Background With the widespread availability of whole-slide imaging, many studies have utilized digital images of hematoxylin and eosin (H&E)-stained breast cancer tissues and applied convolutional neural networks (CNNs) for pathological diagnosis. However, CNN-based diagnosis is largely a black box and may be limited in quantitative morphological research. In this study, we developed a simple algorithm for morphometric analysis of three nuclear atypia features on H&E-stained whole-slide images to predict nuclear grade, hormone receptor status, and Ki-67 levels in breast cancer.Materials and Methods Using 43,183H&E-stained nuclear images larger than 20 µm2 from 131 invasive ductal breast carcinomas, we calculated the following features of nuclear atypia using a computer vision algorithm: anisonucleosis (variation in nuclear size), inhomogeneous chromatin density, and the average size of prominent nucleoli. Anisonucleosis was quantified as the percentage of nuclei larger than 47μm². Inhomogeneous chromatin was defined as the percentage of blue-saturated structures with 0.92-fold luminance or less than the average nuclear luminance. Prominent nucleoli were identified based on blue-saturated structures with 0.87-fold luminance or less, circularity greater than 0.65, and size greater than 1.15μm². Using these values of nuclear atypia features, the thresholds that were the most associated with grade and biomarkers were calculated using receiver operating characteristic curves by Youden index.Results The morphometric algorithm using these thresholds predicted nuclear grade (grade 1 and 3), Ki-67 index ≧ 20%, and hormone receptor negative status with sensitivities of 52.1 to 100% and specificities from 34.3 to 85.3%. Two multivariable logistic regression models combining these three thresholds predicted nuclear grade, Ki-67, and hormone receptor negative with much better accuracy, sensitivities ranging from 57.1 to 91.3%, specificities from 50.9 to 82.2%, and area under curve of 0.70-0.82. The algorithm was applied to an independent set of 42 tumors.Conclusion The present morphological algorithm of nuclear atypia might provide new insights into the computational grading of invasive breast cancer.

A DNA Methylation-based algorithm Improves Lung Cancer risk prediction in the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial.

Under the microscope: microbial life at pore scale under extreme deep-environment conditions.

A fast and simple algorithm for accurate spike detection in HD-MEA recordings.

Instrumental methods of analysis are often calibrated across broad analyte level ranges. Nevertheless, instrumental responses over such extended ranges are frequently nonlinear and heteroscedastic. Accurately assessing these responses, particularly the uncertainty associated with quantifications derived from calibration curves under these conditions, is challenging. Although simpler models may appear attractive, they require more assumptions that can be difficult to verify, even when suitable software is available, which considerably limits their widespread application. This study presents a weighted simulation method that enables reliable and straightforward weighted regression of quadratic relationships between instrumental responses and calibrator concentrations, while also supporting the evaluation of uncertainty from quantifications obtained in such regressions. The proposed tool requires only that the instrumental response follows a quadratic function and that uncertainty in calibrator values is negligible. The algorithm's simplicity is achieved by replicating simulation lines in proportion to the inverse of the signal variance. The method was successfully tested using artificially generated instrumental responses exhibiting a quadratic dependence on analyte concentration and heteroscedastic variance. The approach produced accurate regression coefficients and analyte concentration estimates for unknown samples with low associated uncertainty. Furthermore, the method is potentially applicable to other types of regression and has been implemented in a user-friendly MS Excel spreadsheet. This work aims to democratise access to accurate weighted quadratic regression and uncertainty evaluation for quantifications based on such calibrations, thereby contributing to the improvement of measurement quality in chemistry performed in conformity assessment and research and development.

Pleural effusion is a common occurrence in many different types of health care facilities and has a broad spectrum of potential causes, from benign systemic disorders to potentially fatal infections and tumors. In developing countries and resource-poor environments where sophisticated imaging techniques, specialty laboratory tests, and trained personnel are not always available, timely and accurate diagnosis presents major obstacles. As a result, clinicians utilize both clinical judgment and basic testing to guide the diagnosis of pleural effusion using pragmatic, cost-effective methods. To review and synthesize available evidence on the diagnostic approach to pleural effusion, with a focus on strategies applicable in resource-constrained healthcare environments. The electronic database literature was reviewed through a narrative process. Literature was reviewed from electronic databases (e.g., PubMed, Scopus, and Google Scholar) through the use of keywords: pleural effusion, diagnosis in resource-limited settings, thoracentesis, etc., All articles that met the inclusion criteria (i.e., published articles in English that identify diagnostic methods and challenges faced when diagnosing patients with a pleural effusion in a low-resource setting) and that were published as guidelines, original research articles, and review articles were included in the review. Articles that were not related to diagnostic methods or simply dealt with elaborate methods of imaging were removed from the database before analysis. Clinical assessment, chest X-ray, and diagnostic thoracentesis are key to diagnosing pleural effusions in settings with limited resources. While Light's criteria remain commonly used to help differentiate between transudative and exudative effusions, their use will be limited by laboratory constraints. Where feasible, point-of-care ultrasound represents an incredibly helpful adjunct to these procedures. Tuberculosis and parapneumonic effusions are the most common causes of pleural effusion in low-resource locations, thus requiring a high degree of clinical suspicion. Simplified diagnostic algorithms that combine clinical presentation with findings from simple investigations can help direct management choices. To maximize the benefit of a systematic diagnostic evaluation of pleural effusion in resource-poor environments, an organized approach combining patient history taking (clinical assessment) with a limited range of readily available laboratory tests will yield the greatest results. Investing time in developing cost-effective resources for key diagnostic procedures, establishing standardized clinical pathways for diagnosing pleural effusion, and providing the appropriate level of education/training for physicians to use these resources effectively will enhance both the diagnostic accuracy of the clinician and the overall survival rates of patients diagnosed with pleural effusion in the low-resource setting.

ABSTRACT No previous study has simultaneously examined the influence of diverse feature types, scales, and thresholds on simplification performance. We compared ten algorithms using six measures, seven datasets, and eleven thresholds. Results revealed that ‘Ramer-Douglas-Peucker,’ ‘Sleeve-fitting,’ and ‘Before Opening Window’ produced the largest changes in angularity and vector displacement, while minimizing percentage changes in coordinates and curvilinear segments. The opposite holds for Triangular Routine, Euclidean Distance, and Perpendicular Distance. Performance varies with feature type, scale, and threshold, revealing anomalous trends requiring further investigation. Statistical validation supports these findings, and guidelines help avoid misleading comparisons of newly developed algorithms.

This article considers regularization in expectile regression from a Bayesian framework. Specifically, we proposed two Bayesian regularization approaches for covariate selection and estimation in expectile regression: the Bayesian Lasso and adaptive Lasso expectile regression. Two simple and efficient Gibbs sampling algorithms were developed for posterior inference using a scale mixture of uniform (SMU) representation of the Laplace density. The proposed approaches are illustrated via simulation studies and two real data sets. Compared to some of the existing approaches, results show that the proposed approaches perform very well under a variety of simulation studies and the real data sets.

ABSTRACT Depth contours are essential chart features, portraying seabed morphology and delineating the depth areas used by navigation systems to assess route safety and trigger alarms. Their portrayal must simultaneously satisfy operational safety and visual clarity. For navigational safety, contours must be displaced only toward the adjacent deeper-depth area, ensuring that charted depths never appear deeper than the source bathymetry. Contours must also remain easily interpretable and adhere to Electronic Navigational Chart data constraints, such as the minimum length of line segments. Despite numerous efforts, existing methods suffer from limitations, including parameters difficult for cartographers to interpret, unnatural shapes, uncontrolled contour displacement, and increased vertex density. This paper introduces a side-selective line-simplification method that enforces the safety constraint and controls line displacement, segment length, and bend geometry using parameters expressed in millimeters at the target scale, to produce visually coherent and application-compliant contours. Tests conducted on six testbeds demonstrate a reduction in geometric complexity while preserving the structural form of contours. Comparisons with representative alternatives highlight the method’s advantages in reducing data volume and producing scale-appropriate, consistent outputs. The side-selective method forms an essential building block toward automated workflows for the cartographic generalization of depth contours and other relevant chart features.

Insect pollinators, such as bumblebees, are commonly used to facilitate pollination in strawberry greenhouses. To ensure effective pollination, this study monitored the entry and exit behaviour of bumblebees around the nest box using an industrial camera. Video footage was captured, and a virtual cube-shaped frame was positioned at the entrance of the nest box. Bumblebee detection and counting within this virtual frame were performed using two methods: a) YOLO only and b) YOLO with a simple algorithm. In the algorithm-based method, if a bumblebee crossed the virtual frame an odd number of times, it was classified as having entered or exited the nest box. Conversely, an even number of crossings indicated that the bumblebee had either turned back or re-entered without exiting. The proposed method allowed for the automated counting of bumblebees entering and exiting a nest box. Compared to the YOLO-only method, the proposed method significantly improved performance metrics, including accuracy, precision, and F1 score. The proposed method can effectively support the monitoring of pollinator behaviour in strawberry greenhouses.

A very simple, efficient and accurate dynamic interpolation BFGS algorithm for three-dimensional moment of fluid method

How is consensus reached in groups with limited access to information? Here we run a controlled laboratory experiment with very young children (ages 5-8 years, N = 150) in the USA to identify the attributes that support consensus building. Success is facilitated by two factors. The first is the endogenous adoption of heterogeneous roles: a leader who proposes a solution, a group of debaters who consider the alternatives, and a closer who locks the decision. The second is flexibility in the decision rule, with participants following the wisdom of the crowd with high probability, but not with certainty. These two characteristics allow young children to outperform simple computational algorithms, especially in the more complex conditions with limited observability of the network: children converged 74% of the time, compared with 35% for the algorithm (χ2(1) = 59.83, P < 0.001, 95% confidence interval for the difference in proportions 0.30-0.47). The study also reveals a sharp progression with age (β = 1.49, P < 0.001, 95% confidence interval 0.62-2.36) corresponding to a 4.45-fold increase in the odds of convergence for each one-unit increase in grade. This work contributes to our understanding of how children navigate collective problems in complex social environments.

Metabolic dysfunction-associated steatotic liver disease (MASLD), formerly known as nonalcoholic fatty liver disease, is the leading cause of chronic liver disease in the world, and is closely linked to metabolic syndrome, and cardiovascular-kidney-metabolic (CKM) syndrome. This article discusses key considerations in diagnosis and management of MASLD, its relevance to CKM syndrome, and associated nursing implications through use of a case example. The article reviews the epidemiology and risk factors of MASLD, summarizes noninvasive diagnostic tools, and discusses lifestyle and pharmacologic treatment options. Incretin therapies, such as glucagon-like peptide-1 (GLP-1) receptor agonists and gastric inhibitory polypeptide/GLP-1 receptor agonists are emphasized, due to their systemic benefits. A simplified screening algorithm using Fibrosis-4 Index and staging criteria for liver fibrosis and CKM syndrome are also provided. Nursing considerations include weight bias awareness, patient-centered communication, interdisciplinary coordination, and the realities of insurance obstacles. Future directions call for system-level efforts to improve equitable access, integrate guideline-based practice, and improve early identification and intervention.

From the zero-sum game (ZSG) perspective, this paper proposes an event-triggered distributed robust optimal consensus control approach for nonlinear multi-agent systems (MASs) simultaneously subject to actuator faults and external disturbances, which is a challenging scenario where their adverse effects are coupled and amplified through agent interactions. First, an improved performance index incorporating fault bounds and disturbance penalties is constructed, transforming the robust consensus problem into a tractable optimal control problem within a ZSG framework, thereby decoupling the dual adversarial effects. Second, a simplified critic-only neural network learning algorithm is proposed, which reduces structural complexity and computational burden while maintaining a comparable tracking performance. Third, a Zeno-free dynamic event-triggered mechanism (ETM) with adaptive thresholds is adopted to significantly reduce communication and control updates, adapting to varying operational conditions without sacrificing stability. Finally, simulation studies are conducted to validate the effectiveness and superiority of the proposed scheme.

Large Network Generator: a simple, efficient, and flexible graph formation algorithm

Considerable advances have been made in our understanding of Bardet-Biedl syndrome (BBS), particularly in its core clinical features and molecular genetics, warranting an update to the existing diagnostic criteria framework. Using a rigorous, evidence-based, and consensus-driven process, a multidisciplinary group of international experts and patient-led organizations developed an updated diagnostic algorithm. This algorithm provides practical, updated guidance for clinicians, including a pathway for accurately incorporating genetic findings into the diagnostic process. We recommend that a clinical diagnosis requires either 4 major criteria or 3 major and 2 minor criteria. Revised major criteria are retinal dystrophy, obesity (or overweight in individuals < 2 years old), congenital anomalies of the kidney and urinary tract or chronic kidney disease, hypogonadism/genital anomalies, neurodevelopmental/neurocognitive manifestations, and postaxial polydactyly. The diagnosis can also be established with a positive genetic testing result in patients exhibiting ≥ 1 major criterion, provided that genetic findings should be interpreted in the context of the patient's clinical presentation, age, family history, and overlap with related ciliopathies. These consensus criteria offer a simple algorithm incorporating updated definitions for major and minor criteria and genetic testing to support a timely and accurate diagnosis of patients with BBS, inform genetic counseling, and potentially facilitate earlier access to treatment. Trial Registration: CRIBBS Registry; ClinicalTrials.gov: NCT02329210.

Recent research on photovoltaic/thermal (PV/T) collectors has focused on two key strategies to enhance performance: geometric modifications of the thermal flow channel (such as fins, baffles, and ribbed structures) and the integration of advanced materials like phase change materials (PCMs) and porous media to improve heat transfer and overall efficiency. In this direction, this numerical study investigates the performance enhancement of a photovoltaic/thermal (PV/T) solar collector through the integration of a solid layer along the lower wall of the airflow channel, coupled with a porous medium. The solid layer is introduced to accelerate the airflow and intensify convective heat transfer, thereby improving the thermal management of the photovoltaic cells. To solve the governing transport equations, an in-house computational code was developed in the Fortran programming language based on the finite volume method, coupled with the SIMPLER algorithm. The effects of solid-layer thickness and length, porous-layer thickness, and Darcy number are systematically investigated under a constant Reynolds number (Re = 500) and a uniform heat flux of 1000W/m². The obtained results show that increasing the solid-layer thickness significantly enhances airflow acceleration and leads to a pronounced reduction in PV cell temperature of up to 33°C. Extending the solid-layer length further improves the convective cooling process and increases both electrical and thermal efficiencies. When combined with a sufficiently permeable porous layer, additional performance gains are achieved, particularly at high Darcy numbers (Da = 10- 1). Compared to a conventional PV/T collector, the optimized configuration demonstrates enhancements of up to 60% in thermal efficiency and 28% in electrical efficiency.

Objective.When electrical impedance tomography is applied to a known system, such as the human body, a parameterized model is shown to produce a more accurate reconstruction than a conductivity map. Furthermore, if the number of free parameters is less than the total number of independent measurements, the sensitivity volume method can be employed to identify a significantly reduced number of data measurements with the highest value for distinguishing these parameters.Approach.To achieve direct parametric inversion from this reduced set of measurements, a simple algorithm establishes the correspondence between training data and parameterized model cases. Here two training algorithms will be demonstrated. For sparse sampling, the parameters associated with each trained case are interpolated to generate a high density of invertible data cases. For dense sampling, enough cases are directly measured that a simple nearest-neighbor search in data space can invert the data.Main result.Once the training is established, a simple nearest-neighbor query in data space has a one-to-one correspondence with the model parameters for reconstruction. Sparse sampling is demonstrated with an insulating cylinder in a saltwater tub whose diameter and angular position constitute a two-dimensional (2D) model space, and whose reduced high-value data space consists of 9 independent tetrapolar data measurements made with 15 available electrodes. Dense sampling is demonstrated with a mechanical goldfish in a saltwater tub whose coordinates and orientation define a 3D model space, and whose reduced high-value data space consists of 16 tetrapolar data measurements made from 128 available electrodes.Significance.The parametric method demonstrated here reduces the necessary number of data measurements by orders of magnitude compared to standard electrical impedance tomography to achieve higher accuracy within the parametric representation, and can, in principle, be expanded to complex 3D systems such as organs within the human body to achieve fast, high fidelity parametric reconstructions.

We introduce a computationally simple algorithm for sampling open-chain distributions within the framework of imaginary-time Feynman path integration. The present method is based on the staging algorithm introduced by Pollock and Ceperley [Phys. Rev. B 30, 2555 (1984)] originally developed for computing position-dependent observables. Here, we sample off-diagonal elements of the density matrix, formulated as a distribution describing a linear polymer-like chain of beads, each connected via nearest-neighbor springs to calculate momentum-dependent quantities. This is achieved using a Monte Carlo scheme that ensures efficient and unbiased sampling of all beads along the chain from the free-particle distribution via a staging transformation; we refer to this approach as staging open path integral Monte Carlo (OPIMC). The proposed algorithm is straightforward to implement, as it only involves sampling Gaussian distributions through a transformation defined by a set of recursion relations, followed by a standard Metropolis acceptance/rejection step. The staging OPIMC method accurately reproduces end-to-end and momentum distributions for quantum systems ranging from coupled harmonic oscillators to liquid water.

Dual-energy X-ray absorptiometry (DEXA) is the diagnostic standard for osteoporosis, yet its serial data remains underutilised in predictive analytics. To our knowledge, no published model provides explicit age-based predictions of osteoporosis onset or recovery using serial DEXA T-score trajectories. This proof-of-concept study describes a deterministic mathematical framework for predicting time to osteoporosis (TTO) and time to exit osteoporosis (TEO), defined as the age at which a patient's T-score trajectory reaches or exits the threshold of - 2.5. We developed two deterministic algorithms converting serial hip DEXA T-scores into age-based predictions: a two-point slope algorithm (TTOc) and a multipoint least-squares regression (TTOt). The algorithms were evaluated on 200 patients drawn from an institutional DEXA database using a pre-specified stratified random-sampling rule (50 patients each with 2, 3, 4, and ≥ 5 scans; seed 42). Three validation analyses were performed: (i) onset-age prediction against observed age of first osteoporotic reading using mean absolute error (MAE) and Bland-Altman analysis; (ii) prospective T-score prediction using scans 1 to N - 1 to predict scan N; and (iii) stability analysis examining how prediction intervals narrowed with increasing scan count. 95% prediction intervals were computed for TTOt by inverse prediction. Of 200 patients, 19 experienced observable crossing into osteoporosis and 4 experienced recovery during follow-up. For the 8 patients with sufficient pre-crossing data, TTOc predicted observed onset age with MAE 5.67 years (mean bias + 1.42; 95% limits of agreement - 16.0 to + 18.8). TTOt gave MAE 7.33 years (bias + 4.73; LoA - 14.2 to + 23.7). For prospective T-score prediction across 300 prediction points in 150 patients, TTOt produced lower MAE than TTOc on prospective T-score prediction (0.385 vs. 0.533 T-score units). Stability analysis demonstrated marked narrowing of TTOt prediction intervals with increasing scan count (mean PI width: 654 years at 3 scans, 202 years at 4 scans, 69.5 years at 5 scans). The two algorithms showed complementary behaviour: TTOc produced lower onset-age error in the validation subset (n = 8), while TTOt produced lower error for next-scan T-score extrapolation. The framework translates static DEXA outputs into patient-specific age-based predictions using mathematics that any reader can inspect on one page. TTOc and TTOt showed distinctly complementary behaviour. The two-point method produced lower onset-age error, while regression produced lower error for next-scan T-score prediction. The stability analysis further establishes a minimum data requirement for reliable interval estimation, with prediction intervals narrowing sharply between three and five scans. External validation, confounder adjustment, and evaluation against clinical endpoints are the necessary next steps before the framework could inform deployed decision support tools. Serial DEXA T-scores can be converted into interpretable, age-based threshold predictions using simple deterministic algorithms, with regression-based prediction error within an order of magnitude of the intrinsic measurement noise of DEXA itself. The framework is transparent by construction, providing a concrete methodological foundation on which future clinical decision support tools for bone health can be built and benchmarked.