Light scattering of non-diffracting Tricomi beams by a homogeneous spherical particle.

Non-diffracting beams have attracted enormous research interest for their unique properties and potential applications. As a type of non-diffracting beam described by the Tricomi functions and characterized by multiple parameters, the Tricomi beam exhibits good versatility and adjustability, which may be used as an advantage to enhance or suppress the scattering of particles. In this work, within the framework of the generalized Lorenz-Mie theory, the light scattering of non-diffracting Tricomi beams by spherical particles is investigated.

Associations between Life's Essential 8 and stroke: findings from NHANES 2005-2018.

Background: Stroke poses a significant financial and medical burden as it is the primary cause of death and disability globally. The identification of modifiable risk factors is crucial in the prevention of stroke. Life's Essential 8 (LE8) is the most recent indicator of cardiovascular health, but its association with stroke is unclear.

DiffuSeg: Domain-Driven Diffusion for Medical Image Segmentation.

In recent years, the deployment of supervised machine learning techniques for segmentation tasks has significantly increased. Nonetheless, the annotation process for extensive datasets remains costly, labor-intensive, and error-prone. While acquiring sufficiently large datasets to train deep learning models is feasible, these datasets often experience a distribution shift relative to the actual test data.

MT-CooL: Multi-Task Cooperative Learning via Flat Minima Searching.

While multi-task learning (MTL) has been widely developed for natural image analysis, its potential for enhancing performance in medical imaging remains relatively unexplored. Most methods formulate MTL as a multi-objective problem, inherently forcing all tasks to compete with each other during optimization. In this work, we propose a novel approach by formulating MTL as a multi-level optimization problem, in which the features learned from one task are optimized by benefiting from the other tasks.

MERIT: Multi-view evidential learning for reliable and interpretable liver fibrosis staging.

Accurate staging of liver fibrosis from magnetic resonance imaging (MRI) is crucial in clinical practice. While conventional methods often focus on a specific sub-region, multi-view learning captures more information by analyzing multiple patches simultaneously. However, previous multi-view approaches could not typically calculate uncertainty by nature, and they generally integrate features from different views in a black-box fashion, hence compromising reliability as well as interpretability of the resulting models.

Network pharmacological mechanism analysis and evidence-based medical validation of Dahuang Mudan Decoction in the treatment of acute pancreatitis.

Background: Dahuang Mudan Decoction is commonly used in China for the treatment of acute pancreatitis. Nevertheless, the therapeutic efficacy of the drug remains a subject of debate, and its active ingredients and potential therapeutic targets remain to be determined. The present study used a network pharmacological approach to investigate the active ingredients and possible targets of the drug, and illustrated the clinical effectiveness of Dahuang Mudan Decoction in the treatment of acute pancreatitis by meta-analysis.

Labelling with dynamics: A data-efficient learning paradigm for medical image segmentation.

The success of deep learning on image classification and recognition tasks has led to new applications in diverse contexts, including the field of medical imaging. However, two properties of deep neural networks (DNNs) may limit their future use in medical applications. The first is that DNNs require a large amount of labeled training data, and the second is that the deep learning-based models lack interpretability.

Description and reconstruction of typical structured light beams with vector spherical wave functions.

It is well known that the generalized Lorenz-Mie theory (GLMT) is a rigorous analytical method for dealing with the interaction between light beams and spherical particles, which involves the description and reconstruction of the light beams with vector spherical wave functions (VSWFs). In this paper, a detailed study on the description and reconstruction of the typical structured light beams with VSWFs is reported. We first systematically derive the so-called beam shape coefficients (BSCs) of typical structured light beams, including the fundamental Gaussian beam, Hermite-Gaussian beam, Laguerre-Gaussian beam, Bessel beam, and Airy beam, with the aid of the angular spectrum decomposition method.

Multi-Source Domain Adaptation for Medical Image Segmentation.

Unsupervised domain adaptation(UDA) aims to mitigate the performance drop of models tested on the target domain, due to the domain shift from the target to sources. Most UDA segmentation methods focus on the scenario of solely single source domain. However, in practical situations data with gold standard could be available from multiple sources (domains), and the multi-source training data could provide more information for knowledge transfer.

Effectiveness of Huangqi Guizhi Wuwu decoction combined with rehabilitation training for shoulder hand syndrome after stroke: A systematic review and meta-analysis.

Background: Shoulder hand syndrome (SHS) is a common complication of stroke. This meta-analysis aimed to evaluate the effectiveness of Huangqi Guizhi Wuwu decoction (HGWD) combined with rehabilitation training in managing it, as its efficacy remains inconclusive.

Methods: Seven databases, including PubMed, EMBASE, Cochrane Library, SinoMed, Chinese National Knowledge Infrastructure, Wanfang Data, and VIP database were searched in this study.

Multi-target landmark detection with incomplete images via reinforcement learning and shape prior embedding.

Medical images are generally acquired with limited field-of-view (FOV), which could lead to incomplete regions of interest (ROI), and thus impose a great challenge on medical image analysis. This is particularly evident for the learning-based multi-target landmark detection, where algorithms could be misleading to learn primarily the variation of background due to the varying FOV, failing the detection of targets. Based on learning a navigation policy, instead of predicting targets directly, reinforcement learning (RL)-based methods have the potential to tackle this challenge in an efficient manner.

MyoPS: A benchmark of myocardial pathology segmentation combining three-sequence cardiac magnetic resonance images.

Assessment of myocardial viability is essential in diagnosis and treatment management of patients suffering from myocardial infarction, and classification of pathology on the myocardium is the key to this assessment. This work defines a new task of medical image analysis, i.e.

Water quality characteristics and evaluation of Qilian Mountain National Park section in Heihe River Basin based on water quality indices and 3D fluorescence technology.

The water quality of the Heihe River Basin affects the life quality and health of tens of thousands of residents along it. However, there are relatively few studies that evaluate its water quality. In this study, we used principal component analysis (PCA), an improved comprehensive water quality index (WQI), and three-dimensional (3D) fluorescence technology to identify pollutants and evaluate water quality at nine monitoring sites in the Qilian Mountain National Park in Heihe River Basin.

A New Framework of Swarm Learning Consolidating Knowledge From Multi-Center Non-IID Data for Medical Image Segmentation.

Large training datasets are important for deep learning-based methods. For medical image segmentation, it could be however difficult to obtain large number of labeled training images solely from one center. Distributed learning, such as swarm learning, has the potential to use multi-center data without breaching data privacy.

Scattering analysis of two-dimensional Airy beams by typical non-spherical particles.

The scattering of structured light beams by various particles is an important subject of research with myriad practical applications, such as the manipulation, measurement, and diagnosis of small particles. We carry out an analysis of the scattering of two-dimensional (2D) Airy beams by typical non-spherical particles. The electric and magnetic field vectors of the incident Airy beams are derived by introducing a vector potential in the Lorenz gauge.

Minimizing Estimated Risks on Unlabeled Data: A New Formulation for Semi-Supervised Medical Image Segmentation.

Supervised segmentation can be costly, particularly in applications of biomedical image analysis where large scale manual annotations from experts are generally too expensive to be available. Semi-supervised segmentation, able to learn from both the labeled and unlabeled images, could be an efficient and effective alternative for such scenarios. In this work, we propose a new formulation based on risk minimization, which makes full use of the unlabeled images.

Chirality of optical vortex beams reflected from an air-chiral medium interface.

Chirality plays an important role in understanding of the chiral light-matter interaction. In this work, we study theoretically and numerically the chirality of optical vortex beams reflected from an air-chiral medium interface. A theoretical model that takes into full account the vectorial nature of electromagnetic fields is developed to describe the reflection of optical vortex beams at an interface between air and a chiral medium.

Echogenic swirling pattern, carcinoembryonic antigen, and lactate dehydrogenase in the diagnosis of malignant pleural effusion.

The echogenic swirling pattern has a role in predicting malignant pleural effusion (MPE). However, its predictive ability is suboptimal, and its clinical utility remains to be defined. The aim of this study was to assess the diagnostic potential of the echogenic swirling pattern combined with pleural carcinoembryonic antigen (CEA) and routine laboratory tests of pleural effusion in MPE.

Hydrothermal synthesis of magnetic nano-CoFeO catalyst and its enhanced degradation of amoxicillin by activated permonosulfate.

Advanced oxidation process (AOP) has attracted widespread attention because it can effectively remove antibiotics in water, but its practical engineering application is limited by the problems of the low efficiency and difficult recovery of the catalyst. In the study, nano-spinel CoFeO was prepared by hydrothermal method and served as the peroxymonosulfate (PMS) catalyst to degrade antibiotic amoxicillin (AMX). The reaction parameters such as CoFeO dosage, AMX concentration, and initial pH value were also optimized.

Light scattering of Laguerre-Gaussian vortex beams by arbitrarily shaped chiral particles.

Laguerre-Gaussian (LG) beams with vortex phase possess a handedness, which would produce chiroptical interactions with chiral matter and may be used to probe structural chirality of matter. In this paper, we numerically investigate the light scattering of LG vortex beams by chiral particles. Using the vector potential method, the electric and magnetic field components of the incident LG vortex beams are derived.

Unsupervised Domain Adaptation With Variational Approximation for Cardiac Segmentation.

Unsupervised domain adaptation is useful in medical image segmentation. Particularly, when ground truths of the target images are not available, domain adaptation can train a target-specific model by utilizing the existing labeled images from other modalities. Most of the reported works mapped images of both the source and target domains into a common latent feature space, and then reduced their discrepancy either implicitly with adversarial training or explicitly by directly minimizing a discrepancy metric.

Disentangle domain features for cross-modality cardiac image segmentation.

Unsupervised domain adaptation (UDA) generally learns a mapping to align the distribution of the source domain and target domain. The learned mapping can boost the performance of the model on the target data, of which the labels are unavailable for model training. Previous UDA methods mainly focus on domain-invariant features (DIFs) without considering the domain-specific features (DSFs), which could be used as complementary information to constrain the model.

CF Distance: A New Domain Discrepancy Metric and Application to Explicit Domain Adaptation for Cross-Modality Cardiac Image Segmentation.

Domain adaptation has great values in unpaired cross-modality image segmentation, where the training images with gold standard segmentation are not available from the target image domain. The aim is to reduce the distribution discrepancy between the source and target domains. Hence, an effective measurement for this discrepancy is critical.

Waste Brick as Constructed Wetland Fillers to Treat the Tail Water of Sewage Treatment Plant.

Adopting the concept of "using waste to treat waste", the waste bricks will be used for constructed wetland filling. Integrated vertical-flow constructed wetland (IVCW) studied on the purification effect in influent water under three hydraulic loads (0.15, 0.

Atrial scar quantification via multi-scale CNN in the graph-cuts framework.

Late gadolinium enhancement magnetic resonance imaging (LGE MRI) appears to be a promising alternative for scar assessment in patients with atrial fibrillation (AF). Automating the quantification and analysis of atrial scars can be challenging due to the low image quality. In this work, we propose a fully automated method based on the graph-cuts framework, where the potentials of the graph are learned on a surface mesh of the left atrium (LA) using a multi-scale convolutional neural network (MS-CNN).