Dynamic Local Conformal Reinforcement Network (DLCR) for Aortic Dissection Centerline Tracking.

Pre-extracted aortic dissection (AD) centerline is very useful for quantitative diagnosis and treatment of AD disease. However, centerline extraction is challenging because (i) the lumen of AD is very narrow and irregular, yielding failure in feature extraction and interrupted topology; and (ii) the acute nature of AD requires a quick algorithm, however, AD scans usually contain thousands of slices, centerline extraction is very time-consuming. In this paper, a fast AD centerline extraction algorithm, which is based on a local conformal deep reinforced agent and dynamic tracking framework, is presented.

Academic mentees thrive in big groups, but survive in small groups.

Mentoring is a key component of scientific achievements, contributing to overall measures of career success for mentees and mentors. Within the scientific community, possessing a large research group is often perceived as an indicator of exceptional mentorship and high-quality research. However, such large, competitive groups may also escalate dropout rates, particularly among early-career researchers.

[Coronary artery segmentation based on Transformer and convolutional neural networks dual parallel branch encoder neural network].

Manual segmentation of coronary arteries in computed tomography angiography (CTA) images is inefficient, and existing deep learning segmentation models often exhibit low accuracy on coronary artery images. Inspired by the Transformer architecture, this paper proposes a novel segmentation model, the double parallel encoder u-net with transformers (DUNETR). This network employed a dual-encoder design integrating Transformers and convolutional neural networks (CNNs).

[Classification of Alzheimer's disease based on multi-example learning and multi-scale feature fusion].

Alzheimer's disease (AD) classification models usually segment the entire brain image into voxel blocks and assign them labels consistent with the entire image, but not every voxel block is closely related to the disease. To this end, an AD auxiliary diagnosis framework based on weakly supervised multi-instance learning (MIL) and multi-scale feature fusion is proposed, and the framework is designed from three aspects: within the voxel block, between voxel blocks, and high-confidence voxel blocks. First, a three-dimensional convolutional neural network was used to extract deep features within the voxel block; then the spatial correlation information between voxel blocks was captured through position encoding and attention mechanism; finally, high-confidence voxel blocks were selected and combined with multi-scale information fusion strategy to integrate key features for classification decision.

Targeted Avoidance in Complex Networks.

The study of spreading in networks presents a fascinating topic with a wide array of practical applications. Various strategies have been proposed to attack or immunize networks. However, it is often not feasible or necessary to consider the entire network in the context of real-world systems.

Oogenesis involves a novel nuclear envelop remodeling mechanism in Schmidtea mediterranea.

The cell nuclei of Ophisthokonts, the eukaryotic supergroup defined by fungi and metazoans, is remarkable in the constancy of their double-membraned structure in both somatic and germ cells. Such remarkable structural conservation underscores common and ancient evolutionary origins. Yet, the dynamics of disassembly and reassembly displayed by Ophisthokont nuclei vary extensively.

Multi-omic profiling highlights factors associated with resistance to immuno-chemotherapy in non-small-cell lung cancer.

Although immune checkpoint blockade (ICB) therapies have shifted the treatment paradigm for non-small-cell lung cancer (NSCLC), many patients remain resistant. Here we characterize the tumor cell states and spatial cellular compositions of the NSCLC tumor microenvironment (TME) by analyzing single-cell transcriptomes of 232,080 cells and spatially resolved transcriptomes of tumors from 19 patients before and after ICB-chemotherapy. We find that tumor cells and secreted phosphoprotein 1-positive macrophages interact with collagen type XI alpha 1 chain-positive cancer-associated fibroblasts to stimulate the deposition and entanglement of collagen fibers at tumor boundaries, obstructing T cell infiltration and leading to poor prognosis.

Motifs-based link prediction for heterogeneous multilayer networks.

Link prediction has a wide range of applications in the study of complex networks, and the current research on link prediction based on single-layer networks has achieved fruitful results, while link prediction methods for multilayer networks have to be further developed. Existing research on link prediction for multilayer networks mainly focuses on multiplexed networks with homogeneous nodes and heterogeneous edges, while there are relatively few studies on general multilayer networks with heterogeneous nodes and edges. In this context, this paper proposes a method for heterogeneous multilayer networks based on motifs for link prediction.

Natural variation in the Tn1a promoter regulates tillering in rice.

Rice tillering is an important agronomic trait that influences plant architecture and ultimately affects yield. This can be genetically improved by mining favourable variations in genes associated with tillering. Based on a previous study on dynamic tiller number, we cloned the gene Tiller number 1a (Tn1a), which encodes a membrane-localised protein containing the C2 domain that negatively regulates tillering in rice.

[An ensemble model for assisting early Alzheimer's disease diagnosis based on structural magnetic resonance imaging with dual-time-point fusion].

Alzheimer's Disease (AD) is a progressive neurodegenerative disorder. Due to the subtlety of symptoms in the early stages of AD, rapid and accurate clinical diagnosis is challenging, leading to a high rate of misdiagnosis. Current research on early diagnosis of AD has not sufficiently focused on tracking the progression of the disease over an extended period in subjects.

Productive scientists are associated with lower disruption in scientific publishing.

While scientific researchers often aim for high productivity, prioritizing the quantity of publications may come at the cost of time and effort dedicated to individual research. It is thus important to examine the relationship between productivity and disruption for individual researchers. Here, we show that with the increase in the number of published papers, the average citation per paper will be higher yet the mean disruption of papers will be lower.

Motif-based community detection in heterogeneous multilayer networks.

Multilayer networks composed of intralayer edges and interlayer edges are an important type of complex networks. Considering the heterogeneity of nodes and edges, it is necessary to design more reasonable and diverse community detection methods for multilayer networks. Existing research on community detection in multilayer networks mainly focuses on multiplexing networks (where the nodes are homogeneous and the edges are heterogeneous), but few studies have focused on heterogeneous multilayer networks where both nodes and edges represent different semantics.

FedDUS: Lung tumor segmentation on CT images through federated semi-supervised with dynamic update strategy.

Background And Objective: Lung tumor annotation is a key upstream task for further diagnosis and prognosis. Although deep learning techniques have promoted automation of lung tumor segmentation, there remain challenges impeding its application in clinical practice, such as a lack of prior annotation for model training and data-sharing among centers.

Methods: In this paper, we use data from six centers to design a novel federated semi-supervised learning (FSSL) framework with dynamic model aggregation and improve segmentation performance for lung tumors.

MAD-Former: A Traceable Interpretability Model for Alzheimer's Disease Recognition Based on Multi-Patch Attention.

The integration of structural magnetic resonance imaging (sMRI) and deep learning techniques is one of the important research directions for the automatic diagnosis of Alzheimer's disease (AD). Despite the satisfactory performance achieved by existing voxel-based models based on convolutional neural networks (CNNs), such models only handle AD-related brain atrophy at a single spatial scale and lack spatial localization of abnormal brain regions based on model interpretability. To address the above limitations, we propose a traceable interpretability model for AD recognition based on multi-patch attention (MAD-Former).

Allosteric Feature Collaboration for Model-Heterogeneous Federated Learning.

Although federated learning (FL) has achieved outstanding results in privacy-preserved distributed learning, the setting of model homogeneity among clients restricts its wide application in practice. This article investigates a more general case, namely, model-heterogeneous FL (M-hete FL), where client models are independently designed and can be structurally heterogeneous. M-hete FL faces new challenges in collaborative learning because the parameters of heterogeneous models could not be directly aggregated.

ImageCAS: A large-scale dataset and benchmark for coronary artery segmentation based on computed tomography angiography images.

Cardiovascular disease (CVD) accounts for about half of non-communicable diseases. Vessel stenosis in the coronary artery is considered to be the major risk of CVD. Computed tomography angiography (CTA) is one of the widely used noninvasive imaging modalities in coronary artery diagnosis due to its superior image resolution.

Attractive deep morphology-aware active contour network for vertebral body contour extraction with extensions to heterogeneous and semi-supervised scenarios.

Automatic vertebral body contour extraction (AVBCE) from heterogeneous spinal MRI is indispensable for the comprehensive diagnosis and treatment of spinal diseases. However, AVBCE is challenging due to data heterogeneity, image characteristics complexity, and vertebral body morphology variations, which may cause morphology errors in semantic segmentation. Deep active contour-based (deep ACM-based) methods provide a promising complement for tackling morphology errors by directly parameterizing the contour coordinates.

Feature-correlation-aware history-preserving-sparse-coding framework for automatic vertebra recognition.

Automatic vertebra recognition from magnetic resonance imaging (MRI) is of significance in disease diagnosis and surgical treatment of spinal patients. Although modern methods have achieved remarkable progress, vertebra recognition still faces two challenges in practice: (1) Vertebral appearance challenge: The vertebral repetitive nature causes similar appearance among different vertebrae, while pathological variation causes different appearance among the same vertebrae; (2) Field of view (FOV) challenge: The FOVs of the input MRI images are unpredictable, which exacerbates the appearance challenge because there may be no specific-appearing vertebrae to assist recognition. In this paper, we propose a Feature-cOrrelation-aware history-pReserving-sparse-Coding framEwork (FORCE) to extract highly discriminative features and alleviate these challenges.

A high-frequency mobility big-data reveals how COVID-19 spread across professions, locations and age groups.

As infected and vaccinated population increases, some countries decided not to impose non-pharmaceutical intervention measures anymore and to coexist with COVID-19. However, we do not have a comprehensive understanding of its consequence, especially for China where most population has not been infected and most Omicron transmissions are silent. This paper aims to reveal the complete silent transmission dynamics of COVID-19 by agent-based simulations overlaying a big data of more than 0.

m A promotes planarian regeneration.

Regeneration is the regrowth of damaged tissues or organs, a vital process in response to damages from primitive organisms to higher mammals. Planarian possesses active whole-body regenerative capability owing to its vast reservoir of adult stem cells, neoblasts, providing an ideal model to delineate the underlying mechanisms for regeneration. RNA N -methyladenosine (m A) modification participates in many biological processes, including stem cell self-renewal and differentiation, in particular the regeneration of haematopoietic stem cells and axons.

Quantification of the spatial-temporal patterns of great ideas.

Quantitative understanding of the process of knowledge creation is crucial for accelerating the advance of science. Recent years have witnessed a great effort to address this issue by studying the publication data of scientific journals, leading to a variety of surprising discoveries at both individual level and disciplinary level. However, before scientific journals appeared on a large scale and became the mainstream for publishing research results, there are also intellectual achievements that have changed the world, which have usually become classic and are now referred to as the great ideas of great people.

Predicting the cascading dynamics in complex networks via the bimodal failure size distribution.

Cascading failure as a systematic risk occurs in a wide range of real-world networks. Cascade size distribution is a basic and crucial characteristic of systemic cascade behaviors. Recent research works have revealed that the distribution of cascade sizes is a bimodal form indicating the existence of either very small cascades or large ones.