Segmentation tracking and clustering system enables accurate multi-animal tracking of social behaviors.

Accurate analysis of social behaviors in animals is hindered by methodological challenges. Here, we develop a segmentation tracking and clustering system (STCS) to address two major challenges in computational neuroethology: reliable multi-animal tracking and pose estimation under complex interaction conditions and providing interpretable insights into social differences guided by genotype information. We established a comprehensive, long-term, multi-animal-tracking dataset across various experimental settings.

Damage Evolution Mechanism of Railway Wagon Bogie Adapter 1035 Steel and Damage Parameter Calibration Based on Gursone-Tvergaarde-Needleman Model.

As a critical component of a train, the railway wagon bogie adapter has higher quality requirements. During the forging process, external loads can induce voids, cracks, and other defects in the forging, thereby reducing its service life. Hence, studying the damage behavior of the forging material, specifically AISI 1035 steel, becomes crucial.

A 3D hierarchical cross-modality interaction network using transformers and convolutions for brain glioma segmentation in MR images.

Background: Precise glioma segmentation from multi-parametric magnetic resonance (MR) images is essential for brain glioma diagnosis. However, due to the indistinct boundaries between tumor sub-regions and the heterogeneous appearances of gliomas in volumetric MR scans, designing a reliable and automated glioma segmentation method is still challenging. Although existing 3D Transformer-based or convolution-based segmentation networks have obtained promising results via multi-modal feature fusion strategies or contextual learning methods, they widely lack the capability of hierarchical interactions between different modalities and cannot effectively learn comprehensive feature representations related to all glioma sub-regions.

Multiview Classification Through Learning From Interval-Valued Data.

The classification problem concerning crisp-valued data has been well resolved. However, interval-valued data, where all of the observations' features are described by intervals, are also a common data type in real-world scenarios. For example, the data extracted by many measuring devices are not exact numbers but intervals.

A bidirectional registration neural network for cardiac motion tracking using cine MRI images.

Using cine magnetic resonance imaging (cine MRI) images to track cardiac motion helps users to analyze the myocardial strain, and is of great importance in clinical applications. At present, most of the automatic deep learning-based motion tracking methods compare two images without considering temporal information between MRI frames, which easily leads to the lack of consistency of the generated motion fields. Even though a small number of works take into account the temporal factor, they are usually computationally intensive or have limitations on image length.

A discontinuity-preserving regularization for deep learning-based cardiac image registration.

. Sliding motion may occur between organs in anatomical regions due to respiratory motion and heart beating. This issue is often neglected in previous studies, resulting in poor image registration performance.

A modality-collaborative convolution and transformer hybrid network for unpaired multi-modal medical image segmentation with limited annotations.

Background: Multi-modal learning is widely adopted to learn the latent complementary information between different modalities in multi-modal medical image segmentation tasks. Nevertheless, the traditional multi-modal learning methods require spatially well-aligned and paired multi-modal images for supervised training, which cannot leverage unpaired multi-modal images with spatial misalignment and modality discrepancy. For training accurate multi-modal segmentation networks using easily accessible and low-cost unpaired multi-modal images in clinical practice, unpaired multi-modal learning has received comprehensive attention recently.

Prostate lesion segmentation based on a 3D end-to-end convolution neural network with deep multi-scale attention.

Prostate cancer is one of the deadest cancers among human beings. To better diagnose the prostate cancer, prostate lesion segmentation becomes a very important work, but its progress is very slow due to the prostate lesions small in size, irregular in shape, and blurred in contour. Therefore, automatic prostate lesion segmentation from mp-MRI is a great significant work and a challenging task.

Automatic 3D CT liver segmentation based on fast global minimization of probabilistic active contour.

Purpose: Automatic liver segmentation from computed tomography (CT) images is an essential preprocessing step for computer-aided diagnosis of liver diseases. However, due to the large differences in liver shapes, low-contrast to adjacent tissues, and existence of tumors or other abnormalities, liver segmentation has been very challenging. This study presents an accurate and fast liver segmentation method based on a novel probabilistic active contour (PAC) model and its fast global minimization scheme (3D-FGMPAC), which is explainable as compared with deep learning methods.

Endocrine disruptors affect the expression of estrogen receptor genes and proteins in the white cloud mountain minnow (Teleostei: Cyprinidae).

The endocrine disruptor chemicals (EDCs) are ubiquitous in the environment, and it has raised wide public concern because of the dangers of EDCs for living organisms and the environment. In order to comparatively study the effects of EDCs [17-α-ethinylestradiol (EE), Bisphenol A (BPA) and Nonylphenol (NP)] on the expression of estrogen receptors (ERs: ) at mRNA and protein level, total 520 adult were exposed to E, EE, BPA and NP with three concentrations respectively: EE (1, 5, 25 ng/l), NP (10, 50, 250 μg/l), BPA (100, 500, 2,500 μg/l) for 28 days, E (2, 20, 200 ng/l) being as the positive control. After treatment, the brain, eye, gill, heart, liver, gut, kidney, muscle, testis, and ovary were collected, following by the real-time quantitative PCR (RT-qPCR) and western blot methods to detect the expression levels of , , and in at mRNA and protein level.

The Prognostic Value of Periostin Expression in Non-Small Cell Lung Cancer: A Meta-Analysis.

BACKGROUND Non-small cell lung cancer (NSCLC) is one of the leading causes of cancer-related death in the world and its poor prognosis is a major concern. Periostin was found to be associated with the prognosis of NSCLC. However, the research results were inconsistent.

Multiclass Classification With Fuzzy-Feature Observations: Theory and Algorithms.

The theoretical analysis of multiclass classification has proved that the existing multiclass classification methods can train a classifier with high classification accuracy on the test set, when the instances are precise in the training and test sets with same distribution and enough instances can be collected in the training set. However, one limitation with multiclass classification has not been solved: how to improve the classification accuracy of multiclass classification problems when only imprecise observations are available. Hence, in this article, we propose a novel framework to address a new realistic problem called multiclass classification with imprecise observations (MCIMO), where we need to train a classifier with fuzzy-feature observations.

Therapeutic efficacy of the novel SHP2 degrader SHP2-D26, alone or in combination, against lung cancer is associated with modulation of p70S6K/S6, Bim and Mcl-1.

SHP2, a protein tyrosine phosphatase, plays a critical role in fully activating oncogenic signaling pathways such as Ras/MAPK downstream of cell surface tyrosine receptors (e.g., EGFR), which are often activated in human cancers, and thus has emerged as an attractive cancer therapeutic target.

Automatic CT liver Couinaud segmentation based on key bifurcation detection with attentive residual hourglass-based cascaded network.

This paper presents an automatic Couinaud segmentation method based on deep learning of key point detection. Assuming that the liver mask has been extracted, the proposed method can automatically divide the liver into eight anatomical segments according to Couinaud's definition. Firstly, an attentive residual hourglass-based cascaded network (ARH-CNet) is proposed to identify six key bifurcation points of the hepatic vascular system.

The novel BET degrader, QCA570, is highly active against the growth of human NSCLC cells and synergizes with osimertinib in suppressing osimertinib-resistant EGFR-mutant NSCLC cells.

Lung cancer remains the leading cause of cancer deaths worldwide despite advances in knowledge in cancer biology and options of various targeted therapies. Efforts in identifying innovative and effective therapies are still highly appreciated. Targeting bromodomain and extra terminal (BET) proteins that function as epigenetic readers and master transcription coactivators is now a potential cancer therapeutic strategy.

Identification of tumor microenvironment-based genes associated with acquired resistance to EGFR Tyrosine Kinase Inhibitor in Lung Adenocarcinoma.

The tumor microenvironment evidently affects treatment response and clinical outcome. This study aims to construct a tumor microenvironment-based crosstalk between immunotherapy and epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKI) in lung adenocarcinoma. We used ESTIMATE algorithm to calculate stromal and immune scores.

Overcoming acquired resistance to third-generation EGFR inhibitors by targeting activation of intrinsic apoptotic pathway through Mcl-1 inhibition, Bax activation, or both.

Treatment of EGFR-mutant non-small cell lung cancer (NSCLC) with mutation-selective third-generation EGFR-tyrosine kinase inhibitors (EGFR-TKIs) such as osimertinib has achieved remarkable success in the clinic. However, the immediate challenge is the emergence of acquired resistance, limiting the long-term remission of patients. This study suggests a novel strategy to overcome acquired resistance to osimertinib and other third-generation EGFR-TKIs through directly targeting the intrinsic apoptotic pathway.

Systemic Immune-Inflammation Index Is a Prognostic Factor for Breast Cancer Patients After Curative Resection.

Background: The preoperative systemic immune-inflammation index (SII) is correlated with prognosis in several malignancies. The aim of this study was to investigate the prognosis value of SII in patients with resected breast cancer.

Materials And Methods: A total of 784 breast cancer patients who underwent surgical resection were consecutively investigated.

Lung-CRNet: A convolutional recurrent neural network for lung 4DCT image registration.

Purpose: Deformable image registration (DIR) of lung four-dimensional computed tomography (4DCT) plays a vital role in a wide range of clinical applications. Most of the existing deep learning-based lung 4DCT DIR methods focus on pairwise registration which aims to register two images with large deformation. However, the temporal continuities of deformation fields between phases are ignored.

Ureteral metastasis from pulmonary adenocarcinoma: A case report and literature review.

The occurrence of ureteral metastasis from distant primary tumors is uncommon, and appears to be especially rare when it originates from the lungs. In the case presented here, a patient with lumbago and left hydronephrosis was diagnosed with left ureteral metastasis of pulmonary adenocarcinoma after a CT-guided percutaneous transthoracic needle biopsy of the lung and retroperitoneal laparoscopic left nephroureterectomy. He accepted the targeted therapy because the lung tumor epidermal growth factor receptor mutation (exon19 deletion) was positive, and preoperative staging of lung adenocarcinoma was stage IVA.

APRNet: A 3D Anisotropic Pyramidal Reversible Network With Multi-Modal Cross-Dimension Attention for Brain Tissue Segmentation in MR Images.

Brain tissue segmentation in multi-modal magnetic resonance (MR) images is significant for the clinical diagnosis of brain diseases. Due to blurred boundaries, low contrast, and intricate anatomical relationships between brain tissue regions, automatic brain tissue segmentation without prior knowledge is still challenging. This paper presents a novel 3D fully convolutional network (FCN) for brain tissue segmentation, called APRNet.

Learning U-Net Based Multi-Scale Features in Encoding-Decoding for MR Image Brain Tissue Segmentation.

Accurate brain tissue segmentation of MRI is vital to diagnosis aiding, treatment planning, and neurologic condition monitoring. As an excellent convolutional neural network (CNN), U-Net is widely used in MR image segmentation as it usually generates high-precision features. However, the performance of U-Net is considerably restricted due to the variable shapes of the segmented targets in MRI and the information loss of down-sampling and up-sampling operations.

Comprehensive analysis of competitive endogenous RNA associated with immune infiltration in lung adenocarcinoma.

To identify the prognostic biomarker of the competitive endogenous RNA (ceRNA) and explore the tumor infiltrating immune cells (TIICs) which might be the potential prognostic factors in lung adenocarcinoma. In addition, we also try to explain the crosstalk between the ceRNA and TIICs to explore the molecular mechanisms involved in lung adenocarcinoma. The transcriptome data of lung adenocarcinoma were obtained from The Cancer Genome Atlas (TCGA) database, and the hypergeometric correlation of the differently expressed miRNA-lncRNA and miRNA-mRNA were analyzed based on the starBase.