Siameses

Environmental abiotic constraints are known to reduce plant growth. This effect is largely due to the inhibition of cell division in the leaf and root meristems caused by perturbations of the cell cycle machinery. Progression of the cell cycle is regulated by CDK kinases whose phosphorylation activities are dependent on cyclin proteins. Recent results have emphasized the role of inhibitors of the cyclin-CDK complexes in the impairment of the cell cycle and the resulting growth inhibition under environmental constraints...

Although it is well known that hierarchical transcriptional networks are essential for various aspects of plant development and environmental response, little has been investigated about whether and how they also regulate the plant cell cycle. Recent studies on cell cycle regulation in Arabidopsis thaliana identified SCARECROW-LIKE28 (SCL28), a GRAS-type transcription factor, that constitutes a hierarchical transcriptional pathway comprised of MYB3R, SCL28 and SIAMESE-RELATED (SMR). In this pathway, MYB3R family proteins regulate the G2/M-specific transcription of the SCL28 gene, of which products, in turn, positively regulate the transcription of SMR genes encoding a group of plant-specific inhibitor proteins of cyclin-dependent kinases...

Change detection (CD) is a fundamental and important task for monitoring the land surface dynamics in the earth observation field. Existing deep learning-based CD methods typically extract bi-temporal image features using a weight-sharing Siamese encoder network and identify change regions using a decoder network. These CD methods, however, still perform far from satisfactorily as we observe that 1) deep encoder layers focus on irrelevant background regions and 2) the models' confidence in the change regions is inconsistent at different decoder stages...

We proposed a self-supervised machine learning method to automatically rate the severity of pulmonary edema in the frontal chest X-ray radiographs (CXR) which could be potentially related to COVID-19 viral pneumonia. For this we use the modified radiographic assessment of lung edema (mRALE) scoring system. The new model was first optimized with the simple Siamese network (SimSiam) architecture where a ResNet-50 pretrained by ImageNet database was used as the backbone. The encoder projected a 2048-dimension embedding as representation features to a downstream fully connected deep neural network for mRALE score prediction...

In this paper, we present a novel anchor-free visual tracking framework, referred to as feature dynamic activation siamese network (SiamFDA), which addresses the issue of ignoring global spatial information in current Siamese network-based tracking algorithms. Our approach captures long-range dependencies between distant pixels in space, which enables robustness to unreliable regions. Additionally, we introduce a hierarchical feature selector that adaptively activates features at different layers, and an adaptive sample label assignment method to further improve tracking performance...

A benchmark histopathological Hematoxylin and Eosin (H&E) image dataset for Cervical Adenocarcinoma in Situ (CAISHI), containing 2240 histopathological images of Cervical Adenocarcinoma in Situ (AIS), is established to fill the current data gap, of which 1010 are images of normal cervical glands and another 1230 are images of cervical AIS. The sampling method is endoscope biopsy. Pathological sections are obtained by H&E staining from Shengjing Hospital, China Medical University. These images have a magnification of 100 and are captured by the Axio Scope...

The problem of change detection in remote sensing image processing is both difficult and important. It is extensively used in a variety of sectors, including land resource planning, monitoring and forecasting of agricultural plant health, and monitoring and assessment of natural disasters. Remote sensing images provide a large amount of long-term and fully covered data for earth environmental monitoring. A lot of progress has been made thanks to deep learning's quick development. But the majority of deep learning-based change detection techniques currently in use rely on the well-known Convolutional neural network (CNN)...

To address the challenges of handling imprecise building boundary information and reducing false-positive outcomes during the process of detecting building changes in remote sensing images, this paper proposes a Siamese transformer architecture based on a difference module. This method introduces a layered transformer to provide global context modeling capability and multiscale features to better process building boundary information, and a difference module is used to better obtain the difference features of a building before and after a change...

This paper introduces a novel method that enables robots to identify objects based on user gaze, tracked via eye-tracking glasses. This is achieved without prior knowledge of the objects' categories or their locations and without external markers. The method integrates a two-part system: a category-agnostic object shape and pose estimator using superquadrics and Siamese networks. The superquadrics-based component estimates the shapes and poses of all objects, while the Siamese network matches the object targeted by the user's gaze with the robot's viewpoint...

OBJECTIVES: A comparative assessment of systolic blood pressure (BP) measurement agreement and precision in two commonly used non-invasive BP devices was carried out in conscious cats. METHODS: Systolic BP measurements were obtained from 50 conscious cats as part of their clinical investigations. All measurements were taken by the same operator and were performed according to the American College of Veterinary Internal Medicine (ACVIM) consensus guidelines. The same cuff location and cuff size were used for paired measurements...

A Siamese neural network, which is a deep learning technique, was applied to investigate phase transitions based on polarising microscopic textures of liquid crystals like: antiferroelectric smectic CA * phase and its glass, smectic I phase and its glass, and smectic G and its glass. It is an example of a subtle transition without significant structural changes, where textures above and below the glass transition temperature are similar. The Siamese neural network could distinguish textures of the chosen liquid crystal phases from a glass of that phase...

BACKGROUND: Cell clustering is a pivotal aspect of spatial transcriptomics (ST) data analysis as it forms the foundation for subsequent data mining. Recent advances in spatial domain identification have leveraged graph neural network (GNN) approaches in conjunction with spatial transcriptomics data. However, such GNN-based methods suffer from representation collapse, wherein all spatial spots are projected onto a singular representation. Consequently, the discriminative capability of individual representation feature is limited, leading to suboptimal clustering performance...

Finding the 3D structure of proteins and their complexes has several applications, such as developing vaccines that target viral proteins effectively. Methods such as cryogenic electron microscopy (cryo-EM) have improved in their ability to capture high-resolution images, and when applied to a purified sample containing copies of a macromolecule, they can be used to produce a high-quality snapshot of different 2D orientations of the macromolecule, which can be combined to reconstruct its 3D structure. Instead of purifying a sample so that it contains only one macromolecule, a process that can be difficult, time-consuming, and expensive, a cell sample containing multiple particles can be photographed directly and separated into its constituent particles using computational methods...

This study conducts collateral evaluation from ischemic damage using a deep learning-based Siamese network, addressing the challenges associated with a small and imbalanced dataset. The collateral network provides an alternative oxygen and nutrient supply pathway in ischemic stroke cases, influencing treatment decisions. Research in this area focuses on automated collateral assessment using deep learning (DL) methods to expedite decision-making processes and enhance accuracy. Our study employed a 3D ResNet-based Siamese network, referred to as SCANED, to classify collaterals as good/intermediate or poor...

PURPOSE: Identifying glaucoma patients at high risk of progression based on widely available structural data is an unmet task in clinical practice. We test the hypothesis that baseline or serial structural measures can predict visual field (VF) progression with deep learning (DL). METHODS:   SETTING: Tertiary academic center. DESIGN: Development of a DL algorithm to predict VF progression. STUDY POPULATION: 3,079 eyes (1,765 patients) with various types of glaucoma and ≥5 VFs, and ≥3 years of follow-up...

OBJECTIVES: Respiratory motion-induced displacement of internal organs poses a significant challenge in image-guided radiation therapy, particularly affecting liver landmark tracking accuracy. METHODS: Addressing this concern, we propose a self-supervised method for robust landmark tracking in long liver ultrasound sequences. Our approach leverages a Siamese-based context-aware correlation filter network, trained by using the consistency loss between forward tracking and back verification...

Despite the significant advancements in drone sensory device reliability, data integrity from these devices remains critical in securing successful flight plans. A notable issue is the vulnerability of GNSS to jamming attacks or signal loss from satellites, potentially leading to incomplete drone flight plans. To address this, we introduce SiaN-VO, a Siamese neural network designed for visual odometry prediction in such challenging scenarios. Our preliminary studies have shown promising results, particularly for flights under static conditions (constant speed and altitude); while these findings are encouraging, they do not fully represent the complexities of real-world flight conditions...

BACKGROUND: The progressive cognitive decline, an integral component of Alzheimer's disease (AD), unfolds in tandem with the natural aging process. Neuroimaging features have demonstrated the capacity to distinguish cognitive decline changes stemming from typical brain aging and AD between different chronological points. OBJECTIVE: To disentangle the normal aging effect from the AD-related accelerated cognitive decline and unravel its genetic components using a neuroimaging-based deep learning approach...

Here, we announce the complete genome of a previously undescribed papillomavirus from a betta fish, Betta splendens . The genome is 5,671 bp with a GC content of 38.2%. Variants were detected in public databases. This genome is most similar to papillomaviruses that infect sea bass (52.9 % nucleotide identity).

Image reconstruction from incomplete measurements is one basic task in imaging. While supervised deep learning has emerged as a powerful tool for image reconstruction in recent years, its applicability is limited by its prerequisite on a large number of latent images for model training. To extend the application of deep learning to the imaging tasks where acquisition of latent images is challenging, this paper proposes an unsupervised deep learning method that trains a deep model for image reconstruction with the access limited to measurement data...