Siameses

Timely diagnosis of mild traumatic brain injury (mTBI) remains challenging due to the rapid recovery of acute symptoms and the absence of evidence of injury in static neuroimaging scans. Furthermore, while longitudinal tracking of mTBI is essential in understanding how the diseases progresses/regresses over time for enhancing personalized patient care, a standardized approach for this purpose is not yet available. Recent functional neuroimaging studies have provided evidence of brain function alterations following mTBI, suggesting mTBI-detection models can be built based on these changes...

Automatic and label-free screening methods may help to reduce cervical cancer mortality rates, especially in developing regions. The latest advances of deep learning in the biomedical optics field provide a more automatic approach to solving clinical dilemmas. However, existing deep learning methods face challenges, such as the requirement of manually annotated training sets for clinical sample analysis. Here, we develop Siamese deep learning video flow cytometry for the analysis of clinical cervical cancer cell samples in a smear-free manner...

The use of environmental DNA (eDNA) analysis has demonstrated notable efficacy in detecting the existence of freshwater species, including those that are endangered or uncommon. This application holds significant potential for enhancing environmental monitoring and management efforts. However, the efficacy of eDNA-based detection relies on several factors. In this study, we assessed the impact of rainfall on the detection of eDNA for the Siamese bat catfish (Oreoglanis siamensis). Quantitative polymerase chain reaction (qPCR) analysis indicated that samples from days with average rainfall exceeding 35 mm (classified as heavy and very heavy rain) yielded negative results...

In recent years, the development of intelligent sensor systems has experienced remarkable growth, particularly in the domain of microwave and millimeter wave sensing, thanks to the increased availability of affordable hardware components. With the development of smart Ground-Based Synthetic Aperture Radar (GBSAR) system called GBSAR-Pi, we previously explored object classification applications based on raw radar data. Building upon this foundation, in this study, we analyze the potential of utilizing polarization information to improve the performance of deep learning models based on raw GBSAR data...

Aflatoxins, harmful substances found in peanuts, corn, and their derivatives, pose significant health risks. Addressing this, the presented research introduces an innovative MSGhostDNN model, merging contrastive learning with multi-scale convolutional networks for precise aflatoxin detection. The method significantly enhances feature discrimination, achieving an impressive 97.87% detection accuracy with a pre-trained model. By applying Grad-CAM, it further refines the model to identify key wavelengths, particularly 416 nm, and focuses on 40 key wavelengths for optimal performance with 97...

Cyclam, known for its potent chelation properties, is explored for diverse applications through selective N -functionalization, offering versatile ligands for catalysis, medical research, and materials science. The challenges arising from N -alkylation, which could decrease the coordination properties, are addressed by introducing a robust C -functionalization method. The facile two-step synthesis proposed here involves the click chemistry-based C -functionalization of a hydroxyethyl cyclam derivative using Cu(I)-catalyzed alkyne-azide cycloaddition (CuAAC)...

OBJECTIVE: To develop and validate an artificial intelligence diagnostic model based on fundus images for predicting Carotid Intima-Media Thickness (CIMT) in individuals with Type 2 Diabetes Mellitus (T2DM). METHODS: In total, 1236 patients with T2DM who had both retinal fundus images and CIMT ultrasound records within a single hospital stay were enrolled. Data were divided into normal and thickened groups and sent to eight deep learning models: convolutional neural networks of the eight models were all based on ResNet or ResNeXt...

As a plant-specific endoreplication regulator, the SIAMESE-RELATED ( SMR ) family (a cyclin-dependent kinase inhibitor) plays an important role in plant growth and development and resistance to stress. Although the genes of the maize ( Zea mays ) SMR family have been studied extensively, the ZmSMR10 (Zm00001eb231280) gene has not been reported. In this study, the function of this gene was characterized by overexpression and silencing. Compared with the control, the transgenic plants exhibited the phenotypes of early maturation, dwarfing, and drought resistance...

Jochen Büttner was not included as an author in the original publication [...].

While Siamese object tracking has witnessed significant advancements, its hard real-time behaviour on embedded devices remains inadequately addressed. In many application cases, an embedded implementation should not only have a minimal execution latency, but this latency should ideally also have zero variance, i.e., be predictable. This study aims to address this issue by meticulously analysing real-time predictability across different components of a deep-learning-based video object tracking system. Our detailed experiments not only indicate the superiority of Field-Programmable Gate Array (FPGA) implementations in terms of hard real-time behaviour but also unveil important time predictability bottlenecks...

Wearable devices like smartwatches and smart wristbands have gained substantial popularity in recent years. However, their small interfaces create inconvenience and limit computing functionality. To fill this gap, we propose ViWatch, which enables robust finger interactions under deployment variations, and relies on a single IMU sensor that is ubiquitous in COTS smartwatches. To this end, we design an unsupervised Siamese adversarial learning method. We built a real-time system on commodity smartwatches and tested it with over one hundred volunteers...

OBJECTIVES: An evaluation of the effectiveness of a new computational system proposed for automatic classification, developed based on a Siamese network combined with Convolutional Neural Networks (CNNs), is presented. It aims to identify endodontic technical errors using Cone Beam Computed Tomography (CBCT). The study also aims to compare the performance of the automatic classification system with that of dentists. METHODS: One thousand endodontically treated maxillary molars sagittal and coronal reconstructions were evaluated for the quality of the endodontic treatment and the presence of periapical hypodensities by three board-certified dentists and by an oral and maxillofacial radiologist...

Alzheimer's disease (AD) is the general form of dementia, leading to a progressive neurological disorder characterized by memory loss due to brain cell damage. Artificial Intelligence (AI) assists in the early identification and prediction of AD patients, determining future risks and benefits for radiologists and doctors to save time and cost. Since deep learning (DL) approaches work well with massive datasets and have recently become helpful for AD detection, there remains an area for improvement in automating detection performance...

The public has expressed growing concern for the well-being of fishes, including popular pet species such as the Siamese fighting fish ( Betta splendens ). In captivity, male Bettas behave aggressively, often causing injuries and death if housed together. As a result, they are typically isolated in small fishbowls, which has been widely criticised as cruel. To investigate the impact of keeping Bettas in these conditions, we recorded the behaviour of individual males in containers of different sizes that were either bare or enriched with gravel, large rocks, and live plants...

Chlamydiosis, an infection caused by several Chlamydia species, has been reported in Nile, saltwater, and Siamese crocodiles. Despite its widespread reports in various countries, including Thailand, genetic information on Chlamydia species remains limited. This study presents a whole-genome-based characterization of Siamese crocodile-isolated Chlamydia. The results showed that Siamese crocodile Chlamydia contained a single circular chromosome with a size of 1.22-1.23 Mbp and a plasmid with a size of 7.7-8.0 kbp...

Identifying bacterial strains is essential in microbiology for various practical applications, such as disease diagnosis and quality monitoring of food and water. Classical machine learning algorithms have been utilized to identify bacteria based on their Raman spectra. However, convolutional neural networks (CNNs) offer higher classification accuracy, but they require extensive training sets and retraining of previous untrained class targets can be costly and time-consuming. Siamese networks have emerged as a promising solution...

Studies comparing acoustic signals often rely on pixel-wise differences between spectrograms, as in for example mean squared error (MSE). Pixel-wise errors are not representative of perceptual sensitivity, however, and such measures can be highly sensitive to small local signal changes that may be imperceptible. In computer vision, high-level visual features extracted with convolutional neural networks (CNN) can be used to calculate the fidelity of computer-generated images. Here, we propose the auditory perceptual distance (APD) metric based on acoustic features extracted with an unsupervised CNN and validated by perceptual behavior...

BACKGROUND: Predicting protein-protein interactions (PPIs) from sequence data is a key challenge in computational biology. While various computational methods have been proposed, the utilization of sequence embeddings from protein language models, which contain diverse information, including structural, evolutionary, and functional aspects, has not been fully exploited. Additionally, there is a significant need for a comprehensive neural network capable of efficiently extracting these multifaceted representations...

We propose a geometric deep-learning-based framework, TractGeoNet, for performing regression using diffusion magnetic resonance imaging (dMRI) tractography and associated pointwise tissue microstructure measurements. By employing a point cloud representation, TractGeoNet can directly utilize tissue microstructure and positional information from all points within a fiber tract without the need to average or bin data along the streamline as traditionally required by dMRI tractometry methods. To improve regression performance, we propose a novel loss function, the Paired-Siamese Regression loss, which encourages the model to focus on accurately predicting the relative differences between regression label scores rather than just their absolute values...

Video anomaly detection aims to find the events in a video that do not conform to the expected behavior. The prevalent methods mainly detect anomalies by snippet reconstruction or future frame prediction error. However, the error is highly dependent on the local context of the current snippet and lacks the understanding of normality. To address this issue, we propose to detect anomalous events not only by the local context, but also according to the consistency between the testing event and the knowledge about normality from the training data...