Amyotrophic Lateral Sclerosis (ALS) is a fatal neurodegenerative disorder characterized by rapid progression and poor prognosis. Early diagnosis remains highly challenging due to nonspecific clinical manifestations, leading to persistently high misdiagnosis rates. This study investigates the feasibility of using resting-state electromyography (EMG) signals for accurate ALS identification. Clinically collected EMG signals vary in length across individuals and muscle regions; further, preprocessing via sliding window methods yields inconsistent numbers of segmented samples, posing challenges for traditional neural networks to adapt to variable-length sequences. To address these issues, we propose a unified time-frequency learning framework called TF-Transformer. Centered on a Transformer-based feature extraction module, the framework treats segmented EMG data as sequence data, enabling efficient processing of variable-length sequences without padding or truncation. It synchronously extracts and fuses time and frequency domain features into a unified EMG representation for ALS identification. Experiments using five-fold cross-validation show that this method effectively utilizes the inherent time-frequency characteristics of resting state electromyographic signals, providing new technical insights and methods for clinical ALS diagnosis.

Analyzing functional connectivity patterns from resting-state functional magnetic resonance imaging (fMRI) requires unraveling its interrelations across spatial, temporal, and frequency domains. To comprehensively analyze four-dimensional (4D) fMRI data, we propose the Spatiotemporal Frequency Graph Neural Network (STFreqGNN), which processes dynamic heterogeneous graphs across spatial, temporal, and frequency domains using a transformer-style architecture. To reduce the complexity of multi-domain analysis with small sample sizes for fMRI datasets and ensure domainspecific interpretability, we introduce two structure-informed modules in the spatial and temporal domains to improve knowledge aggregation within each domain. Specifically, the plugin GNNs transmit information within the static homogeneous brain region graphs, and recurrent blocks aggregate features from heterogeneous nodes defined across different temporal windows. Additionally, we design cross-domain masked self-attention blocks to prevent attention captured by irrelevant or redundant token pairs, finally enabling efficient disease-specific feature learning. Experimental results on both public and in-house datasets suggest that the proposed method is not only superior to several state-of-the-art methods on fMRI-based classification but also preserves interpretation ability in all these domains.

Crevasse detection is crucial for glacier and climate research, and provides essential guidance for activities in glacier regions. Ground-penetrating radar(GPR) and machine learning are used to automatically detect crevasse. In this study, a Siam-Gabor-ResNet deep learning framework is proposed to detect crevasse automatically using GPR data. A contrast learning with Siamese network framework is proposed to improve the accuracy of crevasse detection, which aims to increase the feature similarity between crevasses while simultaneously enhancing the feature distinctiveness between crevasse and continuous snow layers. Additionally, a trainable Gabor-ResNet feature extraction module is built by integrating the Gabor filter bank into the ResNet network and used to further reduce the complexity of model training while extracting multi-scale features in a real-time manner. Experiments are performed on the Greenland dataset and the 2015 McMurdo dataset, which illustrate the effectiveness of the proposed method. The average accuracy rate of crevasse detection reaches 94.38%, which can detect the narrowest crevasse (0.6 meters) in two datasets, with an average detection time of only 6.8 milliseconds. Experimental results show that the proposed method can detect crevasse in real-time, automatically, and accurately.

Electromyography (EMG) provides a useful way to identify amyotrophic lateral sclerosis(ALS) disease. EMG signals sampled from different muscles show different sensitivities on ALS disease identification, in which to find out the most sensitive muscles to ALS is meaningful. In this paper, a selective ensemble learning method is proposed for cross-muscle ALS disease identification. First, omics features are extracted from time, frequency and wavelet domains of the original EMG signals and their adaptively decomposed components respectively. Second, the ensemble learning method with selective voting strategy is proposed for ALS identification in cross-individual and cross-muscle scenarios. Finally, the contributions of each sample to the individual identification are comprehensively analyzed using the ridge regression model. Two EMG datasets from different human race and different devices are used to evaluate the performance of the proposed method. Experimental results illustrate the effectiveness of the proposed method on cross-individual and cross-muscle ALS identification, i.e. the classification accuracy and sensitivity improve by 1% ~ 11% and 1%. ~ 18% respectively.

The accumulation rate is a key parameter in computing the mass balance of glaciers and ice sheets to estimate sea level rise. A shallow-layers-detection ice sounding radar (SLDISR) is developed to measure the accumulation rate and shape of near-surface internal layers with high resolution. With a transmitting frequency from 500 to 2000 MHz, this frequency-modulated continuous wave (FMCW) radar provides a range resolution of about 16 cm in free space by using a Hanning window and a penetrating depth about 150 m under polar ice. The spectral analysis and coherent integration techniques are used to obtain a high processing gain and to improve the signal-to-noise ratio of the system. A phase-locked loop with wideband yttrium iron garnet (YIG) oscillator is applied to generate a sweeping chirp signal as an input source for the transmitter. A stable, low-frequency reference chirp signal is generated with a direct digital synthesizer (DDS) integrated in field-programmable gate array (FPGA). To reduce the high-speed requirement to the analog-to-digital converter (ADC), dechirp technology is adopted at the RF section of the receiver. The implementation of the digital unit is based on an FPGA chip. The designed radar has been successfully deployed in Antarctica during the 31st Chinese Antarctic Research Expedition (CHINARE 31) and CHINARE 33, mainly over the East Antarctic Ice Sheet (EAIS). The echograms indicate the effectiveness of the radar system on detecting clear internal reflecting horizons (IRHs) over ice sheets.

Occlusal contact status of teeth is a key indicator for orthodontic and periodontal disease treatment. Digital analysis includes tooth position recognition and occlusal contact distribution estimation. In this study, we propose a cascade two-stage point-wise network named Teeth Segmentation Network (TSegNet) based on self-attention mechanism to address teeth segmentation task. And a template-based registration method is proposed to analyze the status of teeth occlusion. In TSegNet, spatial and channel attention are used to improve the performance feature extraction. Template-registration-based occlusal distribution analysis method reduced the labeled number of training samples. To the best of our knowledge, it is the first study on occlusal contact analyzing by using computer-aided-diagnosis technique. Experiment results illustrate the effectiveness and robustness of our proposed method.

In this paper, we propose a novel and efficient false positive reduction framework for early pulmonary nodules detection with physical examination Computer Tomography (CT) scan. First, a 3DU-NET model is used for detecting small pulmonary nodules in physical examination. Second, the grayscale history image (GHI) of the candidate nodules in multi-view is used as the input of the false positive reduction network to obtain their comprehensive information. Finally, an effective multi-branch false positive reduction network is built to avoid reducing the sensitivity of nodule detection. Experiments are performed in our dataset, i.e. 1,000 samples of Lung Nodules from Physical Examination (LNPE1000), in which nodules are with the average diameter of 5. 3 mm. The sensitivities of the proposed method are 79.9% and 85.8% with each CT contained 0.25 and 0.45 false positives on average respectively, which are more than 8.9% and 3.9% higher than baseline. Experimental results show that the proposed method is robust and efficient, which can successfully detect smaller and larger nodules in physical examination and can effectively reduce false positives while maintain the sensitivity of nodule detection. It may be helpful to further improve the efficiency of doctors in clinical diagnosis.

Electromyography (EMG) analysis is an important means to assist the diagnosis of amyotrophic lateral sclerosis (ALS) and other neuromuscular diseases. Following the idea of omics analysis, this paper adopts omics feature extraction and hybrid feature selection strategies to identify ALS with EMG signal in cross-individual scenarios. Specifically, multiple features from time domain, frequency domain, wavelet domain and nonlinear dynamics are extracted to capture the intrinsic characteristics of the EMG signal to the greatest extent. And then, a hybrid feature selection strategy is designed which combines certain basic feature selection methods in two rounds to screen the discriminative features for classification. Finally, the EMG signals of ALS and the normal control are classified by linear discriminant analysis. Experiments are carried out on the public and private datasets to verify the effectiveness of the proposed method.

Although the needle Electromyography (EMG) is an efficient means for Amyotrophic Lateral Sclerosis (ALS) diagnosis, the proper selection of muscles with high sensitivity is an important issue in clinical application. In this paper, we propose a new deep learning method called Wavelet-based Multi-branch Convolutional Neural Network (WM-CNN) for cross-individual ALS disease identification. The proposed method uses the multi-branch network as framework to learn the relationship among EMG samples of an EMG recording, in which each sub-network with three wavelet-based convolution layers (WCL) is used to extract features of the corresponding sample. Experiments show that the proposed method obtains better performance in cross-individual and cross-muscle experiments compared with other methods, which implies that the proposed method can extract general features of neurogenic injury and could be helpful to find proper muscles more sensitive for ALS disease identification.