Dance Your Latents: Consistent Dance Generation through Spatial-temporal Subspace Attention Guided by Motion Flow

The advancement of generative AI has extended to the realm of Human Dance Generation, demonstrating superior generative capacities. However, current methods still exhibit deficiencies in achieving spatiotemporal consistency, resulting in artifacts like ghosting, flickering, and incoherent motions. In this paper, we present Dance-Your-Latents, a framework that makes latents dance coherently following motion flow to generate consistent dance videos. Firstly, considering that each constituent element moves within a confined space, we introduce spatial-temporal subspace-attention blocks that decompose the global space into a combination of regular subspaces and efficiently model the spatiotemporal consistency within these subspaces. This module enables each patch pay attention to adjacent areas, mitigating the excessive dispersion of long-range attention. Furthermore, observing that body part's movement is guided by pose control, we design motion flow guided subspace align & restore. This method enables the attention to be computed on the irregular subspace along the motion flow. Experimental results in TikTok dataset demonstrate that our approach significantly enhances spatiotemporal consistency of the generated videos.Comment: 10 pages, 5 figure

A Novel Method for ECG Signal Classification Via One-Dimensional Convolutional Neural Network

This paper develops an end-to-end ECG signal classification algorithm based on a novel segmentation strategy and 1D Convolutional Neural Networks (CNN) to aid the classification of ECG signals and alleviate the workload of physicians. The ECG segmentation strategy named R-R-R strategy (i.e., retaining ECG data between the R peaks just before and after the current R peak) is used for segmenting the original ECG data into segments to train and test the 1D CNN models. The novel strategy mimics physicians in scanning ECG to a greater extent, and maximizes the inherent information of ECG segments for diagnosis. The performance of the proposed end to end ECG signal classification algorithm was verified with the ECG signals from 48 records in the MIT-BIH arrhythmia database. When the heartbeat types were divided into the five classes recommended by clinicians, i.e., normal beat, left bundle branch block beat, right bundle branch block beat, premature ventricular contraction, and paced beat, the classification accuracy, the area under the curve (AUC), the sensitivity, and the F1-score achieved by the proposed model were 0.9924, 0.9994, 0.99 and 0.99, respectively. When the heartbeat types were divided into six classes recommended by clinicians, i.e., normal beat, left bundle branch block beat, right bundle branch block beat, premature ventricular contraction, paced beat and other beats, the beat classification accuracy, the AUC, the sensitivity, and the F1-score achieved by the model reached 0.9702, 0.9966, 0.97, and 0.97, respectively. When the heartbeat types were divided into five classes recommended by the Association for Advancement of Medical Instrumentation (AAMI), i.e., normal beat, supraventricular ectopic beat, ventricular ectopic beat, fusion beat, and unknown beat, the beat classification accuracy, the sensitivity, and the F1-score were 0.9745, 0.97, and 0.97, respectively. Experimental results show that the proposed method achieves better performance than the state-of-the-art methods

Key pathways and genes controlling the development and progression of clear cell renal cell carcinoma (ccRCC) based on gene set enrichment analysis

BACKGROUND: Clear-cell renal cell carcinoma (ccRCC) is one of the most common types of kidney cancer in adults; however, its causes are not completely understood. The study was designed to filter the key pathways and genes associated with the occurrence or development of ccRCC, acquaint its pathogenesis at gene and pathway level, to provide more theory evidence and targeted therapy for ccRCC. METHODS: Gene set enrichment analysis (GSEA) and meta-analysis (Meta) were used to screen the critical pathways and genes which may affect the occurrence and progression of ccRCC on the transcription level. Corresponding pathways of significant genes were obtained with the online website DAVID (http://david.abcc.ncifcrf.gov/). RESULTS: Thirty seven consistent pathways and key genes in these pathways related to ccRCC were obtained with combined GSEA and meta-analysis. These pathways were mainly involved in metabolism, organismal systems, cellular processes and environmental information processing. CONCLUSION: The gene pathways that we identified could provide insight concerning the development of ccRCC. Further studies are needed to determine the biological function for the positive genes

Development and Validation of a New Method to Diagnose Apical Hypertrophic Cardiomyopathy By Gated Single-Photon Emission Computed Tomography Myocardial Perfusion Imaging

Aim The aim of this study is to develop and validate a new method to diagnose apical hypertrophic cardiomyopathy (AHCM) by the integral quantitative analysis of myocardial perfusion and wall thickening from gated single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI). Patients and methods Twenty-two consecutive patients, who showed T wave inversion of at least 3 mm in precordial leads and sinus rhythm in ECG, were enrolled. All the patients underwent cardiac magnetic resonance (CMR), gated rest SPECT MPI and echocardiography. According to CMR diagnostic results, 13 patients were categorized as in the AHCM group and the remaining nine patients were categorized as in the non-AHCM group. Operators who were blinded to the CMR diagnosis independently performed the diagnosis by gated SPECT MPI. The regions of interest inside the apical hotspots on the perfusion polar map were drawn and the mean values of wall thickening in the drawn region of interests were calculated. Using MRI diagnosis as the gold standard, AHCM was diagnosed based on receiver operating characteristic analysis of the mean wall thickening in the apical perfusion hotspot. The area under curve, sensitivity, specificity, and accuracy of our method were 0.97, 100, 89, and 95%, respectively. Conclusion Our new method has high sensitivity, specificity, and accuracy against CMR diagnosis. It has great promise to become a clinical tool in the diagnosis of AHCM

NMD-12: A New Machine-Learning Derived Screening Instrument to Detect Mild Cognitive Impairment and Dementia

Introduction Using machine learning techniques, we developed a brief questionnaire to aid neurologists and neuropsychologists in the screening of mild cognitive impairment (MCI) and dementia. Methods With the reduction of the survey size as a goal of this research, feature selection based on information gain was performed to rank the contribution of the 45 items corresponding to patient responses to the specified questions. The most important items were used to build the optimal screening model based on the accuracy, practicality, and interpretability. The diagnostic accuracy for discriminating normal cognition (NC), MCI, very mild dementia (VMD) and dementia was validated in the test group. Results The screening model (NMD-12) was constructed with the 12 items that were ranked the highest in feature selection. The receiver-operator characteristic (ROC) analysis showed that the area under the curve (AUC) in the test group was 0.94 for discriminating NC vs. MCI, 0.88 for MCI vs. VMD, 0.97 for MCI vs. dementia, and 0.96 for VMD vs. dementia, respectively. Discussion The NMD-12 model has been developed and validated in this study. It provides healthcare professionals with a simple and practical screening tool which accurately differentiates NC, MCI, VMD, and dementia

Semantic Segmentation to Extract Coronary Arteries in Invasive Coronary Angiograms

Accurate semantic segmentation of each coronary artery using invasive coronary angiography (ICA) is important for stenosis assessment and coronary artery disease (CAD) diagnosis. In this paper, we propose a multi-step semantic segmentation algorithm based on analyzing arterial sements extraced from ICAs. The proposed algorithm firstly extracts the entire arterial binary mask (binary vascular tree) using a deep learning-based method. Then we extract the centerline of the binary vascular tree and separate it into different arterial segments. Finally, by extracting the underlyingarterial topology, position, and pixel features, we construct a powerful coronary artery segment classifier based on a support vector machine. Each arterial segment is classified into the left coronary artery (LCA), left anterior descending (LAD), and other types of arterial segments. The proposed method was tested on a dataset with 225 ICAs and achieved a mean accuracy of 70.33% for the multi-class artery classification and a mean intersection over union of 0.6868 for semantic segmentation of arteries. The experimental results show the effectiveness of the proposed algorithm, which provides impressive performance for analyzing the individual arteries in ICAs

Myocardial Stunning-Induced Left Ventricular Dyssynchrony On Gated Single-Photon Emission Computed Tomography Myocardial Perfusion Imaging

Objectives Myocardial stunning provides additional nonperfusion markers of coronary artery disease (CAD), especially for severe multivessel CAD. The purpose of this study is to assess the influence of myocardial stunning to the changes of left ventricular mechanical dyssynchrony (LVMD) parameters between stress and rest gated single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI). Patients and methods A total of 113 consecutive patients (88 males and 25 females) who had undergone both stress and rest 99mTc-sestamibi gated SPECT MPI were retrospectively enrolled. Suspected or known patients with CAD were included if they had exercise stress MPI and moderate to severe myocardial ischemia. Segmental scores were summed for the three main coronary arteries according to standard myocardial perfusion territories, and then regional perfusion, wall motion, and wall thickening scores were measured. Myocardial stunning was defined as both ischemia and wall dysfunction within the same coronary artery territory. Patients were divided into the stunning group (n=58) and nonstunning group (n=55). Results There was no significant difference of LVMD parameters between stress and rest in the nonstunning group. In the stunning group, phase SD and phase histogram bandwidth of contraction were significantly larger during stress than during rest (15.05±10.70 vs. 13.23±9.01 and 46.07±34.29 vs. 41.02±32.16, PP\u3c0.05). Conclusion Both systolic and diastolic LVMD parameters deteriorate with myocardial stunning. This kind of change may have incremental values to diagnose CAD

Semantic Segmentation to Extract Coronary Arteries in Invasive Coronary Angiograms

Accurate semantic segmentation of each coronary artery using invasive coronary angiography (ICA) is important for stenosis assessment and coronary artery disease (CAD) diagnosis. In this paper, we propose a multi-step semantic segmentation algorithm based on analyzing arterial sements extraced from ICAs. The proposed algorithm firstly extracts the entire arterial binary mask (binary vascular tree) using a deep learning-based method. Then we extract the centerline of the binary vascular tree and separate it into different arterial segments. Finally, by extracting the underlyingarterial topology, position, and pixel features, we construct a powerful coronary artery segment classifier based on a support vector machine. Each arterial segment is classified into the left coronary artery (LCA), left anterior descending (LAD), and other types of arterial segments. The proposed method was tested on a dataset with 225 ICAs and achieved a mean accuracy of 70.33% for the multi-class artery classification and a mean intersection over union of 0.6868 for semantic segmentation of arteries. The experimental results show the effectiveness of the proposed algorithm, which provides impressive performance for analyzing the individual arteries in ICAs

Ab Initio Studies on Interactions in K3_3C60_{60} under High Pressure

Fullerene solids doped with alkali metals (A$_3$C$_{60}$, A = K, Rb, Cs) exhibit a superconducting transition temperature ($T_c$) as high as 40 K, and their unconventional superconducting properties have been a subject of debate. With application of high pressure on K$_3$C$_{60}$ and Rb$_3$C$_{60}$, the experiments demonstrate the decrease of $T_c$. In this paper, we focus on K$_3$C$_{60}$ and derive the structure of K$_3$C$_{60}$ under different pressures based on first-principles calculations, exploring the trends of Coulomb interactions at various pressures. By utilizing the Maximally Localized Wannier function approach, Constrained Density Functional Perturbation Theory (cDFPT), and Constrained Random Phase Approximation (cRPA), we construct a microscopic low-energy model near the Fermi level. Our results strongly indicate that, in the K$_3$C$_{60}$ system, as pressure increases, the effect of phonons is the key to intraorbital electron pairing. There is a dominance of the phonon-driven superconducting mechanism at high pressure