Seq-HGNN: Learning Sequential Node Representation on Heterogeneous Graph

Recent years have witnessed the rapid development of heterogeneous graph neural networks (HGNNs) in information retrieval (IR) applications. Many existing HGNNs design a variety of tailor-made graph convolutions to capture structural and semantic information in heterogeneous graphs. However, existing HGNNs usually represent each node as a single vector in the multi-layer graph convolution calculation, which makes the high-level graph convolution layer fail to distinguish information from different relations and different orders, resulting in the information loss in the message passing. %insufficient mining of information. To this end, we propose a novel heterogeneous graph neural network with sequential node representation, namely Seq-HGNN. To avoid the information loss caused by the single vector node representation, we first design a sequential node representation learning mechanism to represent each node as a sequence of meta-path representations during the node message passing. Then we propose a heterogeneous representation fusion module, empowering Seq-HGNN to identify important meta-paths and aggregate their representations into a compact one. We conduct extensive experiments on four widely used datasets from Heterogeneous Graph Benchmark (HGB) and Open Graph Benchmark (OGB). Experimental results show that our proposed method outperforms state-of-the-art baselines in both accuracy and efficiency. The source code is available at https://github.com/nobrowning/SEQ_HGNN.Comment: SIGIR 202

Language-Conditioned Imitation Learning with Base Skill Priors under Unstructured Data

The growing interest in language-conditioned robot manipulation aims to develop robots capable of understanding and executing complex tasks, with the objective of enabling robots to interpret language commands and manipulate objects accordingly. While language-conditioned approaches demonstrate impressive capabilities for addressing tasks in familiar environments, they encounter limitations in adapting to unfamiliar environment settings. In this study, we propose a general-purpose, language-conditioned approach that combines base skill priors and imitation learning under unstructured data to enhance the algorithm's generalization in adapting to unfamiliar environments. We assess our model's performance in both simulated and real-world environments using a zero-shot setting. In the simulated environment, the proposed approach surpasses previously reported scores for CALVIN benchmark, especially in the challenging Zero-Shot Multi-Environment setting. The average completed task length, indicating the average number of tasks the agent can continuously complete, improves more than 2.5 times compared to the state-of-the-art method HULC. In addition, we conduct a zero-shot evaluation of our policy in a real-world setting, following training exclusively in simulated environments without additional specific adaptations. In this evaluation, we set up ten tasks and achieved an average 30% improvement in our approach compared to the current state-of-the-art approach, demonstrating a high generalization capability in both simulated environments and the real world. For further details, including access to our code and videos, please refer to our supplementary materials

Modeling genetic imprinting effects of DNA sequences with multilocus polymorphism data

Single nucleotide polymorphisms (SNPs) represent the most widespread type of DNA sequence variation in the human genome and they have recently emerged as valuable genetic markers for revealing the genetic architecture of complex traits in terms of nucleotide combination and sequence. Here, we extend an algorithmic model for the haplotype analysis of SNPs to estimate the effects of genetic imprinting expressed at the DNA sequence level. The model provides a general procedure for identifying the number and types of optimal DNA sequence variants that are expressed differently due to their parental origin. The model is used to analyze a genetic data set collected from a pain genetics project. We find that DNA haplotype GAC from three SNPs, OPRKG36T (with two alleles G and T), OPRKA843G (with alleles A and G), and OPRKC846T (with alleles C and T), at the kappa-opioid receptor, triggers a significant effect on pain sensitivity, but with expression significantly depending on the parent from which it is inherited (p = 0.008). With a tremendous advance in SNP identification and automated screening, the model founded on haplotype discovery and statistical inference may provide a useful tool for genetic analysis of any quantitative trait with complex inheritance

Study on brain damage patterns of COVID-19 patients based on EEG signals

ObjectiveThe coronavirus disease 2019 (COVID-19) is an acute respiratory infectious disease caused by the SARA-CoV-2, characterized by high infectivity and incidence. Clinical data indicates that COVID-19 significantly damages patients’ perception, motor function, and cognitive function. However, the electrophysiological mechanism by which the disease affects the patient’s nervous system is not yet clear. Our aim is to investigate the abnormal levels of brain activity and changes in brain functional connectivity network in patients with COVID-19.MethodsWe compared and analyzed electroencephalography signal sample entropy, energy spectrum, and brain network characteristic parameters in the delta (1–4 Hz), theta (4–8 Hz), alpha (8–13 Hz), and beta (13–30 Hz) bands of 15 patients with COVID-19 and 15 healthy controls at rest.ResultsAt rest, energy values of the four frequency bands in the frontal and temporal lobes of COVID-19 patients were significantly reduced. At the same time, the sample entropy value of the delta band in COVID-19 patients was significantly increased, while the value of the beta band was significantly decreased. However, the average value of the directed transfer function of patients did not show any abnormalities under the four frequency bands. Furthermore, node degree in the temporal lobe of patients was significantly increased, while the input degree of the frontal and temporal lobes was significantly decreased, and the output degree of the frontal and occipital lobes was significantly increased.ConclusionThe level of brain activity in COVID-19 patients at rest is reduced, and the brain functional network undergoes a rearrangement. These results preliminarily demonstrate that COVID-19 patients exhibit certain brain abnormalities during rest, it is feasible to explore the neurophysiological mechanism of COVID-19’s impact on the nervous system by using EEG signals, which can provide a certain technical basis for the subsequent diagnosis and evaluation of COVID-19 using artificial intelligence and the prevention of brain nervous system diseases after COVID-19 infection

Mg3(Bi,Sb)2 single crystals towards high thermoelectric performance

The rapid growth of the thermoelectric cooler market makes the development of novel room temperature thermoelectric materials of great importance. Ternary n-type Mg3(Bi,Sb)2 alloys are promising alternatives to the state-of-the-art Bi2(Te,Se)3 alloys but grain boundary resistance is the most important limitation. n-type Mg3(Bi,Sb)2 single crystals with negligible grain boundaries are expected to have particularly high zT but have rarely been realized due to the demanding Mg-rich growth conditions required. Here, we report, for the first time, the thermoelectric properties of n-type Mg3(Bi,Sb)2 alloyed single crystals grown by a one-step Mg-flux method using sealed tantalum tubes. High weighted mobility ∼140 cm2 V−1 s−1 and a high zT of 0.82 at 315 K are achieved in Y-doped Mg3Bi1.25Sb0.75 single crystals. Through both experimental angle-resolved photoemission spectroscopy and theoretical calculations, we denote the origin of the high thermoelectric performance from a point of view of band widening effect and electronegativity, as well as the necessity to form high Bi/Sb ratio ternary Mg3(Bi,Sb)2 alloys. The present work paves the way for further development of Mg3(Bi,Sb)2 for near room temperature thermoelectric applications

Clinical outcomes and risk factors of coronary artery aneurysms after successful percutaneous coronary intervention and drug-eluting stent implantation for chronic total occlusions

AbstractObjectiveThe study aimed to analyze the risk factors and long-term outcomes associated with coronary artery aneurysms (CAAs) after successful percutaneous coronary intervention (PCI) and drug-eluting stent (DES) implantation in patients with CTOs.BackgroundThere are sporadic data available on post-procedure CAAs after transcatheter revascularization for CTOs.Methods and resultsA total of 141 patients with 149 CTOs who underwent successful CTO-PCI and DES implantation with angiographic follow-up from 2004 to 2010 were included. Patients were divided into CAA group and non-CAA group according to the presence of CAAs in the follow-up angiography. The independent predictors and major adverse cardiac events (MACEs) including cardiac death, myocardial infarction (MI) and target-vessel revascularization (TVR) were compared between two groups. The incidence of CAAs was 11.4% (17/149) after index procedure. Multivariate analysis showed that age (OR: 0.925, CI 0.873–0.980, P = 0.008), ostial occlusion (OR: 6.715, CI 1.473–30.610, P = 0.014), the parallel wire technique (OR: 6.167, CI 1.709–22.259, P = 0.005) and DES length (OR: 1.030, CI 1.002–1.058, P = 0.036) were the independent predictors of CAAs after successful CTO-PCI and DES implantation. MACEs were similar between two groups (adjusted hazard ratio 0.670; 95% CI 0.160–2.808; P = 0.584) during the 5-year follow-up.ConclusionsThe independent predictors of CAAs after successful CTO-PCI and DES implantation are age, ostial occlusion, the parallel wire technique and DES length. CAAs after index procedure are not frequently associated with adverse clinical events under dual antiplatelet therapy. Further large clinical studies are warranted to explore the clinical implications of patients with this distinct new entity

Evaluating reproducibility of differential expression discoveries in microarray studies by considering correlated molecular changes

Motivation: According to current consistency metrics such as percentage of overlapping genes (POG), lists of differentially expressed genes (DEGs) detected from different microarray studies for a complex disease are often highly inconsistent. This irreproducibility problem also exists in other high-throughput post-genomic areas such as proteomics and metabolism. A complex disease is often characterized with many coordinated molecular changes, which should be considered when evaluating the reproducibility of discovery lists from different studies