RDGSL: Dynamic Graph Representation Learning with Structure Learning

Temporal Graph Networks (TGNs) have shown remarkable performance in learning representation for continuous-time dynamic graphs. However, real-world dynamic graphs typically contain diverse and intricate noise. Noise can significantly degrade the quality of representation generation, impeding the effectiveness of TGNs in downstream tasks. Though structure learning is widely applied to mitigate noise in static graphs, its adaptation to dynamic graph settings poses two significant challenges. i) Noise dynamics. Existing structure learning methods are ill-equipped to address the temporal aspect of noise, hampering their effectiveness in such dynamic and ever-changing noise patterns. ii) More severe noise. Noise may be introduced along with multiple interactions between two nodes, leading to the re-pollution of these nodes and consequently causing more severe noise compared to static graphs. In this paper, we present RDGSL, a representation learning method in continuous-time dynamic graphs. Meanwhile, we propose dynamic graph structure learning, a novel supervisory signal that empowers RDGSL with the ability to effectively combat noise in dynamic graphs. To address the noise dynamics issue, we introduce the Dynamic Graph Filter, where we innovatively propose a dynamic noise function that dynamically captures both current and historical noise, enabling us to assess the temporal aspect of noise and generate a denoised graph. We further propose the Temporal Embedding Learner to tackle the challenge of more severe noise, which utilizes an attention mechanism to selectively turn a blind eye to noisy edges and hence focus on normal edges, enhancing the expressiveness for representation generation that remains resilient to noise. Our method demonstrates robustness towards downstream tasks, resulting in up to 5.1% absolute AUC improvement in evolving classification versus the second-best baseline

TIGER: Temporal Interaction Graph Embedding with Restarts

Temporal interaction graphs (TIGs), consisting of sequences of timestamped interaction events, are prevalent in fields like e-commerce and social networks. To better learn dynamic node embeddings that vary over time, researchers have proposed a series of temporal graph neural networks for TIGs. However, due to the entangled temporal and structural dependencies, existing methods have to process the sequence of events chronologically and consecutively to ensure node representations are up-to-date. This prevents existing models from parallelization and reduces their flexibility in industrial applications. To tackle the above challenge, in this paper, we propose TIGER, a TIG embedding model that can restart at any timestamp. We introduce a restarter module that generates surrogate representations acting as the warm initialization of node representations. By restarting from multiple timestamps simultaneously, we divide the sequence into multiple chunks and naturally enable the parallelization of the model. Moreover, in contrast to previous models that utilize a single memory unit, we introduce a dual memory module to better exploit neighborhood information and alleviate the staleness problem. Extensive experiments on four public datasets and one industrial dataset are conducted, and the results verify both the effectiveness and the efficiency of our work.Comment: WWW 2023. Codes: https://github.com/yzhang1918/www2023tige

Risk factors for in-hospital mortality after total arch procedure in patients with acute type A aortic dissection

ObjectKnowledge about the risk factors of in-hospital mortality for acute type A aortic dissection (ATAAD) patients who received total arch procedure is limited. This study aims to investigate preoperative and intraoperative risk factors of in-hospital mortality of these patients.MethodsFrom May 2014 to June 2018, 372 ATAAD patients received the total arch procedure in our institution. These patients were divided into survival and death groups, and patients` in-hospital data were retrospectively collected. Receiver operating characteristic curve analysis was adopted to determine the optimal cut-off value of continuous variables. Univariate and multivariable logistic regression analyses were used to detect independent risk factors for in-hospital mortality.ResultsA total of 321 patients were included in the survival group and 51 in the death group. Preoperative details showed that patients in the death group were older (55.4 ± 11.7 vs. 49.3 ± 12.6, P = 0.001), had more renal dysfunction (29.4% vs. 10.9%, P = 0.001) and coronary ostia dissection (29.4% vs. 12.2%, P = 0.001), and decreased left ventricular ejection fraction (LVEF) (57.5 ± 7.9% vs. 59.8 ± 7.3%, P = 0.032). Intraoperative results showed that more patients in the death group experienced concomitant coronary artery bypass grafting (35.3% vs. 15.3%, P = 0.001) with increased cardiopulmonary bypass (CPB) time (165.7 ± 39.0 vs. 149.4 ± 35.8 min, P = 0.003), cross-clamp time (98.4 ± 24.5 vs. 90.2 ± 26.9 min, P = 0.044), and red blood cell transfusion (913.7 ± 629.0 vs. 709.7 ± 686.6 ml, P = 0.047). Logistic regression analysis showed that age >55 years, renal dysfunction, CPB time >144 min, and RBC transfusion >1,300 ml were independent risk factors for in-hospital mortality in patients with ATAAD.ConclusionIn the present study, we identified that older age, preoperative renal dysfunction, long CPB time, and intraoperative massive transfusion were risk factors for in-hospital mortality in ATAAD patients with the total arch procedure

Heterogeneous network embedding enabling accurate disease association predictions.

BackgroundIt is significant to identificate complex biological mechanisms of various diseases in biomedical research. Recently, the growing generation of tremendous amount of data in genomics, epigenomics, metagenomics, proteomics, metabolomics, nutriomics, etc., has resulted in the rise of systematic biological means of exploring complex diseases. However, the disparity between the production of the multiple data and our capability of analyzing data has been broaden gradually. Furthermore, we observe that networks can represent many of the above-mentioned data, and founded on the vector representations learned by network embedding methods, entities which are in close proximity but at present do not actually possess direct links are very likely to be related, therefore they are promising candidate subjects for biological investigation.ResultsWe incorporate six public biological databases to construct a heterogeneous biological network containing three categories of entities (i.e., genes, diseases, miRNAs) and multiple types of edges (i.e., the known relationships). To tackle the inherent heterogeneity, we develop a heterogeneous network embedding model for mapping the network into a low dimensional vector space in which the relationships between entities are preserved well. And in order to assess the effectiveness of our method, we conduct gene-disease as well as miRNA-disease associations predictions, results of which show the superiority of our novel method over several state-of-the-arts. Furthermore, many associations predicted by our method are verified in the latest real-world dataset.ConclusionsWe propose a novel heterogeneous network embedding method which can adequately take advantage of the abundant contextual information and structures of heterogeneous network. Moreover, we illustrate the performance of the proposed method on directing studies in biology, which can assist in identifying new hypotheses in biological investigation

Utilization of Metallurgy—Beneficiation Combination Strategy to Decrease TiO₂ in Titanomagnetite Concentrate before Smelting

Excessive TiO2 in titanomagnetite concentrates (TC) causes unavoidable problems in subsequent smelting. At present, this issue cannot be addressed using traditional mineral processing technology. Herein, a strategy of metallurgy-beneficiation combination to decrease the TiO2 grade in TC before smelting was proposed. Roasting TC with calcium carbonate (CaCO3) together with magnetic separation proved to be a viable strategy. Under optimal conditions (roasting temperature = 1400 °C, CaCO3 ratio = 20%, and magnetic intensity = 0.18 T), iron and titanium was separated efficiently (Fe grade: 56.6 wt.%; Fe recovery: 70 wt.%; TiO2 grade 3 wt.%; TiO2 removal: 84.1 wt.%). X-ray diffraction, scanning electron microscopy, and energy dispersive spectroscopy analysis were used to study the mechanisms. The results showed that Ti in TC could react with CaO to form CaTiO3, and thermodynamic calculations provided a relevant theoretical basis. In sum, the metallurgy-beneficiation combination strategy was proven as an effective method to decrease unwanted TiO2 in TC

Preliminary Study of the Characteristics of Several Glossy Cabbage (Brassica oleracea var. capitata L.) Mutants

To determine the characteristics and potential practical applications of glossy cabbage (Brassica oleracea var. capitata L.) mutants, five different glossy mutants were studied. The amount of epicuticular wax covering the mutant leaves was only approximately 30% that of the wild-type (WT) leaves. The wax crystals of WT plants were columnar and linear, while they were granular and rod-shaped in the mutants. Additionally, in WT cabbage, the primary wax components were alkanes, alcohols, fatty acids, ketones, and aldehydes. There was a significant decrease in the abundance of alkanes and ketones in the wax of the mutants. The glossy-green trait of the mutants may be the result of an inhibited alkane-forming pathway. Higher rates of chlorophyll leaching and water loss demonstrate that the mutant leaves were more permeable and sensitive to drought stress than the WT leaves. Growth curve results indicated that the growth rate of mutant-1 and mutant-3 was slower than that of the corresponding WT cabbage, resulting in shorter plants. However, the growth rate of mutant-2 was not influenced by the lack of coating wax. An investigation of the agronomic traits and heterosis of the glossy cabbage mutants indicated that all five mutants had glossy-green leaves, which was a favorable characteristic. The F1 plants derived from crosses involving mutant-2 exhibited obvious heterosis, suggesting the observed glossy-green trait is controlled by a dominant gene. Therefore, mutant-2 may be useful as a source of genetic material for future cabbage breeding experiments

Novel Approach for Fine Ilmenite Flotation Using Hydrophobized Glass Bubbles as the Buoyant Carrier

Ilmenite disseminated grain size is relatively fine, and it must be finely ground to fully separate ilmenite from gangue and then produce fine-grained minerals, which deteriorates flotation. A novel method using buoyant carriers to improve the recovery of fine ilmenite in froth flotation was introduced in this study. Hydrophobized glass bubbles (HGB) as carrier materials were obtained by an efficient, simple modification of ordinary glass bubbles. The carrier flotation of fine ilmenite in the presence of HGB was investigated by micro flotation tests, X-ray diffractometer analysis, Fourier transform infrared (FTIR), optical microscope observation, and the extended DLVO theory (XDLVO). Micro-flotation results showed that the recovery of fine ilmenite in presence of HGB was 37.7% higher than that when using NaOL alone at pH 6. FTIR analysis and optical microscope observation revealed that fine ilmenite particles can be closely attached on the HGB surface to increase apparent particle size considerably. The data calculated from the DLVO theory indicated that the acid–base interaction force determined the adsorption between two hydrophobic particles

Chromosome Doubling of Microspore-Derived Plants from Cabbage (Brassica oleracea var. capitata L.) and Broccoli (Brassica oleracea var. italica L.)

Chromosome doubling of microspore-derived plants is an important factor in the practical application of microspore culture technology because breeding programs require a large number of genetically stable, homozygous doubled haploid plants with a high level of fertility. In the present paper, 29 populations of microspore-derived plantlets from cabbage (Brassica oleracea var. capitata) and broccoli (Brassica oleracea var. italica) were used to study the ploidy level and spontaneous chromosome doubling of these populations, the artificial chromosome doubling induced by colchicine, and the influence of tissue culture duration on the chromosomal ploidy of the microspore-derived regenerants. Spontaneous chromosome doubling occurred randomly and was genotype dependent. In the plant populations derived from microspores, there were haploids, diploids, and even a low frequency of polyploids and mixed-ploidy plantlets. The total spontaneous doubling in the 14 cabbage populations ranged from 0 - 76.9%, compared with 52.2 - 100% in the 15 broccoli populations. To improve the rate of chromosome doubling, an efficient and reliable artificial chromosome doubling protocol (i.e., the immersion of haploid plantlet roots in a colchicine solution) was developed for cabbage and broccoli microspore-derived haploids. The optimal chromosome doubling of the haploids was obtained with a solution of 0.2% colchicine for 9 - 12 h or 0.4% colchicine for 3 - 9 h for cabbage and 0.05% colchicine for 6 - 12 h for broccoli. This protocol produced chromosome doubling in over 50% of the haploid genotypes for most of the populations derived from cabbage and broccoli. Notably, after 1 or more years in tissue culture, the chromosomes of the haploids were doubled, and most of the haploids turned into doubled haploid or mixed-ploidy plants. This is the first report indicating that tissue culture duration can change the chromosomal ploidy of microspore-derived regenerants