Attributed Multi-Order Graph Convolutional Network for Heterogeneous Graphs.

Heterogeneous graph neural networks play a crucial role in discovering discriminative node embeddings and relations from multi-relational networks. One of the key challenges in heterogeneous graph learning lies in designing learnable meta-paths, which significantly impact the quality of learned embeddings. In this paper, we propose an Attributed Multi-Order Graph Convolutional Network (AMOGCN), which automatically explores meta-paths that involve multi-hop neighbors by aggregating multi-order adjacency matrices. The proposed model first constructs different orders of adjacency matrices from manually designed node connections. Next, AMOGCN fuses these various orders of adjacency matrices to create an intact multi-order adjacency matrix. This process is supervised by the node semantic information, which is extracted from the node homophily evaluated by attributes. Eventually, we employ a one-layer simplifying graph convolutional network with the learned multi-order adjacency matrix, which is equivalent to the cross-hop node information propagation with multi-layer graph neural networks. Substantial experiments reveal that AMOGCN achieves superior semi-supervised classification performance compared with state-of-the-art competitors.

Protective role of arachidonic acid against diabetic myocardial ischemic injury: a translational study of pigs, rats, and humans.

AIM: Patients with diabetes mellitus have poor prognosis after myocardial ischemic injury. However, the mechanism is unclear and there are no related therapies. We aimed to identify regulators of diabetic myocardial ischemic injury. METHODS AND RESULTS: Mass spectrometry-based, non-targeted metabolomic approach was used to profile coronary sinus blood from diabetic and non-diabetic Bama-mini pigs at 0.5-h post coronary artery ligation. Six metabolites had a |log2 (Fold Change)|> 1.3. Among them, the most changed is arachidonic acid (AA), levels of which were 32 times lower in diabetic pigs than in non-diabetic pigs. The AA-derived products, PGI2 and 6-keto-PGF1α, were also significantly reduced. AA treatment of cultured cardiomyocytes protected against cell death by 30% at 48 h of high glucose and oxygen deprivation, which coincided with increased mitophagic activity (as indicated by increased LC3II/LC3I, decreased p62 and increased parkin & PINK1), improved mitochondrial renewal (upregulation of Drp1 and FIS1), reduced ROS generation and increased ATP production. These cardioprotective effects were abolished by PINK1(a crucial mitophagy protein) knockdown or the autophagy inhibitor 3-Methyladenine. The protective effect of AA was also inhibited by indomethacin and Cay10441, a prostacyclin receptor antagonist. Furthermore, diabetic Sprague Dawley rats were subjected to coronary ligation for 40 min and AA treatment (10 mg/day per animal gavaged) decreased myocardial infarct size, cell apoptosis index, inflammatory cytokines and improved heart function. Scanning electron microscopy showed more intact mitochondria in the border zone of infarcted myocardium in AA treated rats. Lastly, diabetic patients after myocardial infarction had lower plasma levels of AA and 6-keto-PGF1α and reduced cardiac ejection fraction, compared with non-diabetic patients after myocardial infarction. Plasma AA level was inversely correlated with fasting blood glucose. CONCLUSIONS: AA protects against diabetic ischemic myocardial damage by promoting mitochondrial autophagy and renewal, which is related to AA derived PGI2 signaling. AA may represent a new strategy to treat diabetic myocardial ischemic injury.

Redefining Esophagectomy: The Manual Layered Insertion Method That May Reduce Anastomotic Leakage.

INTRODUCTION: Anastomotic leakage post-esophagectomy remains a significant challenge. Despite the use of both mechanical and manual anastomosis, leakage rates remain high. This study evaluated the effectiveness of the manual layered insertion anastomosis technique in addressing this issue. METHODS: A retrospective analysis was conducted on patients who underwent this technique from September 2020 to December 2021. The process involved thoracoscopic release of the esophagus, mediastinal lymph node dissection, laparoscopic stomach release, and its transformation into a tube. The latter was then guided to the neck for anastomosis. The posterior anastomotic wall was reshaped in the neck first for optimal insertion, followed by layered suturing with the gastric conduit. The anterior wall was subsequently sutured and repositioned into the chest. RESULTS: The study included 56 patients (51 men, five women, mean age 65.4 y), with nine having undergone neoadjuvant therapy. All received minimally invasive esophagectomy. Average intraoperative blood loss was 79.8 mL, operation time averaged 331 min, and feeding resumed after an average of 6.3 d. No anastomotic leakages were reported, with reduced incidences of anastomotic stenosis and gastric acid reflux compared to previous studies. CONCLUSIONS: The manual layered insertion anastomosis technique may reduce anastomotic leakage and associated complications, improving the efficacy of esophagectomy, which may improve postoperative results and patient quality of life, suggesting the method's potential suitability for wider clinical application.

Assessing the effectiveness of a residential-scale detention tank operated in a multi-objective approach using SWMM.

The volume capture ratio of annual rainfall (VCRAR) of low-impact development measures is significantly influenced by its operating characteristics, particularly for residential stormwater detention tanks (SWDTs). The multi-objective operation strategy of SWDTs, encompassing toilet flushing (TF), green space irrigation (GSI), combined TF and GSI (TF-GSI), and peak flow reduction (PFR) rate, were compared using a case study in Beijing based on the stormwater management model. The findings indicate that the VCRAR for TF, GSI, and TF-GSI rainwater harvesting targets was 89.05, 77.16, and 91.21%, respectively. The operating scheme and return periods have a significant impact on the PFR rate's effectiveness. When the return period was lower than 10 years, the SWDT does not reach its maximum storage capacity, and the PFR rate was increased with increasing the return period: the PFR rate was 71.47% when the design return period was 10 years. It will also produce the phenomena of water inrush, and the overflow volume will grow rapidly when the SWDT reaches its maximum storage capacity. Hence, the operation of SWDTs may be integrated with real-time control to optimize the VCRAR for rainwater reuse and flood migration, thereby enhancing the volume utilization efficiency of SWDTs.

Effects of expanding imports on urban manufacturing employment: Evidence from China.

Full employment is important to promote the high-quality development of the urban economy. Using urban-level data on China from 2004 to 2018, we analyse the effects and mechanism of expanding imports on urban manufacturing employment. We use the Guiding Opinions on Strengthening Import to Promote Balanced Development of Foreign Trade issued by the China State Council in 2012 as a natural experiment to solve the endogeneity problem. We find that expanding imports significantly increases urban manufacturing employment. This conclusion is still robust after a series of robustness tests. Further mechanism tests reveal that productivity improvements and upgrades to product quality from expanding imports can explain increased urban manufacturing employment. The results of the heterogeneity analysis show that expanding imports promote manufacturing employment in large and medium-sized cities but not small cities. Expanding imports increases employment in manufacturing in cities in different regions, with the largest effects on eastern cities, the second largest effects on western cities, and the smallest effects on central cities. These results suggest that expanding imports is an effective channel for increasing employment.

Aspertaichamide a, a novel cytotoxic prenylated indole alkaloid possessing a bicyclo[2.2.2]diazaoctane framework from a marine algal-derived endophytic fungus aspergillus taichungensis 299.

Filamentous fungi belonging to the genus Aspergillus are prodigious producers of alkaloids, particularly prenylated indole alkaloids, that often exhibit structurally diversified skeletons and potent biological activities. In this study, five prenylated indole alkaloids possessing a bicyclo[2.2.2]diazaoctane core ring system, including a novel derivative, namely aspertaichamide A (1), as well as four known compounds, (+)-stephacidin A (2), sclerotiamide (3), (-)-versicolamide B (4), and (+)-versicolamide B (5), were isolated and identified from A. taichungensis 299, an endophytic fungus obtained from the marine red alga Gelidium amansii. The chemical structures of the compounds were elucidated by comprehensive NMR and HRESIMS spectroscopic analyses. In addition to the previously reported prenylated indole alkaloids, aspertaichamide A (1) was characterized as having an unusual ring structure with the fusion of a 3-pyrrolidone dimethylbenzopyran to the bicyclo[2.2.2]diazaoctane moiety, which was rare in these kinds of compounds. The absolute configuration of 1 was determined by TDDFT-ECD calculations. In vitro cytotoxic assays revealed that the novel compound 1 possessed selective cytotoxic activity against five human tumor cell lines (A549, HeLa, HepG2, HCT-116, and AGS), with IC50 values of 1.7-48.5 µM. Most importantly, compound 1 decreased the viability of AGS cells in a concentration-dependent manner with an IC50 value of 1.7 µM. Further studies indicated that 1 may induce AGS cells programmed cell death via the apoptotic pathway.

Revisiting multi-view learning: A perspective of implicitly heterogeneous Graph Convolutional Network.

Graph Convolutional Network (GCN) has become a hotspot in graph-based machine learning due to its powerful graph processing capability. Most of the existing GCN-based approaches are designed for single-view data. In numerous practical scenarios, data is expressed through multiple views, rather than a single view. The ability of GCN to model homogeneous graphs is indisputable, while it is insufficient in facing the heterophily property of multi-view data. In this paper, we revisit multi-view learning to propose an implicit heterogeneous graph convolutional network that efficiently captures the heterogeneity of multi-view data while exploiting the powerful feature aggregation capability of GCN. We automatically assign optimal importance to each view when constructing the meta-path graph. High-order cross-view meta-paths are explored based on the obtained graph, and a series of graph matrices are generated. Combining graph matrices with learnable global feature representation to obtain heterogeneous graph embeddings at various levels. Finally, in order to effectively utilize both local and global information, we introduce a graph-level attention mechanism at the meta-path level that allocates private information to each node individually. Extensive experimental results convincingly support the superior performance of the proposed method compared to other state-of-the-art approaches.

Development and validation of prediction models for poor sleep quality among older adults in the post-COVID-19 pandemic era.

BACKGROUND: Corona Virus Disease 2019 (COVID-19) has a significant impact on sleep quality. However, the effects on sleep quality in the post-COVID-19 pandemic era remain unclear, and there is a lack of a screening tool for Chinese older adults. This study aimed to understand the prevalence of poor sleep quality and determine sensitive variables to develop an effective prediction model for screening sleep problems during infectious diseases outbreaks. MATERIALS AND METHODS: The Peking University Health Cohort included 10,156 participants enrolled from April to May 2023. The Pittsburgh Sleep Quality Index (PSQI) scale was used to assess sleep quality. The data were randomly divided into a training-testing cohort (n = 7109, 70%) and an independent validation cohort (n = 3027, 30%). Five prediction models with 10-fold cross validation including the Least Absolute Shrinkage and Selection Operator (LASSO), Stochastic Volatility Model (SVM), Random Forest (RF), Artificial Neural Network (ANN), and XGBoost model based on the area under curve (AUC) were used to develop and validate predictors. RESULTS: The prevalence of poor sleep quality (PSQI >7) was 30.69% (3117/10,156). Among the generated models, the LASSO model outperformed SVM (AUC 0.579), RF (AUC 0.626), ANN (AUC 0.615) and XGBoost (AUC 0.606), with an AUC of 0.7. Finally, a total of 12 variables related to sleep quality were used as parameters in the prediction models. These variables included age, gender, ethnicity, educational level, residence, marital status, history of chronic diseases, SARS-CoV-2 infection, COVID-19 vaccination, social support, depressive symptoms, and cognitive impairment among older adults during the post-COVID-19 pandemic. The nomogram illustrated that depressive symptoms contributed the most to the prediction of poor sleep quality, followed by age and residence. CONCLUSIONS: This nomogram, based on twelve-variable, could potentially serve as a practical and reliable tool for early identification of poor sleep quality among older adults during the post-pandemic period.

The poor sleep quality (PSQI >7) was still prevalent among older adults during the post-COVID-19 pandemic.The LASSO model was the best model to predict poor sleep quality among older adults, compared with SVM, RF, ANN and XGBoost.This prediction model, based on twelve variables, may potentially serve as a practical and reliable tool for the early identification of poor sleep quality among older adults during the post-pandemic period.

Carbon-doped boron nitride nanosheets as an efficient metal-free catalyst for the selective oxidation of H2S.

We report the first use of carbon-doped boron nitride (BCN) for H2S-selective catalytic oxidation. The obtained carbon-doped BN with an ultrathin layer structure exhibits outstanding H2S elimination and high S yield. In particular, BN doped carbon nanosheets display better catalytic performance than traditional catalysts, such as iron- and carbon-based catalysts. The findings of the present work shed a new light on metal-free catalysts for efficient catalytic removal of toxic H2S.

RhoA suppresses pseudorabies virus replication in vitro.

The porcine pseudorabies virus (PRV) is one of the most devastating pathogens and brings great economic losses to the swine industry worldwide. Viruses are intracellular parasites that have evolved numerous strategies to subvert and utilize different host processes for their life cycle. Among the different systems of the host cell, the cytoskeleton is one of the most important which not only facilitate viral invasion and spread into neighboring cells, but also help viruses to evade the host immune system. RhoA is a key regulator of cytoskeleton system that may participate in virus infection. In this study, we characterized the function of RhoA in the PRV replication by chemical drugs treatment, gene knockdown and gene over-expression strategy. Inhibition of RhoA by specific inhibitor and gene knockdown promoted PRV proliferation. On the contrary, overexpression of RhoA or activation of RhoA by chemical drug inhibited PRV infection. Besides, our data demonstrated that PRV infection induced the disruption of actin stress fiber, which was consistent with previous report. In turn, the actin specific inhibitor cytochalasin D markedly disrupted the normal fibrous structure of intracellular actin cytoskeleton and decreased the PRV replication, suggesting that actin cytoskeleton polymerization contributed to PRV replication in vitro. In summary, our data displayed that RhoA was a host restriction factor that inhibited PRV replication, which may deepen our understanding the pathogenesis of PRV and provide further insight into the prevention of PRV infection and the development of anti-viral drugs.

Learnable Graph Convolutional Network With Semisupervised Graph Information Bottleneck.

Graph convolutional network (GCN) has gained widespread attention in semisupervised classification tasks. Recent studies show that GCN-based methods have achieved decent performance in numerous fields. However, most of the existing methods generally adopted a fixed graph that cannot dynamically capture both local and global relationships. This is because the hidden and important relationships may not be directed exhibited in the fixed structure, causing the degraded performance of semisupervised classification tasks. Moreover, the missing and noisy data yielded by the fixed graph may result in wrong connections, thereby disturbing the representation learning process. To cope with these issues, this article proposes a learnable GCN-based framework, aiming to obtain the optimal graph structures by jointly integrating graph learning and feature propagation in a unified network. Besides, to capture the optimal graph representations, this article designs dual-GCN-based meta-channels to simultaneously explore local and global relations during the training process. To minimize the interference of the noisy data, a semisupervised graph information bottleneck (SGIB) is introduced to conduct the graph structural learning (GSL) for acquiring the minimal sufficient representations. Concretely, SGIB aims to maximize the mutual information of both the same and different meta-channels by designing the constraints between them, thereby improving the node classification performance in the downstream tasks. Extensive experimental results on real-world datasets demonstrate the robustness of the proposed model, which outperforms state-of-the-art methods with fixed-structure graphs.

An early warning signal for grassland degradation on the Qinghai-Tibetan Plateau.

Intense grazing may lead to grassland degradation on the Qinghai-Tibetan Plateau, but it is difficult to predict where this will occur and to quantify it. Based on a process-based ecosystem model, we define a productivity-based stocking rate threshold that induces extreme grassland degradation to assess whether and where the current grazing activity in the region is sustainable. We find that the current stocking rate is below the threshold in ~80% of grassland areas, but in 55% of these grasslands the stocking rate exceeds half the threshold. According to our model projections, positive effects of climate change including elevated CO2 can partly offset negative effects of grazing across nearly 70% of grasslands on the Plateau, but only in areas below the stocking rate threshold. Our analysis suggests that stocking rate that does not exceed 60% (within 50% to 70%) of the threshold may balance human demands with grassland protection in the face of climate change.

Joint learning of feature and topology for multi-view graph convolutional network.

Graph convolutional network has been extensively employed in semi-supervised classification tasks. Although some studies have attempted to leverage graph convolutional networks to explore multi-view data, they mostly consider the fusion of feature and topology individually, leading to the underutilization of the consistency and complementarity of multi-view data. In this paper, we propose an end-to-end joint fusion framework that aims to simultaneously conduct a consistent feature integration and an adaptive topology adjustment. Specifically, to capture the feature consistency, we construct a deep matrix decomposition module, which maps data from different views onto a feature space obtaining a consistent feature representation. Moreover, we design a more flexible graph convolution that allows to adaptively learn a more robust topology. A dynamic topology can greatly reduce the influence of unreliable information, which acquires a more adaptive representation. As a result, our method jointly designs an effective feature fusion module and a topology adjustment module, and lets these two modules mutually enhance each other. It takes full advantage of the consistency and complementarity to better capture the more intrinsic information. The experimental results indicate that our method surpasses state-of-the-art semi-supervised classification methods.

[Application of Rapid HE Staining in Cytological Rapid On-site Evaluation of 
Peripheral Lung Cancer Needle Biopsy].

BACKGROUND: Rapid on-site evaluation (ROSE) is a technique used for simultaneous evaluation of biopsy specimens through rapid cytology staining. Diff-Quik (DQ) staining is the most commonly employed method for cytological rapid on-site evaluation (C-ROSE). However, the utilization of DQ staining for on-site cytological interpretation remains uncommon among pathologists in China, posing challenges to the implementation of C-ROSE. This study aims to assess the application of rapid hematoxylin-eosin (HE) staining and DQ staining for C-ROSE during percutaneous needle biopsy of peripheral lung cancer and evaluate the value of rapid HE staining in C-ROSE. METHODS: Computed tomography (CT)-guided lung biopsies were conducted on 300 patients diagnosed with peripheral lung cancer. The patients were randomly assigned to two groups for C-ROSE using either rapid HE staining or DQ staining, and subsequently the two methods were compared and evaluated. RESULTS: The concordance rate between C-ROSE and histopathological diagnosis was 96.7%. The median staining time for rapid HE staining was 160 s, while that for DQ staining was 120 s, representing a significant difference between the two groups (P<0.001). However, there were no significant differences observed in terms of total biopsy time, concordance rate with histopathology, cytology specimen peeling rate, and incidence of serious adverse reactions between the two groups (P>0.05). CONCLUSIONS: Both staining methods comply with C-ROSE criteria in the biopsy setting of peripheral lung cancer. Rapid HE staining is more aligned with domestic clinical requirements and holds potential for further promotion and adoption in C-ROSE.

Investigating the influence of substituent groups in TTM based radicals for the excitation process: a theoretical study.

Tri-(2,4,6-trichlorophenyl)methyl (TTM) based radicals can be promising in providing relatively high fluorescence quantum efficiency. In this study, we have evaluated the photoluminescence properties of a series of TTM-based radicals by means of DFT and TD-DFT methods. The optimized structures of the ground states (D0) and the first excited states (D1) of all the radicals are calculated and the computed emission bands are comparable with previous experimental results. knr is determined from transition dipole moments (µ12) and the energy gaps between D0 and D1 (ΔE), both of which can be regulated by the conjugated structures from the substituent groups. knr was derived from the mode-averaging method and is consistent with the experimental results. Factors influencing kr and knr, including the potential energy differences (ΔG0), the vibrational reorganization energies (λ) and the electron coupling term (Hab), are discussed. By comparing kr and knr in solvents with different polarities (cyclohexane, toluene, and chloroform), TTM based radicals in cyclohexane exhibit the most promising fluorescence efficiencies. Besides, two substituted radicals, namely 2Br-TTM-3PCz and 2F-TTM-3PCz, have been fabricated. The results show that fluorine atoms are able to increase ΔG0 and a considerably small knr has been predicted. We expect that our calculation can benefit the design of light-emitting molecules in further experiments.

Dynamic Metastable Characteristics of Carbon Cages Embedded with Er2C2.

Novel endohedral metallofullerenes (EMFs), namely, Er2C2@C2v(5)-C80, Er2C2@Cs(6)-C82, Er2C2@Cs(15)-C84, Er2C2@C2v(9)-C86, Er2C2@Cs(15)-C86, and Er2C2@Cs(32)-C88, had been experimentally synthesized, and the unique structures and many fascinating properties had also been widely explored. Nevertheless, the position of the Er atoms inside the cage shows a severe disorder within the stable EMF monomer, which is difficult to understand and explain from the experimental point of view. In this work, based on the density functional theoretical calculations, the Er2C2@Cs(6)-C82 has 73 directional isomers and 2 Er atoms that are far beyond from Er-Er single bonding and tend to be close to the cage side (marked as "shell"), and the core (Er2C2 units) takes on a butterfly shape as generally revealed. The energy difference between any two of the isomers is in the range of 0.05 to 25.6 kcal/mol, indicating a relatively easy thermodynamic transition between the isomers. The other five Er carbide cluster EMFs (Er2C2@C2v(5)-C80, Er2C2@Cs(15)-C84, Er2C2@C2v(9)-C86, Er2C2@Cs(15)-C86, and Er2C2@Cs(32)-C88) are also studied in the same way, and 30, 37, 39, and 43 most stable Er-oriented sites inside the cage, respectively, are obtained. In addition, the shape of the Er2C2 gradually changed from butterfly to linear. Moreover, the electronic structure and molecular orbital analyses show that it is easy for Er2C2@C80-88 to form a charge transfer state of [Er2C2]4+@[C80-88]4- via the dynamic core-shell coordination equilibrium. Er2C2 with a steep drop in chemical stability is restricted to forming varying degrees of metastable states in the shell, determined by the shell size, to ensure the overall stability. The lowest unoccupied molecular orbital energy level of these EMFs is increased by 0.5-1.1 eV compared with fullerenes C80-88, potentially providing favorable conditions for suitable energy level matching with EMF as an electron acceptor used in organic solar cell devices.

Vegetation structural shift tells environmental changes on the Tibetan Plateau over 40 years.

Structural information of grassland changes on the Tibetan Plateau is essential for understanding alterations in critical ecosystem functioning and their underlying drivers that may reflect environmental changes. However, such information at the regional scale is still lacking due to methodological limitations. Beyond remote sensing indicators only recognizing vegetation productivity, we utilized multivariate data fusion and deep learning to characterize formation-based plant community structure in alpine grasslands at the regional scale of the Tibetan Plateau for the first time and compared it with the earlier version of Vegetation Map of China for historical changes. Over the past 40 years, we revealed that (1) the proportion of alpine meadows in alpine grasslands increased from 50% to 69%, well-reflecting the warming and wetting trend; (2) dominances of Kobresia pygmaea and Stipa purpurea formations in alpine meadows and steppes were strengthened to 76% and 92%, respectively; (3) the climate factor mainly drove the distribution of Stipa purpurea formation, but not the recent distribution of Kobresia pygmaea formation that was likely shaped by human activities. Therefore, the underlying mechanisms of grassland changes over the past 40 years were considered to be formation dependent. Overall, the first exploration for structural information of plant community changes in this study not only provides a new perspective to understand drivers of grassland changes and their spatial heterogeneity at the regional scale of the Tibetan Plateau, but also innovates large-scale vegetation study paradigm.

CBPH assay for the highest sensitive detection of SARS-COV-2 in the semen.

BACKGROUND: Great concerns have been raised on SARS-CoV-2 impact on men's andrological well-being, and many studies have attempted to determine whether SARS-CoV-2 is present in the semen and till now the data are unclear and somehow ambiguous. However, these studies used quantitative real-time (qRT) PCR, which is not sufficiently sensitive to detect nucleic acids in clinical samples with a low viral load. METHODS: The clinical performance of various nucleic acid detection methods (qRT-PCR, OSN-qRT-PCR, cd-PCR, and CBPH) was assessed for SARS-CoV-2 using 236 clinical samples from laboratory-confirmed COVID-19 cases. Then, the presence of SARS-CoV-2 in the semen of 12 recovering patients was investigated using qRT-PCR, OSN-qRT-PCR, cd-PCR, and CBPH in parallel using 24 paired semen, blood, throat swab, and urine samples. RESULTS: The sensitivity and specificity along with AUC of CBPH was markedly higher than the other 3methods. Although qRT-PCR, OSN-qRT-PCR and cdPCR detected no SARS-CoV-2 RNA in throat swab, blood, urine, and semen samples of the 12 patients, CBPH detected the presence of SARS-CoV-2 genome fragments in semen samples, but not in paired urine samples, of 3 of 12 patients. The existing SARS-CoV-2 genome fragments were metabolized over time. CONCLUSIONS: Both OSN-qRT-PCR and cdPCR had better performance than qRT-PCR, and CBPH had the highest diagnostic performance in detecting SARS-CoV-2, which contributed the most improvement to the determination of the critical value in gray area samples with low vrial load, which then provides a rational screening strategy for studying the clearance of coronavirus in the semen over time in patients recovering from COVID-19. Although the presence of SARS-CoV-2 fragments in the semen was demonstrated by CBPH, COVID-19 is unlikely to be sexually transmitted from male partners for at least 3 months after hospital discharge.

Graph Neural Networks With Multiple Prior Knowledge for Multi-Omics Data Analysis.

With the development of biotechnology, a large amount of multi-omics data have been collected for precision medicine. There exists multiple graph-based prior biological knowledge about omics data, such as gene-gene interaction networks. Recently, there has been an increasing interest in introducing graph neural networks (GNNs) into multi-omics learning. However, existing methods have not fully exploited these graphical priors since none have been able to integrate knowledge from multiple sources simultaneously. To solve this problem, we propose a multi-omics data analysis framework by incorporating multiple prior knowledge into graph neural network (MPK-GNN). To the best of our knowledge, this is the first attempt to introduce multiple prior graphs into multi-omics data analysis. Specifically, the proposed method contains four parts: (1) a feature-level learning module to aggregate information from prior graphs; (2) a projection module to maximize the agreement among prior networks by optimizing a contrastive loss; (3) a sample-level module to learn a global representation from input multi-omics features; (4) a task-specific module to flexibly extend MPK-GNN for various downstream multi-omics analysis tasks. Finally, we verify the effectiveness of the proposed multi-omics learning algorithm on the cancer molecular subtype classification task. Experimental results show that MPK-GNN outperforms other state-of-the-art algorithms, including multi-view learning methods and multi-omics integrative approaches.

Aroma profiling of Shine Muscat grape provides detailed insights into the regulatory effect of gibberellic acid and N-(2-chloro-4-pyridinyl)-N-phenylurea applications on aroma quality.

As plant growth regulators, gibberellic acid (GA3) and CPPU [forchlorfenuron, N-(2-chloro-4-pyridinyl)-N-phenylurea] are widely used in the production of table grapes. However, how these compounds regulate the aroma quality remains unclear. By measuring free and bound aroma compounds in Shine Muscat grapes from eight groups during whole growth period, GA3 and CPPU were both found to significantly promote the synthesis of acyclic monoterpenes and (E)-2-hexenal, and double applications were found to further increase the aroma compound contents. On the other hand, GA3 and CPPU obviously promoted the expansion of berries, and the effect of promoting the synthesis of aroma compounds was largely diminished. In conclusion, free compound concentrations in berry were almost unaffected by GA3 and CPPU. From the perspective of aroma compounds, a highly concerted interplay was observed for terpenes, and bound compounds exhibited higher correlations than those of free compounds. In addition, 17 compounds could be used as markers that indicated the developmental timing of berries.