MAG-GNN: Reinforcement Learning Boosted Graph Neural Network

While Graph Neural Networks (GNNs) recently became powerful tools in graph learning tasks, considerable efforts have been spent on improving GNNs' structural encoding ability. A particular line of work proposed subgraph GNNs that use subgraph information to improve GNNs' expressivity and achieved great success. However, such effectivity sacrifices the efficiency of GNNs by enumerating all possible subgraphs. In this paper, we analyze the necessity of complete subgraph enumeration and show that a model can achieve a comparable level of expressivity by considering a small subset of the subgraphs. We then formulate the identification of the optimal subset as a combinatorial optimization problem and propose Magnetic Graph Neural Network (MAG-GNN), a reinforcement learning (RL) boosted GNN, to solve the problem. Starting with a candidate subgraph set, MAG-GNN employs an RL agent to iteratively update the subgraphs to locate the most expressive set for prediction. This reduces the exponential complexity of subgraph enumeration to the constant complexity of a subgraph search algorithm while keeping good expressivity. We conduct extensive experiments on many datasets, showing that MAG-GNN achieves competitive performance to state-of-the-art methods and even outperforms many subgraph GNNs. We also demonstrate that MAG-GNN effectively reduces the running time of subgraph GNNs.Comment: Accepted to NeurIPS 202

Synchronous post-acceleration of laser-driven protons in helical coil targets by controlling the current dispersion

Post-acceleration of protons in helical coil targets driven by intense, ultrashort laser pulses can enhance ion energy by utilizing the transient current from the targets’ self-discharge. The acceleration length of protons can exceed a few millimeters, and the acceleration gradient is of the order of GeV/m. How to ensure the synchronization between the accelerating electric field and the protons is a crucial problem for efficient post-acceleration. In this paper, we study how the electric field mismatch induced by current dispersion affects the synchronous acceleration of protons. We propose a scheme using a two-stage helical coil to control the current dispersion. With optimized parameters, the energy gain of protons is increased by four times. Proton energy is expected to reach 45 MeV using a hundreds-of-terawatts laser, or more than 100 MeV using a petawatt laser, by controlling the current dispersion

Appropriate water and fertilizer supply can increase yield by promoting growth while ensuring the soil ecological environment in melon production

Water and fertilizer management in sustainable agricultural development needs to balance crop yield, quality, and soil ecological environment. Therefore, we conducted trials with nine treatments over two growing seasons in 2020 and 2021. The treatments included the three irrigation levels W1 (75% Ep), W2 (100% Ep), and W3 (125% Ep) and the three fertilization levels F1 (758.44 kg/hm2), F2 (948.05 kg/hm2), and F3 (1137.66 kg/hm2) with a N/P/K ratio of 2:1:3. The net photosynthetic rate (Pn) gradually decreased with the plant growing period, and was significantly (*) affected by fertilization at the flowering and fruiting stages. The chlorophyll content increased by 43.75% from the vine to flowering and fruiting stages and decreased by 12.50% in the mature stage, reaching a maximum following W1 application. W2F2 was the most effective in promoting total dry mass (TDM) during the vine and mature stages. Irrigation, fertilization and the interaction exerted a highly significant effect on the fruit quality and yield. Soluble solids performed best under W1F1, while free amino acid reached a maximum under W3F2. Moreover, water use efficiency increased with the fertilization amount, and was maximized in W1F3. At the same fertilizer level, soil nitrate N and available P content exhibited an increasing then decreasing trend with the increasing irrigation amount, while soil available K content increased with irrigation at all growth stages. Structural equation models of yield and quality formation were then established based on the co-occurrence analysis. Pn indirectly regulated the melon growth by TDM (0.87) (***), while growth was identified as the most important direct factor affecting the yield and quality of melon. Furthermore, soil residues indirectly affected yield and quality composition through efficiency. This study indicates that the sustainable practices for water and fertilizer management are essential to improve melon yield and quality in arid and semi-arid regions, and contribute to reducing the risk of soil contamination from agricultural production

A Computational Method for Classifying Different Human Tissues with Quantitatively Tissue-Specific Expressed Genes

Tissue-specific gene expression has long been recognized as a crucial key for understanding tissue development and function. Efforts have been made in the past decade to identify tissue-specific expression profiles, such as the Human Proteome Atlas and FANTOM5. However, these studies mainly focused on “qualitatively tissue-specific expressed genes” which are highly enriched in one or a group of tissues but paid less attention to “quantitatively tissue-specific expressed genes”, which are expressed in all or most tissues but with differential expression levels. In this study, we applied machine learning algorithms to build a computational method for identifying “quantitatively tissue-specific expressed genes” capable of distinguishing 25 human tissues from their expression patterns. Our results uncovered the expression of 432 genes as optimal features for tissue classification, which were obtained with a Matthews Correlation Coefficient (MCC) of more than 0.99 yielded by a support vector machine (SVM). This constructed model was superior to the SVM model using tissue enriched genes and yielded MCC of 0.985 on an independent test dataset, indicating its good generalization ability. These 432 genes were proven to be widely expressed in multiple tissues and a literature review of the top 23 genes found that most of them support their discriminating powers. As a complement to previous studies, our discovery of these quantitatively tissue-specific genes provides insights into the detailed understanding of tissue development and function

CRISPR/Cas9 Mediated Knockout of the OsbHLH024 Transcription Factor Improves Salt Stress Resistance in Rice (Oryza sativa L.)

Salinity stress is one of the most prominent abiotic stresses that negatively affect crop production. Transcription factors (TFs) are involved in the absorption, transport, or compartmentation of sodium (Na+) or potassium (K+) to resist salt stress. The basic helix–loop–helix (bHLH) is a TF gene family critical for plant growth and stress responses, including salinity. Herein, we used the CRISPR/Cas9 strategy to generate the gene editing mutant to investigate the role of OsbHLH024 in rice under salt stress. The A nucleotide base deletion was identified in the osbhlh024 mutant (A91). Exposure of the A91 under salt stress resulted in a significant increase in the shoot weight, the total chlorophyll content, and the chlorophyll fluorescence. Moreover, high antioxidant activities coincided with less reactive oxygen species (ROS) and stabilized levels of MDA in the A91. This better control of oxidative stress was accompanied by fewer Na+ but more K+, and a balanced level of Ca2+, Zn2+, and Mg2+ in the shoot and root of the A91, allowing it to withstand salt stress. Furthermore, the A91 also presented a significantly up-regulated expression of the ion transporter genes (OsHKT1;3, OsHAK7, and OsSOS1) in the shoot when exposed to salt stress. These findings imply that the OsbHLH024 might play the role of a negative regulator of salt stress, which will help to understand better the molecular basis of rice production improvement under salt stress

Exosomal miRNAs contribute to coal dust particle-induced pulmonary fibrosis in rats

Coal workers’ pneumoconiosis (CWP) is a fatal occupational disease caused by inhalation of coal dust particles, which leads to progressive pulmonary fibrosis. Recently, as new signal carriers for intercellular communication, exosomal miRNAs have been validated in the pathogenesis of multiple diseases. However, the research on exosomal miRNAs in CWP is still in the preliminary stage. Here, using miRNA sequencing, exosomal miRNA profiles in bronchoalveolar lavage fluid (BALF) from rats with pulmonary fibrosis induced by coal dust particles were analyzed, and the underlying biological function of putative target genes was explored by GO term analysis and KEGG pathway enrichment analysis. According to the results, intratracheal instillation of coal dust particles can alter the exosomal miRNAs expression in the BALF of rats. Further bioinformatics analysis provided some clues to reveal their function in pathological process of pneumoconiosis. More importantly, we identified 4 differentially expressed exosomal miRNAs (miRNA-21–5p, miRNA-29a-3p, miRNA-26a-5p, and miRNA-34a-5p) by qRT‑PCR and further verified the temporal changes in the expression of these exosomal miRNAs in animal models from 2 weeks to 16 weeks postexposure. In addition, we conducted a preliminary study on Smad7 as a potential target of miRNA-21–5p and found that exosomal miRNA 21–5p/Smad7 may contribute to the pulmonary fibrosis induced by coal dust particles. Our study confirmed the contribution of exosomal miRNAs to coal dust particle-induced pulmonary fibrosis and provided new insights into the pathogenesis of CWP

Intratumoral heterogeneity in a minority of ovarian low-grade serous carcinomas

Abstract Background Ovarian low-grade serous carcinoma (LGSC) has fewer mutations than ovarian high-grade serous carcinoma (HGSC) and a less aggressive clinical course. However, an overwhelming majority of LGSC patients do not respond to conventional chemotherapy resulting in a poor long-term prognosis comparable to women diagnosed with HGSC. KRAS and BRAF mutations are common in LGSC, leading to clinical trials targeting the MAPK pathway. We assessed the stability of targetable somatic mutations over space and/or time in LGSC, with a view to inform stratified treatment strategies and clinical trial design. Methods Eleven LGSC cases with primary and recurrent paired samples were identified (stage IIB-IV). Tumor DNA was isolated from 1–4 formalin-fixed paraffin-embedded tumor blocks from both the primary and recurrence (n = 37 tumor and n = 7 normal samples). Mutational analysis was performed using the Ion Torrent AmpliSeqTM Cancer Panel, with targeted validation using Fluidigm-MiSeq, Sanger sequencing and/or Raindance Raindrop digital PCR. Results KRAS (3/11), BRAF (2/11) and/or NRAS (1/11) mutations were identified in five unique cases. A novel, non-synonymous mutation in SMAD4 was observed in one case. No somatic mutations were detected in the remaining six cases. In two cases with a single matched primary and recurrent sample, two KRAS hotspot mutations (G12V, G12R) were both stable over time. In three cases with multiple samplings from both the primary and recurrent surgery some mutations (NRAS Q61R, BRAF V600E, SMAD4 R361G) were stable across all samples, while others (KRAS G12V, BRAF G469V) were unstable. Conclusions Overall, the majority of cases with detectable somatic mutations showed mutational stability over space and time while one of five cases showed both temporal and spatial mutational instability in presumed drivers of disease. Investigation of additional cases is required to confirm whether mutational heterogeneity in a minority of LGSC is a general phenomenon that should be factored into the design of clinical trials and stratified treatment for this patient population