KG-BART: Knowledge Graph-Augmented BART for Generative Commonsense Reasoning

Generative commonsense reasoning which aims to empower machines to generate sentences with the capacity of reasoning over a set of concepts is a critical bottleneck for text generation. Even the state-of-the-art pre-trained language generation models struggle at this task and often produce implausible and anomalous sentences. One reason is that they rarely consider incorporating the knowledge graph which can provide rich relational information among the commonsense concepts. To promote the ability of commonsense reasoning for text generation, we propose a novel knowledge graph augmented pre-trained language generation model KG-BART, which encompasses the complex relations of concepts through the knowledge graph and produces more logical and natural sentences as output. Moreover, KG-BART can leverage the graph attention to aggregate the rich concept semantics that enhances the model generalization on unseen concept sets. Experiments on benchmark CommonGen dataset verify the effectiveness of our proposed approach by comparing with several strong pre-trained language generation models, particularly KG-BART outperforms BART by 5.80, 4.60, in terms of BLEU-3, 4. Moreover, we also show that the generated context by our model can work as background scenarios to benefit downstream commonsense QA tasks.Comment: 10 pages, 7 figures, Appear in AAAI 202

Multi-View Multi-Graph Embedding for Brain Network Clustering Analysis

Network analysis of human brain connectivity is critically important for understanding brain function and disease states. Embedding a brain network as a whole graph instance into a meaningful low-dimensional representation can be used to investigate disease mechanisms and inform therapeutic interventions. Moreover, by exploiting information from multiple neuroimaging modalities or views, we are able to obtain an embedding that is more useful than the embedding learned from an individual view. Therefore, multi-view multi-graph embedding becomes a crucial task. Currently, only a few studies have been devoted to this topic, and most of them focus on the vector-based strategy which will cause structural information contained in the original graphs lost. As a novel attempt to tackle this problem, we propose Multi-view Multi-graph Embedding (M2E) by stacking multi-graphs into multiple partially-symmetric tensors and using tensor techniques to simultaneously leverage the dependencies and correlations among multi-view and multi-graph brain networks. Extensive experiments on real HIV and bipolar disorder brain network datasets demonstrate the superior performance of M2E on clustering brain networks by leveraging the multi-view multi-graph interactions

Interpretable Spatio-Temporal Embedding for Brain Structural-Effective Network with Ordinary Differential Equation

The MRI-derived brain network serves as a pivotal instrument in elucidating both the structural and functional aspects of the brain, encompassing the ramifications of diseases and developmental processes. However, prevailing methodologies, often focusing on synchronous BOLD signals from functional MRI (fMRI), may not capture directional influences among brain regions and rarely tackle temporal functional dynamics. In this study, we first construct the brain-effective network via the dynamic causal model. Subsequently, we introduce an interpretable graph learning framework termed Spatio-Temporal Embedding ODE (STE-ODE). This framework incorporates specifically designed directed node embedding layers, aiming at capturing the dynamic interplay between structural and effective networks via an ordinary differential equation (ODE) model, which characterizes spatial-temporal brain dynamics. Our framework is validated on several clinical phenotype prediction tasks using two independent publicly available datasets (HCP and OASIS). The experimental results clearly demonstrate the advantages of our model compared to several state-of-the-art methods

STAT1 as a downstream mediator of ERK signaling contributes to bone cancer pain by regulating MHC II expression in spinal microglia

Major histocompatibility class II (MHC II)-specific activation of CD4+ T helper cells generates specific and persistent adaptive immunity against tumors. Emerging evidence demonstrates that MHC II is also involved in basic pain perception; however, little is known regarding its role in the development of cancer-induced bone pain (CIBP). In this study, we demonstrate that MHC II expression was markedly induced on the spinal microglia of CIBP rats in response to STAT1 phosphorylation. Mechanical allodynia was ameliorated by either pharmacological or genetic inhibition of MHC II upregulation, which was also attenuated by the inhibition of pSTAT1 and pERK but was deteriorated by intrathecal injection of IFNγ. Furthermore, inhibition of ERK signaling decreased the phosphorylation of STAT1, as well as the production of MHC II in vivo and in vitro. These findings suggest that STAT1 contributes to bone cancer pain as a downstream mediator of ERK signaling by regulating MHC II expression in spinal microglia

Dynamic placement of the linker histone H1 associated with nucleosome arrangement and gene transcription in early Drosophila embryonic development

The linker histone H1 is critical to maintenance of higher-order chromatin structures and to gene expression regulation. However, H1 dynamics and its functions in embryonic development remain unresolved. Here, we profiled gene expression, nucleosome positions, and H1 locations in early Drosophila embryos. The results show that H1 binding is positively correlated with the stability of beads-on-a-string nucleosome organization likely through stabilizing nucleosome positioning and maintaining nucleosome spacing. Strikingly, nucleosomes with H1 placement deviating to the left or the right relative to the dyad shift to the left or the right, respectively, during early Drosophila embryonic development. H1 occupancy on genic nucleosomes is inversely correlated with nucleosome distance to the transcription start sites. This inverse correlation reduces as gene transcription levels decrease. Additionally, H1 occupancy is lower at the 5\u27 border of genic nucleosomes than that at the 3\u27 border. This asymmetrical pattern of H1 occupancy on genic nucleosomes diminishes as gene transcription levels decrease. These findings shed new lights into how H1 placement dynamics correlates with nucleosome positioning and gene transcription during early Drosophila embryonic development

Molecular analysis of phosphomannomutase (PMM) genes reveals a unique PMM duplication event in diverse Triticeae species and the main PMM isozymes in bread wheat tissues

BACKGROUND: Phosphomannomutase (PMM) is an essential enzyme in eukaryotes. However, little is known about PMM gene and function in crop plants. Here, we report molecular evolutionary and biochemical analysis of PMM genes in bread wheat and related Triticeae species. RESULTS: Two sets of homoeologous PMM genes (TaPMM-1 and 2) were found in bread wheat, and two corresponding PMM genes were identified in the diploid progenitors of bread wheat and many other diploid Triticeae species. The duplication event yielding PMM-1 and 2 occurred before the radiation of diploid Triticeae genomes. The PMM gene family in wheat and relatives may evolve largely under purifying selection. Among the six TaPMM genes, the transcript levels of PMM-1 members were comparatively high and their recombinant proteins were all enzymatically active. However, PMM-2 homoeologs exhibited lower transcript levels, two of which were also inactive. TaPMM-A1, B1 and D1 were probably the main active isozymes in bread wheat tissues. The three isozymes differed from their counterparts in barley and Brachypodium distachyon in being more tolerant to elevated test temperatures. CONCLUSION: Our work identified the genes encoding PMM isozymes in bread wheat and relatives, uncovered a unique PMM duplication event in diverse Triticeae species, and revealed the main active PMM isozymes in bread wheat tissues. The knowledge obtained here improves the understanding of PMM evolution in eukaryotic organisms, and may facilitate further investigations of PMM function in the temperature adaptability of bread wheat

The impact of type 2 diabetes and its management on the prognosis of patients with severe COVID‐19

Background Although type 2 diabetes mellitus (T2DM) patients with coronavirus disease 2019 (COVID‐19) develop a more severe condition compared to those without diabetes, the mechanisms for this are unknown. Moreover, the impact of treatment with antihyperglycemic drugs and glucocorticoids is unclear. Methods From 1584 COVID‐19 patients, 364 severe/critical COVID‐19 patients with clinical outcome were enrolled for the final analysis, and patients without preexisting T2DM but elevated glucose levels were excluded. Epidemiological data were obtained and clinical status evaluation carried out to assess the impact of T2DM and its management on clinical outcomes. Results Of 364 enrolled severe COVID‐19 inpatients, 114 (31.3%) had a history of T2DM. Twenty‐seven (23.7%) T2DM patients died, who had more severe inflammation, coagulation activation, myocardia injury, hepatic injury, and kidney injury compared with non‐DM patients. In severe COVID‐19 patients with T2DM, we demonstrated a higher risk of all‐cause fatality with glucocorticoid treatment (adjusted hazard ratio [HR], 3.61; 95% CI, 1.14‐11.46; P = .029) and severe hyperglycemia (fasting plasma glucose ≥11.1 mmol/L; adjusted HR, 11.86; 95% CI, 1.21‐116.44; P = .034). Conclusions T2DM status aggravated the clinical condition of COVID‐19 patients and increased their critical illness risk. Poor fasting blood glucose (≥ 11.1 mmol/L) and glucocorticoid treatment are associated with poor prognosis for T2DM patients with severe COVID‐19

Activated networking of platelet activating factor receptor and FAK/STAT1 induces malignant potential in BRCA1-mutant at-risk ovarian epithelium

Objectives It is essential to understand the molecular basis of ovarian cancer etiology and tumor development to provide more effective preventive and therapeutic approaches to reduce mortality. Particularly, the molecular targets and pathways involved in early malignant transformation are still not clear. Pro-inflammatory lipids and pathways have been reported to play significant roles in ovarian cancer progression and metastasis. The major objective of this study was to explore and determine whether platelet activating factor (PAF) and receptor associated networking pathways might significantly induce malignant potential in BRCA1-mutant at-risk epithelial cells. Methods BRCA1-mutant ovarian epithelial cell lines including (HOSE-636, HOSE-642), BRCA1-mutant ovarian cancer cell (UWB1.289), wild type normal ovarian epithelial cell (HOSE-E6E7) and cancerous cell line (OVCA429), and the non-malignant BRCA1-mutant distal fallopian tube (fimbria) tissue specimens were used in this study. Mutation analysis, kinase microarray, western blot, immune staining, co-immune precipitation, cell cycle, apoptosis, proliferation and bioinformatic pathway analysis were applied. Results We found that PAF, as a potent pro-inflammatory mediator, induced significant anti-apoptotic effect in BRCA1-mutant ovarian surface epithelial cells, but not in wild type HOSE cells. With kinase microarray technology and the specific immune approaches, we found that phosphor-STAT1 was activated by 100 nM PAF treatment only in BRCA1-mutant associated at-risk ovarian epithelial cells and ovarian cancer cells, but not in BRCA1-wild type normal (HOSE-E6E7) or malignant (OVCA429) ovarian epithelial cells. Co-immune precipitation revealed that elevated PAFR expression is associated with protein-protein interactions of PAFR-FAK and FAK-STAT1 in BRCA1-mutant ovarian epithelial cells, but not in the wild-type control cells. Conclusion Previous studies showed that potent inflammatory lipid mediators such as PAF and its receptor (PAFR) significantly contribute to cancer progression and metastasis. Our findings suggest that these potent inflammatory lipids and receptor pathways are significantly involved in the early malignant transformation through PAFR-FAK-STAT1 networking and to block apoptosis pathway in BRCA1 dysfunctional at-risk ovarian epithelium

In Vitro Uptake of 140 kDa Bacillus thuringiensis Nematicidal Crystal Proteins by the Second Stage Juvenile of Meloidogyne hapla

Plant-parasitic nematodes (PPNs) are piercing/sucking pests, which cause severe damage to crops worldwide, and are difficult to control. The cyst and root-knot nematodes (RKN) are sedentary endoparasites that develop specialized multinucleate feeding structures from the plant cells called syncytia or giant cells respectively. Within these structures the nematodes produce feeding tubes, which act as molecular sieves with exclusion limits. For example, Heterodera schachtii is reportedly unable to ingest proteins larger than 28 kDa. However, it is unknown yet what is the molecular exclusion limit of the Meloidogyne hapla. Several types of Bacillus thuringiensis crystal proteins showed toxicity to M. hapla. To monitor the entry pathway of crystal proteins into M. hapla, second-stage juveniles (J2) were treated with NHS-rhodamine labeled nematicidal crystal proteins (Cry55Aa, Cry6Aa, and Cry5Ba). Confocal microscopic observation showed that these crystal proteins were initially detected in the stylet and esophageal lumen, and subsequently in the gut. Western blot analysis revealed that these crystal proteins were modified to different molecular sizes after being ingested. The uptake efficiency of the crystal proteins by the M. hapla J2 decreased with increasing of protein molecular mass, based on enzyme-linked immunosorbent assay analysis. Our discovery revealed 140 kDa nematicidal crystal proteins entered M. hapla J2 via the stylet, and it has important implications in designing a transgenic resistance approach to control RKN