Afterthoughts

Writers Sugata Mitra and Payal Arora were invited to provide some afterthoughts having read each other's papers. As Arora observes, the Hole-in-the-Wall approach has shown that the absence of a teacher can sometimes encourage children to explore more bravely than they would in their presence. However, as she again observes, institutional indifference may result in abdication of responsibility and lack of sustainability. It sells its products to schools and hence locates its kiosks on school playgrounds. She also wonders whether placing of computers i

BioBLP: A Modular Framework for Learning on Multimodal Biomedical Knowledge Graphs

Knowledge graphs (KGs) are an important tool for representing complex relationships between entities in the biomedical domain. Several methods have been proposed for learning embeddings that can be used to predict new links in such graphs. Some methods ignore valuable attribute data associated with entities in biomedical KGs, such as protein sequences, or molecular graphs. Other works incorporate such data, but assume that entities can be represented with the same data modality. This is not always the case for biomedical KGs, where entities exhibit heterogeneous modalities that are central to their representation in the subject domain. We propose a modular framework for learning embeddings in KGs with entity attributes, that allows encoding attribute data of different modalities while also supporting entities with missing attributes. We additionally propose an efficient pretraining strategy for reducing the required training runtime. We train models using a biomedical KG containing approximately 2 million triples, and evaluate the performance of the resulting entity embeddings on the tasks of link prediction, and drug-protein interaction prediction, comparing against methods that do not take attribute data into account. In the standard link prediction evaluation, the proposed method results in competitive, yet lower performance than baselines that do not use attribute data. When evaluated in the task of drug-protein interaction prediction, the method compares favorably with the baselines. We find settings involving low degree entities, which make up for a substantial amount of the set of entities in the KG, where our method outperforms the baselines. Our proposed pretraining strategy yields significantly higher performance while reducing the required training runtime. Our implementation is available at https://github.com/elsevier-AI-Lab/BioBLP

Role of Grb2 in myogenesis and epithelial-to-mesenchymal transition

Grb2 is an adaptor protein whose SH2 domain has been shown to interact with phosphorylated tyrosine residues of activated receptors such as EGF and TGF-β and activate intracellular Ras/MAPK signaling pathway which controls the expression of genes required for biological processes such as proliferation and migration necessary for both myogenesis and EMT. Overexpression of Grb2 reduced the differentiation potential of C2C12 myoblasts significantly compared to the control indicating that Grb2 inhibits muscle differentiation. Overexpression of exogenous N-WASP in Grb2 overexpressing cells was found to rescue the inhibition of differentiation mediated by Grb2 indicating that inhibition of differentiation by Grb2 occurs through N-WASP. Alternatively, it was found that GRB2 expression increased significantly, during EMT, in lung adenocarcinoma cell line, A549. GRB2, even in the absence of TGF-β was found to cause drastic reduction in the E-Cadherin expression strengthening the hypothesis that Grb2 could play an independent role in controlling EMT. The expression of mesenchymal transcription factor, Snail was found to increase in GRB2 over expressing cells, before TGF-β stimulation suggesting that the reduction of E-Cadherin expression observed on GRB2 over expression may be mediated by Snail in A549 cells.DOCTOR OF PHILOSOPHY (SBS

Myogenic differentiation depends on the interplay of Grb2 and N-WASP

Myogenesis requires a well-coordinated withdrawal from cell cycle, morphological changes and cell fusion mediated by actin cytoskeleton. Grb2 is an adaptor protein whose central SH2 domain binds to phosphorylated tyrosine residues of activated receptors and activates intracellular signaling pathway, while its N-terminal and C-terminal SH3 domains bind to proline rich proteins such as N-WASP (Neural-Wiskott Aldrich Syndrome Protein). We found that the expression of Grb2 was increased at the beginning of differentiation and remained constant during differentiation in C2C12 myoblasts. Knocking down endogenous Grb2 expression caused a significant increase in the fusion index and expression of MyHC, a terminal differentiation marker when compared with the control. Over expression of Grb2 in C2C12 (C2C12Grb2-Myc) reduced myotube formation and expression of MyHC. Similarly over expression of Grb2P49L-Myc (N-terminal SH3 domain mutant) or Grb2R86K-Myc (SH2 domain mutant) inhibited myogenic differentiation of C2C12 cells. However, the expression of Grb2P206L-Myc (C-terminal SH3 domain mutant) did not inhibit myotube formation and expression of MyHC. This suggests that the C-terminal SH3 domain of Grb2 is critical for the inhibition of myogenic differentiation. The C2C12Grb2-Myc cells have reduced phalloidin staining at late stages of differentiation. Expression of N-WASP in C2C12Grb2-Myc cells rescued the myogenic defect and increased phalloidin staining (increased F-actin) in these cells. Thus our results suggest that Grb2 is a negative regulator of myogenesis and reduces myogenic differentiation by inhibiting actin polymerization/remodeling through its C-terminal SH3 domain.MOE (Min. of Education, S’pore

Electromagnetic Radiations from Heavy Ion Collision

In this review, we have discussed the different sources of photons and dileptons produced in heavy ion collision (HIC). The transverse momentum (pT) spectra of photons for different collision energies are analyzed with a view of extracting the thermal properties of the system formed in HIC. We showed the effect of viscosity on pT spectra of produced thermal photons. The dilepton productions from hot hadrons are considered including the spectral change of light vector mesons in the thermal bath. We have analyzed the pT and invariant mass (M) spectra of dileptons for different collision energies too. As the individual spectra are constrained by certain unambiguous hydrodynamical inputs, so we evaluated the ratio of photon to dilepton spectra, Rem, to overcome those quantities. We argue that the variation of the radial velocity extracted from Rem with M is indicative of a phase transition from the initially produced partons to hadrons. In the calculations of interferometry involving dilepton pairs, it is argued that the nonmonotonic variation of HBT radii with invariant mass of the lepton pairs signals the formation of quark gluon plasma in HIC. Elliptic flow (v2) of dilepton is also studied at sNN=2.76 TeV for 30–40% centrality using the (2+1)d hydrodynamical model

Overexpression of GRB2 Enhances Epithelial to Mesenchymal Transition of A549 Cells by Upregulating SNAIL Expression

GRB2 is an adaptor protein which interacts with phosphorylated TGF-β receptor and is critical for mammary tumour growth. We found that TGF-β1-induced EMT increased GRB2 expression in A549 cells (non-small cell lung cancer). Overexpression of GRB2 (A549GRB2) enhanced cell invasion while knocking down GRB2 (A549GRB2KD) reduced cell migration and invasion, probably due to increased vinculin and reduced Paxillin patches in A549GRB2KD cell. TGF-β1-induced EMT was more pronounced in A549GRB2 cells and attenuated in A549GRB2KD cells. This could be due to the reduced expression of E-cadherin in A549GRB2 and increased expression of E-cadherin in A549GRB2KD cells, even before TGF-β1 stimulation. Expression of SNAIL was elevated in A549GRB2 cells and was further enhanced by TGF-β1 stimulation, suggesting that GRB2 down-regulates E-cadherin by enhancing the expression of SNAIL. The N-SH3 domain of GRB2 was critical for suppressing E-cadherin expression, while the C-SH3 domain of GRB2 mediating interaction with proteins such as N-WASP was critical for promoting invasion, and the SH2 domain was critical for suppressing E-cadherin expression and invasion. Thus, our data suggests that GRB2 enhances EMT by suppressing E-cadherin expression and promoting invasion probably through N-WASP to promote metastasis

BioBLP: a modular framework for learning on multimodal biomedical knowledge graphs

Abstract Background Knowledge graphs (KGs) are an important tool for representing complex relationships between entities in the biomedical domain. Several methods have been proposed for learning embeddings that can be used to predict new links in such graphs. Some methods ignore valuable attribute data associated with entities in biomedical KGs, such as protein sequences, or molecular graphs. Other works incorporate such data, but assume that entities can be represented with the same data modality. This is not always the case for biomedical KGs, where entities exhibit heterogeneous modalities that are central to their representation in the subject domain. Objective We aim to understand how to incorporate multimodal data into biomedical KG embeddings, and analyze the resulting performance in comparison with traditional methods. We propose a modular framework for learning embeddings in KGs with entity attributes, that allows encoding attribute data of different modalities while also supporting entities with missing attributes. We additionally propose an efficient pretraining strategy for reducing the required training runtime. We train models using a biomedical KG containing approximately 2 million triples, and evaluate the performance of the resulting entity embeddings on the tasks of link prediction, and drug-protein interaction prediction, comparing against methods that do not take attribute data into account. Results In the standard link prediction evaluation, the proposed method results in competitive, yet lower performance than baselines that do not use attribute data. When evaluated in the task of drug-protein interaction prediction, the method compares favorably with the baselines. Further analyses show that incorporating attribute data does outperform baselines over entities below a certain node degree, comprising approximately 75% of the diseases in the graph. We also observe that optimizing attribute encoders is a challenging task that increases optimization costs. Our proposed pretraining strategy yields significantly higher performance while reducing the required training runtime. Conclusion BioBLP allows to investigate different ways of incorporating multimodal biomedical data for learning representations in KGs. With a particular implementation, we find that incorporating attribute data does not consistently outperform baselines, but improvements are obtained on a comparatively large subset of entities below a specific node-degree. Our results indicate a potential for improved performance in scientific discovery tasks where understudied areas of the KG would benefit from link prediction methods