Artificial intelligence for topic modelling in Hindu philosophy: Mapping themes between the Upanishads and the Bhagavad Gita.

The Upanishads are known as one of the oldest philosophical texts in the world that form the foundation of Hindu philosophy. The Bhagavad Gita is the core text of Hindu philosophy and is known as a text that summarises the key philosophies of the Upanishads with a major focus on the philosophy of karma. These texts have been translated into many languages and there exist studies about themes and topics that are prominent; however, there is not much done using language models which are powered by deep learning. In this paper, we use advanced language models such as BERT to provide topic modelling of the Upanishads and the Bhagavad Gita. We then map those topics of the Bhagavad Gita and the Upanishads since it is well known that Bhagavad Gita summarizes the key messages in the Upanishads. We also analyse the distinct and overlapping topics amongst the texts and visualise the link of selected texts of the Upanishads with the Bhagavad Gita. Our results show very high similarity between the topics of these two texts with the mean cosine similarity of 73%. We find that out of the fourteen topics extracted from the Bhagavad Gita, nine of them have a cosine similarity of more than 70% with the topics of the Upanishads. We also find that topics generated by the BERT-based models show very high coherence when compared to the conventional models. Our best-performing model gives a coherence score of 73% on the Bhagavad Gita and 69% on the Upanishads. The visualization of the low-dimensional embeddings of these texts shows very clear overlapping themes among their topics adding another level of validation to our results.

Expression of a catalytically inactive transmembrane protein tyrosine phosphatase epsilon (tm-PTP epsilon) delays optic nerve myelination.

Reversible tyrosine phosphorylation is integral to the process of oligodendrocyte differentiation. To interfere with the subset of the phosphorylation cycle overseen by protein tyrosine phosphatase epsilon (PTP epsilon) in oligodendrocytes, we applied a substrate-trapping approach in the development of transgenic mice overexpressing a catalytically inactive, transmembrane PTP epsilon-hemaglutinin (tm-PTP epsilon-HA) from the dual promoter element of the gene encoding the myelin protein 2',3'-cyclic nucleotide 3'-phosphodiesterase (CNP). Transgene expression peaked during the active myelinating period, at 2-3 weeks postnatal. Two tyrosine phosphoproteins, alpha-enolase and beta-actin, were phosphorylated to a greater degree in transgenic mice. Despite a high degree of tm-PTP epsilon-HA expression, myelin was grossly normal in nearly all axonal tracts. Phenotypic abnormalities were limited to optic nerve, where a decrease in the degree of myelination was reflected by reduced levels of myelin proteins on postnatal day 21 (PND21), as well as a decrease in the density of differentiated oligodendrocytes. The optic chiasm was reduced in thickness in transgenic mice; optic nerves similarly exhibited a reduction in transverse width. Further analyses of the optic pathway demonstrated that transgenic protein was unexpectedly present in retinal ganglion cells, whose axons are the targets of myelination by optic nerve oligodendrocytes. On PND28, transgenic protein declined dramatically in both oligodendrocytes and retinal ganglion cells contributing to the recovery of optic nerve myelination. Thus, delayed myelination arises only when tm-PTP epsilon-HA is simultaneously expressed in myelin-forming glia and their neuronal targets. While tm-PTP epsilon related signaling pathways may figure in axon-glial interactions, interfering with tm-PTP epsilon activity does not perceptibly affect the development or myelinating capacity of most oligodendrocytes.