A dynamic equilibrium between DNA methylation and demethylation of neuronal activity-regulated genes is crucial for memory processes. However, the mechanisms underlying this equilibrium remain elusive. Tet1 oxidase has been shown to play a key role in the active DNA demethylation in the CNS. In this study, we used Tet1 gene knockout (Tet1KO) mice to examine the involvement of Tet1 in memory consolidation and storage in the adult brain. We found that Tet1 ablation leads to: altered expression of numerous neuronal activity-regulated genes, compensatory upregulation of active demethylation pathway genes, and upregulation of various epigenetic modifiers. Moreover, Tet1KO mice showed an enhancement in the consolidation and storage of threat recognition (cued and contextual fear conditioning) and object location memories. We conclude that Tet1 plays a critical role in regulating neuronal transcription and in maintaining the epigenetic state of the brain associated with memory consolidation and storage.
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Migraine is defined as recurrent attack of headache that are commonly unilateral and accompanied by gastrointestinal and visual disorders. Migraine is more prevalent in females than males with a ratio of 3:1. It is primarily a complex neurovascular disorder involving local vasodilation of intracranial, extracerebral blood vessels and simultaneous stimulation of surrounding trigeminal sensory nervous pain pathway that results in headache. The activation of 'trigeminovascular system' causes release of various vasodilators, especially calcitonin gene-related peptide (CGRP) that induces pain response. At the same time, decreased levels of neurotransmitter, serotonin have been observed in migraineurs. Serotonin receptors have been found on the trigeminal nerve and cranial vessels and their agonists especially triptans prove effective in migraine treatment. It has been found that triptans act on trigeminovascular system and bring the elevated serum levels of key molecules like calcitonin gene related peptide (CGRP) to normal. Currently CGRP receptor antagonists, olcegepant and telcagepant are under consideration for antimigraine therapeutics. It has been observed that varying levels of ovarian hormones especially estrogen influence serotonin neurotransmission system and CGRP levels making women more predisposed to migraine attacks. This review provides comprehensive information about the role of serotonin and CGRP in migraine, specifically the menstrual migraine.
Document structure extraction has been a widely researched area for decades with recent works performing it as a semantic segmentation task over document images using fullyconvolution networks. Such methods are limited by image resolution due to which they fail to disambiguate structures in dense regions which appear commonly in forms. To mitigate this, we propose Form2Seq, a novel sequenceto-sequence (Seq2Seq) inspired framework for structure extraction using text, with a specific focus on forms, which leverages relative spatial arrangement of structures. We discuss two tasks; 1) Classification of low-level constituent elements (TextBlock and empty fillable Widget) into ten types such as field captions, list items, and others; 2) Grouping lower-level elements into higher-order constructs, such as Text Fields, ChoiceFields and ChoiceGroups, used as information collection mechanism in forms. To achieve this, we arrange the constituent elements linearly in natural reading order, feed their spatial and textual representations to Seq2Seq framework, which sequentially outputs prediction of each element depending on the final task. We modify Seq2Seq for grouping task and discuss improvements obtained through cascaded end-to-end training of two tasks versus training in isolation. Experimental results show the effectiveness of our text-based approach achieving an accuracy of 90% on classification task and an F1 of 75.82, 86.01, 61.63 on groups discussed above respectively, outperforming segmentation baselines. Further we show our framework achieves state of the results for table structure recognition on ICDAR 2013 dataset.
Topic models have been widely used to learn text representations and gain insight into document corpora. To perform topic discovery, most existing neural models either take document bag-of-words (BoW) or sequence of tokens as input followed by variational inference and BoW reconstruction to learn topic-word distribution. However, leveraging topic-word distribution for learning better features during document encoding has not been explored much. To this end, we develop a framework TAN-NTM, which processes document as a sequence of tokens through a LSTM whose contextual outputs are attended in a topic-aware manner. We propose a novel attention mechanism which factors in topic-word distribution to enable the model to attend on relevant words that convey topic related cues. The output of topic attention module is then used to carry out variational inference. We perform extensive ablations and experiments resulting in ∼ 9 -15 percentage improvement over score of existing SOTA topic models in NPMI coherence on several benchmark datasets -20Newsgroups, Yelp Review Polarity and AGNews. Further, we show that our method learns better latent document-topic features compared to existing topic models through improvement on two downstream tasks: document classification and topic guided keyphrase generation.
Document structure extraction has been a widely researched area for decades. Recent work in this direction has been deep learning-based, mostly focusing on extracting structure using fully convolution NN through semantic segmentation. In this work, we present a novel multi-modal approach for form structure extraction. Given simple elements such as textruns and widgets, we extract higher-order structures such as TextBlocks, Text Fields, Choice Fields, and Choice Groups, which are essential for information collection in forms. To achieve this, we obtain a local image patch around each low-level element (reference) by identifying candidate elements closest to it. We process textual and spatial representation of candidates sequentially through a BiLSTM to obtain context-aware representations and fuse them with image patch features obtained by processing it through a CNN. Subsequently, the sequential decoder takes this fused feature vector to predict the association type between reference and candidates. These predicted associations are utilized to determine larger structures through connected components analysis. Experimental results show the effectiveness of our approach achieving a recall of 90.29%, 73.80%, 83.12%, and 52.72% for the above structures, respectively, outperforming semantic segmentation baselines significantly. We show the efficacy of our method through ablations, comparing it against using individual modalities. We also introduce our new rich human-annotated Forms Dataset.
We develop a reinforcement learning based search assistant which can assist users through a set of actions and sequence of interactions to enable them realize their intent. Our approach caters to subjective search where the user is seeking digital assets such as images which is fundamentally different from the tasks which have objective and limited search modalities. Labeled conversational data is generally not available in such search tasks and training the agent through human interactions can be time consuming. We propose a stochastic virtual user which impersonates a real user and can be used to sample user behavior efficiently to train the agent which accelerates the bootstrapping of the agent. We develop A3C algorithm based context preserving architecture which enables the agent to provide contextual assistance to the user. We compare the A3C agent with Q-learning and evaluate its performance on average rewards and state values it obtains with the virtual user in validation episodes. Our experiments show that the agent learns to achieve higher rewards and better states.
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