Browsing by Author "Kumar, T."

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    Compressive Strength of High Plastic Clay Stabilized with Fly Ash-Based Geopolymer and Its Synthesis Parameters
    (Springer Science and Business Media Deutschland GmbH, 2021) Neeraj Varma, N.; Kumar, T.; Thotakura, V.
    Geopolymerization is an effective technique for utilizing industrial solid waste material as stabilizing material. This paper studies the effect of class-F fly ash-based geopolymer on compressible strength characteristics of high plastic clay using unconfined compression strength (UCS) test. Sodium silicate and sodium hydroxide were used as alkali activators in proportions of 60:40, respectively. The fly ash content was varied by 0, 10, 20 and 30% by dry weight of soil, and alkali activator was varied by 5, 10 and 15% by dry weight of soil–fly ash mix. UCS tests were carried out on the specimens contaminated under controlled curing environment. Unconfined compression strength increased with increase in fly ash and liquid activator content. The maximum UCS value of 790 kPa observed at 30% of fly ash content under elevated temperature of 50 °C. The influence of Si/Al and Na/Al ratios on compressive strength of geopolymeric materials was also identified. Further, numerical analysis was carried out to check the significance of factors effecting the compressive strength of the material. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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    Predicting cross-tissue hormone-gene relations using balanced word embeddings
    (Oxford University Press, 2022) Jadhav, A.; Kumar, T.; Raghavendra, M.; Loganathan, T.; Narayanan, M.
    Motivation: Inter-organ/inter-tissue communication is central to multi-cellular organisms including humans, and mapping inter-tissue interactions can advance system-level whole-body modeling efforts. Large volumes of biomedical literature have fostered studies that map within-tissue or tissue-agnostic interactions, but literature-mining studies that infer inter-tissue relations, such as between hormones and genes are solely missing. Results: We present a first study to predict from biomedical literature the hormone-gene associations mediating inter-tissue signaling in the human body. Our BioEmbedS∗ models use neural network-based Biomedical word Embeddings with a Support Vector Machine classifier to predict if a hormone-gene pair is associated or not, and whether an associated gene is involved in the hormone's production or response. Model training relies on our unified dataset Hormone-Gene version 1 of ground-truth associations between genes and endocrine hormones, which we compiled and carefully balanced in the embedded space to handle data disparities, such as between poorly- versus well-studied hormones. Our BioEmbedS model recapitulates known gene mediators of tissue-tissue signaling with 70.4% accuracy; predicts novel inter-tissue communication genes in humans, which are enriched for hormone-related disorders; and generalizes well to mouse, thereby holding promise for its extension to other multi-cellular organisms as well. © 2022 The Author(s).