Publications by authors named "S Anu Priya"

Enantioseparation and enantiorecognition are crucial in the pharmaceutical analysis of chiral substances, impacting safety, efficacy, and regulatory compliance. Enantioseparation refers to the process of separating enantiomers from a mixture, typically achieved through chromatography techniques like HPLC and SFC. In contrast, enantiorecognition involves the identification of enantiomers based on their interaction with a chiral selector without the need for separation.

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Background: Blood transfusion is a double-edged sword, as it is a life-saving intervention but is also associated with various adverse reactions. However, blood transfusion safety can be improved by identifying these adverse reactions and taking appropriate interventions. Therefore, in this study, we aimed to determine the frequency and type of transfusion reactions occurring among in-patients at our hospital, as reported to our institute's blood center.

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Two-spotted spider mite (TSSM), Tetranychus urticae Koch is a devastating polyphagous mite causing considerable economic loss. Acaricides are showered in crops to manage this pest. The pest is known for developing resistance to several classical acaricides.

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Rationale And Objectives: To investigate the effect of ComBat harmonization on the stability of myocardial radiomic features derived from multi-energy CT reconstructions.

Materials And Methods: A retrospective study was conducted on 205 patients who underwent dual-energy chest CTA at a single center. The data was reconstructed into multiple spectral reconstructions (mixed energy simulating standard 120 Kv acquisition and monoenergetic images ranging from 40 to 190 keV in increments of 10).

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Article Synopsis
  • - DNNs are transitioning from simple models (single-modality, single-task) to more complex ones (multi-modality, multi-task), which require advanced hardware solutions to handle their varying layers and complex dependencies.
  • - Heterogeneous systems are being developed, integrating different accelerators to reduce latency, with FPGAs being a key component due to their high density and configurability for machine-learning tasks.
  • - The authors introduce CHEF, a framework that efficiently implements these complex models on heterogeneous FPGA clusters, featuring two main approaches (CHEF-A2F and CHEF-M2A) that significantly reduce latency and search times compared to previous methods.
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