An effective deep residual network based class attention layer with bidirectional LSTM for diagnosis and classification of COVID-19.

In recent days, COVID-19 pandemic has affected several people's lives globally and necessitates a massive number of screening tests to detect the existence of the coronavirus. At the same time, the rise of deep learning (DL) concepts helps to effectively develop a COVID-19 diagnosis model to attain maximum detection rate with minimum computation time. This paper presents a new Residual Network (ResNet) based Class Attention Layer with Bidirectional LSTM called RCAL-BiLSTM for COVID-19 Diagnosis.

Further terpenoids from Euphorbia tirucalli.

The phytochemical investigation of Euphorbia tirucalli L. (Euphorbiaceae) yielded four new compounds, including a rare cadalene-type sesquiterpene (tirucadalenone), two tirucallane triterpenoids, euphorol L and euphorol M, with the latter being described as an epimeric mixture, and a euphane triterpene, namely, euphorol N, together with 7 known compounds. Their structures and absolute configurations were elucidated from analysis of 1D (1H, J-modulated C) and 2D NMR (HSQC, HMBC and NOESY), high-resolution mass spectrometry (HRESIMS), optical rotation, and GIAO NMR shift calculation followed by CP3 analysis, along with comparison with literature reports.