Analysis of DNA Sequence Classification Using CNN and Hybrid Models.

In a general computational context for biomedical data analysis, DNA sequence classification is a crucial challenge. Several machine learning techniques have used to complete this task in recent years successfully. Identification and classification of viruses are essential to avoid an outbreak like COVID-19. Regardless, the feature selection process remains the most challenging aspect of the issue. The most commonly used representations worsen the case of high dimensionality, and sequences lack explicit features. It also helps in detecting the effect of viruses and drug design. In recent days, deep learning (DL) models can automatically extract the features from the input. In this work, we employed CNN, CNN-LSTM, and CNN-Bidirectional LSTM architectures using Label and K-mer encoding for DNA sequence classification. The models are evaluated on different classification metrics. From the experimental results, the CNN and CNN-Bidirectional LSTM with K-mer encoding offers high accuracy with 93.16% and 93.13%, respectively, on testing data.

First Exploration of Neutron Shell Structure below Lead and beyond N=126.

The nuclei below lead but with more than 126 neutrons are crucial to an understanding of the astrophysical r process in producing nuclei heavier than A∼190. Despite their importance, the structure and properties of these nuclei remain experimentally untested as they are difficult to produce in nuclear reactions with stable beams. In a first exploration of the shell structure of this region, neutron excitations in ^{207}Hg have been probed using the neutron-adding (d,p) reaction in inverse kinematics. The radioactive beam of ^{206}Hg was delivered to the new ISOLDE Solenoidal Spectrometer at an energy above the Coulomb barrier. The spectroscopy of ^{207}Hg marks a first step in improving our understanding of the relevant structural properties of nuclei involved in a key part of the path of the r process.

Pharmacological Actions of Glucagon-Like Peptide-1, Gastric Inhibitory Polypeptide, and Glucagon.

Glucagon family of peptide hormones is a group of structurally related brain-gut peptides that exert their pleiotropic actions through interactions with unique members of class B1 G protein-coupled receptors (GPCRs). They are key regulators of hormonal homeostasis and are important drug targets for metabolic disorders such as type-2 diabetes mellitus (T2DM), obesity, and dysregulations of the nervous systems such as migraine, anxiety, depression, neurodegeneration, psychiatric disorders, and cardiovascular diseases. The current review aims to provide a detailed overview of the current understanding of the pharmacological actions and therapeutic advances of three members within this family including glucagon-like peptide-1 (GLP-1), gastric inhibitory polypeptide (GIP), and glucagon.