Leveraging transformers-based language models in proteome bioinformatics

In recent years, the rapid growth of biological data has increased interest in using bioinformatics to analyze and interpret this data. Proteomics, which studies the structure, function, and interactions of proteins, is a crucial area of bioinformatics. Using natural language processing (NLP) techniques in proteomics is an emerging field that combines machine learning and text mining to analyze biological data. Recently, transformer-based NLP models have gained significant attention for their ability to process variable-length input sequences in parallel, using self-attention mechanisms to capture long-range dependencies. In this review paper, we discuss the recent advancements in transformer-based NLP models in proteome bioinformatics and examine their advantages, limitations, and potential applications to improve the accuracy and efficiency of various tasks. Additionally, we highlight the challenges and future directions of using these models in proteome bioinformatics research. Overall, this review provides valuable insights into the potential of transformer-based NLP models to revolutionize proteome bioinformatics.     

Purification and characterization of minor brain proteolipids: use of fast atom bombardment-mass spectrometry for peptide sequencing

A combination of lipophilic gel permeation chromatography and ion-exchange chromatography in organic solvents was used to purify low molecular weight proteolipids from bovine brain. Cleavage peptides were purified by HPLC and studied mainly by the fast atom bombardment--mass spectrometry technique. A proteolipid of Mr 14 000 contains several peptides from the first 113 amino acids of the major myelin proteolipid (MMPL) plus an extra unknown blocked N-terminal peptide. A proteolipid of Mr 16 000 contains smaller peptides belonging to a C-terminal fragment of MMPL of about 160 residues. These two proteolipids do not seem to be artifacts from MMPL.     

Characteristics of a Beetle's Free Flight and a Flapping-Wing System that Mimics Beetle Flight

In this work, we first present a method to experimentally capture the free flight of a beetle (Allomyrina dichotoma), which is not an active flyer. The beetle is suspended in the air by a hanger to induce the free flight. This flight is filmed using two high-speed cameras. The high speed images are then examined to obtain flapping angle, flapping frequency, and wing rotation of the hind wing. The acquired data of beetle free flight are used to design a motor-driven flapper that can approximately mimic the beetle in terms of size, flapping frequency and wing kinematics. The flapper can create a large flapping angle over 140° with a large passive wing rotation angle. Even though the flapping frequency of the flapper is not high enough compared to that of a real beetle due to the limited motor torque, the flapper could produce positive average vertical force. This work will provide important experience for future development of a beetle-mimicking Flapping-Wing Micro Air Vehicle (FWMAV).     

Atherosclerotic plaque development

Atherosclerosis is now recognized as an inflammatory/immunomodulatory reaction to the presence of oxidized low-density lipoproteins within the arterial wall, often times in the setting of such risk factors as family history, hypercholesterolemia, high blood pressure, diabetes mellitus and smoking. The progression to high-risk lesions such as thin-fibrous cap atheromas results in an increased risk of sudden death, acute myocardial infarction and ischemic stroke. The interplay of macrophages, T lymphocytes and mast cells play a central role in both the development but more importantly in the progression of coronary and carotid artery disease to high-risk phenotypes.