Validation that human microbiome phages use alternative genetic coding with TAG stop read as Q

Metagenomic findings suggesting that bacteriophages (phages) can use genetic codes different from those of their host bacteria reveal a new dimension of phage-host interaction dynamics. Whereas reassignment of stop codons to code for amino acids has been predicted, there has been no proteomic validation of alternative coding in phages. In fact, one code where the stop codon TAG is reassigned to glutamine (code 15) has never been experimentally validated in any biological system. Here, we characterized stop codon reassignment in two crAss-like phages found in the human gut microbiome using LC-MS/MS-based metaproteomics. The proteome data from several phage structural proteins clearly demonstrates reassignment of the TAG stop codon to glutamine, establishing for the first time the expression of genetic code 15.

A computational screen for alternative genetic codes in over 250,000 genomes

The genetic code has been proposed to be a 'frozen accident', but the discovery of alternative genetic codes over the past four decades has shown that it can evolve to some degree. Since most examples were found anecdotally, it is difficult to draw general conclusions about the evolutionary trajectories of codon reassignment and why some codons are affected more frequently. To fill in the diversity of genetic codes, we developed Codetta, a computational method to predict the amino acid decoding of each codon from nucleotide sequence data. We surveyed the genetic code usage of over 250,000 bacterial and archaeal genome sequences in GenBank and discovered five new reassignments of arginine codons (AGG, CGA, and CGG), representing the first sense codon changes in bacteria. In a clade of uncultivated Bacilli, the reassignment of AGG to become the dominant methionine codon likely evolved by a change in the amino acid charging of an arginine tRNA. The reassignments of CGA and/or CGG were found in genomes with low GC content, an evolutionary force which likely helped drive these codons to low frequency and enable their reassignment.

Down syndrome detection using modified adaboost algorithm

In human body genetic codes are stored in the genes. All of our inherited traits are associated with these genes and are grouped as structures generally called chromosomes. In typical cases, each cell consists of 23 pairs of chromosomes, out of which each parent contributes half. But if a person has a partial or full copy of chromosome 21, the situation is called Down syndrome. It results in intellectual disability, reading impairment, developmental delay, and other medical abnormalities. There is no specific treatment for Down syndrome. Thus, early detection and screening of this disability are the best styles for down syndrome prevention. In this work, recognition of Down syndrome utilizes a set of facial expression images. Solid geometric descriptor is employed for extracting the facial features from the image set. An AdaBoost method is practiced to gather the required data sets and for the categorization. The extracted information is then assigned and used to instruct the Neural Network using Backpropagation algorithm. This work recorded that the presented model meets the requirement with 98.67% accuracy.

Applications of Artificial Intelligence (AI) in healthcare: A review

Artificial intelligence is revolutionizing — and strengthening — modern healthcarethrough technologies that can predict, grasp, learn, and act, whether it's employed toidentify new relationships between genetic codes or to control surgery-assisting robots.It can detect minor patterns that humans would completely overlook. This studyexplores and discusses the various modern applications of AI in the health sector.Particularly, the study focuses on three most emerging areas of AI-poweredhealthcare: AI-led drug discovery, clinical trials, and patient care. The findings suggestthat pharmaceutical firms have benefited from AI in healthcare by speeding up theirdrug discovery process and automating target identification. Artificial Intelligence (AI)can help also to eliminate time-consuming data monitoring methods. The findings alsoindicate that AI-assisted clinical trials are capable of handling massive volumes of dataand producing highly accurate results. Medical AI companies develop systems thatassist patients at every level. Patients' medical data is also analyzed by clinicalintelligence, which provides insights to assist them improve their quality of life.

Applications of Artificial Intelligence (AI) in healthcare: A review

Artificial intelligence is revolutionizing — and strengthening — modern healthcare through technologies that can predict, grasp, learn, and act, whether it's employed to identify new relationships between genetic codes or to control surgery-assisting robots. It can detect minor patterns that humans would completely overlook. This study explores and discusses the various modern applications of AI in the health sector. Particularly, the study focuses on three most emerging areas of AI-powered healthcare: AI-led drug discovery, clinical trials, and patient care. The findings suggest that pharmaceutical firms have benefited from AI in healthcare by speeding up their drug discovery process and automating target identification. Artificial Intelligence (AI) can help also to eliminate time-consuming data monitoring methods. The findings also indicate that AI-assisted clinical trials are capable of handling massive volumes of data and producing highly accurate results. Medical AI companies develop systems that assist patients at every level. Patients' medical data is also analyzed by clinical intelligence, which provides insights to assist them improve their quality of life.