Prediction of Bioprocess Production Using Deep Neural Network Method

Deep learning enhanced the state-of-the-art methods in genomics allows it to be used in analysing the biological data with high prediction. The training process of neural network with several hidden layers which has been facilitated by deep learning has been subjected into increased interest in achieving remarkable results in various fields. Thus, the extraction of bioprocess production can be implemented by pathway prediction in genomic metabolic network in eschericia coli. As metabolic engineering involves the manipulation of genes which have the potential to increase the yield of metabolite production. A mathematical model of this network is the foundation for the development of computational procedure that directs genetic manipulations that would eventually lead to optimized bioprocess production. Due to the ability of deep learning to be well suited in terms of genomics, modelling for biological network can be implemented. Each layer reveal the insight of biological network which enable pathway analysis to be implemented in order to extract the target bioprocess production. In this study, deep neural network has been to identify any set of gene deletion models that offers optimal results in xylitol production and its growth yield.

Methicillin-resistant staphylococcus aureus from Peninsular Malaysian animal handlers: molecular profile, antimicrobial resistance, immune evasion cluster and genotypic categorization

Staphylococcus aureus (S. aureus) infections, particularly methicillin-resistant Staphylococcus aureus (MRSA) in humans and animals, have become a significant concern globally. The present study aimed to determine the prevalence and antibiogram of S. aureus isolated from animal handlers in Peninsular Malaysia. Furthermore, the genotypic characteristics of S. aureus isolates were also investigated. Nasal and oral swab samples were collected from 423 animal handlers in Peninsular Malaysia. The antibiogram profiles of S. aureus against 18 antibiotics were established using a Kirby–Bauer test. The genotypic profile of S. aureus, including the presence of antimicrobial resistance (AMR), virulence genes and spa genotypes, was investigated using molecular techniques. The overall carriage rate of S. aureus, MRSA and MDRSA was 30.5%, 1.2% and 19.4%, respectively. S. aureus was highly resistant against penicillin (72.3%) and amoxicillin (52.3%). Meanwhile, gentamicin and linezolid were fully effective against all the isolated S. aureus from animal handlers. It was observed that animal handlers with close exposure to poultry were more likely to carry S. aureus that is resistant to tetracycline and erythromycin. S. aureus isolates harboured tetracycline resistance (tetK, tetL and tetM), erythromycin resistance (ermA, ermB, ermC and msrA) and immune evasion cluster (IEC) genes (scn, chp, sak, sea and sep). Seventeen different spa types were detected among the 30 isolates of MDRSA, with t189 (16.7%) and t4171 (16.7%) being the predominant spa type, suggesting wide genetic diversity of the MDRSA isolates. The present study demonstrated the prevalence of S. aureus strains, including MRSA and MDRSA with various antimicrobial resistance and genetic profiles from animal handlers in Peninsular Malaysia