No abstract

− Multidrug-resistant Acinetobacter baumannii and Pseudomonas aeruginosa are highly dangerous nosocomial pathogens, cause the symptoms of skin infections, pressure sores, sepsis, blood stream and wound infections. Unfortunately, these pathogens are immune to the most common antibiotics, such as, carbapenem, aminoglycoside and fluoroquinolone. Therefore, it is imperative that new and effective antibiotics be developed. In the present study, the antimicrobial effects of Aloe vera MAP (modified Aloe polysaccharide) on Staphylococcus aureus and Bacillus subtilis, Escherichia coli and Enterobacter aerogenes, and clinical Pseudomonas aeruginosa and clinical Acinetobacter baumannii were comprehensibly investigated. Prior to the growth inhibition effect measurement and antibiotic disc diffusion assay on gram-positive and gram-negative bacteria and selected multidrugresistant Pseudomonas aeruginosa and Acinetobacter baumannii, antimicrobial resistance screening was performed for the multidrug-resistant bacteria obtained from clinical isolates. The results for showed the Aloe vera MAP had a concentration-dependent effect on all of examined bacteria, particularly on Pseudomonas aeruginosa. Anti-inflammatory and anti-oxidant experiments were also performed dose dependently effects to confirm the beneficial physiological effects of Aloe vera MAP.

<p>A pilot machine learning(ML) program was developed to test ML technique for simulation of biochemical parameters at the coastal area in Korea. Temperature, chlorophyll, solar radiation, daylight time, humidity, nutrient data were collected as training dataset from the public domain and in-house projects of KIOST(Korea Institute of Ocean Science & Technology). Daily satellite chlorophyll data of MODIS(Moderate Resolution Imaging Spectroradiometer) and GOCI(Geostationary Ocean Color Imager) were retrieved from the public services. Daily SST(Sea Surface Temperature) data and ECMWF solar radiation data were retrieved from GHRSST service and Copernicus service. Meteorological observation data and marine observation data were collected from KMA (Korea Meteorological Agency) and KIOST. The output of marine biochemical numerical model of KIOST were also prepared to validate ML model. ML program was configured using LSTM network and TensorFlow. During the data processing process, some chlorophyll data were interpolated because there were many missing data exist in satellite dataset. ML training were conducted repeatedly under varying combinations of sequence length, learning rate, number of hidden layer and iterations. The 75% of training dataset were used for training and 25% were used for prediction. The maximum correlation between training data and predicted data was 0.995 in case that model output data were used as training dataset. When satellite data and observation data were used, correlations were around 0.55. Though the latter corelation is relatively low, the model simulated periodic variation well and some differences were found at peak values. It is thought that ML model can be applied for simulation of chlorophyll data if preparation of sufficient reliable observation data were possible.</p>