Influence of reduced water availability on Pseudomonas putida unsaturated biofilms and the role of alginate in desiccation tolerance

Biofilms are aggregates of cells adhering to surfaces embedded in a matrix of extracellular polymeric substances of their own making. Microbial water availability in many terrestrial habitats is one of the most important factors influencing unsaturated biofilm development and biofilm cell survival and death. In general, Pseudomonas putida strain mt-2 unsaturated biofilm formation proceeds through three distinct developmental phases, culminating in the formation of a microcolony. The form and severity of reduced water availability alters cell morphology. The dehydration treatments resulted in biofilms comprised of smaller cells but they were taller and more porous, and had a thicker exopolysaccharide (EPS) layer at the air interface. In the osmotic stress treatments, cell filamentation occurred more frequently in the presence of high concentrations of ionic, but not non-ionic, solutes and these filamented cells drastically altered biofilm Architecture;P. putida produced more EPS in response to dehydration stress, but not thermodynamically equivalent osmotic stress. Carbohydrate composition analysis showed that alginate was a component of unsaturated biofilm EPS only in the presence of dehydration stress. The absence of alginate altered biofilm architecture in that biofilms were shorter, covered more surface area, and were less porous than when alginate was present. By measuring intracellular water potential changes and monitoring fatty acid alterations during dehydration stress, we demonstrated that alginate slows the rate of cellular dehydration. Alginate production also contributed to the ability of cells to survive a severe desiccation stress. These results suggest that an important consequence of cellular dehydration is alginate production, which contributes to the fitness of P. putida in water-limited environments.;Reduced water availability influenced the temporal and spatial localization of dead cells within unsaturated biofilms. Dead cells were organized in arrays of various lengths one cell width. In general, more dead cells were localized in the lower layers, while active, growing cells were localized primarily in the upper layers.;Taken together, reduction of water availability influences biofilm architecture, EPS production, and cell death patterns, which all reflect a status of adaptation of P. putida to dehydration stress. This study provides new insights into microbial dehydration physiology

Predictive Solution for Radiation Toxicity Based on Big Data

Radiotherapy is a treatment method using radiation for cancer treatment based on a patient treatment planning for each radiotherapy machine. At this time, the dose, volume, device setting information, complication, tumor control probability, etc. are considered as a single-patient treatment for each fraction during radiotherapy process. Thus, these filed-up big data for a long time and numerous patients’ cases are inevitably suitable to produce optimal treatment and minimize the radiation toxicity and complication. Thus, we are going to handle up prostate, lung, head, and neck cancer cases using machine learning algorithm in radiation oncology. And, the promising algorithms as the support vector machine, decision tree, and neural network, etc. will be introduced in machine learning. In conclusion, we explain a predictive solution of radiation toxicity based on the big data as treatment planning decision support system

Prediction of Cancer Patient Outcomes Based on Artificial Intelligence

Knowledge-based outcome predictions are common before radiotherapy. Because there are various treatment techniques, numerous factors must be considered in predicting cancer patient outcomes. As expectations surrounding personalized radiotherapy using complex data have increased, studies on outcome predictions using artificial intelligence have also increased. Representative artificial intelligence techniques used to predict the outcomes of cancer patients in the field of radiation oncology include collecting and processing big data, text mining of clinical literature, and machine learning for implementing prediction models. Here, methods of data preparation and model construction to predict rates of survival and toxicity using artificial intelligence are described

Effects of Textural Properties on the Response of a SnO2-Based Gas Sensor for the Detection of Chemical Warfare Agents

The sensing behavior of SnO2-based thick film gas sensors in a flow system in the presence of a very low concentration (ppb level) of chemical agent simulants such as acetonitrile, dipropylene glycol methyl ether (DPGME), dimethyl methylphosphonate (DMMP), and dichloromethane (DCM) was investigated. Commercial SnO2 [SnO2(C)] and nano-SnO2 prepared by the precipitation method [SnO2(P)] were used to prepare the SnO2 sensor in this study. In the case of DCM and acetonitrile, the SnO2(P) sensor showed higher sensor response as compared with the SnO2(C) sensors. In the case of DMMP and DPGME, however, the SnO2(C) sensor showed higher responses than those of the SnO2(P) sensors. In particular, the response of the SnO2(P) sensor increased as the calcination temperature increased from 400 °C to 800 °C. These results can be explained by the fact that the response of the SnO2-based gas sensor depends on the textural properties of tin oxide and the molecular size of the chemical agent simulant in the detection of the simulant gases (0.1–0.5 ppm)