Degradation and enhanced oil recovery potential of Alcanivorax borkumensis through production of bio-enzyme and bio-surfactant.

Microbial enhanced oil recovery (EOR) has become the focus of oilfield research due to its low cost, environmental friendliness and sustainability. The degradation and EOR capacity of A. borkumensis through the production of bio-enzyme and bio-surfactant were first investigated in this study. The total protein concentration, acetylcholinesterase, esterase, lipase, alkane hydroxylase activity, surface tension, and emulsification index (EI) were determined at different culture times. The bio-surfactant was identified as glycolipid compound, and the yield was 2.6 ± 0.2 g/L. The nC12 and nC13 of crude oil were completely degraded, and more than 40.0 % of nC14-nC24 was degraded by by A. borkumensis. The results of the microscopic etching model displacement and core flooding experiments showed that emulsification was the main mechanism of EOR. A. borkumensis enhanced the recovery rate by 20.2 %. This study offers novel insights for the development of environmentally friendly and efficient oil fields.

Microbial dynamics and biogenic methane production responses to the addition of glycine betaine in shales.

Biogenic methane production depends on microbial community compositions in shale gas reservoirs, and glycine betaine plays an important role in methanogenic metabolic pathways. Previous studies have mainly focused on the microbial community dynamics in the water produced by shale hydraulic fracturing. Here, we used fresh shale as a sample and obtained the methane (CH4) and carbon dioxide (CO2) concentrations, microbial communities, and methanogenic functional gene numbers of solid and liquid groups in anaerobic bottles through gas chromatography, 16S rDNA sequencing (60 samples) and quantitative real-time PCR analysis in all culture stages. With glycine betaine addition, the total CH4 concentrations of the S1, S2 and Sw samples were 1.56, 1.05 and 4.48 times, while CO2 increased by 2.54-, 4.80- and 0.43-fold compared with samples without glycine betaine after 28 days of incubation, respectively. The alpha diversity was reduced when glycine betaine was added. The significant differences in bacterial community abundance at the genus level in samples with glycine betaine were Bacillus, Oceanobacillus, Acinetobacter, and Legionella. The bacterial and archaeal community changes implied that the addition of glycine betaine may promote CH4 production mainly by first forming CO2 and then generating CH4. The results of mrtA, mcrA, and pmoA gene numbers showed that the shale had great potential for producing methane. The addition of glycine betaine to shale changed the original microbial networks and increased the nodes and taxon connectedness of the Spearman association network. Our analyses indicate that the addition of glycine betaine enhances CH4 concentrations, causing the microbial network to be more complex and sustainable which supports the survival and adaptation of microbes in shale formations.

An Adaptive Deep Ensemble Learning Method for Dynamic Evolving Diagnostic Task Scenarios.

Increasingly, machine learning methods have been applied to aid in diagnosis with good results. However, some complex models can confuse physicians because they are difficult to understand, while data differences across diagnostic tasks and institutions can cause model performance fluctuations. To address this challenge, we combined the Deep Ensemble Model (DEM) and tree-structured Parzen Estimator (TPE) and proposed an adaptive deep ensemble learning method (TPE-DEM) for dynamic evolving diagnostic task scenarios. Different from previous research that focuses on achieving better performance with a fixed structure model, our proposed model uses TPE to efficiently aggregate simple models more easily understood by physicians and require less training data. In addition, our proposed model can choose the optimal number of layers for the model and the type and number of basic learners to achieve the best performance in different diagnostic task scenarios based on the data distribution and characteristics of the current diagnostic task. We tested our model on one dataset constructed with a partner hospital and five UCI public datasets with different characteristics and volumes based on various diagnostic tasks. Our performance evaluation results show that our proposed model outperforms other baseline models on different datasets. Our study provides a novel approach for simple and understandable machine learning models in tasks with variable datasets and feature sets, and the findings have important implications for the application of machine learning models in computer-aided diagnosis.

Elucidate microbial characteristics in a full-scale treatment plant for offshore oil produced wastewater.

Oil-produced wastewater treatment plants, especially those involving biological treatment processes, harbor rich and diverse microbes. However, knowledge of microbial ecology and microbial interactions determining the efficiency of plants for oil-produced wastewater is limited. Here, we performed 16S rDNA amplicon sequencing to elucidate the microbial composition and potential microbial functions in a full-scale well-worked offshore oil-produced wastewater treatment plant. Results showed that microbes that inhabited the plant were diverse and originated from oil and marine associated environments. The upstream physical and chemical treatments resulted in low microbial diversity. Organic pollutants were digested in the anaerobic baffled reactor (ABR) dominantly through fermentation combined with sulfur compounds respiration. Three aerobic parallel reactors (APRs) harbored different microbial groups that performed similar potential functions, such as hydrocarbon degradation, acidogenesis, photosynthetic assimilation, and nitrogen removal. Microbial characteristics were important to the performance of oil-produced wastewater treatment plants with biological processes.

Tracking Knowledge Evolution in Cloud Health Care Research: Knowledge Map and Common Word Analysis.

BACKGROUND: With the continuous development of the internet and the explosive growth in data, big data technology has emerged. With its ongoing development and application, cloud computing technology provides better data storage and analysis. The development of cloud health care provides a more convenient and effective solution for health. Studying the evolution of knowledge and research hotspots in the field of cloud health care is increasingly important for medical informatics. Scholars in the medical informatics community need to understand the extent of the evolution of and possible trends in cloud health care research to inform their future research. OBJECTIVE: Drawing on the cloud health care literature, this study aimed to describe the development and evolution of research themes in cloud health care through a knowledge map and common word analysis. METHODS: A total of 2878 articles about cloud health care was retrieved from the Web of Science database. We used cybermetrics to analyze and visualize the keywords in these articles. We created a knowledge map to show the evolution of cloud health care research. We used co-word analysis to identify the hotspots and their evolution in cloud health care research. RESULTS: The evolution and development of cloud health care services are described. In 2007-2009 (Phase I), most scholars used cloud computing in the medical field mainly to reduce costs, and grid computing and cloud computing were the primary technologies. In 2010-2012 (Phase II), the security of cloud systems became of interest to scholars. In 2013-2015 (Phase III), medical informatization enabled big data for health services. In 2016-2017 (Phase IV), machine learning and mobile technologies were introduced to the medical field. CONCLUSIONS: Cloud health care research has been rapidly developing worldwide, and technologies used in cloud health research are simultaneously diverging and becoming smarter. Cloud-based mobile health, cloud-based smart health, and the security of cloud health data and systems are three possible trends in the future development of the cloud health care field.

[Effects of nitrogen application and ridge film furrow planting on water use of winter wheat in dry land of south Shanxi].

A 2-year (2008-2010) field experiment was conducted to study the effects of basal dressing nitrogen, topdressing nitrogen, and ridge film furrow planting on the 0-2 m soil moisture status and the grain yield and water use efficiency of winter wheat in rain-fed area of South Shanxi Province. In all treatments, the soil moisture status during winter wheat growth period had the same change trend, being increased steadily from pre-sowing to revival stage and decreased sharply from revival stage to heading stage, and then increased gradually till maturity stage. From revival stage to heading stage, the soil water consumption was the most. Increasing nitrogen basal application rate or topdressing nitrogen increased the soil water consumption, widened the soil moisture active layer, and deepened the relatively stable layer. Topdressing nitrogen increased grain yield significantly; ridge film furrow planting decreased soil water consumption obviously. The water use efficiency under ridge film furrow planting was 23.4% and 39.1% higher than that under conventional planting system in 2009 and 2010 (P < 0.01). The grain yield under ridge film furrow planting plus top-dressing nitrogen was 3643 kg x hm(-2), which was significantly higher than that under single ridge film furrow planting or topdressing nitrogen, displaying a preferable water-fertilizer coupling effect.