RESUMO
A novel Bacillus licheniformis strain (DM-1) was isolated from a mature reservoir in Dagang oilfield of China. DM-1 showed unique properties to utilize petroleum hydrocarbons and agroindustrial by-product (molasses) for exopolysaccharide (EPS) production under oil recovery conditions. The DM-1 EPS was proven to be a proteoglycan with a molecular weight of 568 kDa. The EPS showed shear thinning properties and had high viscosities at dilute concentrations (<1%, w/v), high salinities, and elevated temperatures. Strain DM-1 could degrade long-chain n-alkanes up to C36. Viscosity reduction test have shown that the viscosity of the crude oil was reduced by 40% compared with that before DM-1 treatment. Sand pack flooding test results under simulated reservoir conditions have shown that the enhanced oil recovery efficiency was 19.2% after 7 days of in-situ bioaugmentation with B. licheniformis DM-1. The obtained results indicate that strain DM-1 is a promising candidate for in situ microbial enhanced oil recovery (MEOR).
Assuntos
Alcanos/metabolismo , Bacillus licheniformis/metabolismo , Biodegradação Ambiental , Hidrocarbonetos/metabolismo , Campos de Petróleo e Gás/microbiologia , Petróleo/metabolismo , Polissacarídeos Bacterianos/metabolismo , Bacillus licheniformis/isolamento & purificaçãoRESUMO
Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death worldwide. The early diagnosis of HCC is greatly helpful to achieve long-term disease-free survival. However, HCC is usually difficult to be diagnosed at an early stage. The aim of this study was to create the prediction model to diagnose HCC based on gene expression programming (GEP). GEP is an evolutionary algorithm and a domain-independent problem-solving technique. Clinical data show that six serum biomarkers, including gamma-glutamyl transferase, C-reaction protein, carcinoembryonic antigen, alpha-fetoprotein, carbohydrate antigen 153, and carbohydrate antigen 199, are related to HCC characteristics. In this study, the prediction of HCC was made based on these six biomarkers (195 HCC patients and 215 non-HCC controls) by setting up optimal joint models with GEP. The GEP model discriminated 353 out of 410 subjects, representing a determination coefficient of 86.28% (283/328) and 85.37% (70/82) for training and test sets, respectively. Compared to the results from the support vector machine, the artificial neural network, and the multilayer perceptron, GEP showed a better outcome. The results suggested that GEP modeling was a promising and excellent tool in diagnosis of hepatocellular carcinoma, and it could be widely used in HCC auxiliary diagnosis. Graphical abstract The process to establish an efficient model for auxiliary diagnosis of hepatocellular carcinoma.
Assuntos
Algoritmos , Carcinoma Hepatocelular/diagnóstico , Carcinoma Hepatocelular/genética , Regulação Neoplásica da Expressão Gênica , Neoplasias Hepáticas/diagnóstico , Neoplasias Hepáticas/genética , Adulto , Idoso , Idoso de 80 Anos ou mais , Inteligência Artificial , Biomarcadores Tumorais/sangue , Carcinoma Hepatocelular/sangue , Estudos de Casos e Controles , Feminino , Humanos , Neoplasias Hepáticas/sangue , Masculino , Pessoa de Meia-Idade , Curva ROC , Adulto JovemRESUMO
In this work, we irradiated a high-definition (HD) industrial camera based on a commercial-off-the-shelf (COTS) CMOS image sensor (CIS) with Cobalt-60 gamma-rays. All components of the camera under test were fabricated without radiation hardening, except for the lens. The irradiation experiments of the HD camera under biased conditions were carried out at 1.0, 10.0, 20.0, 50.0 and 100.0 Gy/h. During the experiment, we found that the tested camera showed a remarkable degradation after irradiation and differed in the dose rates. With the increase of dose rate, the same target images become brighter. Under the same dose rate, the radiation effect in bright area is lower than that in dark area. Under different dose rates, the higher the dose rate is, the worse the radiation effect will be in both bright and dark areas. And the standard deviations of bright and dark areas become greater. Furthermore, through the progressive degradation analysis of the captured image, experimental results demonstrate that the attenuation of signal to noise ratio (SNR) versus radiation time is not obvious at the same dose rate, and the degradation is more and more serious with increasing dose rate. Additionally, the decrease rate of SNR at 20.0, 50.0 and 100.0 Gy/h is far greater than that at 1.0 and 10.0 Gy/h. Even so, we confirm that the HD industrial camera is still working at 10.0 Gy/h during the 8 h of measurements, with a moderate decrease of the SNR (5 dB). The work is valuable and can provide suggestion for camera users in the radiation field.