An experimental study of imbibition process and fluid distribution in tight oil reservoir under different pressures and temperatures

Tight reservoirs are a major focus of unconventional reservoir development. As a means to improve hydrocarbon recovery from tight reservoirs, imbibition has been received increasing attentions in recent years. This study evaluates how the changes in temperature and pressure affect imbibition through conducting experimental tests under various conditions on samples from the Yan Chang formation, a tight reservoir in Ordos Basin. The fluid distribution is compared before and after imbibition in core samples by nuclear magnetic resonance method. The results show that the imbibition recovery is significantly improved through increasing temperature and pressure. A high temperature facilitates molecular thermal movements, increasing oil-water exchange rate. The core samples are characterized with nano-mesopores, which is followed by nano-macropores, micropores, mesopores, and nano-micropores. Comparative analysis of nuclear magnetic resonance shows that the irreducible water saturation increases after imbibition and is mainly distributed in nano-pores. Increasing pressure increases the amount of residual water in nano pores, with the relatively more significant increase in the amount of residual water in nanomacro-pores compared with other types of pores.Cited as: Liang, Y., Lai, F., Dai, Y., Shi, H., Shi, G. An experimental study of imbibition process and fluid distribution in tight oil reservoir under different pressures and temperatures. Capillarity, 2021, 4(4): 66-75, doi: 10.46690/capi.2021.04.0

Validation of the neural network for 3D photon radiation field reconstruction under various source distributions

Introduction: This paper proposes a five-layer fully connected neural network for predicting radiation parameters in a radiation space based on detector readings.Methods: The network is trained and tested using gamma flux values from individual detector positions as input, and is used to predict the gamma radiation field in 3D space under different source term distributions. The method is evaluated using the mean percentage change error (PCT) for the test set under different source term distributions.Results: The results show that the neural network method can accurately predict radiation parameters with an average PCT error range of 0.53% to 3.11%, within the given measurement input error range of ± 10%. The method also demonstrates its ability to directly reconstruct the 3D radiation field with some simple source terms.Discussion: The proposed method has practical value in real operations within radiation spaces, and can be used to improve the accuracy and efficiency of predicting radiation parameters. Further research could explore the use of more complex source term distributions and the integration of other types of sensors for improved accuracy

Integrated glycomic analysis of ovarian cancer side population cells

Additional file 2. The category of representative lectins for glycan profiling

Modulatory Effect of Fermented Papaya Extracts on Mammary Gland Hyperplasia Induced by Estrogen and Progestin in Female Rats

Fermented papaya extracts (FPEs) are obtained by fermentation of papaya by Aspergillus oryzae and yeasts. In this study, we investigated the protective effects of FPEs on mammary gland hyperplasia induced by estrogen and progestogen. Rats were randomly divided into 6 groups, including a control group, an FPE-alone group, a model group, and three FPE treatment groups (each receiving 30, 15, or 5 ml/kg FPEs). Severe mammary gland hyperplasia was induced upon estradiol benzoate and progestin administration. FPEs could improve the pathological features of the animal model and reduce estrogen levels in the serum. Analysis of oxidant indices revealed that FPEs could increase superoxide dismutase (SOD) and glutathione peroxidase (GSH-Px) activities, decrease malondialdehyde (MDA) level in the mammary glands and serum of the animal models, and decrease the proportion of cells positive for the oxidative DNA damage marker 8-oxo-dG in the mammary glands. Additionally, estradiol benzoate and progestin altered the levels of serum biochemical compounds such as aspartate transaminase (AST), total bilirubin (TBIL), and alanine transaminase (ALT), as well as hepatic oxidant indices such as SOD, GSH-Px, MDA, and 8-oxo-2′-deoxyguanosine (8-oxo-dG). These indices reverted to normal levels upon oral administration of a high dose of FPEs. Taken together, our results indicate that FPEs can protect the mammary glands and other visceral organs from oxidative damage

Phase 2 study of buparlisib (BKM120), a pan-class I PI3K inhibitor, in patients with metastatic triple-negative breast cancer

Treatment options for triple-negative breast cancer remain limited. Activation of the PI3K pathway via loss of PTEN and/or INPP4B is common. Buparlisib is an orally bioavailable, pan-class I PI3K inhibitor. We evaluated the safety and efficacy of buparlisib in patients with metastatic triple-negative breast cancer. This was a single-arm phase 2 study enrolling patients with triple-negative metastatic breast cancer. Patients were treated with buparlisib at a starting dose of 100 mg daily. The primary endpoint was clinical benefit, defined as confirmed complete response (CR), partial response (PR), or stable disease (SD) for ≥ 4 months, per RECIST 1.1. Secondary endpoints included progression-free survival (PFS), overall survival (OS), and toxicity. A subset of patients underwent pre- and on-treatment tumor tissue biopsies for correlative studies. Fifty patients were enrolled. Median number of cycles was 2 (range 1-10). The clinical benefit rate was 12% (6 patients, all SD ≥ 4 months). Median PFS was 1.8 months (95% confidence interval [CI] 1.6-2.3). Median OS was 11.2 months (95% CI 6.2-25). The most frequent adverse events were fatigue (58% all grades, 8% grade 3), nausea (34% all grades, none grade 3), hyperglycemia (34% all grades, 4% grade 3), and anorexia (30% all grades, 2% grade 3). Eighteen percent of patients experienced depression (12% grade 1, 6% grade 2) and anxiety (10% grade 1, 8% grade 2). Alterations in PIK3CA / AKT1 / PTEN were present in 6/27 patients with available targeted DNA sequencing (MSK-IMPACT), 3 of whom achieved SD as best overall response though none with clinical benefit ≥ 4 months. Of five patients with paired baseline and on-treatment biopsies, reverse phase protein arrays (RPPA) analysis demonstrated reduction of S6 phosphorylation in 2 of 3 patients who achieved SD, and in none of the patients with progressive disease. Buparlisib was associated with prolonged SD in a very small subset of patients with triple-negative breast cancer; however, no confirmed objective responses were observed. Downmodulation of key nodes in the PI3K pathway was observed in patients who achieved SD. PI3K pathway inhibition alone may be insufficient as a therapeutic strategy for triple-negative breast cancer. Registered on 13 February 2013; . Registered on 27 June 2012

A Survey of SAR Image Target Detection Based on Convolutional Neural Networks

Synthetic Aperture Radar (SAR) target detection is a significant research direction in radar information processing. Aiming at the poor robustness and low detection accuracy of traditional detection algorithms, SAR image target detection based on the Convolutional Neural Network (CNN) is reviewed in this paper. Firstly, the traditional SAR image target detection algorithms are briefly discussed, and their limitations are pointed out. Secondly, the CNN’s network principle, basic structure, and development process in computer vision are introduced. Next, the SAR target detection based on CNN is emphatically analyzed, including some common data sets and image processing methods for SAR target detection. The research status of SAR image target detection based on CNN is summarized and compared in detail with traditional algorithms. Afterward, the challenges of SAR image target detection are discussed and future research is proposed. Finally, the whole article is summarized. By summarizing and analyzing prior research work, this paper is helpful for subsequent researchers to quickly recognize the current development status and identify the connections between various detection algorithms. Beyond that, this paper summarizes the problems and challenges confronting researchers in the future, and also points out the specific content of future research, which has certain guiding significance for promoting the progress of SAR image target detection

A Survey of SAR Image Target Detection Based on Convolutional Neural Networks

Synthetic Aperture Radar (SAR) target detection is a significant research direction in radar information processing. Aiming at the poor robustness and low detection accuracy of traditional detection algorithms, SAR image target detection based on the Convolutional Neural Network (CNN) is reviewed in this paper. Firstly, the traditional SAR image target detection algorithms are briefly discussed, and their limitations are pointed out. Secondly, the CNN’s network principle, basic structure, and development process in computer vision are introduced. Next, the SAR target detection based on CNN is emphatically analyzed, including some common data sets and image processing methods for SAR target detection. The research status of SAR image target detection based on CNN is summarized and compared in detail with traditional algorithms. Afterward, the challenges of SAR image target detection are discussed and future research is proposed. Finally, the whole article is summarized. By summarizing and analyzing prior research work, this paper is helpful for subsequent researchers to quickly recognize the current development status and identify the connections between various detection algorithms. Beyond that, this paper summarizes the problems and challenges confronting researchers in the future, and also points out the specific content of future research, which has certain guiding significance for promoting the progress of SAR image target detection

霉草科: 广东省分布新记录 Triuridaceae: a new record of the family from Guangdong Province, China

报道了广东省霉草科Triuridaceae 1种分布新记录———大柱霉草(Sciaphila megastyla Fukuyama et Suzuki)。 A saprophytic species, Sciaphila megastyla Fukuyama et Suzuki of Triuridaceae, is first reported in Guangdong, China. It was found in the understorey of a bamboo stand on Nankunshan, Guangdong Province in July 2003