Lithofacies Characteristics and Sweet Spot Distribution of Lacustrine Shale Oil: A Case Study from the Dongying Depression, Bohai Bay Basin, China

AbstractLacustrine shale is characterized by rapid lithofacies transformation and compositional heterogeneity, which present challenges in shale oil sweet spot evaluation and distribution prediction and should be systematically studied. Field emission-scanning electron microscopy (FE-SEM), low-pressure adsorption isotherm analysis, mercury intrusion porosimetry (MIP), and triaxial compression testing were employed to comprehensively analyze the oil-bearing capacity, reservoir properties, fluidity, and frackability of different lithofacies. Via analyses of mineral composition, total organic carbon (TOC) content, and sedimentary structure, seven lithofacies were identified: organic-rich calcareous shale (L1), organic-rich laminated calcareous mudstone (L2), organic-rich laminated carbonate-bearing mudstone (L3), intermediate-organic laminated calcareous mudstone (L4), organic-poor laminated calcareous mudstone (L5), organic-poor thin-bedded calcareous mudstone (L6), and organic-rich laminated silty mudstone (L7). Considered together, the oil-bearing capacity, reservoir properties, fluidity, and frackability suggested that the L1 and L7 lithofacies were high-quality sweet spots, with satisfactory oil-bearing capacity (TOC>3.5%; S1>10 mgHC/grock), well-developed pores and microfractures, notable fluidity (as indicated by a high oil saturation index value), and suitable brittleness. The sweet spot distribution was predicted according to multiresolution graph-based clustering analysis of well logs. The results indicate that comprehensive research of the key factors for shale oil and lithofacies prediction can promote sweet spot prediction and enhance shale oil exploration

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Evaluation and Prediction of Higher Education System Based on AHP-TOPSIS and LSTM Neural Network

A healthy and sustainable higher education system plays an important role in social development. The evaluation and prediction of such a system are vital for higher education. Existing models are usually constructed based on fewer indicators and original data are incomplete; thus, evaluation may be inefficient. In addition, these models are generally suitable for specific countries, rather than the whole universe. To tackle these issues, we proceed as follows: Firstly, we select a series of evaluation indicators that cover most aspects of higher education to establish a basic evaluation system. Then, we choose several representative countries to illustrate the system. Next, we use the analytic hierarchy process (AHP) to calculate a weight matrix of the indicators according to their importance. Furthermore, we obtain authoritative data from these countries. Then, we apply the indicators to the technique for order preference by similarity to an ideal solution (TOPSIS) algorithm to ascertain their relative levels. Finally, we combine the weight matrix with the relative levels to achieve a comprehensive evaluation of higher education. So far, a theoretical establishment of a higher education evaluation model has been generally completed. For better practical application, we add a predictive function to our evaluation model. Starting with China, we predict the development of national higher education for the next 20 years. We adopt a long short-term memory (LSTM) neural network as a method of prediction. Considering the significant influences of national policies on higher education, we address the issues under two circumstances: with or without policy influences. At last, we compare our model with existing models. Experimental results show that our model better reflects national higher education levels and provides more reasonable and robust prediction results

\u3ci\u3eAthyrium multidentatum\u3c/i\u3e (Doll.) Ching Extract Induce Apoptosis via Mitochondrial Dysfunction and Oxidative Stress in HepG2 Cells

Athyrium multidentatum (Doll.) Ching (AMC), a unique and nutritious potherb widely distributed in china, has been extensively used in traditional Chinese medicine. Previous studies indicated that AMC extract exhibited antioxidant and antitumor properties. However, the chemical composition of AMC and molecular mechanism of AMC toxicity to HepG2 cells have not yet been elucidated. Hence, this study aimed to investigate the chemical compositions and the underlying mechanisms of the antiproliferative and apoptotic effects of AMC on HepG2. HPLC-MS analysis showed that AMC contain five compounds with chlorogenic acid accounting for 43 percent. Also, AMC strongly inhibited the cell growth and induced apoptosis and cell cycle arrest in HepG2 cells by significantly upregulating the protein expressions of Fas, Fas-L, Bax/Bcl-2, cyto-c, cleaved caspase-3, and PARP in a dose-dependent manner, which indicates AMC induces apoptosis in HepG2 cells through both intrinsic and extrinsic pathways. Moreover, AMC provoked the production of ROS, H2O2, and NO, modulating the PI3K/Akt, MAPK, NFκB and Nrf2 pathways and their downstream transcriptional cascades, ultimately evoked oxidative stress and apoptosis in HpeG2 cells. Further in vivo experiments demonstrated that AMC significantly suppressed the tumor growth, suggesting that AMC may be a novel promising agent for hepatocellular carcinoma treatment