In this work, a bicomponent scaffold with a core-shell and islandlike structure that combines the respective advantages of polylactic acid (PLA) and chitosan (CS) was prepared via electrospinning accompanied by automatic phase separation and crystallization. The objective of this research was to design nanosized topography with highly bioactive CS onto PLA electrospun fiber surface to improve the cell biocompatibility of the PLA fibrous membrane. The morphology, inner structure, surface composition, crystallinity, and thermodynamic analyses of nanofibers with various PLA/CS ratios were carried out, and the turning mechanism of a core-shell or islandlike topography structure was also speculated. The mineralization of hydroxyapatite and culture results of preosteoblast (MC3T3-E1) cells on the modified scaffolds indicate that the outer CS component and rough nanoscale topography on the surface of the nanofibers balanced the hydrophilicity and hydrophobicity of the fibers, enhanced their mineralization ability, and made them more beneficial for the attachment and growth of cells. Moreover, CS and "islandlike" protrusions on the fiber surface increased the alkaline phosphatase activity of the MC3T3-E1 cells seeded on the fibrous membrane and provided a more appropriate interface for cell adhesion and proliferation. These results illustrate that this kind of PLA/CS membrane has the potential in tissue engineering. More importantly, our study provides a new approach to designing PLA scaffolds, with combined topographic and bioactive modification effects at the interface between cells and materials, for biomedicine.
Deep reinforcement learning (DRL) has been envisioned to have a competitive edge in quantitative finance. However, there is a steep development curve for quantitative traders to obtain an agent that automatically positions to win in the market, namely to decide where to trade, at what price and what quantity, due to the error-prone programming and arduous debugging. In this paper, we present the first open-source framework FinRL as a full pipeline to help quantitative traders overcome the steep learning curve. FinRL is featured with simplicity, applicability and extensibility under the key principles, full-stack framework, customization, reproducibility and hands-on tutoring.Embodied as a three-layer architecture with modular structures, FinRL implements fine-tuned state-of-the-art DRL algorithms and common reward functions, while alleviating the debugging workloads. Thus, we help users pipeline the strategy design at a high turnover rate. At multiple levels of time granularity, FinRL simulates various markets as training environments using historical data and live trading APIs. Being highly extensible, FinRL reserves a set of user-import interfaces and incorporates trading constraints such as market friction, market liquidity and investor's risk-aversion. Moreover, serving as practitioners' stepping stones, typical trading tasks are provided as step-by-step tutorials, e.g., stock trading, portfolio allocation, cryptocurrency trading, etc.
CCS CONCEPTS• Computing methodologies → Machine learning; Markov decision processes; Reinforcement learning.
The chemical composition and chemical
structure of selected oil
shales and the kerogens isolated from them were studied by solid-state 13C nuclear magnetic resonance (NMR) and Fourier transform
infrared (FTIR) spectroscopy with curve-fitting analysis, and the
changes during the removal of the inorganic matrix were also investigated.
The 13C NMR results indicate that the oil shales and kerogens
are mainly composed of aliphatic carbon (≥55%). FTIR analysis
indicates that aliphatic hydrocarbons mainly contain the methylene
group, mostly forming long straight chains and a small amount of branched
chains, and the hydroxy groups mainly contain OH–OH and OH–O bonds (≥65%).
Both the 13C NMR and FTIR analyses show that the acid treatment
improved the hydrocarbon-generating ability in kerogen. Furthermore,
the curve-fitting analysis indicates that the HCl and HF treatments
slightly affected the aliphatic and hydroxyl structures, but significantly
affected the oxygen-containing and aromatic structures in the oil
shales. The oxygen-containing groups in the oil shales are mainly
the C–OH group, followed by
the C–O, OC–O, and CO groups, in descending
order. After the acid treatment, the main oxygen-containing groups
were the C–OH and C–O groups, because of the
hydrolysis, substitution,
and ion exchange. The 13C NMR results indicate that the
acid treatment not only decreased the extent of aromatic ring condensation
(fused aromatic rings), but also decreased the number of condensed
aromatic rings.
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