Development of 3D Porous Chitosan-Based Platform for In Vitro Culture

Three-dimensional (3D) cell cultures have been widely used in biological research since two-dimensional (2D) cultures have many limitations including alteration of cell morphology, metabolic pathways and gene expression. Therefore, the application of 2D cell cultures in pharmaceutical companies for drug screening causes reproducibility issues in animal studies, which strongly influences the efficiency of drug development. The aim of my studies is to develop 3D cell culture models to better mimic the extracellular matrix (ECM) in tumor microenvironment, not only for deeper understanding of interaction between cells and ECM, but also for the application in drug screening. Three different compositions of chitosan-alginate (CA) scaffolds with different stiffness were produced to mimic prostate cancer (PCa) progression stages. The results showed that PCa cells demonstrated stiffness independent growth and protein expression. But surprisingly, it was found that CA scaffolds could identify PCa cell phenotypic characteristics. Further, a novel 3D porous chitosan-chondroitin sulfate (C-CS) scaffold, with three CS compositions, were developed to mimic the PCa progression, since the clinical research has suggested that the CS is found in normal prostate tissue, greater in PCa, and further in metastatic sites. The results showed that CS can promote PCa cell metastasis-related gene expression and anti-cancer drug resistance. Although CA and C-CS scaffold provided more insights on PCa, 3D cell culture is more complicated to use than 2D cultures, which limited its application in industry. Therefore, a novel biomaterial format, named Frozen Films, were developed to combine the advantages of 2D and 3D culture while reducing their drawbacks. Cell cultures tested on Frozen Films demonstrated that cells had 3D culture performance, but with much easier operation process. Overall, those studies demonstrated that CA scaffolds, C-CS scaffolds and Frozen Films could be promising in vitro platforms for cellular research, with potential applications for in vitro anti-cancer drug screening

Well Performance from Numerical Methods to Machine Learning Approach: Applications in Multiple Fractured Shale Reservoirs

Horizontal well fracturing technology is widely used in unconventional reservoirs such as tight or shale oil and gas reservoirs. Meanwhile, the potential of enhanced oil recovery (EOR) methods including huff-n-puff miscible gas injection are used to further increase oil recovery in unconventional reservoirs. The complexities of hydraulic fracture properties and multiphase flow make it difficult and time-consuming to understand the well performance (i.e., well production) in fractured shale reservoirs, especially when using conventional numerical methods. Therefore, in this paper, two methods are developed to bridge this gap by using the machine learning technique to forecast well production performance in unconventional reservoirs, especially on the EOR pilot projects. The first method is the artificial neural network, through which we can analyze the big data from unconventional reservoirs to understand the underlying patterns and relationships. A bunch of factors is contained such as hydraulic fracture parameters, well completion, and production data. Then, feature selection is performed to determine the key factors. Finally, the artificial neural network is used to determine the relationship between key factors and well production performance. The second is time series analysis. Since the properties of the unconventional reservoir are the function of time such as fluid properties and reservoir pressure, it is quite suitable to apply the time series analysis to understand the well production performance. Training and test data are from over 10000 wells in different fractured shale reservoirs, including Bakken, Eagle Ford, and Barnett. The results demonstrate that there is a good match between the available and predicated well performance data. The overall R values of the artificial neural network and time series analysis are both above 0.8, indicating that both methods can provide reliable results for the prediction of well performance in fractured shale reservoirs. Especially, when dealing with the EOR field cases, such as huff-n-puff miscible gas injection, Time series analysis can provide more accurate results than the artificial neural network. This paper presents a thorough analysis of the feasibility of machine learning in multiple fractured shale reservoirs. Instead of using the time-consuming numerical methods, it also provides a more robust way and meaningful reference for the evaluation of the well performance

Coronavirus-like Core–Shell-Structured Co@C for Hydrogen Evolution via Hydrolysis of Sodium Borohydride

Constructing a reliable and robust cobalt-based catalyst for hydrogen evolution via hydrolysis of sodium borohydride is appealing but challenging due to the deactivation caused by the metal leaching and re-oxidization of metallic cobalt. A unique core–shell-structured coronavirus-like Co@C microsphere was prepared via pyrolysis of Co-MOF. This special Co@C had a microporous carbon coating to retain the reduced state of cobalt and resist the metal leaching. Furthermore, several nano-bumps grown discretely on the surface afforded enriched active centers. Applied in the pyrolysis of NaBH4, the Co@C-650, carbonized at 650 °C, exhibited the best activity and reliable recyclability. This comparable performance is ascribed to the increased metallic active sites and robust stability

Impact of Climate Change on Alpine Phenology over the Qinghai–Tibet Plateau from 1981 to 2020

Climate change is a major driver of cyclical and seasonal changes in alpine phenology. This study investigated how climate change affects vegetation phenology’s spatial and temporal responses on the Qinghai–Tibet plateau (QTP) from 1981 to 2020. We used the daily two-band enhanced vegetation index (EVI2) at a 0.05° spatial resolution for 1981–2014, 16-day moderate resolution imaging spectroradiometer (MODIS) enhanced vegetation index data for 2000–2020 at 250 m spatial resolution, and climate records from 106 meteorological stations from 1981 to 2020 to construct linear regression models and Mann–Kendall point test to understand the changes on QTP vegetation in response to climate change. We found that the temperature in April, July, and September controls vegetation growth, and spring precipitation (p < 0.05) influences the length of the growing period, with a partial correlation coefficient of −0.69. Over the past 40 years, temperature and precipitation changes on the QTP have not shown abrupt shifts despite the increasingly warm and dry spring climate. We observed a meridional distribution trend in the correlation between precipitation and alpine vegetation greening, browning and the length of the growing period. In regions experiencing strong warming, vegetation growth was hindered by a lack of precipitation. We conclude that climatic factors alone cannot fully explain the changing trends in vegetation phenology across the QTP

Coronavirus-like Core&ndash;Shell-Structured Co@C for Hydrogen Evolution via Hydrolysis of Sodium Borohydride

Constructing a reliable and robust cobalt-based catalyst for hydrogen evolution via hydrolysis of sodium borohydride is appealing but challenging due to the deactivation caused by the metal leaching and re-oxidization of metallic cobalt. A unique core&ndash;shell-structured coronavirus-like Co@C microsphere was prepared via pyrolysis of Co-MOF. This special Co@C had a microporous carbon coating to retain the reduced state of cobalt and resist the metal leaching. Furthermore, several nano-bumps grown discretely on the surface afforded enriched active centers. Applied in the pyrolysis of NaBH4, the Co@C-650, carbonized at 650 &deg;C, exhibited the best activity and reliable recyclability. This comparable performance is ascribed to the increased metallic active sites and robust stability

Advantages of photo-curable collagen-based cell-laden bioinks compared to methacrylated gelatin (GelMA) in digital light processing (DLP) and extrusion bioprinting

The development of cell-laden bioinks that possess high biocompatibility and printability is crucial in the field of bioprinting for the creation of cell-embedded tissue engineering scaffolds. As widely known, methacrylated gelatin (GelMA) is one of the most commonly used photo-crosslinkable bioink for cell-laden bioprinting with different printing methods, but GelMA is the derivative of gelatin, so it loses the unique triple-helix molecular structure of collagen and may not be able to successfully activate the cellular pathways or facilitate cell-matrix interaction as effectively as collagen. Recently, methacrylated collagen (CMA) was developed to be an alternative photocrosslinkable bioink with a good bioactivity, but its low printability and biocompatibility limited that application in tissue engineering. In this study, the synthetic process for CMA was improved by synthesizing under 4 °C and using acidic aqueous solution as solvent. Our CMA bioinks were demonstrated a similar printability as GelMA in extrusion bioprinting, while a better formability in digital light processing (DLP). To further analyze the bioactive properties, CMA bioinks were encapsulated with Schwann cells (SCs) and bone mesenchymal stem cells (BMSCs) for printing. SCs-laden CMA bioinks had a significantly higher proliferation rate and expression of neural stem cell-associated genes than GelMA in DLP bioprinting. While, BMSCs-laden CMA bioinks demonstrated >95% cellular viability, better cell spreading and higher expression of osteogenesis-related genes than that of GelMA. Overall, we speculate that the CMA-based bioink developed in this study could be potential bioinks for 3D cell-laden bioprinting in the future

Quantitative Evaluation of the Eco-Environment in a Coalfield Based on Multi-Temporal Remote Sensing Imagery: A Case Study of Yuxian, China

With the exploitation of coalfields, the eco-environment around the coalfields can become badly damaged. To address this issue, &#8220;mine greening&#8222; has been proposed by the Ministry of Land and Resources of China. The sustainable development of mine environments has now become one of the most prominent issues in China. In this study, we aimed to make use of Landsat 7 ETM+ and Landsat 8 OLI images obtained between 2005 and 2016 to analyze the eco-environment in a coalfield. Land cover was implemented as the basic evaluation factor to establish the evaluation model for the eco-environment. Analysis and investigation of the eco-environment in the Yuxian coalfield was conducted using a novel evaluation model, based on the biological abundance index, vegetation coverage index, water density index, and natural geographical factors. The weight of each indicator was determined by an analytic hierarchy process. Meanwhile, we also used the classic ecological footprint to calculate the ecological carrying capacity in order to verify the effectiveness of the evaluation model. Results showed that the eco-environment index illustrated a slowly increasing tendency over the study period, and the ecological quality could be considered as &#8220;good&#8222;. The results of the evaluation model showed a strong correlation with the ecological carrying capacity with a correlation coefficient of 0.9734. In conclusion, the evaluation method is a supplement to the time-series quantitative evaluation of the eco-environment, and also helps us to explore the eco-environment in the mining area

Is green eco-friendly? How cognitive biases affect residents’ willingness to participate in natural rubber plantation ecological restoration programs in Hainan, China

The misconception that “green is ecological” is widespread, causing the public to overlook severe ecological problems in nature and life. This study aimed to reveal how cognitive biases affect residents’ willingness to participate in ecological restoration and provide a valuable reference for policy design. Based on survey data from 655 urban residents, this study quantified the residents’ cognitive biases regarding the eco-consequences of natural rubber expansion and used a double-hurdle (DH) model to estimate the effect of cognitive biases on their willingness to participate (WP) and their degree of willingness to participate (DWP) in natural rubber plantation ecological restoration (RPER) programs. We further analyzed how ecological consciousness moderates the effects of cognitive biases on residents’ WP and DWP. The results indicate that there are widespread cognitive biases regarding the eco-consequences of rubber expansion among urban residents in Hainan. Cognitive biases cause a decrease in residents’ WP and DWP in the RPER programs. WP was positively influenced by residents’ educational level, and negatively influenced by age. DWP was positively correlated with residents’ household income, and industry cognition and negatively correlated with age, educational level, and residence duration. The moderating effect of the residents’ ecological consciousness significantly weakens the negative effects of cognitive biases on WP and DWP. Moreover, ecological attention exerts a negative moderating effect, whereas ecological identity and ecological practice exert a positive moderating effect. These results have profound implications for policymakers in implementing programs to restore ecosystems

Promotion of Adrenal Pheochromocytoma (PC-12) Cell Proliferation and Outgrowth Using Schwann Cell-Laden Gelatin Methacrylate Substrate

Peripheral nerve injuries cause different degrees of nerve palsy and function loss. Due to the limitations of autografts, nerve tissue engineering (TE) scaffolds incorporated with various neurotrophic factors and cells have been investigated to promote nerve regeneration. However, the molecular mechanism is still poorly understood. In this study, we co-cultured Schwann cells (SCs) and rat adrenal pheochromocytoma (PC-12) cells on 50% degrees of methacryloyl substitution gelatin methacrylate (GelMA) scaffold. The SCs were encapsulated within the GelMA, and PC-12 cells were on the surface. A 5% GelMA was used as the co-culture scaffold since it better supports SCs proliferation, viability, and myelination and promotes higher neurotrophic factors secretion than 10% GelMA. In the co-culture, PC-12 cells demonstrated a higher cell proliferation rate and axonal extension than culturing without SCs, indicating that the secretion of neurotrophic factors from SCs can stimulate PC-12 growth and axonal outgrowth. The mRNA level for neurotrophic factors of SCs in 5% GelMA was further evaluated. We found significant upregulation when compared with a 2D culture, which suggested that this co-culture system could be a potential scaffold to investigate the mechanism of how SCs affect neuronal behaviors

An Optimal Transport Based Global Similarity Index for Remote Sensing Products Comparison

Remote sensing products, such as land cover data products, are essential for a wide range of scientific studies and applications, and their quality evaluation and relative comparison have become a major issue that needs to be studied. Traditional methods, such as error matrices, are not effective in describing spatial distribution because they are based on a pixel-by-pixel comparison. In this paper, the relative quality comparison of two remote sensing products is turned into the difference measurement between the spatial distribution of pixels by proposing a max-sliced Wasserstein distance-based similarity index. According to optimal transport theory, the mathematical expression of the proposed similarity index is firstly clarified, and then its rationality is illustrated, and finally, experiments on three open land cover products (GLCFCS30, FROMGLC, CNLUCC) are conducted. Results show that based on this proposed similarity index-based relative quality comparison method, the spatial difference, including geometric shapes and spatial locations between two different remote sensing products in raster form, can be quantified. The method is particularly useful in cases where there exists misregistration between datasets, while pixel-based methods will lose their robustness