On the determination of a cloud condensation nuclei from satellite : Challenges and possibilities

We use aerosol size distributions measured in the size range from 0.01 to 10+ μm during Transport and Chemical Evolution over the Pacific (TRACE-P) and Aerosol Characterization Experiment-Asia (ACE-Asia), results of chemical analysis, measured/modeled humidity growth, and stratification by air mass types to explore correlations between aerosol optical parameters and aerosol number concentration. Size distributions allow us to integrate aerosol number over any size range expected to be effective cloud condensation nuclei (CCN) and to provide definition of a proxy for CCN (CCNproxy). Because of the internally mixed nature of most accumulation mode aerosol and the relationship between their measured volatility and solubility, this CCNproxy can be linked to the optical properties of these size distributions at ambient conditions. This allows examination of the relationship between CCNproxy and the aerosol spectral radiances detected by satellites. Relative increases in coarse aerosol (e.g., dust) generally add only a few particles to effective CCN but significantly increase the scattering detected by satellite and drive the Angstrom exponent (α) toward zero. This has prompted the use of a so-called aerosol index (AI) on the basis of the product of the aerosol optical depth and the nondimensional α, both of which can be inferred from satellite observations. This approach biases the AI to be closer to scattering values generated by particles in the accumulation mode that dominate particle number and is therefore dominated by sizes commonly effective as CCN. Our measurements demonstrate that AI does not generally relate well to a measured proxy for CCN unless the data are suitably stratified. Multiple layers, complex humidity profiles, dust with very low α mixed with pollution, and size distribution differences in pollution and biomass emissions appear to contribute most to method limitations. However, we demonstrate that these characteristic differences result in predictable influences on AI. These results suggest that inference of CCN from satellites will be challenging, but new satellite and model capabilities could possibly be integrated to improve this retrieval

Preparation and Physicochemical Properties of 10-Hydroxycamptothecin (HCPT) Nanoparticles by Supercritical Antisolvent (SAS) Process

The goal of the present work was to study the feasibility of 10-hydroxycamptothecin (HCPT) nanoparticle preparation using supercritical antisolvent (SAS) precipitation. The influences of various experimental factors on the mean particle size (MPS) of HCPT nanoparticles were investigated. The optimum micronization conditions are determined as follows: HCPT solution concentration 0.5 mg/mL, the flow rate ratio of CO2 and HCPT solution 19.55, precipitation temperature 35 °C and precipitation pressure 20 MPa. Under the optimum conditions, HCPT nanoparticles with a MPS of 180 ± 20.3 nm were obtained. Moreover, the HCPT nanoparticles obtained were characterized by Scanning electron microscopy, Dynamic light scattering, Fourier-transform infrared spectroscopy, High performance liquid chromatography-mass spectrometry, X-ray diffraction and Differential scanning calorimetry analyses. The physicochemical characterization results showed that the SAS process had not induced degradation of HCPT. Finally, the dissolution rates of HCPT nanoparticles were investigated and the results proved that there is a significant increase in dissolution rate compared to unprocessed HCPT

Tuning the Reactivity of Nanoenergetic Gas Generators Based on Bismuth and Iodine oxidizers

There is a growing interest on novel energetic materials called Nanoenergetic Gas- Generators (NGGs) which are potential alternatives to traditional energetic materials including pyrotechnics, propellants, primers and solid rocket fuels. NGGs are formulations that utilize metal powders as a fuel and oxides or hydroxides as oxidizers that can rapidly release large amount of heat and gaseous products to generate shock waves. The heat and pressure discharge, impact sensitivity, long term stability and other critical properties depend on the particle size and shape, as well as assembling procedure and intermixing degree between the components. The extremely high energy density and the ability to tune the dynamic properties of the energetic system makes NGGs ideal candidates to dilute or replace traditional energetic materials for emerging applications. In terms of energy density, performance and controllability of dynamic properties, the energetic materials based on bismuth and iodine compounds are exceptional among the NGGs. The thermodynamic calculations and experimental study confirm that NGGs based on iodine and bismuth compounds mixed with aluminum nanoparticles are the most powerful formulations to date and can be used potentially in microthrusters technology with high thrust-to-weight ratio with controlled combustion and exhaust velocity for space applications. The resulting nano thermites generated significant value of pressure discharge up to 14.8 kPa m3/g. They can also be integrated with carbon nanotubes to form laminar composite yarns with high power actuation of up to 4700 W/kg, or be used in other emerging applications such as biocidal agents to effectively destroy harmful bacteria in seconds, with 22 mg/m2 minimal content over infected area

Lithium, an anti-psychotic drug, greatly enhances the generation of induced pluripotent stem cells

Somatic cells can be reprogrammed into induced pluripotent stem cells (iPSCs) by defined factors. The low efficiency of reprogramming and genomic integration of oncogenes and viral vectors limited the potential application of iPSCs. Here we report that Lithium (Li), a drug used to treat mood disorders, greatly enhances iPSC generation from both mouse embryonic fibroblast and human umbilical vein endothelial cells. Li facilitates iPSC generation with one (Oct4) or two factors (OS or OK). The effect of Li on promoting reprogramming only partially depends on its major target GSK3β. Unlike other GSK3β inhibitors, Li not only increases the expression of Nanog, but also enhances the transcriptional activity of Nanog. We also found that Li exerts its effect by promoting epigenetic modifications via downregulation of LSD1, a H3K4-specific histone demethylase. Knocking down LSD1 partially mimics Li's effect in enhancing reprogramming. Our results not only provide a straightforward method to improve the iPSC generation efficiency, but also identified a histone demethylase as a critical modulator for somatic cell reprogramming

Impact of stoichiometry representation on simulation of genotype-phenotype relationships in metabolic networks.

<div><p>Genome-scale metabolic networks provide a comprehensive structural framework for modeling genotype-phenotype relationships through flux simulations. The solution space for the metabolic flux state of the cell is typically very large and optimization-based approaches are often necessary for predicting the active metabolic state under specific environmental conditions. The objective function to be used in such optimization algorithms is directly linked with the biological hypothesis underlying the model and therefore it is one of the most relevant parameters for successful modeling. Although linear combination of selected fluxes is widely used for formulating metabolic objective functions, we show that the resulting optimization problem is sensitive towards stoichiometry representation of the metabolic network. This undesirable sensitivity leads to different simulation results when using numerically different but biochemically equivalent stoichiometry representations and thereby makes biological interpretation intrinsically subjective and ambiguous. We hereby propose a new method, Minimization of Metabolites Balance (MiMBl), which decouples the artifacts of stoichiometry representation from the formulation of the desired objective functions, by casting objective functions using metabolite turnovers rather than fluxes. By simulating perturbed metabolic networks, we demonstrate that the use of stoichiometry representation independent algorithms is fundamental for unambiguously linking modeling results with biological interpretation. For example, MiMBl allowed us to expand the scope of metabolic modeling in elucidating the mechanistic basis of several genetic interactions in <em>Saccharomyces cerevisiae</em>.</p> </div

Long-Term Sphere Culture Cannot Maintain a High Ratio of Cancer Stem Cells: A Mathematical Model and Experiment

Acquiring abundant and high-purity cancer stem cells (CSCs) is an important prerequisite for CSC research. At present, researchers usually gain high-purity CSCs through flow cytometry sorting and expand them by short-term sphere culture. However, it is still uncertain whether we can amplify high-purity CSCs through long-term sphere culture. We have proposed a mathematical model using ordinary differential equations to derive the continuous variation of the CSC ratio in long-term sphere culture and estimated the model parameters based on a long-term sphere culture of MCF-7 stem cells. We found that the CSC ratio in long-term sphere culture presented as gradually decreased drift and might be stable at a lower level. Furthermore, we found that fitted model parameters could explain the main growth pattern of CSCs and differentiated cancer cells in long-term sphere culture