Formability of Rare Earth-Containing Solid Solution Based on Compounds with Scheelite-Type Structure

The regularities of the solid solutions between the scheelite-type compounds and rare earth molybdates or tungstates were investigated by the atomic parameter-support vector machine method and the intelligent database of phase diagrams of molten salt systems. The crystal structure of scheelite-type compounds having M^1M^′Ⅲ （XO4）2（X = Mo, W） as common formula and the formability of the continuous solid solution between these compounds and rare earth molybdates or tungstates were also investigated. Besides, the cell constants of these compounds can be calculated by some semi-empirical equations. Based on the obtained relationships, the results of computerized prediction of the solid solubility of T1Pr （MoO4）2-Pr2 （MoO4）3 system have good agreement with experimental results.

Relationships between Pitzer's ion interaction coefficients and ionic parameters of electrolyte solutions

Pattern recognition methods were used to treat the experimentally measured data of Pitzer’s coefficients of 107 electrolytes, to find the relationships between the ionic structural parameters of these electrolytes and Pitzer’s coefficients. It is found that these relationships can be approximately expressed as linear equations of four dimensionless numbers, (R_+/R_-), (R_++R_-)/Z_+Z_-, (Z_+/Z_-) and (R_t/R_l), where R_+ and R_- are the cationic and anionic radii respectively; Z_+ and Z_- are the cationic and anionic charge numbers respectively, and (R_t/R_l) denotes the nonsphericity of some non-spherical ions. Besides, it is found that the difference of the nuclear magnetic resonance measured rotational relaxation time of water molecules around cations and anions, |Δτ|, has good correlation with Pitzer’s coefficients. The relationships can be interpreted by the theory of corresponding states of ionic solutions. Based on the relationships, an example of application to some hydrometallurgical process was discussed.

Functional characterization of the NF-kB binding site in the human NOD2 promoter

Nucleotide-binding and oligomerization domain 2(NOD2),a member of the NOD protein family,plays an important role in innate immunity.In response to pathogen attack,NOD2 stimulates cytokine and defensin production by activating nuclear factor(NF)-kB,a key transcription factor responsible for mediating downstream reactions.However,the mechanism linking NOD2 regulation and NF-kB activation is poorly understood.Using bioinformatics,we found a completely preserved canonical NF-kB binding site in the NOD2 core promoter(216 to 225 bp)in both humans and chimpanzees.The functional role of this NF-kB binding site was investigated using the enhanced green fluorescent protein(EGFP)reporter system,site-directed mutagenesis,the NF-kB activation inhibitor(JSH-23)and the chromatin immunoprecipitation(ChIP)assay.The results show that the NF-kB binding site is critical for regulation of the NOD2 gene.Either deletion of the NF-kB binding elements within the NOD2 promoter or treatment with an NF-kB activation inhibitor could lead to a significant loss of NOD2 promoter activity as detected by reporter gene assay.The canonical NF-kB binding site was bound by NF-kB as determined by the ChIP method.Based on these results,we suggest a positive feedback regulation between NF-kB and NOD2,which may represent an efficient mechanism in response to pathogen invasion.

Learning to detect subway arrivals for passengers on a train

The use of traditional positioning technologies, such as GPS and wireless local positioning, rely on un- derlying infrastructure. However, in a subway environment, such positioning systems are not available for the position- ing tasks, such as the detection of the train arrivals for the passengers in the train. An alternative approach is to exploit the contextual information available in the mobile devices of subway riders to detect train arrivals. To this end, we pro- pose to exploit multiple contextual features extracted from the mobile devices of subway riders to precisely detecting train arrivals. Following this line, we first investigate poten- tial contextual features which may be effective to detect train arrivals according to the observations from 3D accelerome- ters and GSM radio. Furthermore, we propose to explore the maximum entropy （MaxEnt） model for training a train ar- rival detector by learning the correlation between contextual features and train arrivals. Finally, we perform extensive ex- periments on several real-world data sets collected from two major subway lines in the Beijing subway system. Experi- mental results validate both the effectiveness and efficiency of the proposed approach.