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Semiconductor nanowires prepared by wet chemical methods are a relatively new field of 1D electronic systems, where the dimensions can be controlled by changing the reaction parameters using solution chemistry. Here, the solution–liquid–solid approach where the nanowire growth is governed by low‐melting‐point catalyst particles, such as Bi nanocrystals, is presented. In particular, the focus is on the preparation and characterization of CdSe nanowires, a material which serves a prototype structure for many kinds of low dimensional semiconductor systems. To investigate the influence of different reaction parameters on the structural and optical properties of the nanowires, a comprehensive synthetic study is presented, and the results are compared with those reported in literature. How the interplay between different reaction parameters affects the diameter, length, crystal structure, and the optical properties of the resultant nanowires are demonstrated. The structural properties are mainly determined by competing reaction pathways, such as the growth of Bi nanocatalysts, the formation and catalytic growth of nanowires, and the formation and uncatalytic growth of quantum dots. Systematic variation of the reaction parameters (e.g., molecular precursors, concentration and concentration ratios, organic ligands, or reaction time, and temperature) enables control of the nanowire diameter from 6 to 33 nm, while their length can be adjusted between several tens of nanometers and tens of micrometers. The obtained CdSe nanowires exhibit an admixture of wurtzite (W) and zinc blende (ZB) structure, which is investigated by X‐ray diffraction. The diameter‐dependent band gaps of these nanowires can be varied between 650 and 700 nm while their fluorescence intensities are mainly governed by the Cd/Se precursor ratio and the ligands used.

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BackgroundCelery is an increasing popular vegetable species, but limited transcriptome and genomic data hinder the research to it. In addition, a lack of celery molecular markers limits the process of molecular genetic breeding. High-throughput transcriptome sequencing is an efficient method to generate a large transcriptome sequence dataset for gene discovery, molecular marker development and marker-assisted selection breeding.Principal FindingsCelery transcriptomes from four tissues were sequenced using Illumina paired-end sequencing technology. De novo assembling was performed to generate a collection of 42,280 unigenes (average length of 502.6 bp) that represent the first transcriptome of the species. 78.43% and 48.93% of the unigenes had significant similarity with proteins in the National Center for Biotechnology Information (NCBI) non-redundant protein database (Nr) and Swiss-Prot database respectively, and 10,473 (24.77%) unigenes were assigned to Clusters of Orthologous Groups (COG). 21,126 (49.97%) unigenes harboring Interpro domains were annotated, in which 15,409 (36.45%) were assigned to Gene Ontology(GO) categories. Additionally, 7,478 unigenes were mapped onto 228 pathways using the Kyoto Encyclopedia of Genes and Genomes Pathway database (KEGG). Large numbers of simple sequence repeats (SSRs) were indentified, and then the rate of successful amplication and polymorphism were investigated among 31 celery accessions.ConclusionsThis study demonstrates the feasibility of generating a large scale of sequence information by Illumina paired-end sequencing and efficient assembling. Our results provide a valuable resource for celery research. The developed molecular markers are the foundation of further genetic linkage analysis and gene localization, and they will be essential to accelerate the process of breeding.

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Continuous blood pressure (BP) estimation using pulse transit time (PTT) is a promising method for unobtrusive BP measurement. However, the accuracy of this approach must be improved for it to be viable for a wide range of applications. This study proposes a novel continuous BP estimation approach that combines data mining techniques with a traditional mechanism-driven model. First, 14 features derived from simultaneous electrocardiogram and photoplethysmogram signals were extracted for beat-to-beat BP estimation. A genetic algorithm-based feature selection method was then used to select BP indicators for each subject. Multivariate linear regression and support vector regression were employed to develop the BP model. The accuracy and robustness of the proposed approach were validated for static, dynamic, and follow-up performance. Experimental results based on 73 subjects showed that the proposed approach exhibited excellent accuracy in static BP estimation, with a correlation coefficient and mean error of 0.852 and -0.001 ± 3.102 mmHg for systolic BP, and 0.790 and -0.004 ± 2.199 mmHg for diastolic BP. Similar performance was observed for dynamic BP estimation. The robustness results indicated that the estimation accuracy was lower by a certain degree one day after model construction but was relatively stable from one day to six months after construction. The proposed approach is superior to the state-of-the-art PTT-based model for an approximately 2-mmHg reduction in the standard derivation at different time intervals, thus providing potentially novel insights for cuffless BP estimation.