BackgroundSiberian apricot (Prunus sibirica L.) has emerged as a novel potential source of biodiesel in China, but the molecular regulatory mechanism of oil accumulation in Siberian apricot seed kernels (SASK) is still unknown at present. To better develop SASK oil as woody biodiesel, it is essential to profile transcriptome and to identify the full repertoire of potential unigenes involved in the formation and accumulation of oil SASK during the different developing stages.ResultsWe firstly detected the temporal patterns for oil content and fatty acid (FA) compositions of SASK in 7 different developing stages. The best time for obtaining the high quality and quantity of SASK oil was characterized at 60 days after flowering (DAF), and the representative periods (10, 30, 50, 60, and 70 DAF) were selected for transcriptomic analysis. By Illumina/Solexa sequencings, approximately 65 million short reads (average length = 96 bp) were obtained, and then assembled into 124,070 unigenes by Trinity strategy (mean size = 829.62 bp). A total of 3,000, 2,781, 2,620, and 2,675 differentially expressed unigenes were identified at 30, 50, 60, and 70 DAF (10 DAF as the control) by DESeq method, respectively. The relationship between the unigene transcriptional profiles and the oil dynamic patterns in developing SASK was comparatively analyzed, and the specific unigenes encoding some known enzymes and transcription factors involved in acetyl-coenzyme A (acetyl-CoA) formation and oil accumulation were determined. Additionally, 5 key metabolic genes implicated in SASK oil accumulation were experimentally validated by quantitative real-time PCR (qRT-PCR). Our findings could help to construction of oil accumulated pathway and to elucidate the molecular regulatory mechanism of increased oil production in developing SASK.ConclusionsThis is the first study of oil temporal patterns, transcriptome sequencings, and differential profiles in developing SASK. All our results will serve as the important foundation to further deeply explore the regulatory mechanism of SASK high-quality oil accumulation, and may also provide some reference for researching the woody biodiesel plants.Electronic supplementary materialThe online version of this article (doi:10.1186/s13068-015-0213-3) contains supplementary material, which is available to authorized users.

We have developed a strategy for synthesizing immediately activable, water-soluble, compact (∼10-12 nm hydrodynamic diameter) quantum dots with a small number of stable and controllable conjugation handles for long distance delivery and subsequent biomolecule conjugation. Upon covalent conjugation with engineered monovalent streptavidin, the sample results in a population consisting of low-valency quantum dots. Alternatively, we have synthesized quantum dots with a small number of biotin molecules that can self-assemble with engineered divalent streptavidin via high-affinity biotin-streptavidin interactions. Being compact, stable and highly specific against biotinylated proteins of interest, these low-valency quantum dots are ideal for labeling and tracking single molecules on the cell surface with high spatiotemporal resolution for different biological systems and applications.

Raman spectroscopy is commonly used in microplastics identification, but equipment variations yield inconsistent data structures that disrupt the development of communal analytical tools. We report a strategy to overcome the issue using a database of high-resolution, full-window Raman spectra. This approach enables customizable analytical tools to be easily createda feature we demonstrate by creating machine-learning classification models using open-source random-forest, K-nearest neighbors, and multi-layer perceptron algorithms. These models yield >95% classification accuracy when trained on spectroscopic data with spectroscopic data downgraded to 1, 2, 4, or 8 cm–1 spacings in Raman shift. The accuracy can be maintained even in non-ideal conditions, such as with spectroscopic sampling rates of 1 kHz and when microplastic particles are outside the focal plane of the laser. This approach enables the creation of classification models that are robust and adaptable to varied spectrometer setups and experimental needs.

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