Genome-wide identification and expression analysis of the trehalose-6-phosphate synthase (TPS) gene family in cucumber (Cucumis sativus L.).

Trehalose-6-phosphate synthase (TPS) is significant in the growth, development and stress resistance of plants. We identified the cucumber TPS family and its physicochemical properties, domains, gene structures, evolutionary relationships, gene locations, cis-acting elements, conserved motifs, and expression patterns using bioinformatics. Our results uncovered seven CsTPS genes in the cucumber genome and named CsTPS1-CsTPS7 according to their locations in the chromosomes. Seven CsTPS genes were randomly distributed in six cucumber chromosomes. Domain analysis showed that the TPS and TPP domains exist in all CsTPSs, and an additional hydrolase-3 domain exist in CsTPS3, CsTPS5 and CsTPS6. Phylogenetic analysis showed that TPS proteins from Arabidopsis, rice, soybean, and cucumber were divided into two subfamilies (Class I and Class II) and they were further divided into seven subgroups. TPS proteins from Arabidopsis and cucumber were grouped together, suggesting a close evolutionary relationship. Gene structure analysis indicated that most Class I genes contained 16-17 introns, while Class II genes (except CsTPS7) had two introns. Motif analysis showed that Class II genes had 10 complete conserved motifs, while Class I genes lacked motif 8 and motif 9. Furthermore, CsTPS genes possessed numerous cis-acting elements related to stress, hormone, and light response in the promoter regions. GO analysis indicated multiple functions for the CsTPS proteins. Expression analysis of CsTPS genes in different tissues found that they were expressed in roots, stems and leaves, with the highest expression levels in roots. The expression analysis of CsTPSs under different treatments showed that CsTPS genes may participate in the response to abiotic stress, plant hormones and sugar treatments.

Fe-Doped CoP Flower-Like Microstructure on Carbon Membrane as Integrated Electrode with Enhanced Sodium Ion Storage.

Poor cyclability and rate performance always impede the development of transition metal phosphide-based anode materials. Many strategies have been used to address the above problems, such as the designing of hierarchical structures, combination with carbon materials, and doping with other metal elements. Considering those strategies, a flower-like Fe-doped CoP material is designed. The synthesis consists of microsheets grown on a carbon membrane (CM, leaves as precursor) through a hydrothermal method and in situ phosphorization. The Fe doping and carbon membrane synergistically induce the formation of a flower-like hierarchical microstructure during the crystal-growing process. The unique hierarchical microstructure increases the contact area between electrode and electrolyte, and accommodates the volume expansion during cycling. The hierarchical Fe-doped CoP grown directly on the carbon membrane increases the active sites for intercalation of sodium species and further promotes the internal electron conduction in the Fe-doped CoP/CM electrode. Thereby, the Fe-doped CoP/CM as the anode electrode for sodium ion batteries exhibits a high specific capacity of 515 mA h g-1 at 100 mA g-1 after 100 cycles. Even if the current density rises to 500 mA g-1 , the specific capacity is still maintained at 324 mA h g-1 after 500 cycles, showing superior rate performances and cyclability.

Enhanced Peroxidase-Like Performance of Gold Nanoparticles by Hot Electrons.

Enzyme mimics have been widely used as alternatives to natural enzymes. However, the catalytic performances of enzyme mimics are often decreased due to different spatial structures or absence of functional groups compared to natural enzymes. Here, we report a highly efficient enzyme-like catalytic performance of gold nanoparticles (AuNPs) by visible-light stimulation. The enzyme-like reaction is evaluated by the catalytic reaction of AuNPs oxidizing a typical chromogenic substrate 3,3',5,5'-tetramethylbenzydine (TMB) with hydrogen peroxide as an oxidant. From investigations of the wavelength-dependent reaction rate, radical capture, hole-donor addition, and dark-field scattering spectroscopy experiments, it is revealed that the strong plasmonic absorption of AuNPs facilitates generation of hot electrons, which are transfered from AuNPs to the adsorbed reactant molecule, greatly promoting the catalytic performance of the enzyme-like catalytic reaction. The present work provides a simple method for improving the performance of enzyme mimics, which is expected to find further application in the field of plasmon-enhanced biocatalysis and biosensors.

Recognition of multiple imbalanced cancer types based on DNA microarray data using ensemble classifiers.

DNA microarray technology can measure the activities of tens of thousands of genes simultaneously, which provides an efficient way to diagnose cancer at the molecular level. Although this strategy has attracted significant research attention, most studies neglect an important problem, namely, that most DNA microarray datasets are skewed, which causes traditional learning algorithms to produce inaccurate results. Some studies have considered this problem, yet they merely focus on binary-class problem. In this paper, we dealt with multiclass imbalanced classification problem, as encountered in cancer DNA microarray, by using ensemble learning. We utilized one-against-all coding strategy to transform multiclass to multiple binary classes, each of them carrying out feature subspace, which is an evolving version of random subspace that generates multiple diverse training subsets. Next, we introduced one of two different correction technologies, namely, decision threshold adjustment or random undersampling, into each training subset to alleviate the damage of class imbalance. Specifically, support vector machine was used as base classifier, and a novel voting rule called counter voting was presented for making a final decision. Experimental results on eight skewed multiclass cancer microarray datasets indicate that unlike many traditional classification approaches, our methods are insensitive to class imbalance.