We report here on the synthesis and photophysical/electrochemical properties of a series of novel triphenylamine (TPA)-based organic dyes (TPAR1, TPAR2, TPAR4, and TPAR5) as well as their application in dye-sensitized nanocrystalline TiO2 solar cells (DSCs). In the four designed dyes, the TPA group and the rhodanine-3-acetic acid take the role of the basic electron donor unit and the electron acceptor, respectively. It was found that introduction of a CH2CH− group into the TPA unit exhibited better photovoltaic performance due to the increase of the electron-density donor moiety and that introduction of a methine (−CHCH−) unit to the π bridge resulted in a red-shift and broadening of the absorption spectrum due to expansion of the π-conjugation system. Density functional theory (DFT) calculation indicated that the electron distribution moved from the donor unit to the electron acceptor under light irradiation, which means efficient intramolecular charge transfer. In particular, the DSCs based on TPAR4 showed the best photovoltaic performance:  a maximum monochromatic incident photon-to-current conversion efficiency (IPCE) of 81%, a short-circuit photocurrent density (J sc) of 18.2 mA cm-2, an open-circuit photovoltage (V oc) of 563 mV, and a fill factor (ff) of 0.61, corresponding to an overall conversion efficiency of 5.84% under AM 1.5 irradiation (100 mW cm-2). This work suggests that the molecular-designed triphenylamine dyes are promising in the application of DSCs.

Some series of 2-alkyl (alkythio)-5-((4-chloro)-3-ethyl-1-methyl-1H-pyrazole-5-yl)-1,3, 4-oxadiazoles (thiadiazoles) were prepared as potential fungicides. Their fungicidal activity was evaluated against rice sheath blight, which is a major disease of rice in China. Structure-activity relationships for the screened compounds were evaluated and discussed. It was found that 5-(4-chloro-3-ethyl-1-methyl-1H-pyrazole-5-yl)-1,3, 4-thiadiazole-2-thione has the higher fungicidal activity.

Gliomas are the most common primary intracranial tumors. Understanding the molecular basis of gliomas' progression is required to develop more effective therapies. The Wnt/β-catenin signaling cascade is an important signal transduction pathway in human cancers. Although, overactivation of this pathway is a hallmark of several forms of cancer, little is known about its role in human gliomas. Here, we aimed to determine the clinical significance of Wnt/β-catenin pathway components in gliomas. Immunohistochemical staining was performed to detect the expression patterns of Wnt1, β-catenin and Cyclin D1 in the biopsies from 96 patients with primary gliomas. Kaplan-Meier survival and Cox regression analyses were performed to evaluate the prognosis of patients. Cytoplasmic staining pattern of Wnt1, membranous, cytoplasmic and nuclear accumulation of β-catenin, and nuclear localization of Cyclin D1 were demonstrated by immunohistochemical staining. The Wnt1 expression significantly correlated with the expression of Cyclin D1 (P < 0.0001). The ratio of tumors with a cytoplasmic-nuclear pattern or a cytoplasmic pattern of β-catenin was significantly higher in Wnt1-positive (P < 0.01) and Cyclin D1-positive (P < 0.01) tumors than in Wnt1-negative and Cyclin D1-negative tumors, respectively. The protein expression levels of Wnt1, β-catenin and Cyclin D1 were all positively correlated with the Karnofsky performance scale (KPS) score and World Health Organization (WHO) grades of patients with gliomas. Furthermore, Wnt1, cytoplasmic-nuclear β-catenin and Cyclin D1 status were all the independent prognostic factors for glioma patients (P = 0.01, 0.007 and 0.005, respectively). These results provide convincing evidence that the Wnt/β-catenin pathway correlated closely with the progression of gliomas and might be a novel prognostic marker for this neoplasm.

Locality and label information of training samples play an important role in image classification. However, previous dictionary learning algorithms do not take the locality and label information of atoms into account together in the learning process, and thus their performance is limited. In this paper, a discriminative dictionary learning algorithm, called the locality-constrained and label embedding dictionary learning (LCLE-DL) algorithm, was proposed for image classification. First, the locality information was preserved using the graph Laplacian matrix of the learned dictionary instead of the conventional one derived from the training samples. Then, the label embedding term was constructed using the label information of atoms instead of the classification error term, which contained discriminating information of the learned dictionary. The optimal coding coefficients derived by the locality-based and label-based reconstruction were effective for image classification. Experimental results demonstrated that the LCLE-DL algorithm can achieve better performance than some state-of-the-art algorithms.