Strip loaded waveguide amplifiers based on erbium-doped nanocomposites with 17†dB internal net gain.

We propose a strip loaded amplifier employing SU-8 as the loaded waveguide and nanoparticles (NPs)-polymethyl methacrylate (PMMA) as the cladding layer. By leveraging the undoped SU-8 loaded waveguide, the polymer waveguide amplifier accomplished remarkably low transmission losses, reaching as low as 1.8 dB/cm at 1530 nm. We prepared NPs-PMMA nanocomposite by utilizing NaLu0.1Y0.7F4: Er3+, Yb3+ @NaLuF4 core-shell nanoparticles, which exhibited a significantly enhanced lifetime of 6.15 ms. An internal net gain of up to 17.7 dB was achieved on a strip loaded waveguide with a length as short as 0.5 cm when the on-chip pump power was 77 mW. Signal enhancement (SE) was measured at different wavelengths, revealing that the strip loaded waveguide exhibited broadband SE ranging from 1510 nm to 1570 nm, covering the C-band. To the best of our knowledge, this work has achieved the highest gain results reported thus far on a polymer matrix and provides an efficient method for optical amplification in passive devices on silicon and Si3N4 platforms, leveraging the ease of integration of polymer materials with diverse photonic platforms.

Synthesis and biological evaluation of BMS-986120 and its deuterated derivatives as PAR4 antagonists.

BMS-986120 is a PAR4 antagonist that is being investigated as an antiplatelet agent in phase I clinical trial. An improved synthesis of BMS-986120 has been developed. Based on the novel synthetic approach to BMS-986120, a series of deuterated derivatives of BMS-986120 have been synthesized and biologically evaluated to search for more potent antiplatelet agents. The in vitro antiplatelet assay by turbidimetry demonstrated that PC-2 and PC-6 had IC50 values of 6.30 nM and 6.97 nM, respectively, versus BMS-986120 with an IC50 of 7.80 nM. The result of in vitro metabolic stability study showed that all of the deuterated compounds had similar half-life (T1/2) and intrinsic clearance (Clint) in comparison with BMS-986120. Further probing the metabolic profile of BMS-986120 is worth being conducted.

Discovery of 2,3-Dihydro[1,4]dioxino[2,3-g]benzofuran Derivatives as Protease Activated Receptor 4 (PAR4) Antagonists with Potent Antiplatelet Aggregation Activity and Low Bleeding Tendency.

Patients with arterial embolic disease have benefited greatly from antiplatelet therapy. However, hemorrhage risk of antiplatelet agents cannot be ignored. Herein, we describe the discovery of 2,3-dihydro[1,4]dioxino[2,3-g]benzofuran compounds as novel PAR4 antagonists. Notably, the isomers 36 and 37 with the chemotype of phenoxyl methylene substituted on the 2,3-dihydro-1,4-dioxine ring exhibited potent in vitro antiplatelet activity (IC50 = 26.13 nM for 36 and 14.26 nM for 37) and significantly improved metabolic stability in human liver microsomes (T1/2 = 97.6 min for 36 and 11.1 min for BMS-986120). 36 also displayed good oral PK profiles (mice: T1/2 = 7.32 h and F = 45.11%). Both of them showed overall potent ex vivo antiplatelet activity at concentrations of 6 and 12 mg/kg, with no impact on the coagulation system and low bleeding liability. Our work will facilitate development of novel PAR4 antagonists as a safer therapeutic option for arterial embolism.

Organocatalytic Para-Selective Amination of Phenols with Iminoquinone Monoacetals.

A highly selective para C-H amination of unprotected phenols with iminoquinone acetals was realized, giving the functional phenols in good to excellent yields. Overall, this transformation is operationally simple, proceeds with readily available phenols, and has wide substrate scope and low catalyst loading. The biarylamine product is stochastically formed via [5,5]-sigmatropic rearrangement of a mixed acetal species which is generated in situ under the reaction conditions.

The recognition of multi-class protein folds by adding average chemical shifts of secondary structure elements.

The recognition of protein folds is an important step in the prediction of protein structure and function. Recently, an increasing number of researchers have sought to improve the methods for protein fold recognition. Following the construction of a dataset consisting of 27 protein fold classes by Ding and Dubchak in 2001, prediction algorithms, parameters and the construction of new datasets have improved for the prediction of protein folds. In this study, we reorganized a dataset consisting of 76-fold classes constructed by Liu et al. and used the values of the increment of diversity, average chemical shifts of secondary structure elements and secondary structure motifs as feature parameters in the recognition of multi-class protein folds. With the combined feature vector as the input parameter for the Random Forests algorithm and ensemble classification strategy, we propose a novel method to identify the 76 protein fold classes. The overall accuracy of the test dataset using an independent test was 66.69%; when the training and test sets were combined, with 5-fold cross-validation, the overall accuracy was 73.43%. This method was further used to predict the test dataset and the corresponding structural classification of the first 27-protein fold class dataset, resulting in overall accuracies of 79.66% and 93.40%, respectively. Moreover, when the training set and test sets were combined, the accuracy using 5-fold cross-validation was 81.21%. Additionally, this approach resulted in improved prediction results using the 27-protein fold class dataset constructed by Ding and Dubchak.

Predicting enzyme subclasses by using random forest with multicharacteristic parameters.

In order to predict enzyme subclasses, this paper builds a new enzyme database in term of previous ideas and methods. Based on protein sequence, by selecting increment of diversity value, low-frequency of power spectral density, matrix scoring values and motif frequency as characteristic parameters to describe the sequence information, a Random Forest algorithm for predicting enzyme subclass is proposed. Using the Jack-knife test, the overall success rate identifying the 18 subclasses of oxidoreductases, the 8 subclasses of transferases, the 5 subclasses of hydrolases, the 6 subclasses of lyases, the 6 subclasses of isomerases, and the 6 subclasses of ligases are 90.86%, 95.24%, 96.42%, 98.60%, 97.53% and 98.03%. Furthermore, the same way is used to the previous database, the better results are obtained.