Optical isolators and circulators are indispensable for photonic integrated circuits (PICs). Despite of significant progress in silicon-on-insulator (SOI) platforms, integrated optical isolators and circulators have been rarely reported on silicon nitride (SiN) platforms. In this paper, we report monolithic integration of magneto-optical (MO) isolators on SiN platforms with record high performances based on standard silicon photonics foundry process and magneto-optical thin film deposition. We successfully grow high quality MO garnet thin films on SiN with large Faraday rotation up to -5900 deg/cm. We show a superior magnetooptical figure of merit (FoM) of MO/SiN waveguides compared to that of MO/SOI in an optimized device design. We demonstrate TM/TE mode broadband and narrow band optical isolators and circulators on SiN with high isolation ratio, low cross talk and low insertion loss.In particular, we observe 1 dB insertion loss and 28 dB isolation ratio in a SiN racetrack resonator-based isolator at 1570.2 nm wavelength. The low thermo-optic coefficient of SiN also ensures excellent temperature stability of the device. Our work paves the way for integration of high performance nonreciprocal photonic devices on SiN platforms.

Background: The location of proteins in a cell can provide important clues to their functions in various biological processes. Thus, the application of machine learning method in the prediction of protein subcellular localization has become a hotspot in bioinformatics. As one of key organelles, the Golgi apparatus is in charge of protein storage, package, and distribution. Objective: The identification of protein location in Golgi apparatus will provide in-depth insights into their functions. Thus, the machine learning-based method of predicting protein location in Golgi apparatus has been extensively explored. The development of protein sub-Golgi apparatus localization prediction should be reviewed for providing a whole background for the fields. Method: The benchmark dataset, feature extraction, machine learning method and published results were summarized. Results: We briefly introduced the recent progresses in protein sub-Golgi apparatus localization prediction using machine learning methods and discussed their advantages and disadvantages. Conclusion: We pointed out the perspective of machine learning methods in protein sub-Golgi localization prediction.

Multiple genetic alterations are attributed to gastric cancer (GC); however, only a few critical genes have been identified so far. In this study, we isolated and characterized a novel gene p42.3, represented as tumor-specific and mitosis phase-dependent expression protein in GC cell line BGC823. Our data showed that the expression of p42.3 was cell cycle-dependent in GC cell lines. Moreover, p42.3 was specifically expressed in primary GC tissues but not in the matched normal mucosa of stomach, and this gene was expressed in diverse embryonic tissues. Furthermore, significant suppression of cell proliferation and tumorigenicity were detected and G 2 /M phase arrest was observed in cell line BGC823 depleted of p42.3 expression by RNAi technique, and we confirmed the expression changes of cyclin B1 and Chk2 following the silence of p42.3. Taken together, we cloned and characterized p42.3 gene that was specifically expressed in GC tumors but not in normal gastric mucosa, and the gene was associated with M-phase regulation. Moreover, p42.3 might be involved in cell proliferation and tumorigenesis; therefore, this gene might have potential applications in the diagnosis or treatment of GC.

Ion channels (IC) are ion-permeable protein pores located in the lipid membranes of all cells. Different ion channels have unique functions in different biological processes. Due to the rapid development of high-throughput mass spectrometry, proteomic data are rapidly accumulating and provide us an opportunity to systematically investigate and predict ion channels and their types. In this paper, we constructed a support vector machine (SVM)-based model to quickly predict ion channels and their types. By considering the residue sequence information and their physicochemical properties, a novel feature-extracted method which combined dipeptide composition with the physicochemical correlation between two residues was employed. A feature selection strategy was used to improve the performance of the model. Comparison results of in jackknife cross-validation demonstrated that our method was superior to other methods for predicting ion channels and their types. Based on the model, we built a web server called IonchanPred which can be freely accessed from .