Prediction of B-cell Linear Epitopes with a Combination of Support Vector Machine Classification and Amino Acid Propensity Identification

Epitopes are antigenic determinants that are useful because they induce B-cell antibody production and stimulate T-cell activation. Bioinformatics can enable rapid, efficient prediction of potential epitopes. Here, we designed a novel B-cell linear epitope prediction system called LEPS, Linear Epitope Prediction by Propensities and Support Vector Machine, that combined physico-chemical propensity identification and support vector machine (SVM) classification. We tested the LEPS on four datasets: AntiJen, HIV, a newly generated PC, and AHP, a combination of these three datasets. Peptides with globally or locally high physicochemical propensities were first identified as primitive linear epitope (LE) candidates. Then, candidates were classified with the SVM based on the unique features of amino acid segments. This reduced the number of predicted epitopes and enhanced the positive prediction value (PPV). Compared to four other well-known LE prediction systems, the LEPS achieved the highest accuracy (72.52%), specificity (84.22%), PPV (32.07%), and Matthews' correlation coefficient (10.36%)

High-speed measurement-device-independent quantum key distribution with integrated silicon photonics

Measurement-device-independent quantum key distribution (MDI-QKD) removes all detector side channels and enables secure QKD with an untrusted relay. It is suitable for building a star-type quantum access network, where the complicated and expensive measurement devices are placed in the central untrusted relay and each user requires only a low-cost transmitter, such as an integrated photonic chip. Here, we experimentally demonstrate a 1.25 GHz silicon photonic chip-based MDI-QKD system using polarization encoding. The photonic chip transmitters integrate the necessary encoding components for a standard QKD source. We implement random modulations of polarization states and decoy intensities, and demonstrate a finite-key secret rate of 31 bps over 36 dB channel loss (or 180 km standard fiber). This key rate is higher than state-of-the-art MDI-QKD experiments. The results show that silicon photonic chip-based MDI-QKD, benefiting from miniaturization, low-cost manufacture and compatibility with CMOS microelectronics, is a promising solution for future quantum secure networks.Comment: 30 pages, 12 figure

Study of an Energetic-oxidant Co-crystal: Preparation, Characterisation, and Crystallisation Mechanism

An energetic co-crystal consisting of the most promising military explosive 2,4,6,8,10,12-hexanitro-2,4,6,8,10,12-hexaazaisowurtzitane (CL-20) and the most well-known oxidant applied in propellants ammonium perchlorate has been prepared with a simple solvent evaporation method. Scanning electron microscopy revealed that the morphology of co-crystal differs greatly from each component. The X-ray diffraction spectrum, FTIR, Raman spectra, and differential scanning calorimetry characterisation further prove the formation of the co-crystal. The result of determination of hygroscopic rate indicated the hygroscopicity was effectively reduced. At last, the crystallisation mechanism has been discussed

Protein-ligand binding region prediction (PLB-SAVE) based on geometric features and CUDA acceleration

[[abstract]]Background Protein-ligand interactions are key processes in triggering and controlling biological functions within cells. Prediction of protein binding regions on the protein surface assists in understanding the mechanisms and principles of molecular recognition. In silico geometrical shape analysis plays a primary step in analyzing the spatial characteristics of protein binding regions and facilitates applications of bioinformatics in drug discovery and design. Here, we describe the novel software, PLB-SAVE, which uses parallel processing technology and is ideally suited to extract the geometrical construct of solid angles from surface atoms. Representative clusters and corresponding anchors were identified from all surface elements and were assigned according to the ranking of their solid angles. In addition, cavity depth indicators were obtained by proportional transformation of solid angles and cavity volumes were calculated by scanning multiple directional vectors within each selected cavity. Both depth and volume characteristics were combined with various weighting coefficients to rank predicted potential binding regions. Results Two test datasets from LigASite, each containing 388 bound and unbound structures, were used to predict binding regions using PLB-SAVE and two well-known prediction systems, SiteHound and MetaPocket2.0 (MPK2). PLB-SAVE outperformed the other programs with accuracy rates of 94.3% for unbound proteins and 95.5% for bound proteins via a tenfold cross-validation process. Additionally, because the parallel processing architecture was designed to enhance the computational efficiency, we obtained an average of 160-fold increase in computational time. Conclusions In silico binding region prediction is considered the initial stage in structure-based drug design. To improve the efficacy of biological experiments for drug development, we developed PLB-SAVE, which uses only geometrical features of proteins and achieves a good overall performance for protein-ligand binding region prediction. Based on the same approach and rationale, this method can also be applied to predict carbohydrate-antibody interactions for further design and development of carbohydrate-based vaccines. PLB-SAVE is available at http://save.cs.ntou.edu.tw.[[booktype]]電子

Experimental measurement-device-independent quantum digital signatures over a metropolitan network

Quantum digital signatures (QDS) provide a means for signing electronic communications with informationtheoretic security. However, all previous demonstrations of quantum digital signatures assume trusted measurement devices. This renders them vulnerable against detector side-channel attacks, just like quantum key distribution. Here, we exploit a measurement-device-independent (MDI) quantum network, over a 200-square-kilometer metropolitan area, to perform a field test of a three-party measurement-device-independent quantum digital signature (MDI-QDS) scheme that is secure against any detector side-channel attack. In so doing, we are able to successfully sign a binary message with a security level of about 1E-7. Remarkably, our work demonstrates the feasibility of MDI-QDS for practical applications.Comment: 5 pages, 1 figure, 2 tables, supplemental materials included as ancillary fil

In Vitro Hemocompatibility and Cytotoxicity Evaluation of Halloysite Nanotubes for Biomedical Application

Halloysite nanotubes (HNTs), due to their unique structures and properties, may play an important role in biomedical applications. In vitro test is usually conducted as a preliminary screening evaluation of the hemocompatibility and cytotoxicity of HNTs for its short term consuming, convenience, and less expense. In this work, HNTs were processed with anticoagulated rabbit blood to detect its blood compatibility. The result of hemolysis test shows that the hemolysis ratios are below 0.5%, indicating nonhemolysis of HNTs. Plasma recalcification time suggests that HNTs are dose-dependently contributing to blood coagulation in platelet poor plasma (PPP). The effect of platelet activation caused by HNTs was also examined by scanning electron microscopy (SEM). Meanwhile, HNTs were labeled with fluorescein isothiocyanate (FITC) to observe its intracellular distribution in A549 cells under confocal microscopy. CCK-8 test and TUNEL test of HNTs at different concentration levels were performed in vitro, respectively. Therefore, the potential usage of HNTs in medicine may be very meaningful in oral dosing, dermal application, dental uses, or medical implants