Multi-dark-state resonances in cold multi-Zeeman-sublevel atoms

We present our experimental and theoretical studies of multi-dark-state resonances (MDSRs) generated in a unique cold rubidium atomic system with only one coupling laser beam. Such MDSRs are caused by different transition strengths of the strong coupling beam connecting different Zeeman sublevels. Controlling the transparency windows in such electromagnetically induced transparency system can have potential applications in multi-wavelength optical communication and quantum information processing.Comment: 11pages, 4figure

The Mutual Beneficial Effect between Medical Imaging and Nanomedicine

The reports on medical imaging and nanomedicine are getting more and more prevalent. Many nanoparticles entering into the body act as contrast agents, or probes in medical imaging, which are parts of nanomedicines. The application extent and the quality of imaging have been improved by nanotechnique. On one hand, nanomedicines advance the sensitivity and specificity of molecular imaging. On the other hand, the biodistribution of nanomedicine can also be studied in vivo by medical imaging, which is necessary in the toxicological research. The toxicity of nanomedicine is a concern which may slow down the application of nanomedical. The quantitative description of the kinetic process is significant. Based on metabolic study on radioactivity tracer, a scheme of pharmacokinetic research of nanomedicine is proposed. In this review, we will discuss the potential advantage of medical imaging in toxicology of nanomedicine, as well as the advancement of medical imaging prompted by nanomedicine

Systematic Analysis of Survival-Associated Alternative Splicing Signatures in Thyroid Carcinoma

Alternative splicing (AS) is a key mechanism involved in regulating gene expression and is closely related to tumorigenesis. The incidence of thyroid cancer (THCA) has increased during the past decade, and the role of AS in THCA is still unclear. Here, we used TCGA and to generate AS maps in patients with THCA. Univariate analysis revealed 825 AS events related to the survival of THCA. Five prognostic models of AA, AD, AT, ES, and ME events were obtained through lasso and multivariate analyses, and the final prediction model was established by integrating all the AS events in the five prediction models. Kaplan–Meier survival analysis revealed that the overall survival rate of patients in the high-risk group was significantly shorter than that of patients in the low-risk group. The ROC results revealed that the prognostic capabilities of each model at 3, 5, and 8 years were all greater than 0.7, and the final prognostic capabilities of the models were all greater than 0.9. By reviewing other databases and utilizing qPCR, we verified the established THCA gene model. In addition, gene set enrichment analysis showed that abnormal AS events might play key roles in tumor development and progression of THCA by participating in changes in molecular structure, homeostasis of the cell environment and in cell energy. Finally, a splicing correlation network was established to reveal the potential regulatory patterns between the predicted splicing factors and AS event candidates. In summary, AS should be considered an important prognostic indicator of THCA. Our results will help to elucidate the underlying mechanism of AS in the process of THCA tumorigenesis and broaden the prognostic and clinical application of molecular targeted therapy for THCA

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Numerically efficient models of hybrid cascaded multilevel converters for high voltage direct current transmission system

The development and integration of renewable energy sources have gained significant attention over the past few decades. It provides a means to cope with the increasing energy consumption demand while reducing carbon emissions. Voltage-source-converter-based high voltage direct current (VSC-HVDC) transmission is a critical enabling technology in integrating hydroelectric power, wind-power plants, and photovoltaic systems into the power grid. To further reduce converter losses and converter station footprint, VSC-HVDC systems have evolved in the past twenty years from the traditional two- and three-level converters to multilevel converters. The modular multilevel converter (MMC) is well-known for its higher modularity, reduced harmonics, and improved converter efficiency, compared to the traditional two- and three-level converters. Recent advancements in VSC-HVDC have brought a new class of multilevel converter topologies, i.e., the so-called hybrid cascaded multilevel converters (HCMCs), which combine the reduced footprint of the conventional VSCs with the lower losses and reduced output harmonics of the MMC. However, simulating large-scale AC-DC networks introduced significant computational challenges in electromagnetic transient (EMT) type programs, as a large number of switching operations and massive input/output data transfer are simulated at a small time step. Thus, the large number of series-connected submodules introduce a high dimension, time-variant matrix to be solved at each time step. Therefore, the efficiency of DM is limited in the electromagnetic transient (EMT) type simulators. This thesis focuses on developing new numerically efficient models, which improve the simulation speed and maintain simulation accuracy for system-level studies simultaneously. The proposed models in this thesis present different levels of simulation details and efficiency, which offer great flexibility to accommodate the requirements of various study tasks. Different from the prior-art models, which mainly focus on the MMC topology, the numerically efficient models of hybrid multilevel converters and their real-time simulation methods are investigated in this thesis.Applied Science, Faculty ofEngineering, School of (Okanagan)Graduat

Facile Synthesis of 5-Arylidene Thiohydantoin by Sequential Sulfonylation/Desulfination Reaction

The sequential sulfonylation/desulfination reactions of 5-benzylthiohydantoin with excess arylsulfonyl chlorides in the presence of triethylamine have been developed to afford a wide range of 5-arylidene thiohydantoin derivatives in moderate to excellent yields. A plausible sulfonylation/desulfination mechanism was proposed. The bioassay showed that these compounds exhibit certain fungicidal activities with the 71.9% inhibition rate of 2K against B. cinerea, and 57.6% inhibition rate of 2m against A. solani, respectively

Classification and Causes Identification of Chinese Civil Aviation Incident Reports

Safety is a primary concern for the civil aviation industry. Airlines record high-frequency but potentially low-severity unsafe events, i.e., incidents, in their reports. Over the past few decades, civil aviation security practitioners have made efforts to analyze these issues. The information in incident reports is valuable for risk analysis. However, incident reports were inefficiently utilized due to incoherence, large volume, and poor structure. In this study, we proposed a technical scheme to intelligently classify and extract risk factors from Chinese civil aviation incident reports. Firstly, we adopted machine learning classifiers and vectorization strategies to classify incident reports into 11 categories. Grid search was used to adjust the parameters of the classifier. In the preliminary experiment, the combination of the extreme gradient boosting (XGBoost) classifier and the occurrence position (OC-POS) vectorization strategy outperformed with an 0.85 weighted F1-score. In addition, we designed a rule-based system to identify the factors related to the occurrence of incidents from 25 empirical causes, which included equipment, human, environment, and organizational causes. For cause identification, we used rules obtained through manual analysis with keywords and discourse. F1-score above 0.90 was obtained on the test set using the causes identification model derived from the training set. The proposed system permits insights into unsafe factors in aviation incidents and prevents reoccurrence. Future works can proceed on this study, such as exploring the causal relationship between causes and incidents