Measurement of SiPM gain and photon detection efficiency at different temperatures and bias voltages

Gain and photon detection efficiency (PDE) of silicon photomultipliers (SiPMs) are important characteristics to understand SiPM-based detector systems in low light level applications. In this work, experimental setups are developed to quantify SiPM gain and PDE at different temperatures and bias voltages with a light source of fixed wavelength 405 nm, where a novel light-tight connected device of two integrating spheres is implemented to produce weak light onto SiPM. We present methods and results of the breakdown voltage, gain and PDE measurements for a Hamamatsu S13360-2050VE MPPC. At 25 Celsius, consistent results are obtained with the datasheet from the manufacturer. The temperature and bias voltage dependence of SiPM performances can guide its usage, such as in gain compensation at readout circuits, optical modeling of SiPMs and optimization of operating conditions of SiPM-based detectors.Comment: 9 pages, 14 figure

Electric Field Measurement by Edge Transient Current Technique on Silicon Low Gain Avalanche Detector

A novel methodology, named the diffusion profile method, is proposed in this research to measure the electric field of a low gain avalanche detector (LGAD).The proposed methodology utilizes the maximum of the time derivative of the edge transient current technique (edge-TCT) test waveform to quantify the dispersion of the light-induced carriers. This method introduces the estimation of the elongation of the carrier cluster caused by diffusion and the divergence of the electric field force during its drift along the detector. The effectiveness of the diffusion profile method is demonstrated through the analysis of both simulated and measured edge-TCT waveforms. Experimental data was collected from a laser scan performed on an LGAD detector along its thickness direction.A simulation procedure has been developed in RASER (RAdiation SEmiconductoR) to generate signals from LGAD.An assumption of immediate one-step carrier multiplication is introduced to simplify the avalanche process.Simulation results were compared with transient current data at the waveform level and showed a favorable match. Both simulation and experimental results have shown that the diffusion profile method could be applied to certain edge-TCT facilities as an alternative of electric field measurement

Association between occlusal support and cognitive impairment in older Chinese adults: a community-based study

IntroductionThe loss of occlusal support due to tooth loss is associated with systemic diseases. However, there was little about the association between occlusal support and cognitive impairment. The cross-sectional study aimed to investigate their association.MethodsCognitive function was assessed and diagnosed in 1,225 community-dwelling adults aged 60 years or older in Jing’an District, Shanghai. Participants were diagnosed with mild cognitive impairment (MCI) by Peterson’s criteria, or dementia, according to the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition. We determined the number of functional occlusal supporting areas according to Eichner classifications. We used multivariate logistic regression models to analyze the relationship between occlusal support and cognitive impairment and mediation effect models to analyze the mediation effect of age.ResultsSix hundred sixty participants were diagnosed with cognitive impairment, averaging 79.92 years old. After adjusting age, sex, education level, cigarette smoking, alcohol drinking, cardiovascular disease, and diabetes, individuals with poor occlusal support had an OR of 3.674 (95%CI 1.141–11.829) for cognitive impairment compared to those with good occlusal support. Age mediated 66.53% of the association between the number of functional occlusal supporting areas and cognitive impairment.DiscussionIn this study, cognitive impairment was significantly associated with the number of missing teeth, functional occlusal areas, and Eichner classifications with older community residents. Occlusal support should be a significant concern for people with cognitive impairment

Leakage current simulations of Low Gain Avalanche Diode with improved Radiation Damage Modeling

We report precise TCAD simulations of IHEP-IME-v1 Low Gain Avalanche Diode (LGAD) calibrated by secondary ion mass spectroscopy (SIMS). Our setup allows us to evaluate the leakage current, capacitance, and breakdown voltage of LGAD, which agree with measurements' results before irradiation. And we propose an improved LGAD Radiation Damage Model (LRDM) which combines local acceptor removal with global deep energy levels. The LRDM is applied to the IHEP-IME-v1 LGAD and able to predict the leakage current well at -30 $^{\circ}$C after an irradiation fluence of $\Phi_{eq}=2.5 \times 10^{15} ~n_{eq}/cm^{2}$. The charge collection efficiency (CCE) is under development

Multi-Label Multi-Kernel Transfer Learning for Human Protein Subcellular Localization

Recent years have witnessed much progress in computational modelling for protein subcellular localization. However, the existing sequence-based predictive models demonstrate moderate or unsatisfactory performance, and the gene ontology (GO) based models may take the risk of performance overestimation for novel proteins. Furthermore, many human proteins have multiple subcellular locations, which renders the computational modelling more complicated. Up to the present, there are far few researches specialized for predicting the subcellular localization of human proteins that may reside in multiple cellular compartments. In this paper, we propose a multi-label multi-kernel transfer learning model for human protein subcellular localization (MLMK-TLM). MLMK-TLM proposes a multi-label confusion matrix, formally formulates three multi-labelling performance measures and adapts one-against-all multi-class probabilistic outputs to multi-label learning scenario, based on which to further extends our published work GO-TLM (gene ontology based transfer learning model for protein subcellular localization) and MK-TLM (multi-kernel transfer learning based on Chou's PseAAC formulation for protein submitochondria localization) for multiplex human protein subcellular localization. With the advantages of proper homolog knowledge transfer, comprehensive survey of model performance for novel protein and multi-labelling capability, MLMK-TLM will gain more practical applicability. The experiments on human protein benchmark dataset show that MLMK-TLM significantly outperforms the baseline model and demonstrates good multi-labelling ability for novel human proteins. Some findings (predictions) are validated by the latest Swiss-Prot database. The software can be freely downloaded at http://soft.synu.edu.cn/upload/msy.rar

Gene ontology based transfer learning for protein subcellular localization

<p>Abstract</p> <p>Background</p> <p>Prediction of protein subcellular localization generally involves many complex factors, and using only one or two aspects of data information may not tell the true story. For this reason, some recent predictive models are deliberately designed to integrate multiple heterogeneous data sources for exploiting multi-aspect protein feature information. Gene ontology, hereinafter referred to as <it>GO</it>, uses a controlled vocabulary to depict biological molecules or gene products in terms of biological process, molecular function and cellular component. With the rapid expansion of annotated protein sequences, gene ontology has become a general protein feature that can be used to construct predictive models in computational biology. Existing models generally either concatenated the <it>GO </it>terms into a flat binary vector or applied majority-vote based ensemble learning for protein subcellular localization, both of which can not estimate the individual discriminative abilities of the three aspects of gene ontology.</p> <p>Results</p> <p>In this paper, we propose a Gene Ontology Based Transfer Learning Model (<it>GO-TLM</it>) for large-scale protein subcellular localization. The model transfers the signature-based homologous <it>GO </it>terms to the target proteins, and further constructs a reliable learning system to reduce the adverse affect of the potential false <it>GO </it>terms that are resulted from evolutionary divergence. We derive three <it>GO </it>kernels from the three aspects of gene ontology to measure the <it>GO </it>similarity of two proteins, and derive two other spectrum kernels to measure the similarity of two protein sequences. We use simple non-parametric cross validation to explicitly weigh the discriminative abilities of the five kernels, such that the time & space computational complexities are greatly reduced when compared to the complicated semi-definite programming and semi-indefinite linear programming. The five kernels are then linearly merged into one single kernel for protein subcellular localization. We evaluate <it>GO-TLM </it>performance against three baseline models: <it>MultiLoc, MultiLoc-GO </it>and <it>Euk-mPLoc </it>on the benchmark datasets the baseline models adopted. 5-fold cross validation experiments show that <it>GO-TLM </it>achieves substantial accuracy improvement against the baseline models: 80.38% against model <it>Euk-mPLoc </it>67.40% with <it>12.98% </it>substantial increase; 96.65% and 96.27% against model <it>MultiLoc-GO </it>89.60% and 89.60%, with <it>7.05% </it>and <it>6.67% </it>accuracy increase on dataset <it>MultiLoc plant </it>and dataset <it>MultiLoc animal</it>, respectively; 97.14%, 95.90% and 96.85% against model <it>MultiLoc-GO </it>83.70%, 90.10% and 85.70%, with accuracy increase <it>13.44%</it>, <it>5.8% </it>and <it>11.15% </it>on dataset <it>BaCelLoc plant</it>, dataset <it>BaCelLoc fungi </it>and dataset <it>BaCelLoc animal </it>respectively. For <it>BaCelLoc </it>independent sets, <it>GO-TLM </it>achieves 81.25%, 80.45% and 79.46% on dataset <it>BaCelLoc plant holdout</it>, dataset <it>BaCelLoc plant holdout </it>and dataset <it>BaCelLoc animal holdout</it>, respectively, as compared against baseline model <it>MultiLoc-GO </it>76%, 60.00% and 73.00%, with accuracy increase <it>5.25%</it>, <it>20.45% </it>and <it>6.46%</it>, respectively.</p> <p>Conclusions</p> <p>Since direct homology-based <it>GO </it>term transfer may be prone to introducing noise and outliers to the target protein, we design an explicitly weighted kernel learning system (called Gene Ontology Based Transfer Learning Model, <it>GO-TLM</it>) to transfer to the target protein the known knowledge about related homologous proteins, which can reduce the risk of outliers and share knowledge between homologous proteins, and thus achieve better predictive performance for protein subcellular localization. Cross validation and independent test experimental results show that the homology-based <it>GO </it>term transfer and explicitly weighing the <it>GO </it>kernels substantially improve the prediction performance.</p

FROM MESSIANIC STRUGGLE TO ZEN'S ENLIGHTENMENT ON J.D SANLINGER'S THE CATCHER IN THE RYE

学位：文学硕士院系专业：外文学院外文系_外国语言学及应用语言学学号：xy00006