Lifetime Prediction method of LED Light-emitting Device Based on Approximate Analysis

Compared with other light sources, LED light source has a longer service life and will not suddenly fail. Due to its high efficiency, energy saving and long life, LED has become the most popular light source at present. However, it is no longer considered to meet the application requirements, and the time that the light flux decays to this level is considered to be the life of the LED. This paper introduces the approximate method used to predict the lumen maintenance life of LED lamps. The experimental results obtained by the approximate method are compared with the TM-21 standard. Eventually, it is concluded that the approximate method provides more reliability information than the original TM-21 standard, and the obtained results are more reference, more accurate and more reliable

Constitutive activation of JAK–STAT3 signaling by BRCA1 in human prostate cancer cells

AbstractGerm-line mutations of the breast cancer susceptibility gene 1 (BRCA1) confer a high risk for breast and ovarian cancer in women and prostate cancer in men. The BRCA1 protein contributes to cell proliferation, cell cycle regulation, DNA repair and apoptosis; however, the mechanisms underlying these functions of BRCA1 remain largely unknown. Here, we showed that, in Du-145 human prostate cancer cells, enhanced expression of BRCA1 resulted in constitutive activation of signal transducer and activator transcription factor 3 (STAT3) tyrosine and serine phosphorylation. Moreover, Janus kinase 1 (JAK1) and JAK2, the upstream activators of STAT3, were also activated by BRCA1. Immunoprecipitation assay showed that BRCA1 interacted with JAK1 and JAK2. Blocking STAT3 activation using antisense oligonucleotides significantly inhibited cell proliferation and triggered apoptosis in Du-145 cells with enhanced expression of BRCA1. These findings indicate that BRCA1 interacts with the components of the JAK–STAT signaling cascade and modulates its activation, which may provide a new critical survival signal for the growth of breast, ovarian and prostate cancers in the presence of normal BRCA1

Metatranscriptomic analysis revealed Prevotella as a potential biomarker of oropharyngeal microbiomes in SARS-CoV-2 infection

Background and objectivesDisease severity and prognosis of coronavirus disease 2019 (COVID-19) disease with other viral infections can be affected by the oropharyngeal microbiome. However, limited research had been carried out to uncover how these diseases are differentially affected by the oropharyngeal microbiome of the patient. Here, we aimed to explore the characteristics of the oropharyngeal microbiota of COVID-19 patients and compare them with those of patients with similar symptoms.MethodsCOVID-19 was diagnosed in patients through the detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by quantitative reverse transcription polymerase chain reaction (RT-qPCR). Characterization of the oropharyngeal microbiome was performed by metatranscriptomic sequencing analyses of oropharyngeal swab specimens from 144 COVID-19 patients, 100 patients infected with other viruses, and 40 healthy volunteers.ResultsThe oropharyngeal microbiome diversity in patients with SARS-CoV-2 infection was different from that of patients with other infections. Prevotella and Aspergillus could play a role in the differentiation between patients with SARS-CoV-2 infection and patients with other infections. Prevotella could also influence the prognosis of COVID-19 through a mechanism that potentially involved the sphingolipid metabolism regulation pathway.ConclusionThe oropharyngeal microbiome characterization was different between SARS-CoV-2 infection and infections caused by other viruses. Prevotella could act as a biomarker for COVID-19 diagnosis and of host immune response evaluation in SARS-CoV-2 infection. In addition, the cross-talk among Prevotella, SARS-CoV-2, and sphingolipid metabolism pathways could provide a basis for the precise diagnosis, prevention, control, and treatment of COVID-19

Fundamental Studies of Organic Silicon Combustion Chemistry and Characterizing the Presence of Silicon Species in Landfill and Sludge Gas

Organic silicon species play important roles as reactants in waste-to-energy systems and in material synthesis processes. This research addresses some of the critical challenges posed by silicon species in biogas, which hinder energy recovery due, in part, to silica production during combustion. Despite over a decade of recognizing the impact of silicon compounds on biogas utilization, comprehensive characterization of their presence in biogas is lacking. In addition, understanding combustion chemistry of silicon compounds is essential to evaluate and mitigate their impacts on biogas combustion and improve material synthesis, but detailed reaction mechanisms and thermochemistry data of these chemicals remain scarce and have high uncertainties and known discrepancies with experimental observations. This study aims to fill key knowledge gaps by advancing the understanding of silicon species including their presence in biogas and fundamental combustion chemistry of some canonical compounds. The technical approach for characterizing waste-to-energy concerns included statistical analysis of biogas data reported in the literature, on-site analysis at landfill gas energy facilities, and qualitative interviews with waste-to-energy stakeholders. For advancing the reaction chemistry, flat flame burners and ignition studies were used. The fundamental laboratory combustion experiments focused on trimethylsilanol (TMSO) and hexamethyldisiloxane (HMDSO) due to the canonical structure of these silicon compounds. Burner studies leveraged well-established initial and boundary conditions and novel application of recent diagnostic advances to measure in situ temperature at unprecedented spatial resolution. The ignition studies leveraged the simplified geometry and well-established data interpretation methods to consider chemistry effects on flame speeds. Key outcomes of this work include a new database on silicon species concentrations in biogas. The longitudinal study showed widespread presence of silicon species in global and U.S. biogas systems. TMSO, octamethylcyclotetrasiloxane (D4) and decamethylcyclopentasiloxane (D5) were identified as sentinel species, where their concentrations were identified correlated with total silicon species concentrations. The database established in this work provide an important quantitative foundation for technology development to recover silicon species from biogas and to develop methods for silica abatement and mitigation. In addition, the new correlations discovered between specific and total silicon species concentrations provide new opportunities for developing methods to monitor silicon species and potentially improve waste-to-energy facilities, e.g., by reducing maintenance costs associated with mitigating the presences of these species in biogas. In laboratory burner studies, x-ray fluorescence (XRF) spectroscopy was applied to measure the in-situ temperature fields for the first time in flames with silicon reactants. Findings provide direct insight into the reaction pathways consuming TMSO and HMDSO. Specifically, TMSO and HMDSO reactions were initiated in low-temperature low-oxygen regions. The results indicate radicals from the methane flame system initiate reactions with TMSO and HMDSO through H-abstraction pathways. Additionally, gas sampling measurements identified TMSO as an intermediate of HMDSO reactions, contrary to recently proposed reaction mechanisms in the literature. Surprisingly, the flame speed measurements showed no significant impact from HMDSO on methane flame speeds at the conditions studied, suggesting transport effects may dominate the HMDSO chemistry. The results are the first attempt to measure siloxane flame speeds and provide important knowledge on how to conduct further measurements in the future with improved accuracy. The original findings of the burner and ignition studies provide new insights into the combustion chemistry of silicon species (namely the radical interaction and production pathways), and provide quantitative data to further advance reaction theory and kinetics.PhDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/192420/1/mqh_1.pd

Research on Chinese Medical Entity Recognition Based on Multi-Neural Network Fusion and Improved Tri-Training Algorithm

Chinese medical texts contain a large number of medically named entities. Automatic recognition of these medical entities from medical texts is the key to developing medical informatics. In the field of Chinese medical information extraction, annotated Chinese medical text data are very few. In the named entity recognition task, there is insufficient labeled data, which leads to low model recognition performance. Therefore, this paper proposes a Chinese medical entity recognition model based on multi-neural network fusion and the improved Tri-Training algorithm. The model performs semi-supervised learning by improving the Tri-Training algorithm. According to the characteristics of the medical entity recognition task and medical data, the method in this paper is improved in terms of the division of the initial sub-training set, the construction of the base classifier, and the integration of the learning voting method. In addition, this paper also proposes a multi-neural network fusion entity recognition model for base classifier construction. The model learns feature information jointly by combining Iterated Dilated Convolutional Neural Network (IDCNN) and BiLSTM. Through experimental verification, the model proposed in this paper outperforms other models and improves the performance of the Chinese medical entity recognition model by incorporating and improving the semi-supervised learning algorithm

Lifetime Prediction method of LED Light-emitting Device Based on Approximate Analysis

Compared with other light sources, LED light source has a longer service life and will not suddenly fail. Due to its high efficiency, energy saving and long life, LED has become the most popular light source at present. However, it is no longer considered to meet the application requirements, and the time that the light flux decays to this level is considered to be the life of the LED. This paper introduces the approximate method used to predict the lumen maintenance life of LED lamps. The experimental results obtained by the approximate method are compared with the TM-21 standard. Eventually, it is concluded that the approximate method provides more reliability information than the original TM-21 standard, and the obtained results are more reference, more accurate and more reliable

Metric-Based Assessment Method for MS-T Formalism with Small Subsets of Torsional Conformers

The multi-structural approximation with torsional anharmonicity (MS-T) method and its variants have been widely used for calculating conformational-rovibrational partition functions of large molecules. The present work aimed to propose a systematic method to assess and explain the performance of various variants of the MS-T method. First, we proposed the simplest variant MS-T(2NN) (two nearest neighborhood torsions are coupled) and systematically validated it for large alkanes n-CnH2n+2 (n = 6-10) and their transition states of hydrogen abstraction reactions. Second, we proposed a metric-based method to explain the underlying reason for the good performance of MS-T(2NN)-it includes the torsional conformers that have dominant contributions to the partition function calculations. These conformers are closer to the lowest-energy conformer in the space of dihedral and energy metrics. Third, the same observation and explanation apply to the other two variants, MS-2DT (any two torsions are coupled) and MS-3DT (any three torsional are coupled), which contain increasingly more torsional conformers than MS-T(2NN) but are subsets of the complete set of torsional conformers considered by the MS-T method. Overall, the present method provides a mathematically rigorous and computationally effective diagnosis tool to assess various MS-T methods dealing with the torsional anharmonicity of large molecules in the partition function calculation

Cavity ringdown spectroscopy of intermediates in the reactions of aromatics + OH

Arom. compds., such as toluene and benzene, are important anthropogenically emitted volatile org. compds. in urban areas, and lead to the prodn. of secondary org. aerosols. However, there are many questions that remain about the mechanism of their atm. oxidn. and very few direct observations of the radial intermediates have been made. Therefore, we have used mid-IR pulsed-laser photolysis cavity ringdown spectroscopy to directly detect the hydroxy-cyclohexadienyl radicals formed from the addn. of OH to toluene and benzene in lab. expts. Vibrational spectra and kinetic models of the reaction chem. will be presented

Research on Chinese Medical Entity Relation Extraction Based on Syntactic Dependency Structure Information

Extracting entity relations from unstructured medical texts is a fundamental task in the field of medical information extraction. In relation extraction, dependency trees contain rich structural information that helps capture the long-range relations between entities. However, many models cannot effectively use dependency information or learn sentence information adequately. In this paper, we propose a relation extraction model based on syntactic dependency structure information. First, the model learns sentence sequence information by Bi-LSTM. Then, the model learns syntactic dependency structure information through graph convolutional networks. Meanwhile, in order to remove irrelevant information from the dependencies, the model adopts a new pruning strategy. Finally, the model adds a multi-head attention mechanism to focus on the entity information in the sentence from multiple aspects. We evaluate the proposed model on a Chinese medical entity relation extraction dataset. Experimental results show that our model can learn dependency relation information better and has higher performance than other baseline models