Detecting Proteins in Highly Autofluorescent Cells Using Quantum Dot Antibody Conjugates

We have applied quantum dot (Qdot) antibody conjugates as a biomolecular probe for cellular proteins important in biogeochemical cycling in the sea. Conventional immunological methods have been hampered by the strong autofluorescence found in cyanobacteria cells. Qdot conjugates provide an ideal alternative for studies that require long-term imaging of cells such as detection of low abundance cellular antigens by fluorescence microscopy. The advantage of Qdot labeled probes over conventional immunological methods is the photostability of the probe. Phycoerythrin bleaches in cyanobacterial cells under prolonged UV or blue light excitation, which means that the semiconducting nanocrystal probe, the Qdot, can yield a strong fluorescent signal without interference from cellular pigments

Detecting Proteins in Highly Autofluorescent Cells Using Quantum Dot Antibody Conjugates

We have applied quantum dot (Qdot) antibody conjugates as a biomolecular probe for cellular proteins important in biogeochemical cycling in the sea. Conventional immunological methods have been hampered by the strong autofluorescence found in cyanobacteria cells. Qdot conjugates provide an ideal alternative for studies that require long-term imaging of cells such as detection of low abundance cellular antigens by fluorescence microscopy. The advantage of Qdot labeled probes over conventional immunological methods is the photostability of the probe. Phycoerythrin bleaches in cyanobacterial cells under prolonged UV or blue light excitation, which means that the semiconducting nanocrystal probe, the Qdot, can yield a strong fluorescent signal without interference from cellular pigments

Leveraging Key Information Modeling to Improve Less-Data Constrained News Headline Generation via Duality Fine-Tuning

Recent language generative models are mostly trained on large-scale datasets, while in some real scenarios, the training datasets are often expensive to obtain and would be small-scale. In this paper we investigate the challenging task of less-data constrained generation, especially when the generated news headlines are short yet expected by readers to keep readable and informative simultaneously. We highlight the key information modeling task and propose a novel duality fine-tuning method by formally defining the probabilistic duality constraints between key information prediction and headline generation tasks. The proposed method can capture more information from limited data, build connections between separate tasks, and is suitable for less-data constrained generation tasks. Furthermore, the method can leverage various pre-trained generative regimes, e.g., autoregressive and encoder-decoder models. We conduct extensive experiments to demonstrate that our method is effective and efficient to achieve improved performance in terms of language modeling metric and informativeness correctness metric on two public datasets.Comment: Accepted by AACL-IJCNLP 2022 main conferenc

Uloma (Uloma) intricornicula Liu, Ren & Wang, 2007 (Coleoptera, Tenebrionidae, Ulomini): Descriptions of the larva and pupa and new distributional records

The genus Uloma Dejean, 1821 (Coleoptera, Tenebrionidae, Ulomini) comprises more than 200 species and subspecies worldwide, 37 of which are recorded from China. However, the morphology of the immature stages of Chinese Uloma have been poorly documented. Up to now, larva and pupa descriptions are available for only one species, Uloma (Uloma) metogana Ren, 2004.The larva and pupa of Uloma (Uloma) intricornicula Liu, Ren & Wang, 2007, from southern China, are described and illustrated for the first time and are compared with those of U. (U.) metogana Ren, 2004. Differences between male and female pupae of this species are highlighted. New distributional data for U. (U.) intricornicula are also provided. Finally, 13 Uloma species from China are formally assigned to the nominated subgenus

FACTORS AFFECTING THE WILLINGNESS OF CHINESE USERS TO CONTINUE USING ONLINE EDUCATION PLATFORMS IN YUNNAN

This research examines the main factors such as platform system quality, course quality, and user interaction influencing users' continuous use intention on the online education platform from the user experience and perceived value perspective. Data was collected through the convenience approach via online survey questionnaires from 422 Yunnan respondents who had a prior online learning experience, including both elementary and higher education level courses, within the past year on an online education platform in China. Yunnan is located at the border of southwest China, where education is costly and inefficient. Data are tested against the research model by using structural equation modeling. The results indicate that user-perceived value will significantly impact users' willingness to continue using online education platforms. Furthermore, users' functional experience and emotional experience have a positive impact on perceived profit, while they have a negative effect on perceived loss. In addition, the quality of the platform system affects users' functional experience and emotional experiences. Besides, course quality, including timeliness, pertinence, authority, and1 Ed.D., Chinese Teacher, Stamford International University, Thailand. [email protected] Ph.D., Assistant Professor, Graduate School of Education, Stamford International University, Thailand. [email protected] Ed.D., Chinese Associate Professor, Stamford International University, Thailand. [email protected] MBA., Stamford International University, Thailand. [email protected] M.Ed., Stamford International University, Thailand. [email protected] M.Ed., Stamford International University, Thailand. [email protected] Ph.D., Lecturer, Stamford International University, Thailand. [email protected]: Human Sciences, ISSN 2586-9388, Vol.14 No.2 (Jul.-Dec. 2022)richness, positively affects users' functional experience and emotional experiences. And Interactions between students and teachers were also found in the study that has a positive influence on users' functional experience and emotional experiences

Determination of focal mechanism solutions of the earthquakes with M≥4.0 occurred in the mainland of China during August to October 2023

In this paper，the regional full waveform inversion using the broadband waveforms recorded by China Seismic Network were conducted, and the focal mechanism solutions of the 26 earthquakes with M≥4.0 occurred in the mainland of China during August to October 2023 were obtained. The types of these focal mechanism solutions show 9 reverse faulting, 14 strike-slip faulting and 3 normal faulting

Smart grid power load type forecasting: research on optimization methods of deep learning models

Introduction: In the field of power systems, power load type prediction is a crucial task. Different types of loads, such as domestic, industrial, commercial, etc., have different energy consumption patterns. Therefore, accurate prediction of load types can help the power system better plan power supply strategies to improve energy utilization and stability. However, this task faces multiple challenges, including the complex topology of the power system, the diversity of time series data, and the correlation between data. With the rapid development of deep learning methods, researchers are beginning to leverage these powerful techniques to address this challenge. This study aims to explore how to optimize deep learning models to improve the accuracy of load type prediction and provide support for efficient energy management and optimization of smart grids.Methods: In this study, we propose a deep learning method that combines graph convolutional networks (GCN) and sequence-to-sequence (Seq2Seq) models and introduces an attention mechanism. The methodology involves multiple steps: first, we use the GCN encoder to process the topological structure information of the power system and encode node features into a graph data representation. Next, the Seq2Seq decoder takes the historical time series data as the input sequence and generates a prediction sequence of the load type. We then introduced an attention mechanism, which allows the model to dynamically adjust its attention to input data and better capture the relationship between time series data and graph data.Results: We conducted extensive experimental validation on four different datasets, including the National Grid Electricity Load Dataset, the Canadian Electricity Load Dataset, the United States Electricity Load Dataset, and the International Electricity Load Dataset. Experimental results show that our method achieves significant improvements in load type prediction tasks. It exhibits higher accuracy and robustness compared to traditional methods and single deep learning models. Our approach demonstrates advantages in improving load type prediction accuracy, providing strong support for the future development of the power system.Discussion: The results of our study highlight the potential of deep learning techniques, specifically the combination of GCN and Seq2Seq models with attention mechanisms, in addressing the challenges of load type prediction in power systems. By improving prediction accuracy and robustness, our approach can contribute to more efficient energy management and the optimization of smart grids