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6,806 results on '"Song, Jia"'

212. Pharmacokinetics of rare earth element lanthanum and the effect of lanthanum on the blood element spectrum

Author

LIN Xiuqin, CHEN Yingsi, LI Ziyin, XU Feifei, WAN Siyu, ZHAO Xiaohan, SONG Jia, WU Weiliang, and YANG Xingfen

Subjects
rare earth elements, lanthanum nitrate, rats, pharmacokinetics, blood element spectrum, Food processing and manufacture, TP368-456, Nutrition. Foods and food supply, TX341-641
Abstract

ObjectiveTo explore the absorption， distribution， accumulation， and excretion after oral exposure to lanthanum and the effects of lanthanum on the blood element spectrum of rats.Methods48 female SPF-grade rats were randomly divided into 8 groups with a one-time gavage of 700 mg/kg·BW lanthanum nitrate solution using single oral exposure experiment. And the blood， heart， liver， spleen， lung， kidneys， brain， femur， and fecal of rats were collected and measured by ICP-MS after gavage for 1， 2， 4， 8， 16， 32， 64， and 128 h. Another 48 SPF-grade rats were randomly divided into 4 groups （half male and female）， each group with a control and an experimental group was observed in experimental time-point in repeat oral exposure experiment. The experimental group repeatedly gavage lanthanum nitrate solution containing lanthanum 66 mg/kg·BW， and the control group gavage an equal amount of physiological saline. Then， on the 7th， 14th， 21st and 28th days， the rats （including the control group and the experimental group） were taken to determine the contents of 44 elements in blood and the contents of lanthanum in liver， kidney， femur， feces and urine by ICP-MS.ResultsAfter single oral exposure to lanthanum， lanthanum peaked in blood for 2 h， the elimination half-life was 69.315 h， and the overall clearance rate was 1.733 L/h/kg. Lanthanum was widely distributed in the heart， liver， spleen， lung， kidneys， brain， and femur with blood， and the level in the femur was the highest （P0.05）.ConclusionIn this experiment， lanthanum can be rapidly absorbed into the blood after oral exposure and is widely distributed in the heart， liver， spleen， lung， kidneys， brain， and femur. After repeat oral exposure to lanthanum， lanthanum is easily deposited in the liver and femur， feces excretion is the main route of elimination， and no effect of lanthanum on the blood element spectrum of rats has been observed.

Published
2023
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213. Copper‐Metallized Porous N‐Heterocyclic Carbene Ligand Polymer‐Catalyzed Regio‐ and Stereoselective 1,2‐Carboboration of Alkynes

Author

Jun‐Song Jia, Jin‐Rong Luo, Wen‐Hao Li, Fei‐Hu Cui, Ying‐Ming Pan, and Hai‐Tao Tang

Subjects
1,2‐carboboration, catalyst design, nano‐catalysis, NHC catalysis, stereoselectivity and regioselectivity, Science
Abstract

Abstract Alkenylboronates are highly versatile building blocks and valuable reagents in the synthesis of complex molecules. Compared with that of monosubstituted alkenylboronates, the synthesis of multisubstituted alkenylboronates is challenging. The copper‐catalyzed carboboration of alkynes is an operationally simple and straightforward method for synthesizing bis/trisubstituted alkenylboronates. In this work, a series of copper‐metallized N‐Heterocyclic Carbene (NHC) ligand porous polymer catalysts are designed and synthesized in accordance with the mechanism of carboboration. By using CuCl@POL‐NHC‐Ph as the optimal nanocatalyst, this study realizes the β‐regio‐ and stereoselective (syn‐addition) 1,2‐carboboration of alkynes (regioselectivity up to >99:1) with satisfactory yields and a wide range of substrates. This work not only overcomes the selectivity of carboboration but also provides a new strategy for the design of nanocatalysts and their application in organic synthesis.

Published
2024
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214. Constructing a Multimodal Music Teaching Model in College by Integrating Emotions

Author

Song Jia

Subjects
caffenet network, speech signal, lrlr, feature fusion, music teaching, 68t05, Mathematics, QA1-939
Abstract

In this study, we enhanced the CaffeNet network for recognizing students’ facial expressions in a music classroom and extracted emotional features from their expressions. Additionally, students’ speech signals were processed through filters to identify emotional characteristics. Using the LRLR fusion strategy, these expression and speech-based emotional features were combined to derive multimodal fusion emotion results. Subsequently, a music teaching model incorporating this multimodal emotion recognition was developed. Our analysis indicates a mere 6.03% discrepancy between the model’s emotion recognition results and manual emotional assessments, underscoring its effectiveness. Implementation of this model in a music teaching context led to a noticeable increase in positive emotional responses—happy and surprised emotions peaked at 30.04% and 27.36%, respectively, during the fourth week. Furthermore, 70% of students displayed a positive learning status, demonstrating a significant boost in engagement and motivation for music learning. This approach markedly enhances student interest in learning and provides a solid basis for improving educational outcomes in music classes.

Published
2024
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215. A Novel Video Salient Object Detection Method via Semi-supervised Motion Quality Perception

Author

Chen, Chenglizhao, Song, Jia, Peng, Chong, Wang, Guodong, and Fang, Yuming

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Previous video salient object detection (VSOD) approaches have mainly focused on designing fancy networks to achieve their performance improvements. However, with the slow-down in development of deep learning techniques recently, it may become more and more difficult to anticipate another breakthrough via fancy networks solely. To this end, this paper proposes a universal learning scheme to get a further 3\% performance improvement for all state-of-the-art (SOTA) methods. The major highlight of our method is that we resort the "motion quality"---a brand new concept, to select a sub-group of video frames from the original testing set to construct a new training set. The selected frames in this new training set should all contain high-quality motions, in which the salient objects will have large probability to be successfully detected by the "target SOTA method"---the one we want to improve. Consequently, we can achieve a significant performance improvement by using this new training set to start a new round of network training. During this new round training, the VSOD results of the target SOTA method will be applied as the pseudo training objectives. Our novel learning scheme is simple yet effective, and its semi-supervised methodology may have large potential to inspire the VSOD community in the future.

Published
2020

216. Structures and Properties of $\beta$-Titanium Doping Trace Transition Metal Elements: a Density Functional Theory Study

Author

Song, Jia, Wang, Luyu, Zhang, Liang, Wu, Kaiqi, Wu, Wenheng, and Gao, Zhibin

Subjects
Condensed Matter - Materials Science, Condensed Matter - Mesoscale and Nanoscale Physics, Physics - Computational Physics
Abstract

We systematically calculate the structure, formation enthalpy, formation free energy, elastic constants and electronic structure of Ti$_{0.98}$X$_{0.02}$ system by density functional theory (DFT) simulations to explore the effect of transition metal X (X=Ag, Cd, Co, Cr, Cu, Fe, Mn, Mo, Nb, Ni, Pd, Rh, Ru, Tc, and Zn) on the stability mechanism of $\beta$-titanium. Based on our calculations, the results of formation enthalpy and free energy show that adding trace X is beneficial to the thermodynamic stability of $\beta$-titanium. This behavior is well explained by the density of state (DOS). However, the tetragonal shear moduli of Ti$_{0.98}$X$_{0.02}$ systems are negative, indicating that $\beta$-titanium doping with a low concentration of X is still elastically unstable at 0 K. Therefore, we theoretically explain that $\beta$-titanium doping with trace transition metal X is unstable in the ground state., Comment: 19 pages, 4 figures, 3 tables

Published
2020

217. ImageBERT: Cross-modal Pre-training with Large-scale Weak-supervised Image-Text Data

Author

Qi, Di, Su, Lin, Song, Jia, Cui, Edward, Bharti, Taroon, and Sacheti, Arun

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

In this paper, we introduce a new vision-language pre-trained model -- ImageBERT -- for image-text joint embedding. Our model is a Transformer-based model, which takes different modalities as input and models the relationship between them. The model is pre-trained on four tasks simultaneously: Masked Language Modeling (MLM), Masked Object Classification (MOC), Masked Region Feature Regression (MRFR), and Image Text Matching (ITM). To further enhance the pre-training quality, we have collected a Large-scale weAk-supervised Image-Text (LAIT) dataset from Web. We first pre-train the model on this dataset, then conduct a second stage pre-training on Conceptual Captions and SBU Captions. Our experiments show that multi-stage pre-training strategy outperforms single-stage pre-training. We also fine-tune and evaluate our pre-trained ImageBERT model on image retrieval and text retrieval tasks, and achieve new state-of-the-art results on both MSCOCO and Flickr30k datasets.

Published
2020

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