Comparison of genetic impact on growth and wood traits between seedlings and clones from the same plus trees of Pinus koraiensis

To evaluate the relationships among clones and open pollinated families from the same plus trees and to select elite breeding materials, growth, and wood characteristics of 33-year-old Pinus koraiensis clones and families were measured and analyzed. The results show that growth and wood characters varied significantly. The variation due to clonal effects was higher than that of family effects. The ratio of genetic to phenotypic coefficient of variation of clones in growth and wood traits was above 90%, and the repeatability of these characteristics was more than 0.8, whereas the ratio of genetic to phenotypic coefficient of variation of families was above 90%. The broad-sense heritability of all characteristics exceeded 0.4, and the narrow-sense family heritability of growth traits was less than 0.3. Growth characteristics were positively correlated with each other, but most wood properties were weakly correlated in both clones and families. Fiber length and width were positively correlated between clones and families. Using the membership function method, eleven clones and four families were selected as superior material for improved diameter growth and wood production, and two families from clonal and open-pollinated trees showed consistently better performance. Generally, selection of the best clones is an effective alternative to deployment of families as the repeatability estimates from clonal trees were higher than narrow-sense heritability estimates from open pollinated families. The results provide valuable insight for improving P. koraiensis breeding programs and subsequent genetic improvement

A Novel Solid-Phase Site-Specific PEGylation Enhances the In Vitro and In Vivo Biostabilty of Recombinant Human Keratinocyte Growth Factor 1

Keratinocyte growth factor 1 (KGF-1) has proven useful in the treatment of pathologies associated with dermal adnexae, liver, lung, and the gastrointestinal tract diseases. However, poor stability and short plasma half-life of the protein have restricted its therapeutic applications. While it is possible to improve the stability and extend the circulating half-life of recombinant human KGF-1 (rhKGF-1) using solution-phase PEGylation, such preparations have heterogeneous structures and often low specific activities due to multiple and/or uncontrolled PEGylation. In the present study, a novel solid-phase PEGylation strategy was employed to produce homogenous mono-PEGylated rhKGF-1. RhKGF-1 protein was immobilized on a Heparin-Sepharose column and then a site-selective PEGylation reaction was carried out by a reductive alkylation at the N-terminal amino acid of the protein. The mono-PEGylated rhKGF-1, which accounted for over 40% of the total rhKGF-1 used in the PEGylation reaction, was purified to homogeneity by SP Sepharose ion-exchange chromatography. Our biophysical and biochemical studies demonstrated that the solid-phase PEGylation significantly enhanced the in vitro and in vivo biostability without affecting the over all structure of the protein. Furthermore, pharmacokinetic analysis showed that modified rhKGF-1 had considerably longer plasma half-life than its intact counterpart. Our cell-based analysis showed that, similar to rhKGF-1, PEGylated rhKGF-1 induced proliferation in NIH 3T3 cells through the activation of MAPK/Erk pathway. Notably, PEGylated rhKGF-1 exhibited a greater hepatoprotection against CCl4-induced injury in rats compared to rhKGF-1

A New Method of Pedestrian Abnormal Behavior Detection Based on Attention Guidance

In public places, some behavior that violates public order and endangers public safety is defined as abnormal behavior. Moreover, it is a necessary auxiliary means to maintain public order and safety by detecting abnormal behavior in a large number of surveillance videos. However, due to the small proportion of abnormal behavior in video data, the extreme imbalance of data seriously restricts the effectiveness of detection. So, weakly supervised learning has become the most suitable and effective detection method. However, existing weakly supervised methods rarely take the locality and slightness of abnormal behavior into account and ignore the details of extracted features. Based on this, an attention-directed abnormal behavior detection model is proposed. In the two common prediction and reconstruction abnormal behavior detection methods based on weak supervision, suitable attention mechanisms are introduced, respectively, and two corresponding attention-directed networks are proposed. In addition, aiming at the problem of inaccurate thresholds for abnormal behavior division, the loss function of the model is improved and a new abnormal behavior evaluation method is proposed. Experiments were carried out on three classical datasets (the USCD Ped1, USCD Ped2, and CUHK Avenue dataset) for abnormal behavior detection. The best results for the area under the curve (AUC) indicator reached 82.7%, 94.5%, and 87.3%, respectively, which are better than many existing literature results

Text Summarization Method Based on Gated Attention Graph Neural Network

Text summarization is an information compression technology to extract important information from long text, which has become a challenging research direction in the field of natural language processing. At present, the text summary model based on deep learning has shown good results, but how to more effectively model the relationship between words, more accurately extract feature information and eliminate redundant information is still a problem of concern. This paper proposes a graph neural network model GA-GNN based on gated attention, which effectively improves the accuracy and readability of text summarization. First, the words are encoded using a concatenated sentence encoder to generate a deeper vector containing local and global semantic information. Secondly, the ability to extract key information features is improved by using gated attention units to eliminate local irrelevant information. Finally, the loss function is optimized from the three aspects of contrastive learning, confidence calculation of important sentences, and graph feature extraction to improve the robustness of the model. Experimental validation was conducted on a CNN/Daily Mail dataset and MR dataset, and the results showed that the model in this paper outperformed existing methods

Text Summarization Method Based on Gated Attention Graph Neural Network

Text summarization is an information compression technology to extract important information from long text, which has become a challenging research direction in the field of natural language processing. At present, the text summary model based on deep learning has shown good results, but how to more effectively model the relationship between words, more accurately extract feature information and eliminate redundant information is still a problem of concern. This paper proposes a graph neural network model GA-GNN based on gated attention, which effectively improves the accuracy and readability of text summarization. First, the words are encoded using a concatenated sentence encoder to generate a deeper vector containing local and global semantic information. Secondly, the ability to extract key information features is improved by using gated attention units to eliminate local irrelevant information. Finally, the loss function is optimized from the three aspects of contrastive learning, confidence calculation of important sentences, and graph feature extraction to improve the robustness of the model. Experimental validation was conducted on a CNN/Daily Mail dataset and MR dataset, and the results showed that the model in this paper outperformed existing methods

Effects of Ferulic Acid Esterase-Producing Lactic Acid Bacteria and Storage Temperature on the Fermentation Quality, In Vitro Digestibility and Phenolic Acid Extraction Yields of Sorghum (Sorghum bicolor L.) Silage

Two lactic acid bacteria (LAB) strains with different ferulic acid esterase (FAE) activities were isolated: Lactobacillus farciminis (LF18) and Lactobacillus plantarum (LP23). The effects of these strains on the fermentation quality, in vitro digestibility and phenolic acid extraction yields of sorghum (Sorghum bicolor L.) silage were studied at 20, 30 and 40 °C. Sorghum was ensiled with no additive (control), LF18 or LP23 for 45 days. At 40 °C, the lactic acid content decreased, whereas the ammonia nitrogen (NH3-N) content significantly increased (p < 0.05). At all three temperatures, the inoculants significantly improved the lactic acid contents and reduced the NH3-N contents (p < 0.05). Neither LP23 nor LF18 significantly improved the digestibility of sorghum silages (p > 0.05). The LP23 group exhibited higher phenolic acid extraction yields at 30 °C (p < 0.05), and the corresponding yields of the LF18 and control groups were improved at 40 °C (p < 0.05). FAE-producing LABs might partially ameliorate the negative effects of high temperature and improve the fermentation quality of sorghum silage. The screened FAE-producing LABs could be candidate strains for preserving sorghum silage at high temperature, and some further insights into the relationship between FAE-producing LABs and ensiling temperatures were obtained

Advances in Ag2Se-based thermoelectrics from materials to applications

Thermoelectric materials and their devices can realize the solid-state energy conversion between thermal and electrical energy, therefore serving as a promising alternative to conventional fossil fuels for energy supply. As one promising thermoelectric material, Ag2Se-based semiconductors exhibit considerably high thermoelectric performance at near-room temperatures and show considerable application potential in self-powering wearable electronics and solid-state refrigeration. Considering the fast development in Ag2Se-based thermoelectrics, a timely review for summarizing their progress, challenges, and outlook is of significance. In this review, we first focus on the fundamentals of Ag2Se, including its thermodynamics, crystal structures, band structures, liquid-like behaviors, and mechanical properties. Then, the advanced strategies employed in Ag2Se-based thermoelectric materials for improving their thermoelectric and mechanical properties are summarized. Besides, this review overviews the development of device designs from computational simulations, fabrication technology, and novel applications. In the end, we discuss the major controversies, challenges, and outlook for the future development of Ag2Se-based thermoelectrics.</p

An Improved Semisoft Threshold Algorithm and Its Evaluation for Denoising Random Walk in GNSS Time Series

The differences in the satellite orbit and signal quality of global navigation satellite positioning system, resulting in the complexity of random walk noise in GNSS time series, has become a bottleneck problem in applying GNSS technology to the high precision deformation monitoring industry. For the complex characteristics of random walk noise, small magnitude, low frequency and low sensitivity, an improved semisoft threshold algorithm is presented. Then it forms a unified system of semisoft threshold function, so as to improve the adaptability of conventional semisoft threshold for random walk noise. In order to verify and evaluate the effect of improved semisoft threshold algorithm, MATLAB platform is used to generate a linear trend, periodic and random walk noise of the GNSS time series, a total of 1700 epochs. The results show that the improved semisoft threshold method is better than the classical method, and has better performance in the SNR and root mean square error. The evaluation results show that the morphological character has been performanced high consistency between the noise reduced by improved method with random walk noise. Further from the view of quantitative point, the evaluation results of spectral index analysis verify the applicability of the improved method for random walk noise

Compressional behavior of natural eclogitic zoisite by synchrotron X-ray single-crystal diffraction to 34 GPa

Zoisite is a typical accessory mineral of eclogite; understanding its compressional behavior is important for the knowledge of the properties and processes within subduction zones. In this study, the compressional behavior of a natural eclogitic zoisite Ca-1.99(Al2.87Fe0.11)Si3.00O12OH was investigated at ambient temperature and high pressure to 34 GPa, using a diamond anvil cell (DAC) combined with synchrotron-based single-crystal X-ray diffraction (XRD) method. Our results indicate that zoisite is stable over the experimental pressure range. The pressure-volume (P-V) data were fitted to a third-order Birch-Murnaghan equation of state (BM3 EoS), and the equation of state coefficients including zero-pressure unit-cell volume (V-0), isothermal bulk modulus (K-T0), and its pressure derivative (KT0) were obtained as: V-0=904.77(8) angstrom(3), K-T0 = 118(1) GPa, and KT0 = 6.3(2), respectively. The axial compressibilities () for a-, b-, and c-axes were also obtained using a parameterized form of the BM3 EoS, and the results show (a0)<(b0)<(c0) with (a0):(b0):(c0)=1:1.28:1.50. In addition, the bulk modulus of this study is very consistent with previously studied zoisite with similar Fe content. However, the axial compressibility is significantly different with the previous study and the compression of zoisite in this study is more isotropic, which may result from the difference in the pressure-transmitting medium