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Chinese quantifiers are heavily loaded with Chinese culture, making it challenging to translate them into idiomatic English. The majority of earlier studies only examined linguistically the English translations of Chinese quantifiers. The interpretation of linguistic information, however, is a developing and pivotal stage of cognition in the translation process. Under the umbrella of cognitive iconicity, the research of cognitive distance between linguistic forms is being conducted. Therefore, it makes sense to talk about cognitive iconicity while discussing the English translation of Chinese nominal quantifiers. Based on the definition as well as the function of Chinese quantifiers, this research summarized four translation methods from a viewpoint of individual nominal quantifiers, namely, the omission of iconicity, the retention of iconicity, the conversion of iconicity, and the addition of iconicity. And then it further discussed the selection process of English translation version of Chinese nominal quantifiers.

Traditional Chinese exercise ("TCE" management modalities), including but not limited to Tai Chi, Baduanjin, and Yijinjing, has a good effect on improving the physical function of patients with knee osteoarthritis, but less attention has been paid to the impact on the psychological health of patients, and currently there is insufficient evidence to support it. We conducted this study to provide a systematic synthesis of best evidence regarding the physical and mental health of patients with knee osteoarthritis treated by traditional Chinese exercise. Literature on the effectiveness of traditional Chinese exercise (Tai Chi, Baduanjin, Yijinjing, Qigong, etc.) versus conventional therapy (muscle‐strength training of the lower extremity and aerobic training, wellness education, quadriceps strengthening exercises, etc.) on Western Ontario and McMaster Universities Arthritis Index (WOMAC), visual analog scale (VAS), Short Form‐36 (SF‐36), Timed Up and Go Test (TUG), and Berg Balance Scale (BBS) in knee osteoarthritis (KOA) from Pubmed, Web of Science, Ovid Technologies, China National Knowledge Infrastructure (CNKI), Chinese Science and Technology Periodical Database (VIP), Wanfang Database, and SinoMed were collected from their inception to April 2022. Thirty‐three studies with 2621 cases were included in this study. The study's results indicated that compared with conventional therapy, traditional Chinese exercise had more advantages on patients' WOMAC score, significantly reducing patients' overall WOMAC score (SMD = −0.99; 95% CI: −1.38, −0.60; p < 0.00001) and relieving pain (SMD = −0.76; 95% CI: −1.11, −0.40; p < 0.0001) in patients with KOA. It also has advantages over conventional therapy in improving mental component score (MCS) (SMD = 0.32; 95% CI: −0.00, 0.65; p = 0.05) and physical component score (PCS) (SMD = 0.34; 95% CI: 0.05, 0.62; p = 0.02). Compared with conventional therapy, traditional Chinese exercise can significantly reduce the effect on timed up and go test (TUG) score (SMD = −0.30; 95% CI: −0.50, −0.11; p = 0.002), beck depression inventory (DBI) score (SMD = −0.62; 95% CI: −1.03, −0.22; p = 0.002), and increase the impact on Berg Balance Scale (BBS) score (SMD = 0.60; 95% CI: 0.37, 0.83; p < 0.00001). The findings of this study indicated that traditional Chinese exercise improved body function and mental health in patients with knee osteoarthritis significantly. More high‐quality clinical evidence‐based data was needed to confirm the therapeutic effect of traditional Chinese exercise on the physical and mental health in KOA patients. [ABSTRACT FROM AUTHOR]

A membrane protein's functions are significantly associated with its type, so it is crucial to identify the types of membrane proteins. Conventional computational methods for identifying the species of membrane proteins tend to ignore two issues: High-order correlation among membrane proteins and the scenarios of multi-modal representations of membrane proteins, which leads to information loss. To tackle those two issues, we proposed a deep residual hypergraph neural network (DRHGNN), which enhances the hypergraph neural network (HGNN) with initial residual and identity mapping in this paper. We carried out extensive experiments on four benchmark datasets of membrane proteins. In the meantime, we compared the DRHGNN with recently developed advanced methods. Experimental results showed the better performance of DRHGNN on the membrane protein classification task on four datasets. Experiments also showed that DRHGNN can handle the over-smoothing issue with the increase of the number of model layers compared with HGNN. The code is available at https://github.com/yunfighting/Identification-of-Membrane-Protein-Types-via-deep-residual-hypergraph-neural-network. [ABSTRACT FROM AUTHOR]

Protein is closely related to life activities. As a kind of protein, DNA-binding protein plays an irreplaceable role in life activities. Therefore, it is very important to study DNA-binding protein, which is a subject worthy of study. Although traditional biotechnology has high precision, its cost and efficiency are increasingly unable to meet the needs of modern society. Machine learning methods can make up for the deficiencies of biological experimental techniques to a certain extent, but they are not as simple and fast as deep learning for data processing. In this paper, a deep learning framework based on parallel long and short-term memory(LSTM) and convolutional neural networks(CNN) was proposed to identify DNA-binding protein. This model can not only further extract the information and features of protein sequences, but also the features of evolutionary information. Finally, the two features are combined for training and testing. On the PDB2272 dataset, compared with PDBP_Fusion model, Accuracy(ACC) and Matthew's Correlation Coefficient (MCC) increased by 3.82% and 7.98% respectively. The experimental results of this model have certain advantages. [ABSTRACT FROM AUTHOR]

Membrane proteins are an essential part of the body's ability to maintain normal life activities. Further research into membrane proteins, which are present in all aspects of life science research, will help to advance the development of cells and drugs. The current methods for predicting proteins are usually based on machine learning, but further improvements in prediction effectiveness and accuracy are needed. In this paper, we propose a dynamic deep network architecture based on lifelong learning in order to use computers to classify membrane proteins more effectively. The model extends the application area of lifelong learning and provides new ideas for multiple classification problems in bioinformatics. To demonstrate the performance of our model, we conducted experiments on top of two datasets and compared them with other classification methods. The results show that our model achieves high accuracy (95.3 and 93.5%) on benchmark datasets and is more effective compared to other methods. [ABSTRACT FROM AUTHOR]

DNA contains the genetic information for the synthesis of proteins and RNA, and it is an indispensable substance in living organisms. DNA-binding proteins are an enzyme, which can bind with DNA to produce complex proteins, and play an important role in the functions of a variety of biological molecules. With the continuous development of deep learning, the introduction of deep learning into DNA-binding proteins for prediction is conducive to improving the speed and accuracy of DNA-binding protein recognition. In this study, the features and structures of proteins were used to obtain their representations through graph convolutional networks. A protein prediction model based on graph convolutional network and contact map was proposed. The method had some advantages by testing various indexes of PDB14189 and PDB2272 on the benchmark dataset. [ABSTRACT FROM AUTHOR]

The growth state of flowers is affected by many factors such as temperature, humidity, and light. Therefore, the maintenance of flowers often requires more professional knowledge. Ordinary people are often at a loss when face with various flower representations and do not know where the problem is. In response to the above problems, this article proposes the use of deep learning to identify the growth status of flowers to assist people in successfully raising flowers. In this article, we propose that the mainstream convolutional neural network has the limitation of only inputting images. In terms of network input, data of the current growth environment of flowers will also be input to supplement the input data of the network. In view of the lack of information interaction in the network, in terms of network structure, the shallow and deep characteristics of the network are integrated to make the network performance more advantageous. Experiments show that this method can effectively improve the recognition rate of flower growth status, so as to correctly distinguish the current growth status of flowers. [ABSTRACT FROM AUTHOR]

Approximately 40–50 percent of all drugs targets are G protein-coupled receptors (GPCRs). Three-dimensional structure of GPCRs is important to probe their biophysical and biochemical functions and their pharmaceutical applications. Lacking reliable and high quality free function is one of the ugent problems of computational predicting the three-dimensional structure in this community. We proposed a GPCR-specified energy function composed of four novel empirical potential energy terms: a two-dimensional contact energy force field, knowledge-based helix pair connection distance energy term, knowledge-based helix pair angle restraint energy term and a disulfide bond energy term. To validate the energy function, we employed an ab initio GPCR three-dimensional structure predictor to test if the energy function improved the accuracy of prediction. We evaluated 28 solved GPCRs and found that 21(75 percent) targets were correctly folded (TM-score>0.5). Also, the average TM-score using the energy function was 0.54, which was improved 134 percent than the TM-score 0.23 for MODELLER energy function and 170 percent than the TM-score 0.20 for Rosetta membrane energy function. The results confirmed that our empirical potential energy function toward ab initio folding is competitive to state-of-the-art solutions for structural prediction of GPCRs. [ABSTRACT FROM AUTHOR]

Background: Increasing attention has been paid to posttraumatic affective disorders. However, orthopedic surgeons dealing with trauma often ignore the harm of such diseases. Objective: To investigate the point prevalence and influencing factors of acute stress disorder (ASD) in elderly patients with osteoporotic fractures (EPOFs) from the perspective of orthopedic surgeons. Patients and Methods: A total of 595 cases of EPOFs were treated at our hospital from January 1, 2018, to June 30, 2019. The patients meeting our inclusion criteria were assessed using a structured interview based on the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-V) criteria to verify the presence of ASD. After diagnosis, the participants were divided into two groups (those with and without ASD). The sociodemographic characteristics, disease characteristics, and Social Support Rating Scale (SSRS) scores were assessed. The chi-square test was used for univariate analysis, and multivariate analysis was performed using binary logistic regression. Results: Of the 524 participants, 32 (6.1%) met the criteria for the diagnosis of ASD. The results of the univariate analysis showed that gender, personality, living alone, monthly family income, initial fear, poor prognosis expectation, anxiety/depression, pain, and social support were associated with ASD in EPOFs (P< 0.05). The multivariate regression analysis showed that isolation, low monthly family income, introversion, poor prognosis expectation, previous traumatic history, and intense pain were the main influencing factors and risk factors (OR> 1) for ASD in EPOFs. Conclusion: Being female, living alone, introversion, poor family income, intense initial fear, poor prognosis expectation, anxiety/depression, intense pain perception and low social support were significantly related to the occurrence of ASD in EPOFs. To achieve optimal recovery in EPOFs, orthopedic surgeons should meet both the physiological and psychological needs of the patients. [ABSTRACT FROM AUTHOR]

The knowledge of DNA-binding proteins would help to understand the functions of proteins better in cellular biological processes. Research on the prediction of DNA-binding proteins can promote the research of drug proteins and computer acidified drugs. In recent years, methods based on machine learning are usually used to predict proteins. Although great predicted performance can be achieved via current methods, researchers still need to invest more research in terms of the improvement of predicted performance. In this study, the prediction of DNA-binding proteins is studied from the perspective of evolutionary information and the support vector machine method. One machine learning model for predicting DNA-binding proteins based on evolutionary features by using Chou's 5-step rule is put forward. The results show that great predicted performance is obtained on benchmark dataset PDB1075 and independent dataset PDB186, achieving the accuracy of 86.05% and 75.30%, respectively. Thus, the method proposed is comparable to a certain degree, and it may work even better than other methods to some extent. [ABSTRACT FROM AUTHOR]

Porous Si3N4 ceramics with a fibrous interlocking microstructure were synthesized directly by carbothermal reduction between SiO2 and carbon and reaction bonding of Si, in the N2 atmosphere. X-ray diffractometry analysis demonstrated that β-Si3N4 was the major phase. The influences of content and powder size of the Si on the microstructure and properties of the samples were investigated. Scanning electron microscope (SEM) analysis exhibited that rod-like β-Si3N4 grains were formed in the samples prepared by using the fine Si powder. With an increase in the Si content, the porosity decreased, and the flexural strength increased accordingly. [ABSTRACT FROM AUTHOR]

Fusarium head blight (FHB) caused by Fusarium graminearum Schwabe is a devastating barley disease world-wide, causing significant yield losses and contaminating cereal products with mycotoxins. Barley grain contaminated with deoxynivalenol (DON) is associated with gushing and may be rejected by the malting and brewing industry. Genetically inherited resistance is the most effective option for the control of the disease. A total of 266 barley cultivars and breeding lines originating from China were evaluated for FHB resistance and concentration of DON in grain. Plants were inoculated with isolates of F. graminearum under field conditions by injecting conidia into a single spikelet of each spike. FHB symptoms were evaluated by visual inspection, and DON content was analysed by HPLC. Significant differences in FHB ratings and DON levels were observed among cultivars. Visual symptoms of FHB varied from 4.88 to 71.75% of infected spikelets 21 days after inoculation and from 7.86 to 113.33 area under the disease progress curve units (AUDPC). Twenty-seven lines were more resistant to FHB than the control resistant cultivar Zhedar 2 and with fewer than 12% infected spikelets. Twenty-one of the above lines originated from the area in the mid to low valley of Yangtze River, where FHB epidemics are frequent. DON levels ranged from 0.05 to 24.39 mg/kg among the tested barley lines. Correlation coefficients were significant between FHB symptom ratings and DON levels. However, there was no significant correlation between symptom rating and plant height and no significant correlation between symptom rating and heading date. [ABSTRACT FROM AUTHOR]

Fusariumhead blight (FHB) is a destructive disease of wheat worldwide. FHB resistance genes from Sumai 3 and its derivatives such as Ning 7840 have been well characterized through molecular mapping. In this study, resistance genes in Wangshuibai, a Chinese landrace with high and stable FHB resistance, were analyzed through molecular mapping. A population of 104 F2-derived F7 recombinant inbred lines (RILs) was developed from the cross between resistant landrace Wangshuibai and susceptible variety Alondra‘s’. A total of 32 informative amplified fragment length polymorphism (AFLP) primer pairs (EcoRI/MseI) amplified 410 AFLP markers segregating among the RILs. Among them, 250 markers were mapped in 23 linkage groups covering a genetic distance of 2,430 cM. In addition, 90 simple sequence repeat (SSR) markers were integrated into the AFLP map. Fifteen markers associated with three quantitative trait loci (QTL) for FHB resistance (P<0.01) were located on two chromosomes. One QTL was mapped on 1B and two others were mapped on 3B. One QTL on 3BS showed a major effect and explained up to 23.8% of the phenotypic variation for type II FHB resistance. [ABSTRACT FROM AUTHOR]

Porous Si3N4 were fabricated by carbothermal reduction between carbon black and diatomite. Diatomite is a non-metallic mineral composed of skeletal remains of single-cell water plants (algae). The main ingredient of diatomite is the amorphous active SiO2. The influence of sintering additives on the microstructure and mechanical properties of porous Si3N4 was analyzed. X-ray diffraction (XRD) analysis demonstrated the formation of Si3N4 except for minor of glass phase. Scanning electron microscope (SEM) analysis showed that porous β-Si3N4 occupied fine microstructure and uniform pore structure. The addition of Y2O3 accelerated the densification of porous Si3N4 ceramics. With an increase in the content of Y2O3, the porosity decreased, the flexural strength increased, and the aspect ratio of β-Si3N4 grains became smaller accordingly. The sample with addition of 1wt% Y2O3 showed finer β-Si3N4 grains with higher aspect ratio. Porous Si3N4 ceramics with the porosity of 70.1-79.5% and the flexural strength of 2.3-14.5 MPa were obtained by this technique. This technique would provide an innovatory approach to the synthesis of porous β-Si3N4 at a drastically reduced processing cost. [ABSTRACT FROM AUTHOR]

Abstract: The co-continuous Si3N4/Al composites with different Al contents were fabricated through the squeeze casting of porous Si3N4 preforms with varied porosities obtained by combination of pressureless sintering and carbothermal reduction. The effects of volume fraction of Al phase and the heat treatment temperature on the mechanical properties were investigated. When the volume percentage of Al increased from 24.13% to 70.23%, the Vickers hardness of the composite decreased from 762.33 to 175.09Hv, the flexural strength decreased from 795.06 to 331.53MPa and the fracture toughness increased from 3.66 to 11.52MPam1/2. The rod-like β-Si3N4 grains contributed to the fracture toughness of composites by pull-out, crack deflection and crack bridging. With an increase in the heat treatment temperature, the degree of interfacial reaction increased, the fracture modes changed from ductile intergranular fracture to brittle intergranular fracture. [ABSTRACT FROM AUTHOR]

Purpose: To explore the function of miR-221-3p in the development and course of chronic periodontitis (CP) and offer a fresh avenue for CP diagnosis and management., Methods: miR-221-3p expression was detected by RT-qPCR. The clinical diagnostic value of miR-221-3p in CP patients was analyzed by receiver operating characteristic (ROC). ELISA was used to determine the IL-1β and IL-6 in CP subjects and healthy controls. Pearson correlation analysis was performed with miR-221-3p. PDLCs were induced by LPS, transfected with miR-221-3p mimics, and their expression was analyzed for the effects of IL-1β, and IL-6., Results: The miR-221-3p expression was lower in the gingival sulcus fluid GCF of CP subjects compared to healthy controls. miR-221-3p showed high potential for clinical diagnosis in CP patients by ROC analysis, with high specificity and sensitivity. miR-221-3p was negatively correlated with Probing pocket depth (PD), Attachment loss (AL), Plaque index (PI), and Bleeding index (BI), and negatively correlated with inflammatory factors IL-1β and IL-6. In LPS-induced PDLCs, IL-1β and IL-6 were significantly increased, whereas miR-221-3p was significantly downregulated. Overexpression of miR-221-3p inhibited the production of inflammatory factors IL-1β and IL-6 in LPS-induced PDLCs., Clinical Significance: miR-221-3p expression may be a potential biological marker for the diagnosis of chronic periodontitis and provide a new direction for its treatment of chronic periodontitis., Competing Interests: The authors declared no conflict of interest. No financial support was received from any organization for the submitted work. Dr. Qi and Dr. Gui contributed equally to the study., (Copyright©American Journal of Dentistry.)

A membrane protein's functions are significantly associated with its type, so it is crucial to identify the types of membrane proteins. Conventional computational methods for identifying the species of membrane proteins tend to ignore two issues: High-order correlation among membrane proteins and the scenarios of multi-modal representations of membrane proteins, which leads to information loss. To tackle those two issues, we proposed a deep residual hypergraph neural network (DRHGNN), which enhances the hypergraph neural network (HGNN) with initial residual and identity mapping in this paper. We carried out extensive experiments on four benchmark datasets of membrane proteins. In the meantime, we compared the DRHGNN with recently developed advanced methods. Experimental results showed the better performance of DRHGNN on the membrane protein classification task on four datasets. Experiments also showed that DRHGNN can handle the over-smoothing issue with the increase of the number of model layers compared with HGNN. The code is available at https://github.com/yunfighting/Identification-of-Membrane-Protein-Types-via-deep-residual-hypergraph-neural-network.

G protein-coupled receptors (GPCRs) account for about 40% to 50% of drug targets. Many human diseases are related to G protein coupled receptors. Accurate prediction of GPCR interaction is not only essential to understand its structural role, but also helps design more effective drugs. At present, the prediction of GPCR interaction mainly uses machine learning methods. Machine learning methods generally require a large number of independent and identically distributed samples to achieve good results. However, the number of available GPCR samples that have been marked is scarce. Transfer learning has a strong advantage in dealing with such small sample problems. Therefore, this paper proposes a transfer learning method based on sample similarity, using XGBoost as a weak classifier and using the TrAdaBoost algorithm based on JS divergence for data weight initialization to transfer samples to construct a data set. After that, the deep neural network based on the attention mechanism is used for model training. The existing GPCR is used for prediction. In short-distance contact prediction, the accuracy of our method is 0.26 higher than similar methods.

Background: Zhongyi paste is a traditional Chinese medicine herbal paste that is externally applied to reduce inflammation and relieve pain., Methods: An acute foot swelling inflammation model in C57BL/6J mice was established by carrageenan-induced pathogenesis. Zhongyi paste raised the pain threshold and also reduced the degree of swelling in mice with carrageenan-induced foot swelling., Results: Analysis indicated that serum tumor necrosis factor-alpha, interleukin-1 beta, and prostaglandin E2 (PGE 2 ) cytokine levels and PGE 2 levels in the paw tissue of the mice were decreased by Zhongyi paste treatment. The quantitative polymerase chain reaction and western blot results showed that Zhongyi paste downregulated the mRNA and protein expression of extracellular signal-regulated kinase 1/2 (ERK1/2), and cyclooxygenase-2 (COX-2), and also downregulated the mRNA expression of PGE 2 . At the same time, the Zhongyi paste exerted a stronger effect as an external drug than that of indomethacin, which is an oral drug, and voltaren, which is an externally applied drug., Conclusions: Our results indicated that Zhongyi paste is a very effective drug to reduce inflammatory swelling of the foot, and its mechanism of action is related to regulation of the ERK1/2-COX-2-PGE 2 pathway.

Background: Electronic cigarettes (e-cigarettes) are a prevalent smoking cessation aid worldwide; however, a consensus regarding their efficacy and safety has yet to be reached., Methods: We conducted a systematic review of the literature from related studies written in English or Chinese and published between January 1, 2003, and July 30, 2017. Eligible studies reporting the number of smokers who reduced or quit smoking and suffered from adverse events after e-cigarette use were selected according to predefined criteria; pertinent data were then extracted for a meta-analysis., Results: Our search produced 198 articles; of these publications, 14 including 35,665 participants were analyzed. The pooled efficacy rate of e-cigarettes ranged from 48.3% to 58.7% for smoking reduction and from 13.2% to 22.9% for smoking cessation. The pooled rate of adverse events associated with e-cigarettes ranged from 49.1% to 51.6% based on 11 studies including 16,406 participants. The most prevalent adverse events were mouth or throat irritation, anxiety, depressed mood, nausea, and insomnia. No significant differences in overall CO2 exhalation (eCO) levels were observed after e-cigarette use according to the data from 5 studies., Conclusion: Our findings suggest that e-cigarettes are moderately effective with regard to smoking reduction and smoking cessation. eCO levels are unreliable for evaluating the efficacy of e-cigarettes. E-cigarette related adverse events frequently occur, especially due to high-dose nicotine-containing cartridges.

Objective: To evaluate the compatibility of novel nano-calcium-deficient hydroxyapatite/poly-amino acid (n-CDHA/PAA) complex biomaterials with muscle and bone tissue in an in vivo model., Methods: Thirty-two New Zealand white rabbits were used in this study. Biomaterials were surgically implanted into each rabbit in the back erector spinae and in tibia with induced defect. Polyethylene was implanted into rabbits in the control group and n-CDHA/PAA into those of the experimental group. Animals were examined at four different points in time: 2 weeks, 4 weeks, 12 weeks, and 24 weeks after surgery. They were euthanized after embolization. Back erector spinae muscles with the surgical implants were examined after hematoxylin and eosin (HE) staining at these points in time. Tibia bones with the surgical implants were examined by X-ray and scanning electron microscopy (SEM) at these points in time to evaluate the interface of the bone with the implanted biomaterials. Bone tissues were sectioned and subjected to HE, Masson, and toluidine blue staining., Results: HE staining of back erector spinae muscles at 4 weeks, 12 weeks, and 24 weeks after implantation of either n-CDHA/PAA or polyethylene showed disappearance of inflammation and normal arrangement in the peripheral tissue of implant biomaterials; no abnormal staining was observed. At 2 weeks after implantation, X-ray imaging of bone tissue samples in both experimental and control groups showed that the peripheral tissues of the implanted biomaterials were continuous and lacked bone osteolysis, absorption, necrosis, or osteomyelitis. The connection between implanted biomaterials and bone tissue was tight. The results of HE, Masson, toluidine blue staining and SEM confirmed that the implanted biomaterials were closely connected to the bone defect and that no rejection had taken place. The n-CDHA/PAA biomaterials induced differentiation of a large number of chondrocytes. New bone trabecula began to form at 4 weeks after implanting n-CDHA/PAA biomaterials, and lamellar bone gradually formed at 12 weeks and 24 weeks after implantation. Routine blood and kidney function tests showed no significant changes at 2 weeks and 24 weeks after implantation of both biomaterials., Conclusion: n-CDHA/PAA composites showed good compatibility in in vivo model. In this study, n-CDHA/PAA were found to be safe, nontoxic, and biologically active in bone repair.

Background: In recent years, many progresses have been made in molecular target therapy for lung cancer, in which anti-angiogenic target therapy is a hot spot drawing researchers' attention. The aim of this study is to explore the expression of canstatin gene transfected into human lymphocytes and its inhibitory effect on growth and metastasis of Lewis lung carcinoma., Methods: The eukaryotic expression vector of pCMV-Script and the recombinant pCMV-Script/Canstatin vector were separately transfected into lymphocytes by electroporation. The expression of canstatin protein in supernatant of lymphocyues was examined by SDS-PAGE assay. Furthermore, Lewis lung carcinoma cells were subcutaneously inoculated to C57BL mice to make animal model of tumor. When the transplanted tumors on the mice developed to 1cm³, the 30 mice were randomized into 3 groups, which were injected with 0.2mL supernatant of lymphocytes transfected with recombinant vector or naked vector, or 0.2mL NS respectively. After the treatment for 14 days, the size and pathological section of subcutaneous tumors were observed, and the number of pulmonary metastatic node was calculated., Results: Canstatin protein was found in supernatant of the lymphocytes in the recombinant vector group by SDS-PAGE assay. After the treatment, the tumor size in the recombinant vector group (1.49cm³±0.18cm³) was significantly smaller than that in the naked vector group (2.44cm³± 0.19cm³) and NS group (2.53cm³±0.18cm³) (P=0.000). The numbers of pulmonary metastatic node were 3.40±1.14, 7.60±2.61 and 7.60±2.41 in the recombinant vector group, naked vector group and NS group respectively (recombinant vector group vs the other two groups, P=0.013)., Conclusions: The pCMV-Script/Canstatin vector can express canstatin protein in human lymphocytes. Canstatin has strongly inhibitory effect on growth and metastasis of mouse Lewis lung carcinoma.

Background: Angiogenesis is essential for tumor's growth and metastasis. Canstatin, a newly found potent endogenous angiogenesis inhibitor, has drawn researcher's attention to its powerful biological activities on endothelial cells. The aim of this experiment is to explore the expression and effects of canstatin gene in lung cancer A549 cells and HUV-ECC cells., Methods: Expression vector of pCMV- Script/Canstatin was transfected into A549 and ECC cells by electroporation, and the positive clone was screened by G418. Growth characteristics of the two cell lines were compared before and after transfection. Expression of canstatin protein in supernatant was examined by SDS-PAGE assay, and cell cycles of the two cell lines were analysed by flow cytometry., Results: The expression of canstatin gene was found in supernatant of the transfected A549 cells and ECC cells. The apoptotic rate in the transfected ECC cells (16.04%) was significantly increased compared with that of the naked plasmid control group (0.43%) and parental cell group (2.92%) (P < 0.01). The growth of the transfected ECC cells was significantly inhibited (P < 0.01). The apoptotic rate in the transfected A549 cells (0.19%) showed no marked difference from the naked plasmid control group (0.13%) and parental cell group (0.07%) (P > 0.05). No significant difference in cell growth was found among the transfected A549 cell, naked plasmid control and parental cell groups., Conclusions: The results indicate that canstatin gene can express in lung cancer A549 cell line and HUV-ECC cell line, and it can specifically inhibit proliferation of endothelial cell and induce its apoptosis.

Wheat head blight caused mainly by Fusarium graminearum, is an important wheat disease, causing yield and quality losses. The breeding of resistant varieties is the key measure to control this disease, but the conventional breeding method is of low efficiency. The marker-assisted selection (MAS) can significantly improve the breeding efficiency. In this study, four RAPD (randomly amplified polymorphic DNA) markers linked to FHB resistance were obtained and one was cloned and sequenced. F7 recombinant inbred lines (RILs) were derived from the F1 of the cross Ning894037 (resistant)/Alondra (susceptible) by the single-seed descent method. Scab resistance of F7 RILs was evaluated in the greenhouse by injecting conidiospores into a central floret. Scab symptoms were evaluated on the 21st day after inoculation. Disease severity was assessed as the percentage of infected spikelets/spike. The F7 RIL population displayed a normal distribution, transgressive segregation and significant variation for FHB severity. DNA from resistant and susceptible parents was analyzed with 520 RAPD primers. Four markers (S1384-640,S1360-600, S1319-350,S1319-820) linked to FHB resistance were obtained. DNA of S1384-640 was recovered, subjected to re-amplification by using S1384 primer and the same protocol as for RAPD analysis and identified the rightness. The PCR product of S1384-640 was ligated into the pUCm-T vector, and cloned into fresh competent cells of Escherichia coli strain DH5alpha RAPD analysis showed that the inserts of the recombinant plasmids were DNA of S1384-640. The sequencing result showed that the cloned fragment was 648 bp.