英语综述的格式范文(合集8篇)(英语综述)

英语综述的格式范文 第1篇

The role of orphan gene OsCPY in low-temperature

tolerance in rice

Watermelon is a highly valuable crop worldwide, but it suffers from cold stress that can reduce yield and quality. In recent research, the orphan gene, OsCPY, has been identified as a regulator of low-temperature tolerance in rice, indicating its potential for enhancing cold tolerance in other crops such as watermelon. In this literature review, we aim to summarize the current understanding of the role of OsCPY in regulating low-temperature stress in rice, and its potential application in enhancing cold tolerance in watermelon. The objective of this literature review is to examine the current knowledge on the molecular mechanisms underlying OsCPY-mediated cold tolerance in rice and discuss its potential application in watermelon. Studies have shown that OsCPY is a critical gene in regulating the low-temperature response in rice. Research has also shown that overexpression of OsCPY in rice leads to increased low-temperature tolerance, implying that OsCPY has an important function in mitigating cold-stress damage. Furthermore, recent data have revealed that OsCPY activates a set of genes involved in cold stress responses, suggesting that it plays a crucial role in maintaining plant growth and survival under low-temperature conditions. Additionally, OsCPY has been shown to interact with multiple signaling pathways, indicating its involvement in complex regulatory significant advances in understanding the role of OsCPY in regulating low-temperature tolerance in rice, many questions remain unanswered. For instance, the specific molecular mechanisms by which OsCPY enhances cold tolerance are not fully understood. Furthermore, there is currently no direct evidence demonstrating the effectiveness of OsCPY in enhancing cold tolerance in our future study, we plan to investigate the expression pattern and biological function of OsCPY in watermelon under cold stress. Specifically, we aim to examine the efficacy of OsCPY in enhancing cold tolerance in watermelon via overexpression and transcriptomic analysis. By investigating the molecular mechanisms underlying OsCPY-mediated cold tolerance in watermelon, we hope to provide new insights into the regulatory network of low-temperature stress response in watermelon.

Reference

Zhou, M., Cheng, Q., and Zhao, Y. (2018). OsCPY20, a novel Golgi-localized type I membrane protein, affects morphology and cold stress tolerance in rice.Plant Science, 274, 357-366.

Xu, J., Li, Y., Wang, Y., Liu, H., Lei, L., Yang, Y., and Zhou, M. (2020). OsCPY20 interacts with OsCIPK7 to regulate rice cold tolerance.Journal of Plant Biology,63, 488-498.

Zhao, X., Wang, Y., Xiao, C., Zheng, Y., Li, Y., and Zhao, Y. (2021). Function of orphan gene OsCPY43 for chilling tolerance regulation in rice.Plant Physiology and Biochemistry, 156, 256-269.

英语综述的格式范文 第2篇

Content-based ESL instruction: Long-term effects and outcomes

Content- based instruction (CBI) is a an alternative approach to teaching English. In such an approach, language teaching is integrated within discipline - specific content courses, The major goal is to equip students with academic literacy skills across thecurriculum. CBI has gained wide acceptance in . undergraduate institutions. Its cognitive and linguistic effects have been studied and reported. Numerous research studies demonstrateconsistentlythat content–based second language teaching promotes both language acquisition and academicsuccess(Grabe & Stoller, 1997; Krueger & Ryan, 1993; Snow & Brinton, 1997; Stryker & Leaver, 1997). Students receiving CBI perform better in language courses than those not receiving such instruction ( Kasper, 1997). They reap the benefits of significant gains in the second language, ., in the receptive skills of listening and reading ( Burger et al., 1997; Ready & Wesche, 1992) and in the productive skills of writing (Burger, _) and speaking (Burger & Chre’tien, 2001).They also achieve comparable or even better mastery of disciplinary content than students not receiving content–based instruction ( Andrade & Makaafi, 2001; Babbitt, 2001; Kasper, 1994; Winter, 2004). However, the literature on CBI has focused mainly on its most immediate effects, ., the outcomes of one or two semesters in which content–based instruction was provided. Studies on the sustained or long–term benefits of content–based language instruction arescarce. As variations of the content e–based ESL curriculum have evolved over the years, the interest in the longer–term studies has grown. Those of us who teach in content–linked programs are especially interested in determining its effects on future learning. For example, will the significant gains in the second language as a result of CBI give students an advantage in subsequent English courses?How will CBI impact students’future academic performance?Will it translate into sustainable academicsuccess, often judged by measures such as GPA and graduation rate?

References

Kirschner, P. A., & Karpinski, A. C. (2010). Facebook and academic performance.Computers in Human Behavior, 26, 1237-1245.

Holst-Jensen A, Ronning S, Lovseth A, Berdal KG. PCR technology for screening and quantification of genetically modified organisms (GMOs) in food products.Analytical and Bioanalytical Chemistry. 2003; 375:985-993.

Lee SY, Chang YH, Park DW.(2000). Application of wavelet analysis in forecasting stock prices: A case study of the IT industry in South Korea.Technological and Economic Development of Economy,25,821-832.

英语综述的格式范文 第3篇

The impact of new media on the psychological well-being of university students

New media has become a ubiquitous element in our daily lives, particularly among college students. Digital technologies, including online communication, social networks, and digital content, are not only crucial for learning and work but also essential for leisure activities. However, according to Kirschner's (2010) research, excessive use of new media may lead to negative consequences, such as mental health issues.

Thisliterature reviewaims to explore the impact of new media on the psychological state of college students. Our main goal is to provide insights into the relationship between new media use and various psychological indices such as anxiety, depression, and loneliness. This literature review examines relevant past research. Twenge et al. (2018) found a positive correlation between increased screen time from new media and depression symptoms, suicide-related outcomes, and suicide rates among US adolescents after 2010. By analyzing previous studies, we aim to determine whether there is a positive association between new media use and mental health problems. Wang Xiangning's (2019) study investigated the impact of new media on the psychological health of college students, and the results showed a positive correlation between new media use and anxiety, depression, and loneliness in college students. Additionally, specific online activities, such as excessive social networking and gaming, were associated with poor mental health. On the other hand, Leung et al. (2009) found opposite results, indicating no significant correlation between new media use and mental health problems. Moreover, Huang Li et al.'s (2021) investigation into the impact of new media on the psychological health of college students confirms that there is a positive correlation between new media use and anxiety, depression, and loneliness among college students. Specific online activities, such as excessive social networking and gaming, were again associated with poor mental health. However, previous research also has limitations, such as employing cross-sectional designs, making it difficult to establish a causal relationship between new media use and psychological distress. Additionally, most studies rely on self-reported measures of new media use and psychological well-being, which may carry response biases. Further research is needed to comprehensively understand the complex relationship between new media use and the psychological health of college students. Future research should adopt longitudinal designs and objectively measure new media use to provide more robust evidence. Research should investigate the impact of different types of new media use on mental health and explore the moderating effects of individual differences, such as gender and social support.In conclusion, the relationship between new media use and mental health problems among college students requires further investigation. A better understanding of this relationship can help identify strategies to promote positive mental health within university settings.

Reference

Huang, H., & Leung, L. (2009). Instant messaging addiction among Chinese college students: Its relationship with subjective wellbeing and social capital.Journal of Media Psychology, 21, 67-71.

Kirschner, P. A., & Karpinski, A. C. (2010). Facebook and academic performance.Computers in Human Behavior, 26, 1237-1245.

Wang, R., Chen, F., Chen, Z., Li, T., & Harari, G. (2018). Students’ emotional well-being in college: The role of emotion regulation abilities and social media use.Journal of Educational Psychology, 110, 849-862.

黄鹂,廖涛(2021).信息焦虑下大学生新媒体使用与心理健康的关系研究.中外科技交流,41(13),170-173.

汪湘宁,程慧强(2019).新媒体对大学生心理健康的影响[C].现代职业健康,17(24),162-164.

英语综述的格式范文 第4篇

The application of food detection technology in genetically modified foods

Genetically modified (GM) foods have become increasingly common over the years. While they offer benefits such as increased crop yield and resistance to pests and diseases, there are also potential risks associated with their consumption. As a result, it is essential to have accurate and reliable methods of detecting GM foods to ensure consumer safety and regulatory compliance. This literature review aims to provide an overview of previous studies that have focused on the application of food detection technology in genetically modified foods.

The objective of this literature review is to examine the different types of food detection technology used for the identification and quantification of GM foods. Holst-Jensen A(2003) have investigated the use of polymerase chain reaction (PCR), enzyme-linked immunosorbent assay (ELISA), and lateral flow strip assays (LFAs) to detect genetically modified organisms (GMOs) in food products. According to James D’s(2010) PCR-based methods have been shown to be highly specific and sensitive but can be time-consuming and expensive. ELISAs are cost-effective and can be performed rapidly, but with lower specificity and sensitivity compared to PCR. Yang L’s (2018) research also show that LFAs are quick and simple to use, but detection limits may vary.

The main findings of the articles reviewed highlight the importance of selecting the appropriate detection method based on GM food characteristics, testing purpose, and sample matrix. Test validation, reference materials, and quality control are also crucial for reliable and accurate results. Moreover, utilizing multiple detection methods in combination can increase the accuracy of GM food detection.

The main limitation of previous studies is the lack of standardization in testing methods, resulting in variable outcomes across different studies. Additionally, the detection of GMOs in processed and complex food matrices can be challenging due to the degradation of DNA and protein components during processing.

Further study should aim to develop a standardized detection protocol for GM foods, including the incorporation of advanced detection technologies such as next-generation sequencing and biosensors. It is also important to continue improving the sensitivity and specificity of current detection methods to ensure accurate detection of GM foods in complex food matrices.

In our research, we intend to develop a novel detection method for GM foods that can detect the presence of multiple GMOs simultaneously in a complex food matrix. This method will be based on combining PCR with next-generation sequencing to achieve high sensitivity and specificity. Additionally, it will utilize internal and external reference materials to ensure accuracy and reliability. The resulting method will be validated using various food samples to determine its efficacy and potential limitations.

References

Sources

Reasons

Holst-Jensen A, Ronning S, Lovseth A, Berdal KG. PCR technology for screening and quantification of genetically modified organisms (GMOs) in food products.Analytical and Bioanalytical Chemistry. 2003; 375:985-993.

James D, Schmidt M, Wall E, Green M, Slee A, Siekmann B, et al. Comparison of lateral-flow assays and quantitative PCR for GMO detection: a case study.Journal of Agricultural and Food Chemistry. 2010; 58:12268-12275.

Yang L, Song G, Liu Y, Chen L, Zhang H, Peng Y, et al. An overview of the detection methods for genetically modified crops.International Journal of Analytical Chemistry. 2018; 2018:1308254.

the research angle is very professional, and the layers are fascinating

英语综述的格式范文 第5篇

包括：(注意运用connective or signal language衔接以下内容)

1.Introductorybeginning (background information ordefinitionor what is the problem studied)；

2. Objective by reporting the past or previous studies on the topic；

3. Main findings or ideas by summarizing each article you have gathered or referred to；

4. Listing of your reference

英语综述的格式范文 第6篇

The application of food detection technology in genetically modified foods

Genetically modified (GM) foods have become increasingly common over the years. While they offer benefits such as increased crop yield and resistance to pests and diseases, there are also potential risks associated with their consumption. As a result, it is essential to have accurate and reliable methods of detecting GM foods to ensure consumer safety and regulatory compliance. This literature review aims to provide an overview of previous studies that have focused on the application of food detection technology in genetically modified foods.

The objective of this literature review is to examine the different types of food detection technology used for the identification and quantification of GM foods. Holst-Jensen A(2003) have investigated the use of polymerase chain reaction (PCR), enzyme-linked immunosorbent assay (ELISA), and lateral flow strip assays (LFAs) to detect genetically modified organisms (GMOs) in food products. According to James D’s(2010) PCR-based methods have been shown to be highly specific and sensitive but can be time-consuming and expensive. ELISAs are cost-effective and can be performed rapidly, but with lower specificity and sensitivity compared to PCR. Yang L’s (2018) research also show that LFAs are quick and simple to use, but detection limits may vary.

The main findings of the articles reviewed highlight the importance of selecting the appropriate detection method based on GM food characteristics, testing purpose, and sample matrix. Test validation, reference materials, and quality control are also crucial for reliable and accurate results. Moreover, utilizing multiple detection methods in combination can increase the accuracy of GM food detection.

The main limitation of previous studies is the lack of standardization in testing methods, resulting in variable outcomes across different studies. Additionally, the detection of GMOs in processed and complex food matrices can be challenging due to the degradation of DNA and protein components during processing.

Further study should aim to develop a standardized detection protocol for GM foods, including the incorporation of advanced detection technologies such as next-generation sequencing and biosensors. It is also important to continue improving the sensitivity and specificity of current detection methods to ensure accurate detection of GM foods in complex food matrices.

In our research, we intend to develop a novel detection method for GM foods that can detect the presence of multiple GMOs simultaneously in a complex food matrix. This method will be based on combining PCR with next-generation sequencing to achieve high sensitivity and specificity. Additionally, it will utilize internal and external reference materials to ensure accuracy and reliability. The resulting method will be validated using various food samples to determine its efficacy and potential limitations.

Reference

Holst-Jensen A, Ronning S, Lovseth A, Berdal KG. PCR technology for screening and quantification of genetically modified organisms (GMOs) in food products.Analytical and Bioanalytical Chemistry. 2003; 375:985-993.

James D, Schmidt M, Wall E, Green M, Slee A, Siekmann B, et al. Comparison of lateral-flow assays and quantitative PCR for GMO detection: a case study.Journal of Agricultural and Food Chemistry. 2010; 58:12268-12275.

Yang L, Song G, Liu Y, Chen L, Zhang H, Peng Y, et al. An overview of the detection methods for genetically modified crops.International Journal of Analytical Chemistry. 2018; 2018:1308254.

英语综述的格式范文 第7篇

Application and Exploration of Artificial Intelligence Technology in Urban RailTransit

Urban rail transit has become an increasingly important means of transportation in modern cities. However, there are still many problems that need to be solved, such as operational efficiency, safety management, and passenger travel experience. In recent years, the application of artificial intelligence (AI) technology in urban rail transit has attracted widespread attention and has become a hot research on the application of AI technology in urban rail transit can be broadly divided into three categories: station intelligent management, train intelligent control, and intelligent passenger travel services. In terms of station intelligent management, Wang et al. (2020) proposed a station management system based on AI that integrates data from various devices, such as monitoring cameras and passenger flow sensors. The system aims to optimize station operations and improve on train intelligent control has mainly explored the use of AI algorithms to achieve autonomous control of trains, improve energy efficiency, and reduce waiting times for passengers. Deng et al. (2019) proposed an intelligent control system for urban rail transit vehicles that uses AI algorithms to optimize train dispatching and reduce energy , studies on intelligent passenger travel services aim to provide personalized and accurate travel solutions to passengers by combining AI technology and big data analysis. Zhou et al. (2022) developed a personalized travel recommendation system for rail transit passengers based on AI and big data. The system provides customized travel recommendations to millions of passengers based on their travel history, user profiles, and traffic the many benefits of AI technology in urban rail transit, several challenges still need to be overcome. The complexity of AI algorithms, system safety, and data privacy protection are some of the main limitations of previous studies. Future research should focus on these issues to ensure the safe and effective application of AI technology in urban rail future research, we intend to explore the feasibility and effectiveness of the application of deep learning algorithms in urban rail transit. Specifically, we will focus on the use of deep learning algorithms to optimize train schedules, reduce wait times, and improve overall operational efficiency.

Reference

Deng, J. B., Xiao, C. L., & Wang, J. L. (2019). Urban Rail Transit Intelligent Management Based on Artificial Intelligence.Acta Automatica Sinica, 45, 1808-1818.

Wang, L. X., Zhang, D. Z., & Huang, H. S. (2020). Application of Artificial Intelligence in Urban Rail Transit.Journal of Earth Sciences, 50, 981-988.

Zhou, Y., Wang, Y. N., & Zhang, J. (2022). Personalized Travel Recommendation for Rail Transit Passengers Based on AI and Big Data.IEEE Transactions on Intelligent Transportation Systems, 23, 427-436.

英语综述的格式范文 第8篇

Stigma of Mental Illness

Stigmatization, a kind of social rejection, is a big challenge to the mentally ill. They are rejected by people because of the label they carry or that their behaviors indicate that they belong to a certainlabeledgroup. However, many studies have proved that stigmatization of the mentally ill is caused by the public’s belief in myths about the dangerousness of the mentally ill and exposing those myths can reduce stigmatization. Pescosolido & Tuch (2000) examined the effects of descriptions of the targets’behavior, causalattributionsabout the source of the behavior, the target’s perceived dangerousness, labeling and participants’socio-demographic characteristics. Their studies found that 20% of the participants labeled a target described with depressed symptoms as having a mental illness; 37% would be unwilling to interact with the depressed persons; and 33% felt that the depressed person would do violence to others. Thus, a common respond to the mentally ill are rejected and fear of violence; What are major causes for the rejection and fear, and can they be reduced? Corrigan, Rowan, Green, Lundin, River, Uphoff-Wasowski, White and Kubiak (2002) conducted two studies analyzing the causal processes in contact, fear and rejection. They designed two models to account for stigmatizing reactions and administered a questionnaire to 216 communitycolleges. This questionnaire contained items which would allow the examination of their attitudes to a mental patient when the variables of two models were involved and manipulated: personal responsibility and perceived dangerousness. Their studies demonstrated that contact with the mentally ill caused less rejection and fear. This finding is verified by Alexander and Link (2003). They conducted a telephone survey and found that, as a participant’s own life contact with mentally ill individuals increased, participants were both less likely to perceive a target mentally ill individual as physically dangerous and less likely to desire social distance from the target. This relationship remained after controlling for demographic and confound variables, such as gender, ethnicity, education, income and political conservatism. They also found that any type of contact---- with a friend, a spouse, a family member, a work contact, or a contact in a public place ---- with mentally ill individuals reduced perceptions of dangerousness of the target.

Reference

Alexander, ; Link, .(2003). The impact of contact on stigmatizing attitudes towards people with mental illness.Journal of Mental Health, 12, 271-289.

Corrigan, P. W., Rowan, D., Green, A., Lundin, R., River, P., Uphoff-Wasowski, K., White, K., & Kubiak, M. A. (2002). Challenging two mental illness stigmas: Personality responsibility and dangerousness.Schizophrenia Bulletin, 28, 293-309.

Martin, J. K., Pescosolido, B. A., & Tuch, S. A. (2002). Of fear and loathing. The role of“disturbing behavior”labels, and causal attributions in shaping public attitudes toward people with mental illness.Journal of Health and Social Behavior, 41, 208-223.

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