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Journal articles on the topic 'AI-2'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Cheng, Jun. "(Invited) Towards AI² Electrochemistry(AI² = AI × ab initio)." ECS Meeting Abstracts MA2024-02, no. 61 (2024): 4097. https://doi.org/10.1149/ma2024-02614097mtgabs.

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It is known that electrode materials undergo dynamic structural changes at in-situ/in-operando conditions. Yet, the majority of computational studies only consider the static structures of electrode materials. When the materials are submerged in liquid solution, dynamic solvation effects are often completely ignored, or treated with dielectric continuum models, often lacking validation. The situations are about to change. Thanks to the latest development of in-situ experimental techniques and state-of-the-art computational methods, dynamics of electrode materials has recently drawn more and mo

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Matsuo, Tatsuru, Masaru Todoriki, and Shin-ichiro Tago. "2. Explainable AI." Journal of The Institute of Image Information and Television Engineers 74, no. 1 (2020): 30–34. http://dx.doi.org/10.3169/itej.74.30.

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Janssen, T. "SP-0541 AI^2: When the AI checks the AI." Radiotherapy and Oncology 182 (May 2023): S427. http://dx.doi.org/10.1016/s0167-8140(23)67400-6.

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Nitta, Hajime Yoshinoand Katsumi. "AI and Law (2)." Journal of Advanced Computational Intelligence and Intelligent Informatics 2, no. 1 (1998): 1. http://dx.doi.org/10.20965/jaciii.1998.p0001.

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In the last issue (Vol.1, No.2), we introduced the Legal Expert System (LES) project led by Hajime Yoshino of Meiji Gakuin University, presenting six papers on the LES project. Those papers were mainly related to higher order reasoning systems such as ase-based reasoning, abductive and inductive logic programming, nonmonotonic reasoning, and analogical reasoning. The objective of the LES project was to develop a legal expert system effective for use by lawyers, so the project covers inference mechanisms, analysis of legal knowledge, and user interfaces. In this second special issue on the LES

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Eaton, Eric, and Amy McGovern. "Welcome to AI Matters, volume 2, issue 2." AI Matters 2, no. 2 (2016): 3. http://dx.doi.org/10.1145/2847557.2847558.

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ปากดี, สุพัตรา, та ธีระภาพ เพชรมาลัยกุล. "แนวทางการประยุกต์ใช้ปัญญาประดิษฐ์ (AI) ในการจัดการศึกษา สังกัดสำนักงานเขตพื้นที่การศึกษามัธยมศึกษากรุงเทพมหานคร เขต 2". Interdisciplinary Academic and Research Journal 4, № 6 (2024): 555–72. https://doi.org/10.60027/iarj.2024.278760.

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ภูมิหลังและวัตถุประสงค์: การจัดการศึกษาเป็นกระบวนการสำคัญในการพัฒนาผู้เรียนให้มีคุณภาพและทักษะในศตวรรษที่ 21 เพื่อตอบสนองต่อการเปลี่ยนแปลงของโลกยุคดิจิทัล การนำเทคโนโลยีดิจิทัลมาประยุกต์ใช้ในด้านการศึกษาจะช่วยเพิ่มประสิทธิภาพการเรียนรู้ของผู้เรียน ปัญญาประดิษฐ์เป็นอีกหนึ่งเทคโนโลยีที่สามารถยกระดับคุณภาพการศึกษา ช่วยปรับปรุงกระบวนการเรียนการสอนและการบริหารจัดการงานต่าง ๆ ในสถานศึกษา วิจัยครั้งนี้จึงมีวัตถุประสงค์เพื่อ 1) เพื่อศึกษาการประยุกต์ใช้ปัญญาประดิษฐ์ (AI) ในการจัดการศึกษา สังกัดสํานักงานเขตพื้นที่การศึกษามัธยมศึกษากรุงเทพมหานคร เขต 2 2) เพื่อวิเคราะห์และเสนอแนวทางการประยุกต์ใช้ปัญญาประด

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Karpatne, Anuj, and Ziyu Yao. "Welcome to AI matters 8(2)." AI Matters 8, no. 2 (2022): 3. http://dx.doi.org/10.1145/3557785.3557786.

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Welcome to the second issue of this year's AI Matters Newsletter. We open with a conference report by Louise Dennis, followed by a summary of events happening during 2022-2023 by Dilini Samarasinghe. This third article is a collection of responses to blue sky questions about AI education from the AAAI/ACM SIGAI New and Future AI Educator Program. Finally, the last issue by Register et al. discusses the pillars of joy and justice for AI, Machine Learning, and Data Science education.

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Pereira, Catarina S., J. Randall McAuley, Michiko E. Taga, Karina B. Xavier, and Stephen T. Miller. "Sinorhizobium meliloti, a bacterium lacking the autoinducer-2 (AI-2) synthase, responds to AI-2 supplied by other bacteria." Molecular Microbiology 70, no. 5 (2008): 1223–35. http://dx.doi.org/10.1111/j.1365-2958.2008.06477.x.

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Leite, Iolanda, and Anuj Karpatne. "Welcome to AI Matters 7(2)." AI Matters 7, no. 2 (2021): 3–4. http://dx.doi.org/10.1145/3478369.3478370.

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Welcome to the second issue of this year's AI Matters Newsletter. We start with a report on upcoming SIGAI Events by Dilini Samarasinghe and Conference reports by Louise Dennis, our conference coordination officer. In our regular Education column, Carolyn Rosé discusses the role of AI in education in a post-pandemic reality. We then bring you our regular Policy column, where Larry Medsker covers interesting and timely discussions on AI policy, for example whether governments should play a role in reducing algorithmic bias. This issue closes with an article contribution from Li Dong, one of the

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McGovern, Amy, and Iolanda Leite. "Welcome to AI matters 5(2)." AI Matters 5, no. 2 (2019): 3. http://dx.doi.org/10.1145/3340470.3340471.

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Leite, Iolanda, and Anuj Karpatne. "Welcome to AI matters 6(2)." AI Matters 6, no. 2 (2021): 3–4. http://dx.doi.org/10.1145/3430581.3430582.

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Wagstaff, Kiri. "Welcome to AI Matters Issue 2." AI Matters 1, no. 2 (2014): 3. http://dx.doi.org/10.1145/2685328.2685329.

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Yao, Ziyu. "Welcome to AI Matters 10(2)." AI Matters 10, no. 2 (2024): 3. https://doi.org/10.1145/3694712.3694713.

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Welcome to the second issue of this year's AI Matters Newsletter. In this issue, Louise A. Dennis will present a summary of recent events sponsored or run in cooperation with ACM SIGAI. This issue also includes a article from Pavan Ghantasala, a lead data scientist from Wells Fargo/Purdue University, discussing their findings of using predictive models to retain customers in the mobile gaming market.

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Pereira, Catarina S., Jessica A. Thompson, and Karina B. Xavier. "AI-2-mediated signalling in bacteria." FEMS Microbiology Reviews 37, no. 2 (2013): 156–81. http://dx.doi.org/10.1111/j.1574-6976.2012.00345.x.

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Esuli, Andrea, Fabrizio Sebastiani, and Ahmed Abasi. "AI and Opinion Mining, Part 2." IEEE Intelligent Systems 25, no. 4 (2010): 72–79. http://dx.doi.org/10.1109/mis.2010.94.

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McGovern, Amy. "Welcome to AI matters 4(2)." AI Matters 4, no. 2 (2018): 3–4. http://dx.doi.org/10.1145/3236644.3236645.

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Yao, Ziyu, and Anuj Karpatne. "Welcome to AI Matters 9(2)." AI Matters 9, no. 2 (2023): 3. http://dx.doi.org/10.1145/3609468.3609469.

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Welcome to the second issue of this year's AI Matters Newsletter. As usual, we will start the issue with a conference report by Louise A. Dennis. Following it, we are very excited to present three award-winning or candidacy papers by Wijmans et al. (ICLR 2023 Outstanding Paper), Ma et al. (SIGKDD 2022 Best Paper), and Li, Liu, and Wu (CVPR 2023, Award Candidate). The papers cover a range of intriguing topics, such as the formation of spatial memory in navigation agents, causal modeling with hypergraphs, and full-body motion estimation from egocentric videos. Finally, we end this issue by a col

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Sakurai, Emiko, and Murakami Ryu. "Ai to gensō no fashizumu. 2 vols." World Literature Today 63, no. 1 (1989): 165. http://dx.doi.org/10.2307/40145289.

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OSADA, Junichi, Toshihiro NISHIZAWA, and Shinichi FUKUZUMUI. "1B3-2 UI/UX Design for AI." Japanese Journal of Ergonomics 60, Supplement (2024): 1B3–2–1B3–2. http://dx.doi.org/10.5100/jje.60.1b3-2.

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Marques, João C., Pedro Lamosa, Caitlin Russell, et al. "Processing the Interspecies Quorum-sensing Signal Autoinducer-2 (AI-2)." Journal of Biological Chemistry 286, no. 20 (2011): 18331–43. http://dx.doi.org/10.1074/jbc.m111.230227.

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Pereira, Catarina S., Anna K. de Regt, Patrícia H. Brito, Stephen T. Miller, and Karina B. Xavier. "Identification of Functional LsrB-Like Autoinducer-2 Receptors." Journal of Bacteriology 191, no. 22 (2009): 6975–87. http://dx.doi.org/10.1128/jb.00976-09.

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ABSTRACT Although a variety of bacterial species have been reported to use the interspecies communication signal autoinducer-2 (AI-2) to regulate multiple behaviors, the molecular mechanisms of AI-2 recognition and signal transduction remain poorly understood. To date, two types of AI-2 receptors have been identified: LuxP, present in Vibrio spp., and LsrB, first identified in Salmonella enterica serovar Typhimurium. In S. Typhimurium, LsrB is the ligand binding protein of a transport system that enables the internalization of AI-2. Here, using both sequence analysis and structure prediction,

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Shao, Hanjuan, Deanna James, Richard J. Lamont, and Donald R. Demuth. "Differential Interaction of Aggregatibacter (Actinobacillus) actinomycetemcomitans LsrB and RbsB Proteins with Autoinducer 2." Journal of Bacteriology 189, no. 15 (2007): 5559–65. http://dx.doi.org/10.1128/jb.00387-07.

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ABSTRACT Our previous studies showed that the Aggregatibacter actinomycetemcomitans RbsB protein interacts with cognate and heterologous autoinducer 2 (AI-2) signals and suggested that the rbsDABCK operon encodes a transporter that may internalize AI-2 (D. James et al., Infect. Immun. 74:4021-4029, 2006.). However, A. actinomycetemcomitans also possesses genes related to the lsr operon of Salmonella enterica serovar Typhimurium which function to import AI-2. Here, we show that A. actinomycetemcomitans LsrB protein competitively inhibits the interaction of the Vibrio harveyi AI-2 receptor (LuxP

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Yu, Jing, Melissa L. Madsen, Michael D. Carruthers, et al. "Analysis of Autoinducer-2 Quorum Sensing in Yersinia pestis." Infection and Immunity 81, no. 11 (2013): 4053–62. http://dx.doi.org/10.1128/iai.00880-13.

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ABSTRACTThe autoinducer-2 (AI-2) quorum-sensing system has been linked to diverse phenotypes and regulatory changes in pathogenic bacteria. In the present study, we performed a molecular and biochemical characterization of the AI-2 system inYersinia pestis, the causative agent of plague. In strain CO92, the AI-2 signal is produced in aluxS-dependent manner, reaching maximal levels of 2.5 μM in the late logarithmic growth phase, and both wild-type and pigmentation (pgm) mutant strains made equivalent levels of AI-2. Strain CO92 possesses a chromosomallsrlocus encoding factors involved in the bi

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Wang, Yang, Li Yi, Zhicheng Zhang, Hongjie Fan, Xiangchao Cheng, and Chengping Lu. "Overexpression ofluxSCannot Increase Autoinducer-2 Production, Only Affect the Growth and Biofilm Formation inStreptococcus suis." Scientific World Journal 2013 (2013): 1–6. http://dx.doi.org/10.1155/2013/924276.

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LuxS/AI-2 quorum sensing (QS) system involves the production of cell signaling molecules vialuxS-based autoinducer-2 (AI-2). LuxS has been reported to plays critical roles in regulating various behaviors of bacteria. AI-2 is a byproduct of the catabolism of S-adenosylhomocysteine (SAH) performed by the LuxS and Pfs enzymes. In our previous study, the function of LuxS in AI-2 production was verified inStreptococcus suis(SS). Decreased levels of SS biofilm formation and host-cell adherence as well as an inability to produce AI-2 were observed in bacteria having aluxSmutant gene. In this study, t

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James, DeAnna, HanJuan Shao, Richard J. Lamont, and Donald R. Demuth. "The Actinobacillus actinomycetemcomitans Ribose Binding Protein RbsB Interacts with Cognate and Heterologous Autoinducer 2 Signals." Infection and Immunity 74, no. 7 (2006): 4021–29. http://dx.doi.org/10.1128/iai.01741-05.

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ABSTRACT Autoinducer 2 (AI-2) produced by the oral pathogen Actinobacillus actinomycetemcomitans influences growth of the organism under iron limitation and regulates the expression of iron uptake genes. However, the cellular components that mediate the response of A. actinomycetemcomitans to AI-2 have not been fully characterized. Analysis of the complete genome sequence of A. actinomycetemcomitans (www.oralgen.lanl.gov ) indicated that the RbsB protein was related to LuxP, the AI-2 receptor of Vibrio harveyi. To determine if RbsB interacts with AI-2, the bioluminescence of the reporter strai

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Sorrell, Vincent L. "What Does AI Mean to You? – Part 2." CASE 6, no. 5 (2022): 199–200. http://dx.doi.org/10.1016/j.case.2022.05.001.

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Branca, Malorye Allison. "Front Row Series 2: Andy Beck on AI." GEN Edge 4, no. 1 (2022): 267–68. http://dx.doi.org/10.1089/genedge.4.1.44.

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Branca, Malorye Allison. "Front Row Series 2: Daphne Koller on AI." GEN Edge 4, no. 1 (2022): 269–70. http://dx.doi.org/10.1089/genedge.4.1.45.

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Domnich, Marharyta, Indrek Sünter, Heido Trofimov, et al. "KappaMask: AI-Based Cloudmask Processor for Sentinel-2." Remote Sensing 13, no. 20 (2021): 4100. http://dx.doi.org/10.3390/rs13204100.

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The Copernicus Sentinel-2 mission operated by the European Space Agency (ESA) provides comprehensive and continuous multi-spectral observations of all the Earth’s land surface since mid-2015. Clouds and cloud shadows significantly decrease the usability of optical satellite data, especially in agricultural applications; therefore, an accurate and reliable cloud mask is mandatory for effective EO optical data exploitation. During the last few years, image segmentation techniques have developed rapidly with the exploitation of neural network capabilities. With this perspective, the KappaMask pro

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Eaton, Eric, and Amy McGovern. "Welcome to AI Matters, volume 2, issue 3." AI Matters 2, no. 3 (2016): 3. http://dx.doi.org/10.1145/2911172.2911173.

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Bansal, Ronak Umesh, Aditya Krishna Vangipurapu, Swarup Shivaji Satav, Ankush Nag, and Rashmi Phalnikar. "HealthWise: AI-Powered Medical Chatbot with Llama 2." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 11 (2024): 1–6. http://dx.doi.org/10.55041/ijsrem38983.

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This research paper presents the development of a medical chatbot utilizing cutting- edge technologies, specifically the Langchain framework and the Llama 2 language model, in conjunction with the PineCone database for efficient data storage and retrieval. The primary objective of this project is to create an intelligent conversational agent capable of providing reliable medical information, assisting users in symptom assessment, and guiding them towards appropriate healthcare resources. The integration of Langchain facilitates seamless interaction between the user interface and the underlying

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Porter-O'Grady, Tim. "What can AI do for you? part 2." Nursing Management 56, no. 2 (2025): 56. https://doi.org/10.1097/nmg.0000000000000213.

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De Keersmaecker, Sigrid C. J., Kathleen Sonck, and Jos Vanderleyden. "Let LuxS speak up in AI-2 signaling." Trends in Microbiology 14, no. 3 (2006): 114–19. http://dx.doi.org/10.1016/j.tim.2006.01.003.

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Eaton, Eric, and Amy McGovern. "Welcome to AI matters, volume 3, issue 2." AI Matters 3, no. 2 (2017): 3. http://dx.doi.org/10.1145/3098888.3098889.

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Li, Min, Amer E. Villaruz, Viveka Vadyvaloo, Daniel E. Sturdevant, and Michael Otto. "AI-2-dependent gene regulation in Staphylococcus epidermidis." BMC Microbiology 8, no. 1 (2008): 4. http://dx.doi.org/10.1186/1471-2180-8-4.

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Wang, Yang, Yuxin Wang, Liyun Sun, Daniel Grenier, and Li Yi. "The LuxS/AI-2 system of Streptococcus suis." Applied Microbiology and Biotechnology 102, no. 17 (2018): 7231–38. http://dx.doi.org/10.1007/s00253-018-9170-7.

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Hegde, M., D. L. Englert, S. Schrock, et al. "Chemotaxis to the Quorum-Sensing Signal AI-2 Requires the Tsr Chemoreceptor and the Periplasmic LsrB AI-2-Binding Protein." Journal of Bacteriology 193, no. 3 (2010): 768–73. http://dx.doi.org/10.1128/jb.01196-10.

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Khodayari, Khosro, Roy J. Smith, and Howard L. Black. "Red Rice (Oryza sativa) Control with Herbicide Treatments in Soybeans (Glycine max)." Weed Science 35, no. 1 (1987): 127–29. http://dx.doi.org/10.1017/s0043174500026916.

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Preplant incorporation of alachlor [2-chloro-N-(2,6-diethylphenyl)-N-(methoxymethyl)acetamide] at 3.6 kg ai/ha, metolachlor [2-chloro-N-(2-ethyl-6-methylphenyl)-N-(2-methoxy-1-methylethyl)acetamide] at 2.2 kg ai/ha, and tank mixtures of imazaquin {2-[4,5-dihydro-4-methyl-4-(1-methylethyl)-5-oxo-1H-imidazol-2-yl]-3-quinolinecarboxylic acid} with either alachlor at 0.14 + 2.2 kg ai/ha or metolachlor at 0.14 + 1.1 kg ai/ha controlled >90% red rice (Oryza sativaL. # ORYSA) in soybeans [Glycine max(L.) Merr.]. Also, FMC-57020 [2-(2-chlorophenyl)methyl-4,4-dimethyl-3-isoxazolidinone] at 1.7 kg ai

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Wanaldi, Wanaldi, Yustinus Eko Sulistio, and Johan Setiawan. "DynaBot: Dynamic Dota 2 Bot." IJNMT (International Journal of New Media Technology) 7, no. 1 (2020): 11–17. http://dx.doi.org/10.31937/ijnmt.v7i1.1695.

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   This research was conducted to find out whether the Dynamic Scripting method that has been used before only on zeus characters can be generalized to be used on other characters on the Dota 2 game. Dynamic scripting works by using the rulebase where the rulebase contains actions that determine the actions performed by Artificial Intelligence (AI). In addition, some adjustments have been made to existing methods. To find out whether the performance of a generalized and adjusted model is better than the previous model, a test has been conducted where AI is made with dynamic s

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Liu, Yirong, Huizhi Hu, and Feng Luo. "Roles of autoinducer-2 mediated quorum sensing in wastewater treatment." Water Science and Technology 84, no. 4 (2021): 793–809. http://dx.doi.org/10.2166/wst.2021.278.

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Abstract Quorum sensing (QS) is considered to be a promising regulation method for biological wastewater treatment (WWT) due to its regulation in extracellular substances (EPS) production, biofilm formation, granulation, colonization and bacterial activity and stability. Recently, autoinducer-2 (AI-2), a kind of interspecies communication QS signal molecule, is being increasingly reported for its roles in regulating bacterial gene expression and aggregation. Consequently, AI-2 mediated QS system is considered as a promising regulatory approach in WWT processes. This article systematically revi

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Jang, Young Eun, Sung Mi Ahn, Sung Ji Park, Hyun Ji Kwon, and Ga Yeon Ko. "Analysis of Trends in Domestic Nursing Education Research Applying Artificial Intelligence." Korean Society for Health and Nursing Convergence 1, no. 1 (2024): 51–58. http://dx.doi.org/10.69663/jkshnc.2024.1.1.51.

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Purpose : The purpose of this study was to analyze the trends in domestic nursing education research applying artificial intelligence (AI) in South Korea by examining recent research trends and suggesting future research directions. Method : This study conducted a literature review to analyze the trends in research on AI-applied domestic nursing education published in domestic academic journals. Results : A total of 55 keywords were identified based on the titles and major keywords from 12 literature sources, including multiple entries. The major keywords included AI (12, 21.9%), nursing stude

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Yunefri, Yogi, Yogi Ersan Fadrial, Sutejo, and Muhamad Sadar. "LITERASI DIGITAL DALAM PENGEMBANGAN PEMBELAJARAN ARTIFICIAL INTELLIGENCE BAGI GURU SMKN 2 PINGGIR." J-COSCIS : Journal of Computer Science Community Service 4, no. 2 (2024): 231–37. http://dx.doi.org/10.31849/jcoscis.v4i2.19135.

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Guru di SMKN 2 Pinggir memerlukan literasi digital yang tinggi untuk mengintegrasikan AI dalam pembelajaran dan mengembangkan media pembelajaran yang relevan dengan teknologi terkini. Pengabdian masyarakat dapat membantu guru-guru ini memahami, mengembangkan, dan mengadopsi AI dalam pendidikan. Melalui analisis situasi mendalam, solusi yang tepat dapat dirancang sesuai kebutuhan mereka. Keterbatasan pengetahuan dan keterampilan dalam literasi digital dan penggunaan AI adalah masalah utama yang dihadapi. Dengan solusi yang dirancang khusus, pengabdian masyarakat dapat membantu guru SMKN 2 Pingg

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Krin, Evelyne, Nesrine Chakroun, Evelyne Turlin, et al. "Pleiotropic Role of Quorum-Sensing Autoinducer 2 in Photorhabdus luminescens." Applied and Environmental Microbiology 72, no. 10 (2006): 6439–51. http://dx.doi.org/10.1128/aem.00398-06.

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ABSTRACT Bacterial virulence is an integrative process that may involve quorum sensing. In this work, we compared by global expression profiling the wild-type entomopathogenic Photorhabdus luminescens subsp. laumondii TT01 to a luxS-deficient mutant unable to synthesize the type 2 quorum-sensing inducer AI-2. AI-2 was shown to regulate more than 300 targets involved in most compartments and metabolic pathways of the cell. AI-2 is located high in the hierarchy, as it controls the expression of several transcriptional regulators. The regulatory effect of AI-2 appeared to be dose dependent. The l

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Helcman, Martin, Karel Šmejkal, Marie Čulenová, Tibor Béres, and Jakub Treml. "Natural Phenolics Disrupt Microbial Communication by Inhibiting Quorum Sensing." Microorganisms 13, no. 2 (2025): 287. https://doi.org/10.3390/microorganisms13020287.

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Quorum sensing, a bacterial cell-to-cell communication mechanism, plays a key role in bacterial virulence and biofilm formation. Targeting quorum-sensing pathways represents a promising strategy for the development of novel antibacterial agents. This study evaluated the anti-quorum-sensing activities of 18 natural compounds, including cannabinoids, arylbenzofurans, flavonoids, caffeine, and chlorogenic acid, using the luminescent biosensor strain Vibrio harveyi MM30. V. harveyi MM30, a mutant strain deficient in the production of autoinducer-2 (AI-2) but responsive to exogenous AI-2, was used

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BLANA, VASILIKI A., AGAPI I. DOULGERAKI, and GEORGE-JOHN E. NYCHAS. "Autoinducer-2–like Activity in Lactic Acid Bacteria Isolated from Minced Beef Packaged under Modified Atmospheres." Journal of Food Protection 74, no. 4 (2011): 631–35. http://dx.doi.org/10.4315/0362-028x.jfp-10-276.

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Fifteen fingerprints (assigned to Leuconostoc spp., Leuconostoc mesenteroides, Weissella viridescens, Leuconostoc citreum, and Lactobacillus sakei) of 89 lactic acid bacteria (LAB) isolated from minced beef stored under modified atmospheres at various temperatures were screened for their ability to exhibit autoinducer-2 (AI-2)–like activity under certain growth conditions. Cell-free meat extracts (CFME) were collected at the same time as the LAB isolates and tested for the presence of AI-2–like molecules. All bioassays were conducted using the Vibrio harveyi BAA-1117 (sensor 1−, sensor 2+) bio

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Armanto, Hendrawan, Harits Ar Rosyid, Muladi Muladi, and Gunawan Gunawan. "Utilization of the Particle Swam Optimization Algorithm in Game Dota 2." Register: Jurnal Ilmiah Teknologi Sistem Informasi 10, no. 2 (2024): 116–26. https://doi.org/10.26594/register.v10i2.3503.

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Dota 2, a Multiplayer Online Battle Arena game, is widely popular among gamers, with many attempting to create efficient artificial intelligence that can play like a human. However, current AI technology still falls short in some areas, despite some AI models being able to play decently. To address this issue, researchers continue to explore ways to enhance AI performance in Dota 2. This study focuses on the process of developing artificial intelligence code in Dota 2 and integrating the particle swarm optimization algorithm into Dota 2 Team's Desire. Although particle swarm optimization is an

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LU, LINGENG, MICHAEL E. HUME, and SURESH D. PILLAI. "Autoinducer-2–like Activity Associated with Foods and Its Interaction with Food Additives." Journal of Food Protection 67, no. 7 (2004): 1457–62. http://dx.doi.org/10.4315/0362-028x-67.7.1457.

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The autoinducer-2 (AI-2) molecule produced by bacteria as part of quorum sensing is considered to be a universal inducer signal in bacteria because it reportedly influences gene expression in a variety of both gram-negative and gram-positive bacteria. The objective of this study was to determine whether selected fresh produce and processed foods have AI-2–like activity and whether specific food additives can act as AI-2 mimics and result in AI-2–like activity. The luminescence-based response of the reporter strain Vibrio harveyi BB170 was used as the basis for determining AI-2 activity in the

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Song, Sooyeon, and Thomas K. Wood. "The Primary Physiological Roles of Autoinducer 2 in Escherichia coli Are Chemotaxis and Biofilm Formation." Microorganisms 9, no. 2 (2021): 386. http://dx.doi.org/10.3390/microorganisms9020386.

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Autoinducer 2 (AI-2) is a ubiquitous metabolite but, instead of acting as a “universal signal,” relatively few phenotypes have been associated with it, and many scientists believe AI-2 is often a metabolic byproduct rather than a signal. Here, the aim is to present evidence that AI-2 influences both biofilm formation and motility (swarming and chemotaxis), using Escherichia coli as the model system, to establish AI-2 as a true signal with an important physiological role in this bacterium. In addition, AI-2 signaling is compared to the other primary signal of E. coli, indole, and it is shown th

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SONI, KAMLESH A., PALMY JESUDHASAN, MARTHA CEPEDA, et al. "Identification of Ground Beef–Derived Fatty Acid Inhibitors of Autoinducer-2–Based Cell Signaling." Journal of Food Protection 71, no. 1 (2008): 134–38. http://dx.doi.org/10.4315/0362-028x-71.1.134.

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Autoinducer-2 (AI-2) molecules are used by several microorganisms to modulate various processes, including bioluminescence, biofilm formation, and virulence expression. Certain food matrices, including ground beef extracts, possess compounds capable of inhibiting AI-2 activity. In the present study, we identified and characterized these AI-2 inhibitors from ground beef extract using hexane solvent extraction and gas chromatography. Gas chromatographic analysis revealed the presence of several fatty acids such as palmitic acid (C16:0), stearic acid (C18:0), oleic acid (C18:1ω9), and linoleic ac

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Ren, Pei-Fang, Chen-Ying Yu, Li-Li Zhu, Wei-Hua Yang, and Ye Shen. "Evaluation of a novel deep learning based screening system for pathologic myopia." International Journal of Ophthalmology 16, no. 9 (2023): 1417–23. http://dx.doi.org/10.18240/ijo.2023.09.07.

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AIM: To evaluate the clinical application value of the artificial intelligence assisted pathologic myopia (PM-AI) diagnosis model based on deep learning. METHODS: A total of 1156 readable color fundus photographs were collected and annotated based on the diagnostic criteria of Meta-pathologic myopia (PM) (2015). The PM-AI system and four eye doctors (retinal specialists 1 and 2, and ophthalmologists 1 and 2) independently evaluated the color fundus photographs to determine whether they were indicative of PM or not and the presence of myopic choroidal neovascularization (mCNV). The performance

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