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Journal articles on the topic 'RAG Model'

Author: Grafiati

Published: 7 June 2025

Last updated: 16 July 2025

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1

Miot, Charline, Rahul Arya, Thomas Burn, Edward M. Behrens, and Craig Bassing. "Elucidating roles of the Rag1 N-terminus and RAG DSBs in shaping the cellular response to TCRa recombination." Journal of Immunology 204, no. 1_Supplement (2020): 80.8. http://dx.doi.org/10.4049/jimmunol.204.supp.80.8.

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Abstract The RAG1/RAG2 (RAG) endonuclease generates lymphocyte antigen receptor gene diversity via V(D)J recombination. The large numbers of V, D, and J segments and inherent imprecision in repair of RAG DNA double strand breaks (DSBs) together establish a vast diversity of antigen receptor specificities, including self-reactive receptors. Mechanisms have evolved to negatively select self-reactive cells and inhibit autoimmunity. In humans, deletion of the RAG1 N-terminus causes Omenn Syndrome, a fatal immunodeficiency with ab T cell-based autoimmunity. We discovered impaired negative selection

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Nagawa, Fumikiyo, Kei-ichiro Ishiguro, Akio Tsuboi, et al. "Footprint Analysis of the RAG Protein Recombination Signal Sequence Complex for V(D)J Type Recombination." Molecular and Cellular Biology 18, no. 1 (1998): 655–63. http://dx.doi.org/10.1128/mcb.18.1.655.

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ABSTRACT We have studied the interaction between recombination signal sequences (RSSs) and protein products of the truncated forms of recombination-activating genes (RAG) by gel mobility shift, DNase I footprinting, and methylation interference assays. Methylation interference with dimethyl sulfate demonstrated that binding was blocked by methylation in the nonamer at the second-position G residue in the bottom strand and at the sixth- and seventh-position A residues in the top strand. DNase I footprinting experiments demonstrated that RAG1 alone, or even a RAG1 homeodomain peptide, gave footp

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Mishra, Ankit, and Aniket Gupta. "Retrieval Augmented Generation (RAG) Model." International Journal of Research Publication and Reviews 6, no. 6 (2025): 4690–93. https://doi.org/10.55248/gengpi.6.0125.0635.

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4

Hall, Lucinda M. C., Stuart C. Fawell, Xiaoju Shi, et al. "Sequence Diversity and Antigenic Variation at the rag Locus of Porphyromonas gingivalis." Infection and Immunity 73, no. 7 (2005): 4253–62. http://dx.doi.org/10.1128/iai.73.7.4253-4262.2005.

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ABSTRACT The rag locus of Porphyromonas gingivalis W50 encodes RagA, a predicted tonB-dependent receptor protein, and RagB, a lipoprotein that constitutes an immunodominant outer membrane antigen. The low G+C content of the locus, an association with mobility elements, and an apparent restricted distribution in the species suggested that the locus had arisen by horizontal gene transfer. In the present study, we have demonstrated that there are four divergent alleles of the rag locus. The original rag allele found in W50 was renamed rag-1, while three novel alleles, rag-2 to rag-4, were found i

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Nagpure, Disha. "Advanced Multi Model RAG Application." International Journal of Scientific Research and Engineering Trends 10, no. 5 (2024): 2399–406. http://dx.doi.org/10.61137/ijsret.vol.10.issue5.305.

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Rogala, Kacper B., Xin Gu, Jibril F. Kedir, et al. "Structural basis for the docking of mTORC1 on the lysosomal surface." Science 366, no. 6464 (2019): 468–75. http://dx.doi.org/10.1126/science.aay0166.

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The mTORC1 (mechanistic target of rapamycin complex 1) protein kinase regulates growth in response to nutrients and growth factors. Nutrients promote its translocation to the lysosomal surface, where its Raptor subunit interacts with the Rag guanosine triphosphatase (GTPase)–Ragulator complex. Nutrients switch the heterodimeric Rag GTPases among four different nucleotide-binding states, only one of which (RagA/B•GTP–RagC/D•GDP) permits mTORC1 association. We used cryo–electron microscopy to determine the structure of the supercomplex of Raptor with Rag-Ragulator at a resolution of 3.2 angstrom

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Byrne, A., B. Beaton, and K. Wells. "224 CONSTRUCTION OF GENE TARGETING VECTORS FOR RAG-1 AND RAG-2." Reproduction, Fertility and Development 26, no. 1 (2014): 226. http://dx.doi.org/10.1071/rdv26n1ab224.

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Gene targeting in mammalian cells has become a routine technique and is currently used to study gene function, create biomedical models, and generate potential tissue sources for xenotransplantation. Severe combined immunodeficiency (SCID) is a condition characterised by the absence of T cells and a lack of B cell function. Severe combined immunodeficiency affects ~1 out of every 100 000 infants. Autosomal recessive SCID can occur due to a mutation within the recombination activating genes (RAG-1/RAG-2) that play a role in recombination of immunoglobulins and T-cell receptors. Gene targeting h

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Shi, Xiaoju, Shirley A. Hanley, Marie-Claire Faray-Kele, et al. "The rag Locus of Porphyromonas gingivalis Contributes to Virulence in a Murine Model of Soft Tissue Destruction." Infection and Immunity 75, no. 4 (2007): 2071–74. http://dx.doi.org/10.1128/iai.01785-06.

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ABSTRACT The rag locus of Porphyromonas gingivalis encodes a putative TonB-dependent outer membrane receptor, RagA, and a 55-kDa immunodominant antigen, RagB. Inactivation of either ragA or ragB prevented expression of both RagA and RagB. Both the ragA and ragB mutants were significantly less virulent than wild-type strains in a murine model of infection.

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Ji, Yanhong, Alicia J. Little, Joydeep K. Banerjee, et al. "Promoters, enhancers, and transcription target RAG1 binding during V(D)J recombination." Journal of Experimental Medicine 207, no. 13 (2010): 2809–16. http://dx.doi.org/10.1084/jem.20101136.

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V(D)J recombination assembles antigen receptor genes in a well-defined order during lymphocyte development. This sequential process has long been understood in the context of the accessibility model, which states that V(D)J recombination is regulated by controlling the ability of the recombination machinery to gain access to its chromosomal substrates. Indeed, many features of “open” chromatin correlate with V(D)J recombination, and promoters and enhancers have been strongly implicated in creating a recombinase-accessible configuration in neighboring chromatin. An important prediction of the a

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Efeyan, Alejo, and David M. Sabatini. "Nutrients and growth factors in mTORC1 activation." Biochemical Society Transactions 41, no. 4 (2013): 902–5. http://dx.doi.org/10.1042/bst20130063.

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Growth factors and nutrients regulate the mTORC1 [mammalian (or mechanistic) target of rapamycin complex 1] by different mechanisms. The players that link growth factors and mTORC1 activation have been known for several years and mouse models have validated its relevance for human physiology and disease. In contrast with the picture for growth factor signalling, the means by which nutrient availability leads to mTORC1 activation have remained elusive until recently, with the discovery of the Rag GTPases upstream of mTORC1. The Rag GTPases recruit mTORC1 to the outer lysosomal surface, where gr

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Silva-Barbosa, Suse D., Gillian S. Butler-Browne, James P. Di Santo, and Vincent Mouly. "Comparative Analysis of Genetically Engineered Immunodeficient Mouse Strains as Recipients for Human Myoblast Transplantation." Cell Transplantation 14, no. 7 (2005): 457–67. http://dx.doi.org/10.3727/000000005783982837.

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The development of an optimized animal model for the in vivo analysis of human muscle cells remains an important goal in the search of therapy for muscular dystrophy. Here we examined the efficiency of human myoblast xenografts in three distinct immunodeficient mouse models. We found that different conditioning regimes used to provoke host muscle regeneration (i.e., cardiotoxin versus cryodamage) had a marked impact on xenograft success. Tibialis anterior muscle of Rag2-, Rag-/γc-, and Rag-/γc-/C5- mice was treated by cardiotoxin or cryodamage, submitted to enzymatic digestion, and analyzed by

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Stankov, Svetlomir, and Desislava Velinova. "APPLICATION OF A LARGE LANGUAGE MODEL FOR AIR POLLUTION ANALYSIS BY USING A RAG SYSTEM." ENVIRONMENT. TECHNOLOGY. RESOURCES. Proceedings of the International Scientific and Practical Conference 2 (June 8, 2025): 345–51. https://doi.org/10.17770/etr2025vol2.8603.

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The paper examines the application of an LLM (Large Language Model) for analysing text information about air pollution using a RAG (Retrieval-Augmented Generation) system. It describes what RAG systems are and how they help increase the credibility of the text generated by LLM models (reducing their hallucinations) using information retrieval from reliable sources. An approach for the development of a RAG system suitable for answering queries on imported text documents by the user is presented. The realisation of this approach is the development of a RAG chatbot using the Python language, answ

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Hartono, Luluk Setiawati, Esther Irawati Setiawan, and Vrijraj Singh. "Retrieval Augmented Generation-Based Chatbot for Prospective and Current University Students." International Journal of Engineering, Science and Information Technology 5, no. 3 (2025): 268–77. https://doi.org/10.52088/ijesty.v5i3.951.

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Universities utilize chatbots as assistants for users, especially prospective and current students, to access information and answer questions with relevant answers. This study introduces a new approach to an open-source model-based QA system using Gemma2-2b-it by combining Retrieval Augmented Generation (RAG) and Fine-tuning (FT) techniques. Previously, some studies have focused on only one approach, but this study will combine and compare both methods separately. Raw conversation data from WhatsApp, the main university website, and university PDF documents are used. The Retrieval Augmented G

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Muratbaev, B.B. "EKONOMETRIK MODELLARDAN FOYDALANGAN HOLDA INNOVATSIYALARNING O'ZBEKISTON IQTISODIYOTINING BARQAROR RIVOJLANISHIGA TASIRINI PROGNOZLASH." Innovations in Science and Technologies 2, no. 3 (2025): 1059–66. https://doi.org/10.5281/zenodo.15106706.

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Maqolada iqtisodiyotning innovatsion rivojlanishiga tasir etuvchi asosiy rag’batlantiruvchi omillarning tasiri bah’olangan. Maqolaning maqsadi iqtisodiyotning innovatsion rivojlanishini prognozlashni modellashtirishdan iborat. Tadqiqotni o’tkazish metodologiyasi korrelyatsion-regression usullarga asoslangan va ulardan foydalanilgan. Ko’p omilli regression tah’lil yordamida moliyaviy investitsiyalarni h’isobga olgan holda iqtisodiyotning innovatsion rivojlanishiga rag’batlantiruvchi omillar tasirini prognozlashning iqtisodiy-statistik modeli hisoblab ch

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Elkin, Peter L., Guresh Mehta, Frank LeHouillier, et al. "Semantic Clinical Artificial Intelligence vs Native Large Language Model Performance on the USMLE." JAMA Network Open 8, no. 4 (2025): e256359. https://doi.org/10.1001/jamanetworkopen.2025.6359.

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ImportanceLarge language models (LLMs) are being implemented in health care. Enhanced accuracy and methods to maintain accuracy over time are needed to maximize LLM benefits.ObjectiveTo evaluate whether LLM performance on the US Medical Licensing Examination (USMLE) can be improved by including formally represented semantic clinical knowledge.Design, Setting, and ParticipantsThis comparative effectiveness research study was conducted between June 2024 and February 2025 at the Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo

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Siriwardhana, Shamane, Rivindu Weerasekera, Elliott Wen, Tharindu Kaluarachchi, Rajib Rana, and Suranga Nanayakkara. "Improving the Domain Adaptation of Retrieval Augmented Generation (RAG) Models for Open Domain Question Answering." Transactions of the Association for Computational Linguistics 11 (2023): 1–17. http://dx.doi.org/10.1162/tacl_a_00530.

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Abstract Retrieval Augment Generation (RAG) is a recent advancement in Open-Domain Question Answering (ODQA). RAG has only been trained and explored with a Wikipedia-based external knowledge base and is not optimized for use in other specialized domains such as healthcare and news. In this paper, we evaluate the impact of joint training of the retriever and generator components of RAG for the task of domain adaptation in ODQA. We propose RAG-end2end, an extension to RAG that can adapt to a domain-specific knowledge base by updating all components of the external knowledge base during training.

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Kaif, Mohd, Sanskar Sharma, and Dr Sadhana Rana. "Gemini MultiPDF Chatbot: Multiple Document RAG Chatbot using Gemini Large Language Model." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (2024): 24–30. http://dx.doi.org/10.22214/ijraset.2024.61195.

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Abstract: The Gemini MultiPDF Chatbot represents a groundbreaking advancement in natural language processing (NLP) by integrating Retrieval-Augmented Generation (RAG) techniques with the Gemini Large Language Model. This innovative chatbot is designed to handle multiple document retrieval and generation tasks, leveraging the extensive knowledge base of the Gemini model. By harnessing RAG methods, the chatbot enhances its ability to acquire, comprehend, and generate responses across diverse knowledge sources contained within multiple PDF documents. The integration of Gemini's powerful language

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Yang, Chi Bok, and Yang Sok Kim. "Implementation of Retrieval Augmented Generation (RAG) Model Using LLM: A RapidMiner-Based Approach." Korean Institute of Smart Media 14, no. 2 (2025): 34–42. https://doi.org/10.30693/smj.2025.14.2.34.

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Generative AI technology, driven by Large Language Models (LLMs), is being increasingly utilized to overcome existing limitations. Retrieval-Augmented Generation (RAG) has emerged as an effective approach to reduce hallucination in LLMs by leveraging up-to-date and domain-specific knowledge beyond training data. However, most studies propose programming-based implementations. This research introduces a GUI-based RAG framework using RapidMiner, to construct RAG systems without programming proficiency. The methodology includes storing and retrieving embeddings with the Qdrant vector database and

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Vaibhav Fanindra Mahajan. "Retrieval-augmented generation: The technical foundation of intelligent AI Chatbots." World Journal of Advanced Research and Reviews 26, no. 1 (2025): 4093–99. https://doi.org/10.30574/wjarr.2025.26.1.1571.

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Retrieval-Augmented Generation (RAG) has emerged as a transformative approach in conversational AI technology, addressing fundamental limitations of traditional chatbot systems. This technical article explores the architecture, mechanisms, and advantages of RAG implementations. Traditional AI chatbots suffer from outdated knowledge bases, hallucination tendencies, and limited context awareness - constraints that RAG effectively overcomes by combining dynamic information retrieval with sophisticated text generation capabilities. The RAG framework operates through a multi-stage process encompass

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Bettridge, John T., Chan-Hyun Na, Akhilesh Pandey, and Stephen Desiderio. "Allosteric Regulation of V(D)J Recombination By Active Chromatin." Blood 128, no. 22 (2016): 3707. http://dx.doi.org/10.1182/blood.v128.22.3707.3707.

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Abstract V(D)J recombination, the process by which antigen receptor genes are assembled, is implicated in the pathogenesis of lymphoid malignancies. Rearrangement is initiated by the RAG recombinase, a heterotetrameric complex consisting of two subunits each of RAG-1 and RAG-2, which cleaves participating DNA segments at conserved signal sequences. The susceptibility of gene segments to undergo recombination is positively correlated with active chromatin marks, such as tri-methylation of histone H3 at lysine 4 (H3K4me3). RAG-2 interacts directly with H3K4me3 through a plant homeodomain (PHD),

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Dietrich, N., and B. Stubbert. "P.121 Bridging the evidence gap: RAG-enabled LLMs in neuroimaging decision support." Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques 52, s1 (2025): S44. https://doi.org/10.1017/cjn.2025.10274.

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Background: Large language models (LLMs) offer potential for clinical decision support but may not fully adhere to current guidelines. Retrieval-augmented generation (RAG) may address this gap by dynamically incorporating external knowledge. This study evaluated LLM adherence with and without RAG to Canadian neuroimaging guidelines. Methods: A novel RAG framework was developed that integrated Canadian Association of Radiologists (CAR) Diagnostic Imaging Referral Guidelines with GPT-4o and o1 models. Clinical scenarios were curated to represent various central nervous system conditions, such as

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Liu, Yuan, Yilong Liu, Ziang Qi, Yao Xiao, and Xiaoyan Guo. "TCNAttention-RAG: Stock Prediction and Fraud Detection Framework Based on Financial Report Analysis." Applied and Computational Engineering 139, no. 1 (2025): 60–66. https://doi.org/10.54254/2755-2721/2025.22702.

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Due to the high volatility of financial markets and the prevalence of financial fraud, real-time stock market forecasting for listed companies remains a challenging task. To address these challenges, this study proposes TCNAttention-RAG, a hybrid deep learning framework integrating Temporal Convolutional Network (TCN), Multi-Layer Perceptron (MLP), Attention Mechanism, and Retrieval-Augmented Generation (RAG) for enhanced stock price forecasting. The model leverages TCN for temporal feature extraction, MLP for nonlinear representation, and Attention for feature weighting, while RAG dynamically

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Ahluwalia, Raju, Erika Vainieri, Joseph Tam, et al. "Surgical Diabetic Foot Debridement: Improving Training and Practice Utilizing the Traffic Light Principle." International Journal of Lower Extremity Wounds 18, no. 3 (2019): 279–86. http://dx.doi.org/10.1177/1534734619853657.

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Comprehensive management of a severe diabetic foot infection focus on clear treatment pathways. Including rapid, radical debridement of all infection in addition to intravenous antibiotics and supportive measures. However, inexperienced surgeons can often underestimate the extent of infection, risking inadequate debridement, repeated theatre episodes, higher hospital morbidity, and hospital length of stay (LOS). This study aims to assess protocolized diabetic-foot-debridement: Red-Amber-Green (RAG) model as part of a value-based driven intervention. The model highlights necrotic/infected tissu

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Kim, Junseo, Seok Jun Kim, Junseok Ahn, and Suehyun Lee. "LLM-Based Response Generation for Korean Adolescents: A Study Using the NAVER Knowledge iN Q&A Dataset with RAG." Healthcare Informatics Research 31, no. 2 (2025): 136–45. https://doi.org/10.4258/hir.2025.31.2.136.

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Objectives: This research aimed to develop a retrieval-augmented generation (RAG) based large language model (LLM) system that offers personalized and reliable responses to a wide range of concerns raised by Korean adolescents. Our work focuses on building a culturally reflective dataset and on designing and validating the system’s effectiveness by comparing the answer quality of RAG-based models with non-RAG models.Methods: Data were collected from the NAVER Knowledge iN platform, concentrating on posts that featured adolescents’ questions and corresponding expert responses during the period

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Kimura, Eizen, Yukinobu Kawakami, Shingo Inoue, and Ai Okajima. "Mapping Drug Terms via Integration of a Retrieval-Augmented Generation Algorithm with a Large Language Model." Healthcare Informatics Research 30, no. 4 (2024): 355–63. http://dx.doi.org/10.4258/hir.2024.30.4.355.

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Objectives: This study evaluated the efficacy of integrating a retrieval-augmented generation (RAG) model and a large language model (LLM) to improve the accuracy of drug name mapping across international vocabularies.Methods: Drug ingredient names were translated into English using the Japanese Accepted Names for Pharmaceuticals. Drug concepts were extracted from the standard vocabulary of OHDSI, and the accuracy of mappings between translated terms and RxNorm was assessed by vector similarity, using the BioBERT-generated embedded vectors as the baseline. Subsequently, we developed LLMs with

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Soong, David, Sriram Sridhar, Han Si, et al. "Improving accuracy of GPT-3/4 results on biomedical data using a retrieval-augmented language model." PLOS Digital Health 3, no. 8 (2024): e0000568. http://dx.doi.org/10.1371/journal.pdig.0000568.

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Large language models (LLMs) have made a significant impact on the fields of general artificial intelligence. General purpose LLMs exhibit strong logic and reasoning skills and general world knowledge but can sometimes generate misleading results when prompted on specific subject areas. LLMs trained with domain-specific knowledge can reduce the generation of misleading information (i.e. hallucinations) and enhance the precision of LLMs in specialized contexts. Training new LLMs on specific corpora however can be resource intensive. Here we explored the use of a retrieval-augmented generation (

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Alam, Sirojul, Jaka Abdul Jabar, Fauzi Abdurrachman, Bambang Suharjo, and H. A. Danang Rimbawa. "Improving Large Language Model’s Ability to Find the Words Relationship." Jurnal Bumigora Information Technology (BITe) 6, no. 2 (2024): 141–48. https://doi.org/10.30812/bite.v6i2.4127.

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Background: It is still possible to enhance the capabilities of popular and widely used large language models (LLMs) such as Generative Pre-trained Transformer (GPT). Using the Retrieval-Augmented Generation (RAG) architecture is one method of achieving enhancement. This architectural approach incorporates outside data into the model to improve LLM capabilities. Objective: The aim of this research is to prove that the RAG can help LLMs respond with greater precision and rationale. Method: The method used in this work is utilizing Huggingface Application Programming Interface (API) for word emb

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Yao, Chengyuan, and Satoshi Fujita. "Adaptive Control of Retrieval-Augmented Generation for Large Language Models Through Reflective Tags." Electronics 13, no. 23 (2024): 4643. http://dx.doi.org/10.3390/electronics13234643.

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While retrieval-augmented generation (RAG) enhances large language models (LLMs), it also introduces challenges that can impact accuracy and performance. In practice, RAG can obscure the intrinsic strengths of LLMs. Firstly, LLMs may become too reliant on external retrieval, underutilizing their own knowledge and reasoning, which can diminish responsiveness. Secondly, RAG may introduce irrelevant or low-quality data, adding noise that disrupts generation, especially with complex tasks. This paper proposes an RAG framework that uses reflective tags to manage retrieval, evaluating documents in p

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Blank, Christian, Kerstin Schuster, Reinhard Andreesen, and Andreas Mackensen. "Lymphopenia-Associated CD4+ T Cell-Mediated Colitis Correlates with Homeostatic but Not with Endogenous Proliferation." Blood 106, no. 11 (2005): 1314. http://dx.doi.org/10.1182/blood.v106.11.1314.1314.

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Abstract Transfer of peripheral T cells into lymphopenic hosts results in a burst-like proliferation, called endogenous proliferation (EP) and an antigen-independent proliferation, called homeostatic proliferation (HP). Formerly both were named lymphopenic or homeostatic proliferation. T cells that undergo either endogenous or homeostatic proliferation acquire an activated phenotype, develop the ability to produce Interferon-gamma (IFN-g) and to lyse target cells specifically, but only homeostatic proliferating T cells acquire a CD44bright CD62Lbright phenotype. We developed a murine model to

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Dreyfus, David. "RAG-1 (Recombination Activating Gene-1) protein is closely related to herpes virus recombinases: Implications for the origins of the acquired immune system. (105.20)." Journal of Immunology 188, no. 1_Supplement (2012): 105.20. http://dx.doi.org/10.4049/jimmunol.188.supp.105.20.

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Abstract The acquired immune system results from recombination of germ-line DNA V(D)J signal sequences by the RAG-1 recombinase. RAG-1 is a dde magnesium binding recombinase related to both retroviral integrases and transposases. However the closest phylogenetic relative to RAG-1 appears to be a family of recombinases encoded by the herpesviridae termed the DBP (DNA binding proteins) that catalyze strand exchange and possibly other recombination events during herpes virus replication. Both the dde magnesium binding triad and the nonamer binding regions of RAG-1 are present and conserved in the

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Xu, Kehan, Kun Zhang, Jingyuan Li, Wei Huang, and Yuanzhuo Wang. "CRP-RAG: A Retrieval-Augmented Generation Framework for Supporting Complex Logical Reasoning and Knowledge Planning." Electronics 14, no. 1 (2024): 47. https://doi.org/10.3390/electronics14010047.

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The Retrieval-Augmented Generation (RAG) framework enhances Large Language Models (LLMs) by retrieving relevant knowledge to broaden their knowledge boundaries and mitigate factual hallucinations stemming from knowledge gaps. However, the RAG Framework faces challenges in effective knowledge retrieval and utilization; invalid or misused knowledge will interfere with LLM generation, reducing reasoning efficiency and answer quality. Existing RAG methods address these issues by decomposing and expanding queries, introducing special knowledge structures, and using reasoning process evaluation and

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Mukhopadhyay, Parthasarathi. "Optimizing Retrieval in Libraries through RAG: A Framework." Indian Journal of Information Library and Society 37, no. 1-2 (2024): 6–22. https://doi.org/10.5281/zenodo.14266525.

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This research study shows through demonstration that it is quite possible to develop a low-cost question-answer-based search system for libraries by using open source tools and open source large language models (LLMs). The typical problems associated with LLMs are hallucinations and out-of-date, non-contextual answer generation. Surprisingly, it has been found that the same LLMs can generate better contextualized answers when augmented with a set of relevant documents. This process of augmenting relevant documents in LLMs before the generation of answers is known as retrieval-augmented generat

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Iaroshev, Ivan, Ramalingam Pillai, Leandro Vaglietti, and Thomas Hanne. "Evaluating Retrieval-Augmented Generation Models for Financial Report Question and Answering." Applied Sciences 14, no. 20 (2024): 9318. http://dx.doi.org/10.3390/app14209318.

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This study explores the application of retrieval-augmented generation (RAG) to improve the accuracy and reliability of large language models (LLMs) in the context of financial report analysis. The focus is on enabling private investors to make informed decisions by enhancing the question-and-answering capabilities regarding the half-yearly or quarterly financial reports of banks. The study adopts a Design Science Research (DSR) methodology to develop and evaluate an RAG system tailored for this use case. The study conducts a series of experiments to explore models in which different RAG compon

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G, Ananya. "RAG based Chatbot using LLMs." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 06 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem35600.

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Historically, Artificial Intelligence (AI) was used to understand and recommend information. Now, Generative AI can also help us create new content. Generative AI builds on existing technologies, like Large Language Models (LLMs) which are trained on large amounts of text and learn to predict the next word in a sentence. Generative AI can not only create new text, but also images, videos, or audio. This project focuses on the implementation of a chatbot based the concepts of Generative AI and Large Language Models which can answer any query regarding the content provided in the PDFs. The prima

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Kolawole, Abimbola O., Mariam B. Gonzalez-Hernandez, Holly Turula, Chenchen Yu, Michael D. Elftman, and Christiane E. Wobus. "Oral Norovirus Infection Is Blocked in Mice Lacking Peyer's Patches and Mature M Cells." Journal of Virology 90, no. 3 (2015): 1499–506. http://dx.doi.org/10.1128/jvi.02872-15.

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ABSTRACTA critical early step in murine norovirus (MNV) pathogenesis is crossing the intestinal epithelial barrier to reach the target cells for replication, i.e., macrophages, dendritic cells, and B cells. Our previous work showed that MNV replication decreases in the intestines of mice conditionally depleted of microfold (M) cells. To define the importance of Peyer's patch (PP) M cells during MNV pathogenesis, we used a model of BALB/c mice deficient in recombination-activating gene 2 (Rag2) and the common gamma chain (γc) (Rag-γc−/−), which lack gut-associated lymphoid tissues (GALT), such

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Kim, Eu Wang, Yeon Ju Shin, Kyong Ju Kim, and Sehoon Kwon. "Development of an Automated Construction Contract Review Framework Using Large Language Model and Domain Knowledge." Buildings 15, no. 6 (2025): 923. https://doi.org/10.3390/buildings15060923.

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Construction contract review demands specialized expertise, requiring comprehensive understanding of both technical and legal aspects. While AI advancements offer potential solutions, two problems exist: LLMs lack sufficient domain-specific knowledge to analyze construction contracts; existing RAG approaches do not effectively utilize domain expertise. This study aims to develop an automated contract review system that integrates domain expertise with AI capabilities while ensuring reliable analysis. By transforming expert knowledge into a structured knowledge base aligned with the SCF classif

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Abou-Bacar, Ahmed, Alexander W. Pfaff, Sophie Georges, et al. "Role of NK Cells and Gamma Interferon in Transplacental Passage of Toxoplasma gondii in a Mouse Model of Primary Infection." Infection and Immunity 72, no. 3 (2004): 1397–401. http://dx.doi.org/10.1128/iai.72.3.1397-1401.2004.

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ABSTRACT Protective immunity in mice infected with Toxoplasma gondii is mainly mediated by NK cells, CD4 and CD8 T cells, and type 1 cytokines, such as gamma interferon (IFN-γ). To clarify the roles of NK cells and IFN-γ in protection against primary congenital toxoplasmosis, we used recombination activating gene 2 knockout (RAG-2−/−) mice, which lack T and B lymphocytes, in comparison with the wild-type BALB/c model. RAG-2−/− mice had a significantly lower risk of fetal toxoplasmosis than BALB/c mice (25 versus 63.9%; P = 0.003). This protection was associated with an increased number of mate

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Dimayuga, Paul C., Fernando H. Y. Cesena, Kuang-Yuh Chyu, et al. "Natural antibodies and complement modulate intimal thickening after arterial injury." American Journal of Physiology-Regulatory, Integrative and Comparative Physiology 297, no. 5 (2009): R1593—R1600. http://dx.doi.org/10.1152/ajpregu.00114.2009.

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Immune factors are involved in modulating neointimal response to arterial wall injury, but the role of individual immune effectors in this response remains unclear. Using a carotid cuff injury model in mice, we tested the role of immunoglobulin isotypes in modulating intimal thickening by using adoptive transfer of splenocytes from WT mice, or the direct administration of IgG or IgM into immune-deficient Rag-1−/− [Rag-1 knockout (Rag-1KO)] mice. The direct role of complement was also tested by depletion of complement. Splenocytes from WT mice were isolated and adoptively transferred to Rag-1KO

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Choi, Yein, Sungwoo Kim, Yipene Cedric Francois Bassole, and Yunsick Sung. "Enhanced Retrieval-Augmented Generation Using Low-Rank Adaptation." Applied Sciences 15, no. 8 (2025): 4425. https://doi.org/10.3390/app15084425.

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Recent advancements in retrieval-augmented generation (RAG) have substantially enhanced the efficiency of information retrieval. However, traditional RAG-based systems still encounter challenges, such as high latency in output decision making, the inaccurate retrieval of road traffic-related laws and regulations, and considerable processing overhead in large-scale searches. This study presents an innovative application of RAG technology for processing road traffic-related laws and regulations, particularly in the context of unmanned systems like autonomous driving. Our approach integrates embe

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Wu, Xiao Dong, Wei Min Li, and Lin Zhang. "Evaluation for the Hypersonic Near-Space Target Threat Based on GRA-TOPSIS Model." Advanced Materials Research 532-533 (June 2012): 1046–50. http://dx.doi.org/10.4028/www.scientific.net/amr.532-533.1046.

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From the need of antagonising the hypersonic near-space target (HNST), a multi-attribute evaluation method of HNST threat based on RAG-TOPSIS is proposed; the target attribute weights are dealt with using RAG; the grey state for people to understand the selection of attributes in the traditional TOPSIS is avoided and the confirming of the weights of target attributes is more scientific. The multi-attribute evaluation model of HNST’s threat is established, then the rationality and effectiveness of the method is verified by an example.

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Ono, Ayumi, Shinichiro Hattori, Ryusho Kariya, et al. "Comparative Study of Human Hematopoietic Cell Engraftment into Balb/c and C57BL/6 Strain of Rag-2/Jak3 Double-Deficient Mice." Journal of Biomedicine and Biotechnology 2011 (2011): 1–6. http://dx.doi.org/10.1155/2011/539748.

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Immunodeficient mice are becoming invaluable tools in human stem cell and tumor research. In this study, we generated Rag-2/Jak3 double-deficient (Rag-2−/−Jak3−/−) mice with a C57/BL6 and Balb/c genetic background and compared the human lymphohematopoietic cell engraftment rate. Human cord blood-derived CD34+hematopoietic stem cells were successfully engrafted into Balb/c Rag-2−/−Jak3−/−mice; however, the engraftment rate was far lower in C57/BL6 Rag-2−/−Jak3−/−mice. Transplantation of human peripheral blood mononuclear cells resulted in the same tendency. Thus, a Balb/c background offers supe

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Chen, Ying-Hao, Shanq-Jang Ruan, and Pei-fu Chen. "Predicting 30-Day Postoperative Mortality and American Society of Anesthesiologists Physical Status Using Retrieval-Augmented Large Language Models: Development and Validation Study." Journal of Medical Internet Research 27 (June 3, 2025): e75052. https://doi.org/10.2196/75052.

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Background Accurately assessing perioperative risk is critical for informed surgical planning and patient safety. However, current prediction models often rely on structured data and overlook the nuanced clinical reasoning embedded in free-text preoperative notes. Recent advances in large language models (LLMs) have opened opportunities for harnessing unstructured clinical data, yet their application in perioperative prediction remains limited by concerns about factual accuracy. Retrieval-augmented generation (RAG) offers a promising solution—enhancing LLM performance by grounding outputs in d

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Pingua, Bhagyajit, Adyakanta Sahoo, Meenakshi Kandpal, et al. "Medical LLMs: Fine-Tuning vs. Retrieval-Augmented Generation." Bioengineering 12, no. 7 (2025): 687. https://doi.org/10.3390/bioengineering12070687.

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Large language models (LLMs) are trained on huge datasets, which allow them to answer questions from various domains. However, their expertise is confined to the data that they were trained on. In order to specialize LLMs in niche domains like healthcare, various training methods can be employed. Two of these commonly known approaches are retrieval-augmented Generation and model fine-tuning. Five models—Llama-3.1-8B, Gemma-2-9B, Mistral-7B-Instruct, Qwen2.5-7B, and Phi-3.5-Mini-Instruct—were fine-tuned on healthcare data. These models were trained using three distinct approaches: retrieval-aug

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Iivanainen, Sanna, Jarkko Lagus, Henri Viertolahti, Lauri Sippola, and Jussi Koivunen. "Investigating large language model (LLM) performance using in-context learning (ICL) for interpretation of ESMO and NCCN guidelines for lung cancer." Journal of Clinical Oncology 42, no. 16_suppl (2024): e13637-e13637. http://dx.doi.org/10.1200/jco.2024.42.16_suppl.e13637.

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e13637 Background: The recent development of advanced LLMs has been suggested to improve patient care across several areas such as clinical-decision support or helping to answer patients’ questions. Hallucinations have been identified as a blocker for the use of LLMs in routine clinical practice. ICL and Retrieval Augmented Generation (RAG) could improve the LLM performance and reduce hallucinations, consecutively making the use of LLMs possible in clinical practice. Methods: A method using ICL and RAG was developed on top of health AI platform (Gosta MedKit) to interpret the most recent ESMO

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Swacha, Jakub, and Michał Gracel. "Retrieval-Augmented Generation (RAG) Chatbots for Education: A Survey of Applications." Applied Sciences 15, no. 8 (2025): 4234. https://doi.org/10.3390/app15084234.

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Retrieval-Augmented Generation (RAG) overcomes the main barrier for the adoption of LLM-based chatbots in education: hallucinations. The uncomplicated architecture of RAG chatbots makes it relatively easy to implement chatbots that serve specific purposes and thus are capable of addressing various needs in the educational domain. With five years having passed since the introduction of RAG, the time has come to check the progress attained in its adoption in education. This paper identifies 47 papers dedicated to RAG chatbots’ uses for various kinds of educational purposes, which are analyzed in

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Webb, Cynthia Vannessa Reinoso, Iurii Koboziev, Kathryn L. Furr, et al. "Intestinal Bacterial Composition of Lymphopenic Mice and Susceptibility to CD45RBhigh T Cell-Induced Colitis." Journal of Immunology 196, no. 1_Supplement (2016): 188.10. http://dx.doi.org/10.4049/jimmunol.196.supp.188.10.

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Abstract Background Following relocation to our current institution we observed a 40% reduction in the incidence of T cell-induced colitis in our well-established mouse model of IBD. The objectives of this study were to: a) quantify and compare the colonic microbiota in healthy and colitic mice obtained from our current (TTUHSC) and former insitution (LSUHSC), and b) determine whether colonization of healthy mice housed at our current institution with feces from healthy or colitic mice from LSUHSC alters the incidence and/or severity of colitis. Methods DNA from frozen feces was isolated using

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Emmanuel, Akinola, Marei Dose, Shilpa Keerthivasan, Katayoun Aghajani та Fotini Gounari. "β-Catenin induces T-cell transformation by promoting genomic instability (HEM4P.237)". Journal of Immunology 192, № 1_Supplement (2014): 116.13. http://dx.doi.org/10.4049/jimmunol.192.supp.116.13.

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Abstract Deregulated activation of β-catenin has been correlated with genomic instability in cancer. During thymocyte development β-catenin activates transcription in partnership with Tcf-1, an essential T-cell specific DNA-binding protein. We previously reported that targeted activation of β-catenin in thymocytes (CAT mice) induces lymphomas that depend on recombination-activating-gene (RAG) and c-Myc activities. Here we show that these lymphomas have recurring Tcra/Myc translocations that resulted from illegitimate RAG-recombination events and resembled oncogenic translocations in ponies. We

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Sekeľová, Frederika, and Andrej Lališ. "Application of resilience assessment grid in production of aircraft components." MAD - Magazine of Aviation Development 7, no. 4 (2020): 6–11. http://dx.doi.org/10.14311/mad.2019.04.01.

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The paper evaluates an application of the resilience measuring tool Resilience Assessment Grid (RAG) in the aircraft components production. For the purpose of the evaluation a specific RAG model for a change process is created. The paper contains a description of change process and the approval process of the engineering change proposal. Paper describes BPMN and FRAM models created to support model RAG and which both clarify relations among the functions inside the management of change process. The paper presents a possible approach for the Resilience Assessment Grid model creation. The outcom

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Liu, Yicheng. "Retrieval-Augmented Generation: Methods, Applications and Challenges." Applied and Computational Engineering 142, no. 1 (2025): 99–108. https://doi.org/10.54254/2755-2721/2025.kl22312.

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The Retrieval-Augmented Generation (RAG) has been proven to have a promising approach. It can address the limitations of purely generative models in knowledge-intensive tasks caused by their reliance on static, pre-trained knowledge. RAG addresses these challenges by integrating a retrieval mechanism with a generative model, enabling dynamic access to external knowledge sources during the generation process. This paper presents a comprehensive study of the RAG framework, focusing on its architecture, training strategies, and applications. The framework combines a dense passage retriever (DPR)

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Zelenko, Zara, Emily Jane Gallagher, Irini Markella Antoniou, et al. "EMT reversal in human cancer cells after IR knockdown in hyperinsulinemic mice." Endocrine-Related Cancer 23, no. 9 (2016): 747–58. http://dx.doi.org/10.1530/erc-16-0142.

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Type 2 diabetes (T2D) is associated with increased cancer risk and cancer-related mortality. Data herein show that we generated an immunodeficient hyperinsulinemic mouse by crossing theRag1−/−mice, which have no mature B or T lymphocytes, with the MKR mouse model of T2D to generate theRag1−/−(Rag/WT) andRag1−/−/MKR+/+(Rag/MKR) mice. The female Rag/MKR mice are insulin resistant and have significantly higher nonfasting plasma insulin levels compared with the Rag/WT controls. Therefore, we used these Rag/MKR mice to investigate the role of endogenous hyperinsulinemia on human cancer progression.

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