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Easy, beautiful handmade rag rugs : 12 step-by-step techniques with patterns and projects, including latch hook, braiding, and punch needle
Do you have a closet full of clothes that you no longer wear? Do you have a pile of scrap fabric from previous crafts lying in a corner? Don't contribute to the landfill when you can learn how to make beautiful rag rugs from your throw-away fabrics! Rag rug making is approachable and fun for crafters of all ages! With no loom and no machines required, rag rug making is a timeless homesteading tradition that emphasizes upcycling scrap materials and fabrics, making the craft low cost, easy for beginners, sustainable, and eco-conscious. After all, why buy new rugs when you can be self-sufficient and make your own? This engaging and accessible project guide will show you step-by-step how to make beautiful rag rug crafts completely by hand from start to finish! The book begins with a stunning gallery of rag rugs made by leading fabric artists from around the world, where they share their inspiration and which techniques they used. You'll then find complete overviews on the tools you'll need, and learn how to design, size, and transfer your own pattern, choose a color palette, use ready-made, recycled, or non-traditional materials, choose a rug backing, and create your own colors with fabric dyeing. Then, the techniques and projects will take you through creating rag rugs with methods including rug hooking, traditional and miniature punch needle, proddy, braiding, latch hooking, and quillie.
BOOK
Pioneering agentic retrieval-augmented generation in software quality: a novel framework for code smell detection via dynamic retrieval
Code smells—subtle indicators of poor design choices—pose significant challenges to software maintainability and readability, particularly in dynamic languages such as Python. Traditional detection methods, including rule-based heuristics and static machine learning classifiers, often suffer from limited adaptability, poor contextual awareness, and lack of explainability. These limitations hinder their effectiveness in evolving codebases and real-world development environments. This study introduces a novel Agentic retrieval-augmented generation (Agentic RAG) framework for code smell detection, marking the first application of agentic reasoning in this domain. By embedding autonomous agents into the retrieval and reasoning pipeline, the proposed system dynamically routes queries, selects optimal retrieval strategies, and synthesizes context-aware explanations using large language models (LLMs). Unlike static classifiers, the proposed framework leverages hybrid retrieval (sparse + dense) and structured prompting to detect and explain Long Method and Large Class smells with high interpretability. Experimental results demonstrate that Agentic RAG—particularly when paired with DeepSeek and chain-of-thought prompting—achieves superior performance, with 89.5% accuracy, a macro F1-score of 78.3%, and a weighted F1 of 88.7%. To assess generalization, Experiment 2 extended the framework to 21 distinct code smell types across multiple programming languages, achieving 94.85% accuracy, a macro F1-score of 90.24%, and a weighted F1-score of 94.93% through stratified five-fold cross-validation, thereby confirming the model’s robustness and scalability. Beyond academic benchmarks, this work lays the foundation for real-world integration into developer platforms, enabling real-time code review, contextual feedback, and actionable refactoring suggestions. By bridging LLMs with dynamic retrieval and agentic reasoning, this framework advances the frontier of intelligent software quality assurance.
Journal Article
Rags to rugs : 30 new weaving designs for repurposed fabrics
\"In Rags to Rugs, Tom Knisely explores the weaving possibilities of a variety of fabrics, from T-shirts and jeans to quilts, linens, towels, and more. He shows you the techniques he uses to get the most from each piece, and gives advice on how best to set up your loom for weaving with rags much thicker than your typical weaving thread. Includes 30 brand-new rug designs\"-- Provided by publisher.
Book
Twenty-five years of mTOR
In my PNAS Inaugural Article, I describe the development of the mTOR field, starting with efforts to understand the mechanism of action of the drug rapamycin, which ∼25 y ago led to the discovery of the mTOR protein kinase. I focus on insights that we have contributed and on work that has been particularly influential to me, as well as provide some personal reflections and stories. We now appreciate that, as part of two distinct complexes, mTORC1 and mTORC2, mTOR is the major regulator of growth (mass accumulation) in animals and is the key link between the availability of nutrients in the environment and the control of most anabolic and catabolic processes. Nutrients signal to mTORC1 through the lysosome-associated Rag GTPases and their many regulators and associated cytosolic and lysosomal nutrient sensors. mTOR signaling is deregulated in common diseases, like cancer and epilepsy, and mTORC1 is a well-validated modulator of aging in multiple model organisms. There is significant excitement around using mTORC1 inhibitors to treat cancer and neurological disease and, potentially, to improve healthspan and lifespan.
Journal Article
A cold-blooded view of adaptive immunity
The adaptive immune system arose 500 million years ago in ectothermic (cold-blooded) vertebrates. Classically, the adaptive immune system has been defined by the presence of lymphocytes expressing recombination-activating gene (RAG)-dependent antigen receptors and the MHC. These features are found in all jawed vertebrates, including cartilaginous and bony fish, amphibians and reptiles and are most likely also found in the oldest class of jawed vertebrates, the extinct placoderms. However, with the discovery of an adaptive immune system in jawless fish based on an entirely different set of antigen receptors — the variable lymphocyte receptors — the divergence of T and B cells, and perhaps innate-like lymphocytes, goes back to the origin of all vertebrates. This Review explores how recent developments in comparative immunology have furthered our understanding of the origins and function of the adaptive immune system.
Journal Article
MEGA-GPT: Artificial Intelligence Guidance and Building Analytical Protocols Using MEGA Software
Over the past three decades, the Molecular Evolutionary Genetics Analysis (MEGA) software has evolved into a powerful tool with an ever-expanding suite of functionalities. Yet, despite its user-friendly design and widespread adoption by researchers and students, the software's extensive feature set can overwhelm both new and experienced users who are unfamiliar with its latest capabilities. To address this challenge, we developed MEGA-GPT, an AI-driven resource that leverages ChatGPT augmented with retrieval techniques to guide users through MEGA's analytical workflows via natural language queries. By integrating MEGA's help documentation, version-specific articles, and other key publications, MEGA-GPT enhances ChatGPT's standard responses to deliver step-by-step protocols, clarify analytical settings, and recommend optimal workflows. Our evaluations indicate that MEGA-GPT offers significantly improved guidance while minimizing the hallucinations and inaccuracies observed in standard ChatGPT outputs. We propose that such customized, retrieval-augmented query interfaces can substantially enhance the usability of complex scientific computing packages. MEGA-GPT is freely available to all users with a ChatGPT account by accessing the URL https://tinyurl.com/gpt-mega, which is also integrated into MEGA's graphical user interface.
Journal Article
A survey on augmenting knowledge graphs (KGs) with large language models (LLMs): models, evaluation metrics, benchmarks, and challenges
Integrating Large Language Models (LLMs) with Knowledge Graphs (KGs) enhances the interpretability and performance of AI systems. This research comprehensively analyzes this integration, classifying approaches into three fundamental paradigms: KG-augmented LLMs, LLM-augmented KGs, and synergized frameworks. The evaluation examines each paradigm’s methodology, strengths, drawbacks, and practical applications in real-life scenarios. The findings highlight the substantial impact of these integrations in fundamentally improving real-time data analysis, efficient decision-making, and promoting innovation across various domains. In this paper, we also describe essential evaluation metrics and benchmarks for assessing the performance of these integrations, addressing challenges like scalability and computational overhead, and providing potential solutions. This comprehensive analysis underscores the profound impact of these integrations on improving real-time data analysis, enhancing decision-making efficiency, and fostering innovation across various domains.
Journal Article
Integrating Retrieval-Augmented Generation with Large Language Models in Nephrology: Advancing Practical Applications
The integration of large language models (LLMs) into healthcare, particularly in nephrology, represents a significant advancement in applying advanced technology to patient care, medical research, and education. These advanced models have progressed from simple text processors to tools capable of deep language understanding, offering innovative ways to handle health-related data, thus improving medical practice efficiency and effectiveness. A significant challenge in medical applications of LLMs is their imperfect accuracy and/or tendency to produce hallucinations—outputs that are factually incorrect or irrelevant. This issue is particularly critical in healthcare, where precision is essential, as inaccuracies can undermine the reliability of these models in crucial decision-making processes. To overcome these challenges, various strategies have been developed. One such strategy is prompt engineering, like the chain-of-thought approach, which directs LLMs towards more accurate responses by breaking down the problem into intermediate steps or reasoning sequences. Another one is the retrieval-augmented generation (RAG) strategy, which helps address hallucinations by integrating external data, enhancing output accuracy and relevance. Hence, RAG is favored for tasks requiring up-to-date, comprehensive information, such as in clinical decision making or educational applications. In this article, we showcase the creation of a specialized ChatGPT model integrated with a RAG system, tailored to align with the KDIGO 2023 guidelines for chronic kidney disease. This example demonstrates its potential in providing specialized, accurate medical advice, marking a step towards more reliable and efficient nephrology practices.
Journal Article
Amino acid-dependent control of mTORC1 signaling: a variety of regulatory modes
The mechanistic target of rapamycin complex 1 (mTORC1) is an essential regulator of cell growth and metabolism through the modulation of protein and lipid synthesis, lysosome biogenesis, and autophagy. The activity of mTORC1 is dynamically regulated by several environmental cues, including amino acid availability, growth factors, energy levels, and stresses, to coordinate cellular status with environmental conditions. Dysregulation of mTORC1 activity is closely associated with various diseases, including diabetes, cancer, and neurodegenerative disorders. The discovery of Rag GTPases has greatly expanded our understanding of the regulation of mTORC1 activity by amino acids, especially leucine and arginine. In addition to Rag GTPases, other factors that also contribute to the modulation of mTORC1 activity have been identified. In this review, we discuss the mechanisms of regulation of mTORC1 activity by particular amino acids.
Journal Article