Nutritional status modulates box C/D snoRNP biogenesis by regulated subcellular relocalization of the R2TP complex

BACKGROUND: Box C/D snoRNPs, which are typically composed of box C/D snoRNA and the four core protein components Nop1, Nop56, Nop58, and Snu13, play an essential role in the modification and processing of pre-ribosomal RNA. The highly conserved R2TP complex, comprising the proteins Rvb1, Rvb2, Tah1, and Pih1, has been shown to be required for box C/D snoRNP biogenesis and assembly; however, the molecular basis of R2TP chaperone-like activity is not yet known. RESULTS: Here, we describe an unexpected finding in which the activity of the R2TP complex is required for Nop58 protein stability and is controlled by the dynamic subcellular redistribution of the complex in response to growth conditions and nutrient availability. In growing cells, the complex localizes to the nucleus and interacts with box C/D snoRNPs. This interaction is significantly reduced in poorly growing cells as R2TP predominantly relocalizes to the cytoplasm. The R2TP-snoRNP interaction is mainly mediated by Pih1. CONCLUSIONS: The R2TP complex exerts a novel regulation on box C/D snoRNP biogenesis that affects their assembly and consequently pre-rRNA maturation in response to different growth conditions. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13059-014-0404-4) contains supplementary material, which is available to authorized users

Multi-domain Recommendation with Embedding Disentangling and Domain Alignment

Multi-domain recommendation (MDR) aims to provide recommendations for different domains (e.g., types of products) with overlapping users/items and is common for platforms such as Amazon, Facebook, and LinkedIn that host multiple services. Existing MDR models face two challenges: First, it is difficult to disentangle knowledge that generalizes across domains (e.g., a user likes cheap items) and knowledge specific to a single domain (e.g., a user likes blue clothing but not blue cars). Second, they have limited ability to transfer knowledge across domains with small overlaps. We propose a new MDR method named EDDA with two key components, i.e., embedding disentangling recommender and domain alignment, to tackle the two challenges respectively. In particular, the embedding disentangling recommender separates both the model and embedding for the inter-domain part and the intra-domain part, while most existing MDR methods only focus on model-level disentangling. The domain alignment leverages random walks from graph processing to identify similar user/item pairs from different domains and encourages similar user/item pairs to have similar embeddings, enhancing knowledge transfer. We compare EDDA with 12 state-of-the-art baselines on 3 real datasets. The results show that EDDA consistently outperforms the baselines on all datasets and domains. All datasets and codes are available at https://github.com/Stevenn9981/EDDA.Comment: Accepted by CIKM'23 as a Long pape

WESE: Weak Exploration to Strong Exploitation for LLM Agents

Recently, large language models (LLMs) have demonstrated remarkable potential as an intelligent agent. However, existing researches mainly focus on enhancing the agent's reasoning or decision-making abilities through well-designed prompt engineering or task-specific fine-tuning, ignoring the procedure of exploration and exploitation. When addressing complex tasks within open-world interactive environments, these methods exhibit limitations. Firstly, the lack of global information of environments leads to greedy decisions, resulting in sub-optimal solutions. On the other hand, irrelevant information acquired from the environment not only adversely introduces noise, but also incurs additional cost. This paper proposes a novel approach, Weak Exploration to Strong Exploitation (WESE), to enhance LLM agents in solving open-world interactive tasks. Concretely, WESE involves decoupling the exploration and exploitation process, employing a cost-effective weak agent to perform exploration tasks for global knowledge. A knowledge graph-based strategy is then introduced to store the acquired knowledge and extract task-relevant knowledge, enhancing the stronger agent in success rate and efficiency for the exploitation task. Our approach is flexible enough to incorporate diverse tasks, and obtains significant improvements in both success rates and efficiency across four interactive benchmarks

Synthesis and Properties of Red Mud-Based Nanoferrite Clinker

Red mud, an industrial waste obtained from alumina plants, is usually discharged into marine or disposed into a landfill polluting the surrounding water, atmosphere, and soil. Thus, disposal of red mud is an environmental concern and it should be recycled in an effective way. Since red mud consists of iron- and aluminum-rich phases, it can potentially be processed into cementitious material and can be used for a construction purpose. This research investigated the synthesis of nanoferrite (NF) clinker by using red mud as a raw material through chemical combustion technology for potential use in cement-based composite. Before the synthesis of NF, red mud was characterized by using XRF, XRD, and SEM techniques. From characterization results, the stoichiometric ratio of raw materials was calculated and experimentally optimized. The sample was then tested at various temperatures (815, 900, 1000, and 1100 degrees C) to find the optimum synthesis temperature. Finally, the hydraulic activity of NF was verified and the contribution to mechanical properties was determined by replacing cement with NF at various substitution levels (0, 5, 10, and 20wt%). Test results showed that the optimum condition for the synthesis of NF was found when the ratio of CaCO3/red mud was 1.5 and the sintering temperature was 815 degrees C. The synthesized NF had an average diameter of 300nm, and the main composition was brownmillerite (C(4)AF) with distinct hydraulic reaction. When NF was used as a substitute of Portland cement in mortar, the flexural strength with a 5% replacement level improved by 15%. Therefore, it can be concluded that the synthesis of NF provides an alternative approach to recycle red mud and could significantly help in reducing environmental pollution

Understanding the planning of LLM agents: A survey

As Large Language Models (LLMs) have shown significant intelligence, the progress to leverage LLMs as planning modules of autonomous agents has attracted more attention. This survey provides the first systematic view of LLM-based agents planning, covering recent works aiming to improve planning ability. We provide a taxonomy of existing works on LLM-Agent planning, which can be categorized into Task Decomposition, Plan Selection, External Module, Reflection and Memory. Comprehensive analyses are conducted for each direction, and further challenges for the field of research are discussed.Comment: 9 pages, 2 tables, 2 figure

A Toll-Like Receptor 7, 8, and 9 Antagonist Inhibits Th1 and Th17 Responses and Inflammasome Activation in a Model of IL-23-Induced Psoriasis

Psoriasis is a chronic inflammatory skin disease that involves the induction of T-helper 1 (Th1) and T-helper 17 (Th17) cell responses and the aberrant expression of proinflammatory cytokines, including IL-1β. Copious evidence suggests that abnormal activation of Toll-like receptors (TLRs) contributes to the initiation and maintenance of psoriasis. We have evaluated an antagonist of TLR7, 8, and 9 as a therapeutic agent in an IL-23-induced psoriasis model in mice. Psoriasis-like skin lesions were induced in C57BL/6 mice by intradermal injection of IL-23 in the ear or dorsum. IL-23-induced increase in ear thickness was inhibited in a dose-dependent manner by treatment with antagonist. Histological examination of ear and dorsal skin tissues demonstrated a reduction in epidermal hyperplasia in mice treated with the antagonist. Treatment with antagonist also reduced the induction of Th1 and Th17 cytokines in skin and/or serum, as well as dermal expression of inflammasome components, NLRP3 and AIM2, and antimicrobial peptides. These results indicate that targeting TLR7, 8, and 9 may provide a way to neutralize multiple inflammatory pathways that are involved in the development of psoriasis. The antagonist has the potential for the treatment of psoriasis and other autoimmune diseases

Towards Open-World Recommendation with Knowledge Augmentation from Large Language Models

Recommender systems play a vital role in various online services. However, the insulated nature of training and deploying separately within a specific domain limits their access to open-world knowledge. Recently, the emergence of large language models (LLMs) has shown promise in bridging this gap by encoding extensive world knowledge and demonstrating reasoning capability. Nevertheless, previous attempts to directly use LLMs as recommenders have not achieved satisfactory results. In this work, we propose an Open-World Knowledge Augmented Recommendation Framework with Large Language Models, dubbed KAR, to acquire two types of external knowledge from LLMs -- the reasoning knowledge on user preferences and the factual knowledge on items. We introduce factorization prompting to elicit accurate reasoning on user preferences. The generated reasoning and factual knowledge are effectively transformed and condensed into augmented vectors by a hybrid-expert adaptor in order to be compatible with the recommendation task. The obtained vectors can then be directly used to enhance the performance of any recommendation model. We also ensure efficient inference by preprocessing and prestoring the knowledge from the LLM. Extensive experiments show that KAR significantly outperforms the state-of-the-art baselines and is compatible with a wide range of recommendation algorithms. We deploy KAR to Huawei's news and music recommendation platforms and gain a 7\% and 1.7\% improvement in the online A/B test, respectively

How Can Recommender Systems Benefit from Large Language Models: A Survey

Recommender systems (RS) play important roles to match users' information needs for Internet applications. In natural language processing (NLP) domains, large language model (LLM) has shown astonishing emergent abilities (e.g., instruction following, reasoning), thus giving rise to the promising research direction of adapting LLM to RS for performance enhancements and user experience improvements. In this paper, we conduct a comprehensive survey on this research direction from an application-oriented view. We first summarize existing research works from two orthogonal perspectives: where and how to adapt LLM to RS. For the "WHERE" question, we discuss the roles that LLM could play in different stages of the recommendation pipeline, i.e., feature engineering, feature encoder, scoring/ranking function, and pipeline controller. For the "HOW" question, we investigate the training and inference strategies, resulting in two fine-grained taxonomy criteria, i.e., whether to tune LLMs or not, and whether to involve conventional recommendation model (CRM) for inference. Detailed analysis and general development trajectories are provided for both questions, respectively. Then, we highlight key challenges in adapting LLM to RS from three aspects, i.e., efficiency, effectiveness, and ethics. Finally, we summarize the survey and discuss the future prospects. We also actively maintain a GitHub repository for papers and other related resources in this rising direction: https://github.com/CHIANGEL/Awesome-LLM-for-RecSys.Comment: 15 pages; 3 figures; summarization table in appendi