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  1. Artikel ; Online: Deciphering protein prenylation in endocytic trafficking in

    Carruthers, Vern B / Dou, Zhicheng

    mBio

    2024  Band 15, Heft 4, Seite(n) e0028324

    Abstract: Toxoplasma ... ...

    Abstract Toxoplasma gondii
    Mesh-Begriff(e) Animals ; Toxoplasma/metabolism ; Protein Prenylation ; Proteins/metabolism ; Organelles/metabolism ; Protein Transport
    Chemische Substanzen Proteins
    Sprache Englisch
    Erscheinungsdatum 2024-02-26
    Erscheinungsland United States
    Dokumenttyp Journal Article ; Comment
    ZDB-ID 2557172-2
    ISSN 2150-7511 ; 2161-2129
    ISSN (online) 2150-7511
    ISSN 2161-2129
    DOI 10.1128/mbio.00283-24
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  2. Artikel ; Online: Fluorescence-based Heme Quantitation in

    Bergmann, Amy / Dou, Zhicheng

    Bio-protocol

    2021  Band 11, Heft 12, Seite(n) e4063

    Abstract: Toxoplasma ... ...

    Abstract Toxoplasma gondii
    Sprache Englisch
    Erscheinungsdatum 2021-06-20
    Erscheinungsland United States
    Dokumenttyp Journal Article
    ZDB-ID 2833269-6
    ISSN 2331-8325 ; 2331-8325
    ISSN (online) 2331-8325
    ISSN 2331-8325
    DOI 10.21769/BioProtoc.4063
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  3. Artikel ; Online: Toxoplasma gondii

    Key, Melanie / Baptista, Carlos Gustavo / Bergmann, Amy / Floyd, Katherine / Blader, Ira J / Dou, Zhicheng

    mSphere

    2024  Band 9, Heft 3, Seite(n) e0009224

    Abstract: Toxoplasma gondii: Importance: Toxoplasma ... ...

    Abstract Toxoplasma gondii
    Importance: Toxoplasma gondii
    Mesh-Begriff(e) Humans ; Toxoplasma/metabolism ; Coproporphyrinogens/metabolism ; Heme ; Coproporphyrinogen Oxidase/genetics ; Hypoxia ; Oxygen/metabolism
    Chemische Substanzen Coproporphyrinogens ; Heme (42VZT0U6YR) ; Coproporphyrinogen Oxidase (EC 1.3.3.3) ; Oxygen (S88TT14065)
    Sprache Englisch
    Erscheinungsdatum 2024-02-27
    Erscheinungsland United States
    Dokumenttyp Journal Article
    ISSN 2379-5042
    ISSN (online) 2379-5042
    DOI 10.1128/msphere.00092-24
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  4. Artikel ; Online: Oxadiazon Derivatives Elicit Potent Intracellular Growth Inhibition against

    Rees, Kerrick C / Dou, Zhicheng / Whitehead, Daniel C

    ACS infectious diseases

    2022  Band 8, Heft 5, Seite(n) 911–917

    Abstract: Infections ... ...

    Abstract Infections of
    Mesh-Begriff(e) Heme ; Humans ; Oxadiazoles/pharmacology ; Toxoplasma
    Chemische Substanzen Oxadiazoles ; Heme (42VZT0U6YR) ; oxadiazon (C6U0E0YTP6)
    Sprache Englisch
    Erscheinungsdatum 2022-04-01
    Erscheinungsland United States
    Dokumenttyp Journal Article ; Research Support, N.I.H., Extramural
    ISSN 2373-8227
    ISSN (online) 2373-8227
    DOI 10.1021/acsinfecdis.2c00020
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  5. Buch ; Online: JDsearch

    Liu, Jiongnan / Dou, Zhicheng / Tang, Guoyu / Xu, Sulong

    A Personalized Product Search Dataset with Real Queries and Full Interactions

    2023  

    Abstract: Recently, personalized product search attracts great attention and many models have been proposed. To evaluate the effectiveness of these models, previous studies mainly utilize the simulated Amazon recommendation dataset, which contains automatically ... ...

    Abstract Recently, personalized product search attracts great attention and many models have been proposed. To evaluate the effectiveness of these models, previous studies mainly utilize the simulated Amazon recommendation dataset, which contains automatically generated queries and excludes cold users and tail products. We argue that evaluating with such a dataset may yield unreliable results and conclusions, and deviate from real user satisfaction. To overcome these problems, in this paper, we release a personalized product search dataset comprised of real user queries and diverse user-product interaction types (clicking, adding to cart, following, and purchasing) collected from JD.com, a popular Chinese online shopping platform. More specifically, we sample about 170,000 active users on a specific date, then record all their interacted products and issued queries in one year, without removing any tail users and products. This finally results in roughly 12,000,000 products, 9,400,000 real searches, and 26,000,000 user-product interactions. We study the characteristics of this dataset from various perspectives and evaluate representative personalization models to verify its feasibility. The dataset can be publicly accessed at Github: https://github.com/rucliujn/JDsearch.

    Comment: Accepted to SIGIR 2023
    Schlagwörter Computer Science - Information Retrieval
    Thema/Rubrik (Code) 005
    Erscheinungsdatum 2023-05-24
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    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  6. Buch ; Online: UniGen

    Li, Xiaoxi / Zhou, Yujia / Dou, Zhicheng

    A Unified Generative Framework for Retrieval and Question Answering with Large Language Models

    2023  

    Abstract: Generative information retrieval, encompassing two major tasks of Generative Document Retrieval (GDR) and Grounded Answer Generation (GAR), has gained significant attention in the area of information retrieval and natural language processing. Existing ... ...

    Abstract Generative information retrieval, encompassing two major tasks of Generative Document Retrieval (GDR) and Grounded Answer Generation (GAR), has gained significant attention in the area of information retrieval and natural language processing. Existing methods for GDR and GAR rely on separate retrieval and reader modules, which hinder simultaneous optimization. To overcome this, we present \textbf{UniGen}, a \textbf{Uni}fied \textbf{Gen}erative framework for retrieval and question answering that integrates both tasks into a single generative model leveraging the capabilities of large language models. UniGen employs a shared encoder and two distinct decoders for generative retrieval and question answering. To facilitate the learning of both tasks, we introduce connectors, generated by large language models, to bridge the gaps between query inputs and generation targets, as well as between document identifiers and answers. Furthermore, we propose an iterative enhancement strategy that leverages generated answers and retrieved documents to iteratively improve both tasks. Through extensive experiments on the MS MARCO and NQ datasets, we demonstrate the effectiveness of UniGen, showcasing its superior performance in both the retrieval and the question answering tasks.
    Schlagwörter Computer Science - Information Retrieval ; Computer Science - Artificial Intelligence ; Computer Science - Computation and Language
    Thema/Rubrik (Code) 004
    Erscheinungsdatum 2023-12-18
    Erscheinungsland us
    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  7. Artikel: Toxoplasma gondii

    Key, Melanie / Baptista, Carlos Gustavo / Bergmann, Amy / Floyd, Katherine / Blader, Ira J / Dou, Zhicheng

    bioRxiv : the preprint server for biology

    2023  

    Abstract: Toxoplasma ... ...

    Abstract Toxoplasma gondii
    Sprache Englisch
    Erscheinungsdatum 2023-11-16
    Erscheinungsland United States
    Dokumenttyp Preprint
    DOI 10.1101/2023.11.16.567449
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  8. Artikel ; Online: The Toxoplasma plant-like vacuolar compartment (PLVAC).

    Stasic, Andrew J / Moreno, Silvia N J / Carruthers, Vern B / Dou, Zhicheng

    The Journal of eukaryotic microbiology

    2022  Band 69, Heft 6, Seite(n) e12951

    Abstract: Toxoplasma gondii belongs to the phylum Apicomplexa and is an important cause of congenital disease and infection in immunocompromised patients. T. gondii shares several characteristics with plants including a nonphotosynthetic plastid termed apicoplast ... ...

    Abstract Toxoplasma gondii belongs to the phylum Apicomplexa and is an important cause of congenital disease and infection in immunocompromised patients. T. gondii shares several characteristics with plants including a nonphotosynthetic plastid termed apicoplast and a multivesicular organelle that was named the plant-like vacuole (PLV) or vacuolar compartment (VAC). The name plant-like vacuole was selected based on its resemblance in composition and function to plant vacuoles. The name VAC represents its general vacuolar characteristics. We will refer to the organelle as PLVAC in this review. New findings in recent years have revealed that the PLVAC represents the lysosomal compartment of T. gondii which has adapted peculiarities to fulfill specific Toxoplasma needs. In this review, we discuss the composition and functions of the PLVAC highlighting its roles in ion storage and homeostasis, endocytosis, exocytosis, and autophagy.
    Mesh-Begriff(e) Humans ; Toxoplasma ; Vacuoles ; Protozoan Proteins ; Apicoplasts ; Plants
    Chemische Substanzen Protozoan Proteins
    Sprache Englisch
    Erscheinungsdatum 2022-10-27
    Erscheinungsland United States
    Dokumenttyp Journal Article ; Review ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ZDB-ID 1147218-2
    ISSN 1550-7408 ; 1066-5234
    ISSN (online) 1550-7408
    ISSN 1066-5234
    DOI 10.1111/jeu.12951
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  9. Buch ; Online: Enhancing Robustness of LLM-Synthetic Text Detectors for Academic Writing

    Dou, Zhicheng / Guo, Yuchen / Chang, Ching-Chun / Nguyen, Huy H. / Echizen, Isao

    A Comprehensive Analysis

    2024  

    Abstract: The emergence of large language models (LLMs), such as Generative Pre-trained Transformer 4 (GPT-4) used by ChatGPT, has profoundly impacted the academic and broader community. While these models offer numerous advantages in terms of revolutionizing work ...

    Abstract The emergence of large language models (LLMs), such as Generative Pre-trained Transformer 4 (GPT-4) used by ChatGPT, has profoundly impacted the academic and broader community. While these models offer numerous advantages in terms of revolutionizing work and study methods, they have also garnered significant attention due to their potential negative consequences. One example is generating academic reports or papers with little to no human contribution. Consequently, researchers have focused on developing detectors to address the misuse of LLMs. However, most existing methods prioritize achieving higher accuracy on restricted datasets, neglecting the crucial aspect of generalizability. This limitation hinders their practical application in real-life scenarios where reliability is paramount. In this paper, we present a comprehensive analysis of the impact of prompts on the text generated by LLMs and highlight the potential lack of robustness in one of the current state-of-the-art GPT detectors. To mitigate these issues concerning the misuse of LLMs in academic writing, we propose a reference-based Siamese detector named Synthetic-Siamese which takes a pair of texts, one as the inquiry and the other as the reference. Our method effectively addresses the lack of robustness of previous detectors (OpenAI detector and DetectGPT) and significantly improves the baseline performances in realistic academic writing scenarios by approximately 67% to 95%.
    Schlagwörter Computer Science - Computation and Language ; Computer Science - Artificial Intelligence
    Erscheinungsdatum 2024-01-15
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    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  10. Buch ; Online: Soaring from 4K to 400K

    Zhang, Peitian / Liu, Zheng / Xiao, Shitao / Shao, Ninglu / Ye, Qiwei / Dou, Zhicheng

    Extending LLM's Context with Activation Beacon

    2024  

    Abstract: The utilization of long contexts poses a big challenge for large language models due to their limited context window length. Although the context window can be extended through fine-tuning, it will result in a considerable cost at both training and ... ...

    Abstract The utilization of long contexts poses a big challenge for large language models due to their limited context window length. Although the context window can be extended through fine-tuning, it will result in a considerable cost at both training and inference time, and exert an unfavorable impact to the LLM's original capabilities. In this work, we propose Activation Beacon, which condenses LLM's raw activations into more compact forms such that it can perceive a much longer context with a limited context window. Activation Beacon is introduced as a plug-and-play module for the LLM. It fully preserves the LLM's original capability on short contexts while extending the new capability on processing longer contexts. Besides, it works with short sliding windows to process the long context, which achieves a competitive memory and time efficiency in both training and inference. Activation Beacon is learned by the auto-regression task conditioned on a mixture of beacons with diversified condensing ratios. Thanks to such a treatment, it can be efficiently trained purely with short-sequence data in just 10K steps, which consumes less than 9 hours on a single 8xA800 GPU machine. The experimental studies show that Activation Beacon is able to extend Llama-2-7B's context length by $\times100$ times (from 4K to 400K), meanwhile achieving a superior result on both long-context generation and understanding tasks. Our model and code will be available at the BGE repository.
    Schlagwörter Computer Science - Computation and Language ; Computer Science - Artificial Intelligence
    Thema/Rubrik (Code) 401
    Erscheinungsdatum 2024-01-07
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    Dokumenttyp Buch ; Online
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