23 research outputs found

    MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models

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    Multimodal Large Language Model (MLLM) relies on the powerful LLM to perform multimodal tasks, showing amazing emergent abilities in recent studies, such as writing poems based on an image. However, it is difficult for these case studies to fully reflect the performance of MLLM, lacking a comprehensive evaluation. In this paper, we fill in this blank, presenting the first MLLM Evaluation benchmark MME. It measures both perception and cognition abilities on a total of 14 subtasks. In order to avoid data leakage that may arise from direct use of public datasets for evaluation, the annotations of instruction-answer pairs are all manually designed. The concise instruction design allows us to fairly compare MLLMs, instead of struggling in prompt engineering. Besides, with such an instruction, we can also easily carry out quantitative statistics. A total of 10 advanced MLLMs are comprehensively evaluated on our MME, which not only suggests that existing MLLMs still have a large room for improvement, but also reveals the potential directions for the subsequent model optimization.Comment: https://github.com/BradyFU/Awesome-Multimodal-Large-Language-Model

    Polygonatum cyrtonema polysaccharides reshape the gut microbiota to ameliorate dextran sodium sulfate-induced ulcerative colitis in mice

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    Ulcerative colitis (UC) is a chronic inflammatory bowel disease characterized inflammatory imbalance, intestinal epithelial mucosal damage, and dysbiosis of the gut microbiota. Polygonatum cyrtonema polysaccharides (PCPs) can regulate gut microbiota and inflammation. Here, the different doses of PCPs were administered to dextran sodium sulfate-induced UC mice, and the effects of the whole PCPs were compared with those of the fractionated fractions PCP-1 (19.9 kDa) and PCP-2 (71.6 and 4.2 kDa). Additionally, an antibiotic cocktail was administered to UC mice to deplete the gut microbiota, and PCPs were subsequently administered to elucidate the potential role of the gut microbiota in these mice. The results revealed that PCP treatment significantly optimized the lost weight and shortened colon, restored the balance of inflammation, mitigated oxidative stress, and restored intestinal epithelial mucosal damage. And, the PCPs exhibited superior efficacy in ameliorating these symptoms compared with PCP-1 and PCP-2. However, depletion of the gut microbiota diminished the therapeutic effects of PCPs in UC mice. Furthermore, fecal transplantation from PCP-treated UC mice to new UC-afflicted mice produced therapeutic effects similar to PCP treatment. So, PCPs significantly ameliorated the symptoms, inflammation, oxidative stress, and intestinal mucosal damage in UC mice, and gut microbiota partially mediated these effects

    A Challenger to GPT-4V? Early Explorations of Gemini in Visual Expertise

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    The surge of interest towards Multi-modal Large Language Models (MLLMs), e.g., GPT-4V(ision) from OpenAI, has marked a significant trend in both academia and industry. They endow Large Language Models (LLMs) with powerful capabilities in visual understanding, enabling them to tackle diverse multi-modal tasks. Very recently, Google released Gemini, its newest and most capable MLLM built from the ground up for multi-modality. In light of the superior reasoning capabilities, can Gemini challenge GPT-4V's leading position in multi-modal learning? In this paper, we present a preliminary exploration of Gemini Pro's visual understanding proficiency, which comprehensively covers four domains: fundamental perception, advanced cognition, challenging vision tasks, and various expert capacities. We compare Gemini Pro with the state-of-the-art GPT-4V to evaluate its upper limits, along with the latest open-sourced MLLM, Sphinx, which reveals the gap between manual efforts and black-box systems. The qualitative samples indicate that, while GPT-4V and Gemini showcase different answering styles and preferences, they can exhibit comparable visual reasoning capabilities, and Sphinx still trails behind them concerning domain generalizability. Specifically, GPT-4V tends to elaborate detailed explanations and intermediate steps, and Gemini prefers to output a direct and concise answer. The quantitative evaluation on the popular MME benchmark also demonstrates the potential of Gemini to be a strong challenger to GPT-4V. Our early investigation of Gemini also observes some common issues of MLLMs, indicating that there still remains a considerable distance towards artificial general intelligence. Our project for tracking the progress of MLLM is released at https://github.com/BradyFU/Awesome-Multimodal-Large-Language-Models.Comment: Total 120 pages. See our project at https://github.com/BradyFU/Awesome-Multimodal-Large-Language-Model

    Generation and Analysis of a Large-Scale Expressed Sequence Tag Database from a Full-Length Enriched cDNA Library of Developing Leaves of <i>Gossypium hirsutum</i> L

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    <div><p>Background</p><p>Cotton (<i>Gossypium hirsutum</i> L.) is one of the world’s most economically-important crops. However, its entire genome has not been sequenced, and limited resources are available in GenBank for understanding the molecular mechanisms underlying leaf development and senescence.</p><p>Methodology/Principal Findings</p><p>In this study, 9,874 high-quality ESTs were generated from a normalized, full-length cDNA library derived from pooled RNA isolated from throughout leaf development during the plant blooming stage. After clustering and assembly of these ESTs, 5,191 unique sequences, representative 1,652 contigs and 3,539 singletons, were obtained. The average unique sequence length was 682 bp. Annotation of these unique sequences revealed that 84.4% showed significant homology to sequences in the NCBI non-redundant protein database, and 57.3% had significant hits to known proteins in the Swiss-Prot database. Comparative analysis indicated that our library added 2,400 ESTs and 991 unique sequences to those known for cotton. The unigenes were functionally characterized by gene ontology annotation. We identified 1,339 and 200 unigenes as potential leaf senescence-related genes and transcription factors, respectively. Moreover, nine genes related to leaf senescence and eleven MYB transcription factors were randomly selected for quantitative real-time PCR (qRT-PCR), which revealed that these genes were regulated differentially during senescence. The qRT-PCR for three <i>GhYLSs</i> revealed that these genes express express preferentially in senescent leaves.</p><p>Conclusions/Significance</p><p>These EST resources will provide valuable sequence information for gene expression profiling analyses and functional genomics studies to elucidate their roles, as well as for studying the mechanisms of leaf development and senescence in cotton and discovering candidate genes related to important agronomic traits of cotton. These data will also facilitate future whole-genome sequence assembly and annotation in <i>G. hirsutum</i> and comparative genomics among <i>Gossypium</i> species.</p></div

    The most abundant putative transcriptional factors (TFs).

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    a<p>Percent = (total number of unigenes)/(total number of putative TFs). There were 200 putative TFs.</p

    Expression patterns of nine putative leaf senescence related genes from upland cotton.

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    <p>(a) Chlorophyll contents per fresh weight of leaves at each of four developmental stages. (b) Changes in transcript levels of the nine putative leaf senescence-related genes at each leaf developmental stage.</p

    Arginine vasopressin in hypothalamic paraventricular nucleus is transferred to the nucleus raphe magnus to participate in pain modulation

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    abstract: Hypothalamic paraventricular nucleus (PVN) is one of the main sources of arginine vasopressin (AVP) synthesis and secretion. AVP is the most important bioactive substance in PVN regulating pain process. Our pervious study has pointed that pain stimulation induced AVP increase in the nucleus raphe magnus (NRM), which plays a role in pain modulation. The present study was designed to investigate the source of AVP in the rat NRM during pain process using the methods of nucleus push–pull perfusion and radioimmunoassay. The results showed that pain stimulation increased the AVP concentration in the NRM perfusion liquid, PVN cauterization inhibited the role that pain stimulation induced the increase of AVP concentration in the NRM perfusion liquid, and PVN microinjection of l-glutamate sodium, which excited the PVN neurons, could increase the AVP concentration in the NRM perfusion liquid. The data suggested that AVP in the PVN might be transferred to the NRM to participate in pain modulation

    Analysis of GhYLS9 relationships.

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    <p>(a) Multiple sequence alignment of GhYLS9 and other homologous proteins in plants: <i>Arabidopsis thaliana</i> YLS9 (AB047812), <i>Casuarina glauca</i> HIN1 (ABZ80409), <i>Nicotiana tabacum</i> HIN1 (BAD22533), <i>Ricinus communis</i> SYP(XP_002532540), <i>Cucumis sativus</i> SYP24 (XP_004136508) and <i>Glycine max</i> SYP24 (XP_003554459). (b) Phylogenetic tree of these plant proteins constructed with MEGA 4 (c) Changes in transcript levels of GhYLS9 genes at each leaf development stage.</p

    Phylogeny analysis of putative MYB transcription factors.

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    <p>Twenty-two putative cotton MYB transcription factors and thirty-one putative MYB transcription factors from other plant species were aligned and analyzed by neighbor-joining in MEGA4.</p
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