3,075 research outputs found

    Gene-induced Multimodal Pre-training for Image-omic Classification

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    Histology analysis of the tumor micro-environment integrated with genomic assays is the gold standard for most cancers in modern medicine. This paper proposes a Gene-induced Multimodal Pre-training (GiMP) framework, which jointly incorporates genomics and Whole Slide Images (WSIs) for classification tasks. Our work aims at dealing with the main challenges of multi-modality image-omic classification w.r.t. (1) the patient-level feature extraction difficulties from gigapixel WSIs and tens of thousands of genes, and (2) effective fusion considering high-order relevance modeling. Concretely, we first propose a group multi-head self-attention gene encoder to capture global structured features in gene expression cohorts. We design a masked patch modeling paradigm (MPM) to capture the latent pathological characteristics of different tissues. The mask strategy is randomly masking a fixed-length contiguous subsequence of patch embeddings of a WSI. Finally, we combine the classification tokens of paired modalities and propose a triplet learning module to learn high-order relevance and discriminative patient-level information.After pre-training, a simple fine-tuning can be adopted to obtain the classification results. Experimental results on the TCGA dataset show the superiority of our network architectures and our pre-training framework, achieving 99.47% in accuracy for image-omic classification. The code is publicly available at https://github.com/huangwudiduan/GIMP

    The scalars from the topcolor scenario and the spin correlations of the top pair production at the LHC

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    The topcolor scenario predicts the existences of some new scalars. In this paper, we consider the contributions of these new particles to the observables, which are related to the top quark pair (ttˉt\bar{t}) production at the LHC. It is found that these new particles can generate significant corrections to the ttˉt\bar{t} production cross section and the ttˉt\bar{t} spin correlations.Comment: 23 pages, 4 figures; discussions and references added; agrees with published versio

    Heritable and Lineage-Specific Gene Knockdown in Zebrafish Embryo

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    BACKGROUND: Reduced expression of developmentally important genes and tumor suppressors due to haploinsufficiency or epigenetic suppression has been shown to contribute to the pathogenesis of various malignancies. However, methodology that allows spatio-temporally knockdown of gene expression in various model organisms such as zebrafish has not been well established, which largely limits the potential of zebrafish as a vertebrate model of human malignant disorders. PRINCIPAL FINDING: Here, we report that multiple copies of small hairpin RNA (shRNA) are expressed from a single transcript that mimics the natural microRNA-30e precursor (mir-shRNA). The mir-shRNA, when microinjected into zebrafish embryos, induced an efficient knockdown of two developmentally essential genes chordin and alpha-catenin in a dose-controllable fashion. Furthermore, we designed a novel cassette vector to simultaneously express an intronic mir-shRNA and a chimeric red fluorescent protein driven by lineage-specific promoter, which efficiently reduced the expression of a chromosomally integrated reporter gene and an endogenously expressed gata-1 gene in the developing erythroid progenitors and hemangioblasts, respectively. SIGNIFICANCE: This methodology provides an invaluable tool to knockdown developmental important genes in a tissue-specific manner or to establish animal models, in which the gene dosage is critically important in the pathogenesis of human disorders. The strategy should be also applicable to other model organisms

    Group Consensus with a Dynamic Leader for Multiagent Systems via Sampled-Data Control

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    This paper considers a group consensus problem with a dynamic leader for multiagent systems in a sampled-data setting. With the leader’s state available to only a fraction of the followers, a distributed linear protocol based on sampled-data control is proposed for group consensus under fixed directed topology. On basis of M-matrix theory, we derive a sufficient condition on the sampling period and the control parameter for ultimate boundedness of the tracking errors. Furthermore, simulation examples are provided to demonstrate the effectiveness of the theoretical results

    Fine-tuning Large Language Model (LLM) Artificial Intelligence Chatbots in Ophthalmology and LLM-based evaluation using GPT-4

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    Purpose: To assess the alignment of GPT-4-based evaluation to human clinician experts, for the evaluation of responses to ophthalmology-related patient queries generated by fine-tuned LLM chatbots. Methods: 400 ophthalmology questions and paired answers were created by ophthalmologists to represent commonly asked patient questions, divided into fine-tuning (368; 92%), and testing (40; 8%). We find-tuned 5 different LLMs, including LLAMA2-7b, LLAMA2-7b-Chat, LLAMA2-13b, and LLAMA2-13b-Chat. For the testing dataset, additional 8 glaucoma QnA pairs were included. 200 responses to the testing dataset were generated by 5 fine-tuned LLMs for evaluation. A customized clinical evaluation rubric was used to guide GPT-4 evaluation, grounded on clinical accuracy, relevance, patient safety, and ease of understanding. GPT-4 evaluation was then compared against ranking by 5 clinicians for clinical alignment. Results: Among all fine-tuned LLMs, GPT-3.5 scored the highest (87.1%), followed by LLAMA2-13b (80.9%), LLAMA2-13b-chat (75.5%), LLAMA2-7b-Chat (70%) and LLAMA2-7b (68.8%) based on the GPT-4 evaluation. GPT-4 evaluation demonstrated significant agreement with human clinician rankings, with Spearman and Kendall Tau correlation coefficients of 0.90 and 0.80 respectively; while correlation based on Cohen Kappa was more modest at 0.50. Notably, qualitative analysis and the glaucoma sub-analysis revealed clinical inaccuracies in the LLM-generated responses, which were appropriately identified by the GPT-4 evaluation. Conclusion: The notable clinical alignment of GPT-4 evaluation highlighted its potential to streamline the clinical evaluation of LLM chatbot responses to healthcare-related queries. By complementing the existing clinician-dependent manual grading, this efficient and automated evaluation could assist the validation of future developments in LLM applications for healthcare.Comment: 13 Pages, 1 Figure, 8 Table

    COVID-19 Lockdown Increased the Risk of Preterm Birth

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    Funding Information: This research was supported by the National Key Research and Development Program of China (2018YFC1002804), National Natural Science Foundation of China (82001571 and 81671412), Program of Shanghai Academic Research Leader (20XD1424100), Outstanding Youth Medical Talents of Shanghai Rising Stars of Medical Talent Youth Development Program, Science and Technology Innovation Fund of ShanghaiPeer reviewedPublisher PD
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