3,075 research outputs found
Gene-induced Multimodal Pre-training for Image-omic Classification
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
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 () production at the LHC. It
is found that these new particles can generate significant corrections to the
production cross section and the spin correlations.Comment: 23 pages, 4 figures; discussions and references added; agrees with
published versio
Heritable and Lineage-Specific Gene Knockdown in Zebrafish Embryo
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
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
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
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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