NavGPT: Explicit Reasoning in Vision-and-Language Navigation with Large Language Models

Trained with an unprecedented scale of data, large language models (LLMs) like ChatGPT and GPT-4 exhibit the emergence of significant reasoning abilities from model scaling. Such a trend underscored the potential of training LLMs with unlimited language data, advancing the development of a universal embodied agent. In this work, we introduce the NavGPT, a purely LLM-based instruction-following navigation agent, to reveal the reasoning capability of GPT models in complex embodied scenes by performing zero-shot sequential action prediction for vision-and-language navigation (VLN). At each step, NavGPT takes the textual descriptions of visual observations, navigation history, and future explorable directions as inputs to reason the agent's current status, and makes the decision to approach the target. Through comprehensive experiments, we demonstrate NavGPT can explicitly perform high-level planning for navigation, including decomposing instruction into sub-goal, integrating commonsense knowledge relevant to navigation task resolution, identifying landmarks from observed scenes, tracking navigation progress, and adapting to exceptions with plan adjustment. Furthermore, we show that LLMs is capable of generating high-quality navigational instructions from observations and actions along a path, as well as drawing accurate top-down metric trajectory given the agent's navigation history. Despite the performance of using NavGPT to zero-shot R2R tasks still falling short of trained models, we suggest adapting multi-modality inputs for LLMs to use as visual navigation agents and applying the explicit reasoning of LLMs to benefit learning-based models

microRNAs in cardiac regeneration and cardiovascular disease

microRNAs (miRNAs) are a class of small non-coding RNAs, which have been shown important to a wide range of biological process by post-transcriptionally regulating the expression of protein-coding genes. miRNAs have been demonstrated essential to normal cardiac development and function. Recently, numerous studies indicate miRNAs are involved in cardiac regeneration and cardiac disease, including cardiac hypertrophy, myocardial infarction and cardiac arrhythmia. These observations suggest miRNAs play important roles in cardiology. In this review, we summarize the recent progress of studying miRNAs in cardiac regeneration and cardiac disease. We also discuss the diagnostic and therapeutic potential of miRNAs in heart disease

NavGPT: Explicit Reasoning in Vision-and-Language Navigation with Large Language Models

Trained with an unprecedented scale of data, large language models (LLMs) like ChatGPT and GPT-4 exhibit the emergence of significant reasoning abilities from model scaling. Such a trend underscored the potential of training LLMs with unlimited language data, advancing the development of a universal embodied agent. In this work, we introduce the NavGPT, a purely LLM-based instruction-following navigation agent, to reveal the reasoning capability of GPT models in complex embodied scenes by performing zero-shot sequential action prediction for vision-and-language navigation (VLN). At each step, NavGPT takes the textual descriptions of visual observations, navigation history, and future explorable directions as inputs to reason the agent's current status, and makes the decision to approach the target. Through comprehensive experiments, we demonstrate NavGPT can explicitly perform high-level planning for navigation, including decomposing instruction into sub-goals, integrating commonsense knowledge relevant to navigation task resolution, identifying landmarks from observed scenes, tracking navigation progress, and adapting to exceptions with plan adjustment. Furthermore, we show that LLMs is capable of generating high-quality navigational instructions from observations and actions along a path, as well as drawing accurate top-down metric trajectory given the agent's navigation history. Despite the performance of using NavGPT to zero-shot R2R tasks still falling short of trained models, we suggest adapting multi-modality inputs for LLMs to use as visual navigation agents and applying the explicit reasoning of LLMs to benefit learning-based models. Code is available at: https://github.com/GengzeZhou/NavGPT

Urban models: Progress and perspective

Urban modelling is an important branch of land use science. It integrates geography, surveying and mapping, information science, system science, economics, sociology and other disciplines to establish urban models, which have been used to provide support for urban policymaking or analyses. Urban models are used to understand, analyse, evaluate and reproduce the process of urban development, and predict the consequence of urban planning scenarios. In this paper, we provide a systematic review of urban models, including the evaluation, classification, application of urban models, and the timeline of urban models’ development. According to their modelling styles and applications, urban models can be classified into three categories: aggregate static models of economic and spatial interaction, urban dynamics models, and behavioural models of individual agents which linked to spatial location. According to the different modelling methods, urban models can be classified into two categories: top-down and bottom-up. Nowadays, emerging technologies, especially Information and Communication Technologies (ICT), are gradually but significantly changing the organization form of urban economic activities. It enables regions to break the location limitation and join in the national even global industry division, and that triggers a new bottom-up rural urbanization process, which formed a significant challenge for urban models. Based on above discussion, we proposed two perspectives for improvements of urban models, inculding the integration of ICT with tradtional urban models and integaration of top-down and bottom-up models

Transferred BCR/ABL DNA from K562 extracellular vesicles causes chronic myeloid leukemia in immunodeficient mice.

Our previous study showed that besides mRNAs and microRNAs, there are DNA fragments within extracellular vesicles (EVs). The BCR/ABL hybrid gene, involved in the pathogenesis of chronic myeloid leukemia (CML), could be transferred from K562 EVs to neutrophils and decrease their phagocytic activity in vitro. Our present study provides evidence that BCR/ABL DNAs transferred from EVs have pathophysiological significance in vivo. Two months after injection of K562 EVs into the tail vein of Sprague-Dawley (SD) rats, they showed some characteristics of CML, e.g., feeble, febrile, and thin, with splenomegaly and neutrophilia but with reduced neutrophil phagocytic activity. These findings were also observed in immunodeficient NOD/SCID mice treated with K562 EVs; BCR/ABL mRNA and protein were found in their neutrophils. The administration of actinomycin D, an inhibitor of de novo mRNA synthesis, prevented the abnormalities caused by K562 EVs in NOD/SCID mice related to CML, including neutrophilia and bone marrow hyperplasia. As a specific inhibitor of tyrosine kinases, imatinib blocked the activity of tyrosine kinases and the expression of phospho-Crkl, induced by the de novo BCR/ABL protein caused by K562 EVs bearing BCR/ABL DNA. Our current study shows the pathophysiological significance of transferred tumor gene from EVs in vivo, which may represent an important mechanism for tumorigenesis, tumor progression, and metastasis

Development and Validation of Prediction Models for Hypertensive Nephropathy, the PANDORA Study

Importance: Hypertension is a leading cause of end-stage renal disease (ESRD), but currently, those at risk are poorly identified. Objective: To develop and validate a prediction model for the development of hypertensive nephropathy (HN). Design Setting and Participants: Individual data of cohorts of hypertensive patients from Kailuan, China served to derive and validate a multivariable prediction model of HN from 12, 656 individuals enrolled from January 2006 to August 2007, with a median follow-up of 6.5 years. The developed model was subsequently tested in both derivation and external validation cohorts. Variables: Demographics, physical examination, laboratory, and comorbidity variables. Main Outcomes and Measures: Hypertensive nephropathy was defined as hypertension with an estimated glomerular filtration rate (eGFR) \u3c 60 ml/min/1.73 m and/or proteinuria. Results: About 8.5% of patients in the derivation cohort developed HN after a median follow-up of 6.5 years that was similar in the validation cohort. Eight variables in the derivation cohort were found to contribute to the risk of HN: salt intake, diabetes mellitus, stroke, serum low-density lipoprotein, pulse pressure, age, hypertension duration, and serum uric acid. The discrimination by concordance statistics (C-statistics) was 0.785 (IQR, 0.770-0.800); the calibration slope was 1.129, the intercept was -0.117; and the overall accuracy by adjusted was 0.998 with similar results in the validation cohort. A simple points scale developed from these data (0, low to 40, high) detected a low morbidity of 7% in the low-risk group (0-10 points) compared with \u3e40% in the high-risk group (\u3e20 points). Conclusions and Relevance: A prediction model of HN over 8 years had high discrimination and calibration, but this model requires prospective evaluation in other cohorts, to confirm its potential to improve patient care

Inhibition of DYRK1A, via histone modification, promotes cardiomyocyte cell cycle activation and cardiac repair after myocardial infarction

BACKGROUND: While the adult mammalian heart undergoes only modest renewal through cardiomyocyte proliferation, boosting this process is considered a promising therapeutic strategy to repair cardiac injury. This study explored the role and mechanism of dual-specificity tyrosine regulated kinase 1A (DYRK1A) in regulating cardiomyocyte cell cycle activation and cardiac repair after myocardial infarction (MI). METHODS: DYRK1A-knockout mice and DYRK1A inhibitors were used to investigate the role of DYRK1A in cardiomyocyte cell cycle activation and cardiac repair following MI. Additionally, we explored the underlying mechanisms by combining genome-wide transcriptomic, epigenomic, and proteomic analyses. FINDINGS: In adult mice subjected to MI, both conditional deletion and pharmacological inhibition of DYRK1A induced cardiomyocyte cell cycle activation and cardiac repair with improved cardiac function. Combining genome-wide transcriptomic and epigenomic analyses revealed that DYRK1A knockdown resulted in robust cardiomyocyte cell cycle activation (shown by the enhanced expression of many genes governing cell proliferation) associated with increased deposition of trimethylated histone 3 Lys4 (H3K4me3) and acetylated histone 3 Lys27 (H3K27ac) on the promoter regions of these genes. Mechanistically, via unbiased mass spectrometry, we discovered that WD repeat-containing protein 82 and lysine acetyltransferase 6A were key mediators in the epigenetic modification of H3K4me3 and H3K27ac and subsequent pro-proliferative transcriptome and cardiomyocyte cell cycle activation. INTERPRETATION: Our results reveal a significant role of DYRK1A in cardiac repair and suggest a drug target with translational potential for treating cardiomyopathy. FUNDING: This study was supported in part by grants from the National Natural Science Foundation of China (81930008, 82022005, 82070296, 82102834), National Key R&D Program of China (2018YFC1312700), Program of Innovative Research Team by the National Natural Science Foundation (81721001), and National Institutes of Health (5R01DK039308-31, 7R37HL023081-37, 5P01HL074940-11)