The Development of LLMs for Embodied Navigation

In recent years, the rapid advancement of Large Language Models (LLMs) such as the Generative Pre-trained Transformer (GPT) has attracted increasing attention due to their potential in a variety of practical applications. The application of LLMs with Embodied Intelligence has emerged as a significant area of focus. Among the myriad applications of LLMs, navigation tasks are particularly noteworthy because they demand a deep understanding of the environment and quick, accurate decision-making. LLMs can augment embodied intelligence systems with sophisticated environmental perception and decision-making support, leveraging their robust language and image-processing capabilities. This article offers an exhaustive summary of the symbiosis between LLMs and embodied intelligence with a focus on navigation. It reviews state-of-the-art models, research methodologies, and assesses the advantages and disadvantages of existing embodied navigation models and datasets. Finally, the article elucidates the role of LLMs in embodied intelligence, based on current research, and forecasts future directions in the field. A comprehensive list of studies in this survey is available at https://github.com/Rongtao-Xu/Awesome-LLM-E

Research progress of CTC, ctDNA, and EVs in cancer liquid biopsy

Circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), and extracellular vehicles (EVs) have received significant attention in recent times as emerging biomarkers and subjects of transformational studies. The three main branches of liquid biopsy have evolved from the three primary tumor liquid biopsy detection targets—CTC, ctDNA, and EVs—each with distinct benefits. CTCs are derived from circulating cancer cells from the original tumor or metastases and may display global features of the tumor. ctDNA has been extensively analyzed and has been used to aid in the diagnosis, treatment, and prognosis of neoplastic diseases. EVs contain tumor-derived material such as DNA, RNA, proteins, lipids, sugar structures, and metabolites. The three provide different detection contents but have strong complementarity to a certain extent. Even though they have already been employed in several clinical trials, the clinical utility of three biomarkers is still being studied, with promising initial findings. This review thoroughly overviews established and emerging technologies for the isolation, characterization, and content detection of CTC, ctDNA, and EVs. Also discussed were the most recent developments in the study of potential liquid biopsy biomarkers for cancer diagnosis, therapeutic monitoring, and prognosis prediction. These included CTC, ctDNA, and EVs. Finally, the potential and challenges of employing liquid biopsy based on CTC, ctDNA, and EVs for precision medicine were evaluated

Genetic manipulation of primary human natural killer cells to investigate the functional and oncogenic roles of PRDM1

Extra-nodal natural killer/T-cell lymphoma, nasal type (ENKTCL) is a highly aggressive lymphoma, where the tumor suppressor gene (TSG) PRDM1 is frequently lost/inactivated. We employed two different CRISPR/Cas9 approaches to generate PRDM1-/- primary NK cells to study its role in NK-cell homeostasis. PRDM1-/- NK cells showed a marked increase in cloning efficiency, higher proliferation rate and less apoptosis compared with their wild type counterparts. Gene expression profiling demonstrated a marked enrichment in pathways associated with proliferation, cell cycle, MYC, MYB and TCR/NK signaling in PRDM1-/- NK cells, but pathways associated with normal cellular functions including cytotoxic functions were down-regulated, suggesting that the loss of PRDM1 shifted NK cells toward proliferation and survival rather than the performance of its normal functions. We were also able to further modify a PRDM1 deleted clone to introduce heterozygous deletions of common TSG in ENKTCL such as TP53, DDX3X, or PTPN6. We have established an in vitro model to elucidate the major pathways through which PRDM1 mediates its homeostatic control of NK-cells. This approach can be applied to the study of other relevant genetic lesions and oncogenic collaborations in lymphoma pathogenesis

Anthropogenic Aerosols Cause Recent Pronounced Weakening of Asian Summer Monsoon Relative to Last Four Centuries

The Asian Summer Monsoon (ASM) affects ecosystems, biodiversity, and food security of billions of people. In recent decades, ASM strength (as represented by precipitation) has been decreasing, but instrumental measurements span only a short period of time. The initiation and the dynamics of the recent trend are unclear. Here for the first time, we use an ensemble of 10 tree ring-width chronologies from the west-central margin of ASM to reconstruct detail of ASM variability back to 1566 CE. The reconstruction captures weak/strong ASM events and also reflects major locust plagues. Notably, we found an unprecedented 80-year trend of decreasing ASM strength within the context of the 448-year reconstruction, which is contrary to what is expected from greenhouse warming. Our coupled climate model shows that increasing anthropogenic sulfate aerosol emissions over the Northern Hemisphere could be the dominant factor contributing to the ASM decrease. Plan Language Summary Monsoonal rainfall has a certain influence on agriculture and industry in the regions of Asian Summer Monsoon (ASM). An understanding of the spatial-temporal variability of the ASM and the associated dynamics is vital for terrestrial ecosystems, water resources, forests, and landscapes. We have developed a 448-year ASM reconstruction back to 1566 CE using 10 tree ring chronologies from the margin region of ASM. We find that historical severe droughts and locust plague disasters during weak ASM events. The recent decreasing ASM trend persisting for over 80 years is unprecedented over the past 448 years. Coupled climate models show that increasing anthropogenic aerosol emissions are the dominant underlying factor. Our aim is that the time series will find a wide range of utility for understanding past climate variability and for predicting future climate change.National Natural Science Foundation of China [41630531]; National Research Program for Key Issues in Air Pollution Control [DQGG0104]; Chinese Academy of Sciences [QYZDJ-SSW-DQC021, XDPB05, GJHZ1777]; Institute of Earth Environment, Chinese Academy of Sciences; State Key Laboratory of Loess and Quaternary Geology6 month embargo; first published: 09 April 2019This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

CREEP FAILURE ANALYSIS OF THERMAL RECOVERY WELLHEAD

In view of the creep failure of the thermal recovery wellhead under the conditions of steam flood and steam stimulation,the creep test of 30CrMo at 390℃ and four different stresses was carried out. The test results were fitted and the parameters of the combined time hardening model were obtained.It is verified by short-term experiments that the error between the calculated value of the combined time hardening model and the test value is less than 15%.Using ANSYS Workbench,the creep failure process of the main pressure parts of the thermal recovery wellhead under constant temperature and load condition and intermittent load condition are simulated,and the failure time of the main pressure parts under two conditions is obtained,which is of great significance to the service life evaluation of the thermal recovery wellhead

Polarized Secretion of Extracellular Vesicles by Mammary Epithelia.

Extracellular vesicles (EVs) are secreted by many cell types and are increasingly investigated for their role in human diseases including cancer. Here we focus on the secretion and potential physiological function of non-pathological EVs secreted by polarized normal mammary epithelial cells. Using a transwell system to allow formation of epithelial polarity and EV collection from the apical versus basolateral compartments, we found that impaired secretion of EVs by knockdown of RAB27A or RAB27B suppressed the establishment of mammary epithelial polarity, and that addition of apical but not basolateral EVs suppressed epithelial polarity in a dose-dependent manner. This suggests that apical EV secretion contributes to epithelial polarity, and a possible mechanism is through removal of certain intracellular molecules. In contrast, basolateral but not apical EVs promoted migration of mammary epithelial cells in a motility assay. The protein contents of apical and basolateral EVs from MCF10A and primary human mammary epithelial cells were determined by mass spectrometry proteomic analysis, identifying apical-EV-enriched and basolateral-EV-enriched proteins that may contribute to different physiological functions. Most of these proteins differentially secreted by normal mammary epithelial cells through polarized EV release no longer showed polarized secretion in MCF10A-derived transformed epithelial cells. Our results suggest an essential role of EV secretion in normal mammary epithelial polarization and distinct protein contents and functions in apical versus basolateral EVs secreted by polarized mammary epithelia

Multi-Scale Semantic Segmentation and Spatial Relationship Recognition of Remote Sensing Images Based on an Attention Model

A comprehensive interpretation of remote sensing images involves not only remote sensing object recognition but also the recognition of spatial relations between objects. Especially in the case of different objects with the same spectrum, the spatial relationship can help interpret remote sensing objects more accurately. Compared with traditional remote sensing object recognition methods, deep learning has the advantages of high accuracy and strong generalizability regarding scene classification and semantic segmentation. However, it is difficult to simultaneously recognize remote sensing objects and their spatial relationship from end-to-end only relying on present deep learning networks. To address this problem, we propose a multi-scale remote sensing image interpretation network, called the MSRIN. The architecture of the MSRIN is a parallel deep neural network based on a fully convolutional network (FCN), a U-Net, and a long short-term memory network (LSTM). The MSRIN recognizes remote sensing objects and their spatial relationship through three processes. First, the MSRIN defines a multi-scale remote sensing image caption strategy and simultaneously segments the same image using the FCN and U-Net on different spatial scales so that a two-scale hierarchy is formed. The output of the FCN and U-Net are masked to obtain the location and boundaries of remote sensing objects. Second, using an attention-based LSTM, the remote sensing image captions include the remote sensing objects (nouns) and their spatial relationships described with natural language. Finally, we designed a remote sensing object recognition and correction mechanism to build the relationship between nouns in captions and object mask graphs using an attention weight matrix to transfer the spatial relationship from captions to objects mask graphs. In other words, the MSRIN simultaneously realizes the semantic segmentation of the remote sensing objects and their spatial relationship identification end-to-end. Experimental results demonstrated that the matching rate between samples and the mask graph increased by 67.37 percentage points, and the matching rate between nouns and the mask graph increased by 41.78 percentage points compared to before correction. The proposed MSRIN has achieved remarkable results

Three-Dimensional-Printed Guiding Template for Unicompartmental Knee Arthroplasty

Background. For unicompartmental knee arthroplasty (UKA), accurate alignment of the limb is crucial. This study is aimed at investigating the efficacy and safety of a three-dimensional printed patient-customized guiding template (3DGT) for UKA. Methods. A total of 22 patients receiving UKA were randomly divided into the 3DGT-UKA group (n=11) and traditional UKA group (T-UKA group; n=11). In the 3DGT-UKA group, the line and angle of osteotomy were decided on a 3D image of the limb reconstructed from imaging data; a guiding template was then designed and printed out. The patients in the T-UKA group underwent conventional UKA. Prosthesis size, operation time, postoperative drainage, hip–knee angle (HKA), pain, and Hospital for Special Surgery (HSS) scores were recorded at day 1, week 1, month 1, and month 3 after surgery. Results. There was no significant difference in the size of prostheses between the preoperatively designed and actually used in the 3DGT-UKA group (p>0.05). HKA was comparable in 3DGT-UKA and T-UKA patients. Operation time was shorter (53.6±6.4 minutes vs. 75.8±7.1 minutes) and wound drainage was less (93.2±3.9 mL vs. 85.2±3.0 mL) in 3DGT-UKA than in T-UKA (p<0.05). Hospital stay was shorter in the 3DGT-UKA group. The 3DGT-UKA group had a lower VAS score on day 1, week 1, and month 1 and a higher HSS score on week 1 and month 1 after surgery. No varus/valgus deformity or prosthesis loosening was observed in either group at the final follow-up. Conclusion. The 3D-printed patient-customized guiding template may help decrease operation time, decrease blood loss, and improve short-term clinical outcomes in patients undergoing UKA surgery