Divalent cations activate TRPV1 through promoting conformational change of the extracellular region

Divalent cations Mg and Ba selectively and directly potentiate transient receptor potential vanilloid type 1 heat activation by lowering the activation threshold into the room temperature range. We found that Mg potentiates channel activation only from the extracellular side; on the intracellular side, Mg inhibits channel current. By dividing the extracellularly accessible region of the channel protein into small segments and perturbing the structure of each segment with sequence replacement mutations, we observed that the S1-S2 linker, the S3-S4 linker, and the pore turret are all required for Mg potentiation. Sequence replacements at these regions substantially reduced or eliminated Mg-induced activation at room temperature while sparing capsaicin activation. Heat activation was affected by many, but not all, of these structural alternations. These observations indicate that extracellular linkers and the turret may interact with each other. Site-directed fluorescence resonance energy transfer measurements further revealed that, like heat, Mg also induces structural changes in the pore turret. Interestingly, turret movement induced by Mg precedes channel activation, suggesting that Mg-induced conformational change in the extracellular region most likely serves as the cause of channel activation instead of a coincidental or accommodating structural adjustment

Do We Really Need a Complex Agent System? Distill Embodied Agent into a Single Model

With the power of large language models (LLMs), open-ended embodied agents can flexibly understand human instructions, generate interpretable guidance strategies, and output executable actions. Nowadays, Multi-modal Language Models~(MLMs) integrate multi-modal signals into LLMs, further bringing richer perception to entity agents and allowing embodied agents to perceive world-understanding tasks more delicately. However, existing works: 1) operate independently by agents, each containing multiple LLMs, from perception to action, resulting in gaps between complex tasks and execution; 2) train MLMs on static data, struggling with dynamics in open-ended scenarios; 3) input prior knowledge directly as prompts, suppressing application flexibility. We propose STEVE-2, a hierarchical knowledge distillation framework for open-ended embodied tasks, characterized by 1) a hierarchical system for multi-granular task division, 2) a mirrored distillation method for parallel simulation data, and 3) an extra expert model for bringing additional knowledge into parallel simulation. After distillation, embodied agents can complete complex, open-ended tasks without additional expert guidance, utilizing the performance and knowledge of a versatile MLM. Extensive evaluations on navigation and creation tasks highlight the superior performance of STEVE-2 in open-ended tasks, with $1.4 \times$ - $7.3 \times$ in performance.Comment: arXiv admin note: text overlap with arXiv:2403.0828

Morphological and Comparative Transcriptome Analysis of Three Species of Five-Needle Pines: Insights Into Phenotypic Evolution and Phylogeny

Pinus koraiensis, Pinus sibirica, and Pinus pumila are the major five-needle pines in northeast China, with substantial economic and ecological values. The phenotypic variation, environmental adaptability and evolutionary relationships of these three five-needle pines remain largely undecided. It is therefore important to study their genetic differentiation and evolutionary history. To obtain more genetic information, the needle transcriptomes of the three five-needle pines were sequenced and assembled. To explore the relationship of sequence information and adaptation to a high mountain environment, data on needle morphological traits [needle length (NL), needle width (NW), needle thickness (NT), and fascicle width (FW)] and 19 climatic variables describing the patterns and intensity of temperature and precipitation at six natural populations were recorded. Geographic coordinates of altitude, latitude, and longitude were also obtained. The needle morphological data was combined with transcriptome information, location, and climate data, for a comparative analysis of the three five-needle pines. We found significant differences for needle traits among the populations of the three five-needle pine species. Transcriptome analysis showed that the phenotypic variation and environmental adaptation of the needles of P. koraiensis, P. sibirica, and P. pumila were related to photosynthesis, respiration, and metabolites. Analysis of orthologs from 11 Pinus species indicated a closer genetic relationship between P. koraiensis and P. sibirica compared to P. pumila. Our study lays a foundation for genetic improvement of these five-needle pines and provides insights into the adaptation and evolution of Pinus species

Accurate identification and measurement of the precipitate area by two-stage deep neural networks in novel chromium-based alloys

The performance of advanced materials for extreme environments is underpinned by their microstructure, such as the size and distribution of nano- to micro-sized reinforcing phase(s). Chromium-based superalloys are a recently proposed alternative to conventional face-centred-cubic superalloys for high-temperature applications, e.g., Concentrated Solar Power. Their development requires the determination of precipitate volume fraction and size distribution using Electron Microscopy (EM), as these properties are crucial for the thermal stability and mechanical properties of chromium superalloys. Traditional approaches to EM image processing utilise filtering with a fixed contrast threshold, leads to weak robustness to background noise and poor generalisability to different materials. It also requires an enormous amount of time for manual object measurements on large datasets. Efficient and accurate object detection and segmentation are therefore highly desired to accelerate the development of novel materials like chromium-based superalloys. To address these bottlenecks, based on YOLOv5 and SegFormer structures, this study proposes an end-to-end, two-stage deep learning scheme, DT-SegNet, to perform object detection and segmentation for EM images. The proposed approach can thus benefit from the training efficiency of CNNs at the detection stage (i.e., a small number of training images required) and the accuracy of the ViT at the segmentation stage. Extensive numerical experiments demonstrate that the proposed DT-SegNet significantly outperforms the state-of-the-art segmentation tools offered by Weka and ilastik regarding a large number of metrics, including accuracy, precision, recall and F1-score. This model forms a useful tool to aid alloy development microstructure examinations, and offers significant advantages to address the large datasets associated with highthroughput alloy development approaches

Accurate identification and measurement of the precipitate area by two-stage deep neural networks in novel chromium-based alloys

The performance of advanced materials for extreme environments is underpinned by their microstructure, such as the size and distribution of nano- to micro-sized reinforcing phase(s). Chromium-based superalloys are a recently proposed alternative to conventional face-centred-cubic superalloys for high-temperature applications, e.g., Concentrated Solar Power. Their development requires the determination of precipitate volume fraction and size distribution using Electron Microscopy (EM), as these properties are crucial for the thermal stability and mechanical properties of chromium superalloys. Traditional approaches to EM image processing utilise filtering with a fixed contrast threshold, leads to weak robustness to background noise and poor generalisability to different materials. It also requires an enormous amount of time for manual object measurements on large datasets. Efficient and accurate object detection and segmentation are therefore highly desired to accelerate the development of novel materials like chromium-based superalloys. To address these bottlenecks, based on YOLOv5 and SegFormer structures, this study proposes an end-to-end, two-stage deep learning scheme, DT-SegNet, to perform object detection and segmentation for EM images. The proposed approach can thus benefit from the training efficiency of CNNs at the detection stage (i.e., a small number of training images required) and the accuracy of the ViT at the segmentation stage. Extensive numerical experiments demonstrate that the proposed DT-SegNet significantly outperforms the state-of-the-art segmentation tools offered by Weka and ilastik regarding a large number of metrics, including accuracy, precision, recall and F1-score. This model forms a useful tool to aid alloy development microstructure examinations, and offers significant advantages to address the large datasets associated with high-throughput alloy development approaches

On electronic structure of polymer-derived amorphous silicon carbide ceramics

The electronic structure of polymer-derived amorphous silicon carbide ceramics was studied by combining measurements of temperature-dependent conductivity and optical absorption. By comparing the experimental results to theoretical models, electronic structure was constructed for a carbon-rich amorphous silicon carbide, which revealed several unique features, such as deep defect energy level, wide band-tail band, and overlap between the band-tail band and defect level. These unique features were discussed in terms of the microstructure of the material and used to explain the electric behavior

Development and validation of a prognostic nomogram for rectal cancer patients who underwent surgical resection

Objective: The purpose of this study was to develop and validate a nomogram model for the prediction of survival outcome in rectal cancer patients who underwent surgical resection.Methods: A total of 9,919 consecutive patients were retrospectively identified using the Surveillance, Epidemiology, and End Results (SEER) database. Significant prognostic factors were determined by the univariate and multivariate Cox analysis. The nomogram model for the prediction of cancer-specific survival (CSS) in rectal cancer patients were developed based on these prognostic variables, and its predictive power was assessed by the concordance index (C-index). Calibration curves were plotted to evaluate the associations between predicted probabilities and actual observations. The internal and external cohort were used to further validate the predictive performance of the prognostic nomogram.Results: All patients from the SEER database were randomly split into a training cohort (n = 6,944) and an internal validation cohort (n = 2,975). The baseline characteristics of two cohorts was comparable. Independent prognostic factors were identified as age, pT stage, lymph node metastasis, serum CEA level, tumor size, differentiation type, perineural invasion, circumferential resection margin involvement and inadequate lymph node yield. In the training cohort, the C-index of the nomogram was 0.719 (95% CI: 0.696–0.742), which was significantly higher than that of the TNM staging system (C-index: 0.606, 95% CI: 0.583–0.629). The nomogram had a C-index of 0.726 (95% CI: 0.691–0.761) for the internal validation cohort, indicating a good predictive power. In addition, an independent cohort composed of 202 rectal cancer patients from our institution were enrolled as the external validation. Compared with the TNM staging system (C-index: 0.573, 95% CI: 0.492–0.654), the prognostic nomogram still showed a better predictive performance, with the C-index of 0.704 (95% CI: 0.626–0.782). Calibration plots showed a good consistency between predicted probability and the actual observation in the training and two validation cohorts.Conclusion: The nomogram showed an excellent predictive ability for survival outcome of rectal cancer patients, and it might provide an accurate prognostic stratification and help clinicians determine individualized treatment strategies

Exploring the therapeutic potential of diterpenes in gastric cancer: Mechanisms, efficacy, and clinical prospects

Gastric cancer (GC) remains a significant global health challenge, particularly prevalent in East Asia. Despite advancements in various treatment modalities, the prognosis for patients, especially those in advanced stages, remains poor, highlighting the need for innovative therapeutic approaches. This review explores the promising potential of diterpenes, naturally occurring compounds with robust anticancer properties, derived from diverse sources such as plants, marine organisms, and fungi. Diterpenes have shown the ability to influence reactive oxygen species (ROS) generation, ferroptosis, and autophagy, positioning them as attractive candidates for novel cancer therapies. This review explores the mechanisms of action of diterpenes and their clinical implications for the treatment of GC. Additionally, it addresses the challenges in translating these compounds from preclinical studies to clinical applications, emphasizing the need for further research to enhance their therapeutic profiles and minimize potential side effects. The discussion underscores the importance of diterpenes in future anticancer strategies, particularly in the fight against gastric cancer