Feature extraction of four-class motor imagery EEG signals based on functional brain network

Objective. A motor-imagery-based brain–computer interface (MI-BCI) provides an alternative way for people to interface with the outside world. However, the classification accuracy of MI signals remains challenging, especially with an increased number of classes and the presence of high variations with data from multiple individual people. This work investigates electroencephalogram (EEG) signal processing techniques, aiming to enhance the classification performance of multiple MI tasks in terms of tackling the challenges caused by the vast variety of subjects. Approach. This work introduces a novel method to extract discriminative features by combining the features of functional brain networks with two other feature extraction algorithms: common spatial pattern (CSP) and local characteristic-scale decomposition (LCD). After functional brain networks are established from the MI EEG signals of the subjects, the measures of degree in the binary networks are extracted as additional features and fused with features in the frequency and spatial domains extracted by the CSP and LCD algorithms. A real-time BCI robot control system is designed and implemented with the proposed method. Subjects can control the movement of the robot through four classes of MI tasks. Both the BCI competition IV dataset 2a and real-time data acquired in our designed system are used to validate the performance of the proposed method. Main results. As for the offline data experiment results, the average classification accuracy of the proposed method reaches 79.7%, outperforming the majority of popular algorithms. Experimental results with real-time data also prove the proposed method to be highly promising in its real-time performance. Significance. The experimental results show that our proposed method is robust in extracting discriminative brain activity features when performing different MI tasks, hence improving the classification accuracy in four-class MI tasks. The high classification accuracy and low computational demand show a considerable practicality for real-time rehabilitation systems

Vote2Cap-DETR++: Decoupling Localization and Describing for End-to-End 3D Dense Captioning

3D dense captioning requires a model to translate its understanding of an input 3D scene into several captions associated with different object regions. Existing methods adopt a sophisticated "detect-then-describe" pipeline, which builds explicit relation modules upon a 3D detector with numerous hand-crafted components. While these methods have achieved initial success, the cascade pipeline tends to accumulate errors because of duplicated and inaccurate box estimations and messy 3D scenes. In this paper, we first propose Vote2Cap-DETR, a simple-yet-effective transformer framework that decouples the decoding process of caption generation and object localization through parallel decoding. Moreover, we argue that object localization and description generation require different levels of scene understanding, which could be challenging for a shared set of queries to capture. To this end, we propose an advanced version, Vote2Cap-DETR++, which decouples the queries into localization and caption queries to capture task-specific features. Additionally, we introduce the iterative spatial refinement strategy to vote queries for faster convergence and better localization performance. We also insert additional spatial information to the caption head for more accurate descriptions. Without bells and whistles, extensive experiments on two commonly used datasets, ScanRefer and Nr3D, demonstrate Vote2Cap-DETR and Vote2Cap-DETR++ surpass conventional "detect-then-describe" methods by a large margin. Codes will be made available at https://github.com/ch3cook-fdu/Vote2Cap-DETR

LL3DA: Visual Interactive Instruction Tuning for Omni-3D Understanding, Reasoning, and Planning

Recent advances in Large Multimodal Models (LMM) have made it possible for various applications in human-machine interactions. However, developing LMMs that can comprehend, reason, and plan in complex and diverse 3D environments remains a challenging topic, especially considering the demand for understanding permutation-invariant point cloud 3D representations of the 3D scene. Existing works seek help from multi-view images, and project 2D features to 3D space as 3D scene representations. This, however, leads to huge computational overhead and performance degradation. In this paper, we present LL3DA, a Large Language 3D Assistant that takes point cloud as direct input and respond to both textual-instructions and visual-prompts. This help LMMs better comprehend human interactions and further help to remove the ambiguities in cluttered 3D scenes. Experiments show that LL3DA achieves remarkable results, and surpasses various 3D vision-language models on both 3D Dense Captioning and 3D Question Answering.Comment: Project Page: https://ll3da.github.io

Cross-modal subspace learning with scheduled adaptive margin constraints

This work has been partially funded by the CMU Portugal research project GoLocal Ref. CMUP-ERI/TIC/0046/2014, by the H2020 ICT project COGNITUS with the grant agreement no 687605 and by the FCT project NOVA LINCS Ref. UID/CEC/04516/2019. We also gratefully acknowledge the support of NVIDIA Corporation with the donation of the GPUs used for this research.Cross-modal embeddings, between textual and visual modalities, aim to organise multimodal instances by their semantic correlations. State-of-the-art approaches use maximum-margin methods, based on the hinge-loss, to enforce a constant margin m, to separate projections of multimodal instances from different categories. In this paper, we propose a novel scheduled adaptive maximum-margin (SAM) formulation that infers triplet-specific constraints during training, therefore organising instances by adaptively enforcing inter-category and inter-modality correlations. This is supported by a scheduled adaptive margin function, that is smoothly activated, replacing a static margin by an adaptively inferred one reflecting triplet-specific semantic correlations while accounting for the incremental learning behaviour of neural networks to enforce category cluster formation and enforcement. Experiments on widely used datasets show that our model improved upon state-of-the-art approaches, by achieving a relative improvement of up to approximate to 12.5% over the second best method, thus confirming the effectiveness of our scheduled adaptive margin formulation.publishersversionpublishe

Circular RNA circNOL10 Inhibits Lung Cancer Development by Promoting SCLM1-Mediated Transcriptional Regulation of the Humanin Polypeptide Family

circNOL10 is a circular RNA expressed at low levels in lung cancer, though its functions in lung cancer remain unknown. Here, the function and molecular mechanism of circNOL10 in lung cancer development are investigated using in vitro and in vivo studies, and it is shown that circNOL10 significantly inhibits the development of lung cancer and that circNOL10 expression is co‐regulated by methylation of its parental gene Pre‐NOL10 and by splicing factor epithelial splicing regulatory protein 1 (ESRP1). circNOL10 promotes the expression of transcription factor sex comb on midleg‐like 1 (SCML1) by inhibiting transcription factor ubiquitination and thus also affects regulation of the humanin (HN) polypeptide family by SCML1. circNOL10 also affects mitochondrial function through regulating the humanin polypeptide family and affecting multiple signaling pathways, ultimately inhibiting cell proliferation and cell cycle progression, and promoting the apoptosis of lung cancer cells, thereby inhibiting lung cancer development. This study investigates the functions and molecular mechanisms of circNOL10 in the development of lung cancer and reveals its involvement in the transcriptional regulation of the HN polypeptide family by SCML1. The results also demonstrate the inhibitory effect of HN on lung cancer cells growth. These findings may identify novel targets for the molecular therapy of lung cancer

The use of PET/MRI in radiotherapy

Abstract Positron emission tomography/magnetic resonance imaging (PET/MRI) is a hybrid imaging technique that quantitatively combines the metabolic and functional data from positron emission tomography (PET) with anatomical and physiological information from MRI. As PET/MRI technology has advanced, its applications in cancer care have expanded. Recent studies have demonstrated that PET/MRI provides unique advantages in the field of radiotherapy and has become invaluable in guiding precision radiotherapy techniques. This review discusses the rationale and clinical evidence supporting the use of PET/MRI for radiation positioning, target delineation, efficacy evaluation, and patient surveillance. Critical relevance statement This article critically assesses the transformative role of PET/MRI in advancing precision radiotherapy, providing essential insights into improved radiation positioning, target delineation, efficacy evaluation, and patient surveillance in clinical radiology practice. Key points • The emergence of PET/MRI will be a key bridge for precise radiotherapy. • PET/MRI has unique advantages in the whole process of radiotherapy. • New tracers and nanoparticle probes will broaden the use of PET/MRI in radiation. • PET/MRI will be utilized more frequently for radiotherapy. Graphical Abstrac

Archaeal Lipids Regulating the Trimeric Structure Dynamics of Bacteriorhodopsin for Efficient Proton Release and Uptake

S-TGA-1 and PGP-Me are native archaeal lipids associated with the bacteriorhodopsin (bR) trimer and contribute to protein stabilization and native dynamics for proton transfer. However, little is known about the underlying molecular mechanism of how these lipids regulate bR trimerization and efficient photocycling. Here, we explored the specific binding of S-TGA-1 and PGP-Me with the bR trimer and elucidated how specific interactions modulate the bR trimeric structure and proton release and uptake using long-term atomistic molecular dynamic simulations. Our results showed that S-TGA-1 and PGP-Me are essential for stabilizing the bR trimer and maintaining the coherent conformational dynamics necessary for proton transfer. The specific binding of S-TGA-1 with W80 and K129 regulates proton release on the extracellular surface by forming a “Glu-shared” model. The interaction of PGP-Me with K40 ensures proton uptake by accommodating the conformation of the helices to recruit enough water molecules on the cytoplasmic side. The present study results could fill in the theoretical gaps of studies on the functional role of archaeal lipids and could provide a reference for other membrane proteins containing similar archaeal lipids

Intelligent Supply Chain Integration and Management Based on Cloud of Things

The fierce global competition and market turbulence has been forcing the enterprises towards to the integration and intelligence for supply chain management, and the seamless information sharing and collaboration as well as operation agility are the challenges which need to be conquered, in terms of the highly distributed and heterogeneous resources located in separated warehouses. Although a number of works have been done to achieve the aforementioned targets, few of them are able to provide an overall integration and intelligence support for such system management. In this context, a novel intelligent supply chain integration and management system based on Cloud of Things is presented, in order to provide flexible and agile approaches to facilitate the resource sharing and participant collaboration in the whole supply chain life cycle. Furthermore, the enabling technologies, such as intelligent supply chain condition perception, heterogeneous network access convergence, and resource servicisation, are also studied. Finally, a case study together with the prototype system is implemented and demonstrates that the developed system can efficiently realise the integration of supply chain processes in the form of services, and also provide the effective intelligence support for physical resource management, so as to achieve an overall performance assurance for the system operation