Generate your neural signals from mine: individual-to-individual EEG converters

Most models in cognitive and computational neuroscience trained on one subject do not generalize to other subjects due to individual differences. An ideal individual-to-individual neural converter is expected to generate real neural signals of one subject from those of another one, which can overcome the problem of individual differences for cognitive and computational models. In this study, we propose a novel individual-to-individual EEG converter, called EEG2EEG, inspired by generative models in computer vision. We applied THINGS EEG2 dataset to train and test 72 independent EEG2EEG models corresponding to 72 pairs across 9 subjects. Our results demonstrate that EEG2EEG is able to effectively learn the mapping of neural representations in EEG signals from one subject to another and achieve high conversion performance. Additionally, the generated EEG signals contain clearer representations of visual information than that can be obtained from real data. This method establishes a novel and state-of-the-art framework for neural conversion of EEG signals, which can realize a flexible and high-performance mapping from individual to individual and provide insight for both neural engineering and cognitive neuroscience.Comment: Proceedings of the 45th Annual Meeting of the Cognitive Science Society (CogSci 2023

rPPG-MAE: Self-supervised Pre-training with Masked Autoencoders for Remote Physiological Measurement

Remote photoplethysmography (rPPG) is an important technique for perceiving human vital signs, which has received extensive attention. For a long time, researchers have focused on supervised methods that rely on large amounts of labeled data. These methods are limited by the requirement for large amounts of data and the difficulty of acquiring ground truth physiological signals. To address these issues, several self-supervised methods based on contrastive learning have been proposed. However, they focus on the contrastive learning between samples, which neglect the inherent self-similar prior in physiological signals and seem to have a limited ability to cope with noisy. In this paper, a linear self-supervised reconstruction task was designed for extracting the inherent self-similar prior in physiological signals. Besides, a specific noise-insensitive strategy was explored for reducing the interference of motion and illumination. The proposed framework in this paper, namely rPPG-MAE, demonstrates excellent performance even on the challenging VIPL-HR dataset. We also evaluate the proposed method on two public datasets, namely PURE and UBFC-rPPG. The results show that our method not only outperforms existing self-supervised methods but also exceeds the state-of-the-art (SOTA) supervised methods. One important observation is that the quality of the dataset seems more important than the size in self-supervised pre-training of rPPG. The source code is released at https://github.com/linuxsino/rPPG-MAE

Environmental Sustainable Development: Study on the Value Realization Mechanism and Diversified Realization Path of Ecological Products under the Background of "Double Carbon"

Under the background of carbon neutrality and common prosperity, the importance of carbon sinks is constantly highlighted. Realizing the value of carbon sink ecological products is not only conducive to the realization of the goal of carbon neutrality, but also an effective way to promote the endogenous development of rural areas and promote common prosperity. Broadening the value transformation channel of carbon sink ecological products and realizing the sustainable transformation from "green water and green hills" to "Jinshan and Yinshan" provide a new way to achieve the goal of carbon neutrality and common prosperity. Based on the theoretical analysis of the traditional connotation, formation mechanism and value of carbon sink ecological products, this paper summarizes the main ways and existing problems of realizing carbon sink ecological value in China, systematically analyzes the two-way promotion relationship between the double carbon target and the realization of carbon sink ecological product value, and emphasizes the important role of carbon sink ecological value realization and participation in carbon market transactions in carbon emission reduction. It also summarizes the experience of international typical cases. Finally, suggestions and reflections were put forward for redistributing the supply of ecological products based on carbon sinks, improving the basic system for calculating the value of ecological products, strengthening the government's guiding role, improving the ecological rights trading market, and innovating financial models, providing reference for optimizing the innovative mechanism and path for realizing the value of ecological products in China under the "dual carbon" goal

Reliability evaluation of a multi-state system with dependent components and imprecise parameters: A structural reliability treatment

Reliability evaluation of a multi-state system (MSS) with dependent components makes much practical sense because the independent identical assumption (i.i.d.) assumption between different components is sometimes impractical in the context of real engineering cases. The task becomes more challenging if imprecision gets involved due to the pervasive uncertainty. The loss of monotony resulting from the introduction of imprecise parameters makes many analytical reliability methods not applied. To address this challenge, in this paper, we develop a survival signature-based reliability framework for an MSS taking into account both dependence and uncertainty. In our framework, the survival function is derived through some unique structural reliability treatments. Vine copula and imprecise probability are integrated and embedded within the framework to address the case that dependence and imprecision simultaneously appear. Implementation-wise, two numerical simulation algorithms are developed to address some complicated cases in which the analytical solution is not available. For demonstration and validation, both the numerical case and application examples are presented. The results show the superiority of the proposed method and its potential in real engineering use

Patchouli alcohol improved diarrhea-predominant irritable bowel syndrome by regulating excitatory neurotransmission in the myenteric plexus of rats

Background and Purpose: Irritable bowel syndrome (IBS) is usually associated with chronic gastrointestinal disorders. Its most common subtype is accompanied with diarrhea (IBS-D). The enteric nervous system (ENS) modulates major gastrointestinal motility and functions whose aberration may induce IBS-D. The enteric neurons are susceptible to long-term neurotransmitter level alterations. The patchouli alcohol (PA), extracted from Pogostemonis Herba, has been reported to regulate neurotransmitter release in the ENS, while its effectiveness against IBS-D and the underlying mechanism remain unknown.Experimental Approach: In this study, we established an IBS-D model in rats through chronic restraint stress. We administered the rats with 5, 10, and 20 mg/kg of PA for intestinal and visceral examinations. The longitudinal muscle myenteric plexus (LMMP) neurons were further immunohistochemically stained for quantitative, morphological, and neurotransmitters analyses.Key Results: We found that PA decreased visceral sensitivity, diarrhea symptoms and intestinal transit in the IBS-D rats. Meanwhile, 10 and 20 mg/kg of PA significantly reduced the proportion of excitatory LMMP neurons in the distal colon, decreased the number of acetylcholine (Ach)- and substance P (SP)-positive neurons in the distal colon and restored the levels of Ach and SP in the IBS-D rats.Conclusion and Implications: These findings indicated that PA modulated LMMP excitatory neuron activities, improved intestinal motility and alleviated IBS-induced diarrheal symptoms, suggesting the potential therapeutic efficacy of PA against IBS-D

Identification of ferroptosis-related molecular clusters and genes for diabetic osteoporosis based on the machine learning

BackgroundDiabetic osteoporosis exhibits heterogeneity at the molecular level. Ferroptosis, a controlled form of cell death brought on by a buildup of lipid peroxidation, contributes to the onset and development of several illnesses. The aim was to explore the molecular subtypes associated with ferroptosis in diabetic osteoporosis at the molecular level and to further elucidate the potential molecular mechanisms.MethodsIntegrating the CTD, GeneCards, FerrDb databases, and the microarray data of GSE35958, we identified ferroptosis-related genes (FRGs) associated with diabetic osteoporosis. We applied unsupervised cluster analysis to divide the 42 osteoporosis samples from the GSE56814 microarray data into different subclusters based on FRGs. Subsequently, FRGs associated with two ferroptosis subclusters were obtained by combining database genes, module-related genes of WGCNA, and differentially expressed genes (DEGs). Eventually, the key genes from FRGs associated with diabetic osteoporosis were identified using the least absolute shrinkage and selection operator (LASSO), Boruta, support vector machine recursive feature elimination (SVM ­ RFE), and extreme gradient boosting (XGBoost) machine learning algorithms. Based on ROC curves of external datasets (GSE56815), the model’s efficiency was examined.ResultsWe identified 15 differentially expressed FRGs associated with diabetic osteoporosis. In osteoporosis, two distinct molecular clusters related to ferroptosis were found. The expression results and GSVA analysis indicated that 15 FRGs exhibited significantly different biological functions and pathway activities in the two ferroptosis subclusters. Therefore, we further identified 17 FRGs associated with diabetic osteoporosis between the two subclusters. The results of the comprehensive analysis of 17 FRGs demonstrated that these genes were heterogeneous and had a specific interaction between the two subclusters. Ultimately, the prediction model had a strong foundation and excellent AUC values (0.84 for LASSO, 0.84 for SVM ­ RFE, 0.82 for Boruta, and 0.81 for XGBoost). IDH1 is a common gene to all four algorithms thus being identified as a key gene with a high AUC value (AUC = 0.698).ConclusionsAs a ferroptosis regulator, IDH1 is able to distinguish between distinct molecular subtypes of diabetic osteoporosis, which may offer fresh perspectives on the pathogenesis of the disease’s clinical symptoms and prognostic heterogeneity

PyPose v0.6: The Imperative Programming Interface for Robotics

PyPose is an open-source library for robot learning. It combines a learning-based approach with physics-based optimization, which enables seamless end-to-end robot learning. It has been used in many tasks due to its meticulously designed application programming interface (API) and efficient implementation. From its initial launch in early 2022, PyPose has experienced significant enhancements, incorporating a wide variety of new features into its platform. To satisfy the growing demand for understanding and utilizing the library and reduce the learning curve of new users, we present the fundamental design principle of the imperative programming interface, and showcase the flexible usage of diverse functionalities and modules using an extremely simple Dubins car example. We also demonstrate that the PyPose can be easily used to navigate a real quadruped robot with a few lines of code

Contralateral delay activity as a marker of visual working memory capacity: a multi-site registered replication

Visual working memory (VWM) is a temporary storage system capable of retaining information that can be accessed and manipulated by higher cognitive processes, thereby facilitating a wide range of cognitive functions. Electroencephalography (EEG) is used to understand the neural correlates of VWM with high temporal precision, and one commonly used EEG measure is an event-related potential called the contralateral delay activity (CDA). In a landmark study by Vogel and Machizawa (2004), the authors found that the CDA amplitude increases with the number of items stored in VWM and plateaus around three to four items, which is thought to represent the typical adult working memory capacity. Critically, this study also showed that the increase in CDA amplitude between two-item and four-item arrays correlated with individual subjects’ VWM performance. Although these results have been supported by subsequent studies, a recent study suggested that the number of subjects used in experiments investigating the CDA may not be sufficient to detect differences in set size and to provide a reliable account of the relationship between behaviorally measured VWM capacity and the CDA amplitude. To address this, the current study, as part of the #EEGManyLabs project, aims to conduct a multi-site replication of Vogel and Machizawa's (2004) seminal study on a large sample of participants, with a pre-registered analysis plan. Through this, our goal is to contribute to deepening our understanding of the neural correlates of visual working memory