Multiple Subjects Research Articles

A class alignment network based on self-attention for cross-subject EEG classification.

Due to the inherent variability in EEG signals across different individuals, domain adaptation and adversarial learning strategies are being progressively utilized to develop subject-specific classification models by leveraging data from other subjects. These approaches primarily focus on domain alignment and tend to overlook the critical task-specific class boundaries. This oversight can result in weak correlation between the extracted features and categories. To address these challenges, we propose a novel model that uses the known information from multiple subjects to bolster EEG classification for an individual subject through adversarial learning strategies. Our method begins by extracting both shallow and attention-driven deep features from EEG signals. Subsequently, we employ a class discriminator to encourage the same-class features from different domains to converge while ensuring that the different-class features diverge. This is achieved using our proposed discrimination loss function, which is designed to minimize the feature distance for samples of the same class across different domains while maximizing it for those from different classes. Additionally, our model incorporates two parallel classifiers that are harmonious yet distinct and jointly contribute to decision-making. Extensive testing on two publicly available EEG datasets has validated our model's efficacy and superiority.

Fluorescence Lifetime Imaging Enables In vivo Quantification of PD-L1 Expression and Inter-tumoral Heterogeneity.

Patient selection for cancer immunotherapy requires precise, quantitative readouts of biomarker expression in intact tumors that can be reliably compared across multiple subjects over time. The current clinical standard biomarker for assessing immunotherapy response is programmed death-ligand-1 (PD-L1) expression, typically quantified using immunohistochemistry. This method, however, only provides snapshots of PD-L1 expression status in microscopic regions of ex vivo specimens. While various targeted probes have been investigated for in vivo imaging of PD-L1, non-specific probe accumulation within the tumor microenvironment (TME) has hindered accurate quantification, limiting the utility for preclinical and clinical studies. Here, we demonstrated that in vivo time-domain (TD) fluorescence imaging of an anti-PD-L1 antibody tagged with the near-infrared fluorophore IRDye 800CW (αPDL1-800) can yield quantitative estimates of baseline tumor PD-L1 heterogeneity across untreated mice, as well as variations in PD-L1 expression in mice undergoing clinically relevant anti-PD1 treatment. The fluorescence lifetime (FLT) of PD-L1 bound αPDL1-800 was significantly longer than the FLT of nonspecifically accumulated αPDL1-800 in the TME. This FLT contrast allowed quantification of PD-L1 expression across mice both in superficial breast tumors using planar FLT imaging and in deep-seated liver tumors (>5 mm depth) using the asymptotic TD algorithm for fluorescence tomography. These findings suggest that fluorescence lifetime imaging can accelerate the preclinical investigation and clinical translation of new immunotherapy treatments by enabling robust quantification of receptor expression across subjects.

High performance P300 spellers using GPT2 word prediction with cross-subject training

ABSTRACT Amyotrophic lateral sclerosis (ALS) severely impairs patients’ ability to communicate, often leading to a decline in their quality of life within a few years of diagnosis. The P300 speller brain–computer interface (BCI) offers an alternative communication method by interpreting a subject’s EEG response to flashing characters presented on a grid interface. This paper addresses the common speed limitations encountered in training efficient P300-based multi-subject classifiers by introducing innovative ‘across-subject’ classifiers. We leverage a combination of the second-generation Generative Pre-Trained Transformer (GPT2) and Dijkstra’s algorithm to optimize stimuli and suggest word completion choices based on subjects’ typing history. Additionally, we employ a multi-layered smoothing technique to accommodate out-of-vocabulary (OOV) words. Through extensive simulations employing random sampling of EEG data from multiple subjects, we demonstrate significant speed enhancements in typing passages containing rare and OOV words. These optimizations result in approximately 10 % improvement in character-level typing speed and up to 40 % improvement in multi-word prediction. We demonstrate that augmenting standard row/column highlighting techniques with layered word prediction yields close-to-optimal performance. Furthermore, we explore both ‘within-subject’ and ‘across-subject’ training techniques, showing that speed improvements are consistent across both approaches.

Substantial projections from the lateral division of the lateral habenula to the dorsal raphe nucleus and from the lateral habenula to the contralateral ventral tegmental area

The lateral habenular nucleus (LHb) projects to the dorsal raphe nucleus (DRN) and ventral tegmental area (VTA). Prior studies have reported that the medial division of the LHb (LHb-m) mainly projects to the DRN, while the LHb mainly projects to the ipsilateral VTA; however, due to only a few studies of projection ratio analysis, the degree of projection of minor and major pathways remains unclear, and the potential significance of minor pathways may be overlooked. After injecting the retrograde tracer into the mice DRN, the proportion of labeled neurons was 63.50% in the LHb-m and 36.50% in the lateral division of the LHb (LHb-l). The proportion distributions of labeled neurons were 26.90% in the anterior LHb-m and 73.10% in the posterior LHb-m. When the retrograde tracer was injected into the VTA, the percentage of labeled neurons was 64.85% in the ipsilateral LHb and 35.15% in the contralateral LHb. The number of cells projecting from anterior and posterior ipsilateral LHb-m to the VTA varied among the individual injection sites. Our quantitative analysis with multiple subjects showed that the projections from LHb-l to DRN and from LHb to contralateral VTA were indeed minor pathways, however, they were substantial. These results provide an anatomical basis for the potential importance of minor pathways projected from the LHb to the VTA and DRN and for the difference in the densities of neurons projecting from the anterior and posterior LHb-m to the DRN.

Justice‐centered STEM education with multilingual learners to address societal challenges: A conceptual framework

AbstractWe propose a conceptual framework for STEM education that is centered around justice for minoritized groups. Justice‐centered STEM education engages all students in multiple STEM subjects, including data science and computer science, to explain and design solutions to societal challenges disproportionately impacting minoritized groups. We articulate the affordances of justice‐centered STEM education for one minoritized student group that has been traditionally denied meaningful STEM learning: multilingual learners (MLs). Justice‐centered STEM education with MLs leverages the assets they bring to STEM learning, including their transnational experiences and knowledge as well as their rich repertoire of meaning‐making resources. In this position paper, we propose our conceptual framework to chart a new research agenda on justice‐centered STEM education to address societal challenges with all students, especially MLs. Our conceptual framework incorporates four interrelated components by leveraging the convergence of multiple STEM disciplines to promote justice‐centered STEM education with MLs: (a) societal challenges in science education, (b) justice‐centered data science education, (c) justice‐centered computer science education, and (d) justice‐centered engineering education. The article illustrates our conceptual framework using the case of the COVID‐19 pandemic, which has presented an unprecedented societal challenge but also an unprecedented opportunity to cultivate MLs' assets toward promoting justice in STEM education. Finally, we describe how our conceptual framework establishes the foundation for a new research agenda that addresses increasingly complex, prevalent, and intractable societal challenges disproportionately impacting minoritized groups. We also consider broader issues pertinent to our conceptual framework, including the social and emotional impacts of societal challenges; the growth of science denial and misinformation; and factors associated with politics, ideology, and religion. Justice‐centered STEM education contributes to solving societal challenges that K‐12 students currently face while preparing them to shape a more just society.

Using Nobel Prize case-based learning in Medical Immunology to cultivate critical thinking dispositions for medical undergraduates

BackgroundCritical thinking has been regarded as an essential skill for college students, which is especially imperative for medical students to possess the ability to have in-depth insight into complex clinical situations. Medical Immunology is an overarching principle connecting multiple medical subjects, which emphasizes that immunity is both beneficial and harmful to the host, thus putting higher demands on students’ critical thinking. The utilization of Nobel Prize stories has been cited as a thematic framework for classroom teaching of other courses, showing numerous educational benefits. Therefore, this study rejuvenated a case-based teaching approach by creating and introducing a vibrant material library centered on numerous iconic Nobel Prize cases in Medical Immunology and evaluating its effects on the critical thinking of medical students.MethodsA total of 70 second-year medical undergraduates from the Beijing University of Chinese Medicine were divided into a control group and an experimental group of 35 cases each. Throughout the semester, the control received the traditional teaching method, and the experimental group adopted case-based teaching based on the Nobel prize-centered material library. The process of teaching design and practice was described using “Antitoxin and immunoserum therapy” as a classical example. A unified assessment of the critical thinking dispositions of participants was conducted at the beginning and end of the semester using the Critical Thinking Disposition Inventory-Chinese Version (CTDI-CV).ResultsIntra-group longitudinal comparisons and inter-group parallel evaluations indicated that, compared with the conventional teaching approach, Nobel Prize case-based learning induced a statistically significant increase in the overall score of the CTDI-CV, as well as the scores within the subdimensions of truth-seeking, analyticity, and maturity in judgment (p < 0.05). Pearson correlation analysis further indicated a positive correlation between the total score of the CTDI-CV and the final grade (p < 0.05), which emphasized the crucial role of critical thinking dispositions in academic achievement.ConclusionThe case-based classroom teaching centered on Nobel Prize cases for Medical Immunology can effectively improve the critical thinking dispositions of medical undergraduates, which contributes to cultivating high-level medical and healthcare talents with excellent comprehensive quality in the new era.

Study on the Problems and Countermeasures of Socialized Reform of Higher Education Logistics

The logistics of colleges and universities involves the clothing, food, housing and transportation of teachers and students, and is an important guarantee for the normal operation of colleges and universities and for teachers and students to work, study and live without fear. After nearly 40 years of socialization reform, China's colleges and universities have explored diversified logistics management models, mainly self-built logistics model, outsourcing logistics model and corporate logistics model. This paper analyzes the problems of “insufficient reform drive”, “lack of function of the main body of governance” and “poor mechanism” in the process of socialized reform of college logistics, and puts forward the countermeasures for the deepening and development of socialized reform of college logistics, which are: change the concept of strengthening the management of logistics, and improving the management of logistics. The countermeasures for the development of the socialization reform of college logistics are: to change the concept to enhance the driving force, to improve the governance capacity of multiple subjects in the logistics reform, to effectively play the role of governance, and to improve the synergistic mechanism.

‘Creating strong Sámi children’: teachers’ positioning of heritage language students

ABSTRACT This article presents an ethnographic study of Sámi heritage language education in public schools in a city in Norway. The Sámi people, the only recognised Indigenous people in Europe, are reclaiming their language and culture after intense assimilatory efforts by the Norwegian state. Based on ten weeks of fieldwork in Sámi heritage language (HL) education in an urban setting far from the Sámi core areas, the current article investigates how Sámi teachers and students negotiate subject positions in HL education based on Sámi culture, values, and traditional knowledge. The researcher observed lessons, wrote field notes, collected teaching materials, conducted interviews, and made video and audio recordings of classroom interactions. The analysis demonstrates how the teachers purposefully and iteratively attempted to position their students as Sámi and how the students responded to their teachers’ construction of a Sámi subject position. The findings suggest that HL teachers need to recognise and accommodate the complex and diverse connections that HL students have with their culture, language, and community, particularly in contexts of historical colonisation and assimilation. By making multiple subject positions available to students, teachers can contribute to a more inclusive and empowering learning environment that acknowledges the nuances of HL students’ subjectivities.

Bidding Strategy for the Alliance of Prosumer Aggregators in the Distribution Market

Photovoltaic energy storage system (PV-ESS) prosumer aggregators are characterized by a large number but small scale in the distribution system and are not competitive enough to participate in market transactions. For this reason, a prosumer aggregator alliance is proposed to participate in the distribution market bidding strategy. Firstly, based on the framework for prosumer aggregator alliances participating in distribution market trading, a bilevel bidding model is constructed. The upper level represents the optimal decision-making model for the prosumer aggregators, while the lower level constitutes the distribution market-clearing model. Secondly, the additional benefits obtained by the alliance are distributed more fairly using the improved Shapley value based on the PV self-consumption rate. Given the problem that the traditional diagonalization algorithm (DA) has an excessive number of iterations when solving the game equilibrium problem of multiple subjects, the DA is improved by optimizing the initial value of the inputs. Finally, case studies are conducted based on the improved IEEE-33 bus distribution system to validate the feasibility and economic viability of the proposed strategy. The case study results show that forming cooperative alliances to participate in market bidding can significantly increase overall profits. The improved DA reduces the number of bids and computation time by 75% and 80%, respectively. Additionally, the improved Shapley value facilitates compensation for some of the aggregators.

Mystery Box Game: A Game-Based Learning to Improve the Academic Achievement of Grade 10 Learners in Probability

This action research investigated the effectiveness of integrating the Mystery Box game to improve the academic achievement of grade 10 learners in Probability at a public school in Ozamiz City. The study, conducted in the 2024 school year, employed a classroom-based action research design with 41 purposively sampled participants. Pre and posttests assessed probability understanding, while a comprehensive lesson plan and a questionnaire gathered information on improvements after a game intervention. Statistical analysis included frequency, Percentage, mean, standard deviation, T-test, and thematic analysis. The findings revealed that integrating the Mystery Box game significantly increased learners' academic achievement in Probability. In conclusion, the post-intervention outcomes decisively confirmed the game's effectiveness in enhancing probability learning, fostering a love for learning, and promoting academic performance and social connections. The research recommended consistently integrating game-based learning in Probability, teacher training in gamification, monitoring and feedback on student progress, and applying the Mystery Box Game to enhance engagement and comprehension across multiple subjects.

The Corrective Feedback in the Students’ Speaking Ability: A Comparative Study

This study investigated whether extensive and comprehensive oral recasting is more effective than explicit correction. The primary focus was on how feedback affects English language learning. Recasts that are intensive are defined as those that receive feedback on a single topic, whereas extensive feedback is defined as errors on multiple subjects and domains. Four groups of ten students each were formed from forty students from a Senior High School. A general English book called Think 4, a storybook, and an English grammar book called Grammar in Use were the study's instruments. In line with the study's aims, they were given feedback that was either explicit or implicit, thorough or in-depth. In accordance with the study's aims, they were given feedback that was either explicit or implicit, long or brief. The findings showed that there wasn't much of a difference between students who got implicit and explicit feedback. Additionally, it was discovered that every technique had the same effect on students' capacity to communicate.

Making sense of big questions that require multiple subjects: preliminary theorisation of an integrative philosophy of knowledge and empirical indications of a lack of subject connection within school curricula

AbstractThis paper provides theorisation about a novel concept for education: an integrative philosophy of knowledge (IPK). This is proposed for school curricula to relate multiple subjects to big questions of personal and existential importance. Critical contemporary issues such as climate change education require multiple subject contributions but there is little clarity in the English education guidance on this drawing together, beyond the encouragement to do so in priority areas. Leading British thinkers have engaged the idea of the unity of knowledge, not without difficulty and criticism. The paper identifies reasons for a practical approach to consilience in school curricula. In addition to preliminary theorisation, in methodological ‘harmony’, this paper adds tentative empirical findings from a related research project that provides quantitative indications that such multi-subject connection is likely not happening. The findings are not definitive, but the perceptions of three groups of beginning and experienced teachers (secondary Religious Education (RE), secondary science and primary teachers) indicate that school curricula do not reflect an integrative approach to climate change education, and sex and relationships education, despite policy priority. A significant national survey could not detect many signs of connection. The paper speculates that RE could have a key epistemic role for the curriculum as a whole in this regard.

Cohesion Model and Scenario Simulation for Risk Prevention of “Blockchain + Agriculture Crowdfunding” from the Synergistic Perspective

Aiming at the problems such as scattered subjects, static mode and single function, this paper explores a dynamic synergistic risk prevention mode of agricultural crowdfunding that adapts to the age of data intelligence. Based on multiple prevention subjects, the blockchain technology and cohesion principle are introduced into agricultural crowdfunding prevention, and then a risk prevention dynamic model of “blockchain + agricultural crowdfunding” is constructed from the synergistic perspective. Fuzzy cognitive map model and its reasoning mechanism are used to simulate four scenarios of 1-subject, 2-subjects, 3-subjects, and 4-subjects respectively in the dynamic model and the static model. The results show that: as the number of risk prevention subjects increases, so does the cohesion, and the cohesion of multi-subjects reaches a higher state in less time and the steady-state faster, even though the fluctuation range reduces. The risk value for the multi-agent reduces rapidly and reaches a lower risk value in a shorter period of time. Overall, cohesion increases while risk reduces. In the same scenario, dynamic mode cohesion is stronger than static mode cohesion, and it takes less time to attain the steady-state value. Under various scenarios, the dynamic model’s cohesiveness steady-state value increases with the number of subjects, which is stronger than the static model’s. This study not only enriches and develops the theory of risk management of agricultural crowdfunding, but also provides a decision-making basis for the enterprises and the government, etc., to formulate, adjust, and implement risk prevention measures.

Traditional Chinese Medicine (TCM)-Inspired Fully Printed Soft Pressure Sensor Array with Self-Adaptive Pressurization for Highly Reliable Individualized Long-Term Pulse Diagnostics.

Reliable, non-invasive, continuous monitoring of pulse and blood pressure is essential for the prevention and diagnosis of cardiovascular diseases. However, the pulse wave varies drastically among individuals or even over time in the same individual, presenting significant challenges for the existing pulse sensing systems. Inspired by pulse diagnosis methods in traditional Chinese medicine (TCM), this work reports a self-adaptive pressure sensing platform (PSP) that combines the fully printed flexible pressure sensor array with an adaptive wristband-style pressure system can identify the optimal pulse signal. Besides the detected pulse rate/width/length, "Cun, Guan, Chi" position, and "floating, moderate, sinking" pulse features, the PSP combined with a machine learning-based linear regression model can also accurately predict blood pressure such as systolic, diastolic, and mean arterial pressure values. The developed diagnostic platform is demonstrated for highly reliable long-term monitoring and analysis of pulse and blood pressure across multiple human subjects over time. The design concept and proof-of-the-concept demonstrations also pave the way for the future developments of flexible sensing devices/systems for adaptive individualized monitoring in the complex practical environments for personalized medicine, along with the support for the development of digital TCM.

MSS-JDA: Multi-source self-selected joint domain adaptation method based on cross-subject EEG emotion recognition

The electroencephalogram (EEG) is widely used to identify different emotional states. However, cross-subject emotion recognition based on EEG data is challenging due to physiological differences among participants. Many studies have proposed using multi-source domain adaptation methods to address cross-subject issues and have achieved promising results. multi-source domain adaptation involves constructing independent branches for EEG data from multiple subjects, using one-to-one domain adaptation, and extracting domain-specific features, followed by reasoning through multiple branches. However, a current practical obstacle is the increasing computational time cost as the number of source domains grows. Moreover, too many source domains may lead to domain shift issues. To address these challenges, we propose a novel emotion recognition method based on Multi-Source Domain Branch Self-Selected Joint Domain Adaptation (MSS-JDA). Initially, we construct a shared feature extractor for multiple source domain branches to extract common low-level features. Next, we use a one-to-one associated joint adaptation method to extract features specific to each domain. During training, our method of multi-source domain branch self-selection, guided by prior knowledge in the early training phase, prunes source domain branches that deviate significantly from the target domain, retaining only a certain number of source domain branches close to the target domain for continued training. Finally, domain-specific features are input into their respective classifiers to obtain N predictions, and their average is taken as the final result. Extensive experiments on the SEED and SEED-IV datasets demonstrate that the MSS-JDA model achieves accuracy and standard deviation scores of 93.78%, 3.39 and 78.93%, 7.39 respectively, surpassing most existing models. The experiments indicate that the proposed MSS-JDA method improves the accuracy of EEG emotion recognition classification, thus benefiting practical applications in EEG classification.

Detecting dynamic interaction of multiple subjects in policy information communication in China

PurposeAs an important part of national governance, the online communication of education policies usually attracts the attention of many subjects, including the public and the media. Existing research mainly focuses on analysing communication behaviour of a single subject. However, with the rapid development of social media, policy information communication is often accompanied by the participation of multiple subjects and forms diversified communication behaviours and interaction patterns. The comprehensive identification of multiple subjects and their interactions can accurately depict the communication process and effectively support the efficient communication of policies. Therefore, this paper aims to conduct fine-grained analysis on the multiple subjects in information communication of the education policy.Design/methodology/approachThis paper explored the communication and interaction process of the education policy via multidimensional analysis. Specifically, the authors firstly obtained multi-source communication data to identify key communication subjects. Secondly, the authors mined the communication contents generated by communication subjects to measure the diversified correlations between subjects. Finally, the authors depicted the interaction of subjects in policy information communication.FindingsThe experimental results reveal that there are multiple key subjects in the policy information communication, and the communication roles of the subjects change with the communication process, including dominance role, one-way or two-way effect role. This further indicates the need to allocate resources dynamically in the process of policy communication.Originality/valueAnalysing the process of policy communication and identifying the dynamic interaction between communication subjects can provide more a comprehensive and detailed decision-making basis for policy formulation and implementation. In addition, the research ideas and methods presented in this paper expand the perspective of information communication research.

Multi-source Selective Graph Domain Adaptation Network for cross-subject EEG emotion recognition

Affective brain-computer interface is an important part of realizing emotional human–computer interaction. However, existing objective individual differences among subjects significantly hinder the application of electroencephalography (EEG) emotion recognition. Existing methods still lack the complete extraction of subject-invariant representations for EEG and the ability to fuse valuable information from multiple subjects to facilitate the emotion recognition of the target subject. To address the above challenges, we propose a Multi-source Selective Graph Domain Adaptation Network (MSGDAN), which can better utilize data from different source subjects and perform more robust emotion recognition on the target subject. The proposed network extracts and selects the individual information specific to each subject, where public information refers to subject-invariant components from multi-source subjects. Moreover, the graph domain adaptation network captures both functional connectivity and regional states of the brain via a dynamic graph network and then integrates graph domain adaptation to ensure the invariance of both functional connectivity and regional states. To evaluate our method, we conduct cross-subject emotion recognition experiments on the SEED, SEED-IV, and DEAP datasets. The results demonstrate that the MSGDAN has superior classification performance.

Cultivation of Youth Patriotism in the New Era: Current Situation, Objectives and Practical Approaches

Youth patriotism education is an important part of maintaining national unity and promoting the growth and success of young people to realize the great rejuvenation of the Chinese nation in the new era. However, youth patriotism education in the new era presents the following status quo: firstly, the education method and content are single and boring; secondly, it is deeply impacted by the negative information on the Internet and the invasion of diversified ideologies and cultures; and thirdly, the cultural self-confidence is weakened, and the influence of traditional culture is declining. Patriotic education for young people in the new era should aim at realizing young people's emotional identity, ideological identity and behavioral identity of knowledge and action. To this end, patriotic education in the new era can be adjusted from the adjustment of the form and content of patriotic education, strengthening the governance of multiple subjects in cyberspace, as well as basing on traditional culture and firming up cultural self-confidence, with a view to enhancing the effectiveness of patriotic education for young people in the new era.

Multiscale mapping of transcriptomic signatures for cardiotoxic drugs

Drug-induced gene expression profiles can identify potential mechanisms of toxicity. We focus on obtaining signatures for cardiotoxicity of FDA-approved tyrosine kinase inhibitors (TKIs) in human induced-pluripotent-stem-cell-derived cardiomyocytes, using bulk transcriptomic profiles. We use singular value decomposition to identify drug-selective patterns across cell lines obtained from multiple healthy human subjects. Cellular pathways affected by cardiotoxic TKIs include energy metabolism, contractile, and extracellular matrix dynamics. Projecting these pathways to published single cell expression profiles indicates that TKI responses can be evoked in both cardiomyocytes and fibroblasts. Integration of transcriptomic outlier analysis with whole genomic sequencing of our six cell lines enables us to correctly reidentify a genomic variant causally linked to anthracycline-induced cardiotoxicity and predict genomic variants potentially associated with TKI-induced cardiotoxicity. We conclude that mRNA expression profiles when integrated with publicly available genomic, pathway, and single cell transcriptomic datasets, provide multiscale signatures for cardiotoxicity that could be used for drug development and patient stratification.

AR3n: A Reinforcement Learning-Based Assist-as-Needed Controller for Robotic Rehabilitation

In this article, we present AR3n (pronounced as Aaron), an assist-as-needed (AAN) controller that utilizes reinforcement learning (RL), to supply adaptive assistance during a robot-assisted handwriting rehabilitation task. Unlike previous AAN controllers, our method does not rely on patient-specific controller parameters or physical models. We propose the use of a virtual patient model to generalize AR3n across multiple subjects. The system modulates robotic assistance in real time based on a subject’s tracking errors while minimizing the amount of robotic assistance. The controller is experimentally validated through a set of simulations and human subject experiments. Finally, a comparative study with a traditional rule-based controller is conducted to analyze differences in the assistance mechanisms of the two controllers.