Interdisciplinary perspective-taking within argumentation: students’ strategies across science and religious education

The importance of developing students’ argumentation skills is well established across the curriculum: students should grasp how claims are made and supported in different disciplines. One challenge is to follow and thereby agree with or critique the arguments of others, which requires perspective-taking, in tracing these other reasons and reasoning. This challenge is increased when disciplines construct argumentation and perspective-taking differently. Here, we consider the role of perspective-taking in argumentation within and between science education and pluralistic religious education, where the former aims at the justification of scientific claims and the latter at both an empathetic understanding of different religions and worldviews, and personal reasoning. We interpretively analyze student data to identify salient features of students’ strategies to perspective-taking within argumentation. Data from 324 pupils across nine schools are explored in relation to students’ challenges in perspective-taking, strategies for perspective-taking within argumentation, and the use of perspective-taking to construct personal argumentation. The analysis shows some barriers to perspective-taking within argumentation, the range of students’ perspective-taking strategies within argumentation, and how personal argumentation could hermeneutically build upon perspective-taking strategies. The importance and implications of perspective-taking within argumentation across the curriculum are considered highlighting challenges in the etic/emic shift, both within the individual subject as well as across them, and some reflections on how this provides a fresh pedagogical perspective on the science/religions debate are made. To end, we conclude with the wider challenges for disciplines and perspective-taking across schooling and university.

Anthropological approach to the concept of legal values: classic, non-classical and post-non-classical

The article is devoted to the study of the concept of legal values, their classification. Analysis of legal literature led to the conclusion that legal values are considered only in line with legal positivism, which have a faade in relation to the subject of law. According to the author, anthropological approaches - classical, non-classical and post-non-classical - can become a milestone in a conceptually different understanding of the values of law. In this regard, the purpose of the paper is to conduct a study of the values of law in line with anthropological research programs. To achieve the goal, the following tasks were set: 1) to describe the classical (neoclassical), non-classical and post-non-classical anthropological programs; 2) to formulate the concept of legal values and truth within the framework of three paradigms of legal thinking; 3) to classify and rank the values of law. As a result of the study, the following conclusions were made: The legal value in the classical (neoclassical) anthropological paradigm lies in the safe-guarding and protection of inviolability of the biopsychophysiological integrity of the organism, which identifies an individual as a physical person. Therefore, the law has an objectified and alienated from the individual subject character. The value of law in non-classical anthropological discourse is imperative-attributive experiences (legal psyche) or intentional acts of consciousness that constitute legal reality, with which a person is identified - a legal personality. The post-non-classical model of cognition is focused on the discovery of the true essence of a person through identification with a legal being (or pure consciousness), which is an absolute value and creator of transpersonal and extra-social legal reality.

Temporal response characterization across individual multiomics profiles of prediabetic and diabetic subjects

Longitudinal deep multi-omics profiling, which combines biomolecular, physiological, environmental and clinical measures data, shows great promise for precision health. However, integrating and understanding the complexity of such data remains a big challenge. Here we propose a bottom-up framework starting from assessing single individuals' multi-omics time series, and using individual responses to assess multi-individual grouping based directly on similarity of their longitudinal deep multi-omics profiles. We applied our method to individual profiles from a study profiling longitudinal responses in type 2 diabetes mellitus. After generating periodograms for individual subject omics signals, we constructed within-person omics networks and analyzed personal-level immune changes. The results showed that our method identified both individual-level responses to immune perturbation, and the clusters of individuals that have similar behaviors in immune response and which was associated to measures of their diabetic status.

Robust anticipation of continuous steering actions from electroencephalographic data during simulated driving

Driving a car requires high cognitive demands, from sustained attention to perception and action planning. Recent research investigated the neural processes reflecting the planning of driving actions, aiming to better understand the factors leading to driving errors and to devise methodologies to anticipate and prevent such errors by monitoring the driver’s cognitive state and intention. While such anticipation was shown for discrete driving actions, such as emergency braking, there is no evidence for robust neural signatures of continuous action planning. This study aims to fill this gap by investigating continuous steering actions during a driving task in a car simulator with multimodal recordings of behavioural and electroencephalography (EEG) signals. System identification is used to assess whether robust neurophysiological signatures emerge before steering actions. Linear decoding models are then used to determine whether such cortical signals can predict continuous steering actions with progressively longer anticipation. Results point to significant EEG signatures of continuous action planning. Such neural signals show consistent dynamics across participants for anticipations up to 1 s, while individual-subject neural activity could reliably decode steering actions and predict future actions for anticipations up to 1.8 s. Finally, we use canonical correlation analysis to attempt disentangling brain and non-brain contributors to the EEG-based decoding. Our results suggest that low-frequency cortical dynamics are involved in the planning of steering actions and that EEG is sensitive to that neural activity. As a result, we propose a framework to investigate anticipatory neural activity in realistic continuous motor tasks.

Linguistics and Deception Detection (DD): A Work in Progress

Linguistic Deception Detection DD is a well-established part of forensic linguistics and an area that continues to attract attention on the part of researchers, self-styled experts, and the public at large. In this article, the various approaches to DD within the general field of linguistics are examined. The basic method is to treat language as a form of behaviour and to equate marked linguistic behaviour with other marked forms of behaviour. Such a comparison has been identified in other fields such as psychology and kinesics as being associated with stress linked to the attempt to deceive, typically in such contexts as examined here. Representative authentic examples of some of the most common linguistic indicators of deception that have been identified are discussed, dividing them into two general categories which we here introduce: language as revealer and language as concealer. We will argue that linguistic analysis for DD should be conducted relative to the subject’s individual linguistic patterns of behaviour, not on absolutes related to broad generalisations about what is supposedly normal or unmarked in the population at large. We will also briefly discuss some structured methods for linguistic analysis for DD and the prospect that technology and artificial intelligence will provide the means to automate and digitalise the linguistic DD process. We maintain that caution is advisable when considering these, as DD will, in all probability, always remain a work in progress, with the need for a flexible human evaluator ready to take into account many different aspects of the individual subject and the case in question.

Concordance rate of a four-quadrant plot for repeated measurements

Background To assure the equivalence between new clinical measurement methods and the standard methods, the four-quadrant plot and the plot’s concordance rate is used in clinical practice, along with Bland-Altman analysis. The conventional concordance rate does not consider the correlation among the data on individual subjects, which may affect its proper evaluation. Methods We propose a new concordance rate for the four-quadrant plot based on multivariate normal distribution to take into account the covariance within each individual subject. The proposed concordance rate is formulated as the conditional probability of the agreement. It contains a parameter to set the minimum concordant number between two measurement methods, which is regarded as agreement. This parameter allows flexibility in the interpretation of the results. Results Through numerical simulations, the AUC value of the proposed method was 0.967, while that of the conventional concordance rate was 0.938. In the application to a real example, the AUC value of the proposed method was 0.999 and that of the conventional concordance rate was 0.964. Conclusion From the results of numerical simulations and a real example, the proposed concordance rate showed better accuracy and higher diagnosability than the conventional approaches.

Anatomically Standardized Detection of MRI Atrophy Patterns in Early-Stage Alzheimer’s Disease

MRI studies have consistently identified atrophy patterns in Alzheimer’s disease (AD) through a whole-brain voxel-based analysis, but efforts to investigate morphometric profiles using anatomically standardized and automated whole-brain ROI analyses, performed at the individual subject space, are still lacking. In this study we aimed (i) to utilize atlas-derived measurements of cortical thickness and subcortical volumes, including of the hippocampal subfields, to identify atrophy patterns in early-stage AD, and (ii) to compare cognitive profiles at baseline and during a one-year follow-up of those previously identified morphometric AD subtypes to predict disease progression. Through a prospectively recruited multi-center study, conducted at four Austrian sites, 120 patients were included with probable AD, a disease onset beyond 60 years and a clinical dementia rating of ≤1. Morphometric measures of T1-weighted images were obtained using FreeSurfer. A principal component and subsequent cluster analysis identified four morphometric subtypes, including (i) hippocampal predominant (30.8%), (ii) hippocampal-temporo-parietal (29.2%), (iii) parieto-temporal (hippocampal sparing, 20.8%) and (iv) hippocampal-temporal (19.2%) atrophy patterns that were associated with phenotypes differing predominately in the presentation and progression of verbal memory and visuospatial impairments. These morphologically distinct subtypes are based on standardized brain regions, which are anatomically defined and freely accessible so as to validate its diagnostic accuracy and enhance the prediction of disease progression.

Decoding individualized motor cortical excitability states from whole-brain electroencephalography

OBJECTIVE: Brain state-dependent transcranial magnetic stimulation (TMS) requires real-time identification of cortical excitability states. Here, we aimed to identify individualized, subject-specific motor cortex (M1) excitability states from whole-scalp electroencephalography (EEG) signals. METHODS: We analyzed a pre-existing dataset that delivered 600 single TMS pulses to the right M1 during EEG and electromyography (EMG) recordings. Subject-specific multivariate pattern classification was used to discriminate between brain states during which TMS elicited small or large motor-evoked potentials (MEPs). RESULTS: Classifiers trained at the individual subject level successfully discriminated between low and high M1 excitability states. MEPs elicited during classifier-predicted high excitability states were significantly larger than those elicited during classifier-predicted low excitability states. Classifiers trained on subject-specific data obtained immediately before TMS delivery performed better than classifiers trained on data from earlier time points, and subject-specific classifiers generalized weakly but significantly across subjects. CONCLUSION: Decoding individualized M1 excitability states from whole-brain EEG activity is feasible and robust. SIGNIFICANCE: Deploying subject-specific classifiers during brain state-dependent TMS may enable effective, fully individualized neuromodulation in the future.

Systematic characterization of human response to H1N1 influenza vaccination through the construction and integration of personalized transcriptome response profiles

Gene expression data is commonly used in vaccine studies to characterize differences between treatment groups or sampling time points. Group-wise comparisons of the transcriptional perturbations induced by vaccination have been applied extensively for investigating the mechanisms of action of vaccines. Such approaches, however, may not be sensitive enough for detecting changes occurring within a minority of the population under investigation or in single individuals. In this study, we developed a data analysis framework to characterize individual subject response profiles in the context of repeated measure experiments, which are typical of vaccine mode of action studies. Following the definition of the methodology, this was applied to the analysis of human transcriptome responses induced by vaccination with a subunit influenza vaccine. Results highlighted a substantial heterogeneity in how different subjects respond to vaccination. Moreover, the extent of transcriptional modulation experienced by each individual subject was found to be associated with the magnitude of vaccine-specific functional antibody response, pointing to a mechanistic link between genes involved in protein production and innate antiviral response. Overall, we propose that the improved characterization of the intersubject heterogeneity, enabled by our approach, can help driving the improvement and optimization of current and next-generation vaccines.

Serotonin 1B receptor density mapping of the human brainstem using positron emission tomography and autoradiography

The serotonin 1B (5-HT1B) receptor has lately received considerable interest in relation to psychiatric and neurological diseases, partly due to findings based on quantification using Positron Emission Tomography (PET). Although the brainstem is an important structure in this regard, PET radioligand binding quantification in brainstem areas often shows poor reliability. This study aims to improve PET quantification of 5-HT1B receptor binding in the brainstem. Volumes of interest (VOIs) were selected based on a 3D [3H]AZ10419369 Autoradiography brainstem model, which visualized 5-HT1B receptor distribution in high resolution. Two previously developed VOI delineation methods were tested and compared to a conventional manual method. For a method based on template data, a [11C]AZ10419369 PET template was created by averaging parametric binding potential (BPND) images of 52 healthy subjects. VOIs were generated based on a predefined volume and BPND thresholding and subsequently applied to test-retest [11C]AZ10419369 parametric BPND images of 8 healthy subjects. For a method based on individual subject data, VOIs were generated directly on each individual parametric image. Both methods showed improved reliability compared to a conventional manual VOI. The VOIs created with [11C]AZ10419369 template data can be automatically applied to future PET studies measuring 5-HT1B receptor binding in the brainstem.