Research on Adaptive Grasping with a Prosthetic Hand Based on Perceptual Information on Hardness and Surface Roughness.

In order to solve the problems of methods that use a single form of sensing, the ease of causing deformation damage to the targets with a low hardness during grasping, and the slow sliding inhibition of a prosthetic hand when the grasping target slides, which are problems that exist in most current intelligent prosthetic hands, this study introduces an adaptive control strategy for prosthetic hands based on multi-sensor sensing. Using a force-sensing resistor (FSR) to collect changes in signals generated after contact with a target, a prosthetic hand can classify the target's hardness level and adaptively provide the desired grasping force so as to reduce the deformation of and damage to the target in the process of grasping. A fiber-optic sensor collects the light reflected by the object to identify its surface roughness, so that the prosthetic hand adaptively adjusts the sliding inhibition method according to the surface roughness information to improve the grasping efficiency. By integrating information on the hardness and surface roughness of the target, an adaptive control strategy for a prosthetic hand is proposed. The experimental results showed that the adaptive control strategy was able to reduce the damage to the target by enabling the prosthetic hand to achieve stable grasping; after grasping the target with an initial force and generating sliding, the efficiency of slippage inhibition was improved, the target could be stably grasped in a shorter time, and the hardness, roughness and weight ranges of targets that could be grasped by the prosthetic hand were enlarged, thus improving the success rate of stable grasping under extreme conditions.

The tail of the caudate is sensitive to both gain and loss feedback during information integration categorization.

Although most category learning studies use feedback for training, little attention has been paid to how individuals utilize feedback implemented as gains or losses during categorization. We compared skilled categorization under three different conditions: Gain (earn points for correct answers), Gain and Loss (earn points for correct answers and lose points for wrong answers) and Correct or Wrong (accuracy feedback only). We also manipulated difficulty and point value, with near boundary stimuli having the highest number of points to win or lose, and stimuli far from the boundary having the lowest point value. We found that the tail of the caudate was sensitive to feedback condition, with highest activity when both Gain and Loss feedback were present and least activity when only Gain or accuracy feedback was present. We also found that activity across the caudate was affected by distance from the decision bound, with greatest activity for the near boundary high value stimuli, and lowest for far low value stimuli. Overall these results indicate that the tail of the caudate is sensitive not only to positive rewards but also to loss and punishment, consistent with recent animal research finding tail of the caudate activity in aversive learning.

Prediction of drug-target binding affinity based on deep learning models.

The prediction of drug-target binding affinity (DTA) plays an important role in drug discovery. Computerized virtual screening techniques have been used for DTA prediction, greatly reducing the time and economic costs of drug discovery. However, these techniques have not succeeded in reversing the low success rate of new drug development. In recent years, the continuous development of deep learning (DL) technology has brought new opportunities for drug discovery through the DTA prediction. This shift has moved the prediction of DTA from traditional machine learning methods to DL. The DL frameworks used for DTA prediction include convolutional neural networks (CNN), graph convolutional neural networks (GCN), and recurrent neural networks (RNN), and reinforcement learning (RL), among others. This review article summarizes the available literature on DTA prediction using DL models, including DTA quantification metrics and datasets, and DL algorithms used for DTA prediction (including input representation of models, neural network frameworks, valuation indicators, and model interpretability). In addition, the opportunities, challenges, and prospects of the application of DL frameworks for DTA prediction in the field of drug discovery are discussed.

Hierarchical consciousness: the Nested Observer Windows model.

Foremost in our experience is the intuition that we possess a unified conscious experience. However, many observations run counter to this intuition: we experience paralyzing indecision when faced with two appealing behavioral choices, we simultaneously hold contradictory beliefs, and the content of our thought is often characterized by an internal debate. Here, we propose the Nested Observer Windows (NOW) Model, a framework for hierarchical consciousness wherein information processed across many spatiotemporal scales of the brain feeds into subjective experience. The model likens the mind to a hierarchy of nested mosaic tiles-where an image is composed of mosaic tiles, and each of these tiles is itself an image composed of mosaic tiles. Unitary consciousness exists at the apex of this nested hierarchy where perceptual constructs become fully integrated and complex behaviors are initiated via abstract commands. We define an observer window as a spatially and temporally constrained system within which information is integrated, e.g. in functional brain regions and neurons. Three principles from the signal analysis of electrical activity describe the nested hierarchy and generate testable predictions. First, nested observer windows disseminate information across spatiotemporal scales with cross-frequency coupling. Second, observer windows are characterized by a high degree of internal synchrony (with zero phase lag). Third, observer windows at the same spatiotemporal level share information with each other through coherence (with non-zero phase lag). The theoretical framework of the NOW Model accounts for a wide range of subjective experiences and a novel approach for integrating prominent theories of consciousness.

Validity of information integration based on subjective and physiological data from a real sports condition: application to the judgment of fatigue in sport.

The objective of the present study was to confirm the convergent validity of information integration theory in the judgment of fatigue in sport, using information integration, subjective, and physiological data. Twenty healthy athletes were confronted with six cycling scenarios in two experimental conditions. In the laboratory condition, the athletes imagined the scenarios and had to cognitively combine the exercise intensity (30%, 50%, and 70% of the maximal intensity) and the exercise duration (15 and 30 min) when judging their expected level of fatigue. In the real sports condition, the athletes enacted each scenario and then rated their subjective fatigue. The heart rate was recorded continuously, so that the physiological training impulse could be calculated. We applied analyses of variance to the data and analyzed correlations between variables. The information integration data from the laboratory condition, the subjective data from the real sports condition, and the objective (physiological) data from the real sports condition were strongly correlated. The information integration patterns concerning fatigue as a function of the exercise duration and intensity obtained respectively from the three data sets were extremely similar.

Mapping Risk Judgment and Risk Taking in Mountain Hiking: An Information Integration Approach.

Risk analysis is essential for promoting hiking-based tourism. Our objective in the present study was to map 395 mountain hikers' positions on risk judgment and risk taking, according to how they integrated three antecedent factors of confidence (environment, team, and self). For integrating information, people can develop an additive rule whereby they apply the same weight to all information or use interaction rules (i.e., conjunctive or disjunctive), to give different weights to information. In the questionnaire our participants completed, there were eight scenarios that combined the three confidence antecedent factors as information cues. We applied cluster analysis, repeated-measures analyses of variance, chi-square tests, and bivariate correlation analyses to the questionnaire results to identify three participant risk positions. In the first risk position (cluster 1), participants used a disjunctive integration rule for both risk judgment and risk taking. In the second risk position (Clusters 2 and 4), they used an additive integration rule for risk judgment while they used a disjunctive integration rule for risk taking. In the third risk position (cluster 3), they used an additive integration rule for both risk judgment and risk taking. In each risk position, confidence in the three antecedent factors (environment, team, and self) negatively affected risk judgment and positively affected risk taking. We found the compositions of the clusters to be related to the participants' sex, and we discuss various advantages of applying information integration for mountain hiking practitioners and promoters.

A bifurcation integrates information from many noisy ion channels and allows for milli-Kelvin thermal sensitivity in the snake pit organ.

In various biological systems, information from many noisy molecular receptors must be integrated into a collective response. A striking example is the thermal imaging organ of pit vipers. Single nerve fibers in the organ reliably respond to milli-Kelvin (mK) temperature increases, a thousand times more sensitive than their molecular sensors, thermo-transient receptor potential (TRP) ion channels. Here, we propose a mechanism for the integration of this molecular information. In our model, amplification arises due to proximity to a dynamical bifurcation, separating a regime with frequent and regular action potentials (APs), from a regime where APs are irregular and infrequent. Near the transition, AP frequency can have an extremely sharp dependence on temperature, naturally accounting for the thousand-fold amplification. Furthermore, close to the bifurcation, most of the information about temperature available in the TRP channels' kinetics can be read out from the times between consecutive APs even in the presence of readout noise. A key model prediction is that the coefficient of variation in the distribution of interspike times decreases with AP frequency, and quantitative comparison with experiments indeed suggests that nerve fibers of snakes are located very close to the bifurcation. While proximity to such bifurcation points typically requires fine-tuning of parameters, we propose that having feedback act from the order parameter (AP frequency) onto the control parameter robustly maintains the system in the vicinity of the bifurcation. This robustness suggests that similar feedback mechanisms might be found in other sensory systems which also need to detect tiny signals in a varying environment.

Temporal integration by multi-level regularities fosters the emergence of dynamic conscious experience.

The relationship between integration and awareness is central to contemporary theories and research on consciousness. Here, we investigated whether and how information integration over time, by incorporating the underlying regularities, contributes to our awareness of the dynamic world. Using binocular rivalry, we demonstrated that structured visual streams, constituted by shape, motion, or idiom sequences containing perceptual- or semantic-level regularities, predominated over their nonstructured but otherwise matched counterparts in the competition for visual awareness. Despite the apparent resemblance, a substantial dissociation of the observed rivalry advantages emerged between perceptual- and semantic-level regularities. These effects stem from nonconscious and conscious temporal integration processes, respectively, with the former but not the latter being vulnerable to perturbations in the spatiotemporal integration window. These findings corroborate the essential role of structure-guided information integration in visual awareness and highlight a multi-level mechanism where temporal integration by perceptually and semantically defined regularities fosters the emergence of continuous conscious experience.

Distribution-dependent representations in auditory category learning and generalization.

A fundamental objective in Auditory Sciences is to understand how people learn to generalize auditory category knowledge in new situations. How we generalize to novel scenarios speaks to the nature of acquired category representations and generalization mechanisms in handling perceptual variabilities and novelty. The dual learning system (DLS) framework proposes that auditory category learning involves an explicit, hypothesis-testing learning system, which is optimal for learning rule-based (RB) categories, and an implicit, procedural-based learning system, which is optimal for learning categories requiring pre-decisional information integration (II) across acoustic dimensions. Although DLS describes distinct mechanisms of two types of category learning, it is yet clear the nature of acquired representations and how we transfer them to new contexts. Here, we conducted three experiments to examine differences between II and RB category representations by examining what acoustic and perceptual novelties and variabilities affect learners' generalization success. Learners can successfully categorize different sets of untrained sounds after only eight blocks of training for both II and RB categories. The category structures and novel contexts differentially modulated the generalization success. The II learners significantly decreased generalization performances when categorizing new items derived from an untrained perceptual area and in a context with more distributed samples. In contrast, RB learners' generalizations are resistant to changes in perceptual regions but are sensitive to changes in sound dispersity. Representational similarity modeling revealed that the generalization in the more dispersed sampling context was accomplished differently by II and RB learners. II learners increased representations of perceptual similarity and decision distance to compensate for the decreased transfer of category representations, whereas the RB learners used a more computational cost strategy by default, computing the decision-bound distance to guide generalization decisions. These results suggest that distinct representations emerged after learning the two types of category structures and using different computations and flexible mechanisms in resolving generalization challenges when facing novel perceptual variability in new contexts. These findings provide new evidence for dissociated representations of auditory categories and reveal novel generalization mechanisms in resolving variabilities to maintain perceptual constancy.

Pacing strategy in trail running: A cognitive subtractive model of the affective balance between effort and pleasure?

The objective of the present study was to undercover the cognitive rules developed by athletes in pacing strategy during a trail running competition. Fifty participants completed a questionnaire on how decisions were made around pacing. Each questionnaire consisted of 12 scenarios that featured the two components of affective balance (effort and pleasure) as information cues. We applied repeated-measures analyses of variance and Tukey's post hoc tests to the data. The results showed that pleasure and effort had a significant effect on deciding to reduce the pace and deciding to maintain the pace. The type of cognitive rule depended on the pacing outcome, with a subtractive integration rule when deciding to maintain the pace and a conjunction integration rule when deciding to reduce the pace. The presence of two different cognitive rules emphasized the importance of information integration in pacing strategy.

What Is It like to Be a Brain Organoid? Phenomenal Consciousness in a Biological Neural Network.

It has been shown that three-dimensional self-assembled multicellular structures derived from human pluripotent stem cells show electrical activity similar to EEG. More recently, neurons were successfully embedded in digital game worlds. The biologically inspired neural network (BNN), expressing human cortical cells, was able to show internal modification and learn the task at hand (predicting the trajectory of a digital ball while moving a digital paddle). In other words, the system allowed to read motor information and write sensory data into cell cultures. In this article, we discuss Neural Correlates of Consciousness (NCC) theories, and their capacity to predict or even allow for consciousness in a BNN. We found that Information Integration Theory (IIT) is the only NCC that offers the possibility for a BNN to show consciousness, since the Φ value in the BNN is >0. In other words, the recording of real-time neural activity responding to environmental stimuli. IIT argues that any system capable of integrating information will have some degree of phenomenal consciousness. We argue that the pattern of activity appearing in the BNN, with increased density of sensory information leading to better performance, implies that the BNN could be conscious. This may have profound implications from a psychological, philosophical, and ethical perspective.

SEMPAI: a Self-Enhancing Multi-Photon Artificial Intelligence for Prior-Informed Assessment of Muscle Function and Pathology.

Deep learning (DL) shows notable success in biomedical studies. However, most DL algorithms work as black boxes, exclude biomedical experts, and need extensive data. This is especially problematic for fundamental research in the laboratory, where often only small and sparse data are available and the objective is knowledge discovery rather than automation. Furthermore, basic research is usually hypothesis-driven and extensive prior knowledge (priors) exists. To address this, the Self-Enhancing Multi-Photon Artificial Intelligence (SEMPAI) that is designed for multiphoton microscopy (MPM)-based laboratory research is presented. It utilizes meta-learning to optimize prior (and hypothesis) integration, data representation, and neural network architecture simultaneously. By this, the method allows hypothesis testing with DL and provides interpretable feedback about the origin of biological information in 3D images. SEMPAI performs multi-task learning of several related tasks to enable prediction for small datasets. SEMPAI is applied on an extensive MPM database of single muscle fibers from a decade of experiments, resulting in the largest joint analysis of pathologies and function for single muscle fibers to date. It outperforms state-of-the-art biomarkers in six of seven prediction tasks, including those with scarce data. SEMPAI's DL models with integrated priors are superior to those without priors and to prior-only approaches.

A knowledge and data-driven optimal planning scheme for multi-modal vision transmission systems.

Vision transmission systems (VTS) manages to achieve the optimal information propagation effect given reasonable strategies. How to automatically generate the optimal planning strategies for VTS under specific conditions is always facing challenges. Currently, related research studies have dealt with this problem with assistance of single-modal vision features. However, there are also some other information from different modalities that can make contributions to this issue. Thus, in the paper, we propose a data-driven optimal planning scheme for multimodal VTS. For one thing, the vision features are employed as the basic mechanism foundation for mathematical modeling. For another, the data from other modalities, such as numerical and semantic information, are also introduced to improve robustness for the modeling process. On such basis, optimal planning strategies can be generated, so that proper communication effect can be obtained. Finally, some simulation experiments are conducted on real-world VTS scenes in simulative platforms, and the observed simulation results can well prove efficiency and proactivity of the proposal.

Enhanced disease-disease association with information enriched disease representation.

OBJECTIVE: Quantification of disease-disease association (DDA) enables the understanding of disease relationships for discovering disease progression and finding comorbidity. For effective DDA strength calculation, there is a need to address the main challenge of integration of various biomedical aspects of DDA is to obtain an information rich disease representation. MATERIALS AND METHODS: An enhanced and integrated DDA framework is developed that integrates enriched literature-based with concept-based DDA representation. The literature component of the proposed framework uses PubMed abstracts and consists of improved neural network model that classifies DDAs for an enhanced literature-based DDA representation. Similarly, an ontology-based joint multi-source association embedding model is proposed in the ontology component using Disease Ontology (DO), UMLS, claims insurance, clinical notes etc. Results and Discussion: The obtained information rich disease representation is evaluated on different aspects of DDA datasets such as Gene, Variant, Gene Ontology (GO) and a human rated benchmark dataset. The DDA scores calculated using the proposed method achieved a high correlation mainly in gene-based dataset. The quantified scores also shown better correlation of 0.821, when evaluated on human rated 213 disease pairs. In addition, the generated disease representation is proved to have substantial effect on correlation of DDA scores for different categories of disease pairs. CONCLUSION: The enhanced context and semantic DDA framework provides an enriched disease representation, resulting in high correlated results with different DDA datasets. We have also presented the biological interpretation of disease pairs. The developed framework can also be used for deriving the strength of other biomedical associations.

The Impact of Information Integration in a Simulation of Future Submarine Command and Control.

OBJECTIVE: Examine the extent to which increasing information integration across displays in a simulated submarine command and control room can reduce operator workload, improve operator situation awareness, and improve team performance. BACKGROUND: In control rooms, the volume and number of sources of information are increasing, with the potential to overwhelm operator cognitive capacity. It is proposed that by distributing information to maximize relevance to each operator role (increasing information integration), it is possible to not only reduce operator workload but also improve situation awareness and team performance. METHOD: Sixteen teams of six novice participants were trained to work together to combine data from multiple sensor displays to build a tactical picture of surrounding contacts at sea. The extent that data from one display were available to operators at other displays was manipulated (information integration) between teams. Team performance was assessed as the accuracy of the generated tactical picture. RESULTS: Teams built a more accurate tactical picture, and individual team members had better situation awareness and lower workload, when provided with high compared with low information integration. CONCLUSION: A human-centered design approach to integrating information in command and control settings can result in lower workload, and enhanced situation awareness and team performance. APPLICATION: The design of modern command and control rooms, in which operators must fuse increasing volumes of complex data from displays, may benefit from higher information integration based on a human-centered design philosophy, and a fundamental understanding of the cognitive work that is carried out by operators.

The cheese was green with envy: An EEG study on minimal fictional descriptions.

Inconsistent information can be hard to understand, but in cases like fiction readers can integrate it with little to no difficulties. The present study aimed at examining if perspective switching can take place when only a minimal fictional description is provided (fictional world condition), as compared with general world knowledge (real world condition). Participants read sentences where food items had animated or inanimate features while EEG was recorded and performed a sentence completion task to evaluate recall. In the real-world condition, the N400 was significantly larger for sentences incongruent, rather than congruent, with general world knowledge. In the fictional world condition, the N400 elicited by congruent and incongruent sentences did not differ, confirming that the minimal description impacted online information processing. Information consistent with general knowledge was better recalled in both conditions. The current results highlight how contextual information is integrated during sentence comprehension.

TIST: Transcriptome and Histopathological Image Integrative Analysis for Spatial Transcriptomics.

Sequencing-based spatial transcriptomics (ST) is an emerging technology to study in situ gene expression patterns at the whole-genome scale. Currently, ST data analysis is still complicated by high technical noises and low resolution. In addition to the transcriptomic data, matched histopathological images are usually generated for the same tissue sample along the ST experiment. The matched high-resolution histopathological images provide complementary cellular phenotypical information, providing an opportunity to mitigate the noises in ST data. We present a novel ST data analysis method called transcriptome and histopathological image integrative analysis for ST (TIST), which enables the identification of spatial clusters (SCs) and the enhancement of spatial gene expression patterns by integrative analysis of matched transcriptomic data and images. TIST devises a histopathological feature extraction method based on Markov random field (MRF) to learn the cellular features from histopathological images, and integrates them with the transcriptomic data and location information as a network, termed TIST-net. Based on TIST-net, SCs are identified by a random walk-based strategy, and gene expression patterns are enhanced by neighborhood smoothing. We benchmark TIST on both simulated datasets and 32 real samples against several state-of-the-art methods. Results show that TIST is robust to technical noises on multiple analysis tasks for sequencing-based ST data and can find interesting microstructures in different biological scenarios. TIST is available at http://lifeome.net/software/tist/ and https://ngdc.cncb.ac.cn/biocode/tools/BT007317.

The functional contributions of consciousness.

The most widely endorsed philosophical and scientific theories of consciousness assume that it contributes a single functional capacity to an organism's information processing toolkit. However, conscious processes are a heterogeneous class of psychological phenomena supported by a variety of neurobiological mechanisms. This suggests a plurality of functional contributions of consciousness (FCCs), in the sense that conscious experience facilitates different functional capacities in different psychological domains. In this paper, I first develop a general methodological framework for isolating the psychological functions that are associated with conscious experience. I then use this to show that the leading accounts-Global Workspace Theories, Higher Order Thought Theory and Information Integration Theory-all fail to acknowledge this functional pluralism, which limits their applicability as theories of consciousness.

Directed Brain Network Analysis for Fatigue Driving Based on EEG Source Signals.

Fatigue driving is one of the major factors that leads to traffic accidents. Long-term monotonous driving can easily cause a decrease in the driver's attention and vigilance, manifesting a fatigue effect. This paper proposes a means of revealing the effects of driving fatigue on the brain's information processing abilities, from the aspect of a directed brain network based on electroencephalogram (EEG) source signals. Based on current source density (CSD) data derived from EEG signals using source analysis, a directed brain network for fatigue driving was constructed by using a directed transfer function. As driving time increased, the average clustering coefficient as well as the average path length gradually increased; meanwhile, global efficiency gradually decreased for most rhythms, suggesting that deep driving fatigue enhances the brain's local information integration abilities while weakening its global abilities. Furthermore, causal flow analysis showed electrodes with significant differences between the awake state and the driving fatigue state, which were mainly distributed in several areas of the anterior and posterior regions, especially under the theta rhythm. It was also found that the ability of the anterior regions to receive information from the posterior regions became significantly worse in the driving fatigue state. These findings may provide a theoretical basis for revealing the underlying neural mechanisms of driving fatigue.

Integration of ESG Information Into Individual Investors' Corporate Investment Decisions: Utilizing the UTAUT Framework.

Environmental, Social, and Governance (ESG) criteria are now considered significant, global non-financial evaluating factors of corporate value. However, no attention is given to what influences the integration of ESG information by individual investors in their investment decisions. This study first identifies different types of information investors use to make investment decisions. Risks identified in information integration in investment decision making is reviewed. Next, the Unified Theory of Acceptance and Use of Technology (UTAUT) model is used to identify individual investors' investment tendencies and the factors affecting integration of ESG information into investment decisions. Each of four categories for UTAUT innovation adoption factors (performance expectancy, effort expectancy, social influences, and facilitating conditions) are discussed in relation to how they affect individual investors' integration of ESG information. Standardization of ESG reporting and evaluation frameworks would reduce efforts to adopt ESG information and could build a strong foundation for facilitating ESG information integration. Corporates' efforts to further communicate their ESG management through their investor relations and active governmental well as non-governmental organizations' participation are recommended.