Embodied tactile perception and learning

Various living creatures exhibit embodiment intelligence, which is reflected by a collaborative interaction of the brain, body, and environment. The actual behavior of embodiment intelligence is generated by a continuous and dynamic interaction between a subject and the environment through information perception and physical manipulation. The physical interaction between a robot and the environment is the basis for realizing embodied perception and learning. Tactile information plays a critical role in this physical interaction process. It can be used to ensure safety, stability, and compliance, and can provide unique information that is difficult to capture using other perception modalities. However, due to the limitations of existing sensors and perception and learning methods, the development of robotic tactile research lags significantly behind other sensing modalities, such as vision and hearing, thereby seriously restricting the development of robotic embodiment intelligence. This paper presents the current challenges related to robotic tactile embodiment intelligence and reviews the theory and methods of robotic embodied tactile intelligence. Tactile perception and learning methods for embodiment intelligence can be designed based on the development of new large‐scale tactile array sensing devices, with the aim to make breakthroughs in the neuromorphic computing technology of tactile intelligence.

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Large-scale RNA editing profiling in different adult chicken tissues

10.1101/319871 ◽

2018 ◽

Cited By ~ 1

Author(s):

Hamid Shafiei ◽

Mohammad Reza Bakhtiarizadeh ◽

Abdolreza Salehi

Keyword(s):

Rna Editing ◽

Large Scale ◽

Critical Role ◽

Sequencing Data ◽

Adult Chicken ◽

Chicken Tissues ◽

Sequencing Technologies ◽

Synonymous Sites ◽

Interspersed Repeats ◽

The Brain

AbstractRNA editing is a post-transcription maturation process that diversifies genomically encoded information and can lead to diversity and complexity of transcriptome, especially in the brain. Thanks to next-generation sequencing technologies, a large number of editing sites have been identified in different species, especially in human, mouse and rat. While this mechanism is well described in mammals, only a few studies have been performed in the chicken. Here, we developed a rigorous computational strategy to identify RNA editing sites in eight different tissues of the chicken (brain, spleen, colon, lung, kidney, heart, testes and liver), based on RNA sequencing data alone. We identified 68 A-to-G editing sites in 46 genes. Only two of these were previously reported in chicken. We found no C-to-U sites, attesting the lack of this type of editing mechanism in the chicken. Similar to mammals, the editing sites were enriched in non-coding regions, rarely resulted in change of amino acids, showed a critical role in nervous system and had a low guanosine level upstream of the editing site and some enrichment downstream from the site. Moreover, in contrast to mammals, editing sites were weakly enriched in interspersed repeats and the frequency and editing ratio of non-synonymous sites were higher than those of synonymous sites.Interestingly, we found several tissue-specific edited genes including GABRA3, SORL1 and HTR1D in brain and RYR2 and FHOD3 in heart that were associated with functional processes relevant to the corresponding tissue. This finding highlighted the importance of the RNA editing in several chicken tissues, especially the brain. This study extends our understanding of RNA editing in chicken tissues and establish a foundation for further exploration of this process.

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Nursing professionals’ attitudes toward use of physical restraints in Styrian nursing homes Austria

Pflege ◽

10.1024/1012-5302/a000649 ◽

2019 ◽

Vol 32 (1) ◽

pp. 57-63

Author(s):

Hannes Mayerl ◽

Tanja Trummer ◽

Erwin Stolz ◽

Éva Rásky ◽

Wolfgang Freidl

Keyword(s):

Nursing Homes ◽

Large Scale ◽

Critical Role ◽

Physical Restraint ◽

Care Practice ◽

Physical Restraints ◽

Convenience Sample ◽

Restraint Use ◽

Positive Attitudes ◽

Nursing Professionals

Abstract. Background: Given that nursing staff play a critical role in the decision regarding use of physical restraints, research has examined nursing professionals’ attitudes toward this practice. Aim: Since nursing professionals’ views on physical restraint use have not yet been examined in Austria to date, we aimed to explore nursing professionals’ attitudes concerning use of physical restraints in nursing homes of Styria (Austria). Method: Data were collected from a convenience sample of nursing professionals (N = 355) within 19 Styrian nursing homes, based on a cross-sectional study design. Attitudes toward the practice of restraint use were assessed by means of the Maastricht Attitude Questionnaire in the German version. Results: The overall results showed rather positive attitudes toward the use of physical restraints, yet the findings regarding the sub-dimensions of the questionnaire were mixed. Although nursing professionals tended to deny “good reasons” for using physical restraints, they evaluated the consequences of physical restraint use rather positive and considered restraint use as an appropriate health care practice. Nursing professionals’ views regarding the consequences of using specific physical restraints further showed that belts were considered as the most restricting and discomforting devices. Conclusions: Overall, Austrian nursing professionals seemed to hold more positive attitudes toward the use of physical restraints than counterparts in other Western European countries. Future nationwide large-scale surveys will be needed to confirm our findings.

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A Drug Target Interaction Prediction Based on LINE-RF Learning

Current Bioinformatics ◽

10.2174/1574893615666191227092453 ◽

2020 ◽

Vol 15 (7) ◽

pp. 750-757

Author(s):

Jihong Wang ◽

Yue Shi ◽

Xiaodan Wang ◽

Huiyou Chang

Keyword(s):

Network Topology ◽

Drug Target ◽

Large Scale ◽

Representation Learning ◽

New Drugs ◽

Combination Method ◽

Learning Methods ◽

Network Representation ◽

On Line ◽

Clinical Experiments

Background: At present, using computer methods to predict drug-target interactions (DTIs) is a very important step in the discovery of new drugs and drug relocation processes. The potential DTIs identified by machine learning methods can provide guidance in biochemical or clinical experiments. Objective: The goal of this article is to combine the latest network representation learning methods for drug-target prediction research, improve model prediction capabilities, and promote new drug development. Methods: We use large-scale information network embedding (LINE) method to extract network topology features of drugs, targets, diseases, etc., integrate features obtained from heterogeneous networks, construct binary classification samples, and use random forest (RF) method to predict DTIs. Results: The experiments in this paper compare the common classifiers of RF, LR, and SVM, as well as the typical network representation learning methods of LINE, Node2Vec, and DeepWalk. It can be seen that the combined method LINE-RF achieves the best results, reaching an AUC of 0.9349 and an AUPR of 0.9016. Conclusion: The learning method based on LINE network can effectively learn drugs, targets, diseases and other hidden features from the network topology. The combination of features learned through multiple networks can enhance the expression ability. RF is an effective method of supervised learning. Therefore, the Line-RF combination method is a widely applicable method.

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Implantable neural interfaces

10.1093/oso/9780199674923.003.0050 ◽

2018 ◽

Author(s):

Stefano Vassanelli

Keyword(s):

Brain Function ◽

Large Scale ◽

Ionic Channels ◽

Patch Clamp Technique ◽

Advantages And Disadvantages ◽

Optogenetic Stimulation ◽

Physical Interfaces ◽

The Brain

Establishing direct communication with the brain through physical interfaces is a fundamental strategy to investigate brain function. Starting with the patch-clamp technique in the seventies, neuroscience has moved from detailed characterization of ionic channels to the analysis of single neurons and, more recently, microcircuits in brain neuronal networks. Development of new biohybrid probes with electrodes for recording and stimulating neurons in the living animal is a natural consequence of this trend. The recent introduction of optogenetic stimulation and advanced high-resolution large-scale electrical recording approaches demonstrates this need. Brain implants for real-time neurophysiology are also opening new avenues for neuroprosthetics to restore brain function after injury or in neurological disorders. This chapter provides an overview on existing and emergent neurophysiology technologies with particular focus on those intended to interface neuronal microcircuits in vivo. Chemical, electrical, and optogenetic-based interfaces are presented, with an analysis of advantages and disadvantages of the different technical approaches.

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What Has Direct Cortical and Subcortical Electrostimulation Taught Us about Neurolinguistics?

The Oxford Handbook of Neurolinguistics ◽

10.1093/oxfordhb/9780190672027.013.8 ◽

2019 ◽

pp. 185-211

Author(s):

Hugues Duffau

Keyword(s):

White Matter ◽

Large Scale ◽

Functional Neuroimaging ◽

Great Accuracy ◽

Subcortical White Matter ◽

Cerebral Lesions ◽

Physiological Basis ◽

Postoperative Mri ◽

The Brain

Investigating the neural and physiological basis of language is one of the most important challenges in neurosciences. Direct electrical stimulation (DES), usually performed in awake patients during surgery for cerebral lesions, is a reliable tool for detecting both cortical and subcortical (white matter and deep grey nuclei) regions crucial for cognitive functions, especially language. DES transiently interacts locally with a small cortical or axonal site, but also nonlocally, as the focal perturbation will disrupt the entire subnetwork sustaining a given function. Thus, in contrast to functional neuroimaging, DES represents a unique opportunity to identify with great accuracy and reproducibility, in vivo in humans, the structures that are actually indispensable to the function, by inducing a transient virtual lesion based on the inhibition of a subcircuit lasting a few seconds. Currently, this is the sole technique that is able to directly investigate the functional role of white matter tracts in humans. Thus, combining transient disturbances elicited by DES with the anatomical data provided by pre- and postoperative MRI enables to achieve reliable anatomo-functional correlations, supporting a network organization of the brain, and leading to the reappraisal of models of language representation. Finally, combining serial peri-operative functional neuroimaging and online intraoperative DES allows the study of mechanisms underlying neuroplasticity. This chapter critically reviews the basic principles of DES, its advantages and limitations, and what DES can reveal about the neural foundations of language, that is, the large-scale distribution of language areas in the brain, their connectivity, and their ability to reorganize.

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Tactile Perception Technologies and Their Applications in Minimally Invasive Surgery: A Review

Frontiers in Physiology ◽

10.3389/fphys.2020.611596 ◽

2020 ◽

Vol 11 ◽

Author(s):

Chao Huang ◽

Qizhuo Wang ◽

Mingfu Zhao ◽

Chunyan Chen ◽

Sinuo Pan ◽

...

Keyword(s):

Minimally Invasive Surgery ◽

Minimally Invasive ◽

Invasive Surgery ◽

Tactile Perception ◽

Industrial Robots ◽

Great Promise ◽

Support Vector ◽

Tactile Information ◽

Conventional Open Surgery ◽

Sense Of Touch

Minimally invasive surgery (MIS) has been the preferred surgery approach owing to its advantages over conventional open surgery. As a major limitation, the lack of tactile perception impairs the ability of surgeons in tissue distinction and maneuvers. Many studies have been reported on industrial robots to perceive various tactile information. However, only force data are widely used to restore part of the surgeon’s sense of touch in MIS. In recent years, inspired by image classification technologies in computer vision, tactile data are represented as images, where a tactile element is treated as an image pixel. Processing raw data or features extracted from tactile images with artificial intelligence (AI) methods, including clustering, support vector machine (SVM), and deep learning, has been proven as effective methods in industrial robotic tactile perception tasks. This holds great promise for utilizing more tactile information in MIS. This review aims to provide potential tactile perception methods for MIS by reviewing literatures on tactile sensing in MIS and literatures on industrial robotic tactile perception technologies, especially AI methods on tactile images.

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Content-Based Estimation of Brain MRI Tilt in Three Orthogonal Directions

Journal of Digital Imaging ◽

10.1007/s10278-020-00400-7 ◽

2021 ◽

Author(s):

Pooja Prabhu ◽

A. K. Karunakar ◽

Sanjib Sinha ◽

N. Mariyappa ◽

G. K. Bhargava ◽

...

Keyword(s):

Large Scale ◽

Brain Mri ◽

Similarity Measures ◽

Principal Component ◽

Brain Anatomy ◽

Morphological Operations ◽

Mr Images ◽

Tilt Correction ◽

Brain Mr Images ◽

The Brain

AbstractIn a general scenario, the brain images acquired from magnetic resonance imaging (MRI) may experience tilt, distorting brain MR images. The tilt experienced by the brain MR images may result in misalignment during image registration for medical applications. Manually correcting (or estimating) the tilt on a large scale is time-consuming, expensive, and needs brain anatomy expertise. Thus, there is a need for an automatic way of performing tilt correction in three orthogonal directions (X, Y, Z). The proposed work aims to correct the tilt automatically by measuring the pitch angle, yaw angle, and roll angle in X-axis, Z-axis, and Y-axis, respectively. For correction of the tilt around the Z-axis (pointing to the superior direction), image processing techniques, principal component analysis, and similarity measures are used. Also, for correction of the tilt around the X-axis (pointing to the right direction), morphological operations, and tilt correction around the Y-axis (pointing to the anterior direction), orthogonal regression is used. The proposed approach was applied to adjust the tilt observed in the T1- and T2-weighted MR images. The simulation study with the proposed algorithm yielded an error of 0.40 ± 0.09°, and it outperformed the other existing studies. The tilt angle (in degrees) obtained is ranged from 6.2 ± 3.94, 2.35 ± 2.61, and 5 ± 4.36 in X-, Z-, and Y-directions, respectively, by using the proposed algorithm. The proposed work corrects the tilt more accurately and robustly when compared with existing studies.

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A new method to predict anomaly in brain network based on graph deep learning

Reviews in the Neurosciences ◽

10.1515/revneuro-2019-0108 ◽

2020 ◽

Vol 31 (6) ◽

pp. 681-689

Author(s):

Jalal Mirakhorli ◽

Hamidreza Amindavar ◽

Mojgan Mirakhorli

Keyword(s):

Deep Learning ◽

Large Scale ◽

Brain Plasticity ◽

Brain Network ◽

High Order ◽

Brain Diseases ◽

Simultaneous Occurrence ◽

Generative Adversarial Network ◽

The Brain ◽

Brain Connections

AbstractFunctional magnetic resonance imaging a neuroimaging technique which is used in brain disorders and dysfunction studies, has been improved in recent years by mapping the topology of the brain connections, named connectopic mapping. Based on the fact that healthy and unhealthy brain regions and functions differ slightly, studying the complex topology of the functional and structural networks in the human brain is too complicated considering the growth of evaluation measures. One of the applications of irregular graph deep learning is to analyze the human cognitive functions related to the gene expression and related distributed spatial patterns. Since a variety of brain solutions can be dynamically held in the neuronal networks of the brain with different activity patterns and functional connectivity, both node-centric and graph-centric tasks are involved in this application. In this study, we used an individual generative model and high order graph analysis for the region of interest recognition areas of the brain with abnormal connection during performing certain tasks and resting-state or decompose irregular observations. Accordingly, a high order framework of Variational Graph Autoencoder with a Gaussian distributer was proposed in the paper to analyze the functional data in brain imaging studies in which Generative Adversarial Network is employed for optimizing the latent space in the process of learning strong non-rigid graphs among large scale data. Furthermore, the possible modes of correlations were distinguished in abnormal brain connections. Our goal was to find the degree of correlation between the affected regions and their simultaneous occurrence over time. We can take advantage of this to diagnose brain diseases or show the ability of the nervous system to modify brain topology at all angles and brain plasticity according to input stimuli. In this study, we particularly focused on Alzheimer’s disease.

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Engineered glycomaterial implants orchestrate large-scale functional repair of brain tissue chronically after severe traumatic brain injury

Science Advances ◽

10.1126/sciadv.abe0207 ◽

2021 ◽

Vol 7 (10) ◽

pp. eabe0207

Author(s):

Charles-Francois V. Latchoumane ◽

Martha I. Betancur ◽

Gregory A. Simchick ◽

Min Kyoung Sun ◽

Rameen Forghani ◽

...

Keyword(s):

Traumatic Brain Injury ◽

Brain Injury ◽

Severe Traumatic Brain Injury ◽

Large Scale ◽

Growth Factor Signaling ◽

Neuronal Circuit ◽

Reach To Grasp ◽

Function Recovery ◽

Cellular Repair ◽

The Brain

Severe traumatic brain injury (sTBI) survivors experience permanent functional disabilities due to significant volume loss and the brain’s poor capacity to regenerate. Chondroitin sulfate glycosaminoglycans (CS-GAGs) are key regulators of growth factor signaling and neural stem cell homeostasis in the brain. However, the efficacy of engineered CS (eCS) matrices in mediating structural and functional recovery chronically after sTBI has not been investigated. We report that neurotrophic factor functionalized acellular eCS matrices implanted into the rat M1 region acutely after sTBI significantly enhanced cellular repair and gross motor function recovery when compared to controls 20 weeks after sTBI. Animals subjected to M2 region injuries followed by eCS matrix implantations demonstrated the significant recovery of “reach-to-grasp” function. This was attributed to enhanced volumetric vascularization, activity-regulated cytoskeleton (Arc) protein expression, and perilesional sensorimotor connectivity. These findings indicate that eCS matrices implanted acutely after sTBI can support complex cellular, vascular, and neuronal circuit repair chronically after sTBI.

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Transcriptional coregualtor NUPR1 maintains tamoxifen resistance in breast cancer cells

Cell Death and Disease ◽

10.1038/s41419-021-03442-z ◽

2021 ◽

Vol 12 (2) ◽

Author(s):

Lingling Wang ◽

Jiashen Sun ◽

Yueyuan Yin ◽

Yanan Sun ◽

Jinyi Ma ◽

...

Keyword(s):

Breast Cancer ◽

Cancer Cells ◽

Breast Cancer Cells ◽

Critical Role ◽

Endocrine Resistance ◽

Autophagic Flux ◽

Physical Interaction ◽

Premature Senescence ◽

Breast Cancers ◽

Transcriptional Coregulator

AbstractTo support cellular homeostasis and mitigate chemotherapeutic stress, cancer cells must gain a series of adaptive intracellular processes. Here we identify that NUPR1, a tamoxifen (Tam)-induced transcriptional coregulator, is necessary for the maintenance of Tam resistance through physical interaction with ESR1 in breast cancers. Mechanistically, NUPR1 binds to the promoter regions of several genes involved in autophagy process and drug resistance such as BECN1, GREB1, RAB31, PGR, CYP1B1, and regulates their transcription. In Tam-resistant ESR1 breast cancer cells, NUPR1 depletion results in premature senescence in vitro and tumor suppression in vivo. Moreover, enforced-autophagic flux augments cytoplasmic vacuolization in NUPR1-depleted Tam resistant cells, which facilitates the transition from autophagic survival to premature senescence. Collectively, these findings suggest a critical role for NUPR1 as a transcriptional coregulator in enabling endocrine persistence of breast cancers, thus providing a vulnerable diagnostic and/or therapeutic target for endocrine resistance.

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