scholarly journals Transcriptional regulation of neonatal neural stem cells is a determinant of social behavior

2021 ◽  
Author(s):  
Takeshi Hiramoto ◽  
Shuken Boku ◽  
Gina Kang ◽  
Seiji Abe ◽  
Mariel Barbachan e Silva ◽  
...  
Keyword(s):  
Social Behavior ◽  
Neurodevelopmental Disorders ◽  
Time Window ◽  
Copy Number Variant ◽  
Social Dimension ◽  
Gene Variants ◽  
Neural Stem Progenitor Cells ◽  
Rare Gene ◽  
Mechanistic Basis ◽  
High Level

Rare gene variants confer a high level of penetrance to neurodevelopmental disorders, but their developmental origin and cellular substrates remain poorly understood. To address this limitation, we explored the role of TBX1, a gene encoded in a rare copy number variant, in cell and mouse models. Here, we report that neonatal Tbx1 deficiency contributes to defective peripubertal social behavior and impairs the proliferation of neonatal neural stem/progenitor cells. Moreover, TBX1 transcriptionally regulates genes linked to post-embryonic neurogenesis and neurodevelopmental disorders associated with other rare gene variants. Our data indicate a precise time window and cell type through which the social dimension is altered by a gene encoded in a rare CNV and provide a potential common mechanistic basis for a group of neurodevelopmental disorders.

scholarly journals Inhibition of mTOR during a postnatal critical sensitive window rescues deficits in GABAergic PV cell connectivity and social behavior caused by loss of TSC1

2020 ◽  
Author(s):  
Mayukh Choudhury ◽  
Clara A. Amegandjin ◽  
Vidya Jadhav ◽  
Josianne Nunes Carriço ◽  
Ariane Quintal ◽  
...  
Keyword(s):  
Social Behavior ◽  
Time Course ◽  
Cell Types ◽  
Developmental Time ◽  
Adult Mice ◽  
Mtorc1 Signaling ◽  
Pv Cell ◽  
Mechanistic Basis

ABSTRACTMutations in regulators of the Mechanistic Target Of Rapamycin Complex 1 (mTORC1), such as Tsc1/2, lead to neurodevelopmental disorders associated with autism, intellectual disabilities and epilepsy. Whereas the effects of mTORC1 signaling dysfunction within diverse cell types are likely critical for the onset of the diverse neurological symptoms associated with mutations in mTORC1 regulators, they are not well understood. In particular, the effects of mTORC1 dys-regulation in specific types of inhibitory interneurons are unclear.Here, we showed that Tsc1 haploinsufficiency in parvalbumin (PV)-positive GABAergic interneurons either in cortical organotypic cultures or in vivo caused a premature increase in their perisomatic innervations, followed by a striking loss in adult mice. This effects were accompanied by alterations of AMPK-dependent autophagy in pre-adolescent but not adult mice. PV cell-restricted Tsc1 mutant mice showed deficits in social behavior. Treatment with the mTOR inhibitor Rapamycin restricted to the third postnatal week was sufficient to permanently rescue deficits in both PV cell innervation and social behavior in adult conditional haploinsufficient mice. All together, these findings identify a novel role of Tsc1-mTORC1 signaling in the regulation of the developmental time course and maintenance of cortical PV cell connectivity and provide a mechanistic basis for the targeted rescue of autism-related behaviors in disorders associated with deregulated mTORC1 signaling.

scholarly journals PENERAPAN METODE BERMAIN AKTIF UNTUK MENINGKATKAN PERILAKU IMAN DAN SOSIAL ANAK USIA KELAS BALITA DI SEKOLAH MINGGU GEREJA BETHEL INDONESIA TANAH ABANG [THE IMPLEMENTATION OF THE ACTIVE PLAY METHOD TO IMPROVE THE FAITH AND SOCIAL BEHAVIOR OF CHILDREN IN THE SUNDAY SCHOOL OF BETHEL CHURCH INDONESIA, TANAH ABANG]

2021 ◽  
Vol 1 (1) ◽  
pp. 35
Author(s):  
Michaella Prawatya
Keyword(s):  
Social Behavior ◽  
Lesson Plan ◽  
Sunday School ◽  
Research Subjects ◽  
Active Play ◽  
Learning Time ◽  
Study Results ◽  
Field Notes ◽  
High Level ◽  
Bahasa Indonesia

<p>The Nursery Sunday School of GBI Tanah Abang was accustomed to share God’s words by story telling method. Nevertheless, the method raised issues, where the children easily got bored and their faith as well as social behavior were not improved. Based on those issues, the teacher was eager to look for improvement by carrying out classroom action research through active play method. This study aimed at: (1) describing the implementation of active playing method, (2) analyzing children’s faith and social behaviors, (3) identifying the obstacles of active-play method’s implementation. Prior to this study, seven children were chosen as research subjects to participate in three cycles. The data was obtained by observation, field notes, teacher’s interview, lesson plan documentation, and parents’ questionnaires. The study results: (1) improving actions are done according to the active playing method sequences with improvements on the next cycle (2) 86% of all students achieve high level of faith behavior (3) 71% of all students reach intermediate to high level of social behavior (4) arising obstacles are the limitations of students in communication, lack of knowledge and preparation of teachers regarding to playing method, learning time constraints, as well as the limitations of existing facilities.</p><p><strong>BAHASA INDONESIA ABSTRACT: </strong>Sekolah minggu GBI Tanah Abang memiliki kelas untuk anak usia Balita, di mana, firman Tuhan biasa dibagikan dengan menggunakan metode bercerita. Namun hal tersebut membuat anak-anak cepat bosan serta iman dan perilaku sosialnya tidak berkembang. Berdasarkan kerisauan guru tersebut maka dilakukan tindakan perbaikan berupa penelitian tindakan kelas dengan menerapkan metode bermain. Tujuan penelitian ini adalah: (1) mendeskripsikan penerapan metode bermain, (2) menganalisis perilaku iman dan sosial anak, (3) mengidentifikasi kendala-kendala dalam penerapan metode bermain. Penelitian ini dilaksanakan dalam tiga siklus, dengan subjek tujuh anak. Data didapat melalui  observasi, catatan lapangan, wawancara guru, dokumentasi RPP, dan kuesioner kepada orang tua. Setelah itu data dianalisis secara kualitatif deskriptif. Hasil dari penelitian ini menunjukkan: (1) tindakan perbaikan dilakukan sesuai dengan langkah metode bermain dengan penyempurnaan pada siklus berikutnya, (2) terlihat peningkatan perilaku iman hingga 86% siswa mencapai tingkat tinggi, (3) terlihat peningkatan perilaku sosial hingga 71% siswa mencapai tingkat sedang hingga tinggi, (4) kendala-kendala yang dihadapi antara lain keterbatasan siswa dalam berkomunikasi, kurangnya pengetahuan dan persiapan guru mengenai metode bermain, keterbatasan waktu pembelajaran, serta keterbatasan fasilitas yang ada.</p>

scholarly journals Asymmetric Residual Neural Network for Accurate Human Activity Recognition

Information ◽  
2019 ◽  
Vol 10 (6) ◽  
pp. 203 ◽  
Author(s):  
Jun Long ◽  
Wuqing Sun ◽  
Zhan Yang ◽  
Osolo Ian Raymond
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Time Window ◽  
Research Problem ◽  
Human Activity Recognition ◽  
Sensor Data ◽  
Practical Applications ◽  
Long Time ◽  
Benchmark Datasets ◽  
High Level

Human activity recognition (HAR) using deep neural networks has become a hot topic in human–computer interaction. Machines can effectively identify human naturalistic activities by learning from a large collection of sensor data. Activity recognition is not only an interesting research problem but also has many real-world practical applications. Based on the success of residual networks in achieving a high level of aesthetic representation of automatic learning, we propose a novel asymmetric residual network, named ARN. ARN is implemented using two identical path frameworks consisting of (1) a short time window, which is used to capture spatial features, and (2) a long time window, which is used to capture fine temporal features. The long time window path can be made very lightweight by reducing its channel capacity, while still being able to learn useful temporal representations for activity recognition. In this paper, we mainly focus on proposing a new model to improve the accuracy of HAR. In order to demonstrate the effectiveness of the ARN model, we carried out extensive experiments on benchmark datasets (i.e., OPPORTUNITY, UniMiB-SHAR) and compared the results with some conventional and state-of-the-art learning-based methods. We discuss the influence of networks parameters on performance to provide insights about its optimization. Results from our experiments show that ARN is effective in recognizing human activities via wearable datasets.

scholarly journals Attention-deficit hyperactivity disorder shares copy number variant risk with schizophrenia and autism spectrum disorder

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Olafur O. Gudmundsson ◽  
G. Bragi Walters ◽  
Andres Ingason ◽  
Stefan Johansson ◽  
Tetyana Zayats ◽  
...  
Keyword(s):  
Attention Deficit Hyperactivity Disorder ◽  
Attention Deficit ◽  
Neurodevelopmental Disorders ◽  
Copy Number ◽  
Neurodevelopmental Disorder ◽  
Copy Number Variant ◽  
Pleiotropic Effect ◽  
Autism Spectrum ◽  
Copy Number Variations ◽  
Hyperactivity Disorder

Abstract Attention-deficit/hyperactivity disorder (ADHD) is a highly heritable common childhood-onset neurodevelopmental disorder. Some rare copy number variations (CNVs) affect multiple neurodevelopmental disorders such as intellectual disability, autism spectrum disorders (ASD), schizophrenia and ADHD. The aim of this study is to determine to what extent ADHD shares high risk CNV alleles with schizophrenia and ASD. We compiled 19 neuropsychiatric CNVs and test 14, with sufficient power, for association with ADHD in Icelandic and Norwegian samples. Eight associate with ADHD; deletions at 2p16.3 (NRXN1), 15q11.2, 15q13.3 (BP4 & BP4.5–BP5) and 22q11.21, and duplications at 1q21.1 distal, 16p11.2 proximal, 16p13.11 and 22q11.21. Six of the CNVs have not been associated with ADHD before. As a group, the 19 CNVs associate with ADHD (OR = 2.43, P = 1.6 × 10−21), even when comorbid ASD and schizophrenia are excluded from the sample. These results highlight the pleiotropic effect of the neuropsychiatric CNVs and add evidence for ADHD, ASD and schizophrenia being related neurodevelopmental disorders rather than distinct entities.

Software Agents in Today's Digital Economy

2001 ◽  
pp. 88-100 ◽  
Author(s):  
Mahesh S. Raisinghani
Keyword(s):  
Intelligent Agents ◽  
New Technology ◽  
Software Agents ◽  
Information Filtering ◽  
Intelligent Agent ◽  
Social Dimension ◽  
Economic Changes ◽  
Software Products ◽  
Intelligent Software Agents ◽  
High Level

One of the most discussed topics in the information systems literature today is software agent/intelligent agent technology. Software agents are high-level software abstractions with inherent capabilities for communication, decision making, control, and autonomy. They are programs that perform functions such as information gathering, information filtering, or mediation (running in the background) on behalf of a person or entity. They have several aliases such as agents, bots, chatterbots, databots, intellibots, and intelligent software agents/robots. They provide a powerful mechanism to address complex software engineering problems such as abstraction, encapsulation, modularity, reusability, concurrency, and distributed operations. Much research has been devoted to this topic, and more and more new software products billed as having intelligent agent functionality are being introduced on the market every day. The research that is being done, however, does not wholeheartedly endorse this trend. The current research into intelligent agent software technology can be divided into two main areas: technological and social. The latter area is particularly important since, in the excitement of new and emergent technology, people often forget to examine what impact the new technology will have on people’s lives. In fact, the social dimension of all technology is the driving force and most important consideration of technology itself. This chapter presents a socio-technical perspective on intelligent agents and proposes a framework based on the data lifecycle and knowledge discovery using intelligent agents. One of the key ideas of this chapter is best stated by Peter F. Drucker in Management Challenges for the 21st Century when he suggests that in this period of profound social and economic changes, managers should focus on the meaning of information, not the technology that collects it.

scholarly journals Classification of Drivers' Workload Using Physiological Signals in Conditional Automation

2021 ◽  
Vol 12 ◽  
Author(s):  
Quentin Meteier ◽  
Marine Capallera ◽  
Simon Ruffieux ◽  
Leonardo Angelini ◽  
Omar Abou Khaled ◽  
...  
Keyword(s):  
Secondary Task ◽  
Time Window ◽  
Mental Workload ◽  
Cognitive Task ◽  
Physiological Signals ◽  
Machine Learning Techniques ◽  
Physiological Data ◽  
Automated Driving ◽  
Physiological Indicators ◽  
High Level

The use of automation in cars is increasing. In future vehicles, drivers will no longer be in charge of the main driving task and may be allowed to perform a secondary task. However, they might be requested to regain control of the car if a hazardous situation occurs (i.e., conditionally automated driving). Performing a secondary task might increase drivers' mental workload and consequently decrease the takeover performance if the workload level exceeds a certain threshold. Knowledge about the driver's mental state might hence be useful for increasing safety in conditionally automated vehicles. Measuring drivers' workload continuously is essential to support the driver and hence limit the number of accidents in takeover situations. This goal can be achieved using machine learning techniques to evaluate and classify the drivers' workload in real-time. To evaluate the usefulness of physiological data as an indicator for workload in conditionally automated driving, three physiological signals from 90 subjects were collected during 25 min of automated driving in a fixed-base simulator. Half of the participants performed a verbal cognitive task to induce mental workload while the other half only had to monitor the environment of the car. Three classifiers, sensor fusion and levels of data segmentation were compared. Results show that the best model was able to successfully classify the condition of the driver with an accuracy of 95%. In some cases, the model benefited from sensors' fusion. Increasing the segmentation level (e.g., size of the time window to compute physiological indicators) increased the performance of the model for windows smaller than 4 min, but decreased for windows larger than 4 min. In conclusion, the study showed that a high level of drivers' mental workload can be accurately detected while driving in conditional automation based on 4-min recordings of respiration and skin conductance.

scholarly journals The microbiota promotes social behavior by neuro-immune modulation of neurite complexity

2020 ◽  
Author(s):  
Joseph J. Bruckner ◽  
Sarah J. Stednitz ◽  
Max Z. Grice ◽  
Johannes Larsch ◽  
Alexandra Tallafuss ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Social Behavior ◽  
Recent Work ◽  
Early Development ◽  
Neurodevelopmental Disorders ◽  
Immune Modulation ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Developmental Programs ◽  
Developmental Mechanisms

AbstractHost-associated microbiotas normally guide the trajectory of intrinsically encoded developmental programs, and dysbiosis is linked to neurodevelopmental disorders such as autism spectrum disorder. Recent work suggests that microbiotas modulate social phenotypes associated with these disorders, though developmental mechanisms linking microbiotas to social behavior are not well understood. We discovered that the zebrafish microbiota is required for normal social behavior. Using this model to examine neuronal features modulated by the microbiota during early development, we found that the microbiota restrains neurite complexity and targeting of specific forebrain neurons required for normal social behavior. The microbiota is also required for normal forebrain infiltration of microglia, the brain’s resident phagocytes that remodel neuronal arbors, suggesting the microbiota modulates arborization via a neuro-immune route. Our work establishes a foundation for study of microbial and host mechanisms that link the microbiota and social behavior in an experimentally tractable model vertebrate.

scholarly journals Intrauterine Viral Infections: Impact of Inflammation on Fetal Neurodevelopment

2021 ◽  
Vol 15 ◽  
Author(s):  
Sourav Ganguli ◽  
Pavithra L. Chavali
Keyword(s):  
Neurodevelopmental Disorders ◽  
Viral Infections ◽  
Inflammatory Responses ◽  
Fetal Brain ◽  
Preterm Births ◽  
Autism Spectrum ◽  
Future Research ◽  
Increased Risk ◽  
Mechanistic Basis ◽  
Simplex Virus

Intrauterine viral infections during pregnancy by pathogens such as Zika virus, Cytomegalovirus, Rubella and Herpes Simplex virus can lead to prenatal as well as postnatal neurodevelopmental disorders. Although maternal viral infections are common during pregnancy, viruses rarely penetrate the trophoblast. When they do cross, viruses can cause adverse congenital health conditions for the fetus. In this context, maternal inflammatory responses to these neurotropic pathogens play a significant role in negatively affecting neurodevelopment. For instance, intrauterine inflammation poses an increased risk of neurodevelopmental disorders such as microcephaly, schizophrenia, autism spectrum disorder, cerebral palsy and epilepsy. Severe inflammatory responses have been linked to stillbirths, preterm births, abortions and microcephaly. In this review, we discuss the mechanistic basis of how immune system shapes the landscape of the brain and how different neurotropic viral pathogens evoke inflammatory responses. Finally, we list the consequences of neuroinflammation on fetal brain development and discuss directions for future research and intervention strategies.

scholarly journals The Human Dynamic Clamp reveals the fronto-parietal network linking real-time social coordination and cognition

2019 ◽  
Author(s):  
G. Dumas ◽  
Q. Moreau ◽  
E. Tognoli ◽  
J.A.S. Kelso
Keyword(s):  
Social Cognition ◽  
Social Behavior ◽  
Real Time ◽  
Dynamic Clamp ◽  
Sensorimotor Coordination ◽  
Self And Other ◽  
The Right ◽  
High Level ◽  
Sensorimotor Mechanisms ◽  
The Brain

AbstractHow does the brain allow us to interact with others, and above all how does it handle situations when the goals of the interactors overlap (i.e. cooperation) or differ (i.e. competition)? Social neuroscience has already provided some answers to these questions but has tended to treat high-level, cognitive interpretations of social behavior separately from the sensorimotor mechanisms upon which they rely. The goal here is to identify the underlying neural processes and mechanisms linking sensorimotor coordination and intention attribution. We combine the Human Dynamic Clamp (HDC), a novel paradigm for studying realistic social behavior between self and other in well-controlled laboratory conditions, with high resolution electroencephalography (EEG). The collection of humanness and intention attribution reports, kinematics and neural data affords an opportunity to relate brain activity to the behavior of the HDC as well as to what the human is doing. Behavioral results demonstrate that sensorimotor coordination influences judgements of cooperativeness and humanness. Analysis of brain dynamics reveals two distinct networks related to integration of visuo-motor information from self and other. The two networks overlap over the right parietal region, an area known to be important for interpersonal motor interactions. Furthermore, connectivity analysis highlights how the judgement of humanness and cooperation of others modulate the connection between the right parietal hub and prefrontal cortex. These results reveal how distributed neural dynamics integrates information from ‘low-level’ sensorimotor mechanisms and ‘high-level’ social cognition to support the realistic social behaviors that play out in real time during interactive scenarios.Significance StatementDaily social interactions require us to coordinate with others and to reflect on their potential motives. This study investigates the brain and behavioral dynamics of these two key aspects of social cognition. Combining high-density electroencephalography and the Human Dynamic Clamp (a Virtual Partner endowed with human-based coordination dynamics), we show first, that several features of sensorimotor coordination influence attribution of intention and judgement of humanness; second, that the right parietal lobe is a key integration hub between information related to self- and other-behavior; and third, that the posterior online social hub is functionally coupled to anterior offline brain structures to support mentalizing about others. Our results stress the complementary nature of low-level and high-level mechanisms that underlie social cognition.

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