Cognitive Capabilities Research Articles

Prenatal exposure to air pollution during the early and middle stages of pregnancy is associated with adverse neurodevelopmental outcomes at ages 1 to 3 years

BackgroundA large body of data shows that fetal brain development is vulnerable to disruption by air pollution experienced by the mother during pregnancy, adversely affecting cognitive and psychomotor capabilities during childhood (De Asis-Cruz et al., Biol Psychiatry 7:480–90, 2022; Morgan ZEM et al., Environ Health 22:11, 2023). This study has sought to identify gestational windows of susceptibility to prenatal exposure to air pollution.Methods470 African American and Latina mother/child pairs participated in a prospective cohort study based in the low-income communities of Northern Manhattan and the South Bronx, New York City. Gestational exposure to respirable particulate matter (PM2.5) and nitrogen dioxide (NO2) was assessed through validated models in relation to cognitive and motor development assessed at ages 1, 2, and 3 years using the Bayley-II Scales. Multiple linear regression models and distributed lag models (DLM) were used to identify critical windows of exposure by trimester and week of pregnancy.ResultsBy linear regression, average exposures to NO2 during the first and second trimesters and the entire pregnancy were significantly and negatively associated with the mental developmental index (MDI) at age 1. Average exposures to PM2.5 during the second trimester and the entire pregnancy were also significantly, inversely associated with age 1 MDI. No significant associations were found between these exposures and MDI at age 2. NO2 exposure during the first trimester was significantly negatively associated with MDI at age 3. Using DLM, exposures to NO2 at lags 29–30 weeks (within the first trimester) and PM2.5 at lags 17–18 weeks (second trimester) were significantly and inversely associated with MDI at age 1. Significant, inverse associations were found between exposures to NO2 at lag 29 weeks and PM2.5 at lags 27–29 weeks and children’s MDI at age 3. No significant associations were found between psychomotor index (PDI) and prenatal exposures to NO2 or PM2.5 at ages 1, 2 or 3.ConclusionsOur finding that prenatal exposure to air pollution in the first and second trimesters was associated with lower scores for cognitive development at ages 1 and 3 is of concern because of the potential consequences of these outcomes for long-term functioning. They underscore the need for stronger policies to protect pregnant individuals and offspring, particularly during vulnerable, early life-stage of development.

The Effectiveness of Al-Ta'hil Al-Fiqhiy Method in Improving the Cognitive Ability of STIBA Makassar Students

The objective of this study is to assess the efficacy of the Al-Ta’hῑl Al-Fiqhῑy learning approach in enhancing students' cognitive capabilities at STIBA Makassar. A true experiment with a posttest-only control group design was employed to assess the substantial impact of the method on students in their seventh semester. The findings of this study indicate that implementing the Al-Ta’hῑl Al-Fiqhῑy method markedly enhanced students' cognitive abilities. The calculated value of Cohen's d reached 1.26, indicating a huge effect size with a probability of 81.4% that the observed difference is due to the intervention and not other confounding factors. Moreover, the results of Cohen's U3 test demonstrated that 89.7% of students in the experimental group exhibited higher cognitive scores than those in the control group. These findings suggest that the Al-Ta’hῑl Al-Fiqhῑy method represents an effective alternative for enhancing the quality of education and fostering deeper comprehension in the field of fiqh. Keywords: Effectiveness, Al-Ta’hῑl Al-Fiqhῑy Method, Cognitive Ability.

Iron load in the normal aging brain measured with QSM and R2* at 7T: findings of the SENIOR cohort

BackgroundIron accumulates in the brain during aging and is the focus of intensive research as an abnormal load, particularly in Deep Gray Matter (DGM), is related to neurodegeneration. Magnetic Resonance Imaging (MRI) metrics such as Quantitative Susceptibility Mapping (QSM) and apparent transverse relaxation rate R2* can be used to follow up iron in vivo. While the influence of age and sex on iron levels has already been reported, a careful consideration of neuronal risk factors, as well as for an enhanced sensitivity, is needed to define the normal evolution.MethodsQSM and R2* at ultra-high field MRI are used to study iron in DGM using a carefully-characterized cohort of the healthy aging brain (SENIOR). Seventy-seven cognitively healthy elders (from 54 to 78 y/o) with clinical, biology, genetics, and cardiovascular risk factors careful evaluation. Differences linked with age, sex, cardiovascular risk factors and weight are studied.ResultsAge and sex have an influence on the brain iron deposition measured by QSM and R2* in a context of normal aging, without appearance of a pathological neurodegenerative process. Iron deposition shows higher values in the caudate and the putamen in older participants. Female participants present a higher level of iron in the amygdala, and males in the thalamus. Female participants also present differences in the accumbens, caudate and hippocampus when evaluating the joint age and sex effect. Participants with higher cardiovascular risk factors showed higher values of the iron, even without any impairment in their cognitive capability. An overweight is related with a higher iron load in the putamen for QSM and R2* in female participants. We controlled that these modifications of iron deposition are not related to a specific profile in the genotype of ApoE loci.ConclusionsEstablishing baseline values of QSM and R2* as iron probes in the context of aging is essential to determine differences in the process of neurodegeneration. Age and sex of participants are important factors that affect brain iron normal values. On the other hand, the presence of cardiovascular risk factors, which can be associated with age related diseases, can also potentially be linked with the iron deposition in the brain.

Semaglutide ameliorates Alzheimer's disease and restores oxytocin in APP/PS1 mice and human brain organoid models

AimsTo investigate the therapeutic effects and mechanisms of Semaglutide in Alzheimer's disease (AD), and identify its potential targets. MethodsWe systematically evaluated the effect of Semaglutide on Alzheimer's disease (AD), using both mice and human organoid models. ResultsBehavioral analyses on APP/PS1 mice demonstrated that Semaglutide improved the cognitive capabilities, particularly in the learning and memory domains. Biochemical investigations further highlighted its role in reducing amyloid plaque deposition and down-regulating the expression of glial fibrillary acidic protein (GFAP) and ionized calcium binding adaptor molecule 1 (Iba1) expression in the mouse brain tissues. Meanwhile, oxytocin (OXT) was up-regulated after Semaglutide treatment. Subsequent studies using human AD-brain organoids (BOs) models revealed that, upon Semaglutide treatment, these AD-BO models also exhibited reduced levels of amyloid-beta (Aβ), phosphorylated Tau (p-Tau) and GFAP expression as well as increased OXT level. ConclusionsSemaglutide can ameliorate Alzheimer's disease in pre-clinical models, suggesting the promising therapeutic potential in AD patients.

Collaborative Robots with Cognitive Capabilities for Industry 4.0 and Beyond

Collaborative Robots with Cognitive Capabilities for Industry 4.0 and Beyond

Technological scenarios of posthumanism: which [built] environment towards sustainability

Abstract Human kind has always been an explorer and technological innovation over the centuries is been implemented thanks to his thirst for novelty. Technological progress today clashes with the limitations of available resources and environmental impacts. The acceleration in the introduction of technological innovations determines rapid obsolescence of human artifacts and the need for continuous replacement, reducing the useful life of products. Having exhausted the physicality of planet Earth, human exploration now continues in two directions: on the one hand, the colonization of Space, on the other, the construction of new virtual worlds (Metaverse) and new physical and cognitive capabilities (biotechnology and artificial intelligence). However, these explorations are the source of considerable energy consumption (e.g., of space flights and data centers) and contribute to climate change, increasing the critical issues regarding the sustainability of lifestyles. Technological and scientific progress, after having changed the natural environment, today comes to change human nature (posthumanism), and consequently the living environment of human kind. The research is approached through the comparison of different philosophical and scientific positions (literature analysis), transferring some general considerations to the building sector. What next generation built environment will be?

Context-dependent communication under environmental constraints

There is significant evidence that real-world communication cannot be reduced to sending signals with context-independent meaning. In this work, based on a variant of the classical Lewis (1969) signalling model, we explore the conditions for the emergence of context-dependent communication in an agent-based situated model. In particular, we demonstrate that pressure to minimise the vocabulary size is sufficient for such emergence. At the same time, we study the environmental conditions and cognitive capabilities that enable contextual disambiguation of symbol meanings. We show that a) regularities in the context are not necessary for context-dependent communication and that b) environmental constraints on the receiver’s referent choice can be unilaterally exploited by the sender, without disambiguation capabilities on the receiver’s end. Consistent with common assumptions, the sender’s awareness of the context appears to be required for contextual communication. We suggest that context-dependent communication is a situated multilayered phenomenon, crucially influenced by environment properties such as distribution of objects in the context. The computational model developed in this work is a demonstration of how signals may be ambiguous out of context, but still allow for near-perfect communication accuracy.

Listening to two-talker conversations in quiet settings: the role of listeners’ cognitive processing capabilities for memory and listening effort

Listening to conversing talkers in quiet environments and remembering the content is a common activity. However, research on the cognitive demands involved is limited. This study investigates the relevance of individuals’ cognitive functions for listeners’ memory of two-talker conversations and their listening effort in quiet listening settings. A dual-task paradigm was employed to explore memory of conversational content and listening effort while analyzing the role of participants’ (n = 29) working memory capacity (measured through the operation span task), attention (Frankfurt attention inventory 2), and information-processing speed (trail making test). In the primary task, participants listened to a conversation between a male and female talker and answered content-related questions. The two talkers’ audio signals were presented through headphones, either spatially separated (+ /– 60°) or co-located (0°). Participants concurrently performed a vibrotactile pattern recognition task as a secondary task to measure listening effort. Results indicated that attention and processing speed were related to memory of conversational content and that all three cognitive functions were related to listening effort. Memory performance and listening effort were similar for spatially separated and co-located talkers when considering the psychometric measures. This research offers valuable insights into cognitive processes during two-talker conversations in quiet settings.

Enhancing Learning Abilities in Students Using a Cognitive Neuroscience Model Based on Brain-Computer Interface Signal Analysis

Computational intelligence is used to create artificially intelligent systems with the ability to learn, adapt, and solve problems. Learners’ computational abilities can be improved with the help of Cognitive Neuroscience techniques. Computational intelligence refers to the ability of a learner based on the complex operations of the brain in the four relevant dimensions: Visual (V), Aural (A), Kinematic (K), and Reading/Writing (R/W). Our proposed framework consists of electroencephalogram (EEG) data acquisition, signal extraction, and EEG signal categorization to assess students’ cognitive learning abilities. Our proposed approach uses an EEG device equipped with a microprocessor, a Think Gear (TGAM) EEG sensor, and a PCB of 16 dry electrodes. The EEG device and the remote processing device are connected by Bluetooth. The EEG signal provides the students with neurofeedback on their cognitive learning capability. The feedback obtained through the learning process will be endowing to improvise computational intelligence. The statistical derivative, Pearson Co-efficient, is used to find the correlation among the derived attributes. The attributes considered are the learner’s gender, stream, age, and geographical region. The results obtained highlight that gender, stream, and age have no correlation with the detectable learning index, and the most accurate learners are kinesthetic ones. Bi-modal learners, who can maintain focus while reading and using their kinematic abilities, had the second highest learning capacity.

Cerebellar Molecular Signatures in Non-Human Primates.

Cerebellar molecular signatures in primates remain largely unexplored. Here, we investigated the immunoreactivity of neuroplasticity-related molecular markers, including aldolase C (Aldoc), phospholipase C beta 3 (PLCB3), and phospholipase C beta 4 (PLCB4) in the cerebellar cortex and associated nuclei of rhesus macaque monkeys (Macaca mulatta). Our main findings are as follows: First, the cerebellar vermis in macaques exhibited striped compartmentalization for all markers, with the striped expression boundary of PLCB3 being less distinct than those of Aldoc and PLCB4. Second, the striped pattern was less pronounced in the cerebellar hemisphere compared to the vermis, with signals in the hemisphere being predominantly intense throughout. Third, distinct zonal patterns and elevated signals for Aldoc and PLCB3 were observed in the cerebellar deep nuclei. Specifically, the fastigial nucleus displayed intense Aldoc signals in both caudal and rostral regions, while the dentate nucleus displayed strong Aldoc signals in both ventral and dorsal regions. Compared to previous rodent studies, the macaque cerebellum demonstrated a higher proportion of intense signal areas and distinct compartmentalization patterns in both cortical and deep nuclei. These findings offer crucial insights into the unique molecular organization of the primate cerebellum, enhancing our understanding of the advanced neuroplasticity, cognitive, and motor capabilities in primates.

The Medaka Approach to Evolutionary Social Neuroscience.

Previously, the integration of comparative biological and neuroscientific approaches has led to significant advancements in social neuroscience. This review elucidates the potential and future directions of evolutionary social neuroscience research utilizing medaka fishes (the family Adrianichthyidae) including Japanese medaka (Oryzias latipes). We focus on medaka's social cognitive capabilities and mate choice behavior, particularly emphasizing mate preference using visual cues. Medaka fishes are also advantageous due to their abundant genetic resources, extensive genomic information, and the relative ease of laboratory breeding and genetic manipulation. Here we present some research examples of both the conventional neuroscience approach and evolutionary approach involving medaka fishes and other species. We also discuss the prospects of uncovering the molecular and cellular mechanisms underlying the diversity of visual mate preference among species. Especially, we introduce that the single-cell transcriptome technology, particularly in conjunction with 'Adaptive Circuitry Census', is an innovative tool that bridges comparative biological methods and neuroscientific approaches. Evolutionary social neuroscience research using medaka has the potential to unveil fundamental principles in neuroscience and elucidate the mechanisms responsible for generating diversity in mating strategies.

Parents' Distress and Demands for Children With Cancer-Related Cognitive Impairment in Western China: A Qualitative Study.

Cancer-related cognitive impairment is one of the common complications in children with cancer, but our understanding of their experience with cognitive deficits remains limited. From the perspective of parents, this study aimed to explore the distress and demands faced by children with cancer-related cognitive impairment, to provide references for developing targeted intervention strategies for cancer children. We used a purposeful sampling method to conduct semi-structured interviews with the parents of 18 children with cancer-related cognitive impairment. The transcripts were analyzed using Colaizzi's method. Three categories and 11 subcategories were obtained from the data analysis, including diverse levels of cognitive impairment in children (speech communication difficulties, impaired executive function, attention deficit, and intellectual disability), persistent negative emotions (anxiety and worry, complaints and resentment, negative avoidance and positive experiences after psychological adjustment), multiple needs (need for disease information, need for professional management, and an urgent need for more external support). Parents of children with cancer-related cognitive impairment face a significant psychological burden, coupled with confusion and numerous inquiries regarding the symptoms and management of their children's cognitive impairment. It is imperative for medical professionals to strengthen the dissemination of information related to cancer-associated cognitive impairments, while promptly recognizing and intervening in related symptoms. Rational allocation of resources, establishment of targeted support systems, and enhancement of social acceptance may be the key points that policymakers could consider. These efforts hold immense significance, as they play a pivotal role in elevating cognitive capabilities and enhancing the overall quality of life for children with cancer.

The Silicon Architect: Transformation Towards a Data-Centric Mindset

This paper investigates the emergence of a novel ‘data-centric’ mindset within architecture and its implications for the architectural design process. Defined by engagement with new technology (Data Science, Big Data, Machine Learning) this mindset is driving new insight toward novel aesthetics and ultimately new disciplinary hypotheses. The literature review first tracks distinguishable transitions in the architectural mindset through the architects that have embodied them (Master Builder, Beaux-Art, Modernist, and Parametric Architect) culminating with what is here termed the ‘Silicon Architect’. Next, three archetypal case studies reveal how the architectural design process is re-potentialized through a data-centric mindset, allowing architects to ultimately escape their imaginative limits and arrive at new disciplinary ambitions. The data-centric inclinations of these architects have resulted in a fusion of human-machine cognition. Through this ‘composite’ cognition, architects can now push beyond more typical ambitions (i.e. the creation of novel forms) toward an encounter with notions of ‘hypotheses generation’ and ‘disciplinary prospection’ via non-human cognitive input. This new mindset emerging in the Silicon Architect is set to re-direct the architectural design process, and in doing so, help the discipline escape the limits of its own paradigmatic imagination in ways that operate beyond human cognitive capabilities. In this sense, research sheds light on the influences that may shape future architectural design processes and the architects who may evolve.

Potential promises and perils of artificial intelligence in psychotherapy -The AI Psychotherapist (APT).

Since the release of ChatGPT, popular demand has driven the use of social chatbots as pseudo-AI psychotherapists. With time, it is inevitable that these technologies will be deployed in some form as dedicated psychotherapy interventions. Here, we attempt to forecast the implications for psychotherapy including the unique benefits to distributive justice as well as concerns for the quality of the therapy and its societal impact. An AI psychotherapist (APT) has the potential to provide engaging clinical interactions given its capacity for highly realistic interaction as well as its high level cognitive and emotional capabilities. Moreover, it can potentially address financial and workforce limitations on access to therapy. However, an APT may cause significant iatrogenic harm if released without adequate quality control and oversight by trained psychotherapists. If not appropriately designed and regulated, APTs have potential to mislead and reinforce maladaptive coping behaviours. Given societal drivers and possible benefits, these technologies will inevitably be deployed; thus, it is incumbent upon us as a professional body to consider their regulation.

Human-to-Robot Handover Based on Reinforcement Learning.

This study explores manipulator control using reinforcement learning, specifically targeting anthropomorphic gripper-equipped robots, with the objective of enhancing the robots' ability to safely exchange diverse objects with humans during human-robot interactions (HRIs). The study integrates an adaptive HRI hand for versatile grasping and incorporates image recognition for efficient object identification and precise coordinate estimation. A tailored reinforcement-learning environment enables the robot to dynamically adapt to diverse scenarios. The effectiveness of this approach is validated through simulations and real-world applications. The HRI hand's adaptability ensures seamless interactions, while image recognition enhances cognitive capabilities. The reinforcement-learning framework enables the robot to learn and refine skills, demonstrated through successful navigation and manipulation in various scenarios. The transition from simulations to real-world applications affirms the practicality of the proposed system, showcasing its robustness and potential for integration into practical robotic platforms. This study contributes to advancing intelligent and adaptable robotic systems for safe and dynamic HRIs.

Surrogate model-based cognitive digital twin for smart remote maintenance of fusion reactor: modeling and implementation

Abstract A remote maintenance robot system (RMRS) plays a critical role in safeguarding the fusion energy experimental device’s security and stability. State-of-the-art intelligent technology such as cognitive digital twins (CDT) is widely considered capable of improving complex equipment’s performance and reducing management burden using a visualized system. However, the CDT virtual space cannot mirror the RMRS which is a kind of flexible multi-body system in real-time and with high fidelity. Therefore, we propose a cognitive digital twin (CDT) modeling method based on a surrogate model for the RMRS. Firstly, model-based system engineering (MBSE) is leveraged to build a structural modular architecture, which can decrease the modeling complexity of CDT and increase the modeling efficiency. Then, the surrogate models are self-learning within the CDT physical space, which reconstructs the RMRS’s real-time dynamic performances and endows CDT with cognitive capabilities. Finally, after integrating the CDT system, a smart decision-making plan that compensates for the operation error is generated for RMRS’s accurate control. We take a China Fusion Engineering Test Reactor (CFETR) multi-purpose overload robot (CMOR) as an example to demonstrate the implementation process. According to the results, CDT can achieve real-time (230 ms time delay) high-fidelity (5 mm control error) monitoring and accurate control, and CMOR conducts smart maintenance based on the simulation results. This method improves the efficiency of RM and provides solutions for high-duty cycle time of CFETR, it can also be applied to other tokamak fusion energy devices.

Artificial intelligence in academic writing: composing a for-and-against essay

Over the past years, Artificial Intelligence (AI) and Chat Generative Pre-Trained Transformer (ChatGPT) as its type have been extensively investigated and proved to be effectually employed within various spheres including education, offering promising opportunities for enhancing teaching and learning processes. However, the realization of these opportunities within the realm of Academic Writing (AW) instruction have not yet been thoroughly studied. Therefore, this article addresses this matter and exposes the results of a conducted analysis of the potential for integrating ChatGPT into AW instruction within the University curriculum. Specifically, it focuses on the issue of teaching master students how to create a successful for-and-against academic essay. Besides, the aim of this article is to develop an overarching framework for the incremental incorporation of ChatGPT into the AW course. To achieve this aim, the article resorts to such methods as theoretical positioning, comparative analysis, and qualitative research. Accordingly, the presented study expounds on the phenomenon of ChatGPT, its attributes, merits, and shortcomings, while also outlining the notion, features, key elements, and stages of producing for-and-against academic essays. In addition, it delineates the possibilities of ChatGPT’s application at various stages of essay writing, which may appear challenging for students. Furthermore, it recognizes potential drawbacks of ChatGPT, such as the risk of its overutilization and overreliance, which may result in obliterating students’ cognitive capabilities and AW skills. The presented study also underscores the necessity for additional immersion in the matter under discussion to address the issue related to long-term effects of integrating ChatGPT into AW instruction. Ultimately, the article sheds light on the AI’s dynamic integration in modern educational settings and provides implications for further research in this field.

Research on the Application of Information Visualization Design Based on the Three-level Theory of Emotions

As society progresses, environmental challenges have emerged as a central concern in modern discourse. The degradation of ecosystems has led to the endangerment and extinction of numerous plant species. Moreover, the overwhelming influx of information available to individuals often hampers their understanding of the significance of these endangered species. Conventional written narratives are frequently inadequate in fulfilling the audience’s informational needs. As a nascent medium for scientific communication, information visualization presents an innovative and effective approach for individuals to swiftly acquire and interpret data. This paper concentrates on the endangered aquatic flora within the South China Botanical Garden, grounded in a three-tiered emotional theory framework. Strategies are developed across instinctual, behavioral, and reflective dimensions. The outlined strategic goals aim to elevate design aesthetic literacy, broaden cognitive processing capability, and enhance the efficiency of information dissemination. By employing endangered aquatic plants as a focal point for design initiatives in the South China Botanical Garden and integrating it with the theoretical framework, the objective is to present information regarding these vulnerable species in a more direct, inventive, and concise manner. This methodology aspires to raise public awareness surrounding the conservation of endangered plant life.

Empowering Chemical AI Through Systems Chemistry

AbstractThis work presents some ambitious perspectives on how Systems Chemistry can contribute to developing the quite new research line of Chemical Artificial Intelligence (CAI). CAI refers to the efforts of devising liquid chemical systems mimicking some performances of biological and human intelligence, which ultimately emerge from wetware. The CAI systems implemented so far assist humans in making decisions. However, such CAI systems lack autonomy and cannot substitute humans. The development of autonomous chemical systems will allow the colonization of the molecular world with remarkable repercussions on human well‐being. As a beneficial side effect, this research line will help establish a deeper comprehension of the mesmerizing phenomenon of the origin of life on Earth and how cognitive capabilities emerge at a basic physico‐chemical level.

Leap to Greatness: Unveiling the Transformative Influence of Rope Skipping among Pe Students

This research investigates the incorporation of a complete rope skipping program into the physical education curriculum to improve students' physical fitness and emotional resilience. The rope skipping exercise was chosen for its versatility and affordability since it can enhance cardiovascular endurance, muscular strength, agility, coordination, and mental concentration. The research used a pre-test and post-test approach to evaluate the influence of frequent rope-skipping sessions on students' competence in essential fitness domains. Students were assessed on fundamental jumping methods, the ability to maintain rhythm and timing, coordination and balance, different footwork variants, pace control, endurance, and stamina. In addition, the study investigated the lasting impact of rope skipping on cardiovascular endurance, muscular strength, agility, speed, weight control, and mental resilience. The findings demonstrated substantial improvements in all areas from the first assessment to the final evaluation, with pupils displaying notable advancements in their physical and cognitive capabilities. The post-test results revealed a transition from mediocre to excellent competence in all domains, indicating the efficacy of the rope-skipping intervention. Pearson's r correlation study provided more evidence of beneficial associations between rope skipping skills and improved athletic performance, namely in endurance, coordination, and mental resilience. The research finds that rope skipping is a valuable and versatile activity for students of school age, promoting enhancements in physical fitness, mental resilience, and good living choices. The program's implementation may be beneficial for educators to foster comprehensive student growth and a lifetime commitment to physical exercise.