Don't forget the learner: an essential aspect for developing effective hypermedia online learning in continuing medical education

There is increasing use of hypermedia online learning in continuing medical education (CME) that presents the learner with a wide range of different learning resources, requiring the learner to use self-regulated learning (SRL) skills. This study is the first to apply an SRL perspective to understand how learners engage with hypermedia online learning in CME. We found that the main SRL skills used by learners were use of strategies and monitoring. The increasing use of strategies was associated with increasing interest in the topic and with increasing satisfaction with the learning experience. Further research is recommended to understand SRL processes and its impact on learning in other aspects of hypermedia online learning across the different phases of medical education. Research is also recommended to implement and evaluate the learning impact of a variety of approaches to develop the SRL skills of hypermedia online learners in CME.     

Auditory-vocal cholinergic pathway in the songbird brain

Male zebra finches learn to imitate a tutor's song through auditory and motor learning. The two main song control nuclei in the zebra finch forebrain, the higher vocal center (HVC) and the robust nucleus of the archistriatum (RA), receive cholinergic innervation from the ventral paleostriatum (VP) of the basal forebrain which may play a key role in song learning. By injecting neuroanatomical tracers, we found a topographically segregated pathway from nucleus ovoidalis (Ov) to VP that in turn projects in a topographic fashion to HVC and RA. Ov is a major relay in the main ascending auditory pathway. The results suggest that the cholinergic neurons in the VP responsible for song learning are regulated by auditory information from the Ov.     

Glutamate receptors and signal transduction in learning and memory

The plasticity of the central nervous system helps form the basis for the neurobiology of learning and memory. Long-term potentiation (LTP) is the main form of synaptic plasticity, reflecting the activity level of the synaptic information storage process, and provides a good model to study the underlying mechanisms of learning and memory. The glutamate receptor-mediated signal pathway plays a key role in the induction and maintenance of LTP, and hence the regulation of learning and memory. The progress in the understanding of the glutamate receptors and related signal transduction systems in learning and memory research are reviewed in this article.     

Organization Of The Learning Process In Educational Facilities Using Microcomputers

The question of the organization of the learning process in educational facilities equipped with microcomputers remains the main component of a set of recommendations of a didactic, educational, psychological, health, and ergonomic nature on the problem of computer learning. The posing and the resolution of this question require a special grounding in methods of organizing learning activity and developing effective organizational models that include the use of computers. The purpose of the present article is to analyze a group learning system as an organizational unit of learning activity and to define a set of conditions for using computers to organize learning activity.     

In silico,in vitro,and in vivo machine learning in synthetic biology and metabolic engineering

Among the main learning methods reviewed in this study and used in synthetic biology and metabolic engineering are supervised learning, reinforcement and active learning, and in vitro or in vivo learning.In the context of biosynthesis, supervised machine learning is being exploited to predict biological sequence activities, predict structures and engineer sequences, and optimize culture conditions.Active and reinforcement learning methods use training sets acquired through an iterative process generally involving experimental measurements. They are applied to design, engineer, and optimize metabolic pathways and bioprocesses.The nascent but promising developments with in vitro and in vivo learning comprise molecular circuits performing simple tasks such as pattern recognition and classification.     

Associative learning and memory in Drosophila: beyond olfactory conditioning

The associative learning abilities of the fruit fly, Drosophila melanogaster, have been demonstrated in both classical and operant conditioning paradigms. Efforts to identify the neural pathways and cellular mechanisms of learning have focused largely on olfactory classical conditioning. Results derived from various genetic and molecular manipulations provide considerable evidence that this form of associative learning depends critically on neural activity and cAMP signaling in brain neuropil structures called mushroom bodies. Three other behavioral learning paradigms in Drosophila serve as the main subject of this review. These are (1) visual and motor learning of flies tethered in a flight simulator, (2) a form of spatial learning that is independent of visual and olfactory cues, and (3) experience-dependent changes in male courtship behavior. The present evidence suggests that at least some of these modes of learning are independent of mushroom bodies. Applying targeted genetic manipulations to these behavioral paradigms should allow for a more comprehensive understanding of neural mechanisms responsible for diverse forms of associative learning and memory.     

Everything is connected: Graph neural networks

In many ways, graphs are the main modality of data we receive from nature. This is due to the fact that most of the patterns we see, both in natural and artificial systems, are elegantly representable using the language of graph structures. Prominent examples include molecules (represented as graphs of atoms and bonds), social networks and transportation networks. This potential has already been seen by key scientific and industrial groups, with already-impacted application areas including traffic forecasting, drug discovery, social network analysis and recommender systems. Further, some of the most successful domains of application for machine learning in previous years—images, text and speech processing—can be seen as special cases of graph representation learning, and consequently there has been significant exchange of information between these areas. The main aim of this short survey is to enable the reader to assimilate the key concepts in the area, and position graph representation learning in a proper context with related fields.     

A neural network model for kindling of focal epilepsy: basic mechanism

A simple neural network model is proposed for kindling — the phenomenon of generating epilepsy by means of repeated electrical stimulation. The model satisfies Dale's hypothesis, incorporates a Hebb-like learning rule and has low periodic activity in absence of shocks. Many of the experimental observations are reproduced and some new experiments are suggested. It is proposed that the main reason for kindling is the formation of a large number of excitatory synaptic connections due to learning.     

Education for sustainability in higher education: a multiple-case study of three courses

Education for sustainability (EfS) in higher education is an emerging specialisation within the general field of EfS. EfS encompasses cognitive, affective and behavioural aspects, and aims at enhancing a variety of learning outcomes in these domains and reaching students from all programmes. One of the main challenges for higher education educators is to design courses in a way that will effectively promote the various learning outcomes of EfS. A central question is how sustainability should be integrated into the curriculum; which topics should be taught and which pedagogies ought to be applied to improve students’ knowledge, skills and motivation to promote sustainable living. The present study aimed to contribute to the knowledge about students’ learning outcomes yielded by different designs of EfS courses. This multiple-case study of three courses used a mixed-methods design. For each course, we identified its characteristics and analysed students’ self-reported learning outcomes. We found that: (1) a course with a higher degree of participatory learning, employing a system approach, promoted the highest and most varied learning outcomes; (2) the lecture-based course yielded the fewest learning outcomes; and (3) field trips promoted learning outcomes only when accompanied by more advanced pedagogies.     

EEG Beta Power but Not Background Music Predicts the Recall Scores in a Foreign-Vocabulary Learning Task

As tantalizing as the idea that background music beneficially affects foreign vocabulary learning may seem, there is—partly due to a lack of theory-driven research—no consistent evidence to support this notion. We investigated inter-individual differences in the effects of background music on foreign vocabulary learning. Based on Eysenck’s theory of personality we predicted that individuals with a high level of cortical arousal should perform worse when learning with background music compared to silence, whereas individuals with a low level of cortical arousal should be unaffected by background music or benefit from it. Participants were tested in a paired-associate learning paradigm consisting of three immediate word recall tasks, as well as a delayed recall task one week later. Baseline cortical arousal assessed with spontaneous EEG measurement in silence prior to the learning rounds was used for the analyses. Results revealed no interaction between cortical arousal and the learning condition (background music vs. silence). Instead, we found an unexpected main effect of cortical arousal in the beta band on recall, indicating that individuals with high beta power learned more vocabulary than those with low beta power. To substantiate this finding we conducted an exact replication of the experiment. Whereas the main effect of cortical arousal was only present in a subsample of participants, a beneficial main effect of background music appeared. A combined analysis of both experiments suggests that beta power predicts the performance in the word recall task, but that there is no effect of background music on foreign vocabulary learning. In light of these findings, we discuss whether searching for effects of background music on foreign vocabulary learning, independent of factors such as inter-individual differences and task complexity, might be a red herring. Importantly, our findings emphasize the need for sufficiently powered research designs and exact replications of theory-driven experiments when investigating effects of background music and inter-individual variation on task performance.     

Network-based diffusion analysis: a new method for detecting social learning

Social learning has been documented in a wide diversity of animals. In free-living animals, however, it has been difficult to discern whether animals learn socially by observing other group members or asocially by acquiring a new behaviour independently. We addressed this challenge by developing network-based diffusion analysis (NBDA), which analyses the spread of traits through animal groups and takes into account that social network structure directs social learning opportunities. NBDA fits agent-based models of social and asocial learning to the observed data using maximum-likelihood estimation. The underlying learning mechanism can then be identified using model selection based on the Akaike information criterion. We tested our method with artificially created learning data that are based on a real-world co-feeding network of macaques. NBDA is better able to discriminate between social and asocial learning in comparison with diffusion curve analysis, the main method that was previously applied in this context. NBDA thus offers a new, more reliable statistical test of learning mechanisms. In addition, it can be used to address a wide range of questions related to social learning, such as identifying behavioural strategies used by animals when deciding whom to copy.     

The evolution of conformist social learning can cause population collapse in realistically variable environments

Why do societies collapse? We use an individual-based evolutionary model to show that, in environmental conditions dominated by low-frequency variation (“red noise”), extirpation may be an outcome of the evolution of cultural capacity. Previous analytical models predicted an equilibrium between individual learners and social learners, or a contingent strategy in which individuals learn socially or individually depending on the circumstances. However, in red noise environments, whose main signature is that variation is concentrated in relatively large, relatively rare excursions, individual learning may be selected from the population. If the social learning system comes to lack sufficient individual learning or cognitively costly adaptive biases, behavior ceases tracking environmental variation. Then, when the environment does change, fitness declines and the population may collapse or even be extirpated. The modeled scenario broadly fits some human population collapses and might also explain nonhuman extirpations. Varying model parameters showed that the fixation of social learning is less likely when individual learning is less costly, when the environment is less red or more variable, with larger population sizes, and when learning is not conformist or is from parents rather than from the general population. Once social learning is fixed, extirpation is likely except when social learning is biased towards successful models. Thus, the risk of population collapse may be reduced by promoting individual learning and innovation over cultural conformity, or by preferential selection of relatively fit individuals as models for social learning.     

高校《普通动物学》教学中实施自主学习的探索

课堂教学是学生获取知识的主渠道,教会学生如何自主学习已经成为世界各国基础教育的根本任务之一。在现代社会自主学习是学习者所必备的重要能力之一,在课堂教学模式下,让学生走自主学习之路是学会学习的有效途径。作者对本校《普通动物学》课堂教学模式下实施自主学习现状进行了分析、对比、改进,目的在于培养学生自主学习的意识,增强自主学习能力,提高学习效率。结果证明学生在自主式教学模式下积极性被充分调动,学习成绩显著提高。     

The effect of memorization processes on the structural organization of natural human sleep

The effect of sleep on learning was investigated, comparing results of memorizing and reproduction of an unknown text before and after subsequent sleep, with a detailed analysis of sleep patterns. Psychological tests excluded the possibility of emotional and stress factors. Presleep learning did not influence mean values of such sleep parameters as total sleep time or duration of different sleep stages. The main finding ot the present experiment was a redistribution of stage REM during nocturnal sleep following learning--its increase in the second sleep cycle with a corresponding decrease toward the end of night. Also, individual difficulties in learning were inversely related to REM latency. Changes in sleep patterns after learning didn't influence the total number of sleep cycles. It is suggested that the REM phase of sleep might be involved in the processing of information acquired during wakefulness.     

Deep Learning and Its Applications in Biomedicine

Advances in biological and medical technologies have been providing us explosive volumes of biological and physiological data, such as medical images, electroencephalography, genomic and protein sequences. Learning from these data facilitates the understanding of human health and disease. Developed from artificial neural networks, deep learning-based algorithms show great promise in extracting features and learning patterns from complex data. The aim of this paper is to provide an overview of deep learning techniques and some of the state-of-the-art applications in the biomedical field. We first introduce the development of artificial neural network and deep learning. We then describe two main components of deep learning, i.e., deep learning architectures and model optimization. Subsequently, some examples are demonstrated for deep learning applications, including medical image classification, genomic sequence analysis, as well as protein structure classification and prediction. Finally, we offer our perspectives for the future directions in the field of deep learning.     

A pheromone that rapidly promotes learning in the newborn

Mammalian neonates depend on their mother's food supply and use a defined sequence of actions to find her mammary area. Their behavior is initially uncertain and demanding but rapidly becomes optimal. Efficient learning is thus operating in newborns. For instance, European rabbit (Oryctolagus cuniculus) pups localize the nipples through typical orocephalic movements. These movements are released by the mammary pheromone secreted in milk or by prenatally learned odor cues. During daily nursing, they also learn odors associated with the mother, supposedly with sucking as the main reinforcer. We here investigate the role of the mammary pheromone as an enforcer of early olfactory learning in newborn rabbits. In testing more than 950 pups, we show that the mammary pheromone promotes learning of neutral odorants paired with the pheromone in single and short trials. The pheromone-induced learning is efficient from birth and supports successive acquisition of distinct odorants. This reveals that a mammalian pheromone can function as a "cognitive organizer" that promotes early learning of relevant environmental cues.     

NR2B tyrosine phosphorylation modulates fear learning as well as amygdaloid synaptic plasticity

Phosphorylation of neural proteins in response to a diverse array of external stimuli is one of the main mechanisms underlying dynamic changes in neural circuitry. The NR2B subunit of the NMDA receptor is tyrosine-phosphorylated in the brain, with Tyr-1472 its major phosphorylation site. Here, we generate mice with a knockin mutation of the Tyr-1472 site to phenylalanine (Y1472F) and show that Tyr-1472 phosphorylation is essential for fear learning and amygdaloid synaptic plasticity. The knockin mice show impaired fear-related learning and reduced amygdaloid long-term potentiation. NMDA receptor-mediated CaMKII signaling is impaired in YF/YF mice. Electron microscopic analyses reveal that the Y1472F mutant of the NR2B subunit shows improper localization at synapses in the amygdala. We thus identify Tyr-1472 phosphorylation as a key mediator of fear learning and amygdaloid synaptic plasticity.     

Extendable and explainable deep learning for pan-cancer radiogenomics research

Radiogenomics is a field where medical images and genomic profiles are jointly analyzed to answer critical clinical questions. Specifically, people want to identify non-invasive imaging biomarkers that are associated with both genomic features and clinical outcomes. Deep learning is an advanced computer science technique that has been applied in many fields, including medical image and genomic data analysis. This review summarizes the current state of deep learning in pan-cancer radiogenomic research, discusses its limitations, and indicates the potential future directions. Traditional machine learning in radiomics, genomics, and radiogenomics have also been briefly discussed. We also summarize the main pan-cancer radiogenomic research resources. Two characteristics of deep learning are emphasized when discussing its application to pan-cancer radiogenomics, which are extendibility and explainability.