Leader-follower dynamics during early social interactions matter for infant word learning.

We know little about the mechanisms through which leader-follower dynamics during dyadic play shape infants' language acquisition. We hypothesized that infants' decisions to visually explore a specific object signal focal increases in endogenous attention, and that when caregivers respond to these proactive behaviors by naming the object it boosts infants' word learning. To examine this, we invited caregivers and their 14-mo-old infants to play with novel objects, before testing infants' retention of the novel object-label mappings. Meanwhile, their electroencephalograms were recorded. Results showed that infants' proactive looks toward an object during play associated with greater neural signatures of endogenous attention. Furthermore, when caregivers named objects during these episodes, infants showed greater word learning, but only when caregivers also joined their focus of attention. Our findings support the idea that infants' proactive visual explorations guide their acquisition of a lexicon.

Reducing false positive rate of docking-based virtual screening by active learning.

Machine learning-based scoring functions (MLSFs) have become a very favorable alternative to classical scoring functions because of their potential superior screening performance. However, the information of negative data used to construct MLSFs was rarely reported in the literature, and meanwhile the putative inactive molecules recorded in existing databases usually have obvious bias from active molecules. Here we proposed an easy-to-use method named AMLSF that combines active learning using negative molecular selection strategies with MLSF, which can iteratively improve the quality of inactive sets and thus reduce the false positive rate of virtual screening. We chose energy auxiliary terms learning as the MLSF and validated our method on eight targets in the diverse subset of DUD-E. For each target, we screened the IterBioScreen database by AMLSF and compared the screening results with those of the four control models. The results illustrate that the number of active molecules in the top 1000 molecules identified by AMLSF was significantly higher than those identified by the control models. In addition, the free energy calculation results for the top 10 molecules screened out by the AMLSF, null model and control models based on DUD-E also proved that more active molecules can be identified, and the false positive rate can be reduced by AMLSF.

Simultaneous multidimensional impacts of active learning revealed in a first implementation in the MENA region.

Enhancing science education in developing countries has been a focal point of many studies and efforts, but reform has mainly been driven by top-down approaches that often face impediments. A shift to active learning pedagogies can potentially address these challenges, but it has thus far been predominantly implemented and understood in developed countries. Thanks to the growing accessibility of open education resources and ubiquitous technologies, education reform can now be carried out from the bottom up. Here, we present the results of a two-year implementation of active learning in five core physics and astronomy courses comprising 2,145 students from the Middle East and North Africa (MENA) region. Simultaneous improvements are observed in both students' performance and their perception of the quality of learning; means improved by 9% (0.5 SD) and 25% (1.5 SD), respectively. The performance gap between students in the bottom quartile and those in the top quartiles was narrowed by 17%. The failure rate was reduced to a third of that in traditional classes; this is 36% better than the results in developed countries, indicating a greater need for active pedagogies by MENA students. Our findings reveal a multidimensional positive influence of active learning, the viability of its grassroots implementation with open resources, and its sustainability and reproducibility. We suggest that wider implementation can boost education-driven economic growth by 1% in per capita gross domestic product [GDP], substantially cut costs of repeating courses, and produce a more competent STEM workforce-all of which are urgently needed to stimulate development and growth.

Meta-learning shows great potential in plant disease recognition under few available samples.

Plant diseases worsen the threat of food shortage with the growing global population, and disease recognition is the basis for the effective prevention and control of plant diseases. Deep learning has made significant breakthroughs in the field of plant disease recognition. Compared with traditional deep learning, meta-learning can still maintain more than 90% accuracy in disease recognition with small samples. However, there is no comprehensive review on the application of meta-learning in plant disease recognition. Here, we mainly summarize the functions, advantages, and limitations of meta-learning research methods and their applications for plant disease recognition with a few data scenarios. Finally, we outline several research avenues for utilizing current and future meta-learning in plant science. This review may help plant science researchers obtain faster, more accurate, and more credible solutions through deep learning with fewer labeled samples.

Active-learning for global optimization of Ni-Ceria nanoparticles: The case of Ce4-xNixO8- x (x = 1, 2, 3).

Ni-CeO2 nanoparticles (NPs) are promising nanocatalysts for water splitting and water gas shift reactions due to the ability of ceria to temporarily donate oxygen to the catalytic reaction and accept oxygen after the reaction is completed. Therefore, elucidating how different properties of the Ni-Ceria NPs relate to the activity and selectivity of the catalytic reaction, is of crucial importance for the development of novel catalysts. In this work the active learning (AL) method based on machine learning regression and its uncertainty is used for the global optimization of Ce(4-x)NixO(8-x) (x = 1, 2, 3) nanoparticles, employing density functional theory calculations. Additionally, further investigation of the NPs by mass-scaled parallel-tempering Born-Oppenheimer molecular dynamics resulted in the same putative global minimum structures found by AL, demonstrating the robustness of our AL search to learn from small datasets and assist in the global optimization of complex electronic structure systems.

From UK-2A to florylpicoxamid: Active learning to identify a mimic of a macrocyclic natural product.

Scaffold replacement as part of an optimization process that requires maintenance of potency, desirable biodistribution, metabolic stability, and considerations of synthesis at very large scale is a complex challenge. Here, we consider a set of over 1000 time-stamped compounds, beginning with a macrocyclic natural-product lead and ending with a broad-spectrum crop anti-fungal. We demonstrate the application of the QuanSA 3D-QSAR method employing an active learning procedure that combines two types of molecular selection. The first identifies compounds predicted to be most active of those most likely to be well-covered by the model. The second identifies compounds predicted to be most informative based on exhibiting low predicted activity but showing high 3D similarity to a highly active nearest-neighbor training molecule. Beginning with just 100 compounds, using a deterministic and automatic procedure, five rounds of 20-compound selection and model refinement identifies the binding metabolic form of florylpicoxamid. We show how iterative refinement broadens the domain of applicability of the successive models while also enhancing predictive accuracy. We also demonstrate how a simple method requiring very sparse data can be used to generate relevant ideas for synthetic candidates.

Preschoolers search longer when there is more information to be gained.

What drives children to explore and learn when external rewards are uncertain or absent? Across three studies, we tested whether information gain itself acts as an internal reward and suffices to motivate children's actions. We measured 24-56-month-olds' persistence in a game where they had to search for an object (animal or toy), which they never find, hidden behind a series of doors, manipulating the degree of uncertainty about which specific object was hidden. We found that children were more persistent in their search when there was higher uncertainty, and therefore, more information to be gained with each action, highlighting the importance of research on artificial intelligence to invest in curiosity-driven algorithms. RESEARCH HIGHLIGHTS: Across three studies, we tested whether information gain itself acts as an internal reward and suffices to motivate preschoolers' actions. We measured preschoolers' persistence when searching for an object behind a series of doors, manipulating the uncertainty about which specific object was hidden. We found that preschoolers were more persistent when there was higher uncertainty, and therefore, more information to be gained with each action. Our results highlight the importance of research on artificial intelligence to invest in curiosity-driven algorithms.

Detecting goals of care conversations in clinical notes with active learning.

OBJECTIVE: Goals of care (GOC) discussions are an increasingly used quality metric in serious illness care and research. Wide variation in documentation practices within the Electronic Health Record (EHR) presents challenges for reliable measurement of GOC discussions. Novel natural language processing approaches are needed to capture GOC discussions documented in real-world samples of seriously ill hospitalized patients' EHR notes, a corpus with a very low event prevalence. METHODS: To automatically detect sentences documenting GOC discussions outside of dedicated GOC note types, we proposed an ensemble of classifiers aggregating the predictions of rule-based, feature-based, and three transformers-based classifiers. We trained our classifier on 600 manually annotated EHR notes among patients with serious illnesses. Our corpus exhibited an extremely imbalanced ratio between sentences discussing GOC and sentences that do not. This ratio challenges standard supervision methods to train a classifier. Therefore, we trained our classifier with active learning. RESULTS: Using active learning, we reduced the annotation cost to fine-tune our ensemble by 70% while improving its performance in our test set of 176 EHR notes, with 0.557 F1-score for sentence classification and 0.629 for note classification. CONCLUSION: When classifying notes, with a true positive rate of 72% (13/18) and false positive rate of 8% (13/158), our performance may be sufficient for deploying our classifier in the EHR to facilitate bedside clinicians' access to GOC conversations documented outside of dedicated notes types, without overburdening clinicians with false positives. Improvements are needed before using it to enrich trial populations or as an outcome measure.

Fine-tuning coreference resolution for different styles of clinical narratives.

OBJECTIVE: Coreference resolution (CR) is a natural language processing (NLP) task that is concerned with finding all expressions within a single document that refer to the same entity. This makes it crucial in supporting downstream NLP tasks such as summarization, question answering and information extraction. Despite great progress in CR, our experiments have highlighted a substandard performance of the existing open-source CR tools in the clinical domain. We set out to explore some practical solutions to fine-tune their performance on clinical data. METHODS: We first explored the possibility of automatically producing silver standards following the success of such an approach in other clinical NLP tasks. We designed an ensemble approach that leverages multiple models to automatically annotate co-referring mentions. Subsequently, we looked into other ways of incorporating human feedback to improve the performance of an existing neural network approach. We proposed a semi-automatic annotation process to facilitate the manual annotation process. We also compared the effectiveness of active learning relative to random sampling in an effort to further reduce the cost of manual annotation. RESULTS: Our experiments demonstrated that the silver standard approach was ineffective in fine-tuning the CR models. Our results indicated that active learning should also be applied with caution. The semi-automatic annotation approach combined with continued training was found to be well suited for the rapid transfer of CR models under low-resource conditions. The ensemble approach demonstrated a potential to further improve accuracy by leveraging multiple fine-tuned models. CONCLUSION: Overall, we have effectively transferred a general CR model to a clinical domain. Our findings based on extensive experimentation have been summarized into practical suggestions for rapid transferring of CR models across different styles of clinical narratives.

The outcomes of team-based learning vs small group interactive learning in the obstetrics and gynecology course for undergraduate students.

INTRODUCTION: Team-based learning (TBL) is a well-established active teaching method which has been shown to have pedagogical advantages in some areas such as business education and preclinical disciplines in undergraduate medical education. Increasingly, it has been adapted to clinical disciplines. However, its superiority over conventional learning methods used in clinical years of medical school remains unclear. The aim of this study was to compare TBL with traditional seminars delivered in small group interactive learning (SIL) format in terms of knowledge acquisition and retention, satisfaction and engagement of undergraduate medical students during the 6-week obstetrics and gynecology clerkship. MATERIAL AND METHODS: The study was conducted at Karolinska Institutet, a medical university in Sweden, and had a prospective, crossover design. All fifth-year medical students attending the obstetrics and gynecology clerkship, at four different teaching hospitals in Stockholm (approximately 40 students per site), in the Autumn semester of 2022 were invited to participate. Two seminars (one in obstetrics and one in gynecology) were designed and delivered in two different formats, ie TBL and SIL. The student:teacher ratio was approximately 10:1 in the traditional SIL seminars and 20:1 in the TBL. All TBL seminars were facilitated by a single teacher who had been trained and certified in TBL. Student knowledge acquisition and retention were assessed by final examination scores, and the engagement and satisfaction were assessed by questionnaires. For the TBL seminars, individual and team readiness assurance tests were also performed and evaluated. RESULTS: Of 148 students participating in the classrooms, 132 answered the questionnaires. No statistically significant differences were observed between TBL and SIL methods with regard to student knowledge acquisition and retention, engagement and satisfaction. CONCLUSIONS: We found no differences in student learning outcomes or satisfaction using TBL or SIL methods. However, as TBL had a double the student to teacher ratio as compared with SIL, in settings where teachers are scarce and suitable rooms are available for TBL sessions, the method may be beneficial in reducing faculty workload without compromising students' learning outcomes.

Deep learning model for pleural effusion detection via active learning and pseudo-labeling: a multisite study.

BACKGROUND: The study aimed to develop and validate a deep learning-based Computer Aided Triage (CADt) algorithm for detecting pleural effusion in chest radiographs using an active learning (AL) framework. This is aimed at addressing the critical need for a clinical grade algorithm that can timely diagnose pleural effusion, which affects approximately 1.5 million people annually in the United States. METHODS: In this multisite study, 10,599 chest radiographs from 2006 to 2018 were retrospectively collected from an institution in Taiwan to train the deep learning algorithm. The AL framework utilized significantly reduced the need for expert annotations. For external validation, the algorithm was tested on a multisite dataset of 600 chest radiographs from 22 clinical sites in the United States and Taiwan, which were annotated by three U.S. board-certified radiologists. RESULTS: The CADt algorithm demonstrated high effectiveness in identifying pleural effusion, achieving a sensitivity of 0.95 (95% CI: [0.92, 0.97]) and a specificity of 0.97 (95% CI: [0.95, 0.99]). The area under the receiver operating characteristic curve (AUC) was 0.97 (95% DeLong's CI: [0.95, 0.99]). Subgroup analyses showed that the algorithm maintained robust performance across various demographics and clinical settings. CONCLUSION: This study presents a novel approach in developing clinical grade CADt solutions for the diagnosis of pleural effusion. The AL-based CADt algorithm not only achieved high accuracy in detecting pleural effusion but also significantly reduced the workload required for clinical experts in annotating medical data. This method enhances the feasibility of employing advanced technological solutions for prompt and accurate diagnosis in medical settings.

A predicted-loss based active learning approach for robust cancer pathology image analysis in the workplace.

BACKGROUND: Convolutional neural network-based image processing research is actively being conducted for pathology image analysis. As a convolutional neural network model requires a large amount of image data for training, active learning (AL) has been developed to produce efficient learning with a small amount of training data. However, existing studies have not specifically considered the characteristics of pathological data collected from the workplace. For various reasons, noisy patches can be selected instead of clean patches during AL, thereby reducing its efficiency. This study proposes an effective AL method for cancer pathology that works robustly on noisy datasets. METHODS: Our proposed method to develop a robust AL approach for noisy histopathology datasets consists of the following three steps: 1) training a loss prediction module, 2) collecting predicted loss values, and 3) sampling data for labeling. This proposed method calculates the amount of information in unlabeled data as predicted loss values and removes noisy data based on predicted loss values to reduce the rate at which noisy data are selected from the unlabeled dataset. We identified a suitable threshold for optimizing the efficiency of AL through sensitivity analysis. RESULTS: We compared the results obtained with the identified threshold with those of existing representative AL methods. In the final iteration, the proposed method achieved a performance of 91.7% on the noisy dataset and 92.4% on the clean dataset, resulting in a performance reduction of less than 1%. Concomitantly, the noise selection ratio averaged only 2.93% on each iteration. CONCLUSIONS: The proposed AL method showed robust performance on datasets containing noisy data by avoiding data selection in predictive loss intervals where noisy data are likely to be distributed. The proposed method contributes to medical image analysis by screening data and producing a robust and effective classification model tailored for cancer pathology image processing in the workplace.

Acute effect of physical activity on academic outcomes in school-aged youth: A systematic review and multivariate meta-analysis.

BACKGROUND: There has been an increase in the number of studies examining the effect of acute and chronic physical activity on academic outcomes in children and adolescents in the last two decades. We aimed to systematically determine the acute effects of physical activity on academic outcomes in school-aged youth and to examine possible moderators. METHODS: We conducted a systematic search using PubMed, Web of Science, SPORTDiscus, and PsycINFO databases (from inception to 11th January 2023) for studies assessing the acute effects of physical activity on academic performance-related outcomes in school-aged youth. A univariate and multivariate meta-analysis was conducted based on a random-effects model with restricted maximum likelihood used to pool the academic outcomes results (Hedge's g). RESULTS: We included 11 articles (803 children and adolescents [range: 6-16 years]) in the systematic review. Overall, acute physical activity increased academic outcomes (Hedge's g = 0.35, 95% CI: 0.20-0.50). Multivariate meta-analyses revealed that physical activity increased academic performance in mathematics (Hedge's g = 0.29, 95% CI: 0.16-0.42) and language (Hedge's g = 0.28, 95% CI: 0.09-0.47). Only behavior change techniques (Hedge's g = 0.54, 95% CI, 0.18-0.90, p < 0.001) played a significant role in this relationship. CONCLUSIONS: A single bout of physical activity can improve academic outcomes in school-aged youth, which may serve as a complementary tool for the educational field. However, the observed heterogeneity in the results indicates that we should interpret the findings obtained with caution.

Preschoolers ask questions about unknown words on video chat and in live interactions at similar rates.

The COVID-19 pandemic has led to a major increase in digital interactions in early experience. A crucial question, given expanding virtual platforms, is whether preschoolers' active word learning behaviors extend to their interactions over video chat. When not provided with sufficient information to link new words to meanings, preschoolers drive their word learning by asking questions. In person, 5-year-olds focus their questions on unknown words compared with known words, highlighting their active word learning. Here, we investigated whether preschoolers' question-asking over video chat differs from in-person question-asking. In the study, 5-year-olds were instructed to move toys in response to known and unknown verbs on a video conferencing call (i.e., Zoom). Consistent with in-person results, video chat participants (n = 18) asked more questions about unknown words than about known words. The rate of question-asking about words across video chat and in-person formats did not differ. Differences in the types of questions asked about words indicate, however, that although video chat does not hinder preschoolers' active word learning, the use of video chat may influence how preschoolers request information about words.

Learning experiences from an online QI fellowship programme during COVID-19 - a qualitative study.

BACKGROUND: During the COVID-19 pandemic in the United Kingdom, multiple aspects of everyday human existence were disrupted. In contrast, almost all levels of educational learning continued, albeit with modifications, including adaptation to virtual-or online-classroom experiences. This pedagogic transition also occurred in the National Institute of Health and Care Research Applied Research Collaboration Northwest London's (NIHR ARC NWL) Improvement Leader Fellowship, an annual programme focusing on quality improvement (QI). This qualitative study aimed to understand how these changes impacted the Fellows' learning experience. METHODS: We explored the experiences of two cohorts of programme Fellows (n = 18, 2020-2021 and n = 15, 2021-2022) with focus groups, analysed under a constructivist qualitative research paradigm. RESULTS: The two primary and four sub-themes that emerged were: Online QI learning experience (benefits and challenges) and Implementing online QI learning (facilitators and barriers). While benefits had three further sub-themes (i.e., digital flexibility, connection between learners, and respite from impact of COVID-19), challenges had four (i.e., lack of interaction, technological challenges and digital exclusion, human dimension, and digital fatigue). While the facilitators had three sub-themes (i.e., mutual and programmatic support, online resource access, and personal resilience), barriers had one (i.e., preventing implementation and lack of protected time). CONCLUSION: Despite challenges to in-person ways of working, online learning generally worked for action-orientated QI learning, but changes are needed to ensure the effectiveness of future use of virtual learning for QI. Understanding the challenges of the translation of learning into action is crucial for implementation learning, gaining insight into how improvement Fellows navigated this translation when learning remotely and implementing directly in their workplace is key to understanding the evolving nature of implementation over the pandemic years and beyond.

The Evolution of Active Learning in Response to the Pandemic: The Role of Technology.

Active learning has consistently played a significant role in education. Through interactive tasks, group projects, and a variety of engaging activities, students are encouraged to forge connections with the subject matter. However, the pandemic has necessitated that educators adapt and refine their active learning techniques to accommodate the online environment. This has resulted in stimulating innovations in the field, encompassing virtual simulations, online collaboration tools, and interactive multimedia. The COVID-19 pandemic has rapidly transformed the landscape of teaching and learning, particularly in higher education. One of the most prominent shifts has been the widespread adoption of active learning techniques, which have increased student engagement and fostered deeper learning experiences. In this chapter, we examine the evolution of active learning during the pandemic, emphasizing its advantages and challenges. Furthermore, we delve into the role of advances in artificial intelligence and their potential to enhance the effectiveness of active learning approaches. As we once focused on leveraging the opportunities of remote teaching, we must now shift our attention to harnessing the power of AI responsibly and ethically to benefit our students. Drawing from our expertise in educational innovation, we provide insights and recommendations for educators aiming to maximize the benefits of active learning in the post-pandemic era.

Volitional learning promotes theta phase coding in the human hippocampus.

Electrophysiological studies in rodents show that active navigation enhances hippocampal theta oscillations (4-12 Hz), providing a temporal framework for stimulus-related neural codes. Here we show that active learning promotes a similar phase coding regime in humans, although in a lower frequency range (3-8 Hz). We analyzed intracranial electroencephalography (iEEG) from epilepsy patients who studied images under either volitional or passive learning conditions. Active learning increased memory performance and hippocampal theta oscillations and promoted a more accurate reactivation of stimulus-specific information during memory retrieval. Representational signals were clustered to opposite phases of the theta cycle during encoding and retrieval. Critically, during active but not passive learning, the temporal structure of intracycle reactivations in theta reflected the semantic similarity of stimuli, segregating conceptually similar items into more distant theta phases. Taken together, these results demonstrate a multilayered mechanism by which active learning improves memory via a phylogenetically old phase coding scheme.

Context-informed incremental learning improves both the performance and resilience of myoelectric control.

Despite its rich history of success in controlling powered prostheses and emerging commercial interests in ubiquitous computing, myoelectric control continues to suffer from a lack of robustness. In particular, EMG-based systems often degrade over prolonged use resulting in tedious recalibration sessions, user frustration, and device abandonment. Unsupervised adaptation is one proposed solution that updates a model's parameters over time based on its own predictions during real-time use to maintain robustness without requiring additional user input or dedicated recalibration. However, these strategies can actually accelerate performance deterioration when they begin to classify (and thus adapt) incorrectly, defeating their own purpose. To overcome these limitations, we propose a novel adaptive learning strategy, Context-Informed Incremental Learning (CIIL), that leverages in situ context to better inform the prediction of pseudo-labels. In this work, we evaluate these CIIL strategies in an online target acquisition task for two use cases: (1) when there is a lack of training data and (2) when a drastic and enduring alteration in the input space has occurred. A total of 32 participants were evaluated across the two experiments. The results show that the CIIL strategies significantly outperform the current state-of-the-art unsupervised high-confidence adaptation and outperform models trained with the conventional screen-guided training approach, even after a 45-degree electrode shift (p < 0.05). Consequently, CIIL has substantial implications for the future of myoelectric control, potentially reducing the training burden while bolstering model robustness, and leading to improved real-time control.

Twelve tips for designing and implementing peer-led assessment writing schemes in health professions education.

Peer-led assessment (PLA) has gained increasing prominence within health professions education as an effective means of engaging learners in the process of assessment writing and practice. Involving students in various stages of the assessment lifecycle, including item writing, quality assurance, and feedback, not only facilitates the creation of high-quality item banks with minimal faculty input but also promotes the development of students' assessment literacy and fosters their growth as teachers. The advantages of involving students in the generation of assessments are evident from a pedagogical standpoint, benefiting both students and faculty. However, faculty members may face uncertainty when it comes to implementing such approaches effectively. To address this concern, this paper presents twelve tips that offer guidance on important considerations for the successful implementation of peer-led assessment schemes in the context of health professions education.

Breaking down barriers and building up facilitators of lecture free curriculum in medical education: An interpretive structural modeling.

INTRODUCTION: The field of medical education has seen a growing interest in lecture free curriculum. However, it comes with its own set of challenges and obstacles. In this article, we aim to identify the prerequisites, facilitators, challenges, and barriers of lecture-free curriculum in medical education and examine their interrelationships using interpretive structural modeling (ISM) technique. METHODS: In this mixed-method study initially, we performed a scoping review and semi-structured interviews and determined the main prerequisites, facilitators, challenges, and barriers of lecture-free curriculum in medical education using qualitative content analysis approach. The interrelationships among these components were investigated using ISM. Therefore, self-interactive structural matrices were formed, initial and final reachability matrices were achieved, and MICMAC analysis was conducted to classify the factors. RESULTS: Finally, two ISM models of prerequisites and facilitators with 27 factors in 10 levels and challenges and obstacles with 25 factors in eight levels were developed. Each of the models was divided into three parts: key, strategic, and dependent factors. 'Providing relevant evidence regarding lecture free curriculum' emerged as the most important prerequisite and facilitator, and 'insufficient support from the university' was identified as the most critical barrier and challenge. CONCLUSIONS: The study highlights the significant importance of lecture-free curriculum in medical education and provides insights into its prerequisites, facilitators, challenges, and barriers. The findings can be utilized by educational managers and decision-makers to implement necessary changes in the design and implementation of lecture-free in medical education, leading to more effective improvements in the quality and success of education.