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Can adults with developmental dyslexia apply statistical knowledge to a new context?

COGNITIVE PROCESSING 2023年第1期24卷 129-145页

作者： Schiff, Rachel Cohen, Hadar Kahta, Shani Sasson, Ayelet Bar Ilan Univ Fac Educ Learning Disabil MA Program IL-52900 Ramat Gan Israel Bar Ilan Univ Fac Educ Haddad Ctr Dyslexia & Learning Disabil IL-52900 Ramat Gan Israel Bar Ilan Univ Fac Humanities English Foreign Language Unit IL-52900 Ramat Gan Israel

We investigated transfer of artificial grammar learning in adults with and without dyslexia in 3 experiments. In Experiment 1, participants implicitly learned an artificial grammar system and were tested on new items that included the same symbols. In Experiment 2, participants were given practice with letter strings and then tested on strings created with a different letter set. In Experiment 3, participants were given practice with shapes and then tested on strings created with different shapes. Results show that in Experiment 1, both groups demonstrated utilization of pre-trained instances in the subsequent grammaticality judgement task, while in Experiments 2 (orthographic) and 3 (nonorthographic), only typically developed participants demonstrated application of knowledge from training to test. A post hoc analysis comparing between the experiments suggests that being trained and tested on an orthographic task leads to better performance than a nonorthographic task among typically developed adults but not among adults with dyslexia. Taken together, it appears that following extensive training, individuals with dyslexia are able to form stable representations from sequential stimuli and use them in a subsequent task that utilizes strings of similar symbols. However, the manipulation of the symbols challenges this ability.

关键词： Developmental dyslexia (DD) Artificial grammar learning (AGL) Statistical learning Transfer Adults Orthographic nonorthographic stimuli

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Hippocampal output suppresses orbitofrontal cortex schema cell formation

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NATURE NEUROSCIENCE 2025年第5期28卷 1-13页

作者： Zong, Wenhui Zhou, Jingfeng Gardner, Matthew P. H. Zhang, Zhewei Costa, Kaue Machado Schoenbaum, Geoffrey Natl Inst Drug Abuse Intramural Res Program Baltimore MD 20852 USA Beijing Normal Univ State Key Lab Cognit Neurosci & Learning Beijing Peoples R China Chinese Inst Brain Res Beijing Peoples R China Concordia Univ Montreal PQ Canada Univ Alabama Birmingham Birmingham AL USA

Both the orbitofrontal cortex (OFC) and the hippocampus (HC) are implicated in the formation of cognitive maps and their generalization into schemas. However, how these areas interact in supporting this function remains unclear, with some proposals supporting a serial model in which the OFC draws on task representations created by the HC to extract key behavioral features and others suggesting a parallel model in which both regions construct representations that highlight different types of information. In the present study, we tested between these two models by asking how schema correlates in rat OFC would be affected by inactivating the output of the HC, after learning and during transfer across problems. We found that the prevalence and content of schema correlates were unaffected by inactivating one major HC output area, the ventral subiculum, after learning, whereas inactivation during transfer accelerated their formation. These results favor the proposal that the OFC and HC operate in parallel to extract different features defining cognitive maps and schemas.

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Structural Forecasting for Short-Term Tropical Cyclone Intensity Guidance

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WEATHER AND FORECASTING 2023年第6期38卷 985-998页

作者： McNeely, Trey Khokhlov, Pavel Dalmasso, Niccolo Wood, Kimberly M. Lee, Ann B. Carnegie Mellon Univ Dept Stat & Data Sci Pittsburgh PA 15213 USA Carnegie Mellon Univ Machine Learning Dept Pittsburgh PA USA Mississippi State Univ Dept Geosci Mississippi State MS USA Microsoft AI Dev Accelerat Program Cambridge MA USA

Because geostationary satellite (Geo) imagery provides a high temporal resolution window into tropical cyclone (TC) behavior, we investigate the viability of its application to short-term probabilistic forecasts of TC convective structure to subsequently predict TC intensity. Here, we present a prototype model that is trained solely on two inputs: Geo infrared imag-ery leading up to the synoptic time of interest and intensity estimates up to 6 h prior to that time. To estimate future TC struc-ture, we compute cloud-top temperature radial profiles from infrared imagery and then simulate the evolution of an ensemble of those profiles over the subsequent 12 h by applying a deep autoregressive generative model (PixelSNAIL). To forecast TC in-tensities at hours 6 and 12, we input operational intensity estimates up to the current time (0 h) and simulated future radial pro-files up to 112 h into a "nowcasting" convolutional neural network. We limit our inputs to demonstrate the viability of our approach and to enable quantification of value added by the observed and simulated future radial profiles beyond operational intensity estimates alone. Our prototype model achieves a marginally higher error than the National Hurricane Center's official forecasts despite excluding environmental factors, such as vertical wind shear and sea surface temperature. We also demonstrate that it is possible to reasonably predict short-term evolution of TC convective structure via radial profiles from Geo infrared im-agery, resulting in interpretable structural forecasts that may be valuable for TC operational guidance.

关键词： Hurricanes typhoons Satellite observations Probability forecasts models distribution Deep learning Artificial intelligence Short-range prediction

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Assessing Darkness of the Human Kinome from a Medicinal Chemistry Perspective

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JOURNAL OF MEDICINAL CHEMISTRY 2024年第19期67卷 17919-17928页

作者： Vossen, Selina Xerxa, Elena Bajorath, Juergen Rhein Friedrich Wilhelms Univ Dept Life Sci Informat & Data Sci B IT D-53115 Bonn Germany Lamarr Inst Machine Learning & Artificial Intellig D-53115 Bonn Germany Rhein Friedrich Wilhelms Univ LIMES Inst Program Unit Chem Biol & Med Chem D-53115 Bonn Germany

In drug discovery, human protein kinases (PKs) represent one of the major target classes due to their central role in cellular signaling, implication in various diseases as a consequence of deregulated signaling, and notable druggability. Individual PKs and their disease biology have been explored to different degrees, giving rise to heterogeneous functional knowledge and disease associations across the human kinome. The U.S. National Institutes of Health previously designated 162 understudied ("dark") human PKs and lipid kinases due to the lack of functional annotations and high-quality molecular probes for functional investigations. Given the large volumes of available PK inhibitors (PKIs) and activity data, we have systematically analyzed the distribution of PKIs and associated data at different confidence levels across the human kinome and distinguished between chemically explored, underexplored, and unexplored PKs. The analysis provides a medicinal chemistry-centric view of PK exploration and further extends prior assessment of the dark kinome.

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Which Factors Predict the Chat Experience of a Natural Language Generation Dialogue Service?

arXiv

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arXiv 2023年

作者： Chen, Eason National Taiwan Normal University Program of Learning Sciences Taipei Taiwan

In this paper, we proposed a conceptual model to predict the chat experience in a natural language generation dialog system. We evaluated the model with 120 participants with Partial Least Squares Structural Equation Modeling (PLS-SEM) and obtained an R-square (R2) with 0.541. The model considers various factors, including the prompts used for generation;coherence, sentiment, and similarity in the conversation;and users' perceived dialog agents' favorability. We then further explore the effectiveness of the subset of our proposed model. The results showed that users' favorability and coherence, sentiment, and similarity in the dialogue are positive predictors of users' chat experience. Moreover, we found users may prefer dialog agents with characteristics of Extroversion, Openness, Conscientiousness, Agreeableness, and Non-Neuroticism. Through our research, an adaptive dialog system might use collected data to infer factors in our model, predict the chat experience for users through these factors, and optimize it by adjusting prompts. © 2023, CC BY.

关键词： Forecasting

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Protocol for the implementation and referral of the ecofit physical activity intervention within Diabetes Alliance program Plus among regional, rural and remote people with type 2 diabetes in a primary care setting

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CONTEMPORARY CLINICAL TRIALS 2025年 148卷 107774-107774页

作者： Jansson, Anna K. Beacroft, Sam Duncan, Mitch J. Cox, Emily R. Robards, Sara L. Ferris, Wendy Hure, Alexis Acharya, Shamasunder Plotnikoff, Ronald C. Univ Newcastle Ctr Act Living & Learning Sch Educ Callaghan NSW Australia Hunter Med Res Inst Act Living & Learning Res Program New Lambton Hts NSW Australia Univ Newcastle Sch Med & Publ Hlth Callaghan NSW Australia Univ Newcastle Sch Biomed Sci & Pharm Callaghan NSW Australia Hunter Med Res Inst Kookaburra Circuit New Lambton Hts NSW Australia John Hunter Hosp Hunter New England Local Hlth Dist New Lambton Hts NSW Australia

Background: This paper outlines the protocol for the ecofit implementation-effectiveness trial, a multi-component mobile Health (mHealth) intervention that aims to increase resistance and aerobic physical activity in primary care-based adults with type 2 diabetes (T2D). This study will be conducted as part of the Diabetes Alliance program Plus (DAP+), a large-scale integrated health service intervention in a large health district in Australia. The ecofit program has previously demonstrated efficacy and effectiveness in insufficiently active people with (or at risk of) T2D and community dwelling-adults, respectively. The aim of this study is to assess the reach (primary outcome), adoption, appropriateness, feasibility and fidelity of the implementation of ecofit and the overall effectiveness of the intervention. Research design and methods: Prospective participants are adults diagnosed with T2D, who attend primary care settings enrolled in DAP+, and are identified and referred to ecofit by a primary care clinician. To support the implementation of ecofit a host of strategies will be utilised, which includes the education and upskilling of primary care clinicians enrolled in DAP+ using brief training sessions, the supply of an information package and access to professional development. The co-primary outcomes of reach will be defined as the number of participant registrations on the ecofit platform and the number of primary care clinicians who have been introduced to ecofit. Conclusion: This study will evaluate the implementation of ecofit among adults with T2D within the primary care setting. The results may help improve T2D lifestyle interventions in primary care settings across Australia.

关键词： Physical activity Resistance training mHealth Type 2 diabetes Primary care

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"It is not just the shape, there is more": students' learning of enzyme-substrate interactions with immersive Virtual Reality

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CHEMISTRY EDUCATION RESEARCH AND PRACTICE 2025年第1期26卷 259-270页

作者： Matovu, Henry Won, Mihye Tasker, Roy Mocerino, Mauro Treagust, David Franklin Ungu, Dewi Ayu Kencana Tsai, Chin-Chung Univ Sydney Sch Chem Camperdown NSW Australia Monash Univ Fac Educ Clayton Vic Australia Western Sydney Univ Sch Sci Penrith NSW Australia Curtin Univ Sch Mol & Life Sci Perth WA Australia Curtin Univ Sch Educ Perth WA Australia Nanyang Technol Univ Inst Pedag Innovat Res & Excellence InsPIRE Singapore Singapore Natl Taiwan Normal Univ Program Learning Sci Taipei Taiwan Natl Taiwan Normal Univ Inst Res Excellence Learning Sci Taipei Taiwan

Immersive Virtual Reality (iVR) can help students visualise and explore complex chemical concepts, such as protein enzyme structures and interactions. We designed a set of collaborative iVR-based learning tasks on the interaction between a protein enzyme and its substrate. We investigated how 18 pairs (36 students) in undergraduate chemistry courses changed their understanding of enzyme-substrate interactions through iVR learning tasks. Videos of pre- and post-interviews and student-generated diagrams were analysed. Before iVR, students had abstract models of the structure of a protein enzyme or its interaction with a substrate molecule. Over 90 per cent of the students (33/36) explained enzyme-substrate interactions using simplistic lock-and-key diagrams, exclusively focusing on the shape. Although many students employed key scientific terms like activation energy in their explanations, they were unsure how enzymes lowered activation energy or how catalytic reactions occurred. After iVR, all students discussed the inadequacy of 2D diagrams for representing complex enzyme-substrate interactions. About 90 per cent of students (32/36) used concrete ideas such as electron density and orientation of reactants in the active site to explain the probability of successful interactions between the enzyme and its substrate. Our findings provide evidence of how interactive iVR learning tasks can help students explore complex molecular structures, integrate ideas, and build a concrete understanding of challenging science concepts.

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learning characteristics of graph neural networks predicting protein-ligand affinities

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NATURE MACHINE INTELLIGENCE 2023年第12期5卷 1427-1436页

作者： Mastropietro, Andrea Pasculli, Giuseppe Bajorath, Juergen Sapienza Univ Rome Dept Comp Control & Management Engn Antonio Rubert Rome Italy Rhein Friedrich Wilhelms Univ Dept Life Sci Informat LIMES Program Unit Chem Biol & Med Chem B IT Bonn Germany Lamarr Inst Machine Learning & Artificial Intellig Bonn Germany

In drug design, compound potency prediction is a popular machine learning application. Graph neural networks (GNNs) predict ligand affinity from graph representations of protein-ligand interactions typically extracted from X-ray structures. Despite some promising findings leading to claims that GNNs can learn details of protein-ligand interactions, such predictions are also controversially viewed. For example, evidence has been presented that GNNs might not learn protein-ligand interactions but memorize ligand and protein training data instead. We have carried out affinity predictions with six GNN architectures on community-standard datasets and rationalized the predictions using explainable artificial intelligence. The results confirm a strong influence of ligand-but not protein-memorization during GNN learning and also show that some GNN architectures increasingly prioritize interaction information for predicting high affinities. Thus, while GNNs do not comprehensively account for protein-ligand interactions and physical reality, depending on the model, they balance ligand memorization with learning of interaction patterns. Graph neural networks have proved useful in modelling proteins and their ligand interactions, but it is not clear whether the patterns they identify have biological relevance or whether interactions are merely memorized. Mastropietro et al. use a Shapley value-based method to identify important edges in protein interaction graphs, enabling explanatory analysis of the model mechanisms.

关键词： Ligands

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A program to Provide Clinicians with Feedback on Their Diagnostic Performance in a learning Health System

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JOINT COMMISSION JOURNAL ON QUALITY AND PATIENT SAFETY 2021年第2期47卷 120-126页

作者： Meyer, Ashley N. D. Upadhyay, Divvy K. Collins, Charlotte A. Fitzpatrick, Michael H. Kobylinski, Maria Bansal, Amit B. Torretti, Dennis Singh, Hardeep Michael E DeBakey VA US Vet Affairs Med Ctr Ctr Innovat Qual Effectiveness & Safety IQuESt Hlth Policy Qual & Informat Program Houston TX USA Baylor Coll Med Houston TX 77030 USA Geisinger Safer Dx Learning Lab Danville PA USA Geisinger Div Qual Safety & Patient Experience Danville PA USA Geisinger Ctr Professionalism & Provider Well Being Danville PA USA Geisinger Emergency Med Danville PA USA Geisinger Community Med Danville PA USA Geisinger Hosp Med Cent Reg Med Inst Danville PA USA Geisinger Med Ctr Danville PA USA Geisinger Div Med Danville PA USA Michael E DeBakey VA Med Ctr IQuESt Hlth Policy Qual & Informat Program Houston TX 77030 USA

Problem: Reducing diagnostic errors requires improving both systems and individual clinical reasoning. One strategy to achieve diagnostic excellence is learning from feedback. However, clinicians remain uncomfortable receiving feedback on their diagnostic performance. Thus, a team of researchers and clinical leaders aimed to develop and implement a diagnostic performance feedback program for learning that mitigates potential clinician discomfort. Approach: The program was developed as part of a larger project to create a learning health system around diagnostic safety at Geisinger, a large, integrated health care system in rural Pennsylvania. Steps included identifying potential missed opportunities in diagnosis (MODs) from various sources (for example, risk management, clinician reports, patient complaints);confirming MODs through chart review;and having trained facilitators provide feedback to clinicians about MODs as learning opportunities. The team developed a guide for facilitators to conduct effective diagnostic feedback sessions and surveyed facilitators and recipients about their experiences and perceptions of the feedback sessions. Outcomes: 28 feedback sessions occurred from January 2019 to June 2020, involving MODs from emergency medicine, primary care, and hospital medicine. Most facilitators (90.6% [29/32]) reported that recipients were receptive to learning and discussing MODs. Most recipients reported that conversations were constructive and nonpunitive (83.3% [25/30]) and allowed them to take concrete steps toward improving diagnosis (76.7% [23/30]). Both groups believed discussions would improve future diagnostic safety (93.8% [30/32] and 70.0% [21/30], respectively). Key Insights and Next Steps: An institutional program was developed and implemented to deliver diagnostic performance feedback. Such a program may facilitate learning and improvement to reduce MODs. Future efforts should assess long-term effects on diagnostic performance and patient ou

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Facilitating Training Simulations for Health Emergency 52nd

Facilitating Training Simulations for Health Emergency

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52nd International Simulation and Gaming Association Conference, ISAGA 2021

作者： Utunen, Heini Harviainen, J. Tuomas Gamhewage, Gaya M. Tampere University Tampere Finland Learning and Capacity Development Unit Health Emergencies Program World Health Organization Geneva Switzerland

ISBN: (纸本)9783031099588

The World Health Organization’s (WHO) training simulation exer- cises (SimEx) are immersive activities that capacitate their audiences by enabling them to practice and perform various functions in an emergency response. This paper discusses four tools used in SimEx to maximize the facilitators’ technical experience to meet the overall learning outcomes and objectives. The findings have been collected from 12 exercises through feedback sessions and debriefings with facilitators, written feedback and exercise management observations. They show that facilitation methods and their usability vary depending on the scope of the exercise and the facilitators’ experience. Exercise planning needs to fully align the SimEx activities with their learning objectives for optimal outcomes. © 2022, Springer Nature Switzerland AG.

关键词： E-learning

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