Discover artificial intelligence最新文献

{"title":"The adaptive learning taxonomy for responsible large language model integration in higher education.","authors":"Simon Baradziej, Adrianna Kochanska","doi":"10.1007/s44163-026-01528-1","DOIUrl":"10.1007/s44163-026-01528-1","url":null,"abstract":"<p><p>Large Language Models (LLMs) are being rapidly deployed in higher education, yet institutions lack integrated, theoretically grounded frameworks to guide responsible adoption. While meta-analytic evidence reports large positive effects on academic performance (g = 0.867; Wang and Fan 2025), emerging research reveals a \"cognitive paradox\": performance gains may coincide with diminished metacognitive accuracy and self-regulation-a gap no existing framework comprehensively addresses across adaptation design, data governance, pedagogical targeting, and learner agency simultaneously.</p><p><strong>Methodology: </strong>Through a targeted narrative synthesis of 35 empirical studies published between January 2024 and February 2025, supplemented by selected post-window studies incorporated during peer review, and grounded in four learning theories-Connectivism, Distributed Cognition, Cognitive Load Theory, and Self-Regulated Learning-this paper develops the Adaptive Learning Taxonomy for Educational Decision-Making (ALT-ED). The framework structures institutional decision-making across four operational dimensions: Adaptation Trigger, Data Granularity, Pedagogical Locus, and Agency.</p><p><strong>Key findings: </strong>Three central findings emerge: (1) the cognitive paradox requires shifting the pedagogical locus from cognitive scaffolding to metacognitive development; (2) high-resolution data collection amplifies algorithmic bias, supporting a default of progressive data minimisation; and (3) the policy-practice chasm demands transparent, auditable design frameworks rather than prohibition-based approaches.</p><p><strong>Recommendations: </strong>Institutions should prioritise a metacognitive pedagogical locus to foster \"learning to learn\" skills, employ progressive data granularity to balance personalisation with student privacy, and default to learner-negotiated agency as a structural safeguard against algorithmic determinism.</p><p><strong>Limitations: </strong>The framework has not yet been empirically validated through prospective institutional implementation. Additional limitations include reliance on predominantly short-term quasi-experimental evidence, the absence of standardised metrics for real-time metacognitive efficiency, and a literature base that is overwhelmingly English-language and Western-centric. Future research should focus on cross-institutional pilot validations, longitudinal studies of cognitive entrainment, and the development of standardised instruments for measuring metacognitive efficiency in AI-augmented environments.</p>","PeriodicalId":520312,"journal":{"name":"Discover artificial intelligence","volume":"6 1","pages":"703"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13400592/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148595379","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated adversarial red-teaming for evaluating robustness in LLM-based recommender systems.","authors":"Sarama Shehmir, Rasha Kashef","doi":"10.1007/s44163-026-01546-z","DOIUrl":"https://doi.org/10.1007/s44163-026-01546-z","url":null,"abstract":"<p><p>Robustness testing for large language model based recommender systems (LLM4Rec) typically relies on a handful of handwritten attack prompts drawn from one injection pattern at a single perturbation rate. These narrow test suites miss most of the attack surface and paint an overly optimistic picture of system security. We introduce an automated red teaming framework that replaces static templates with an adaptive loop. An attacker model generates diverse adversarial prompts across sixteen attack categories; a judge then scores each one using ranking distortion metrics. Successful attacks are fed back into the defense for iterative hardening. We run the framework against RoLLMRec on MovieLens, Amazon Books, and Yelp. The loop exposes attack success rates of 42.4%, 48.1%, and 52.0% on the three benchmarks, well above static, paraphrase, and PAIR-style baselines. After the hardening cycle, vulnerability drops by 56.4%, 74.3%, and 68.8% while false positive rates stay at or below 0.6%; module ablations indicate the four defense components combine super-additively, with the prompt shield contributing the largest single share. The full attack corpus is publicly available to support reproducible adversarial benchmarking in LLM4Rec.</p>","PeriodicalId":520312,"journal":{"name":"Discover artificial intelligence","volume":"6 1","pages":"759"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13447462/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148690746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identifying conceptual dimensions of trust in artificial intelligence from qualitative content analysis of open-ended responses.","authors":"Sandrine Toudjui, Jayden Tang, Hamna Akhter, Eunseo Cho, Éloïse Côté, Jason E Plaks","doi":"10.1007/s44163-026-02150-x","DOIUrl":"10.1007/s44163-026-02150-x","url":null,"abstract":"<p><strong>Background: </strong>Trust in artificial intelligence (AI) has received growing attention in academic research, ethical debates, and societal discourse. However, limited research has examined how laypeople conceptualize trust in AI assistants.</p><p><strong>Purpose: </strong>This study aims to identify recurring conceptual dimensions that underlie lay definitions of trust in AI.</p><p><strong>Methods: </strong>Open-ended survey responses were collected from English-speaking participants (<i>N</i> = 204) recruited via two online platforms. Responses were analyzed using inductive qualitative content analysis within a postpositivist framework.</p><p><strong>Results: </strong>Three core themes emerged. The most prevalent dimension mentioned was performance (85%), with participants defining trust primarily in terms of accuracy, source quality, and competence. The second dimension emphasized safety (38%), reflecting participants' concerns about harm prevention, protection and responsible system behavior. The third dimension involved perceived moral integrity of AI (26%), including references to unbiasedness, honesty, and fairness.</p><p><strong>Conclusions: </strong>The findings indicate that lay conceptualizations of AI trust are predominantly anchored in performance expectations, but with significant and noteworthy secondary emphasis on moral attributes related to in safety and integrity. These results offer an empirically grounded framework for understanding public trust in AI and may inform future measurement and design initiatives.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at https://doi.org/10.1007/s44163-026-02150-x.</p>","PeriodicalId":520312,"journal":{"name":"Discover artificial intelligence","volume":"6 1","pages":"1036"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13541890/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148898845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Systematic investigation of pre-processing and feature extraction techniques in medical image analysis.","authors":"Pegah Dehbozorgi, Oleg Ryabchykov, Thomas W Bocklitz","doi":"10.1007/s44163-026-01768-1","DOIUrl":"10.1007/s44163-026-01768-1","url":null,"abstract":"<p><p>As medical imagery remains the cornerstone of diagnosis, the success of complex classification tasks depends on high-quality data and diagnostic features. This study evaluates image pre-processing and feature extraction to optimize model performance. Our investigation assesses how variations in pre-processing and feature extraction impact classification efficiency across three imaging modalities: radiology (chest X-ray images), pathology (H&E (Hematoxylin and Eosin)-stained patches), and ophthalmology (OCT (Optical Coherence Tomography) scans). The experimental framework incorporates nine pre-processing techniques, adjustment (brightness, contrast, and histogram equalization), filtering (mean, median, and Gaussian), and three normalization schemes, systematically combined for each modality. Features were extracted using five pre-trained architectures (VGG16, ResNet50, DenseNet121, MobileNetV2, and InceptionV3) and classified via a PCA-LDA (Principal Component Analysis-Linear Discriminant Analysis) pipeline. Performance was evaluated using the mean sensitivity score. Mean sensitivity scores improved significantly: H&E-stained images increased from 74·9% to 96·95%, chest X-rays from 89·9% to 96·65%, and OCT scans from 82·4% to 98·8%. No single pre-processing configuration consistently dominated; optimal settings varied by modality, confirming the absence of a universal solution. VGG16 and DenseNet121 exhibited the greatest robustness. Implementing at least one pre-processing step combined with a robust DL feature extractor can substantially improve model efficacy in diagnostic detection tasks.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s44163-026-01768-1.</p>","PeriodicalId":520312,"journal":{"name":"Discover artificial intelligence","volume":"6 1","pages":"700"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13395947/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148586564","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Roadmap for using large language models (LLMs) to accelerate cross-disciplinary research with an example from computational biology.","authors":"Ruian Ke, Ruy M Ribeiro","doi":"10.1007/s44163-026-01589-2","DOIUrl":"10.1007/s44163-026-01589-2","url":null,"abstract":"<p><p>Large language models (LLMs) are powerful artificial intelligence (AI) tools transforming how research is conducted. However, their use in research has been met with skepticism, due to concerns about hallucinations, biases and potential harms to research. These emphasize the importance of clearly understanding the strengths and weaknesses of LLMs to ensure their effective and responsible use. Here, we present a roadmap for integrating LLMs into cross-disciplinary research, where effective communication, knowledge transfer and collaboration across diverse fields are essential but often challenging. We examine the capabilities and limitations of LLMs and provide a detailed computational biology case study (on modeling HIV rebound dynamics) demonstrating how iterative interactions with an LLM can facilitate interdisciplinary collaboration and research. We argue that LLMs are best used as augmentative tools within a human-in-the-loop framework. Looking forward, we envisage that the responsible use of LLMs will enhance innovative cross-disciplinary research and substantially accelerate scientific discoveries.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s44163-026-01589-2.</p>","PeriodicalId":520312,"journal":{"name":"Discover artificial intelligence","volume":"6 1","pages":"932"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13518380/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148842195","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A scoping review of applications of natural language processing for chronic pain research.","authors":"Swati Rajwal, Selen Bozkurt, Jeanmarie Perrone, Anne Marie McKenzie-Brown, Abeed Sarker","doi":"10.1007/s44163-026-01417-7","DOIUrl":"10.1007/s44163-026-01417-7","url":null,"abstract":"<p><strong>Background: </strong>Chronic pain poses a significant global health burden, with pertinent contextual information relevant to it encapsulated in free text format across sources such as electronic health records (EHRs), published literature, and social media. Natural language processing (NLP), including recent advances in large language models (LLMs), presents a transformative opportunity to analyze this unstructured data, but the literature is fragmented across disciplines, and there is a need to consolidate existing knowledge, identify gaps in the literature, and inform future research directions in this emerging field.</p><p><strong>Objective: </strong>This review aims to investigate and characterize NLP-based methods designed for chronic pain research.</p><p><strong>Methods: </strong>A structured search was conducted across five databases (PubMed, Web of Science, Scopus, IEEE Xplore, ACL Anthology) for English-language studies published between 2014 and 2025. Included studies were analyzed for design, research question, data sources, NLP methods, population characteristics, and reproducibility.</p><p><strong>Results: </strong>From 155 records, 34 met inclusion criteria. The majority of studies (23/34; 67%) were published in the past three years. Most studies (12/34) relied on EHR data, with others utilizing social media data (n=7), synthesizing literature (n=2), or leveraging other data sources. Earlier studies focused on rule-based methods, while recent work adopted transformer-based models (e.g., BERT, RoBERTa, BioBERT) and LLMs (e.g., GPT-3.5 for zero- or few-shot learning frameworks). Common challenges included limited dataset diversity, lack of standardized evaluation frameworks, and poor reproducibility practices.</p><p><strong>Conclusions: </strong>While the application of NLP for chronic pain research is promising, the review revealed a paucity of research on the topic, with opportunities for future explorations such as validation of generalizable approaches involving diverse data and cohorts, multimodal data validation systems, public release of data and models, and the development of standardized evaluation metrics to enhance reproducibility and equity in chronic pain research. The emerging use of NLP offers clinicians new ways to observe patient needs, communicate more clearly, and make pain care more responsive and humane.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at https://doi.org/10.1007/s44163-026-01417-7.</p>","PeriodicalId":520312,"journal":{"name":"Discover artificial intelligence","volume":"6 1","pages":"1126"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13569585/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148957788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Model centric collaboration reduces data sharing barriers in medical artificial intelligence.","authors":"Yanan Dai, Yue Cai, Yuchen Xu, Qingqing Cai, Shilong Zhang, Yan Wang, Yunhao Xie, Yuan Liu, Leilei Cheng","doi":"10.1007/s44163-026-00914-z","DOIUrl":"10.1007/s44163-026-00914-z","url":null,"abstract":"<p><p>Medical artificial intelligence (AI) offers transformative potential for earlier disease detection, equitable access, and safer, more consistent care. However, its advancement depends on large-scale, multimodal, and longitudinal clinical data, which are highly sensitive and difficult to share under increasingly strict privacy and regulatory constraints. Federated learning-based on local training with gradient or parameter sharing-has emerged as a partial response to these challenges, yet it continues to face important limitations, including reliance on centralized aggregation, communication overhead, and reduced efficiency in heterogeneous settings. This perspective outlines a conceptual framework for rethinking collaboration in medical AI. We introduce the forward-looking notion of a data-model network, a model-centric paradigm that shifts collaboration from direct data exchange toward structured model interaction. In this framework, healthcare institutions train models locally and exchange model artifacts-such as parameters, distilled representations, or weak-to-strong generalizations-within a decentralized ecosystem, without transferring raw patient data. This theoretical model highlights how model-centric interaction can enable collective learning across heterogeneous clinical environments while preserving data sovereignty and embedding privacy protection by design. At the same time, we emphasize that this paradigm entails its own trade-offs and implementation challenges, including governance complexity, knowledge incompleteness, and trust management. By articulating these opportunities and limitations, this perspective argues that privacy-aware collaboration architectures can reposition privacy compliance from a constraint into a potential driver of innovation in medical AI-without presenting it as a panacea.</p>","PeriodicalId":520312,"journal":{"name":"Discover artificial intelligence","volume":"6 1","pages":"208"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12995956/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147489336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analyzing and redesigning deployment pathways for internally developed artificial intelligence tools in clinical education and health research.","authors":"Bo Xie, Ruoke Zhang","doi":"10.1007/s44163-026-02220-0","DOIUrl":"10.1007/s44163-026-02220-0","url":null,"abstract":"<p><p>Artificial intelligence (AI) tools for clinical education and health research are increasingly easy to imagine and prototype, but deploying internally developed tools into legitimate institutional use remains difficult. We conducted a retrospective comparative case study of four AI tools supported by the Nursing AI Studio in one academic health and social sciences environment. The four tools each was designed to address one of the following: clinical evaluation, tailored caregiver support, clinical simulation, and statistical assistance for biomedical and social-behavioral data. We operationalized deployment burden as the organizational, technical, and coordination work required to move a locally developed functioning AI-powered web application into legitimate institutional use. Across cases, early setup steps such as Amazon Web Services access, institutional subdomain configuration, and Information Security Office review were comparatively stable when institutional knowledge and reusable assets were available. The heavier burden occurred in first-team pathway discovery, enterprise integration, and cross-institutional access. The first case included approximately two-week gaps before later requests were initiated, reflecting time spent identifying the next institutional step rather than formal review time. Later internal cases reused such pathway knowledge, but recurring integration and changing compliance requirements persisted. The cross-institutional case introduced a distinct access problem that was not solved by the internal pathway. These findings suggest that deployment burden is not a single delay but a layered process involving discovery, reuse, recurrence, and context-specific bottlenecks. Shared studio-like support mechanisms may reduce repeated rediscovery and help health and social sciences teams plan more realistically for governance, infrastructure, collaboration, and sustainability.</p>","PeriodicalId":520312,"journal":{"name":"Discover artificial intelligence","volume":"6 1","pages":"1240"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13612711/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149067111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ai-guided vectorization for efficient storage and semantic retrieval of visual data.","authors":"Ahmed A Harby, Farhana Zulkernine, Hanady M Abdulsalam","doi":"10.1007/s44163-025-00713-y","DOIUrl":"10.1007/s44163-025-00713-y","url":null,"abstract":"<p><p>The rapid growth of multimedia content has increased the demand for effective methods to reduce storage requirements while maintaining quality and enabling fast data transmission. Existing standards and generative model approaches often involve high computational cost, require extensive parameter tuning, and produce inconsistent results, particularly in environments with limited processing resources. This paper presents a convolutional autoencoder framework for reducing the storage footprint of image and video data. The proposed method is designed for efficient integration with existing storage and retrieval systems. Several autoencoder architectures are developed and evaluated on diverse datasets including CelebA, IMDb Faces, Oxford Flowers 102, MNIST, and UCF101. The results show 56.6% to 70.8% for image data volume with minimal degradation in perceptual quality. The system incorporates a latent representation module that supports compact storage, efficient indexing, and accurate reconstruction. These capabilities are essential for practical deployment in multimedia platforms. Experimental evaluation demonstrates that the proposed approach performs competitively with recent techniques while providing greater consistency and reduced computational overhead. In comparison to generative models, the method achieves a higher peak signal to noise ratio and improved structural fidelity. This study offers a practical and reproducible solution for storage reduction, well suited for large scale image and video archiving and retrieval under constrained or high-throughput conditions.</p>","PeriodicalId":520312,"journal":{"name":"Discover artificial intelligence","volume":"6 1","pages":"16"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12795887/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145971579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A feasibility study of an artificial intelligence based decision support system for personalised housing adaptations and assistive technology.","authors":"Divya Saleela, Adekunle S Oyegoke, Jamiu A Dauda, Saheed O Ajayi","doi":"10.1007/s44163-026-01130-5","DOIUrl":"10.1007/s44163-026-01130-5","url":null,"abstract":"<p><p>This feasibility study explores the development of the HomeAI-Enable system, an AI-driven decision support platform designed to provide personalised housing adaptations and assistive technology recommendations for older adults and individuals with disabilities. The study follows a constructive research approach including focus groups and brainstorming exercises in addition to development research techniques. Three different datasets comprising one, five, and over 500 diseases sourced from reputable databases such as the NHS and Mayo Clinic were used. The datasets include symptoms, diseases and associated housing adaptation and assistive technologies for the identified symptoms and diseases. The system integrates these datasets to offer personalised solutions based on symptoms, disease, body functions, and environmental factors, using advanced techniques like fuzzy matching, natural language processing, and advanced machine learning models. The feasibility study involves the assessment of multiple AI models to determine their effectiveness in providing tailored recommendations for assistive technology and housing adaptations. A proof of concept was developed, and early technical evaluation demonstrates the viability of the proposed architecture and recommendation workflow for housing adaptation decision support. This study evaluates the technical feasibility of the system at a foundational stage within a broader research programme, with clinical validation involving occupational therapists and end users planned for subsequent phases.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s44163-026-01130-5.</p>","PeriodicalId":520312,"journal":{"name":"Discover artificial intelligence","volume":"6 1","pages":"763"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13453988/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148709369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}