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CellContrast: Reconstructing spatial relationships in single-cell RNA sequencing data via deep contrastive learning CellContrast：通过深度对比学习重建单细胞 RNA 测序数据中的空间关系
IF 6.5
Patterns Pub Date : 2024-07-09 DOI: 10.1016/j.patter.2024.101022
Shumin Li, Jiajun Ma, Tianyi Zhao, Yuran Jia, Bo Liu, Ruibang Luo, Yuanhua Huang
{"title":"CellContrast: Reconstructing spatial relationships in single-cell RNA sequencing data via deep contrastive learning","authors":"Shumin Li, Jiajun Ma, Tianyi Zhao, Yuran Jia, Bo Liu, Ruibang Luo, Yuanhua Huang","doi":"10.1016/j.patter.2024.101022","DOIUrl":"https://doi.org/10.1016/j.patter.2024.101022","url":null,"abstract":"<p>A vast amount of single-cell RNA sequencing (SC) data have been accumulated via various studies and consortiums, but the lack of spatial information limits its analysis of complex biological activities. To bridge this gap, we introduce CellContrast, a computational method for reconstructing spatial relationships among SC cells from spatial transcriptomics (ST) reference. By adopting a contrastive learning framework and training with ST data, CellContrast projects gene expressions into a hidden space where proximate cells share similar representation values. We performed extensive benchmarking on diverse platforms, including SeqFISH, Stereo-seq, 10X Visium, and MERSCOPE, on mouse embryo and human breast cells. The results reveal that CellContrast substantially outperforms other related methods, facilitating accurate spatial reconstruction of SC. We further demonstrate CellContrast’s utility by applying it to cell-type co-localization and cell-cell communication analysis with real-world SC samples, proving the recovered cell locations empower more discoveries and mitigate potential false positives.</p>","PeriodicalId":36242,"journal":{"name":"Patterns","volume":"85 1","pages":""},"PeriodicalIF":6.5,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141572472","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A proposal in Brazil to use generative AI in education threatens quality and equity 巴西关于在教育中使用生成式人工智能的提案威胁到教育质量和公平性
IF 6.5
Patterns Pub Date : 2024-07-01 DOI: 10.1016/j.patter.2024.101020
Fernanda D.A.O. Matos, Gildo Girotto Junior, Ana de Medeiros Arnt, Adriana Lippi
{"title":"A proposal in Brazil to use generative AI in education threatens quality and equity","authors":"Fernanda D.A.O. Matos, Gildo Girotto Junior, Ana de Medeiros Arnt, Adriana Lippi","doi":"10.1016/j.patter.2024.101020","DOIUrl":"https://doi.org/10.1016/j.patter.2024.101020","url":null,"abstract":"<p>Artificial intelligence (AI) is considered one of the most revolutionary technological developments today. But can it replace teachers in education? A new proposal in São Paulo, Brazil, suggests this might be possible, but it raises significant concerns about educational quality and equity.</p>","PeriodicalId":36242,"journal":{"name":"Patterns","volume":"79 1","pages":""},"PeriodicalIF":6.5,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141501965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Why brain organoids are not conscious yet 为什么大脑有机体还没有意识
IF 6.5
Patterns Pub Date : 2024-06-24 DOI: 10.1016/j.patter.2024.101011
Kenneth S. Kosik
{"title":"Why brain organoids are not conscious yet","authors":"Kenneth S. Kosik","doi":"10.1016/j.patter.2024.101011","DOIUrl":"https://doi.org/10.1016/j.patter.2024.101011","url":null,"abstract":"<p>Rapid advances in human brain organoid technologies have prompted the question of their consciousness. Although brain organoids resemble many facets of the brain, their shortcomings strongly suggest that they do not fit any of the operational definitions of consciousness. As organoids gain internal processing systems through statistical learning and closed loop algorithms, interact with the external world, and become embodied through fusion with other organ systems, questions of biosynthetic consciousness will arise.</p>","PeriodicalId":36242,"journal":{"name":"Patterns","volume":"3 1","pages":""},"PeriodicalIF":6.5,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141501967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A hierarchically annotated dataset drives tangled filament recognition in digital neuron reconstruction 分层注释数据集推动数字神经元重建中的缠结细丝识别
IF 6.5
Patterns Pub Date : 2024-06-21 DOI: 10.1016/j.patter.2024.101007
Wu Chen, Mingwei Liao, Shengda Bao, Sile An, Wenwei Li, Xin Liu, Ganghua Huang, Hui Gong, Qingming Luo, Chi Xiao, Anan Li
{"title":"A hierarchically annotated dataset drives tangled filament recognition in digital neuron reconstruction","authors":"Wu Chen, Mingwei Liao, Shengda Bao, Sile An, Wenwei Li, Xin Liu, Ganghua Huang, Hui Gong, Qingming Luo, Chi Xiao, Anan Li","doi":"10.1016/j.patter.2024.101007","DOIUrl":"https://doi.org/10.1016/j.patter.2024.101007","url":null,"abstract":"<p>Reconstructing neuronal morphology is vital for classifying neurons and mapping brain connectivity. However, it remains a significant challenge due to its complex structure, dense distribution, and low image contrast. In particular, AI-assisted methods often yield numerous errors that require extensive manual intervention. Therefore, reconstructing hundreds of neurons is already a daunting task for general research projects. A key issue is the lack of specialized training for challenging regions due to inadequate data and training methods. This study extracted 2,800 challenging neuronal blocks and categorized them into multiple density levels. Furthermore, we enhanced images using an axial continuity-based network that improved three-dimensional voxel resolution while reducing the difficulty of neuron recognition. Comparing the pre- and post-enhancement results in automatic algorithms using fluorescence micro-optical sectioning tomography (fMOST) data, we observed a significant increase in the recall rate. Our study not only enhances the throughput of reconstruction but also provides a fundamental dataset for tangled neuron reconstruction.</p>","PeriodicalId":36242,"journal":{"name":"Patterns","volume":"38 1","pages":""},"PeriodicalIF":6.5,"publicationDate":"2024-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141501966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Explainability pitfalls: Beyond dark patterns in explainable AI 可解释性陷阱：超越可解释人工智能的黑暗模式
IF 6.5
Patterns Pub Date : 2024-06-14 DOI: 10.1016/j.patter.2024.100971
Upol Ehsan, Mark O. Riedl
{"title":"Explainability pitfalls: Beyond dark patterns in explainable AI","authors":"Upol Ehsan, Mark O. Riedl","doi":"10.1016/j.patter.2024.100971","DOIUrl":"https://doi.org/10.1016/j.patter.2024.100971","url":null,"abstract":"<p>To make explainable artificial intelligence (XAI) systems trustworthy, understanding harmful effects is important. In this paper, we address an important yet unarticulated type of negative effect in XAI. We introduce explainability pitfalls (EPs), unanticipated negative downstream effects from AI explanations manifesting even when there is no intention to manipulate users. EPs are different from dark patterns, which are intentionally deceptive practices. We articulate the concept of EPs by demarcating it from dark patterns and highlighting the challenges arising from uncertainties around pitfalls. We situate and operationalize the concept using a case study that showcases how, despite best intentions, unsuspecting negative effects, such as unwarranted trust in numerical explanations, can emerge. We propose proactive and preventative strategies to address EPs at three interconnected levels: research, design, and organizational. We discuss design and societal implications around reframing AI adoption, recalibrating stakeholder empowerment, and resisting the “move fast and break things” mindset.</p>","PeriodicalId":36242,"journal":{"name":"Patterns","volume":"14 1","pages":""},"PeriodicalIF":6.5,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141521185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
The receiver operating characteristic curve accurately assesses imbalanced datasets 接收者操作特征曲线可准确评估不平衡数据集
IF 6.5
Patterns Pub Date : 2024-05-31 DOI: 10.1016/j.patter.2024.100994
Eve Richardson, Raphael Trevizani, Jason A. Greenbaum, Hannah Carter, Morten Nielsen, Bjoern Peters
{"title":"The receiver operating characteristic curve accurately assesses imbalanced datasets","authors":"Eve Richardson, Raphael Trevizani, Jason A. Greenbaum, Hannah Carter, Morten Nielsen, Bjoern Peters","doi":"10.1016/j.patter.2024.100994","DOIUrl":"https://doi.org/10.1016/j.patter.2024.100994","url":null,"abstract":"<p>Many problems in biology require looking for a “needle in a haystack,” corresponding to a binary classification where there are a few positives within a much larger set of negatives, which is referred to as a class imbalance. The receiver operating characteristic (ROC) curve and the associated area under the curve (AUC) have been reported as ill-suited to evaluate prediction performance on imbalanced problems where there is more interest in performance on the positive minority class, while the precision-recall (PR) curve is preferable. We show via simulation and a real case study that this is a misinterpretation of the difference between the ROC and PR spaces, showing that the ROC curve is robust to class imbalance, while the PR curve is highly sensitive to class imbalance. Furthermore, we show that class imbalance cannot be easily disentangled from classifier performance measured via PR-AUC.</p>","PeriodicalId":36242,"journal":{"name":"Patterns","volume":"26 1","pages":""},"PeriodicalIF":6.5,"publicationDate":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141191060","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
MetaGate: Interactive analysis of high-dimensional cytometry data with metadata integration MetaGate：利用元数据集成对高维细胞测量数据进行交互式分析
IF 6.5
Patterns Pub Date : 2024-05-13 DOI: 10.1016/j.patter.2024.100989
Eivind Heggernes Ask, Astrid Tschan-Plessl, Hanna Julie Hoel, Arne Kolstad, Harald Holte, Karl-Johan Malmberg
{"title":"MetaGate: Interactive analysis of high-dimensional cytometry data with metadata integration","authors":"Eivind Heggernes Ask, Astrid Tschan-Plessl, Hanna Julie Hoel, Arne Kolstad, Harald Holte, Karl-Johan Malmberg","doi":"10.1016/j.patter.2024.100989","DOIUrl":"https://doi.org/10.1016/j.patter.2024.100989","url":null,"abstract":"<p>Flow cytometry is a powerful technology for high-throughput protein quantification at the single-cell level. Technical advances have substantially increased data complexity, but novel bioinformatical tools often show limitations in statistical testing, data sharing, cross-experiment comparability, or clinical data integration. We developed MetaGate as a platform for interactive statistical analysis and visualization of manually gated high-dimensional cytometry data with integration of metadata. MetaGate provides a data reduction algorithm based on a combinatorial gating system that produces a small, portable, and standardized data file. This is subsequently used to produce figures and statistical analyses through a fast web-based user interface. We demonstrate the utility of MetaGate through a comprehensive mass cytometry analysis of peripheral blood immune cells from 28 patients with diffuse large B cell lymphoma along with 17 healthy controls. Through MetaGate analysis, our study identifies key immune cell population changes associated with disease progression.</p>","PeriodicalId":36242,"journal":{"name":"Patterns","volume":"109 1","pages":""},"PeriodicalIF":6.5,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140933481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Federated learning for privacy-preserving depression detection with multilingual language models in social media posts 利用社交媒体帖子中的多语言语言模型，为保护隐私的抑郁检测提供联合学习
IF 6.5
Patterns Pub Date : 2024-05-13 DOI: 10.1016/j.patter.2024.100990
Samar Samir Khalil, Noha S. Tawfik, Marco Spruit
{"title":"Federated learning for privacy-preserving depression detection with multilingual language models in social media posts","authors":"Samar Samir Khalil, Noha S. Tawfik, Marco Spruit","doi":"10.1016/j.patter.2024.100990","DOIUrl":"https://doi.org/10.1016/j.patter.2024.100990","url":null,"abstract":"<p>The incidences of mental health illnesses, such as suicidal ideation and depression, are increasing, which highlights the urgent need for early detection methods. There is a growing interest in using natural language processing (NLP) models to analyze textual data from patients, but accessing patients’ data for research purposes can be challenging due to privacy concerns. Federated learning (FL) is a promising approach that can balance the need for centralized learning with data ownership sensitivity. In this study, we examine the effectiveness of FL models in detecting depression by using a simulated multilingual dataset. We analyzed social media posts in five different languages with varying sample sizes. Our findings indicate that FL achieves strong performance in most cases while maintaining clients’ privacy for both independent and non-independent client partitioning.</p>","PeriodicalId":36242,"journal":{"name":"Patterns","volume":"9 1","pages":""},"PeriodicalIF":6.5,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140934296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
AI deception: A survey of examples, risks, and potential solutions 人工智能欺骗：实例、风险和潜在解决方案调查
IF 6.5
Patterns Pub Date : 2024-05-10 DOI: 10.1016/j.patter.2024.100988
Peter S. Park, Simon Goldstein, Aidan O’Gara, Michael Chen, Dan Hendrycks
{"title":"AI deception: A survey of examples, risks, and potential solutions","authors":"Peter S. Park, Simon Goldstein, Aidan O’Gara, Michael Chen, Dan Hendrycks","doi":"10.1016/j.patter.2024.100988","DOIUrl":"https://doi.org/10.1016/j.patter.2024.100988","url":null,"abstract":"<p>This paper argues that a range of current AI systems have learned how to deceive humans. We define deception as the systematic inducement of false beliefs in the pursuit of some outcome other than the truth. We first survey empirical examples of AI deception, discussing both special-use AI systems (including Meta’s CICERO) and general-purpose AI systems (including large language models). Next, we detail several risks from AI deception, such as fraud, election tampering, and losing control of AI. Finally, we outline several potential solutions: first, regulatory frameworks should subject AI systems that are capable of deception to robust risk-assessment requirements; second, policymakers should implement bot-or-not laws; and finally, policymakers should prioritize the funding of relevant research, including tools to detect AI deception and to make AI systems less deceptive. Policymakers, researchers, and the broader public should work proactively to prevent AI deception from destabilizing the shared foundations of our society.</p>","PeriodicalId":36242,"journal":{"name":"Patterns","volume":"253 1","pages":""},"PeriodicalIF":6.5,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140933222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Navigating color integrity in data visualization 数据可视化中的色彩完整性导航
IF 6.5
Patterns Pub Date : 2024-05-10 DOI: 10.1016/j.patter.2024.100972
Fabio Crameri, Sari Hason
{"title":"Navigating color integrity in data visualization","authors":"Fabio Crameri, Sari Hason","doi":"10.1016/j.patter.2024.100972","DOIUrl":"https://doi.org/10.1016/j.patter.2024.100972","url":null,"abstract":"<p>Color is crucial in scientific visualization, yet it is often misused. Addressing this, we think accessible and accurate techniques, such as color-blind friendly palettes and perceptually even gradients, are vital. Accountability and basic knowledge in data visualization are key in fostering a culture of color integrity, ensuring accurate and inclusive data representation.</p>","PeriodicalId":36242,"journal":{"name":"Patterns","volume":"66 1","pages":""},"PeriodicalIF":6.5,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140933308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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