当前共找到 1331 篇文献分享,本页显示第 421 - 440 篇。
421.
庞庞
(2024-02-29 21:11):
#paper Machine learning in major depression: From classification to treatment outcome prediction doi 10.1111/cns.13048 这是篇综述机器学习在抑郁症脑影像数据中应用的文章,角度主要是分类和疗效预测。我们可以发现,大部分的此类研究用的都是小样本数据集,这就导致模型的泛化性有限。近年来,已经有越来越多的研究使用多中心大样本抑郁症数据集,但是这些研究的模型准确率相应的会降低。如何对抑郁症进行分亚型,进行特征筛选,选择合适的机器学习乃至深度学习的模型,保证泛化性的同时提高准确率,是抑郁症判别和疗效预测研究未来的重要方向。
Abstract:
AIMS: Major depression disorder (MDD) is the single greatest cause of disability and morbidity, and affects about 10% of the population worldwide. Currently, there are no clinically useful diagnostic biomarkers …
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AIMS: Major depression disorder (MDD) is the single greatest cause of disability and morbidity, and affects about 10% of the population worldwide. Currently, there are no clinically useful diagnostic biomarkers that are able to confirm a diagnosis of MDD from bipolar disorder (BD) in the early depressive episode. Therefore, exploring translational biomarkers of mood disorders based on machine learning is in pressing need, though it is challenging, but with great potential to improve our understanding of these disorders.DISCUSSIONS: In this study, we review popular machine-learning methods used for brain imaging classification and predictions, and provide an overview of studies, specifically for MDD, that have used magnetic resonance imaging data to either (a) classify MDDs from controls or other mood disorders or (b) investigate treatment outcome predictors for individual patients. Finally, challenges, future directions, and potential limitations related to MDD biomarker identification are also discussed, with a goal of offering a comprehensive overview that may help readers to better understand the applications of neuroimaging data mining in depression.CONCLUSIONS: We hope such efforts may highlight the need for an urgently needed paradigm shift in treatment, to guide personalized optimal clinical care.
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422.
哪有情可长
(2024-02-29 21:01):
#paper A quantitative genomics map of rice provides genetic insights and guides breeding, Nature genetics,01 February 2021, doi.org/10.1038/s41588-020-00769-9. 这篇文章先收集水稻中各种性状前人已经定位的QTL,对QTL区间内的关键功能变异位点锚定到水稻基因组精确的位置上,获取了一个包含348个变异位点和562个等位基因的分子图谱(QTN)。然后对另外收集的404份种质材料,构建包含前面鉴定的等位基因的数据库,方便后人进行遗传改良过程亲本的选择。作者有对基因变异的遗传效应进行评估,来鉴定变异位点的效应方向和量化变化的强弱关系。利用水稻QTN图谱和遗传图,论文作者系统分析了水稻基因组中存在的遗传累赘,并针对杂交-回交-自交、群体样本量、导入位点数等各类情形进行了大数据仿真模拟,获得了育种设计路线的优化参数。这篇文章对我与现在处理大量的小麦GWAS得到的显著的SNP位点,如何进行量化管理,形成对育种家有用的数据很有启发。
Abstract:
Extensive allelic variation in agronomically important genes serves as the basis of rice breeding. Here, we present a comprehensive map of rice quantitative trait nucleotides (QTNs) and inferred QTN effects …
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Extensive allelic variation in agronomically important genes serves as the basis of rice breeding. Here, we present a comprehensive map of rice quantitative trait nucleotides (QTNs) and inferred QTN effects based on eight genome-wide association study cohorts. Population genetic analyses revealed that domestication, local adaptation and heterosis are all associated with QTN allele frequency changes. A genome navigation system, RiceNavi, was developed for QTN pyramiding and breeding route optimization, and implemented in the improvement of a widely cultivated indica variety. This work presents an efficient platform that bridges ever-increasing genomic knowledge and diverse improvement needs in rice.
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423.
小W
(2024-02-29 20:28):
#paper doi:arXiv:2203.13906 Biolink Model: A Universal Schema for Knowledge Graphs in
Clinical, Biomedical, and Translational Science 本文介绍了欧洲分子生物学实验室对于生命进程的认识 Biolink 模型,其使用yaml变体 linkml ( Linked data Modeling Language )定义一组分层的、相互关联的类以及它们之间的关系,以此来表征转化科学中的实体以及这些实体之间的联系。其工作包含标准生物模式、样本、TranslatorMinimal三个模型库以及使用其模型关联不同本体数据的方法。基于此模型,其他团队开发了NIH 的Biomedical Data Translator项目,以及 2023 发表于 Nat. Biotechnol 的 BioCypher 。
arXiv,
2022.
DOI: 10.48550/arXiv.2203.13906
Abstract:
Within clinical, biomedical, and translational science, an increasing numberof projects are adopting graphs for knowledge representation. Graph-based datamodels elucidate the interconnectedness between core biomedical concepts,enable data structures to be easily …
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Within clinical, biomedical, and translational science, an increasing numberof projects are adopting graphs for knowledge representation. Graph-based datamodels elucidate the interconnectedness between core biomedical concepts,enable data structures to be easily updated, and support intuitive queries,visualizations, and inference algorithms. However, knowledge discovery acrossthese "knowledge graphs" (KGs) has remained difficult. Data set heterogeneityand complexity; the proliferation of ad hoc data formats; poor compliance withguidelines on findability, accessibility, interoperability, and reusability;and, in particular, the lack of a universally-accepted, open-access model forstandardization across biomedical KGs has left the task of reconciling datasources to downstream consumers. Biolink Model is an open source data modelthat can be used to formalize the relationships between data structures intranslational science. It incorporates object-oriented classification andgraph-oriented features. The core of the model is a set of hierarchical,interconnected classes (or categories) and relationships between them (orpredicates), representing biomedical entities such as gene, disease, chemical,anatomical structure, and phenotype. The model provides class and edgeattributes and associations that guide how entities should relate to oneanother. Here, we highlight the need for a standardized data model for KGs,describe Biolink Model, and compare it with other models. We demonstrate theutility of Biolink Model in various initiatives, including the Biomedical DataTranslator Consortium and the Monarch Initiative, and show how it has supportedeasier integration and interoperability of biomedical KGs, bringing togetherknowledge from multiple sources and helping to realize the goals oftranslational science.
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424.
Vincent
(2024-02-29 17:06):
#paper Transfer learning enables predictions in network biology. Nature. 2023. doi: https://doi.org/10.1038/s41586-023-06139-9. 学习基因互作网络通常需要大量数据,对于数据较少的生物研究来说,利用迁移学习和预训练模型能够有效降低对数据量的需求。这篇文章提出了一种基于transformer的深度学习模型geneformer,其使用了大量的单细胞数据集进行预训练(自监督学习)。在模型训练中,geneformer 并未使用gene的原始表达值,而是使用了gene expression rank(相当于数据降噪)来学习基因网络。对于下游任务,利用少量数据对模型微调就能够很好的增强预测准确率。文章列举了geneformer在基因剂量, 染色质,基因网络方面的例子,预测准确性相较传统的机器学习模型均有明显提升。
Abstract:
Mapping gene networks requires large amounts of transcriptomic data to learn the connections between genes, which impedes discoveries in settings with limited data, including rare diseases and diseases affecting clinically …
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Mapping gene networks requires large amounts of transcriptomic data to learn the connections between genes, which impedes discoveries in settings with limited data, including rare diseases and diseases affecting clinically inaccessible tissues. Recently, transfer learning has revolutionized fields such as natural language understanding and computer vision by leveraging deep learning models pretrained on large-scale general datasets that can then be fine-tuned towards a vast array of downstream tasks with limited task-specific data. Here, we developed a context-aware, attention-based deep learning model, Geneformer, pretrained on a large-scale corpus of about 30 million single-cell transcriptomes to enable context-specific predictions in settings with limited data in network biology. During pretraining, Geneformer gained a fundamental understanding of network dynamics, encoding network hierarchy in the attention weights of the model in a completely self-supervised manner. Fine-tuning towards a diverse panel of downstream tasks relevant to chromatin and network dynamics using limited task-specific data demonstrated that Geneformer consistently boosted predictive accuracy. Applied to disease modelling with limited patient data, Geneformer identified candidate therapeutic targets for cardiomyopathy. Overall, Geneformer represents a pretrained deep learning model from which fine-tuning towards a broad range of downstream applications can be pursued to accelerate discovery of key network regulators and candidate therapeutic targets.
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425.
小年
(2024-02-29 10:51):
Choo, ZN., Behr, J.M., Deshpande, A. et al. Most large structural variants in cancer genomes can be detected without long reads. Nat Genet 55, 2139–2148 (2023). https://doi-org-443.webvpn.las.ac.cn/10.1038/s41588-023-01540-6
短读测序(SRS)普遍应用于癌症基因组学研究,但SRS对于检测癌症结构变异(SVs,包括拷贝数改变和重排)的灵敏度有限,特别是大型染色体结构改变,这是因为人类基因组中有许多同源序列。本研究分析了短读全基因组中的“松散末端”——相邻DNA片段之间质量平衡的局部违反,用于检测短读测序遗漏的SVs。作者在1,330个高纯度癌症全基因组的松散末端景观中,发现大多数大于10kb的克隆SVs在人类基因组87%的区域内可以被短读测序完全解析,并且可以准确检测拷贝数。值得注意的是,一些松散末端代表新端粒,可将其作为替代性端粒延长表型的标志,以上发现通过还38例乳腺癌和黑色素瘤病例的长读长测序得到验证。本项研究的结果表明,异常同源重组不太可能驱动大多数大型癌症SVs,总得来说,全基因组SRS数据中的质量平衡分析提供了癌症染色体结构的一个出人意料的完整景象。("松散末端"是指那些在短读测序数据中没有找到匹配的断点末端。这些末端可能是因为基因组重排事件而产生的,这些事件将本不相连的DNA片段的末端连接在一起,形成了新的结合点)
IF:31.700Q1
Nature genetics,
2023-Dec.
DOI: 10.1038/s41588-023-01540-6
PMID: 37945902
PMCID:PMC10703688
Abstract:
Short-read sequencing is the workhorse of cancer genomics yet is thought to miss many structural variants (SVs), particularly large chromosomal alterations. To characterize missing SVs in short-read whole genomes, we …
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Short-read sequencing is the workhorse of cancer genomics yet is thought to miss many structural variants (SVs), particularly large chromosomal alterations. To characterize missing SVs in short-read whole genomes, we analyzed 'loose ends'-local violations of mass balance between adjacent DNA segments. In the landscape of loose ends across 1,330 high-purity cancer whole genomes, most large (>10-kb) clonal SVs were fully resolved by short reads in the 87% of the human genome where copy number could be reliably measured. Some loose ends represent neotelomeres, which we propose as a hallmark of the alternative lengthening of telomeres phenotype. These pan-cancer findings were confirmed by long-molecule profiles of 38 breast cancer and melanoma cases. Our results indicate that aberrant homologous recombination is unlikely to drive the majority of large cancer SVs. Furthermore, analysis of mass balance in short-read whole genome data provides a surprisingly complete picture of cancer chromosomal structure.
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426.
徐炳祥
(2024-02-29 10:20):
#paper doi: 10.1038/s41467-024-44761-x Nature communications, 2024, Orchestrating chromosome conformation capture analysis with Bioconductor。全基因组染色质构象捕获技术(Hi-C)及其衍生技术是当前研究真核细胞染色质空间构象的最主流技术手段,基于其的研究所涉及的大体量,多模、多目标的多组学分析问题对生物信息技术提出了许多重大挑战。本文系统性的总结了过去十几年来依托R语言和Bioconductor平台开发的一系列Hi-C及衍生数据分析工具包。按分析流程详细描述了数据的获取和预处理,结果的导入导出,核心数据结构,各拓扑结构单元的识别,数据可视化等数据分析的方方面面。本文的总结对学习Hi-C数据分析有重要参考价值,同时也对定制化的分析流程开发有指导意义。
Abstract:
Genome-wide chromatin conformation capture assays provide formidable insights into the spatial organization of genomes. However, due to the complexity of the data structure, their integration in multi-omics workflows remains challenging. …
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Genome-wide chromatin conformation capture assays provide formidable insights into the spatial organization of genomes. However, due to the complexity of the data structure, their integration in multi-omics workflows remains challenging. We present data structures, computational methods and visualization tools available in Bioconductor to investigate Hi-C, micro-C and other 3C-related data, in R. An online book ( https://bioconductor.org/books/OHCA/ ) further provides prospective end users with a number of workflows to process, import, analyze and visualize any type of chromosome conformation capture data.
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427.
🐼太真实
(2024-02-29 10:04):
#paper ProPainter: Improving Propagation and Transformer for Video Inpainting 本文介绍了一种新的视频修复技术——ProPainter,通过双域传播和掩码引导稀疏视频Transformer的设计,实现了高效而准确的视频修复。文章详细介绍了ProPainter的三个关键组成部分:循环流场完成、双域传播和掩码引导稀疏视频Transformer,并提供了相应的技术细节和实验结果。
arXiv,
2023.
DOI: 10.48550/arXiv.2309.03897
Abstract:
Flow-based propagation and spatiotemporal Transformer are two mainstreammechanisms in video inpainting (VI). Despite the effectiveness of thesecomponents, they still suffer from some limitations that affect theirperformance. Previous propagation-based approaches are …
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Flow-based propagation and spatiotemporal Transformer are two mainstreammechanisms in video inpainting (VI). Despite the effectiveness of thesecomponents, they still suffer from some limitations that affect theirperformance. Previous propagation-based approaches are performed separatelyeither in the image or feature domain. Global image propagation isolated fromlearning may cause spatial misalignment due to inaccurate optical flow.Moreover, memory or computational constraints limit the temporal range offeature propagation and video Transformer, preventing exploration ofcorrespondence information from distant frames. To address these issues, wepropose an improved framework, called ProPainter, which involves enhancedProPagation and an efficient Transformer. Specifically, we introducedual-domain propagation that combines the advantages of image and featurewarping, exploiting global correspondences reliably. We also propose amask-guided sparse video Transformer, which achieves high efficiency bydiscarding unnecessary and redundant tokens. With these components, ProPainteroutperforms prior arts by a large margin of 1.46 dB in PSNR while maintainingappealing efficiency.
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428.
颜林林
(2024-02-29 09:02):
#paper doi:10.1038/s41592-024-02201-0. Nature Methods, 2024, scGPT: toward building a foundation model for single-cell multi-omics using generative AI. 这篇文章使用了生成式AI大模型,来进行单细胞测序数据分析。文章并未自己收集样本和测序,而仅仅依靠已发表的公开数据或来自公共数据库的数据,进行模型训练、工具开发和性能验证,属于典型的纯生信文章,借着生成式AI概念的火热,加上结果性能表现良好,这篇文章发表到了Nature Methods杂志,很值得生信专业者学习和模仿。文章在九个多月前,就已预发表在bioRxiv上,当时整合使用了1000万个细胞的数据,在这次的正式发表版本中,整合的细胞数量增加到了3300万,模型性能也得到了进一步的改进。文章开发的模型名为scGPT,它基于生成式预训练变换器(Transformer)架构的单细胞基础模型,旨在处理和解析大规模的单细胞数据。scGPT模型展示了在多种下游任务中,如细胞类型注释、遗传扰动反应预测、多批次整合以及多组学数据整合等方面的卓越性能。研究的创新点在于首次将基础模型概念应用于单细胞生物学领域,通过自监督预训练和任务特定的微调,有效捕获和理解细胞和基因之间复杂的生物学关系。scGPT利用其强大的学习能力揭示了特定条件下的基因-基因互作,展现了转移学习中的扩展性和上下文效应。相比传统的机器学习模型,大模型能够捕捉到更为细致和全面的生物学特征,尤其是一些长距离依赖和复杂的数据关系,比如隐藏在数据背后的未知细胞类型或细胞相互作用,这大概也是这篇文章将其用于单细胞数据分析的重要出发点。
scGPT:利用生成式 AI 构建单细胞多组学基础模型
Abstract:
Generative pretrained models have achieved remarkable success in various domains such as language and computer vision. Specifically, the combination of large-scale diverse datasets and pretrained transformers has emerged as a …
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Generative pretrained models have achieved remarkable success in various domains such as language and computer vision. Specifically, the combination of large-scale diverse datasets and pretrained transformers has emerged as a promising approach for developing foundation models. Drawing parallels between language and cellular biology (in which texts comprise words; similarly, cells are defined by genes), our study probes the applicability of foundation models to advance cellular biology and genetic research. Using burgeoning single-cell sequencing data, we have constructed a foundation model for single-cell biology, scGPT, based on a generative pretrained transformer across a repository of over 33 million cells. Our findings illustrate that scGPT effectively distills critical biological insights concerning genes and cells. Through further adaptation of transfer learning, scGPT can be optimized to achieve superior performance across diverse downstream applications. This includes tasks such as cell type annotation, multi-batch integration, multi-omic integration, perturbation response prediction and gene network inference.
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429.
muton
(2024-02-28 23:02):
#paper The Conceptual Structure of Human Relationships Across Modern and Historical Cultures preprint 人类社会复杂性的特征就是关系的复杂性,我们会和家庭、学校、工作、社区甚至社交网络的各类人群建立不同的关系。但我们应该如何理解如此复杂的人际关系系统?通过使用自然语言处理(NLP)、在线调查、实验室认知任务和计算建模,对世界各地的各种现代文化进行研究。(n = 20425)和跨越3,000年历史的古代文化,作者发现了关系概念的普遍表征空间,由五个主要维度组成(正式、主动、效价、交换、平等)和三个核心范畴(敌对、公共和私人关系)。这一工作推进了对人类社会性的理解。并且通过比较不同国家文化差异,作者发现中美存在巨大文化距离,在理解人际关系中的亲密程度时,美国人似乎更关注物理距离,而中国人更关注心理距离。
PsyArXiv Preprints,
2023.
DOI: 10.31234/osf.io/ut6qp
Abstract:
A defining characteristic of social complexity in Homo sapiens is the diversity of our relationships. We build various types of connections with people in families, schools, workplaces, neighborhoods, and online …
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A defining characteristic of social complexity in Homo sapiens is the diversity of our relationships. We build various types of connections with people in families, schools, workplaces, neighborhoods, and online communities. How do we make sense of such complex systems of human relationships? By using natural language processing, online surveys, laboratory cognitive tasks, and computational modelling on diverse modern cultures across the world (n = 20,425) and ancient cultures across 3,000 years of history, we discovered a universal representational space of relationship concepts, comprised of five principal dimensions (formality, activeness, valence, exchange, equality) and three core categories (hostile, public and private relationships). Our work reveals the fundamental cognitive constructs and cultural principles of relationship knowledge and advances our understanding of human sociality.
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430.
尹志
(2024-02-28 22:09):
#paper An introduction to Topological Data Analysis: fundamental and practical aspects for data scientists doi: https://doi.org/10.48550/arXiv.1710.04019 生成式AI风光无两,Sora甚嚣尘上,虽然我还做不到这样的效果(对,我就是酸),但我却认为这不是终极方案,特别是对于物理世界、生物系统。The Bitter Lesson中对scaling law的强调甚至信奉,在语言、视频这样的领域有其价值,但生命科学、物理系统有数十亿年的的历史(物理系统应该是创始之初把),生命的演化、物理系统的本源,人类对其千百年来积累的原理性探索,应该是更优的先验。哦,回到这篇paper的主题。拓扑数据分析,是一种将系统的拓扑与几何性质引入分析建模过程,从而对系统获取更深刻的理解的工具。本篇综述对这个工具做了细致的讲解并对它的应用领域做了分析和tutorial。对拓扑数据分析这门技术的数学前置也做了简单但细致的介绍,主要是代数拓扑和计算几何。之所以有前面一段的碎碎念,就是因为我结合最近的一些实践,切实感受到拓扑和几何这些抽象的数学工具与生成式AI的结合,对生物系统和物理世界的描述,也许是优于目前暴力怼计算的一种更高效的建模方式,能够更深入系统的物理本质。如果你也相信物理系统和生命世界的简单高效的,是美丽简洁的,建议尝试一下这些新的技术。对了,这篇综述的revison信息是[Submitted on 11 Oct 2017 (v1), last revised 25 Feb 2021 (this version, v2)], 是不是说明了点什么呢?
arXiv,
2017.
DOI: 10.48550/arXiv.1710.04019
Abstract:
Topological Data Analysis is a recent and fast growing field providing a setof new topological and geometric tools to infer relevant features for possiblycomplex data. This paper is a brief …
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Topological Data Analysis is a recent and fast growing field providing a setof new topological and geometric tools to infer relevant features for possiblycomplex data. This paper is a brief introduction, through a few selectedtopics, to basic fundamental and practical aspects of \tda\ for non experts.
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431.
李翛然
(2024-02-28 18:11):
#paper A computational framework for neural network-based variational Monte Carlo with Forward Laplacian doi: https://doi.org/10.1038/s42256-024-00794-x 北大和字节跳动合作的文章,关注是因为一直在看计算化学领域的一些新进展。字节跳动和北京大学团队共同研究,针对神经网络变分蒙特卡罗(NN-VMC)在处理大规模量子系统时计算成本高的问题。
2. 研究团队创新性地提出了“Forward Laplacian”计算框架,通过前向传播直接高效计算神经网络相关拉普拉斯部分,显著提升NN-VMC计算效率。
3. 他们还设计了名为“LapNet”的高效神经网络结构,利用Forward Laplacian优势,大幅减少了模型训练所需的计算资源。
4. 结合Forward Laplacian和LapNet的NN-VMC方法在多种化学系统中展现出优越的性能,可准确计算绝对能量和相对能量,与实验数据和金标准计算方法吻合度高。
5. 尽管已有显著进步,但团队指出,未来还需要将更多化学和物理知识融入NN-VMC方法中以解决部分应用场景中的差异问题,同时Forward Laplacian有望在更广泛的量子力学及基于神经网络的偏微分方程求解领域发挥作用。
432.
前进
(2024-02-28 10:57):
#paper Mckenzie E M , Santhanam A , Ruan D ,et al.Multimodality image registration in the head‐and‐neck using a deep learning‐derived synthetic CT as a bridge[J].Medical Physics, 2020, 47(3).DOI:10.1002/mp.13976.
本文提出并验证一种利用深度学习驱动的跨模态综合技术的头颈多模式图像配准方法。
采用CycleGAN将MRI 转化为合成CT(sCT),将头颈部的MRI-CT多模态配准转化为sCT-CT的单模态配准。配准方法采用传统的B-spline方法。实验结果表明sCT→CT 配准精度好于MRI→CT。平均配准误差从9.8mm下降到6.0mm
Abstract:
PURPOSE: To develop and demonstrate the efficacy of a novel head-and-neck multimodality image registration technique using deep-learning-based cross-modality synthesis.METHODS AND MATERIALS: Twenty-five head-and-neck patients received magnetic resonance (MR) and computed …
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PURPOSE: To develop and demonstrate the efficacy of a novel head-and-neck multimodality image registration technique using deep-learning-based cross-modality synthesis.METHODS AND MATERIALS: Twenty-five head-and-neck patients received magnetic resonance (MR) and computed tomography (CT) (CTaligned ) scans on the same day with the same immobilization. Fivefold cross validation was used with all of the MR-CT pairs to train a neural network to generate synthetic CTs from MR images. Twenty-four of 25 patients also had a separate CT without immobilization (CTnon-aligned ) and were used for testing. CTnon-aligned 's were deformed to the synthetic CT, and compared to CTnon-aligned registered to MR. The same registrations were performed from MR to CTnon-aligned and from synthetic CT to CTnon-aligned . All registrations used B-splines for modeling the deformation, and mutual information for the objective. Results were evaluated using the 95% Hausdorff distance among spinal cord contours, landmark error, inverse consistency, and Jacobian determinant of the estimated deformation fields.RESULTS: When large initial rigid misalignment is present, registering CT to MRI-derived synthetic CT aligns the cord better than a direct registration. The average landmark error decreased from 9.8 ± 3.1 mm in MR→CTnon-aligned to 6.0 ± 2.1 mm in CTsynth →CTnon-aligned deformable registrations. In the CT to MR direction, the landmark error decreased from 10.0 ± 4.3 mm in CTnon-aligned →MR deformable registrations to 6.6 ± 2.0 mm in CTnon-aligned →CTsynth deformable registrations. The Jacobian determinant had an average value of 0.98. The proposed method also demonstrated improved inverse consistency over the direct method.CONCLUSIONS: We showed that using a deep learning-derived synthetic CT in lieu of an MR for MR→CT and CT→MR deformable registration offers superior results to direct multimodal registration.
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433.
盼盼
(2024-02-27 16:38):
DOI: 10.1038/s41586-018-0453-z
目前我们已经发现数千种长链非编码RNA(lncRNA),但是明确功能的只有十几种。德克萨斯西南医学研究中心Mendell教授发现lncRNA-NORAD可以在维持基因组稳定性上非常重要。此外,还报道了PUMILIO是NORAD唯一相互作用的RNA结合蛋白,但是相关的作用机制我们并不清楚。最近 哈弗-麻省理工学院lander教授提出了NORAD和RNA结合蛋白PUMILIO的相互作用,对NORAD功能发挥具有重要作用。NORAD和PUMILIO结合后,NORAD调节PUMILIO组装拓补异构酶复合物的能力,该复合物在维持基因组稳定性具有重要作用。实验证明细胞在PUMILIO敲除后的表型,与PUMILIO敲除表型密切相关,都表现为染色质分离增加,复制叉速度降低和细胞周期改变。在PUMILIO正常表达的细胞,补充NORAD,可以补救NORAD缺失引起的基因组不稳定,但是在NORAD的作用位点缺失以后,挽救效果就很差。这说明NORAD是通过特定位点与PUMILIO相互作用,促进拓补异构酶复合物组装并参与维持基因组稳定性。但是对于NORAD与PUMILIO的相互作用如何促进拓补异构酶复合物组装的,lander教授提出了多个可能的机制,这些机制还有待进一步实验验证。
Abstract:
The human genome contains thousands of long non-coding RNAs, but specific biological functions and biochemical mechanisms have been discovered for only about a dozen. A specific long non-coding RNA-non-coding RNA …
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The human genome contains thousands of long non-coding RNAs, but specific biological functions and biochemical mechanisms have been discovered for only about a dozen. A specific long non-coding RNA-non-coding RNA activated by DNA damage (NORAD)-has recently been shown to be required for maintaining genomic stability, but its molecular mechanism is unknown. Here we combine RNA antisense purification and quantitative mass spectrometry to identify proteins that directly interact with NORAD in living cells. We show that NORAD interacts with proteins involved in DNA replication and repair in steady-state cells and localizes to the nucleus upon stimulation with replication stress or DNA damage. In particular, NORAD interacts with RBMX, a component of the DNA-damage response, and contains the strongest RBMX-binding site in the transcriptome. We demonstrate that NORAD controls the ability of RBMX to assemble a ribonucleoprotein complex-which we term NORAD-activated ribonucleoprotein complex 1 (NARC1)-that contains the known suppressors of genomic instability topoisomerase I (TOP1), ALYREF and the PRPF19-CDC5L complex. Cells depleted for NORAD or RBMX display an increased frequency of chromosome segregation defects, reduced replication-fork velocity and altered cell-cycle progression-which represent phenotypes that are mechanistically linked to TOP1 and PRPF19-CDC5L function. Expression of NORAD in trans can rescue defects caused by NORAD depletion, but rescue is significantly impaired when the RBMX-binding site in NORAD is deleted. Our results demonstrate that the interaction between NORAD and RBMX is important for NORAD function, and that NORAD is required for the assembly of the previously unknown topoisomerase complex NARC1, which contributes to maintaining genomic stability. In addition, we uncover a previously unknown function for long non-coding RNAs in modulating the ability of an RNA-binding protein to assemble a higher-order ribonucleoprotein complex.
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434.
DeDe宝
(2024-02-15 16:06):
#paper doi:10.7554/eLife.91825.3 A spatial-attentional mechanism underlies action-related distortions of time judgment,eLife 时间绑定(Temporal binding)指一种动作和稍微延迟的感官事件之间的时间吸引力的错觉,由动作绑定(action binding,即被报告为较晚发生的动作)和结果绑定(outcome binding,即被报告为较早发生的感觉事件)组成。然而,在使用Libet时钟方法测量时间判断时,视觉空间注意力的影响被严重忽视。结果绑定通常是通过比较动作声音(AS)条件和仅声音(SO)条件来获得的,被试报告声音播放时时钟指针指向的位置,AS 条件下报告的时间比 SO 条件下的报告时间早。动作绑定通过比较AS条件和AO条件,AS 条件下报告的按键时间晚于 AO 条件下报告的按键时间。在本研究的四个实验中,研究者使用时钟方法证明了由动作和感觉刺激引起的时间绑定中注意力调节的不同模式。此外,单独使用注意力测量的计算模型可以重现时间绑定效应,为时间绑定的注意力假设提供强有力的支持证据。
Abstract:
Temporal binding has been understood as an illusion in timing judgment. When an action triggers an outcome (e.g. a sound) after a brief delay, the action is reported to occur …
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Temporal binding has been understood as an illusion in timing judgment. When an action triggers an outcome (e.g. a sound) after a brief delay, the action is reported to occur later than if the outcome does not occur, and the outcome is reported to occur earlier than a similar outcome not caused by an action. We show here that an attention mechanism underlies the seeming illusion of timing judgment. In one method, participants watch a rotating clock hand and report event times by noting the clock hand position when the event occurs. We find that visual spatial attention is critically involved in shaping event time reports made in this way. This occurs because action and outcome events result in shifts of attention around the clock rim, thereby biasing the perceived location of the clock hand. Using a probe detection task to measure attention, we show a difference in the distribution of visual spatial attention between a single-event condition (sound only or action only) and a two-event agency condition (action plus sound). Participants accordingly report the timing of the same event (the sound or the action) differently in the two conditions: spatial attentional shifts masquerading as temporal binding. Furthermore, computational modeling based on the attention measure can reproduce the temporal binding effect. Studies that use time judgment as an implicit marker of voluntary agency should first discount the artefactual changes in event timing reports that actually reflect differences in spatial attention. The study also has important implications for related results in mental chronometry obtained with the clock-like method since Wundt, as attention may well be a critical confounding factor in the interpretation of these studies.
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435.
龙海晨
(2024-02-09 19:34):
#paper Ahmed Mostafa G, Mohamed Ibrahim H, Al Sayed Shehab A, Mohamed Magdy S, AboAbdoun Soliman N, Fathy El-Sherif D. Up-regulated serum levels of interleukin (IL)-17A and IL-22 in Egyptian pediatric patients with COVID-19 and MIS-C: Relation to the disease outcome. Cytokine. 2022 Jun;154:155870. doi: 10.1016/j.cyto.2022.155870. Epub 2022 Apr 4. PMID: 35398721; PMCID: PMC8977483.这是一篇研究新冠感染儿童血清中各种细胞因子与新冠严重程度的文章。检测患者40名(其中 18 名患者患有 COVID-19,22 名患者患有多系统炎症综合征“MIS-C”)年龄从两个月到16周岁,48名健康儿童作为对照组。COVID-19 和 MIS-C 患者的血清 IL-17A 和 IL-22 水平显着高于健康对照儿童。患者的血清 IL-17A 和 IL-22 水平均升高。90% 的患者发现 CRP 和血清铁蛋白水平升高。IL-17和IL-22都被认为是组织信号细胞因子,有利于肺、皮肤和胃肠道等上皮屏障器官的保护和再生。IL-17 和 IL-22 在宿主抵御微生物以及急性和慢性炎症性疾病的发展中发挥着重要作用。但康复的患者与死亡的患者生前留存的血清中对比 IL-17A、IL-22 没有显著差异, IL-17A、IL-22 的升高与否不能作为预后指标。
Abstract:
Both IL-17A and IL-22 share cellular sources and signaling pathways. They have synergistic action on epithelial cells to stimulate their production of antimicrobial peptides which are protective against infections. However, …
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Both IL-17A and IL-22 share cellular sources and signaling pathways. They have synergistic action on epithelial cells to stimulate their production of antimicrobial peptides which are protective against infections. However, both interleukins may contribute to ARDS pathology if their production is not controlled. This study aimed to investigate serum levels of IL-17A and IL-22 in relation to the disease outcome in patients with SARS-CoV-2. Serum IL-17A and IL-22 were measured by ELISA in 40 patients with SARS-CoV-2, aged between 2 months and 16 years, (18 had COVID-19 and 22 had multisystem inflammatory syndrome in children "MIS-C") in comparison to 48 age- and sex-matched healthy control children. Patients with COVID-19 and MIS-C had significantly higher serum IL-17A and IL-22 levels than healthy control children (P < 0.001). Increased serum IL-17A and IL-22 levels were found in all patients. Elevated CRP and serum ferritin levels were found in 90% of these patients. Lymphopenia, neutrophilia, neutropenia, thrombocytopenia and elevated ALT, LDH and D-dimer were found in 45%, 42.5 %, 2.5%, 30%, 32.5%, 82.5%, and 65%, respectively of these patients. There were non-significant differences between patients who recovered and those who died or had a residual illness in serum levels of IL-17A, IL-22 and the routine inflammatory markers of COVID-19. In conclusions, serum IL-17A and IL-22 levels were up-regulated in all patients with COVID-19 and MIS-C. Levels of serum IL-17A, IL-22 and the routine inflammatory markers of COVID-19 were not correlated with the disease outcome. Our conclusions are limited by the sample size.
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436.
na na na
(2024-01-31 23:53):
#paper doi: 10.1038/s41591-023-02371-y. Epub 2023 May 29.Pan-cancer T cell atlas links a cellular stress response state to immunotherapy resistance 。T细胞是肿瘤免疫中非常重要的存在,肿瘤细胞被发现之后,T细胞就会以指数方式增殖向效应T细胞和记忆T细胞分化,快速消灭外来病原体/肿瘤,但经历不断的抗原刺激和免疫抑制信号的干扰,一些T细胞也会进入疲软期,分化为功能丧失的状态,称为T细胞耗竭(TEX)。MD安德森癌症中心对T细胞状态的广泛多样性以及它们在复杂的肿瘤微环境中的关系和作用提供了更深入的了解,为理解癌症免疫治疗效果带来了新的视角。文章主要提出了一个新的概念:T细胞应激反应状态(T cell stress response state),是指当细胞面临不利环境或压力时触发的一系列变化,以保持细胞的稳态和适应环境。适度的应激刺激可以激活细胞的防御机制,促进修复和适应能力的提高。在肿瘤中,TSTR细胞可以被认为是一类“压力过大”的T细胞。但与TEX细胞的不同,T细胞是通过两条截然不同的途径分化为TEX细胞与TSTR细胞。新的T细胞类型的发现对指导之后的肿瘤免疫微环境分析很大的价值,值得对作者数据再次进行挖掘,例如得到新的细胞亚型signature。推荐给做肿瘤科研的小伙伴,值得一读。
Abstract:
Tumor-infiltrating T cells offer a promising avenue for cancer treatment, yet their states remain to be fully characterized. Here we present a single-cell atlas of T cells from 308,048 transcriptomes …
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Tumor-infiltrating T cells offer a promising avenue for cancer treatment, yet their states remain to be fully characterized. Here we present a single-cell atlas of T cells from 308,048 transcriptomes across 16 cancer types, uncovering previously undescribed T cell states and heterogeneous subpopulations of follicular helper, regulatory and proliferative T cells. We identified a unique stress response state, T, characterized by heat shock gene expression. T cells are detectable in situ in the tumor microenvironment across various cancer types, mostly within lymphocyte aggregates or potential tertiary lymphoid structures in tumor beds or surrounding tumor edges. T cell states/compositions correlated with genomic, pathological and clinical features in 375 patients from 23 cohorts, including 171 patients who received immune checkpoint blockade therapy. We also found significantly upregulated heat shock gene expression in intratumoral CD4/CD8 cells following immune checkpoint blockade treatment, particularly in nonresponsive tumors, suggesting a potential role of T cells in immunotherapy resistance. Our well-annotated T cell reference maps, web portal and automatic alignment/annotation tool could provide valuable resources for T cell therapy optimization and biomarker discovery.
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437.
林海onrush
(2024-01-31 23:47):
#paper, doi.org/10.1038/s41586-023-06747-5, Solving olympiad geometry without human demonstrations, 此文介绍了一种解决数学奥林匹克竞赛中复杂几何问题的创新方法。论文中提出的AlphaGeometry是一种结合神经语言模型和符号推理引擎的神经符号系统。它能够生成包括定理和证明在内的合成数据,有效克服了此领域训练数据的稀缺性。AlphaGeometry在解决难度较高的奥林匹克级别问题方面表现出色,其性能可与国际数学奥林匹克竞赛(IMO)金牌得主相媲美。它不仅能以人类可读格式合成证明,还发现了一个已知IMO定理的更通用版本。AlphaGeometry在自动定理证明领域取得了重要进展,展示了神经符号系统在解决复杂数学问题方面的潜力,为不依赖人类生成数据的人工智能研究提供了新方向。这一发展对数学和人工智能领域产生深远影响。
Abstract:
Proving mathematical theorems at the olympiad level represents a notable milestone in human-level automated reasoning, owing to their reputed difficulty among the world's best talents in pre-university mathematics. Current machine-learning …
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Proving mathematical theorems at the olympiad level represents a notable milestone in human-level automated reasoning, owing to their reputed difficulty among the world's best talents in pre-university mathematics. Current machine-learning approaches, however, are not applicable to most mathematical domains owing to the high cost of translating human proofs into machine-verifiable format. The problem is even worse for geometry because of its unique translation challenges, resulting in severe scarcity of training data. We propose AlphaGeometry, a theorem prover for Euclidean plane geometry that sidesteps the need for human demonstrations by synthesizing millions of theorems and proofs across different levels of complexity. AlphaGeometry is a neuro-symbolic system that uses a neural language model, trained from scratch on our large-scale synthetic data, to guide a symbolic deduction engine through infinite branching points in challenging problems. On a test set of 30 latest olympiad-level problems, AlphaGeometry solves 25, outperforming the previous best method that only solves ten problems and approaching the performance of an average International Mathematical Olympiad (IMO) gold medallist. Notably, AlphaGeometry produces human-readable proofs, solves all geometry problems in the IMO 2000 and 2015 under human expert evaluation and discovers a generalized version of a translated IMO theorem in 2004.
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438.
钟鸣
(2024-01-31 23:44):
#paper doi:10.1177/0146167218783195 The Cynical Genius Illusion: Exploring and Debunking Lay Beliefs About Cynicism and Competence
本文讨论的话题是“犬儒主义/愤世嫉俗/玩世不恭”,即那些精致利己、精明世俗、常持怀疑论且做预防性推理的人,与之对立的是非犬儒主义者/理想主义者,他们对陌生人的无私行为报以更少的动机揣度。通常,在艺术作品中愤世嫉俗者如福尔摩斯总被刻画成有能力、洞察力强、对环境的适应能力更强的人。为了确认“犬儒主义者是否拥有更强的能力(本文中泛指认知能力、学历、技能等具体技能)”,作者做了6个调查。前3个调查发现,世人的观念中,普遍认为犬儒主义者比非犬儒主义者能力更高,然而在后三个调查中,通过大规模的调查,作者发现真实情况与世人认识相反:非犬儒主义者的能力更强一些,绝大多数参与者预计愤世嫉俗的人在一系列认知任务和认知能力测试中比不愤世嫉俗的人表现得更好。作者分析认为,这可能是因为更高水平的教育和能力可能有助于个人首先发现并避免潜在的欺骗,从而减少负面社会经历的可能性,这反过来可能有助于对人性产生更积极的看法,此外,高水平的能力可能使个人能够正确识别其环境的“腐败”,并调整自己的愤世嫉俗程度以与之相匹配。最后,艺术作品中过于强调“愤世嫉俗的天才”也会强化公众的认知偏见。
Abstract:
Cynicism refers to a negative appraisal of human nature-a belief that self-interest is the ultimate motive guiding human behavior. We explored laypersons' beliefs about cynicism and competence and to what …
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Cynicism refers to a negative appraisal of human nature-a belief that self-interest is the ultimate motive guiding human behavior. We explored laypersons' beliefs about cynicism and competence and to what extent these beliefs correspond to reality. Four studies showed that laypeople tend to believe in cynical individuals' cognitive superiority. A further three studies based on the data of about 200,000 individuals from 30 countries debunked these lay beliefs as illusionary by revealing that cynical (vs. less cynical) individuals generally do worse on cognitive ability and academic competency tasks. Cross-cultural analyses showed that competent individuals held contingent attitudes and endorsed cynicism only if it was warranted in a given sociocultural environment. Less competent individuals embraced cynicism unconditionally, suggesting that-at low levels of competence-holding a cynical worldview might represent an adaptive default strategy to avoid the potential costs of falling prey to others' cunning.
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439.
小W
(2024-01-31 23:23):
#paper doi:doi.org/10.1038/s41586-023-06377-x A high-performance speech neuroprosthesis
本文介绍了脑机接口在将脑神经信号转化为文本语言的尝试。文章在患有延髓性肌萎缩侧索硬化症 (ALS)患者的大脑6v (entral premotor cortex)区域和44 (布洛卡区)使用四个微电极阵列检测神经活动信号,训练了一个循环神经网络 (RNN) 解码器,以在每 80 毫秒的时间步长预测当时说出每个音素的概率,将这些概率与语言模型相结合,神经活动信号以每分钟 62 个单词的速度被解码。在 50 个单词的数据集中实现了 9.1% 的单词错误率,125000 个单词的数据集的单词错误率为 23.8%。
同时布洛卡区作用在语言产生的高阶方面,但它似乎几乎不包含音素或单词的信息,即使在瘫痪多年后,患者仍存在音素发音的细节,说明仅从 6v 小区域检测的神经活动信号开发出以正常会话速度恢复瘫痪患者通信设备的可行性。
Abstract:
AbstractSpeech brain–computer interfaces (BCIs) have the potential to restore rapid communication to people with paralysis by decoding neural activity evoked by attempted speech into text1,2 or sound3,4. Early demonstrations, although …
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AbstractSpeech brain–computer interfaces (BCIs) have the potential to restore rapid communication to people with paralysis by decoding neural activity evoked by attempted speech into text1,2 or sound3,4. Early demonstrations, although promising, have not yet achieved accuracies sufficiently high for communication of unconstrained sentences from a large vocabulary1–7. Here we demonstrate a speech-to-text BCI that records spiking activity from intracortical microelectrode arrays. Enabled by these high-resolution recordings, our study participant—who can no longer speak intelligibly owing to amyotrophic lateral sclerosis—achieved a 9.1% word error rate on a 50-word vocabulary (2.7 times fewer errors than the previous state-of-the-art speech BCI2) and a 23.8% word error rate on a 125,000-word vocabulary (the first successful demonstration, to our knowledge, of large-vocabulary decoding). Our participant’s attempted speech was decoded at 62 words per minute, which is 3.4 times as fast as the previous record8 and begins to approach the speed of natural conversation (160 words per minute9). Finally, we highlight two aspects of the neural code for speech that are encouraging for speech BCIs: spatially intermixed tuning to speech articulators that makes accurate decoding possible from only a small region of cortex, and a detailed articulatory representation of phonemes that persists years after paralysis. These results show a feasible path forward for restoring rapid communication to people with paralysis who can no longer speak.
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440.
muton
(2024-01-31 23:04):
# paper:DOI: 10.1126/sciadv.abj4383 Emerged human-like facial expression representation in a deep convolutional neural network 最近的研究发现,经过训练以识别面部身份的深度卷积神经网络(DCNN)自发地学习了支持面部表情识别的特征,反之亦然。作者通过比较pretrain的VGG-Face,untrained VGG-Face以及VGG 16三个模型发现,只有pretrain的VGG-Face最后一层的1.25%的units表现出了和人类类似的面部表情识别以及表情混淆的特征。这些研究结果揭示了特定单元的面孔识别视觉经验对面孔表情知觉发展的必要性。
Abstract:
Recent studies found that the deep convolutional neural networks (DCNNs) trained to recognize facial identities spontaneously learned features that support facial expression recognition, and vice versa. Here, we showed that …
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Recent studies found that the deep convolutional neural networks (DCNNs) trained to recognize facial identities spontaneously learned features that support facial expression recognition, and vice versa. Here, we showed that the self-emerged expression-selective units in a VGG-Face trained for facial identification were tuned to distinct basic expressions and, importantly, exhibited hallmarks of human expression recognition (i.e., facial expression confusion and categorical perception). We then investigated whether the emergence of expression-selective units is attributed to either face-specific experience or domain-general processing by conducting the same analysis on a VGG-16 trained for object classification and an untrained VGG-Face without any visual experience, both having the identical architecture with the pretrained VGG-Face. Although similar expression-selective units were found in both DCNNs, they did not exhibit reliable human-like characteristics of facial expression perception. Together, these findings revealed the necessity of domain-specific visual experience of face identity for the development of facial expression perception, highlighting the contribution of nurture to form human-like facial expression perception.
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