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Nearest neighbours reveal fast and slow components of motor learning 期刊论文
NATURE, 2020, 577 (7791) : 526-+
作者:  Kollmorgen, Sepp;  Hahnloser, Richard H. R.;  Mante, Valerio
收藏  |  浏览/下载：23/0  |  提交时间：2020/07/03

A new method for analysing change in high-dimensional data is based on nearest-neighbour statistics and is applied here to song dynamics during vocal learning in zebra finches, but could potentially be applied to other biological and artificial behaviours.


Changes in behaviour resulting from environmental influences, development and learning(1-5) are commonly quantified on the basis of a few hand-picked features(2-4,6,7) (for example, the average pitch of acoustic vocalizations(3)), assuming discrete classes of behaviours (such as distinct vocal syllables)(2,3,8-10). However, such methods generalize poorly across different behaviours and model systems and may miss important components of change. Here we present a more-general account of behavioural change that is based on nearest-neighbour statistics(11-13), and apply it to song development in a songbird, the zebra finch(3). First, we introduce the concept of '  repertoire dating'  , whereby each rendition of a behaviour (for example, each vocalization) is assigned a repertoire time, reflecting when similar renditions were typical in the behavioural repertoire. Repertoire time isolates the components of vocal variability that are congruent with long-term changes due to vocal learning and development, and stratifies the behavioural repertoire into '  regressions'  , '  anticipations'  and '  typical renditions'  . Second, we obtain a holistic, yet low-dimensional, description of vocal change in terms of a stratified '  behavioural trajectory'  , revealing numerous previously unrecognized components of behavioural change on fast and slow timescales, as well as distinct patterns of overnight consolidation(1,2,4,14,15) across the behavioral repertoire. We find that diurnal changes in regressions undergo only weak consolidation, whereas anticipations and typical renditions consolidate fully. Because of its generality, our nonparametric description of how behaviour evolves relative to itself-rather than to a potentially arbitrary, experimenter-defined goal(2,3,14,16)-appears well suited for comparing learning and change across behaviours and species(17,18), as well as biological and artificial systems(5).


  
The single-cell pathology landscape of breast cancer 期刊论文
NATURE, 2020, 578 (7796) : 615-+
作者:  Fouda, Abdelrahman Y.
收藏  |  浏览/下载：40/0  |  提交时间：2020/07/03

Single-cell analyses have revealed extensive heterogeneity between and within human tumours(1-4), but complex single-cell phenotypes and their spatial context are not at present reflected in the histological stratification that is the foundation of many clinical decisions. Here we use imaging mass cytometry(5) to simultaneously quantify 35 biomarkers, resulting in 720 high-dimensional pathology images of tumour tissue from 352 patients with breast cancer, with long-term survival data available for 281 patients. Spatially resolved, single-cell analysis identified the phenotypes of tumour and stromal single cells, their organization and their heterogeneity, and enabled the cellular architecture of breast cancer tissue to be characterized on the basis of cellular composition and tissue organization. Our analysis reveals multicellular features of the tumour microenvironment and novel subgroups of breast cancer that are associated with distinct clinical outcomes. Thus, spatially resolved, single-cell analysis can characterize intratumour phenotypic heterogeneity in a disease-relevant manner, with the potential to inform patient-specific diagnosis.


A single-cell, spatially resolved analysis of breast cancer demonstrates the heterogeneity of tumour and stroma tissue and provides a more-detailed method of patient classification than the current histology-based system.


  
Classification with a disordered dopantatom network in silicon 期刊论文
NATURE, 2020, 577 (7790) : 341-+
作者:  Vagnozzi, Ronald J.;  Maillet, Marjorie;  Sargent, Michelle A.;  Khalil, Hadi;  Johansen, Anne Katrine Z.;  Schwanekamp, Jennifer A.;  York, Allen J.;  Huang, Vincent;  Nahrendorf, Matthias;  Sadayappan, Sakthivel;  Molkentin, Jeffery D.
收藏  |  浏览/下载：38/0  |  提交时间：2020/07/03

Classification is an important task at which both biological and artificial neural networks excel(1,2). In machine learning, nonlinear projection into a high-dimensional feature space can make data linearly separable(3,4), simplifying the classification of complex features. Such nonlinear projections are computationally expensive in conventional computers. A promising approach is to exploit physical materials systems that perform this nonlinear projection intrinsically, because of their high computational density(5), inherent parallelism and energy efficiency(6,7). However, existing approaches either rely on the systems'  time dynamics, which requires sequential data processing and therefore hinders parallel computation(5,6,8), or employ large materials systems that are difficult to scale up(7). Here we use a parallel, nanoscale approach inspired by filters in the brain(1) and artificial neural networks(2) to perform nonlinear classification and feature extraction. We exploit the nonlinearity of hopping conduction(9-11) through an electrically tunable network of boron dopant atoms in silicon, reconfiguring the network through artificial evolution to realize different computational functions. We first solve the canonical two-input binary classification problem, realizing all Boolean logic gates(12) up to room temperature, demonstrating nonlinear classification with the nanomaterial system. We then evolve our dopant network to realize feature filters(2) that can perform four-input binary classification on the Modified National Institute of Standards and Technology handwritten digit database. Implementation of our material-based filters substantially improves the classification accuracy over that of a linear classifier directly applied to the original data(13). Our results establish a paradigm of silicon-based electronics for smallfootprint and energy-efficient computation(14).


  
Seasonal precipitation prediction via data-adaptive principal component regression 期刊论文
INTERNATIONAL JOURNAL OF CLIMATOLOGY, 2017, 37
作者:  Kim, Joonpyo;  Oh, Hee-Seok;  Lim, Yaeji;  Kang, Hyun-Suk
收藏  |  浏览/下载：13/0  |  提交时间：2019/04/09
data-adaptive principal component analysis  high-dimensional data  precipitation  prediction  principal component analysis  regularized regression  skewed data