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Clonally expanded CD8 T cells patrol the cerebrospinal fluid in Alzheimer's disease 期刊论文
NATURE, 2020, 577 (7790) : 399-+
作者:  Gate, David;  Saligrama, Naresha;  Leventhal, Olivia;  Yang, Andrew C.;  Unger, Michael S.;  Middeldorp, Jinte;  Chen, Kelly;  Lehallier, Benoit;  Channappa, Divya;  De Los Santos, Mark B.;  McBride, Alisha;  Pluvinage, John;  Elahi, Fanny;  Tam, Grace Kyin-Ye;  Kim, Yongha;  Greicius, Michael;  Wagner, Anthony D.;  Aigner, Ludwig;  Galasko, Douglas R.;  Davis, Mark M.;  Wyss-Coray, Tony
收藏  |  浏览/下载：6/0  |  提交时间：2020/07/03

Alzheimer'  s disease is an incurable neurodegenerative disorder in which neuroinflammation has a critical function(1). However, little is known about the contribution of the adaptive immune response in Alzheimer'  s disease(2). Here, using integrated analyses of multiple cohorts, we identify peripheral and central adaptive immune changes in Alzheimer'  s disease. First, we performed mass cytometry of peripheral blood mononuclear cells and discovered an immune signature of Alzheimer'  s disease that consists of increased numbers of CD8(+) T effector memory CD45RA(+) (T-EMRA) cells. In a second cohort, we found that CD8(+) T-EMRA cells were negatively associated with cognition. Furthermore, single-cell RNA sequencing revealed that T cell receptor (TCR) signalling was enhanced in these cells. Notably, by using several strategies of single-cell TCR sequencing in a third cohort, we discovered clonally expanded CD8(+) T-EMRA cells in the cerebrospinal fluid of patients with Alzheimer'  s disease. Finally, we used machine learning, cloning and peptide screens to demonstrate the specificity of clonally expanded TCRs in the cerebrospinal fluid of patients with Alzheimer'  s disease to two separate Epstein-Barr virus antigens. These results reveal an adaptive immune response in the blood and cerebrospinal fluid in Alzheimer'  s disease and provide evidence of clonal, antigen-experienced T cells patrolling the intrathecal space of brains affected by age-related neurodegeneration.


  
Accelerated discovery of CO2 electrocatalysts using active machine learning 期刊论文
NATURE, 2020, 581 (7807) : 178-+
作者:  Lan, Jun;  Ge, Jiwan;  Yu, Jinfang;  Shan, Sisi;  Zhou, Huan;  Fan, Shilong;  Zhang, Qi;  Shi, Xuanling;  Wang, Qisheng;  Zhang, Linqi;  Wang, Xinquan
收藏  |  浏览/下载：88/0  |  提交时间：2020/07/03

The rapid increase in global energy demand and the need to replace carbon dioxide (CO2)-emitting fossil fuels with renewable sources have driven interest in chemical storage of intermittent solar and wind energy(1,2). Particularly attractive is the electrochemical reduction of CO2 to chemical feedstocks, which uses both CO2 and renewable energy(3-8). Copper has been the predominant electrocatalyst for this reaction when aiming for more valuable multi-carbon products(9-16), and process improvements have been particularly notable when targeting ethylene. However, the energy efficiency and productivity (current density) achieved so far still fall below the values required to produce ethylene at cost-competitive prices. Here we describe Cu-Al electrocatalysts, identified using density functional theory calculations in combination with active machine learning, that efficiently reduce CO2 to ethylene with the highest Faradaic efficiency reported so far. This Faradaic efficiency of over 80 per cent (compared to about 66 per cent for pure Cu) is achieved at a current density of 400 milliamperes per square centimetre (at 1.5 volts versus a reversible hydrogen electrode) and a cathodic-side (half-cell) ethylene power conversion efficiency of 55 +/- 2 per cent at 150 milliamperes per square centimetre. We perform computational studies that suggest that the Cu-Al alloys provide multiple sites and surface orientations with near-optimal CO binding for both efficient and selective CO2 reduction(17). Furthermore, in situ X-ray absorption measurements reveal that Cu and Al enable a favourable Cu coordination environment that enhances C-C dimerization. These findings illustrate the value of computation and machine learning in guiding the experimental exploration of multi-metallic systems that go beyond the limitations of conventional single-metal electrocatalysts.


  
The proteome landscape of the kingdoms of life 期刊论文
NATURE, 2020
作者:  Arzi, Anat;  Rozenkrantz, Liron;  Gorodisky, Lior;  Rozenkrantz, Danit;  Holtzman, Yael;  Ravia, Aharon;  Bekinschtein, Tristan A.;  Galperin, Tatyana;  Krimchansky, Ben-Zion;  Cohen, Gal;  Oksamitni, Anna;  Aidinoff, Elena;  Sacher, Yaron;  Sobel, Noam
收藏  |  浏览/下载：14/0  |  提交时间：2020/07/03

Proteins carry out the vast majority of functions in all biological domains, but for technological reasons their large-scale investigation has lagged behind the study of genomes. Since the first essentially complete eukaryotic proteome was reported(1), advances in mass-spectrometry-based proteomics(2)have enabled increasingly comprehensive identification and quantification of the human proteome(3-6). However, there have been few comparisons across species(7,8), in stark contrast with genomics initiatives(9). Here we use an advanced proteomics workflow-in which the peptide separation step is performed by a microstructured and extremely reproducible chromatographic system-for the in-depth study of 100 taxonomically diverse organisms. With two million peptide and 340,000 stringent protein identifications obtained in a standardized manner, we double the number of proteins with solid experimental evidence known to the scientific community. The data also provide a large-scale case study for sequence-based machine learning, as we demonstrate by experimentally confirming the predicted properties of peptides fromBacteroides uniformis. Our results offer a comparative view of the functional organization of organisms across the entire evolutionary range. A remarkably high fraction of the total proteome mass in all kingdoms is dedicated to protein homeostasis and folding, highlighting the biological challenge of maintaining protein structure in all branches of life. Likewise, a universally high fraction is involved in supplying energy resources, although these pathways range from photosynthesis through iron sulfur metabolism to carbohydrate metabolism. Generally, however, proteins and proteomes are remarkably diverse between organisms, and they can readily be explored and functionally compared at www.proteomesoflife.org.


  
Integrating genomic features for non-invasive early lung cancer detection 期刊论文
NATURE, 2020, 580 (7802) : 245-+
作者:  Wang, Qinyang;  Wang, Yupeng;  Ding, Jingjin;  Wang, Chunhong;  Zhou, Xuehan;  Gao, Wenqing;  Huang, Huanwei;  Shao, Feng;  Liu, Zhibo
收藏  |  浏览/下载：15/0  |  提交时间：2020/07/03

Circulating tumour DNA in blood is analysed to identify genomic features that distinguish early-stage lung cancer patients from risk-matched controls, and these are integrated into a machine-learning method for blood-based lung cancer screening.


Radiologic screening of high-risk adults reduces lung-cancer-related mortality(1,2)  however, a small minority of eligible individuals undergo such screening in the United States(3,4). The availability of blood-based tests could increase screening uptake. Here we introduce improvements to cancer personalized profiling by deep sequencing (CAPP-Seq)(5), a method for the analysis of circulating tumour DNA (ctDNA), to better facilitate screening applications. We show that, although levels are very low in early-stage lung cancers, ctDNA is present prior to treatment in most patients and its presence is strongly prognostic. We also find that the majority of somatic mutations in the cell-free DNA (cfDNA) of patients with lung cancer and of risk-matched controls reflect clonal haematopoiesis and are non-recurrent. Compared with tumour-derived mutations, clonal haematopoiesis mutations occur on longer cfDNA fragments and lack mutational signatures that are associated with tobacco smoking. Integrating these findings with other molecular features, we develop and prospectively validate a machine-learning method termed '  lung cancer likelihood in plasma'  (Lung-CLiP), which can robustly discriminate early-stage lung cancer patients from risk-matched controls. This approach achieves performance similar to that of tumour-informed ctDNA detection and enables tuning of assay specificity in order to facilitate distinct clinical applications. Our findings establish the potential of cfDNA for lung cancer screening and highlight the importance of risk-matching cases and controls in cfDNA-based screening studies.


  
Microbiome analyses of blood and tissues suggest cancer diagnostic approach 期刊论文
NATURE, 2020, 579 (7800) : 567-+
作者:  Shao, Zhengping;  Flynn, Ryan A.;  Crowe, Jennifer L.;  Zhu, Yimeng;  Liang, Jialiang;  Jiang, Wenxia;  Aryan, Fardin;  Aoude, Patrick;  Bertozzi, Carolyn R.;  Estes, Verna M.;  Lee, Brian J.;  Bhagat, Govind;  Zha, Shan;  Calo, Eliezer
收藏  |  浏览/下载：54/0  |  提交时间：2020/07/03

Microbial nucleic acids are detected in samples of tissues and blood from more than 10,000 patients with cancer, and machine learning is used to show that these can be used to discriminate between and among different types of cancer, suggesting a new microbiome-based diagnostic approach.


Systematic characterization of the cancer microbiome provides the opportunity to develop techniques that exploit non-human, microorganism-derived molecules in the diagnosis of a major human disease. Following recent demonstrations that some types of cancer show substantial microbial contributions(1-10), we re-examined whole-genome and whole-transcriptome sequencing studies in The Cancer Genome Atlas(11) (TCGA) of 33 types of cancer from treatment-naive patients (a total of 18,116 samples) for microbial reads, and found unique microbial signatures in tissue and blood within and between most major types of cancer. These TCGA blood signatures remained predictive when applied to patients with stage Ia-IIc cancer and cancers lacking any genomic alterations currently measured on two commercial-grade cell-free tumour DNA platforms, despite the use of very stringent decontamination analyses that discarded up to 92.3% of total sequence data. In addition, we could discriminate among samples from healthy, cancer-free individuals (n = 69) and those from patients with multiple types of cancer (prostate, lung, and melanoma  100 samples in total) solely using plasma-derived, cell-free microbial nucleic acids. This potential microbiome-based oncology diagnostic tool warrants further exploration.


  
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：23/0  |  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).


  
Supercomputer scours fossil record for Earth's hidden extinctions 期刊论文
NATURE, 2020, 577 (7791) : 458-459
作者:  Callaway, Ewen
收藏  |  浏览/下载：6/0  |  提交时间：2020/07/03

Palaeontologists have charted 300 million years of Earth'  s history in breathtaking detail.


Palaeontologists have charted 300 million years of Earth'  s history in breathtaking detail.


  
ARTIFICIAL INTELLIGENCE A social spin on language analysis 期刊论文
NATURE, 2017, 545 (7653) : 166-167
作者:  Rose, Carolyn Penstein
收藏  |  浏览/下载：0/0  |  提交时间：2019/11/27