Graphene is the nature's thinnest elastic membrane, with exceptional mechanical and electrical properties. We report the direct observation and creation of one-dimensional (1D) and 2D periodic ripples in suspended graphene sheets, using spontaneously and thermally induced longitudinal strains on patterned substrates, with control over their orientations and wavelengths.We also provide the first measurement of graphene's thermal expansion coefficient, which is anomalously large and negative, ~ -7x10 -6 K -1 at 300K. Our work enables novel strain-based engineering of graphene devices.

The most critical attribute of human language is its unbounded combinatorial nature: smaller elements can be combined into larger structures based on a grammatical system, resulting in a hierarchy of linguistic units, e.g., words, phrases, and sentences. Mentally parsing and representing such structures, however, poses challenges for speech comprehension. In speech, hierarchical linguistic structures do not have boundaries clearly defined by acoustic cues and must therefore be internally and incrementally constructed during comprehension. Here we demonstrate that during listening to connected speech, cortical activity of different time scales concurrently tracks the time course of abstract linguistic structures at different hierarchical levels, e.g. words, phrases, and sentences. Critically, the neural tracking of hierarchical linguistic structures is dissociated from the encoding of acoustic cues as well as from the predictability of incoming words. The results demonstrate that a hierarchy of neural processing timescales underlies grammar-based internal construction of hierarchical linguistic structure.

Recent work has made significant progress in improving spatial resolution for pixelwise labeling with Fully Convolutional Network (FCN) framework by employing Dilated/Atrous convolution, utilizing multi-scale features and refining boundaries. In this paper, we explore the impact of global contextual information in semantic segmentation by introducing the Context Encoding Module, which captures the semantic context of scenes and selectively highlights class-dependent featuremaps. The proposed Context Encoding Module significantly improves semantic segmentation results with only marginal extra computation cost over FCN. Our approach has achieved new state-of-theart results 51.7% mIoU on PASCAL-Context, 85.9% mIoU on PASCAL VOC 2012. Our single model achieves a final score of 0.5567 on ADE20K test set, which surpasses the winning entry of COCO-Place Challenge 2017. In addition, we also explore how the Context Encoding Module can improve the feature representation of relatively shallow networks for the image classification on CIFAR-10 dataset. Our 14 layer network has achieved an error rate of 3.45%, which is comparable with state-of-the-art approaches with over 10× more layers. The source code for the complete system are publicly available 1 .

The process of epithelial-to-mesenchymal transition (EMT) is an essential type of cellular plasticity associated with a change from epithelial cells that function as a barrier consisting of a sheet of tightly connected cells to cells with properties of mesenchyme that are not attached to their neighbors and are highly motile. This phenotypic change occurs during development and also contributes to pathological processes, such as cancer progression. The molecular mechanisms controlling the switch between the fully epithelial and fully mesenchymal phenotypes and cells that have characteristics of both (partial EMT) are controversial, and multiple theoretical models have been proposed. To test these theoretical models, we systematically measured the changes in the abundance of proteins, mRNAs, and microRNAs (miRNAs) that represent the core regulators of EMT induced by transforming growth factor-β1 (TGF-β1) in the human breast epithelial cell line MCF10A at the population and single-cell levels. We provide experimental confirmation for a model of cascading switches in phenotypes associated with TGF-β1-induced EMT of MCF10A cells that involves two double-negative feedback loops: one between the transcription factor SNAIL1 and the miR-34 family and another between the transcription factor ZEB1 and the miR-200 family. Furthermore, our data showed that whereas the transition from epithelial to partial EMT was reversible for MCF10A cells, the transition from partial EMT to mesenchymal was mostly irreversible at high concentrations of TGF-β1.

The exact role of a defect structure on transition metal compounds for electrocatalytic oxygen evolution reaction (OER), which is a very dynamic process, remains unclear. Studying the structure–activity relationship of defective electrocatalysts under operando conditions is crucial for understanding their intrinsic reaction mechanism and dynamic behavior of defect sites. Co3O4 with rich oxygen vacancy (VO) has been reported to efficiently catalyze OER. Herein, we constructed pure spinel Co3O4 and VO-rich Co3O4 as catalyst models to study the defect mechanism and investigate the dynamic behavior of defect sites during the electrocatalytic OER process by various operando characterizations. Operando electrochemical impedance spectroscopy (EIS) and cyclic voltammetry (CV) implied that the VO could facilitate the pre-oxidation of the low-valence Co (Co2+, part of which was induced by the VO to balance the charge) at a relatively lower applied potential. This observation confirmed that the VO could initialize the surface reconstruction of VO–Co3O4 prior to the occurrence of the OER process. The quasi-operando X-ray photoelectron spectroscopy (XPS) and operando X-ray absorption fine structure (XAFS) results further demonstrated the oxygen vacancies were filled with OH• first for VO–Co3O4 and facilitated pre-oxidation of low-valence Co and promoted reconstruction/deprotonation of intermediate Co–OOH•. This work provides insight into the defect mechanism in Co3O4 for OER in a dynamic way by observing the surface dynamic evolution process of defective electrocatalysts and identifying the real active sites during the electrocatalysis process. The current finding would motivate the community to focus more on the dynamic behavior of defect electrocatalysts.

A piezoresistive sensor based on ultralight and superelastic aerogel is reported to fabricate MXene/reduced graphene oxide (MX/rGO) hybrid 3D structures and utilize their pressure-sensitive characteristics. The MX/rGO aerogel not only combines the rGO's large specific surface area and the MXene's (TiC T ) high conductivity but also exhibits rich porous structure, which leads to performance better than that of single-component rGO or MXene in terms of the pressure sensor. The large nanosheets of rGO can prevent the poor oxidization of MXene by wrapping MXene inside the aerogel. More importantly, the piezoresistive sensor based on the MX/rGO aerogel shows extremely high sensitivity (22.56 kPa), fast response time (<200 ms), and good stability over 10 000 cycles. The piezoresistive sensor based on the MX/rGO hybrid 3D aerogel can easily capture the signal below 10 Pa, thus clearly testing the pulse of an adult at random. Based on its superior performance, it also demonstrates potential applications in measuring pressure distribution, distinguishing subtle strain, and monitoring healthy activity.

In order to engineer a band gap into graphene, covalent bond-forming reactions can be used to change the hybridization of the graphitic atoms from sp 2 to sp 3 , thereby modifying the conjugation length of the delocalized carbon lattice; similar side-wall chemistry has been shown to introduce a band gap into metallic single-walled carbon nanotubes. Here we demonstrate that the application of such covalent bond-forming chemistry modifies the periodicity of the graphene network thereby introducing a band gap (∼0.4 eV), which is observable in the angle-resolved photoelectron spectroscopy of aryl-functionalized graphene. We further show that the chemically-induced changes can be detected by Raman spectroscopy; the in-plane vibrations of the conjugated π-bonds exhibit characteristic Raman spectra and we find that the changes in D, G, and 2D-bands as a result of chemical functionalization of the graphene basal plane are quite distinct from that due to localized, physical defects in sp 2 -conjugated carbon.

In decision from experience, the source of probability information affects how probability is distorted in the decision task. Understanding how and why probability is distorted is a key issue in understanding the peculiar character of experience-based decision. We consider how probability information is used not just in decision-making but also in a wide variety of cognitive, perceptual, and motor tasks. Very similar patterns of distortion of probability/frequency information have been found in visual frequency estimation, frequency estimation based on memory, signal detection theory, and in the use of probability information in decision-making under risk and uncertainty. We show that distortion of probability in all cases is well captured as linear transformations of the log odds of frequency and/or probability, a model with a slope parameter, and an intercept parameter. We then consider how task and experience influence these two parameters and the resulting distortion of probability. We review how the probability distortions change in systematic ways with task and report three experiments on frequency distortion where the distortions change systematically in the same task. We found that the slope of frequency distortions decreases with the sample size, which is echoed by findings in decision from experience. We review previous models of the representation of uncertainty and find that none can account for the empirical findings.