Transmission electron microscopy is a powerful imaging tool that has found broad application in materials science, nanoscience and biology. With the introduction of aberration-corrected electron lenses, both the spatial resolution and the image quality in transmission electron microscopy have been significantly improved and resolution below 0.5 ångströms has been demonstrated. To reveal the three-dimensional (3D) structure of thin samples, electron tomography is the method of choice, with cubic-nanometre resolution currently achievable. Discrete tomography has recently been used to generate a 3D atomic reconstruction of a silver nanoparticle two to three nanometres in diameter, but this statistical method assumes prior knowledge of the particle's lattice structure and requires that the atoms fit rigidly on that lattice. Here we report the experimental demonstration of a general electron tomography method that achieves atomic-scale resolution without initial assumptions about the sample structure. By combining a novel projection alignment and tomographic reconstruction method with scanning transmission electron microscopy, we have determined the 3D structure of an approximately ten-nanometre gold nanoparticle at 2.4-ångström resolution. Although we cannot definitively locate all of the atoms inside the nanoparticle, individual atoms are observed in some regions of the particle and several grains are identified in three dimensions. The 3D surface morphology and internal lattice structure revealed are consistent with a distorted icosahedral multiply twinned particle. We anticipate that this general method can be applied not only to determine the 3D structure of nanomaterials at atomic-scale resolution, but also to improve the spatial resolution and image quality in other tomography fields.

Dislocations and their interactions strongly influence many material properties, ranging from the strength of metals and alloys to the efficiency of light-emitting diodes and laser diodes. Several experimental methods can be used to visualize dislocations. Transmission electron microscopy (TEM) has long been used to image dislocations in materials, and high-resolution electron microscopy can reveal dislocation core structures in high detail, particularly in annular dark-field mode. A TEM image, however, represents a two-dimensional projection of a three-dimensional (3D) object (although stereo TEM provides limited information about 3D dislocations). X-ray topography can image dislocations in three dimensions, but with reduced resolution. Using weak-beam dark-field TEM and scanning TEM, electron tomography has been used to image 3D dislocations at a resolution of about five nanometres (refs 15, 16). Atom probe tomography can offer higher-resolution 3D characterization of dislocations, but requires needle-shaped samples and can detect only about 60 per cent of the atoms in a sample. Here we report 3D imaging of dislocations in materials at atomic resolution by electron tomography. By applying 3D Fourier filtering together with equal-slope tomographic reconstruction, we observe nearly all the atoms in a multiply twinned platinum nanoparticle. We observed atomic steps at 3D twin boundaries and imaged the 3D core structure of edge and screw dislocations at atomic resolution. These dislocations and the atomic steps at the twin boundaries, which appear to be stress-relief mechanisms, are not visible in conventional two-dimensional projections. The ability to image 3D disordered structures such as dislocations at atomic resolution is expected to find applications in materials science, nanoscience, solid-state physics and chemistry.

The fruit fly can evaluate its energy state and decide whether to pursue food-related cues. Here, we reveal that the mushroom body (MB) integrates hunger and satiety signals to control food-seeking behavior. We have discovered five pathways in the MB essential for hungry flies to locate and approach food. Blocking the MB-intrinsic Kenyon cells (KCs) and the MB output neurons (MBONs) in these pathways impairs food-seeking behavior. Starvation bi-directionally modulates MBON responses to a food odor, suggesting that hunger and satiety controls occur at the KC-to-MBON synapses. These controls are mediated by six types of dopaminergic neurons (DANs). By manipulating these DANs, we could inhibit food-seeking behavior in hungry flies or promote food seeking in fed flies. Finally, we show that the DANs potentially receive multiple inputs of hunger and satiety signals. This work demonstrates an information-rich central circuit in the fly brain that controls hunger-driven food-seeking behavior.

Coherent diffraction imaging (CDI) is high-resolution lensless microscopy that has been applied to image a wide range of specimens using synchrotron radiation, X-ray free-electron lasers, high harmonic generation, soft X-ray lasers and electrons. Despite recent rapid advances, it remains a challenge to reconstruct fine features in weakly scattering objects such as biological specimens from noisy data. Here an effective iterative algorithm, termed oversampling smoothness (OSS), for phase retrieval of noisy diffraction intensities is presented. OSS exploits the correlation information among the pixels or voxels in the region outside of a support in real space. By properly applying spatial frequency filters to the pixels or voxels outside the support at different stages of the iterative process ( a smoothness constraint), OSS finds a balance between the hybrid input-output (HIO) and error reduction (ER) algorithms to search for a global minimum in solution space, while reducing the oscillations in the reconstruction. Both numerical simulations with Poisson noise and experimental data from a biological cell indicate that OSS consistently outperforms the HIO, ER-HIO and noise robust (NR)-HIO algorithms at all noise levels in terms of accuracy and consistency of the reconstructions. It is expected that OSS will find application in the rapidly growing CDI field, as well as other disciplines where phase retrieval from noisy Fourier magnitudes is needed. The (The MathWorks Inc., Natick, MA, USA) source code of the OSS algorithm is freely available from http://www.physics.ucla.edu/research/imaging.