Development of a Multiscale Atomistic Code to Investigate Self-Organized Pattern Formation Induced by Ion Irradiation

Various self-organized patterns including ripples and quantum dots can be induced by ion beam sputtering (IBS). For the past decades, the understanding of such phenomenon has been mainly relied on the Bradley-Harper theory that attributes the formation of self-organized patterns to the interplay between roughening by curvature dependence of erosion and smoothening by surface diffusion. Recently, the development of the crater function theory has overturned this erosion-based paradigm to a redistribution-based paradigm. The theory has proved that erosion is irrelevant and negligible in the pattern formation at low and intermediate incidence angles. Despite the success, there are still some questions open to discuss. The role of erosion for the ripple formation at glancing angles is still unclear. Furthermore, the current application of the crater function theory is limited in the linear regime. The applicability in the nonlinear regime is unknown. In this work, a hybrid MD/kMC (Molecular Dynamics/kinetic Monte Carlo) multiscale atomistic model is developed to elucidate these unknown issues. This model uses the crater functions, which are obtained by MD simulations, to model the prompt mass redistribution due to single-ion impacts. Defect migration, which is missing in previous models using crater functions, is treated by a kMC Arrhenius model. Using this model, a systematic study was performed for silicon bombarded by Ar+ions of various energies (100 eV, 250 eV, 500 eV, 700 eV and 1000 eV) at incidence angles of 0° to 80° with fluence up to 1018 ions/cm2 to cover both the linear and nonlinear regimes. The simulation results are in very good agreement with the experimental findings and the moment-description continuum theory in many features of surface evolution, namely, the phase diagram, wavelength dependence of ion energy and incidence angle, and the nonlinear evolution of surface roughness. The simulations elucidate that erosion plays the dominant role in the pattern formation at glancing angles. In the nonlinear regimes, the ripples first undergo coarsening and then reach saturation state. The surface roughness obeys the scaling theory and yields the growth exponent β=0.358, which is very close to the experimental finding. Ion irradiation with simultaneous sample rotation is also simulated, resulting in the formation of arrays of squared ordered dots. The patterns with sample rotation are found to be strongly correlated to the rotation speed and the pattern types formed without sample rotation

Morphological diversity of single neurons in molecularly defined cell types.

Dendritic and axonal morphology reflects the input and output of neurons and is a defining feature of neuronal types1,2, yet our knowledge of its diversity remains limited. Here, to systematically examine complete single-neuron morphologies on a brain-wide scale, we established a pipeline encompassing sparse labelling, whole-brain imaging, reconstruction, registration and analysis. We fully reconstructed 1,741 neurons from cortex, claustrum, thalamus, striatum and other brain regions in mice. We identified 11 major projection neuron types with distinct morphological features and corresponding transcriptomic identities. Extensive projectional diversity was found within each of these major types, on the basis of which some types were clustered into more refined subtypes. This diversity follows a set of generalizable principles that govern long-range axonal projections at different levels, including molecular correspondence, divergent or convergent projection, axon termination pattern, regional specificity, topography, and individual cell variability. Although clear concordance with transcriptomic profiles is evident at the level of major projection type, fine-grained morphological diversity often does not readily correlate with transcriptomic subtypes derived from unsupervised clustering, highlighting the need for single-cell cross-modality studies. Overall, our study demonstrates the crucial need for quantitative description of complete single-cell anatomy in cell-type classification, as single-cell morphological diversity reveals a plethora of ways in which different cell types and their individual members may contribute to the configuration and function of their respective circuits

Transforming Oriental classics into Western canon

As one of the greatest British translators and Sinologists of the 20th century, Arthur Waley (1889−1966) translated many Oriental classics and exerted a deep and profound impact on Western literary culture. His translations of classical Chinese poems won him the Queen’s Medal for Poetry in 1953 and were not only well received in the academic world but also highly praised by prominent contemporary poets such as Yeats, Pound, Woolf and Mervin. Indeed, his versions of Chinese poems were ranked highly as English poems in their own right and included in several popular anthologies and ESL textbooks. Furthermore, his English translations proved popular enough to be translated into various other Western languages and set to music. Both academic and commercial publishers competed for the right to publish his translations, which sold well and enjoyed high circulation in both university and public libraries. Thus, Western authors and scholars became accustomed to citing his translations, while poets looked to them as an important resource in their poetic compositions. In addition to the prevalence and canonization of Waley’s translations in the West, Waley’s translation is notable and significant when we contemplate the confluence between the East and West, the process of cultural globalization, and the role that literary translation played in this process.</jats:p

Automatic 3D Neuron Tracing from Optical Microscopy Images.

Neuron tracing is the process of reconstructing three-dimensional morphology of neurons from microscopy images. It is essential for delivering more comprehensive understanding of the relationship between neuronal structure and function, which is the fundamental to know how the brain works. However, currently neuron tracing remains a challenging task, due to the natural complexity of neuronal structure, inadequate available data and computational limitation. In recent years, many automatic neuron tracing methods have been developed in the research field, with limited success on specific issues. The lack of a robust neuron tracing method with more general applicability greatly restrains systematic characterisation and analysis on neuronal morphology. To address aforementioned challenges, we first establish a pipeline to generate more standard data, in which we specifically propose a novel approach for automatic refinement on semi-manual reconstruction. Following the pipeline, we manage to generate more than 1000 full morphology data. Second, based on the generated standard reconstruction, we conduct a systematic and quantitative analysis to identify the most critical obstacles in neuron tracing. Third, we propose a novel neuron tracing method by embedding occupancy learning with curve skeleton extraction, which tackles the major issues of weak and punctuated signal, as concluded from the previous analysis. We curated a large dataset to train and test the model. The experimental results show it exceeds other counterpart approaches in most terms of evaluation metrics. At last, we propose a novel learning model for automatic neuron tracing, which learns to directly extracts the skeleton from a raw image. It addresses the main issue of close but irrelevant signal, as concluded previously. We train and bench test it on the curated dataset, as well as a public dataset. Experiments show it achieves state-of-the-art performances in all cases

<b>Multi-dimensional characterization of cellular states in ovarian cancer reveals clinically relevant immunological subtypes and therapeutic vulnerabilities</b>

we depicted and characterized an immunophenotypic landscape of 3,099 OV samples and 80,044 cells based on a machine learning framework</p