In the past few years, 3D electron microscopy (3DEM) has undergone a revolution in instrumentation and methodology. One of the central players in this wide-reaching change is the continuous development of image processing software. Here we present Scipion, a software framework for integrating several 3DEM software packages through a workflow-based approach. Scipion allows the execution of reusable, standardized, traceable and reproducible image-processing protocols. These protocols incorporate tools from different programs while providing full interoperability among them. Scipion is an open-source project that can be downloaded from http://scipion.cnb.csic.es.

Biophysical modeling of neuronal networks helps to integrate and interpret rapidly growing and disparate experimental datasets at multiple scales. The NetPyNE tool (www.netpyne.org) provides both programmatic and graphical interfaces to develop data-driven multiscale network models in NEURON. NetPyNE clearly separates model parameters from implementation code. Users provide specifications at a high level via a standardized declarative language, for example connectivity rules, to create millions of cell-to-cell connections. NetPyNE then enables users to generate the NEURON network, run efficiently parallelized simulations, optimize and explore network parameters through automated batch runs, and use built-in functions for visualization and analysis – connectivity matrices, voltage traces, spike raster plots, local field potentials, and information theoretic measures. NetPyNE also facilitates model sharing by exporting and importing standardized formats (NeuroML and SONATA). NetPyNE is already being used to teach computational neuroscience students and by modelers to investigate brain regions and phenomena.

Highlights d Open Source Brain: an online resource of standardized models of neurons and circuits d Automated 3D visualization, analysis, and simulation of models through the browser d Open source infrastructure and tools for collaborative model development and testing d Accessible, transparent, up-to-date models from different brain regions

5Biophysical modeling of neuronal networks helps to integrate and interpret rapidly growing and 6 disparate experimental datasets at multiple scales. The NetPyNE tool (www.netpyne.org) provides 7 both programmatic and graphical interfaces to develop data-driven multiscale network models in 8 NEURON. NetPyNE clearly separates model parameters from implementation code. Users provide 9 specifications at a high level via a standardized declarative language, e.g., a connectivity rule, instead 10 of tens of loops to create millions of cell-to-cell connections. Users can then generate the NEURON 11 network, run efficiently parallelized simulations, optimize and explore network parameters through 12 automated batch runs, and use built-in functions for visualization and analysis -connectivity matrices, 13 voltage traces, raster plots, local field potentials, and information theoretic measures. NetPyNE also 14 facilitates model sharing by exporting and importing using NeuroML and SONATA standardized 15 formats. NetPyNE is already being used to teach computational neuroscience students and by modelers 16 to investigate different brain regions and phenomena. 17 1 Introduction 18The worldwide upsurge of neuroscience research through the BRAIN Initiative, Human Brain Project, and 19 other efforts is yielding unprecedented levels of experimental findings from many different species, brain 20 regions, scales and techniques. As highlighted in the BRAIN Initiative 2025 report, 1 these initiatives 21 require computational tools to consolidate and interpret the data, and translate isolated findings into an 22 understanding of brain function. Biophysically-detailed multiscale modeling (MSM) provides a unique 23 method for integrating, organizing and bridging these many types of data. For example, data coming from 24 brain slices must be compared and consolidated with in vivo data. These data domains cannot be 25 compared directly, but can be potentially compared through simulations that permit one to switch readily 26 back-and-forth between slice-simulation and in vivo simulation. Furthermore, these multiscale models 27 permit one to develop hypotheses about how biological mechanisms underlie brain function. The MSM 28 approach is essential to understand how subcellular, cellular and circuit-level components of complex neural 29 systems interact to yield neural function and behavior. [2][3][4] It also provides the bridge to more compact 30 theoretical domains, such as low-dimensional dynamics, analytic modeling and information theory. 5-7 31 NEURON is the leading simulator in the domain of multiscale neuronal modeling. 8 It has 648 models 32 available via ModelDB, 9 and over 2,000 NEURON-based publications 33 (neuron.yale.edu/neuron/publications/neuron-bibliography). However, building data-driven large-scale 34 networks and running parallel simulations in NEURON is technically challenging, 10 requiring integration of 35 custom frameworks needed to build and organize complex model components across multiple scales. Other 36...

Image formation in bright field electron microscopy can be described with the help of the contrast transfer function (CTF). In this work the authors describe the “CTF Estimation Challenge”, called by the Madrid Instruct Image Processing Center (I2PC) in collaboration with the National Center for Macromolecular Imaging (NCMI) at Houston. Correcting for the effects of the CTF requires accurate knowledge of the CTF parameters, but these have often been difficult to determine. In this challenge, researchers have had the opportunity to test their ability in estimating some of the key parameters of the electron microscope CTF on a large micrograph data set produced by well-known laboratories on a wide set of experimental conditions. This work presents the first analysis of the results of the CTF Estimation Challenge, including an assessment of the performance of the different software packages under different conditions, so as to identify those areas of research where further developments would be desirable in order to achieve high-resolution structural information.