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SUMMARYDevelopment of the palate in vertebrates involves cranial neural crest migration, convergence of facial prominences and extension of the cartilaginous framework. Dysregulation of palatogenesis results in orofacial clefts, which represent the most common structural birth defects. Detailed analysis of zebrafish palatogenesis revealed distinct mechanisms of palatal morphogenesis: extension, proliferation and integration. We show that wnt9a is required for palatal extension, wherein the chondrocytes form a proliferative front, undergo morphological change and intercalate to form the ethmoid plate. Meanwhile, irf6 is required specifically for integration of facial prominences along a V-shaped seam. This work presents a mechanistic analysis of palate morphogenesis in a clinically relevant context.

Neurodata Without Borders: Neurophysiology (NWB:N) is a data standard for neurophysiology, providing neuroscientists with a common standard to share, archive, use, and build common analysis tools for neurophysiology data. With NWB:N version 2.0 (NWB:N 2.0) we made significant advances towards creating a usable standard, software ecosystem, and vibrant community for standardizing neurophysiology data. In this manuscript we focus in particular on the NWB:N data standard schema and present advances towards creating an accessible data standard for neurophysiology. IntroductionMotivation: Brain function is produced by the coordinated activity of multiple neuronal types that are widely distributed across many brain regions. Neuronal signals are acquired using extra-and intracellular recordings, and increasingly optical imaging, during sensory, motor, and cognitive tasks. Neurophysiology research generates large, complex and heterogeneous datasets at terabyte scale. The data size and complexity is expected to continue to grow with the increasing sophistication of experimental apparatuses. Lack of standards for neurophysiology data and related metadata is the single greatest impediment to fully extracting return-on-investment from neurophysiology experiments, impeding interchange and reuse of data and reproduction of derived conclusions. This gap motivated the launch of the Neurodata Without Borders : Neurophysiology (NWB:N) data standards project. The goal of NWB:N is to develop a standardized format and methods for neurophysiology data and metadata.Background: The first NWB:N 1.0.x standard was the result of a 1 year pilot project in 2015 12 . As part of this pilot, neurophysiologists and software developers met during two hackathons to create a common data format for recordings and metadata of cellular electro-and optical physiology experiments (Fig. 1, top). Despite the important advances that NWB:N 1.0 made towards creating a neurophysiology data standard, the standard was not easily accessible to users. To enhance broad adoption, a sustainable software and community strategy and easy-to-use, high-level application programming interfaces (APIs) were desperately needed. Here we describe NWB:N 2.0, a modern ecosystem for data standardization and accessible data standard for neurophysiology.A Brief History of NWB:N 2.0: The development of the second version of NWB:N began in Janurary 2017 with the start of the Kavli funded NWB4HPC project. The goal was to develop infrastructure and algorithms to enable data-driven discovery and dissemination on high-performance computing systems for the BRAIN Initiative (Fig. 1, bottom). One main goal of the project was to develop the next version of NWB:N to enhance its adoption, with an initial focus on high-level APIs for read, write, and extension of the original NWB:N 1.0.x standard. This standard represented a critical first step toward a unified framework for neural data, but it became clear that in order to achieve these goals we needed an advanced software architecture, a well...

Neuroprostheses have the potential to restore communication to people who cannot speak or type due to paralysis. However, it is unclear if silent attempts to speak can be used to control a communication neuroprosthesis. Here, we translated direct cortical signals in a clinical-trial participant (ClinicalTrials.gov; NCT03698149) with severe limb and vocal-tract paralysis into single letters to spell out full sentences in real time. We used deep-learning and language-modeling techniques to decode letter sequences as the participant attempted to silently spell using code words that represented the 26 English letters (e.g. “alpha” for “a”). We leveraged broad electrode coverage beyond speech-motor cortex to include supplemental control signals from hand cortex and complementary information from low- and high-frequency signal components to improve decoding accuracy. We decoded sentences using words from a 1,152-word vocabulary at a median character error rate of 6.13% and speed of 29.4 characters per minute. In offline simulations, we showed that our approach generalized to large vocabularies containing over 9,000 words (median character error rate of 8.23%). These results illustrate the clinical viability of a silently controlled speech neuroprosthesis to generate sentences from a large vocabulary through a spelling-based approach, complementing previous demonstrations of direct full-word decoding.