Ultrastructural differences impact cilia shape and external exposure across cell classes in the visual cortex.

A primary cilium is a membrane-bound extension from the cell surface that contains receptors for perceiving and transmitting signals that modulate cell state and activity. Primary cilia in the brain are less accessible than cilia on cultured cells or epithelial tissues because in the brain they protrude into a deep, dense network of glial and neuronal processes. Here, we investigated cilia frequency, internal structure, shape, and position in large, high-resolution transmission electron microscopy volumes of mouse primary visual cortex.

Multi-layered maps of neuropil with segmentation-guided contrastive learning.

Maps of the nervous system that identify individual cells along with their type, subcellular components and connectivity have the potential to elucidate fundamental organizational principles of neural circuits. Nanometer-resolution imaging of brain tissue provides the necessary raw data, but inferring cellular and subcellular annotation layers is challenging. We present segmentation-guided contrastive learning of representations (SegCLR), a self-supervised machine learning technique that produces representations of cells directly from 3D imagery and segmentations.

Nanometer-scale views of visual cortex reveal anatomical features of primary cilia poised to detect synaptic spillover.

A primary cilium is a thin membrane-bound extension off a cell surface that contains receptors for perceiving and transmitting signals that modulate cell state and activity. While many cell types have a primary cilium, little is known about primary cilia in the brain, where they are less accessible than cilia on cultured cells or epithelial tissues and protrude from cell bodies into a deep, dense network of glial and neuronal processes. Here, we investigated cilia frequency, internal structure, shape, and position in large, high-resolution transmission electron microscopy volumes of mouse primary visual cortex.

CAVE: Connectome Annotation Versioning Engine.

Advances in Electron Microscopy, image segmentation and computational infrastructure have given rise to large-scale and richly annotated connectomic datasets which are increasingly shared across communities. To enable collaboration, users need to be able to concurrently create new annotations and correct errors in the automated segmentation by proofreading. In large datasets, every proofreading edit relabels cell identities of millions of voxels and thousands of annotations like synapses.

Fusion Object Detection and Action Recognition to Predict Violent Action.

In the context of Shared Autonomous Vehicles, the need to monitor the environment inside the car will be crucial. This article focuses on the application of deep learning algorithms to present a fusion monitoring solution which was three different algorithms: a violent action detection system, which recognizes violent behaviors between passengers, a violent object detection system, and a lost items detection system. Public datasets were used for object detection algorithms (COCO and TAO) to train state-of-the-art algorithms such as YOLOv5.

Emerging roles of oligodendrocyte precursor cells in neural circuit development and remodeling.

Oligodendrocyte precursor cells (OPCs) are non-neuronal brain cells that give rise to oligodendrocytes, glia that myelinate the axons of neurons in the brain. Classically known for their contributions to myelination via oligodendrogenesis, OPCs are increasingly appreciated to play diverse roles in the nervous system, ranging from blood vessel formation to antigen presentation. Here, we review emerging literature suggesting that OPCs may be essential for the establishment and remodeling of neural circuits in the developing and adult brain via mechanisms that are distinct from the production of oligodendrocytes.

Sensor Fusion Approach for Multiple Human Motion Detection for Indoor Surveillance Use-Case.

Multi-human detection and tracking in indoor surveillance is a challenging task due to various factors such as occlusions, illumination changes, and complex human-human and human-object interactions. In this study, we address these challenges by exploring the benefits of a low-level sensor fusion approach that combines grayscale and neuromorphic vision sensor (NVS) data. We first generate a custom dataset using an NVS camera in an indoor environment.

Uncovering COVID-19 infection determinants in Portugal: towards an evidence-based spatial susceptibility index to support epidemiological containment policies.

Background: COVID-19 caused the largest pandemic of the twenty-first century forcing the adoption of containment policies all over the world. Many studies on COVID-19 health determinants have been conducted, mainly using multivariate methods and geographic information systems (GIS), but few attempted to demonstrate how knowing social, economic, mobility, behavioural, and other spatial determinants and their effects can help to contain the disease. For example, in mainland Portugal, non-pharmacological interventions (NPI) were primarily dependent on epidemiological indicators and ignored the spatial variation of susceptibility to infection.

Foundation model of neural activity predicts response to new stimulus types and anatomy.

The complexity of neural circuits makes it challenging to decipher the brain's algorithms of intelligence. Recent breakthroughs in deep learning have produced models that accurately simulate brain activity, enhancing our understanding of the brain's computational objectives and neural coding. However, these models struggle to generalize beyond their training distribution, limiting their utility.

NEURD offers automated proofreading and feature extraction for connectomics.

We are now in the era of millimeter-scale electron microscopy (EM) volumes collected at nanometer resolution. Dense reconstruction of cellular compartments in these EM volumes has been enabled by recent advances in Machine Learning (ML). Automated segmentation methods produce exceptionally accurate reconstructions of cells, but post-hoc proofreading is still required to generate large connectomes free of merge and split errors.

In Situ Co-Amorphization of Olanzapine in the Matrix and on the Coat of Pellets.

In situ amorphization is a promising approach, considered in the present work, to enhance the solubility and dissolution rate of olanzapine, while minimizing the exposure of the amorphous material to the stress conditions applied during conventional processing. The production of pellets by extrusion/spheronization and the coating of inert beads were investigated as novel methods to promote the co-amorphization of olanzapine, a poorly water-soluble drug, and saccharin. Samples were characterized using differential scanning calorimetry, X-ray powder diffraction, Fourier-transform infrared spectroscopy and scanning electron microscopy, and dissolution and stability testing.

Oligodendrocyte precursor cells ingest axons in the mouse neocortex.

Neurons in the developing brain undergo extensive structural refinement as nascent circuits adopt their mature form. This physical transformation of neurons is facilitated by the engulfment and degradation of axonal branches and synapses by surrounding glial cells, including microglia and astrocytes. However, the small size of phagocytic organelles and the complex, highly ramified morphology of glia have made it difficult to define the contribution of these and other glial cell types to this crucial process.

AI based monitoring violent action detection data for in-vehicle scenarios.

With the evolution of technology associated with mobility and autonomy, Shared Autonomous Vehicles will be a reality. To ensure passenger safety, there is a need to create a monitoring system inside the vehicle capable of recognizing human actions. We introduce two datasets to train human action recognition inside the vehicle, focusing on violence detection.

Amorphous and Co-Amorphous Olanzapine Stability in Formulations Intended for Wet Granulation and Pelletization.

The preparation of amorphous and co-amorphous systems (CAMs) effectively addresses the solubility and bioavailability issues of poorly water-soluble chemical entities. However, stress conditions imposed during common pharmaceutical processing (e.g.

Sulfonic Acid Derivatives in the Production of Stable Co-Amorphous Systems for Solubility Enhancement.

Co-amorphization is a promising approach to stabilize drugs in the amorphous form. Olanzapine, a poorly water-soluble drug was used in this study. Sulfonic acids (saccharin, cyclamic acid and acesulfame), free and in salt forms, were used as co-formers and compared with carboxylic acids commonly used in the preparation of co-amorphous systems.

Downstream Processing of Amorphous and Co-Amorphous Olanzapine Powder Blends.

The work evaluates the stability of amorphous and co-amorphous olanzapine (OLZ) in tablets manufactured by direct compression. The flowability and the compressibility of amorphous and co-amorphous OLZ with saccharin (SAC) and the properties of the tablets obtained were measured and compared to those of tablets made with crystalline OLZ. The flowability of the amorphous and mostly of the co-amorphous OLZ powders decreased in comparison with the crystalline OLZ due to the higher cohesiveness of the former materials.

The Precision and Accuracy of 3D Printing of Tablets by Fused Deposition Modelling.

Tablet manufacture by fused deposition modelling (FDM) can be carried out individually (one tablet printed per run) or as a group (i.e., 'multiple printing' in one run) depending on patient's needs.

A petascale automated imaging pipeline for mapping neuronal circuits with high-throughput transmission electron microscopy.

Electron microscopy (EM) is widely used for studying cellular structure and network connectivity in the brain. We have built a parallel imaging pipeline using transmission electron microscopes that scales this technology, implements 24/7 continuous autonomous imaging, and enables the acquisition of petascale datasets. The suitability of this architecture for large-scale imaging was demonstrated by acquiring a volume of more than 1 mm of mouse neocortex, spanning four different visual areas at synaptic resolution, in less than 6 months.

Measurement of the amorphous fraction of olanzapine incorporated in a co-amorphous formulation.

Amorphous and co-amorphous formulations have been used to enhance the solubility and bioavailability of poorly water-soluble drugs. However, during handling and/or storage amorphous solids present inherent instability and overtime recrystallize back into their crystalline counterpart. The development of tools capable of quantifying and monitoring the recrystallization of amorphous materials is required to ensure the delivery of solid dosage forms with improved performance.

Publisher Correction: Flexible Learning-Free Segmentation and Reconstruction of Neural Volumes.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has been fixed in the paper.

Visual physiology of the layer 4 cortical circuit in silico.

Despite advances in experimental techniques and accumulation of large datasets concerning the composition and properties of the cortex, quantitative modeling of cortical circuits under in-vivo-like conditions remains challenging. Here we report and publicly release a biophysically detailed circuit model of layer 4 in the mouse primary visual cortex, receiving thalamo-cortical visual inputs. The 45,000-neuron model was subjected to a battery of visual stimuli, and results were compared to published work and new in vivo experiments.

Large-scale neuroanatomy using LASSO: Loop-based Automated Serial Sectioning Operation.

Serial section transmission electron microscopy (ssTEM) is the most promising tool for investigating the three-dimensional anatomy of the brain with nanometer resolution. Yet as the field progresses to larger volumes of brain tissue, new methods for high-yield, low-cost, and high-throughput serial sectioning are required. Here, we introduce LASSO (Loop-based Automated Serial Sectioning Operation), in which serial sections are processed in "batches.

Flexible Learning-Free Segmentation and Reconstruction of Neural Volumes.

Imaging is a dominant strategy for data collection in neuroscience, yielding stacks of images that often scale to gigabytes of data for a single experiment. Machine learning algorithms from computer vision can serve as a pair of virtual eyes that tirelessly processes these images, automatically detecting and identifying microstructures. Unlike learning methods, our Flexible Learning-free Reconstruction of Imaged Neural volumes (FLoRIN) pipeline exploits structure-specific contextual clues and requires no training.