The Allen Mouse Brain Common Coordinate Framework: A 3D Reference Atlas.

Recent large-scale collaborations are generating major surveys of cell types and connections in the mouse brain, collecting large amounts of data across modalities, spatial scales, and brain areas. Successful integration of these data requires a standard 3D reference atlas. Here, we present the Allen Mouse Brain Common Coordinate Framework (CCFv3) as such a resource. We constructed an average template brain at 10 µm voxel resolution by interpolating high resolution in-plane serial two-photon tomography images with 100 µm z-sampling from 1,675 young adult C57BL/6J mice. Then, using multimodal reference data, we parcellated the entire brain directly in 3D, labeling every voxel with a brain structure spanning 43 isocortical areas and their layers, 329 subcortical gray matter structures, 81 fiber tracts, and 8 ventricular structures. CCFv3 can be used to analyze, visualize, and integrate multimodal and multiscale datasets in 3D and is openly accessible (https://atlas.brain-map.org/).

Integrated Morphoelectric and Transcriptomic Classification of Cortical GABAergic Cells.

Neurons are frequently classified into distinct types on the basis of structural, physiological, or genetic attributes. To better constrain the definition of neuronal cell types, we characterized the transcriptomes and intrinsic physiological properties of over 4,200 mouse visual cortical GABAergic interneurons and reconstructed the local morphologies of 517 of those neurons. We find that most transcriptomic types (t-types) occupy specific laminar positions within visual cortex, and, for most types, the cells mapping to a t-type exhibit consistent electrophysiological and morphological properties. These properties display both discrete and continuous variation among t-types. Through multimodal integrated analysis, we define 28 met-types that have congruent morphological, electrophysiological, and transcriptomic properties and robust mutual predictability. We identify layer-specific axon innervation pattern as a defining feature distinguishing different met-types. These met-types represent a unified definition of cortical GABAergic interneuron types, providing a systematic framework to capture existing knowledge and bridge future analyses across different modalities.

Cryo-EM structure of the periplasmic tunnel of T7 DNA-ejectosome at 2.7 Å resolution.

Hershey and Chase used bacteriophage T2 genome delivery inside Escherichia coli to demonstrate that DNA, not protein, is the genetic material. Seventy years later, our understanding of viral genome delivery in prokaryotes remains limited, especially for short-tailed phages of the Podoviridae family. These viruses expel mysterious ejection proteins found inside the capsid to form a DNA-ejectosome for genome delivery into bacteria. Here, we reconstitute the phage T7 DNA-ejectosome components gp14, gp15, and gp16 and solve the periplasmic tunnel structure at 2.7 Å resolution. We find that gp14 forms an outer membrane pore, gp15 assembles into a 210 Å hexameric DNA tube spanning the host periplasm, and gp16 extends into the host cytoplasm forming a ∼4,200 residue hub. Gp16 promotes gp15 oligomerization, coordinating peptidoglycan hydrolysis, DNA binding, and lipid insertion. The reconstituted gp15:gp16 complex lacks channel-forming activity, suggesting that the pore for DNA passage forms only transiently during genome ejection.

Survey of spiking in the mouse visual system reveals functional hierarchy.

The anatomy of the mammalian visual system, from the retina to the neocortex, is organized hierarchically1. However, direct observation of cellular-level functional interactions across this hierarchy is lacking due to the challenge of simultaneously recording activity across numerous regions. Here we describe a large, open dataset-part of the Allen Brain Observatory2-that surveys spiking from tens of thousands of units in six cortical and two thalamic regions in the brains of mice responding to a battery of visual stimuli. Using cross-correlation analysis, we reveal that the organization of inter-area functional connectivity during visual stimulation mirrors the anatomical hierarchy from the Allen Mouse Brain Connectivity Atlas3. We find that four classical hierarchical measures-response latency, receptive-field size, phase-locking to drifting gratings and response decay timescale-are all correlated with the hierarchy. Moreover, recordings obtained during a visual task reveal that the correlation between neural activity and behavioural choice also increases along the hierarchy. Our study provides a foundation for understanding coding and signal propagation across hierarchically organized cortical and thalamic visual areas.

Human neocortical expansion involves glutamatergic neuron diversification.

The neocortex is disproportionately expanded in human compared with mouse1,2, both in its total volume relative to subcortical structures and in the proportion occupied by supragranular layers composed of neurons that selectively make connections within the neocortex and with other telencephalic structures. Single-cell transcriptomic analyses of human and mouse neocortex show an increased diversity of glutamatergic neuron types in supragranular layers in human neocortex and pronounced gradients as a function of cortical depth3. Here, to probe the functional and anatomical correlates of this transcriptomic diversity, we developed a robust platform combining patch clamp recording, biocytin staining and single-cell RNA-sequencing (Patch-seq) to examine neurosurgically resected human tissues. We demonstrate a strong correspondence between morphological, physiological and transcriptomic phenotypes of five human glutamatergic supragranular neuron types. These were enriched in but not restricted to layers, with one type varying continuously in all phenotypes across layers 2 and 3. The deep portion of layer 3 contained highly distinctive cell types, two of which express a neurofilament protein that labels long-range projection neurons in primates that are selectively depleted in Alzheimer's disease4,5. Together, these results demonstrate the explanatory power of transcriptomic cell-type classification, provide a structural underpinning for increased complexity of cortical function in humans, and implicate discrete transcriptomic neuron types as selectively vulnerable in disease.

Hierarchical organization of cortical and thalamic connectivity.

The mammalian cortex is a laminar structure containing many areas and cell types that are densely interconnected in complex ways, and for which generalizable principles of organization remain mostly unknown. Here we describe a major expansion of the Allen Mouse Brain Connectivity Atlas resource1, involving around a thousand new tracer experiments in the cortex and its main satellite structure, the thalamus. We used Cre driver lines (mice expressing Cre recombinase) to comprehensively and selectively label brain-wide connections by layer and class of projection neuron. Through observations of axon termination patterns, we have derived a set of generalized anatomical rules to describe corticocortical, thalamocortical and corticothalamic projections. We have built a model to assign connection patterns between areas as either feedforward or feedback, and generated testable predictions of hierarchical positions for individual cortical and thalamic areas and for cortical network modules. Our results show that cell-class-specific connections are organized in a shallow hierarchy within the mouse corticothalamic network.

Criteria2Query 3.0: Leveraging generative large language models for clinical trial eligibility query generation.

OBJECTIVE: Automated identification of eligible patients is a bottleneck of clinical research. We propose Criteria2Query (C2Q) 3.0, a system that leverages GPT-4 for the semi-automatic transformation of clinical trial eligibility criteria text into executable clinical database queries. MATERIALS AND METHODS: C2Q 3.0 integrated three GPT-4 prompts for concept extraction, SQL query generation, and reasoning. Each prompt was designed and evaluated separately. The concept extraction prompt was benchmarked against manual annotations from 20 clinical trials by two evaluators, who later also measured SQL generation accuracy and identified errors in GPT-generated SQL queries from 5 clinical trials. The reasoning prompt was assessed by three evaluators on four metrics: readability, correctness, coherence, and usefulness, using corrected SQL queries and an open-ended feedback questionnaire. RESULTS: Out of 518 concepts from 20 clinical trials, GPT-4 achieved an F1-score of 0.891 in concept extraction. For SQL generation, 29 errors spanning seven categories were detected, with logic errors being the most common (n = 10; 34.48 %). Reasoning evaluations yielded a high coherence rating, with the mean score being 4.70 but relatively lower readability, with a mean of 3.95. Mean scores of correctness and usefulness were identified as 3.97 and 4.37, respectively. CONCLUSION: GPT-4 significantly improves the accuracy of extracting clinical trial eligibility criteria concepts in C2Q 3.0. Continued research is warranted to ensure the reliability of large language models.

Multi-task deep learning-based radiomic nomogram for prognostic prediction in locoregionally advanced nasopharyngeal carcinoma.

PURPOSE: Prognostic prediction is crucial to guide individual treatment for locoregionally advanced nasopharyngeal carcinoma (LA-NPC) patients. Recently, multi-task deep learning was explored for joint prognostic prediction and tumor segmentation in various cancers, resulting in promising performance. This study aims to evaluate the clinical value of multi-task deep learning for prognostic prediction in LA-NPC patients. METHODS: A total of 886 LA-NPC patients acquired from two medical centers were enrolled including clinical data, [18F]FDG PET/CT images, and follow-up of progression-free survival (PFS). We adopted a deep multi-task survival model (DeepMTS) to jointly perform prognostic prediction (DeepMTS-Score) and tumor segmentation from FDG-PET/CT images. The DeepMTS-derived segmentation masks were leveraged to extract handcrafted radiomics features, which were also used for prognostic prediction (AutoRadio-Score). Finally, we developed a multi-task deep learning-based radiomic (MTDLR) nomogram by integrating DeepMTS-Score, AutoRadio-Score, and clinical data. Harrell's concordance indices (C-index) and time-independent receiver operating characteristic (ROC) analysis were used to evaluate the discriminative ability of the proposed MTDLR nomogram. For patient stratification, the PFS rates of high- and low-risk patients were calculated using Kaplan-Meier method and compared with the observed PFS probability. RESULTS: Our MTDLR nomogram achieved C-index of 0.818 (95% confidence interval (CI): 0.785-0.851), 0.752 (95% CI: 0.638-0.865), and 0.717 (95% CI: 0.641-0.793) and area under curve (AUC) of 0.859 (95% CI: 0.822-0.895), 0.769 (95% CI: 0.642-0.896), and 0.730 (95% CI: 0.634-0.826) in the training, internal validation, and external validation cohorts, which showed a statistically significant improvement over conventional radiomic nomograms. Our nomogram also divided patients into significantly different high- and low-risk groups. CONCLUSION: Our study demonstrated that MTDLR nomogram can perform reliable and accurate prognostic prediction in LA-NPC patients, and also enabled better patient stratification, which could facilitate personalized treatment planning.

Investigation of the wake region behind a hemispherical turret via laser Rayleigh scattering.

Planar and volumetric density measurements in the wake region behind a mounted hemispherical turret are obtained using laser Rayleigh scattering. The measurements are conducted in a Mach 2 wind tunnel facility at the Kirtland Air Force Base. Quantitative measurements of density and contour plots with lines of constant density are computed, thus enabling visualization of the turret wake's fluid dynamics. A new, to the best of our knowledge, laser diagnostic methodology and configuration for capturing laser images is also presented. This methodology enables further suppression of background light scattering. Multi-dimensional single-shot and time-average measurements are recorded at multiple axial locations behind the turret. The images acquired reveal turbulent regions of the wake flow, and a discussion of the observed phenomena is presented.

Enhancing medical image registration via appearance adjustment networks.

Deformable image registration is fundamental for many medical image analyses. A key obstacle for accurate image registration lies in image appearance variations such as the variations in texture, intensities, and noise. These variations are readily apparent in medical images, especially in brain images where registration is frequently used. Recently, deep learning-based registration methods (DLRs), using deep neural networks, have shown computational efficiency that is several orders of magnitude faster than traditional optimization-based registration methods (ORs). DLRs rely on a globally optimized network that is trained with a set of training samples to achieve faster registration. DLRs tend, however, to disregard the target-pair-specific optimization inherent in ORs and thus have degraded adaptability to variations in testing samples. This limitation is severe for registering medical images with large appearance variations, especially since few existing DLRs explicitly take into account appearance variations. In this study, we propose an Appearance Adjustment Network (AAN) to enhance the adaptability of DLRs to appearance variations. Our AAN, when integrated into a DLR, provides appearance transformations to reduce the appearance variations during registration. In addition, we propose an anatomy-constrained loss function through which our AAN generates anatomy-preserving transformations. Our AAN has been purposely designed to be readily inserted into a wide range of DLRs and can be trained cooperatively in an unsupervised and end-to-end manner. We evaluated our AAN with three state-of-the-art DLRs - Voxelmorph (VM), Diffeomorphic Voxelmorph (DifVM), and Laplacian Pyramid Image Registration Network (LapIRN) - on three well-established public datasets of 3D brain magnetic resonance imaging (MRI) - IBSR18, Mindboggle101, and LPBA40. The results show that our AAN consistently improved existing DLRs and outperformed state-of-the-art ORs on registration accuracy, while adding a fractional computational load to existing DLRs.

Real-time high-spectral-resolution mid-infrared spectroscopy with a signal-to-noise ratio of ten thousand.

We developed a mid-infrared spectroscopy system with high spectral resolution and a high signal-to-noise ratio using an extremely high-order germanium immersion grating. The spectroscopic system covers wavelengths from 3 to 5 µm and has a spectral resolution of 1 GHz with a single-shot bandwidth of 2 THz. We proposed a method of improving the signal-to-noise ratio and achieved a ratio of over 3000 with a data acquisition rate of 125 Hz in the presence of fluctuations in the light source and environment. A signal-to-noise ratio of 10,000 was achieved with 0.1-s integration for 100-µW mid-infrared light.

Automatic identification of myopic maculopathy related imaging features in optic disc region via machine learning methods.

BACKGROUND: Myopic maculopathy (MM) is the most serious and irreversible complication of pathologic myopia, which is a major cause of visual impairment and blindness. Clinic proposed limited number of factors related to MM. To explore additional features strongly related with MM from optic disc region, we employ a machine learning based radiomics analysis method, which could explore and quantify more hidden or imperceptible MM-related features to the naked eyes and contribute to a more comprehensive understanding of MM and therefore may assist to distinguish the high-risk population in an early stage. METHODS: A total of 457 eyes (313 patients) were enrolled and were divided into severe MM group and without severe MM group. Radiomics analysis was applied to depict features significantly correlated with severe MM from optic disc region. Receiver Operating Characteristic were used to evaluate these features' performance of classifying severe MM. RESULTS: Eight new MM-related image features were discovered from the optic disc region, which described the shapes, textural patterns and intensity distributions of optic disc region. Compared with clinically reported MM-related features, these newly discovered features exhibited better abilities on severe MM classification. And the mean values of most features were markedly changed between patients with peripapillary diffuse chorioretinal atrophy (PDCA) and macular diffuse chorioretinal atrophy (MDCA). CONCLUSIONS: Machine learning and radiomics method are useful tools for mining more MM-related features from the optic disc region, by which complex or even hidden MM-related features can be discovered and decoded. In this paper, eight new MM-related image features were found, which would be useful for further quantitative study of MM-progression. As a nontrivial byproduct, marked changes between PDCA and MDCA was discovered by both new image features and clinic features.

Metal-Binding Q-Proline Macrocycles.

We introduce the efficient Fmoc-SPPS and peptoid synthesis of Q-proline-based, metal-binding macrocycles (QPMs), which bind metal cations and display nine functional groups. Metal-free QPMs are disordered, evidenced by NMR and a crystal structure of QPM-3 obtained through racemic crystallization. Upon addition of metal cations, QPMs adopt ordered structures. Notably, the addition of a second functional group at the hydantoin amide position (R2) converts the proline ring from Cγ-endo to Cγ-exo, due to steric interactions.

Supercritical Carbon Dioxide Treatment of Porous Silicon Increases Biocompatibility with Cardiomyocytes.

Porous silicon is of current interest for cardiac tissue engineering applications. While porous silicon is considered to be a biocompatible material, it is important to assess whether post-etching surface treatments can further improve biocompatibility and perhaps modify cellular behavior in desirable ways. In this work, porous silicon was formed by electrochemically etching with hydrofluoric acid, and was then treated with oxygen plasma or supercritical carbon dioxide (scCO2). These processes yielded porous silicon with a thickness of around 4 µm. The different post-etch treatments gave surfaces that differed greatly in hydrophilicity: oxygen plasma-treated porous silicon had a highly hydrophilic surface, while scCO2 gave a more hydrophobic surface. The viabilities of H9c2 cardiomyocytes grown on etched surfaces with and without these two post-etch treatments was examined; viability was found to be highest on porous silicon treated with scCO2. Most significantly, the expression of some key genes in the angiogenesis pathway was strongly elevated in cells grown on the scCO2-treated porous silicon, compared to cells grown on the untreated or plasma-treated porous silicon. In addition, the expression of several apoptosis genes were suppressed, relative to the untreated or plasma-treated surfaces.

SPST-CNN: Spatial pyramid based searching and tagging of liver's intraoperative live views via CNN for minimal invasive surgery.

Laparoscopic liver surgery is challenging to perform because of compromised ability of the surgeon to localize subsurface anatomy due to minimal invasive visibility. While image guidance has the potential to address this barrier, intraoperative factors, such as insufflations and variable degrees of organ mobilization from supporting ligaments, may generate substantial deformation. The navigation ability in terms of searching and tagging within liver views has not been characterized, and current object detection methods do not account for the mechanics of how these features could be applied to the liver images. In this research, we have proposed spatial pyramid based searching and tagging of liver's intraoperative views using convolution neural network (SPST-CNN). By exploiting a hybrid combination of an image pyramid at input and spatial pyramid pooling layer at deeper stages of SPST-CNN, we reveal the gains of full-image representations for searching and tagging variable scaled liver live views. SPST-CNN provides pinpoint searching and tagging of intraoperative liver views to obtain up-to-date information about the location and shape of the area of interest. Downsampling input using image pyramid enables SPST-CNN framework to deploy input images with a diversity of resolutions for achieving scale-invariance feature. We have compared the proposed approach to the four recent state-of-the-art approaches and our method achieved better mAP up to 85.9%.

A mesoscale connectome of the mouse brain.

Comprehensive knowledge of the brain's wiring diagram is fundamental for understanding how the nervous system processes information at both local and global scales. However, with the singular exception of the C. elegans microscale connectome, there are no complete connectivity data sets in other species. Here we report a brain-wide, cellular-level, mesoscale connectome for the mouse. The Allen Mouse Brain Connectivity Atlas uses enhanced green fluorescent protein (EGFP)-expressing adeno-associated viral vectors to trace axonal projections from defined regions and cell types, and high-throughput serial two-photon tomography to image the EGFP-labelled axons throughout the brain. This systematic and standardized approach allows spatial registration of individual experiments into a common three dimensional (3D) reference space, resulting in a whole-brain connectivity matrix. A computational model yields insights into connectional strength distribution, symmetry and other network properties. Virtual tractography illustrates 3D topography among interconnected regions. Cortico-thalamic pathway analysis demonstrates segregation and integration of parallel pathways. The Allen Mouse Brain Connectivity Atlas is a freely available, foundational resource for structural and functional investigations into the neural circuits that support behavioural and cognitive processes in health and disease.

Transcriptional landscape of the prenatal human brain.

The anatomical and functional architecture of the human brain is mainly determined by prenatal transcriptional processes. We describe an anatomically comprehensive atlas of the mid-gestational human brain, including de novo reference atlases, in situ hybridization, ultra-high-resolution magnetic resonance imaging (MRI) and microarray analysis on highly discrete laser-microdissected brain regions. In developing cerebral cortex, transcriptional differences are found between different proliferative and post-mitotic layers, wherein laminar signatures reflect cellular composition and developmental processes. Cytoarchitectural differences between human and mouse have molecular correlates, including species differences in gene expression in subplate, although surprisingly we find minimal differences between the inner and outer subventricular zones even though the outer zone is expanded in humans. Both germinal and post-mitotic cortical layers exhibit fronto-temporal gradients, with particular enrichment in the frontal lobe. Finally, many neurodevelopmental disorder and human-evolution-related genes show patterned expression, potentially underlying unique features of human cortical formation. These data provide a rich, freely-accessible resource for understanding human brain development.

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. Simulations reproduced a variety of observations, including effects of optogenetic perturbations. Critical to the agreement between responses in silico and in vivo were the rules of functional synaptic connectivity between neurons. Interestingly, after extreme simplification the model still performed satisfactorily on many measurements, although quantitative agreement with experiments suffered. These results emphasize the importance of functional rules of cortical wiring and enable a next generation of data-driven models of in vivo neural activity and computations.

Cancer type prediction based on copy number aberration and chromatin 3D structure with convolutional neural networks.

BACKGROUND: With the developments of DNA sequencing technology, large amounts of sequencing data have been produced that provides unprecedented opportunities for advanced association studies between somatic mutations and cancer types/subtypes which further contributes to more accurate somatic mutation based cancer typing (SMCT). In existing SMCT methods however, the absence of high-level feature extraction is a major obstacle in improving the classification performance. RESULTS: We propose DeepCNA, an advanced convolutional neural network (CNN) based classifier, which utilizes copy number aberrations (CNAs) and HiC data, to address this issue. DeepCNA first pre-process the CNA data by clipping, zero padding and reshaping. Then, the processed data is fed into a CNN classifier, which extracts high-level features for accurate classification. Experimental results on the COSMIC CNA dataset indicate that 2D CNN with both cell lines of HiC data lead to the best performance. We further compare DeepCNA with three widely adopted classifiers, and demonstrate that DeepCNA has at least 78% improvement of performance. CONCLUSIONS: This paper demonstrates the advantages and potential of the proposed DeepCNA model for processing of somatic point mutation based gene data, and proposes that its usage may be extended to other complex genotype-phenotype association studies.