Organization of the human intestine at single-cell resolution.

The intestine is a complex organ that promotes digestion, extracts nutrients, participates in immune surveillance, maintains critical symbiotic relationships with microbiota and affects overall health1. The intesting has a length of over nine metres, along which there are differences in structure and function2. The localization of individual cell types, cell type development trajectories and detailed cell transcriptional programs probably drive these differences in function. Here, to better understand these differences, we evaluated the organization of single cells using multiplexed imaging and single-nucleus RNA and open chromatin assays across eight different intestinal sites from nine donors. Through systematic analyses, we find cell compositions that differ substantially across regions of the intestine and demonstrate the complexity of epithelial subtypes, and find that the same cell types are organized into distinct neighbourhoods and communities, highlighting distinct immunological niches that are present in the intestine. We also map gene regulatory differences in these cells that are suggestive of a regulatory differentiation cascade, and associate intestinal disease heritability with specific cell types. These results describe the complexity of the cell composition, regulation and organization for this organ, and serve as an important reference map for understanding human biology and disease.

Annotation of spatially resolved single-cell data with STELLAR.

Accurate cell-type annotation from spatially resolved single cells is crucial to understand functional spatial biology that is the basis of tissue organization. However, current computational methods for annotating spatially resolved single-cell data are typically based on techniques established for dissociated single-cell technologies and thus do not take spatial organization into account. Here we present STELLAR, a geometric deep learning method for cell-type discovery and identification in spatially resolved single-cell datasets. STELLAR automatically assigns cells to cell types present in the annotated reference dataset and discovers novel cell types and cell states. STELLAR transfers annotations across different dissection regions, different tissues and different donors, and learns cell representations that capture higher-order tissue structures. We successfully applied STELLAR to CODEX multiplexed fluorescent microscopy data and multiplexed RNA imaging datasets. Within the Human BioMolecular Atlas Program, STELLAR has annotated 2.6 million spatially resolved single cells with dramatic time savings.

Deep learning identifies robust gender differences in functional brain organization and their dissociable links to clinical symptoms in autism.

BACKGROUND: Autism spectrum disorder (ASD) is a highly heterogeneous disorder that affects nearly 1 in 189 females and 1 in 42 males. However, the neurobiological basis of gender differences in ASD is poorly understood, as most studies have neglected females and used methods ill-suited to capture such differences. AIMS: To identify robust functional brain organisation markers that distinguish between females and males with ASD and predict symptom severity. METHOD: We leveraged multiple neuroimaging cohorts (ASD n = 773) and developed a novel spatiotemporal deep neural network (stDNN), which uses spatiotemporal convolution on functional magnetic resonance imaging data to distinguish between groups. RESULTS: stDNN achieved consistently high classification accuracy in distinguishing between females and males with ASD. Notably, stDNN trained to distinguish between females and males with ASD could not distinguish between neurotypical females and males, suggesting that there are gender differences in the functional brain organisation in ASD that differ from normative gender differences. Brain features associated with motor, language and visuospatial attentional systems reliably distinguished between females and males with ASD. Crucially, these results were observed in a large multisite cohort and replicated in a fully independent cohort. Furthermore, brain features associated with the motor network's primary motor cortex node predicted the severity of restricted/repetitive behaviours in females but not in males with ASD. CONCLUSIONS: Our replicable findings reveal that the brains of females and males with ASD are functionally organised differently, contributing to their clinical symptoms in distinct ways. They inform the development of gender-specific diagnoses and treatment strategies for ASD, and ultimately advance precision psychiatry.

Preparation and Application of Nanocomposite Thin-Film Temperature Sensor during the Milling Process.

During the titanium alloy milling process, high temperatures in the tool-chip contact area will affect the tool life and precision of titanium alloy machining. Therefore, it is essential to measure the temperature of the tool-chip contact area continuously. In this paper, a finite element simulation model of the milling process was established using ABAQUS2020 to obtain the highest temperature location in the tool-chip contact area when milling titanium alloy. The integration of the wire with the alumina ceramic substrate formed an integrated wire substrate. Furthermore, NiCr, NiSi, and SiO2 films were deposited on the substrate sequentially using the DC pulsed magnetron sputtering technique. Finally, its microscopic morphology and static and dynamic performance were tested. The results show that the developed thin-film thermocouple temperature sensor has a Seebeck coefficient of 40.72 µV/°C and a dynamic response time of 0.703 ms. The application of the sensor to our titanium alloy milling experiments showed that the sensor can monitor the transient temperature in the tool-chip contact area, and its temperature measurement performance showed no detrimental effect from wearing. The effect of each milling parameter on the milling temperature was analyzed using ANOVA, and a regression model with an R-sq of 96.76% was obtained for the milling temperature.

A predictive model for respiratory distress in patients with COVID-19: a retrospective study.

BACKGROUND: Coronavirus disease 2019 (COVID-19), associated with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has become a global public health crisis. We retrospectively evaluated 863 hospitalized patients with COVID-19 infection, designated IWCH-COVID-19. METHODS: We built a successful predictive model after investigating the risk factors to predict respiratory distress within 30 days of admission. These variables were analyzed using Kaplan-Meier and Cox proportional hazards (PHs) analyses. Hazard ratios (HRs) and performance of the final model were determined. RESULTS: Neutrophil count >6.3×109/L, D-dimer level ≥1.00 mg/L, and temperature ≥37.3 °C at admission showed significant positive association with the outcome of respiratory distress in the final model. Complement C3 (C3) of 0.9-1.8 g/L, platelet count >350×109/L, and platelet count of 125-350×109/L showed a significant negative association with outcomes of respiratory distress in the final model. The final model had a C statistic of 0.891 (0.867-0.915), an Akaike's information criterion (AIC) of 567.65, and a bootstrap confidence interval (CI) of 0.866 (0.842-0.89). This five-factor model could help in early allocation of medical resources. CONCLUSIONS: The predictive model based on the five factors obtained at admission can be applied for calculating the risk of respiratory distress and classifying patients at an early stage. Accordingly, high-risk patients can receive timely and effective treatment, and health resources can be allocated effectively.

An optical nose chip based on mesoporous colloidal photonic crystal beads.

An optical nose chip is developed using surface functionalized mesoporous colloidal photonic crystal beads as elements. The prepared optical nose chip displays excellent discrimination among a very wide range of compounds, not only the simplex organic vapors from the different or same chemical family, but also the complex expiratory air from different people.

Hybrid mesoporous colloid photonic crystal array for high performance vapor sensing.

A hybrid mesoporous photonic crystal vapor sensing chip was developed by introducing fluorescent dyes into mesoporous colloidal crystals. The sensing chip was capable of discriminating various kinds of vapors, as well as their concentrations, according to their fluorescence and reflective responses to vapor analytes.

Spherical porphyrin sensor array based on encoded colloidal crystal beads for VOC vapor detection.

A spherical porphyrin sensor array using colloidal crystal beads (CCBs) as the encoding microcarriers has been developed for VOC vapor detection. Six different porphyrins were coated onto the CCBs with distinctive encoded reflection peaks via physical adsorption and the sensor array was fabricated by placing the prepared porphyrin-modified CCBs together. The change in fluorescence color of the porphyrin-modified CCBs array serves as the detection signal for discriminating between different VOC vapors and the reflection peak of the CCBs serves as the encoding signal to distinguish between different sensors. It was demonstrated that the VOC vapors detection using the prepared sensor array showed excellent discrimination: not only could the compounds from the different chemical classes be easily differentiated (e.g., alcohol vs acids vs ketones) but similar compounds from the same chemical family (e.g., methanol vs ethanol) and the same compound with different concentration ((e.g., Sat. ethanol vs 60 ppm ethanol vs 10 ppm ethanol) could also be distinguished. The detection reproducibility and the humidity effect were also investigated. The present spherical sensor array, with its simple preparation, rapid response, high sensitivity, reproducibility, and humidity insensitivity, and especially with stable and high-throughput encoding, is promising for real applications in artificial olfactory systems.