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Major challenges remain to precisely detect low-abundance proteins rapidly and cost-effectively from diverse biofluids. Here we present a gold nanoparticle (AuNP)-supported, rapid electronic detection (NasRED) platform with sub-femtomolar sensitivity and high specificity. Surface-functionalized AuNPs act as multivalent detectors to recognize target antigens and antibodies through high-affinity binding, subsequently forming aggregates precipitated in a microcentrifuge tube and producing a solution color change. The residual floating AuNPs’ optical extinction is digitized using customized circuitry incorporating inexpensive optoelectronics and feedback mechanisms for stabilized readout. NasRED introduces active fluidic forces through engineered centrifugation and vortex agitation, effectively promoting low-concentration protein detection and accelerating signal transduction. Using SARS-CoV-2 as a demonstration, NasRED enables detection of both antibodies and antigens from a small sample volume (6 µL), distinguishes the viral antigens from those of human coronaviruses, and delivers test results in <15 min. The limits of detection (LoDs) for antibody detection are approximately 49 aM (7 fg/mL) in phosphate-buffered saline (PBS), or >3,000 times more sensitive than Enzyme-Linked Immunosorbent Assay (ELISA), ~76 aM (11 fg/mL) in human pooled serum and in the femtomolar range in diluted whole blood. For nucleocapsid protein detection, NasRED LoDs are ~190 aM (10 fg/mL) in human saliva and ~2 fM (100 fg/mL) in nasal fluid. Unlike centralized platforms, NasRED is a one-pot, in-solution assay without the needs for washing, labeling, expensive instrumentation or highly trained operators. With low reagent costs and a compact system footprint, this modular digital platform is well-suited for accurate, near-patient diagnosis and screening of a wide range of infectious and chronic diseases. 

Abstract We present a new clustering-enabled regression approach to investigate how functional connectivity (FC) of the entire brain changes from childhood to old age. By applying this method to resting-state functional magnetic resonance imaging data aggregated from three Human Connectome Project studies, we cluster brain regions that undergo identical age-related changes in FC and reveal diverse patterns of these changes for different region clusters. While most brain connections between pairs of regions show minimal yet statistically significant FC changes with age, only a tiny proportion of connections exhibit practically significant age-related changes in FC. Among these connections, FC between region clusters from the same functional network tends to decrease over time, whereas FC between region clusters from different networks demonstrates various patterns of age-related changes. Moreover, our research uncovers sex-specific trends in FC changes. Females show much higher FC mainly within the default mode network, whereas males display higher FC across several more brain networks. These findings underscore the complexity and heterogeneity of FC changes in the brain throughout the lifespan. 

Abstract Genome-wide profiling of chromatin accessibility by DNase-seq or ATAC-seq has been widely used to identify regulatory DNA elements and transcription factor binding sites. However, enzymatic DNA cleavage exhibits intrinsic sequence biases that confound chromatin accessibility profiling data analysis. Existing computational tools are limited in their ability to account for such intrinsic biases and not designed for analyzing single-cell data. Here, we present Simplex Encoded Linear Model for Accessible Chromatin (SELMA), a computational method for systematic estimation of intrinsic cleavage biases from genomic chromatin accessibility profiling data. We demonstrate that SELMA yields accurate and robust bias estimation from both bulk and single-cell DNase-seq and ATAC-seq data. SELMA can utilize internal mitochondrial DNA data to improve bias estimation. We show that transcription factor binding inference from DNase footprints can be improved by incorporating estimated biases using SELMA. Furthermore, we show strong effects of intrinsic biases in single-cell ATAC-seq data, and develop the first single-cell ATAC-seq intrinsic bias correction model to improve cell clustering. SELMA can enhance the performance of existing bioinformatics tools and improve the analysis of both bulk and single-cell chromatin accessibility sequencing data. 

Abstract Efficient delivery of biomolecules into neurons has significant impacts on therapeutic applications in the central nervous system (CNS) and fundamental neuroscience research. Existing viral and non‐viral delivery methods often suffer from inefficient intracellular access due to the endocytic pathway. Here, a neuron‐targeting and direct cytosolic delivery platform is discovered by using a 15‐amino‐acid peptide, termed the N1 peptide, which enables neuron‐specific targeting and cytosolic delivery of functional biomolecules. The N1 peptide initially binds hyaluronan in the extracellular matrix and subsequently passes the membrane of neurons without being trapped into endosome. This mechanism facilitates the efficient delivery of cell‐impermeable and photo‐stable fluorescent dye for super‐resolution imaging of dendritic spines, and functional proteins, such as Cre recombinase, for site‐specific genome modification. Importantly, the N1 peptide exhibits robust neuronal specificity across diverse species, including mice, rats, tree shrews, and zebra finches. Its targeting capability is further demonstrated through various administration routes, including intraparenchymal, intrathecal, and intravenous (i.v.) injections after blood‐brain barrier (BBB) opening with focused ultrasound (FUS). These findings establish the N1 peptide as a versatile and functional platform with significant potential for bioimaging and advanced therapeutic applications. 

Network embedding is an effective approach to learn the low-dimensional representations of vertices in networks, aiming to capture and preserve the structure and inherent properties of networks. The vast majority of existing network embedding methods exclusively focus on vertex proximity of networks, while ignoring the network internal community structure. However, the homophily principle indicates that vertices within the same community are more similar to each other than those from different communities, thus vertices within the same community should have similar vertex representations. Motivated by this, we propose a novel network embedding framework NECS to learn the Network Embedding with Community Structural information, which preserves the high-order proximity and incorporates the community structure in vertex representation learning. We formulate the problem into a principled optimization framework and provide an effective alternating algorithm to solve it. Extensive experimental results on several benchmark network datasets demonstrate the effectiveness of the proposed framework in various network analysis tasks including network reconstruction, link prediction and vertex classification. 

Abstract Alterations in vascular networks, including angiogenesis and capillary regression, play key roles in disease, wound healing, and development. The spatial structures of blood vessels can be captured through imaging, but effective characterization of network architecture requires both metrics for quantification and software to carry out the analysis in a high‐throughput and unbiased fashion. We present Rapid Editable Analysis of Vessel Elements Routine (REAVER), an open‐source tool that researchers can use to analyze high‐resolution 2D fluorescent images of blood vessel networks, and assess its performance compared to alternative image analysis programs. Using a dataset of manually analyzed images from a variety of murine tissues as a ground‐truth, REAVER exhibited high accuracy and precision for all vessel architecture metrics quantified, including vessel length density, vessel area fraction, mean vessel diameter, and branchpoint count, along with the highest pixel‐by‐pixel accuracy for the segmentation of the blood vessel network. In instances where REAVER's automated segmentation is inaccurate, we show that combining manual curation with automated analysis improves the accuracy of vessel architecture metrics. REAVER can be used to quantify differences in blood vessel architectures, making it useful in experiments designed to evaluate the effects of different external perturbations (eg, drugs or disease states).