Molecular basis of human noradrenaline transporter reuptake and inhibition.

Noradrenaline, also known as norepinephrine, has a wide range of activities and effects on most brain cell types1. Its reuptake from the synaptic cleft heavily relies on the noradrenaline transporter (NET) located in the presynaptic membrane2. Here we report the cryo-electron microscopy (cryo-EM) structures of the human NET in both its apo state and when bound to substrates or antidepressant drugs, with resolutions ranging from 2.5 Å to 3.5 Å. The two substrates, noradrenaline and dopamine, display a similar binding mode within the central substrate binding site (S1) and within a newly identified extracellular allosteric site (S2). Four distinct antidepressants, namely, atomoxetine, desipramine, bupropion and escitalopram, occupy the S1 site to obstruct substrate transport in distinct conformations. Moreover, a potassium ion was observed within sodium-binding site 1 in the structure of the NET bound to desipramine under the KCl condition. Complemented by structural-guided biochemical analyses, our studies reveal the mechanism of substrate recognition, the alternating access of NET, and elucidate the mode of action of the four antidepressants.

Towards large-scale single-shot millimeter-wave imaging for low-cost security inspection.

Millimeter-Wave (MMW) imaging is a promising technique for contactless security inspection. However, the high cost of requisite large-scale antenna arrays hinders its widespread application in high-throughput scenarios. Here, we report a large-scale single-shot MMW imaging framework, achieving low-cost high-fidelity security inspection. We first analyzed the statistical ranking of each array element through 1934 full-sampled MMW echoes. The highest-ranked elements are preferentially selected based on the ranking, building the experimentally optimal sparse sampling strategy that reduces antenna array cost by one order of magnitude. Additionally, we derived an untrained interpretable learning scheme, realizing robust and accurate MMW image reconstruction from sparsely sampled echoes. Last, we developed a neural network for automatic object detection, and experimentally demonstrated successful detection of concealed centimeter-sized targets using 10% sparse array, whereas all the other contemporary approaches failed at such a low sampling ratio. With the strong detection ability and order-of-magnitude cost reduction, we anticipate that this technique provides a practical way for large-scale single-shot MMW imaging.

Enhancing Hi-C contact matrices for loop detection with Capricorn: a multiview diffusion model.

MOTIVATION: High-resolution Hi-C contact matrices reveal the detailed three-dimensional architecture of the genome, but high-coverage experimental Hi-C data are expensive to generate. Simultaneously, chromatin structure analyses struggle with extremely sparse contact matrices. To address this problem, computational methods to enhance low-coverage contact matrices have been developed, but existing methods are largely based on resolution enhancement methods for natural images and hence often employ models that do not distinguish between biologically meaningful contacts, such as loops and other stochastic contacts. RESULTS: We present Capricorn, a machine learning model for Hi-C resolution enhancement that incorporates small-scale chromatin features as additional views of the input Hi-C contact matrix and leverages a diffusion probability model backbone to generate a high-coverage matrix. We show that Capricorn outperforms the state of the art in a cross-cell-line setting, improving on existing methods by 17% in mean squared error and 26% in F1 score for chromatin loop identification from the generated high-coverage data. We also demonstrate that Capricorn performs well in the cross-chromosome setting and cross-chromosome, cross-cell-line setting, improving the downstream loop F1 score by 14% relative to existing methods. We further show that our multiview idea can also be used to improve several existing methods, HiCARN and HiCNN, indicating the wide applicability of this approach. Finally, we use DNA sequence to validate discovered loops and find that the fraction of CTCF-supported loops from Capricorn is similar to those identified from the high-coverage data. Capricorn is a powerful Hi-C resolution enhancement method that enables scientists to find chromatin features that cannot be identified in the low-coverage contact matrix. AVAILABILITY AND IMPLEMENTATION: Implementation of Capricorn and source code for reproducing all figures in this paper are available at https://github.com/CHNFTQ/Capricorn.

A whole-slide foundation model for digital pathology from real-world data.

Digital pathology poses unique computational challenges, as a standard gigapixel slide may comprise tens of thousands of image tiles1-3. Prior models have often resorted to subsampling a small portion of tiles for each slide, thus missing the important slide-level context4. Here we present Prov-GigaPath, a whole-slide pathology foundation model pretrained on 1.3 billion 256 × 256 pathology image tiles in 171,189 whole slides from Providence, a large US health network comprising 28 cancer centres. The slides originated from more than 30,000 patients covering 31 major tissue types. To pretrain Prov-GigaPath, we propose GigaPath, a novel vision transformer architecture for pretraining gigapixel pathology slides. To scale GigaPath for slide-level learning with tens of thousands of image tiles, GigaPath adapts the newly developed LongNet5 method to digital pathology. To evaluate Prov-GigaPath, we construct a digital pathology benchmark comprising 9 cancer subtyping tasks and 17 pathomics tasks, using both Providence and TCGA data6. With large-scale pretraining and ultra-large-context modelling, Prov-GigaPath attains state-of-the-art performance on 25 out of 26 tasks, with significant improvement over the second-best method on 18 tasks. We further demonstrate the potential of Prov-GigaPath on vision-language pretraining for pathology7,8 by incorporating the pathology reports. In sum, Prov-GigaPath is an open-weight foundation model that achieves state-of-the-art performance on various digital pathology tasks, demonstrating the importance of real-world data and whole-slide modelling.

GPro: generative AI-empowered toolkit for promoter design.

MOTIVATION: Promoters with desirable properties are crucial in biotechnological applications. Generative AI (GenAI) has demonstrated potential in creating novel synthetic promoters with significantly enhanced functionality. However, these methods' reliance on various programming frameworks and specific task-oriented contexts limits their flexibilities. Overcoming these limitations is essential for researchers to fully leverage the power of GenAI to design promoters for their tasks. RESULTS: Here, we introduce GPro (Generative AI-empowered toolkit for promoter design), a user-friendly toolkit that integrates a collection of cutting-edge GenAI-empowered approaches for promoter design. This toolkit provides a standardized pipeline covering essential promoter design processes, including training, optimization, and evaluation. Several detailed demos are provided to reproduce state-of-the-art promoter design pipelines. GPro's user-friendly interface makes it accessible to a wide range of users including non-AI experts. It also offers a variety of optional algorithms for each design process, and gives users the flexibility to compare methods and create customized pipelines. AVAILABILITY AND IMPLEMENTATION: GPro is released as an open-source software under the MIT license. The source code for GPro is available on GitHub for Linux, macOS, and Windows: https://github.com/WangLabTHU/GPro, and is available for download via Zenodo repository at https://zenodo.org/doi/10.5281/zenodo.10681733.

A Portable Integrated Electrochemical Sensing System for On-Site Nitrite Detection in Food.

Ensuring an appropriate nitrite level in food is essential to keep the body healthy. However, it still remains a huge challenge to offer a portable and low-cost on-site food nitrite analysis without any expensive equipment. Herein, a portable integrated electrochemical sensing system (IESS) is developed to achieve rapid on-site nitrite detection in food, which is composed of a low-cost disposable microfluidic electrochemical patch for few-shot nitrite detection, and a reusable smartphone-assisted electronic device based on self-designed circuit board for signal processing and wireless transmission. The electrochemical patch based on MXene-Ti3C2Tx/multiwalled carbon nanotubes-cyanocobalamin (MXene/MWCNTs-VB12)-modified working electrode achieves high sensitivity of 10.533 µA mm-1 and low nitrite detection limit of 4.22 µm owing to strong electron transfer ability of hybrid MXene/MWCNTs conductive matrix and high nitrite selectivity of VB12 bionic enzyme-based ion-selective layer. Moreover, the portable IESS can rapidly collect pending testing samples through a microfluidic electrochemical patch within 1.0 s to conduct immediate nitrite analysis, and then wirelessly transmit data from a signal-processing electronic device to a smartphone via Bluetooth module. Consequently, this proposed portable IESS demonstrates rapid on-site nitrite analysis and wireless data transmission within one palm-sized electronic device, which would pave a new avenue in food safety and personal bespoke therapy.