High-quality semiconductor fibres via mechanical design.

Recent breakthroughs in fibre technology have enabled the assembly of functional materials with intimate interfaces into a single fibre with specific geometries1-11, delivering diverse functionalities over a large area, for example, serving as sensors, actuators, energy harvesting and storage, display, and healthcare apparatus12-17. As semiconductors are the critical component that governs device performance, the selection, control and engineering of semiconductors inside fibres are the key pathways to enabling high-performance functional fibres. However, owing to stress development and capillary instability in the high-yield fibre thermal drawing, both cracks and deformations in the semiconductor cores considerably affect the performance of these fibres. Here we report a mechanical design to achieve ultralong, fracture-free and perturbation-free semiconductor fibres, guided by a study on stress development and capillary instability at three stages of the fibre formation: the viscous flow, the core crystallization and the subsequent cooling stage. Then, the exposed semiconductor wires can be integrated into a single flexible fibre with well-defined interfaces with metal electrodes, thereby achieving optoelectronic fibres and large-scale optoelectronic fabrics. This work provides fundamental insights into extreme mechanics and fluid dynamics with geometries that are inaccessible in traditional platforms, essentially addressing the increasing demand for flexible and wearable optoelectronics.

Biological carbon pump estimate based on multidecadal hydrographic data.

The transfer of photosynthetically produced organic carbon from surface to mesopelagic waters draws carbon dioxide from the atmosphere1. However, current observation-based estimates disagree on the strength of this biological carbon pump (BCP)2. Earth system models (ESMs) also exhibit a large spread of BCP estimates, indicating limited representations of the known carbon export pathways3. Here we use several decades of hydrographic observations to produce a top-down estimate of the strength of the BCP with an inverse biogeochemical model that implicitly accounts for all known export pathways. Our estimate of total organic carbon (TOC) export at 73.4 m (model euphotic zone depth) is 15.00 ± 1.12 Pg C year-1, with only two-thirds reaching 100 m depth owing to rapid remineralization of organic matter in the upper water column. Partitioned by sequestration time below the euphotic zone, τ, the globally integrated organic carbon production rate with τ > 3 months is 11.09 ± 1.02 Pg C year-1, dropping to 8.25 ± 0.30 Pg C year-1 for τ > 1 year, with 81% contributed by the non-advective-diffusive vertical flux owing to sinking particles and vertically migrating zooplankton. Nevertheless, export of organic carbon by mixing and other fluid transport of dissolved matter and suspended particles remains regionally important for meeting the respiratory carbon demand. Furthermore, the temperature dependence of the sequestration efficiency inferred from our inversion suggests that future global warming may intensify the recycling of organic matter in the upper ocean, potentially weakening the BCP.

Reconstructed covalent organic frameworks.

Covalent organic frameworks (COFs) are distinguished from other organic polymers by their crystallinity1-3, but it remains challenging to obtain robust, highly crystalline COFs because the framework-forming reactions are poorly reversible4,5. More reversible chemistry can improve crystallinity6-9, but this typically yields COFs with poor physicochemical stability and limited application scope5. Here we report a general and scalable protocol to prepare robust, highly crystalline imine COFs, based on an unexpected framework reconstruction. In contrast to standard approaches in which monomers are initially randomly aligned, our method involves the pre-organization of monomers using a reversible and removable covalent tether, followed by confined polymerization. This reconstruction route produces reconstructed COFs with greatly enhanced crystallinity and much higher porosity by means of a simple vacuum-free synthetic procedure. The increased crystallinity in the reconstructed COFs improves charge carrier transport, leading to sacrificial photocatalytic hydrogen evolution rates of up to 27.98 mmol h-1 g-1. This nanoconfinement-assisted reconstruction strategy is a step towards programming function in organic materials through atomistic structural control.

The Skp2-SCF E3 ligase regulates Akt ubiquitination, glycolysis, herceptin sensitivity, and tumorigenesis.

Akt kinase plays a central role in cell growth, metabolism, and tumorigenesis. The TRAF6 E3 ligase orchestrates IGF-1-mediated Akt ubiquitination and activation. Here, we show that Akt ubiquitination is also induced by activation of ErbB receptors; unexpectedly, and in contrast to IGF-1 induced activation, the Skp2 SCF complex, not TRAF6, is a critical E3 ligase for ErbB-receptor-mediated Akt ubiquitination and membrane recruitment in response to EGF. Skp2 deficiency impairs Akt activation, Glut1 expression, glucose uptake and glycolysis, and breast cancer progression in various tumor models. Moreover, Skp2 overexpression correlates with Akt activation and breast cancer metastasis and serves as a marker for poor prognosis in Her2-positive patients. Finally, Skp2 silencing sensitizes Her2-overexpressing tumors to Herceptin treatment. Our study suggests that distinct E3 ligases are utilized by diverse growth factors for Akt activation and that targeting glycolysis sensitizes Her2-positive tumors to Herceptin treatment.

Identification of regulators of polyploidization presents therapeutic targets for treatment of AMKL.

The mechanism by which cells decide to skip mitosis to become polyploid is largely undefined. Here we used a high-content image-based screen to identify small-molecule probes that induce polyploidization of megakaryocytic leukemia cells and serve as perturbagens to help understand this process. Our study implicates five networks of kinases that regulate the switch to polyploidy. Moreover, we find that dimethylfasudil (diMF, H-1152P) selectively increased polyploidization, mature cell-surface marker expression, and apoptosis of malignant megakaryocytes. An integrated target identification approach employing proteomic and shRNA screening revealed that a major target of diMF is Aurora kinase A (AURKA). We further find that MLN8237 (Alisertib), a selective inhibitor of AURKA, induced polyploidization and expression of mature megakaryocyte markers in acute megakaryocytic leukemia (AMKL) blasts and displayed potent anti-AMKL activity in vivo. Our findings provide a rationale to support clinical trials of MLN8237 and other inducers of polyploidization and differentiation in AMKL.

Bioinspired iontronic synapse fibers for ultralow-power multiplexing neuromorphic sensorimotor textiles.

Artificial neuromorphic devices can emulate dendric integration, axonal parallel transmission, along with superior energy efficiency in facilitating efficient information processing, offering enormous potential for wearable electronics. However, integrating such circuits into textiles to achieve biomimetic information perception, processing, and control motion feedback remains a formidable challenge. Here, we engineer a quasi-solid-state iontronic synapse fiber (ISF) comprising photoresponsive TiO2, ion storage Co-MoS2, and an ion transport layer. The resulting ISF achieves inherent short-term synaptic plasticity, femtojoule-range energy consumption, and the ability to transduce chemical/optical signals. Multiple ISFs are interwoven into a synthetic neural fabric, allowing the simultaneous propagation of distinct optical signals for transmitting parallel information. Importantly, IFSs with multiple input electrodes exhibit spatiotemporal information integration. As a proof of concept, a textile-based multiplexing neuromorphic sensorimotor system is constructed to connect synaptic fibers with artificial fiber muscles, enabling preneuronal sensing information integration, parallel transmission, and postneuronal information output to control the coordinated motor of fiber muscles. The proposed fiber system holds enormous promise in wearable electronics, soft robotics, and biomedical engineering.

Benchmarking multi-omics integration algorithms across single-cell RNA and ATAC data.

Recent advancements in single-cell sequencing technologies have generated extensive omics data in various modalities and revolutionized cell research, especially in the single-cell RNA and ATAC data. The joint analysis across scRNA-seq data and scATAC-seq data has paved the way to comprehending the cellular heterogeneity and complex cellular regulatory networks. Multi-omics integration is gaining attention as an important step in joint analysis, and the number of computational tools in this field is growing rapidly. In this paper, we benchmarked 12 multi-omics integration methods on three integration tasks via qualitative visualization and quantitative metrics, considering six main aspects that matter in multi-omics data analysis. Overall, we found that different methods have their own advantages on different aspects, while some methods outperformed other methods in most aspects. We therefore provided guidelines for selecting appropriate methods for specific scenarios and tasks to help obtain meaningful insights from multi-omics data integration.

scDecouple: decoupling cellular response from infected proportion bias in scCRISPR-seq.

Single-cell clustered regularly interspaced short palindromic repeats-sequencing (scCRISPR-seq) is an emerging high-throughput CRISPR screening technology where the true cellular response to perturbation is coupled with infected proportion bias of guide RNAs (gRNAs) across different cell clusters. The mixing of these effects introduces noise into scCRISPR-seq data analysis and thus obstacles to relevant studies. We developed scDecouple to decouple true cellular response of perturbation from the influence of infected proportion bias. scDecouple first models the distribution of gene expression profiles in perturbed cells and then iteratively finds the maximum likelihood of cell cluster proportions as well as the cellular response for each gRNA. We demonstrated its performance in a series of simulation experiments. By applying scDecouple to real scCRISPR-seq data, we found that scDecouple enhances the identification of biologically perturbation-related genes. scDecouple can benefit scCRISPR-seq data analysis, especially in the case of heterogeneous samples or complex gRNA libraries.

scTML: a pan-cancer single-cell landscape of multiple mutation types.

Investigating mutations, including single nucleotide variations (SNVs), gene fusions, alternative splicing and copy number variations (CNVs), is fundamental to cancer study. Recent computational methods and biological research have demonstrated the reliability and biological significance of detecting mutations from single-cell transcriptomic data. However, there is a lack of a single-cell-level database containing comprehensive mutation information in all types of cancer. Establishing a single-cell mutation landscape from the huge emerging single-cell transcriptomic data can provide a critical resource for elucidating the mechanisms of tumorigenesis and evolution. Here, we developed scTML (http://sctml.xglab.tech/), the first database offering a pan-cancer single-cell landscape of multiple mutation types. It includes SNVs, insertions/deletions, gene fusions, alternative splicing and CNVs, along with gene expression, cell states and other phenotype information. The data are from 74 datasets with 2 582 633 cells, including 35 full-length (Smart-seq2) transcriptomic single-cell datasets (all publicly available data with raw sequencing files), 23 datasets from 10X technology and 16 spatial transcriptomic datasets. scTML enables users to interactively explore multiple mutation landscapes across tumors or cell types, analyze single-cell-level mutation-phenotype associations and detect cell subclusters of interest. scTML is an important resource that will significantly advance deciphering intra-tumor and inter-tumor heterogeneity, and how mutations shape cell phenotypes.

NeuronMotif: Deciphering cis-regulatory codes by layer-wise demixing of deep neural networks.

Discovering DNA regulatory sequence motifs and their relative positions is vital to understanding the mechanisms of gene expression regulation. Although deep convolutional neural networks (CNNs) have achieved great success in predicting cis-regulatory elements, the discovery of motifs and their combinatorial patterns from these CNN models has remained difficult. We show that the main difficulty is due to the problem of multifaceted neurons which respond to multiple types of sequence patterns. Since existing interpretation methods were mainly designed to visualize the class of sequences that can activate the neuron, the resulting visualization will correspond to a mixture of patterns. Such a mixture is usually difficult to interpret without resolving the mixed patterns. We propose the NeuronMotif algorithm to interpret such neurons. Given any convolutional neuron (CN) in the network, NeuronMotif first generates a large sample of sequences capable of activating the CN, which typically consists of a mixture of patterns. Then, the sequences are "demixed" in a layer-wise manner by backward clustering of the feature maps of the involved convolutional layers. NeuronMotif can output the sequence motifs, and the syntax rules governing their combinations are depicted by position weight matrices organized in tree structures. Compared to existing methods, the motifs found by NeuronMotif have more matches to known motifs in the JASPAR database. The higher-order patterns uncovered for deep CNs are supported by the literature and ATAC-seq footprinting. Overall, NeuronMotif enables the deciphering of cis-regulatory codes from deep CNs and enhances the utility of CNN in genome interpretation.

Dead-zone-compensated design as general method of flow field optimization for redox flow batteries.

One essential element of redox flow batteries (RFBs) is the flow field. Certain dead zones that cause local overpotentials and side effects are present in all conventional designs. To lessen the detrimental effects, a dead-zone-compensated design of flow field optimization is proposed. The proposed architecture allows for the detection of dead zones and their compensation on existing flow fields. Higher reactant concentrations and uniformity factors can be revealed in the 3D multiphysical simulation. The experiments also demonstrate that at an energy efficiency (EE) of 80%, the maximum current density of the novel flow field is 205 mA cm-2, which is much higher than the values for the previous ones (165 mA cm-2) and typical serpentine flow field (153 mA cm-2). Extensions of the design have successfully increased system EE (2.7 to 4.3%) for a variety of flow patterns. As a result, the proposed design is demonstrated to be a general method to support the functionality and application of RFBs.

Decoding functional cell-cell communication events by multi-view graph learning on spatial transcriptomics.

Cell-cell communication events (CEs) are mediated by multiple ligand-receptor (LR) pairs. Usually only a particular subset of CEs directly works for a specific downstream response in a particular microenvironment. We name them as functional communication events (FCEs) of the target responses. Decoding FCE-target gene relations is: important for understanding the mechanisms of many biological processes, but has been intractable due to the mixing of multiple factors and the lack of direct observations. We developed a method HoloNet for decoding FCEs using spatial transcriptomic data by integrating LR pairs, cell-type spatial distribution and downstream gene expression into a deep learning model. We modeled CEs as a multi-view network, developed an attention-based graph learning method to train the model for generating target gene expression with the CE networks, and decoded the FCEs for specific downstream genes by interpreting trained models. We applied HoloNet on three Visium datasets of breast cancer and liver cancer. The results detangled the multiple factors of FCEs by revealing how LR signals and cell types affect specific biological processes, and specified FCE-induced effects in each single cell. We conducted simulation experiments and showed that HoloNet is more reliable on LR prioritization in comparison with existing methods. HoloNet is a powerful tool to illustrate cell-cell communication landscapes and reveal vital FCEs that shape cellular phenotypes. HoloNet is available as a Python package at https://github.com/lhc17/HoloNet.

scDiffusion: conditional generation of high-quality single-cell data using diffusion model.

MOTIVATION: Single-cell RNA sequencing (scRNA-seq) data are important for studying the laws of life at single-cell level. However, it is still challenging to obtain enough high-quality scRNA-seq data. To mitigate the limited availability of data, generative models have been proposed to computationally generate synthetic scRNA-seq data. Nevertheless, the data generated with current models are not very realistic yet, especially when we need to generate data with controlled conditions. In the meantime, diffusion models have shown their power in generating data with high fidelity, providing a new opportunity for scRNA-seq generation. RESULTS: In this study, we developed scDiffusion, a generative model combining the diffusion model and foundation model to generate high-quality scRNA-seq data with controlled conditions. We designed multiple classifiers to guide the diffusion process simultaneously, enabling scDiffusion to generate data under multiple condition combinations. We also proposed a new control strategy called Gradient Interpolation. This strategy allows the model to generate continuous trajectories of cell development from a given cell state. Experiments showed that scDiffusion could generate single-cell gene expression data closely resembling real scRNA-seq data. Also, scDiffusion can conditionally produce data on specific cell types including rare cell types. Furthermore, we could use the multiple-condition generation of scDiffusion to generate cell type that was out of the training data. Leveraging the Gradient Interpolation strategy, we generated a continuous developmental trajectory of mouse embryonic cells. These experiments demonstrate that scDiffusion is a powerful tool for augmenting the real scRNA-seq data and can provide insights into cell fate research. AVAILABILITY AND IMPLEMENTATION: scDiffusion is openly available at the GitHub repository https://github.com/EperLuo/scDiffusion or Zenodo https://zenodo.org/doi/10.5281/zenodo.13268742.

Convergent estimates of marine nitrogen fixation.

Uncertainty in the global patterns of marine nitrogen fixation limits our understanding of the response of the ocean's nitrogen and carbon cycles to environmental change. The geographical distribution of and ecological controls on nitrogen fixation are difficult to constrain with limited in situ measurements. Here we present convergent estimates of nitrogen fixation from an inverse biogeochemical and a prognostic ocean model. Our results demonstrate strong spatial variability in the nitrogen-to-phosphorus ratio of exported organic matter that greatly increases the global nitrogen-fixation rate (because phytoplankton manage with less phosphorus when it is in short supply). We find that the input of newly fixed nitrogen from microbial fixation and external inputs (atmospheric deposition and river fluxes) accounts for up to 50 per cent of carbon export in subtropical gyres. We also find that nitrogen fixation and denitrification are spatially decoupled but that nevertheless nitrogen sources and sinks appear to be balanced over the past few decades. Moreover, we propose a role for top-down zooplankton grazing control in shaping the global patterns of nitrogen fixation. Our findings suggest that biological carbon export in the ocean is higher than expected and that stabilizing nitrogen-cycle feedbacks are weaker than previously thought.

Fibres-threads of intelligence-enable a new generation of wearable systems.

Fabrics represent a unique platform for seamlessly integrating electronics into everyday experiences. The advancements in functionalizing fabrics at both the single fibre level and within constructed fabrics have fundamentally altered their utility. The revolution in materials, structures, and functionality at the fibre level enables intimate and imperceptible integration, rapidly transforming fibres and fabrics into next-generation wearable devices and systems. In this review, we explore recent scientific and technological breakthroughs in smart fibre-enabled fabrics. We examine common challenges and bottlenecks in fibre materials, physics, chemistry, fabrication strategies, and applications that shape the future of wearable electronics. We propose a closed-loop smart fibre-enabled fabric ecosystem encompassing proactive sensing, interactive communication, data storage and processing, real-time feedback, and energy storage and harvesting, intended to tackle significant challenges in wearable technology. Finally, we envision computing fabrics as sophisticated wearable platforms with system-level attributes for data management, machine learning, artificial intelligence, and closed-loop intelligent networks.

An Ionic Assisted Enhancement Strategy Enabled High Performance Flexible Pressure-Temperature Dual Sensor.

Flexible pressure sensors with a broad range and high sensitivity are greatly desired yet challenging to build. Herein, we have successfully fabricated a pressure-temperature dual sensor via an ionic assisted charge enhancement strategy. Benefiting from the immobilization effect for [EMIM+] [TFSI-] ion pairs and charge transfer between ionic liquid (IL) and HFMO (H10Fe3Mo21O51), the formed IL-HFMO-TPU pressure sensor shows a high sensitivity of 25.35 kPa-1 and broad sensing range (∼10 MPa), respectively. Furthermore, the sensor device exhibits high durability and stability (5000 cycles@1 MPa). The IL-HFMO-TPU sensor also shows the merit of good temperature sensing properties. Attributed to these superior properties, the proposed sensor device could detect pressure in an ultrawide sensing range (from Pa to MPa), including breathe and biophysical signal monitoring etc. The proposed ionic assisted enhancement approach is a generic strategy for constructing high performance flexible pressure-temperature dual sensor.

Self-Adhesive, Stretchable, and Thermosensitive Iontronic Hydrogels for Highly Sensitive Neuromorphic Sensing-Synaptic Systems.

Artificial sensory afferent nerves that emulate receptor nanochannel perception and synaptic ionic information processing in chemical environments are highly desirable for bioelectronics. However, challenges persist in achieving life-like nanoscale conformal contact, agile multimodal sensing response, and synaptic feedback with ions. Here, a precisely tuned phase transition poly(N-isopropylacrylamide) (PNIPAM) hydrogel is introduced through the water molecule reservoir strategy. The resulting hydrogel with strongly cross-linked networks exhibits excellent mechanical performance (∼2000% elongation) and robust adhesive strength. Importantly, the hydrogel's enhanced ionic conductance and heterogeneous structure of the temperature-sensitive component enable highly sensitive strain information perception (GFmax = 7.94, response time ∼ 87 ms), temperature information perception (TCRmax = -1.974%/°C, response time ∼ 270 ms), and low energy consumption synaptic plasticity (42.2 fJ/spike). As a demonstration, a neuromorphic sensing-synaptic system is constructed integrating iontronic strain/temperature sensors with fiber synapses for real-time information sensing, discrimination, and feedback. This work holds enormous potential in bioinspired robotics and bioelectronics.

In Situ Formation of Stable Dual-Layer Solid Electrolyte Interphase for Enhanced Stability and Cycle Life in All-Solid-State Lithium Metal Batteries.

All-solid-state lithium metal batteries have emerged as a promising solution to overcoming the energy density and safety challenges associated with conventional lithium-ion batteries. Solid polymer electrolytes, particularly those based on poly(vinylidene fluoride) (PVDF) and dimethylformamide (DMF), demonstrate significant potential. However, interfacial side reactions between residual DMF solvents and lithium metal present substantial challenges. In this study, we investigate the in situ formation of solid electrolyte interphase protective layers to mitigate these side reactions. By incorporating F-rich additives, such as fluoroethylene carbonate and lithium difluorophosphate, we successfully establish a dual-layer inorganic SEI structure characterized by an outer LiF layer and an inner Li2O layer. Consequently, our approach extends the cycle life of lithium symmetric batteries to 3000 h. Additionally, the Li||LiFePO4 solid-state battery demonstrates exceptional stability, enduring 400 cycles at a 1C rate with an impressive capacity retention of 84%. This strategic methodology effectively leverages the benefits of residual solvents, ensuring both enhanced battery efficiency and long-term operational stability for PVDF-based all-solid-state lithium metal batteries.

Constructing Photocatalytic Covalent Organic Frameworks with Aliphatic Linkers.

Photocatalytic covalent organic frameworks (COFs) are typically constructed with rigid aromatic linkers for crystallinity and extended π-conjugation. However, the essential hydrophobicity of the aromatic backbone can limit their performances in water-based photocatalytic reactions. Here, we for the first time report the synthesis of hydrophilic COFs with aliphatic linkers [tartaric acid dihydrazide (TAH) and butanedioic acid dihydrazide] that can function as efficient photocatalysts for H2O2 and H2 evolution. In these hydrophilic aliphatic linkers, the specific multiple hydrogen bonding networks not only enhance crystallization but also ensure an ideal compatibility of crystallinity, hydrophilicity, and light harvesting. The resulting aliphatic linker COFs adopt an unusual ABC stacking, giving rise to approximately 0.6 nm nanopores with an improved interaction with water guests. Remarkably, both aliphatic linker-based COFs show strong visible light absorption, along with a narrow optical band gap of ∼1.9 eV. The H2O2 evolution rate for TAH-COF reaches up to 6003 µmol h-1 g-1, in the absence of sacrificial agents, surpassing the performance of all previously reported COF-based photocatalysts. Theoretical calculations reveal that the TAH linker can enhance the indirect two-electron oxygen reduction reaction for H2O2 production by improving the O2 adsorption and stabilizing the *OOH intermediate. This study opens a new avenue for constructing semiconducting COFs using nonaromatic linkers.

Esketamine inhibits the c-Jun N-terminal kinase pathway in the spinal dorsal horn to relieve bone cancer pain in rats.

Cancer-induced bone pain (CIBP) is one of the most common and feared symptoms in patients with advanced tumors. The X-C motif chemokine ligand 12 (CXCL12) and the CXCR4 receptor have been associated with glial cell activation in bone cancer pain. Moreover, mitogen-activated protein kinases (MAPKs), as downstream CXCL12/CXCR4 signals, and c-Jun, as activator protein AP-1 components, contribute to the development of various types of pain. However, the specific CIBP mechanisms remain unknown. Esketamine is a non-selective N-methyl-d-aspartic acid receptor (NMDA) inhibitor commonly used as an analgesic in the clinic, but its analgesic mechanism in bone cancer pain remains unclear. We used a tumor cell implantation (TCI) model and explored that CXCL12/CXCR4, p-MAPKs, and p-c-Jun were stably up-regulated in the spinal cord. Immunofluorescence images showed activated microglia in the spinal cord on day 14 after TCI and co-expression of CXCL12/CXCR4, p-MAPKs (p-JNK, p-ERK, p-p38 MAPK), and p-c-Jun in microglia. Intrathecal injection of the CXCR4 inhibitor AMD3100 reduced JNK and c-Jun phosphorylations, and intrathecal injection of the JNK inhibitor SP600125 and esketamine also alleviated TCI-induced pain and reduced the expression of p-JNK and p-c-Jun in microglia. Overall, our data suggest that the CXCL12/CXCR4-JNK-c-Jun signaling pathway of microglia in the spinal cord mediates neuronal sensitization and pain hypersensitivity in cancer-induced bone pain and that esketamine exerts its analgesic effect by inhibiting the JNK-c-Jun pathway.