Nature Communications最新文献

Sphingosine-1-phosphate (S1P) is a signaling lysolipid critical to heart development, immunity, and hearing. Accordingly, mutations in the S1P transporter SPNS2 are associated with reduced white cell count and hearing defects. SPNS2 also exports the S1P-mimicking FTY720-P (Fingolimod) and thereby is central to the pharmacokinetics of this drug when treating multiple sclerosis. Here, we use a combination of cryo-electron microscopy, immunofluorescence, in vitro binding and in vivo S1P export assays, and molecular dynamics simulations to probe SPNS2’s substrate binding and transport. These results reveal the transporter’s binding mode to its native substrate S1P, the therapeutic FTY720-P, and the reported SPNS2-targeting inhibitor 33p. Further capturing an inward-facing apo state, our structures illuminate the protein’s mechanism for exchange between inward-facing and outward-facing conformations. Finally, using these structural, localization, and S1P transport results, we identify how pathogenic mutations ablate the protein’s export activity and thereby lead to hearing loss.

Electrostriction is an important electro-mechanical property in poly (vinylidene fluoride) (PVDF) films, which describes the proportional relation between the electro-stimulated deformation and the square of the electric field. Generally, traditional methods to improve the electrostriction of PVDF either sacrifice other crystalline-related key properties or only influence minimal regions around the surface. Here, we design a unique electret structure to fully exploit the benefits of internal crystal in PVDF films. Through the 3D printing of charged ink, we have obtained the best electrostrictive and ferroelectric properties among PVDF-based materials so far. The optimized electrostrictive coefficient M₃₃ (324 × 10⁻¹⁸ m² V⁻²) is 10⁴ times that of normal PVDF films, and the piezoelectric constant d₃₃ (298 pm V⁻¹) is close to 10 times its traditional limit. The proposed 3D electret structure and the bottom-up approach to ‘print the charge’ open up a new way to design and adapt the electroactive polymers in smart devices and systems.

Accurately basecalling sequence backbones in the presence of nucleotide modifications remains a substantial challenge in nanopore sequencing bioinformatics. It has been extensively demonstrated that state-of-the-art basecallers are less compatible with modification-induced sequencing signals. A precise basecalling, on the other hand, serves as the prerequisite for virtually all the downstream analyses. Here, we report that basecallers exposed to diverse training modifications gain the generalizability to analyze novel modifications. With synthesized oligos as the model system, we precisely basecall various out-of-sample RNA modifications. From the representation learning perspective, we attribute this generalizability to basecaller representation space expanded by diverse training modifications. Taken together, we conclude increasing the training data diversity as a paradigm for building modification-tolerant nanopore sequencing basecallers.

Atomically precise synthesis of graphene nanostructures on semiconductors and insulators has been a formidable challenge. In particular, the metallic substrates needed to catalyze cyclodehydrogenative planarization reactions limit subsequent applications that exploit the electronic and/or magnetic structure of graphene derivatives. Here, we introduce a protocol in which an on-surface reaction is initiated and carried out regardless of the substrate type. We demonstrate that, counterintuitively, atomic hydrogen can play the role of a catalyst in the cyclodehydrogenative planarization reaction. The high efficiency of the method is demonstrated by the nanographene synthesis on metallic Au, semiconducting TiO₂, Ge:H, as well as on inert and insulating Si/SiO₂ and thin NaCl layers. The hydrogen-catalyzed cyclodehydrogenation reaction reported here leads towards the integration of graphene derivatives in optoelectronic devices as well as developing the field of on-surface synthesis by means of catalytic transformations. It also inspires merging of atomically shaped graphene-based nanostructures with low-dimensional inorganic units into functional devices.

Separation of multi-component mixtures in an energy-efficient manner has important practical impact in chemical industry but is highly challenging. Especially, targeted simultaneous removal of multiple impurities to purify the desired product in one-step separation process is an extremely difficult task. We introduced a pore integration strategy of modularizing ordered pore structures with specific functions for on-demand assembly to deal with complex multi-component separation systems, which are unattainable by each individual pore. As a proof of concept, two ultramicroporous nanocrystals (one for C₂H₂-selective and the other for CO₂-selective) as the shell pores were respectively grown on a C₂H₆-selective ordered porous material as the core pore. Both of the respective pore-integrated materials show excellent one-step ethylene production performance in dynamic breakthrough separation experiments of C₂H₂/C₂H₄/C₂H₆ and CO₂/C₂H₄/C₂H₆ gas mixture, and even better than that from traditional tandem-packing processes originated from the optimized mass/heat transfer. Thermodynamic and dynamic simulation results explained that the pre-designed pore modules can perform specific target functions independently in the pore-integrated materials.

Estimation of the energy of quantum many-body systems is a paradigmatic task in various research fields. In particular, efficient energy estimation may be crucial in achieving a quantum advantage for a practically relevant problem. For instance, the measurement effort poses a critical bottleneck for variational quantum algorithms. We aim to find the optimal strategy with single-qubit measurements that yields the highest provable accuracy given a total measurement budget. As a central tool, we establish tail bounds for empirical estimators of the energy. They are helpful for identifying measurement settings that improve the energy estimate the most. This task constitutes an NP-hard problem. However, we are able to circumvent this bottleneck and use the tail bounds to develop a practical, efficient estimation strategy, which we call ShadowGrouping. As the name indicates, it combines shadow estimation methods with grouping strategies for Pauli strings. In numerical experiments, we demonstrate that ShadowGrouping improves upon state-of-the-art methods in estimating the electronic ground-state energies of various small molecules, both in provable and practical accuracy benchmarks. Hence, this work provides a promising way, e.g., to tackle the measurement bottleneck associated with quantum many-body Hamiltonians.

With recent advancements in gene editing technology using the CRISPR/Cas system, there is a demand for more effective gene editors. A key factor facilitating efficient gene editing is effective CRISPR delivery into cells, which is known to be associated with the size of the CRISPR system. Accordingly, compact CRISPR-Cas systems derived from various strains are discovered, among which Un1Cas12f1 is 2.6 times smaller than SpCas9, providing advantages for gene therapy research. Despite extensive engineering efforts to improve Un1Cas12f1, the editing efficiency of Un1Cas12f1 is still shown to be low depending on the target site. To overcome this limitation, we develop enhanced Cas12f1 (eCas12f1), which exhibits gene editing activity similar to SpCas9 and AsCpf1, even in gene targets where previously improved Un1Cas12f1 variants showed low gene editing efficiency. Furthermore, we demonstrate that eCas12f1 efficiently induces apoptosis in cancer cells and is compatible with base editing and regulation of gene expression, verifying its high utility and applicability in gene therapy research.

The beta decay of the lightest charmed baryon ({Lambda }_{c}^{+}) provides unique insights into the fundamental mechanism of strong and electro-weak interactions, serving as a testbed for investigating non-perturbative quantum chromodynamics and constraining the Cabibbo-Kobayashi-Maskawa (CKM) matrix parameters. This article presents the first observation of the Cabibbo-suppressed decay ({Lambda }_{c}^{+}to n{e}^{+}{nu }_{e}), utilizing 4.5 fb⁻¹ of electron-positron annihilation data collected with the BESIII detector. A novel Graph Neural Network based technique effectively separates signals from dominant backgrounds, notably ({Lambda }_{c}^{+}to Lambda {e}^{+}{nu }_{e}), achieving a statistical significance exceeding 10σ. The absolute branching fraction is measured to be (3.57 ± 0.34_stat. ± 0.14_syst.) × 10⁻³. For the first time, the CKM matrix element (leftvert {V}_{cd}rightvert) is extracted via a charmed baryon decay as (0.208pm 0.01{1}_{{{{rm{exp.}}}}}pm 0.00{7}_{{{{rm{LQCD}}}}}pm 0.00{1}_{{tau }_{{Lambda }_{c}^{+}}}). This work highlights a new approach to further understand fundamental interactions in the charmed baryon sector, and showcases the power of modern machine learning techniques in experimental high-energy physics.

Complex experimental protocols often require multi-modal data acquisition with precisely aligned timing, as well as state- and behavior-dependent interventions. Tailored solutions are mostly restricted to individual experimental setups and lack flexibility and interoperability. We present an open-source, Linux-based integrated software solution, called ‘Syntalos’, for simultaneous acquisition and synchronization of data from an arbitrary number of sources, including multi-channel electrophysiological recordings and different live imaging devices, as well as closed-loop, real-time interventions with different actuators. Precisely matching timestamps for all inputs are ensured by continuous statistical analysis and correction of individual devices’ timestamps. New data sources can be integrated with minimal programming skills. Data is stored in a comprehensively structured format to facilitate pooling or sharing data between different laboratories. Syntalos enables precisely synchronized multi-modal recordings as well as closed-loop interventions for multiple experimental approaches. Preliminary neuroscientific experiments on mice with different research questions show the successful performance and easy-to-learn structure of the software suite.

A suspected 443-486 kt of methane escaped from the Nord Stream pipelines in September 2022 at four explosion sites across three pipelines. Much of this methane rapidly escaped to the atmosphere, while an unknown amount was dissolved. We use sustained high-resolution observations of methane concentrations from autonomous gliders and an instrumented ship of opportunity to reveal the timing and spread of dissolved methane across different Baltic regions and marine protected areas. Estimates of methane spread and concentrations are essential to understand the ecosystem response, and for establishing accurate priors for atmospheric outgassing and transport models. A numerical model, initialized by engineering estimates and our observations, enables us to constrain the mass of locally dissolved Nord Stream methane (9.5-14.7 kt). We show that dissolved methane decreased rapidly through outgassing, however initial concentrations were so high that 14% of the Baltic Sea still experienced concentrations 5 times greater than average natural levels.