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Thermal boundary resistance (TBR) is a key property for the thermal management of high power micro- and opto-electronic devices and for the development of high efficiency thermal barrier coatings and thermoelectric materials. Prediction of TBR is important for guiding the discovery of interfaces with very low or very high TBR. In this study, we report the prediction of TBR by the machine learning method. We trained machine learning models using the collected experimental TBR data as training data and materials properties that might affect TBR as descriptors. We found that the machine learning models have much better predictive accuracy than the commonly used acoustic mismatch model and diffuse mismatch model. Among the trained models, the Gaussian process regression and the support vector regression models have better predictive accuracy. Also, by comparing the prediction results using different descriptor sets, we found that the film thickness is an important descriptor in the prediction of TBR. These results indicate that machine learning is an accurate and cost-effective method for the prediction of TBR.

A central step in synaptic vesicle priming is the transition of syntaxin-1 between two very stable complexes: with Munc18 and with the other SNARE complex components. How this transition is promoted is now explored by NMR and FRET studies, revealing that the MUN domain of Munc13 uses weak interactions to pry open the closed syntaxin-1 in complex with Munc18. During the priming step that leaves synaptic vesicles ready for neurotransmitter release, the SNARE syntaxin-1 transitions from a closed conformation that binds Munc18-1 tightly to an open conformation within the highly stable SNARE complex. Control of this conformational transition is important for brain function, but the underlying mechanism is unknown. NMR and fluorescence experiments now show that the Munc13-1 MUN domain, which plays a central role in vesicle priming, markedly accelerates the transition from the syntaxin-1–Munc18-1 complex to the SNARE complex. This activity depends on weak interactions of the MUN domain with the syntaxin-1 SNARE motif, and probably with Munc18-1. Together with available physiological data, these results provide a defined molecular basis for synaptic vesicle priming, and they illustrate how weak protein-protein interactions can play crucial biological roles by promoting transitions between high-affinity macromolecular assemblies.

Neurotransmitter release depends critically on Munc18-1, Munc13, the Ca 2+ sensor synaptotagmin-1, and the soluble N-ethylmaleimide—sensitive factor (NSF) attachment protein (SNAP) receptors (SNAREs) syntaxin-1, synaptobrevin, and SNAP-25. In vitro reconstitutions have shown that syntaxin-1—SNAP-25 liposomes fuse efficiently with synaptobrevin liposomes in the presence of synaptotagmin-1—Ca 2+ , but neurotransmitter release also requires Munc18-1 and Munc13 in vivo. We found that Munc18-1 could displace SNAP-25 from syntaxin-1 and that fusion of syntaxin-1—Munc18-1 liposomes with synaptobrevin liposomes required Munc13, in addition to SNAP-25 and synaptotagmin-1-Ca 2+ . Moreover, when starting with syntaxin-1—SNAP-25 liposomes, NSF—α-SNAP disassembled the syntaxin-1—SNAP-25 heterodimers and abrogated fusion, which then required Munc18-1 and Munc13. We propose that fusion does not proceed through syntaxin-1—SNAP-25 heterodimers but starts with the syntaxin-1—Munc18-1 complex; Munc18-1 and Munc13 then orchestrate membrane fusion together with the SNAREs and synaptotagmin-1-Ca 2+ in an NSF- and SNAP-resistant manner.

Three-dimensional graphene architectures in the macroworld can in principle maintain all the extraordinary nanoscale properties of individual graphene flakes. However, current 3D graphene products suffer from poor electrical conductivity, low surface area and insufficient mechanical strength/elasticity; the interconnected self-supported reproducible 3D graphenes remain unavailable. Here we report a sugar-blowing approach based on a polymeric predecessor to synthesize a 3D graphene bubble network. The bubble network consists of mono- or few-layered graphitic membranes that are tightly glued, rigidly fixed and spatially scaffolded by micrometre-scale graphitic struts. Such a topological configuration provides intimate structural interconnectivities, freeway for electron/phonon transports, huge accessible surface area, as well as robust mechanical properties. The graphene network thus overcomes the drawbacks of presently available 3D graphene products and opens up a wide horizon for diverse practical usages, for example, high-power high-energy electrochemical capacitors, as highlighted in this work. Three-dimensional graphene offers an ideal sheet-to-sheet connectivity of assembled graphenes, but often suffers from poor electrochemical performance. Wang et al . present a sugar-blowing technique to prepare a 3D graphene, which overcomes such problems and shows potential in supercapacitor applications.

Thermoelectric (TE) materials offer a promising pathway toward achieving carbon neutrality by converting waste heat into electricity. The enhancement of their figure-of-merit (zT) depends on optimizing the composition of materials and nanostructures, reducing the thermal conductivity, and increasing the power factor. Cu 2 Se, a superionic material, achieves a zT of 0.4 at 300 K by facilitating Cu ion movement within its face-centered cubic lattice, effectively suppressing thermal conductivity. Herein, we present a novel TE material developed by doping Cu x Se crystals of different compositions with FeSi 2 . We report a remarkable zT of 0.69 at 298 K for Cu 2 Se-based materials and reveal the presence of the CuO and Cu 2 O tiny crystals on the material surface, uniform dispersion of Si within the film, and formation of distinctive amorphous FeO. Our strategy holds great potential for notably advancing waste heat recovery in sustainable TE materials.

Various factors affect the interfacial thermal resistance (ITR) between two materials, making ITR prediction a high-dimensional mathematical problem. Machine learning is a cost-effective method to address this. Here, we report ITR predictive models based on experimental data. The physical, chemical, and material properties of ITR are categorized into three sets of descriptors, and three algorithms are used for the models. Those descriptors assist the models in reducing the mismatch between predicted and experimental values and reaching high predictive performance of 96%. Over 80,000 material systems composed of 293 materials were inputs for predictions. Among the top-100 high-ITR predictions by the three different algorithms, 25 material systems are repeatedly predicted by at least two algorithms. One of the 25 material systems, Bi/Si achieved the ultra-low thermal conductivity in our previous work. We believe that the predicted high-ITR material systems are potential candidates for thermoelectric applications. This study proposed a strategy for material exploration for thermal management by means of machine learning.

Understanding the structural biology of the insulin receptor and how it signals is of key importance in the development of insulin analogs to treat diabetes. We report here a cryo-electron microscopy structure of a single insulin bound to a physiologically relevant, high-affinity version of the receptor ectodomain, the latter generated through attachment of C-terminal leucine zipper elements to overcome the conformational flexibility associated with ectodomain truncation. The resolution of the cryo-electron microscopy maps is 3.2 Å in the insulin-binding region and 4.2 Å in the membrane-proximal region. The structure reveals how the membrane proximal domains of the receptor come together to effect signalling and how insulin’s negative cooperativity of binding likely arises. Our structure further provides insight into the high affinity of certain super-mitogenic insulins. Together, these findings provide a new platform for insulin analog investigation and design. The insulin receptor plays a key role in many physiological processes, yet how insulin effects receptor signaling at the structural level remains incomplete. Here the authors present a high-resolution cryo-EM structure of a high-affinity form of the insulin-bound insulin receptor ectodomain that sheds light on the mechanism of signal transduction.

BackgroundCoronary artery disease (CAD) represents a significant global health challenge. Despite advancements like percutaneous coronary intervention (PCI), patients remain at risk for recurrent cardiovascular events and persistent symptoms such as angina. Chinese herbal medicine (CHM) and acupuncture are commonly used adjunctive therapies, particularly in East Asia, but robust evidence synthesising their effects when combined with standard post-PCI care is needed. Therefore, this systematic review aims to evaluate the efficacy and safety of adding CHM or acupuncture to standard care compared with standard care alone (with or without placebo/sham) in adult patients following PCI.Methods and analysisWe will include parallel-group randomised controlled trials (RCTs) evaluating CHM or acupuncture plus standard care vs standard care (± placebo/sham) in adults post-PCI for CAD. Major international and Chinese electronic databases and clinical trial registries will be searched from their inception without language restrictions. Two reviewers will independently perform study selection based on predefined eligibility criteria and extract data using a standardised form. Risk of bias for included RCTs will be assessed using the Cochrane RoB 2 tool. Where appropriate based on clinical and methodological homogeneity (assessed using the I² statistic), pairwise meta-analyses will be conducted using random-effects models. We will calculate risk ratios (RR) or OR for dichotomous outcomes like major adverse cardiovascular events (MACE) and mortality, and mean differences (MD) or standardised mean differences (SMD) for continuous outcomes such as angina scores and health-related quality of life (HRQoL). Furthermore, if the evidence network permits, a network meta-analysis (NMA) will be performed to indirectly compare the relative effectiveness and safety of CHM vs acupuncture. If quantitative synthesis (pairwise or network) is not feasible, a narrative synthesis will be provided. The certainty of the evidence for key outcomes will be assessed using the GRADE approach. This evaluation will encompass the certainty derived from direct pairwise comparisons as well as the indirect comparisons and overall network estimates derived from the NMA.Ethics and disseminationThere are no ethical issues as this study did not conduct any experiments, surveys or human trials. We will ensure that the findings are shared through pertinent channels.PROSPERO registration numberCRD420251040037.

Research on complete mitochondrial genomes can help in understanding the molecular evolution and phylogenetic relationships of various species. In this study, the complete mitogenome of Hua aristarchorum was characterized to supplement the limited mitogenomic information on the genus Hua . Three distinct assembly methods, GetOrganelle, NovoPlasty and SPAdes, were used to ensure reliable assembly. The 15,691 bp mitogenome contains 37 genes and an AT-rich region. Notably, the cytochrome c oxidase subunit I ( COX1 ) gene, commonly used for species identification, appears to be slow-evolving and less variable, which may suggest the inclusion of rapidly evolving genes (NADH dehydrogenase subunit 6 [ ND6 ] or NADH dehydrogenase subunit 2 [ ND2 ]) as markers in diagnostic, detection, and population genetic studies of Cerithioidea. Moreover, we identified the unreliability of annotations (e.g., the absence of annotations for NADH dehydrogenase subunit 4L [ ND4L ] in NC_037771) and potential misidentifications (NC_023364) in public databases, which indicate that data from public databases should be manually curated in future research. Phylogenetic analyses of Cerithioidea based on different datasets generated identical trees using maximum likelihood and Bayesian inference methods. The results confirm that Semisulcospiridae is closely related to Pleuroceridae. The sequences of Semisulcospiridae clustered into three clades, of which H. aristarchorum is one; H. aristarchorum is sister to the other two clades. The findings of this study will contribute to a better understanding of the characteristics of the H. aristarchorum mitogenome and the phylogenetic relationships of Semisulcospiridae. The inclusion of further mitochondrial genome sequences will improve knowledge of the phylogeny and origin of Cerithioidea.

Background Single-cell transcriptome sequencing (scRNA-seq) has revolutionized the study of immune cells by overcoming the limitations of traditional antibody-based identification and isolation methods. This advancement allows us to obtain comprehensive gene expression profiles from a diverse array of vertebrate species, facilitating the identification of various cell types. Comparative immunology across vertebrates presents a promising approach to understanding the evolution of immune cell types. In this study, we conducted a comparative transcriptome analysis of peripheral blood mononuclear cells (PBMCs) at the single-cell level across 12 species. Results Our findings shed light on the cellular compositional features of PBMCs, spanning from fish to mammals. Notably, we identified genes that exhibit vertebrate universality in characterizing immune cells. Moreover, our investigation revealed that monocytes have maintained a conserved transcriptional regulatory program throughout evolution, emphasizing their pivotal role in orchestrating immune cells to execute immune programs. Conclusions This comprehensive analysis provides valuable insights into the evolution of immune cells across vertebrates.