Molecular and structural basis of an ATPase-nuclease dual-enzyme anti-phage defense complex.

Coupling distinct enzymatic effectors emerges as an efficient strategy for defense against phage infection in bacterial immune responses, such as the widely studied nuclease and cyclase activities in the type III CRISPR-Cas system. However, concerted enzymatic activities in other bacterial defense systems are poorly understood. Here, we biochemically and structurally characterize a two-component defense system DUF4297-HerA, demonstrating that DUF4297-HerA confers resistance against phage infection by cooperatively cleaving dsDNA and hydrolyzing ATP. DUF4297 alone forms a dimer, and HerA alone exists as a nonplanar split spiral hexamer, both of which exhibit extremely low enzymatic activity. Interestingly, DUF4297 and HerA assemble into an approximately 1 MDa supramolecular complex, where two layers of DUF4297 (6 DUF4297 molecules per layer) linked via inter-layer dimerization of neighboring DUF4297 molecules are stacked on top of the HerA hexamer. Importantly, the complex assembly promotes dimerization of DUF4297 molecules in the upper layer and enables a transition of HerA from a nonplanar hexamer to a planar hexamer, thus activating their respective enzymatic activities to abrogate phage infection. Together, our findings not only characterize a novel dual-enzyme anti-phage defense system, but also reveal a unique activation mechanism by cooperative complex assembly in bacterial immunity.

Co-Assembly of Polyoxometalates and Porphyrins as Anode for High-Performance Lithium-Ion Batteries.

Polyoxometalates (POMs) have been considered as one of the most promising anode candidates for lithium-ion batteries (LIBs) in virtue of their high theoretical capacity and reversible multielectron redox properties. However, the poor intrinsic electronic conductivity, low specific surface area and high solubility in organic electrolytes hinder their widespread applications in LIBs. Herein, a novel hybrid nanomaterial is synthesized by co-assembling POMs and porphyrins (PMo12/CoTPyP) through a facile solvothermal method. The POMs clusters are stabilized by porphyrin units through electrostatic interactions, which simultaneously realizes the uniform dispersion of POMs and porphyrin units. Benefiting from the generated sub-1 nm channels for fast ion transport and the synergistic effect between evenly distributed PMo12 clusters and high-conductive CoTPyP units, the LIB based on the optimized PMo12/CoTPyP anode exhibits significant improved Li+ storage capability as well as superior rate and cycling performance. The results of density functional theory (DFT) simulations further reveal that the co-assembly of PMo12 and CoTPyP can accelerate the mobility of Li+ and electrons, which in turn promotes the enhancement of LIBs performance. This work paves a strategy for synthesizing POMs-based anode materials with simultaneously high dispersibility, redox activity and stability. This article is protected by copyright. All rights reserved.

Interfacial Metal-Support Interaction and Catalytic Performance of Perovskite LaCrO3-Supported Ru Catalyst.

Interfacial metal-support interaction (MSI) significantly affects the dispersion of active metals on the surface of the catalyst support and impacts catalyst performance. Understanding MSI is crucial for developing highly active and stable catalysts with a low metal loading, particularly for noble metal catalysts. In this work, we synthesized LaRuxCr1-xO3 catalysts with low Ru loading (x = 0.005, 0.01, and 0.02) using the sol-gel self-combustion method. We found that all of the Ru atoms immediately above or below the metal-support interface are closely bonded to the perovskite LaCrO3 surface lattice through Ru-O bonds, enhancing the MSI via interfacial reaction and charge transfer mechanisms. We identified a variety of Ru species, including small 3D Ru nanoparticles, 2D dispersed Ru surface atoms, and even 0D Ru single atoms. These highly dispersed Ru species exhibit high activity and stability under dry reforming of methane (DRM) conditions. The LaRu0.01Cr0.99O3 catalyst with very low Ru loading (0.42 wt %) was stable over a 50 h DRM test and the carbon deposition was negligible. The CH4 and CO2 conversions at 750 °C reached 83 and 86%, respectively, approaching the theoretical thermodynamic equilibrium values.

Heterogeneity and predictors of the effects of AI assistance on radiologists.

The integration of artificial intelligence (AI) in medical image interpretation requires effective collaboration between clinicians and AI algorithms. Although previous studies demonstrated the potential of AI assistance in improving overall clinician performance, the individual impact on clinicians remains unclear. This large-scale study examined the heterogeneous effects of AI assistance on 140 radiologists across 15 chest X-ray diagnostic tasks and identified predictors of these effects. Surprisingly, conventional experience-based factors, such as years of experience, subspecialty and familiarity with AI tools, fail to reliably predict the impact of AI assistance. Additionally, lower-performing radiologists do not consistently benefit more from AI assistance, challenging prevailing assumptions. Instead, we found that the occurrence of AI errors strongly influences treatment outcomes, with inaccurate AI predictions adversely affecting radiologist performance on the aggregate of all pathologies and on half of the individual pathologies investigated. Our findings highlight the importance of personalized approaches to clinician-AI collaboration and the importance of accurate AI models. By understanding the factors that shape the effectiveness of AI assistance, this study provides valuable insights for targeted implementation of AI, enabling maximum benefits for individual clinicians in clinical practice.

Broadly neutralizing antibodies derived from the earliest COVID-19 convalescents protect mice from SARS-CoV-2 variants challenge.

Coronavirus disease 2019 (COVID-19) was first reported three years ago, when a group of individuals were infected with the original SARS-CoV-2 strain, based on which vaccines were developed. Here, we develop six human monoclonal antibodies (mAbs) from two elite convalescents in Wuhan and show that these mAbs recognize diverse epitopes on the receptor binding domain (RBD) and can inhibit the infection of SARS-CoV-2 original strain and variants of concern (VOCs) to varying degrees, including Omicron strains XBB and XBB.1.5. Of these mAbs, the two most broadly and potently neutralizing mAbs (7B3 and 14B1) exhibit prophylactic activity against SARS-CoV-2 WT infection and therapeutic effects against SARS-CoV-2 Delta variant challenge in K18-hACE2 KI mice. Furthermore, post-exposure treatment with 7B3 protects mice from lethal Omicron variants infection. Cryo-EM analysis of the spike trimer complexed with 14B1 or 7B3 reveals that these two mAbs bind partially overlapped epitopes onto the RBD of the spike, and sterically disrupt the binding of human angiotensin-converting enzyme 2 (hACE2) to RBD. Our results suggest that mAbs with broadly neutralizing activity against different SARS-CoV-2 variants are present in COVID-19 convalescents infected by the ancestral SARS-CoV-2 strain, indicating that people can benefit from former infections or vaccines despite the extensive immune escape of SARS-CoV-2.

Evaluating progress in automatic chest X-ray radiology report generation.

Artificial intelligence (AI) models for automatic generation of narrative radiology reports from images have the potential to enhance efficiency and reduce the workload of radiologists. However, evaluating the correctness of these reports requires metrics that can capture clinically pertinent differences. In this study, we investigate the alignment between automated metrics and radiologists' scoring of errors in report generation. We address the limitations of existing metrics by proposing new metrics, RadGraph F1 and RadCliQ, which demonstrate stronger correlation with radiologists' evaluations. In addition, we analyze the failure modes of the metrics to understand their limitations and provide guidance for metric selection and interpretation. This study establishes RadGraph F1 and RadCliQ as meaningful metrics for guiding future research in radiology report generation.

Structural and functional insights into the modulation of T cell costimulation by monkeypox virus protein M2.

The rapid spread of monkeypox in multiple countries has resulted in a global public health threat and has caused international concerns since May 2022. Poxvirus encoded M2 protein is a member of the poxvirus immune evasion family and plays roles in host immunomodulation via the regulation of innate immune response mediated by the NF-κB pathway and adaptive immune response mediated by B7 ligands. However, the interaction of monkeypox virus (MPXV) M2 with B7 ligands and structural insight into poxviral M2 function have remained elusive. Here we reveal that MPXV M2, co-existing as a hexamer and a heptamer, recognizes human B7.1 and B7.2 (hB7.1/2) with high avidities. The binding of oligomeric MPXV M2 interrupts the interactions of hB7.1/2 with CD28 and CTLA4 and subverts T cell activation mediated by B7.1/2 costimulatory signals. Cryo-EM structures of M2 in complex with hB7.1/2 show that M2 binds to the shallow concave face of hB7.1/2 and displays sterically competition with CD28 and CTLA4 for the binding to hB7.1/2. Our findings provide structural mechanisms of poxviral M2 function and immune evasion deployed by poxviruses.

Single-cell Heterogeneity-aware Transformer-guided Multiple Instance Learning for Cancer Aneuploidy Prediction from Whole Slide Histopathology Images.

Aneuploidy is a hallmark of aggressive malignancies associated with therapeutic resistance and poor survival. Measuring aneuploidy requires expensive specialized techniques that are not clinically applicable. Deep learning analysis of routine histopathology slides has revealed associations with genetic mutations. However, existing studies focus on image patches or tiles, and there is no prior work that predicts aneuploidy using single-cell analysis. Here, we present a single-cell heterogeneity-aware and transformer-guided deep learning framework to predict aneuploidy from whole slide histopathology images. First, we perform nuclei segmentation and classification to obtain individual cancer cells, which are clustered into multiple subtypes. The cell subtype distributions are computed to measure cancer cell heterogeneity. Additionally, morphological features of different cell subtypes are extracted. Further, we leverage a multiple instance learning module with Transformer, which encourages the network to focus on the most informative cancer cells. Lastly, a hybrid network is built to unify cell heterogeneity, morphology, and deep features for aneuploidy prediction. We train and validate our method on two public datasets from TCGA: lung adenocarcinoma (LUAD) and head and neck squamous cell carcinoma (HNSC), with 339 and 245 patients. Our model achieves promising performance with AUC of 0.818 (95% CI: 0.718-0.919) and 0.827 (95% CI: 0.704-0.949) on the LUAD and HNSC test sets, respectively. Through extensive ablation and comparison studies, we demonstrate the effectiveness of each component of the model and superior performance over alternative networks. In conclusion, we present a novel deep learning approach to predict aneuploidy from histopathology images, which could inform personalized cancer treatment.

Dispersing small Ru nanoparticles into boron nitride remodified by reduced graphene oxide for high-efficient electrocatalytic hydrogen evolution reaction.

Ruthenium (Ru) electrocatalysts suffer from excessive aggregation during the hydrogen evolution reaction (HER), which hinders their practical application for hydrogen production. Hexagonal boron nitride (h-BN) is a potential carrier that could solve the above problem, but its wide band gap and low conductivity become obstacles. Herein, we provide a new, facile, low-cost, and effective strategy (killing two birds with one stone) to overcome the above issues. After modifying h-BN with reduced graphene oxide (rGO), a small amount of Ru nanoparticles (NPs) (2.2 %) are dispersed into BN with approximately uniform distribution and size control of Ru nanoparticles (∼3.85 nm). The strong synergy between Ru NPs and BN@C in the optimal Ru/BN@C (Ru wt.% = 2.22 %) electrocatalyst endows it an outstanding HER activity, with small HER overpotentials (η10 = 32 mV, 35 mV) and low Tafel slopes (33.89 mV dec-1, 37.66 mV dec-1) in both 1 M KOH and 0.5 M H2SO4 media, respectively, along with good long-term stability for 50 h. Based on density functional theory (DFT) calculations, the addition of Ru to BN has been successful in creating fresh active sites for H*, with good possible adsorption/desorption ability (ΔGH* = -0.24 eV) while preserving low water dissociation (ΔGb = 0.46 eV) in an alkaline environment. As a result, the Ru/BN composite exhibits outstanding HER activity in both acidic and alkaline conditions. Furthermore, this study provides, for the first time, a template-free strategy to develop a good and low-cost supporter (BN) for dispersing other noble metals and the formation of highly efficient HER/OER electrocatalysts.

Two-dimensional ultrathin surfactant-encapsulating polyoxometalate assemblies as carriers for monodispersing noble-metal nanoparticles with high catalytic activity and stability.

Noble metal nanoparticles (NMNPs) with excellent catalytic activity and stability play an important role in the field of environmental governance. A uniform distribution and a strong binding force with the carriers of the noble metal nanoparticles are important, but avoidance of the use of additional reducing agents is a promising direction of research. Herein, 2D ultrathin surfactant-encapsulating polyoxometalate (SEP) nanosheets constructed by the self-assembly of dodecyldimethylammonium bromide (DODA) and molybdophosphate (H3PMo12O40, PMo12) are designed to be versatile carriers for Ag nanoparticles. Under the synergistic effect of the well-arranged PMo12 units, encapsulating hydrophobic oleic acid (OA) and reductive molybdophosphate under Xe lamp irradiation, the silver oleate (AgOA)-derived Ag nanoparticles (5 ± 2 nm) are monodispersed on the DODA-PMo12 assemblies and form the Agx/DODA-PMo12 composite. The optimized Ag4.89/DODA-PMo12 composite exhibits high catalytic activity and stability in the degradation of 4-nitrophenol (4-NP), which reaches a superior rate constant of 6.49 × 10-3 s-1 and without significant deterioration after three recycles. This technique can be facilely promoted to other noble metal nanoparticles with excellent catalytic activity and stability.

Pt-O bond as an active site superior to Pt0 in hydrogen evolution reaction.

The oxidized platinum (Pt) can exhibit better electrocatalytic activity than metallic Pt0 in the hydrogen evolution reaction (HER), which has aroused great interest in exploring the role of oxygen in Pt-based catalysts. Herein, we select two structurally well-defined polyoxometalates Na5[H3Pt(IV)W6O24] (PtW6O24) and Na3K5[Pt(II)2(W5O18)2] (Pt2(W5O18)2) as the platinum oxide model to investigate the HER performance. Electrocatalytic experiments show the mass activities of PtW6O24/C and Pt2(W5O18)2/C are 20.175 A mg-1 and 10.976 A mg-1 at 77 mV, respectively, which are better than that of commercial 20% Pt/C (0.398 A mg-1). The in situ synchrotron radiation experiments and DFT calculations suggest that the elongated Pt-O bond acts as the active site during the HER process, which can accelerate the coupling of proton and electron and the rapid release of H2. This work complements the knowledge boundary of Pt-based electrocatalytic HER, and suggests another way to update the state-of-the-art electrocatalyst.

In Vitro, Ex Vivo and In Vivo Evaluation of Microcontainers for Oral Delivery of Insulin.

Enhancing the oral bioavailability of peptides has received a lot of attention for decades but remains challenging, partly due to low intestinal membrane permeability. Combining a permeation enhancer (PE) with unidirectionally releasing microcontainers (MCs) has previously been shown to increase insulin permeation across Caco-2 cell monolayers. In the present work, this setup was further employed to compare three common PEs-sodium caprate (C10), sodium dodecyl sulfate (SDS), and lauroyl carnitine. The concept was also studied using porcine intestinal tissue with the inclusion of 70 kDa fluorescein isothiocyanate-dextran (FD70) as a pathogen marker. Moreover, a combined proteolysis and Caco-2 cell permeation setup was developed to investigate the effect of soybean trypsin inhibitor (STI) in the MCs. Lastly, in vivo performance of the MCs was tested in an oral gavage study in rats by monitoring blood glucose and insulin absorption. SDS proved to be the most potent PE without increasing the ex vivo uptake of FD70, while the implementation of STI further improved insulin permeation in the combined proteolysis Caco-2 cell setup. However, no insulin absorption in rats was observed upon oral gavage of MCs loaded with insulin, PE and STI. Post-mortem microscopic examination of their gastrointestinal tract indicated lack of intestinal retention and optimal orientation by the MCs, possibly precluding the potential advantage of unidirectional release.

Electrocatalytic performance of ultrasmall Mo2C affected by different transition metal dopants in hydrogen evolution reaction.

Molybdenum carbides are considered as one type of privileged noble-metal-free electrocatalysts for hydrogen evolution reactions (HER) due to their d-band electron structure, which is similar to Pt. Especially, the electronic structure of such materials can be further adjusted by elemental doping to improve their electrocatalytic activity. Herein, we selected the Anderson-type polyoxometalates (POMs) (NH4)n[TMMo6O24H6]·5H2O (TM = Ni2+, Co2+, n = 4; TM = Fe3+, Cr3+, n = 3) as precursors to prepare new transition-metal-doped Mo2C materials. When these POMs were mixed with dicyandiamide (DCA) by solid grinding, and carbonized at a high temperature, a series of Ni-, Co-, Fe-, and Cr-doped Mo2C composite nanoparticles covered by few-layer graphitic carbon shells (abbr. TM-Mo2C@C) were obtained. All these nanoparticles possess a similar size, morphology, and TM/Mo component ratio, and thus it is feasible to systematically investigate the influence of different TM dopants on the electrocatalytic activity of Mo2C for HER. Both electrocatalytic experiments and DFT calculations reveal that TM dopants have a significant effect on the hydrogen binding energy (ΔGH*) and the catalytic activity of Mo2C. The sequence of HER electrocatalytic activity is as follows: Ni-Mo2C > Co-Mo2C > Fe-Mo2C > Cr-Mo2C. As a result, Ni-Mo2C@C possesses the best HER performance, which required an overpotential of 72 mV at a current density of 10 mA cm-2 and the Tafel slope is 65.8 mV dec-1. This work suggests a shortcut to reasonably investigate the effects of elemental doping on molybdenum carbides and explore new high-efficient and low-cost electrocatalysts for HER.

A Co2 P/WC Nano-Heterojunction Covered with N-Doped Carbon as Highly Efficient Electrocatalyst for Hydrogen Evolution Reaction.

The hydrogen evolution reaction (HER) produces clean hydrogen through an electrochemical process. However, new nonprecious-metal electrocatalysts for the HER are required to reduce the consumption of energy. Herein, we report a new Co2 P/WC nano-heterojunction that consists of Co2 P and WC composite phases coated with a few-layer N-doped graphitic carbon shells (Co2 P/WC@NC). The composite was prepared by a one-step annealing of the polyoxometalate Na9 (NH4 )5 [{(B-α-PW9 O34 )Co3 (OH)(H2 O)2 (Ale)}2 Co]⋅35 H2 O (Co7 P6 W18 ) and dicyandiamide (DCA). The preparation method consisted of the simultaneous phosphorization of Co and carbonization of W in a confined space to isolate a Co2 P/WC nano-heterojunction phase for the first time. Co2 P/WC@NC facilitated the generation of hydrogen in the electrolysis process, which had an overpotential of only 91 mV at a current density of 10 mA cm-2 in the acid solution; an excellent HER performance (2 H+ +2 e- →H2 ) and Tafel slope (40 mV dec-1 ) as well as durability over a period of 50 h were achieved. Theoretical calculations showed that the Co2 P, WC, and Npyridinic C dopants in the material synergistically promoted the HER activity rather than the individual constituents. Furthermore, Co2 P/WC@NC nano-heterojunctions showed good HER performance in the whole pH range of electrolytes and considerable durability in acidic media containing transition metal ions, which may attract more attention for the exploration and optimization of nano-heterojunction catalysts for the HER.

Semantic content analysis and annotation of histological images.

This paper presents a novel two-dimensional (2-D) stochastic method for semantic analysis of the content of histological images Specifically, we propose a 2-D generalization of the traditional hidden Markov model (HMM). The generalization is called spatial-hidden Markov model (SHMM) that captures the contextual characteristics of complex biological features in histological images The model employs a second-order neighborhood system and assumes the conditional independence of vertical and horizontal transitions between hidden states. The notion of 'past' in SHMM is defined as what have been observed in a row-wise raster scan. This paper focuses on two fundamental problems: the best states decoding problem and the estimation of generation probability of an image by a SHMM. Based on our independence assumption of horizontal and vertical transitions, we derive computational tractable solutions to those problems. These solutions are direct extensions of their counterparts, i.e., the Viterbi algorithm and Forward-Backward algorithm, for 1-D HMM. Our experiments were carried on a medical image database with 200 images and compared with a state-of-the-art approach that was run on the same database. The annotation results demonstrated that SHMM consistently outperforms the previous approach and ameliorates many of its drawbacks. In addition, performance comparison with HMM has also validated the superiority of SHMM.