Omnidirectional anisotropic embedded 3D bioprinting.

Anisotropic microstructures resulting from a well-ordered arrangement of filamentous extracellular matrix (ECM) components or cells can be found throughout the human body, including skeletal muscle, corneal stroma, and meniscus, which play a crucial role in carrying out specialized physiological functions. At present, due to the isotropic characteristics of conventional hydrogels, the construction of freeform cell-laden anisotropic structures with high-bioactive hydrogels is still a great challenge. Here, we proposed a method for direct embedded 3D cell-printing of freeform anisotropic structure with shear-oriented bioink (GelMA/PEO). This study focuses on the establishment of an anisotropic embedded 3D bioprinting system, which effectively utilizes the shear stress generated during the extrusion process to create cells encapsulating tissues with distinct anisotropy. In conjunction with the water-solubility of PEO and the in-situ encapsulation effect provided by the carrageenan support bath, high-precise cell-laden bioprinting of intricate anisotropic and porous bionic artificial tissues can be effectively implemented in one-step. Additionally, anisotropic permeable blood vessel has been taken as a representation to validate the effectiveness of the shear-oriented bioink system in fabricating intricate structures with distinct directional characteristics. Lastly, the successful preparation of muscle patches with anisotropic properties and their guiding role for cell cytoskeleton extension have provided a significant research foundation for the application of the anisotropic embedded 3D bioprinting system in the ex-vivo production and in-vivo application of anisotropic artificial tissues.

Mechanistic insights into temperature-driven retention and speciation changes of heavy metals (HMs) in ash residues from Co-combustion of refuse-derived fuel (RDF) and red mud.

Red mud is a promising candidate for promoting the incineration of Refuse Derived Fuel (RDF) and stabilizing the resulting incineration ash. The combustion conditions, notably temperature, significantly steers the migration and transformation of harmful metal components during combustion, and ultimately affect their retention and speciation in the ash residue. The study attempted to investigate the effect of co-combustion temperature on the enrichment and stability of Cr, Ni, Cu, Zn, Cd and Pb within bottom ashes, and to reveal the underlined promotion mechanism of red mud addition. As temperature increased, red mud's active components formed a robust matrix, helping the formation, melting, and vitrification of silicates and aluminosilicates in the bottom ashes. The process significantly contributed to the encapsulation and stabilization of heavy metals such as Ni, Cu, Zn, Cd, and Pb, with their residual fractions ascending to 71.37%, 55.75%, 74.78%, 84.24%, and 93.54%, respectively. Conversely, high temperatures led to an increase in the proportion of Cr in the extremely unstable acid-soluble fraction of the bottom ashes, reaching 31.52%, posing a heightened risk of environmental migration. Considering the stability of heavy metals in the bottom ashes and the combustion characteristics, 800 °C is identified as the optimal temperature for the co-combustion of RDF and red mud, balancing efficiency and environmental safety. The findings will provide valuable insights for the co-utilization strategy of RDF and red mud, contributing to more informed decision-making in waste-to-energy processes.

Van der Waals phase transition investigation toward high-voltage layered cathodes.

High-voltage phase transition constitutes the major barrier to accessing high energy density in layered cathodes. However, questions remain regarding the origin of phase transition, because the interlayer weak bonding features cannot get an accurate description by experiments. Here, we determined van der Waals (vdW) interaction (vdWi) in LixCoO2 via visualizing its electron density, elucidating the origin of O3─O1 phase transition. The charge around oxygen is distorted by the increasing Co─O covalency. The charge distortion causes the difference of vdW gap between O3 and O1 phases, verified by a gap corrected vdW equation. In a high charging state, excessive covalency breaks the vdW gap balance, driving the O3 phase toward a stable O1 one. This interpretation of vdWi-dominated phase transition can be applied to other layered materials, as shown by a map regarding degree of covalence. Last, we introduce the cationic potential to provide a solution for designing high-voltage layered cathodes.

Oxophilic gallium single atoms bridged ruthenium clusters for practical anion-exchange membrane electrolyzer.

The development of highly efficient and durable alkaline hydrogen evolution reaction (HER) catalysts is crucial for achieving high-performance practical anion exchange membrane water electrolyzer (AEMWE) at ampere-level current density. Herein, we report a design concept by employing Ga single atoms as an electronic bridge to stabilize the Ru clusters for boosting alkaline HER performance in practical AEMWE. Experimental and theoretical results collectively reveal that the bridged Ga sites trigger strong metal-support interaction for the homogeneous distribution of Ru clusters with high density, as well as optimize the Ru-H bond strength due to the electron transfer between Ru and Ga for enhanced intrinsic HER activity. Moreover, the oxophilic Ga sites near the Ru clusters tend to adsorb the hydroxyl species and accelerate the water dissociation for sufficient proton supplement in an alkaline medium. The Ru-GaSA/N-C catalyst exhibits a low overpotential of 4 ± 1 mV (10 mA cm-2) and high mass activity of 9.3 ± 0.5 A mg-1Ru at -0.05 V vs RHE. In particular, the Ru-GaSA/N-C-based AEMWE in 1 M KOH delivers a voltage of only 1.74 V to reach an industrial current density of 1 A cm-2, and can steadily operate at 1 A cm-2 for more than 170 h.

A New Benchmark: Clinical Uncertainty and Severity Aware Labeled Chest X-Ray Images with Multi-Relationship Graph Learning.

Chest radiography, commonly known as CXR, is frequently utilized in clinical settings to detect cardiopulmonary conditions. However, even seasoned radiologists might offer different evaluations regarding the seriousness and uncertainty associated with observed abnormalities. Previous research has attempted to utilize clinical notes to extract abnormal labels for training deep-learning models in CXR image diagnosis. However, these methods often neglected the varying degrees of severity and uncertainty linked to different labels. In our study, we initially assembled a comprehensive new dataset of CXR images based on clinical textual data, which incorporated radiologists' assessments of uncertainty and severity. Using this dataset, we introduced a multi-relationship graph learning framework that leverages spatial and semantic relationships while addressing expert uncertainty through a dedicated loss function. Our research showcases a notable enhancement in CXR image diagnosis and the interpretability of the diagnostic model, surpassing existing state-of-the-art methodologies. The dataset address of disease severity and uncertainty we extracted is: https://physionet.org/content/cad-chest/1.0/.

An orbital strategy for regulating the Jahn-Teller effect.

The Jahn-Teller effect (JTE) arising from lattice-electron coupling is a fascinating phenomenon that profoundly affects important physical properties in a number of transition-metal compounds. Controlling JT distortions and their corresponding electronic structures is highly desirable to tailor the functionalities of materials. Here, we propose a local coordinate strategy to regulate the JTE through quantifying occupancy in the [Formula: see text] and [Formula: see text] orbitals of Mn and scrutinizing the symmetries of the ligand oxygen atoms in MnO6 octahedra in LiMn2O4 and Li0.5Mn2O4. The effectiveness of such a strategy has been demonstrated by constructing P2-type NaLi x Mn1 - x O2 oxides with different Li/Mn ordering schemes. In addition, this strategy is also tenable for most 3d transition-metal compounds in spinel and perovskite frameworks, indicating the universality of local coordinate strategy and the tunability of the lattice-orbital coupling in transition-metal oxides. This work demonstrates a useful strategy to regulate JT distortion and provides useful guidelines for future design of functional materials with specific physical properties.

Rethinking masked image modelling for medical image representation.

Masked Image Modelling (MIM), a form of self-supervised learning, has garnered significant success in computer vision by improving image representations using unannotated data. Traditional MIMs typically employ a strategy of random sampling across the image. However, this random masking technique may not be ideally suited for medical imaging, which possesses distinct characteristics divergent from natural images. In medical imaging, particularly in pathology, disease-related features are often exceedingly sparse and localized, while the remaining regions appear normal and undifferentiated. Additionally, medical images frequently accompany reports, directly pinpointing pathological changes' location. Inspired by this, we propose Masked medical Image Modelling (MedIM), a novel approach, to our knowledge, the first research that employs radiological reports to guide the masking and restore the informative areas of images, encouraging the network to explore the stronger semantic representations from medical images. We introduce two mutual comprehensive masking strategies, knowledge-driven masking (KDM), and sentence-driven masking (SDM). KDM uses Medical Subject Headings (MeSH) words unique to radiology reports to identify symptom clues mapped to MeSH words (e.g., cardiac, edema, vascular, pulmonary) and guide the mask generation. Recognizing that radiological reports often comprise several sentences detailing varied findings, SDM integrates sentence-level information to identify key regions for masking. MedIM reconstructs images informed by this masking from the KDM and SDM modules, promoting a comprehensive and enriched medical image representation. Our extensive experiments on seven downstream tasks covering multi-label/class image classification, pneumothorax segmentation, and medical image-report analysis, demonstrate that MedIM with report-guided masking achieves competitive performance. Our method substantially outperforms ImageNet pre-training, MIM-based pre-training, and medical image-report pre-training counterparts. Codes are available at https://github.com/YtongXie/MedIM.

Frequency-aware Feature Fusion for Dense Image Prediction.

Dense image prediction tasks demand features with strong category information and precise spatial boundary details at high resolution. To achieve this, modern hierarchical models often utilize feature fusion, directly adding upsampled coarse features from deep layers and high-resolution features from lower levels. In this paper, we observe rapid variations in fused feature values within objects, resulting in intra-category inconsistency due to disturbed high-frequency features. Additionally, blurred boundaries in fused features lack accurate high frequency, leading to boundary displacement. Building upon these observations, we propose Frequency-Aware Feature Fusion (FreqFusion), integrating an Adaptive Low-Pass Filter (ALPF) generator, an offset generator, and an Adaptive High-Pass Filter (AHPF) generator. The ALPF generator predicts spatially-variant low-pass filters to attenuate high-frequency components within objects, reducing intra-class inconsistency during upsampling. The offset generator refines large inconsistent features and thin boundaries by replacing inconsistent features with more consistent ones through resampling, while the AHPF generator enhances high-frequency detailed boundary information lost during downsampling. Comprehensive visualization and quantitative analysis demonstrate that FreqFusion effectively improves feature consistency and sharpens object boundaries. Extensive experiments across various dense prediction tasks confirm its effectiveness. The code is made publicly available at https://github.com/Linwei-Chen/FreqFusion.

Electric Field-Manipulated Optical Chirality in Ferroelectric Vortex Domains.

Manipulating optical chirality via electric fields has garnered considerable attention in the realm of both fundamental physics and practical applications. Chiral ferroelectrics, characterized by their inherent optical chirality and switchable spontaneous polarization, are emerging as a promising platform for electronic-photonic integrated circuits applications. Unlike organics with chiral carbon centers, integrating chirality into technologically mature inorganic ferroelectrics has posed a long-standing challenge. Here, the successful introduction of chirality is reported into self-assembly La-doped BiFeO3 nanoislands, which exhibit ferroelectric vortex domains. By employing synergistic experimental techniques with piezoresponse force microscopy and nonlinear optical second-harmonic generation probes, a clear correlation between chirality and polarization configuration within these ferroelectric nanoislands is established. Furthermore, the deterministic control of ferroelectric vortex domains and chirality is demonstrated by applying electric fields, enabling reversible and nonvolatile generation and elimination of optically chiral signals. These findings significantly expand the repertoire of field-controllable chiral systems and lay the groundwork for the development of innovative ferroelectric optoelectronic devices.

Trace Small Molecular/Nano-Colloidal Multiscale Electrolyte Additives Enable Ultra-Long Lifespan of Zinc Metal Anodes.

Electrolyte additives are efficient to improve the performance of aqueous zinc-ion batteries (AZIBs), yet the current electrolyte additives are limited to fully water-soluble additives (FWAs) and water-insoluble additives (WIAs). Herein, trace slightly water-soluble additives (SWAs) of zinc acetylacetonate (ZAA) were introduced to aqueous ZnSO4 electrolytes. The SWA system of ZAA is composed of a FWA part and a WIA part in a dynamic manner of dissolution equilibrium. The FWA part exists as soluble small molecules, which efficiently regulate Zn2+ ion solvation structure, while the WIA part exists as insoluble nano-colloids, which in-situ form a thick and robust solid electrolyte interface film on zinc metal anodes (ZMAs). Such small molecular/nano-colloidal multiscale electrolyte additives of ZAA are capable to not only improve ionic conductivity and transference number but also inhibit corrosion, hydrogen evolution, and Zn dendrite on ZMAs. The SWA-based Zn∥Zn half battery delivers a superb cumulative plating capacity of 15 Ah cm-2 under 1 mAh cm-2 and 20 mA cm-2, and the SWA-based NH4V4O10∥Zn pouch cell obtains a capacity retention of 67.8% within 4000 cycles under 4 A g-1. The study provides innovative insights for rational design of electrolyte additives, which may pave the way for the practicality of AZIBs.

Syntropic spin alignment at the interface between ferromagnetic and superconducting nitrides.

The magnetic correlations at the superconductor/ferromagnet (S/F) interfaces play a crucial role in realizing dissipation-less spin-based logic and memory technologies, such as triplet-supercurrent spin-valves and 'π' Josephson junctions. Here we report the observation of an induced large magnetic moment at high-quality nitride S/F interfaces. Using polarized neutron reflectometry and DC SQUID measurements, we quantitatively determined the magnetization profile of the S/F bilayer and confirmed that the induced magnetic moment in the adjacent superconductor only exists below T C. Interestingly, the direction of the induced moment in the superconductors was unexpectedly parallel to that in the ferromagnet, which contrasts with earlier findings in S/F heterostructures based on metals or oxides. First-principles calculations verified that the unusual interfacial spin texture observed in our study was caused by the Heisenberg direct exchange coupling with constant J∼4.28 meV through d-orbital overlapping and severe charge transfer across the interfaces. Our work establishes an incisive experimental probe for understanding the magnetic proximity behavior at S/F interfaces and provides a prototype epitaxial 'building block' for superconducting spintronics.

Enhanced electrocatalytic biomass oxidation at low voltage by Ni2+-O-Pd interfaces.

Challenges in direct catalytic oxidation of biomass-derived aldehyde and alcohol into acid with high activity and selectivity hinder the widespread biomass application. Herein, we demonstrate that a Pd/Ni(OH)2 catalyst with abundant Ni2+-O-Pd interfaces allows electrooxidation of 5-hydroxymethylfurfural to 2, 5-furandicarboxylic acid with a selectivity near 100 % and 2, 5-furandicarboxylic acid yield of 97.3% at 0.6 volts (versus a reversible hydrogen electrode) in 1 M KOH electrolyte under ambient conditions. The rate-determining step of the intermediate oxidation of 5-hydroxymethyl-2-furancarboxylic acid is promoted by the increased OH species and low C-H activation energy barrier at Ni2+-O-Pd interfaces. Further, the Ni2+-O-Pd interfaces prevent the agglomeration of Pd nanoparticles during the reaction, greatly improving the stability of the catalyst. In this work, Pd/Ni(OH)2 catalyst can achieve 100% 5-hydroxymethylfurfural conversion and >90% 2, 5-furandicarboxylic acid selectivity in a flow-cell and work stably over 200 h under a fixed cell voltage of 0.85 V.

Inferior Interfacial Superconductivity in 1 UC FeSe/SrVO3/SrTiO3 with Screened Interfacial Electron-Phonon Coupling.

Single-unit cell (1 UC) FeSe interfaced with TiOx or FeOx exhibits significantly enhanced superconductivity compared to that of bulk FeSe, with interfacial electron-phonon coupling (EPC) playing a crucial role. However, the reduced dimensionality in 1 UC FeSe, which may drive superconducting fluctuations, complicates our understanding of the enhancement mechanisms. We construct a new superconducting interface, 1 UC FeSe/SrVO3/SrTiO3. Here, the itinerant electrons of highly metallic SrVO3 films can screen all high-energy Fuchs-Kliewer phonons, including those of SrTiO3, making it the first FeSe/oxide system with screened interfacial EPC while maintaining the 1 UC FeSe thickness. Despite comparable doping levels, the heavily electron-doped 1 UC FeSe/SrVO3 exhibits a pairing temperature (Tg ∼ 48 K) lower than those of FeSe/SrTiO3 and FeSe/LaFeO3. Our findings disentangle the contributions of interfacial EPC from dimensionality in terms of enhancing Tg in FeSe/oxide interfaces, underscoring the critical importance of interfacial EPC. This FeSe/VOx interface also provides a platform for studying interfacial superconductivity.

Atomic-Layer IrOx Enabling Ligand Effect Boosts Water Oxidation Electrocatalysis.

An in situ formed IrOx (x ≤ 2) layer driven by anodic bias serves as the essential active site of Ir-based materials for oxygen evolution reaction (OER) electrocatalysis. Once being confined to atomic thickness, such an IrOx layer possesses both a favorable ligand effect and maximized active Ir sites with a lower O-coordination number. However, limited by a poor understanding of surface reconstruction dynamics, obtaining atomic layers of IrOx remains experimentally challenging. Herein, we report an idea of material design using intermetallic IrVMn nanoparticles to induce in situ formation of an ultrathin IrOx layer (O-IrVMn/IrOx) to enable the ligand effect for achieving superior OER electrocatalysis. Theoretical calculations predict that a strong electronic interaction originating from an orderly atomic arrangement can effectively hamper the excessive leaching of transition metals, minimizing vacancies for oxygen coordination. Linear X-ray absorption near edge spectra analysis, extended X-ray absorption fine structure fitting outcomes, and X-ray photoelectron spectroscopy collectively confirm that Ir is present in lower oxidation states in O-IrVMn/IrOx due to the presence of unsaturated O-coordination. Consequently, the O-IrVMn/IrOx delivers excellent acidic OER performances with an overpotential of only 279 mV at 10 mA cm-2 and a high mass activity of 2.3 A mg-1 at 1.53 V (vs RHE), exceeding most Ir-based catalysts reported. Moreover, O-IrVMn/IrOx also showed excellent catalytic stability with only 0.05 at. % Ir dissolution under electrochemical oxidation, much lower than that of disordered D-IrVMn/IrOx (0.20 at. %). Density functional theory calculations unravel that the intensified ligand effect optimizes the adsorption energies of multiple intermediates involved in the OER and stabilizes the as-formed catalytic IrOx layer.

Interpretable medical image Visual Question Answering via multi-modal relationship graph learning.

Medical Visual Question Answering (VQA) is an important task in medical multi-modal Large Language Models (LLMs), aiming to answer clinically relevant questions regarding input medical images. This technique has the potential to improve the efficiency of medical professionals while relieving the burden on the public health system, particularly in resource-poor countries. However, existing medical VQA datasets are small and only contain simple questions (equivalent to classification tasks), which lack semantic reasoning and clinical knowledge. Our previous work proposed a clinical knowledge-driven image difference VQA benchmark using a rule-based approach (Hu et al., 2023). However, given the same breadth of information coverage, the rule-based approach shows an 85% error rate on extracted labels. We trained an LLM method to extract labels with 62% increased accuracy. We also comprehensively evaluated our labels with 2 clinical experts on 100 samples to help us fine-tune the LLM. Based on the trained LLM model, we proposed a large-scale medical VQA dataset, Medical-CXR-VQA, using LLMs focused on chest X-ray images. The questions involved detailed information, such as abnormalities, locations, levels, and types. Based on this dataset, we proposed a novel VQA method by constructing three different relationship graphs: spatial relationships, semantic relationships, and implicit relationship graphs on the image regions, questions, and semantic labels. We leveraged graph attention to learn the logical reasoning paths for different questions. These learned graph VQA reasoning paths can be further used for LLM prompt engineering and chain-of-thought, which are crucial for further fine-tuning and training multi-modal large language models. Moreover, we demonstrate that our approach has the qualities of evidence and faithfulness, which are crucial in the clinical field. The code and the dataset is available at https://github.com/Holipori/Medical-CXR-VQA.

Versatile Method of Engineering the Band Alignment and the Electron Wavefunction Hybridization of Hybrid Quantum Devices.

Hybrid devices that combine superconductors (S) and semiconductors (Sm) have attracted great attention due to the integration of the properties of both materials, which relies on the interface details and the resulting coupling strength and wavefunction hybridization. However, until now, none of the experiments have reported good control of the band alignment of the interface, as well as its tunability to the coupling and hybridization. Here, the interface is modified by inducing specific argon milling while maintaining its high quality, e.g., atomic connection, which results in a large induced superconducting gap and ballistic transport. By comparing with Schrödinger-Poisson calculations, it is proven that this method can vary the band bending/coupling strength and the electronic spatial distribution. In the strong coupling regime, the coexistence and tunability of crossed Andreev reflection and elastic co-tunneling-key ingredients for the Kitaev chain-are confirmed. This method is also generic for other materials and achieves a hard and huge superconducting gap in lead and indium antimonide nanowire (Pb-InSb) devices. Such a versatile method, compatible with the standard fabrication process and accompanied by the well-controlled modification of the interface, will definitely boost the creation of more sophisticated hybrid devices for exploring physics in solid-state systems.

Voltage control of multiferroic magnon torque for reconfigurable logic-in-memory.

Magnons, bosonic quasiparticles carrying angular momentum, can flow through insulators for information transmission with minimal power dissipation. However, it remains challenging to develop a magnon-based logic due to the lack of efficient electrical manipulation of magnon transport. Here we show the electric excitation and control of multiferroic magnon modes in a spin-source/multiferroic/ferromagnet structure. We demonstrate that the ferroelectric polarization can electrically modulate the magnon-mediated spin-orbit torque by controlling the non-collinear antiferromagnetic structure in multiferroic bismuth ferrite thin films with coupled antiferromagnetic and ferroelectric orders. In this multiferroic magnon torque device, magnon information is encoded to ferromagnetic bits by the magnon-mediated spin torque. By manipulating the two coupled non-volatile state variables-ferroelectric polarization and magnetization-we further present reconfigurable logic operations in a single device. Our findings highlight the potential of multiferroics for controlling magnon information transport and offer a pathway towards room-temperature voltage-controlled, low-power, scalable magnonics for in-memory computing.

Real-world evidence of 18F-fluciclovine Positron emission tomography/computed tomography performance for recurrent prostate cancer in the Veterans Affairs Health System.

BACKGROUND: There are no population-level studies assessing 18F-fluciclovine (fluciclovine) utilization of Positron emission tomography/computed tomography (PET/CT) for biochemically recurrent prostate cancer (PC). We assessed fluciclovine PET/CT in the Veterans Affairs Health Care System. METHODS: Of 1153 men with claims suggesting receipt of fluciclovine PET/CT, we randomly reviewed charts of 300 who indeed underwent fluciclovine PET/CT. The primary outcome was fluciclovine PET/CT result (positive or negative). Comparison among groups stratified by androgen deprivation therapy (ADT) (yes vs. no) and prostate-specific antigen (PSA) (≤1 vs. >1 ng/mL) at imaging were performed. Logistic regression tested associations between PSA, ADT receipt, and race with fluciclovine PET/CT positive imaging. RESULTS: Fluciclovine PET/CT positivity rate was 33% for patients with PSA 0-0.5 ng/mL, 21% for >0.5-1.0, 54% for >1.0-2.0, and 66% for >2.0 (p < 0.01). A 59% positivity rate ocurred in patients treated with concurrent ADT versus 37% in those not on ADT (p < 0.01). White were more likely to have a positive scan versus Black patients (55% vs. 38%; p = 0.02). Patients whose primary treatment was radical prostatectomy had a lower positivity rate (33%) versus those treated with radiotherapy (55%) (p < 0.001). On multivariable logistic regression, PSA > 1 ng/mL (all men odds ratio [OR]: 4.06, 95% confidence interval [CI]: 2.07-7.96; men on ADT only OR: 4.42, 95% CI: 1.73-11.26) and use of ADT (OR: 3.94, 95% CI: 1.32-11.75), and White (all men OR: 2.22, 95% CI: 1.20-4.17) predicted positive fluciclovine PET/CT. CONCLUSION: This real-world study assessing 18F-fluciclovine PET/CT performance in an equal access health care system confirms higher detection rates than traditional imaging methods, but positivity is highly influenced by PSA at time of imaging. Additionally, patients currently receiving ADT have at least four times higher likelihood of a positive scan, showing that scan positivity isn't negatively affected by ADT status in this study. Finally, White men were more likely to have a positive scan, the reasons for which should be explored in future studies.