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The China Spallation Neutron Source is expected to produce its first beam in 2017. Hesheng Chen and Xun-Li Wang provide an overview of this user facility and what it means for science in China and elsewhere.

Recent progress in machine learning (ML), together with advanced computational power, have provided new research opportunities in cardiovascular modeling. While classifying patient outcomes and medical image segmentation with ML have already shown significant promising results, ML for the prediction of biomechanics such as blood flow or tissue dynamics is in its infancy. This perspective article discusses some of the challenges in using ML for replacing well-established physics-based models in cardiovascular biomechanics. Specifically, we discuss the large landscape of input features in 3D patient-specific modeling as well as the high-dimensional output space of field variables that vary in space and time. We argue that the end purpose of such ML models needs to be clearly defined and the tradeoff between the loss in accuracy and the gained speedup carefully interpreted in the context of translational modeling. We also discuss several exciting venues where ML could be strategically used to augment traditional physics-based modeling in cardiovascular biomechanics. In these applications, ML is not replacing physics-based modeling, but providing opportunities to solve ill-defined problems, improve measurement data quality, enable a solution to computationally expensive problems, and interpret complex spatiotemporal data by extracting hidden patterns. In summary, we suggest a strategic integration of ML in cardiovascular biomechanics modeling where the ML model is not the end goal but rather a tool to facilitate enhanced modeling.

Amorphous materials have no long-range order, but there are ordered structures at short range (2–5 Å), medium range (5–20 Å) and even longer length scales 1 – 5 . While regular 6 , 7 and semiregular polyhedra 8 – 10 are often found as short-range ordering in amorphous materials, the nature of medium-range order has remained elusive 11 – 14 . Consequently, it is difficult to determine whether there exists any structural link at medium range or longer length scales between the amorphous material and its crystalline counterparts. Moreover, an amorphous material often crystallizes into a phase of different composition 15 , with very different underlying structural building blocks, further compounding the issue. Here, we capture an intermediate crystalline cubic phase in a Pd-Ni-P amorphous alloy and reveal the structure of the medium-range order, a six-membered tricapped trigonal prism cluster (6M-TTP) with a length scale of 12.5 Å. We find that the 6M-TTP can pack periodically to several tens of nanometres to form the cube phase. Our experimental observations provide evidence of a structural link between the amorphous and crystalline phases in a Pd-Ni-P alloy at the medium-range length scale and suggest that it is the connectivity of the 6M-TTP clusters that distinguishes the crystalline and amorphous phases. These findings will shed light on the structure of amorphous materials at extended length scales beyond that of short-range order. An intermediate cube phase with a medium-range order structure is identified in Pd-Ni-P metallic glass, which links the amorphous and crystalline phases.

Eddy-resolving turbulence simulations are essential for understanding and controlling complex unsteady fluid dynamics, with significant implications for engineering and scientific applications. Traditional numerical methods, such as direct numerical simulations (DNS) and large eddy simulations (LES), provide high accuracy but face severe computational limitations, restricting their use in high-Reynolds number or real-time scenarios. Recent advances in deep learning-based surrogate models offer a promising alternative by providing efficient, data-driven approximations. However, these models often rely on deterministic frameworks, which struggle to capture the chaotic and stochastic nature of turbulence, especially under varying physical conditions and complex, irregular geometries. Here, we introduce the Conditional Neural Field Latent Diffusion (CoNFiLD) model, a generative learning framework for efficient high-fidelity stochastic generation of spatiotemporal turbulent flows in complex, three-dimensional domains. CoNFiLD synergistically integrates conditional neural field encoding with latent diffusion processes, enabling memory-efficient and robust generation of turbulence under diverse conditions. Leveraging Bayesian conditional sampling, CoNFiLD flexibly adapts to various turbulence generation scenarios without retraining. This capability supports applications such as zero-shot full-field flow reconstruction from sparse sensor data, super-resolution generation, and spatiotemporal data restoration. Extensive numerical experiments demonstrate CoNFiLD’s capability to accurately generate inhomogeneous, anisotropic turbulent flows within complex domains. These findings underscore CoNFiLD’s potential as a versatile, computationally efficient tool for real-time unsteady turbulence simulation, paving the way for advancements in digital twin technology for fluid dynamics. By enabling rapid, adaptive high-fidelity simulations, CoNFiLD can bridge the gap between physical and virtual systems, allowing real-time monitoring, predictive analysis, and optimization of complex fluid processes. Available eddy-resolved simulations predict turbulent flows accurately but are too computationally demanding for widespread application. This study presents a Conditional Neural Field Latent Diffusion model that efficiently simulates complex spatiotemporal dynamics in turbulent systems, even within challenging 3D irregular domains.

The current understanding of lactate extends from its origins as a byproduct of glycolysis to its role in tumor metabolism, as identified by studies on the Warburg effect. The lactate shuttle hypothesis suggests that lactate plays an important role as a bridging signaling molecule that coordinates signaling among different cells, organs and tissues. Lactylation is a posttranslational modification initially reported by Professor Yingming Zhao’s research group in 2019. Subsequent studies confirmed that lactylation is a vital component of lactate function and is involved in tumor proliferation, neural excitation, inflammation and other biological processes. An indispensable substance for various physiological cellular functions, lactate plays a regulatory role in different aspects of energy metabolism and signal transduction. Therefore, a comprehensive review and summary of lactate is presented to clarify the role of lactate in disease and to provide a reference and direction for future research. This review offers a systematic overview of lactate homeostasis and its roles in physiological and pathological processes, as well as a comprehensive overview of the effects of lactylation in various diseases, particularly inflammation and cancer.

Low thermal conductivity is favorable for preserving the temperature gradient between the two ends of a thermoelectric material, in order to ensure continuous electron current generation. In high-performance thermoelectric materials, there are two main low thermal conductivity mechanisms: the phonon anharmonic in PbTe and SnSe, and phonon scattering resulting from the dynamic disorder in AgCrSe 2 and CuCrSe 2 , which have been successfully revealed by inelastic neutron scattering. Using neutron scattering and ab initio calculations, we report here a mechanism of static local structure distortion combined with phonon-anharmonic-induced ultralow lattice thermal conductivity in α -MgAgSb. Since the transverse acoustic phonons are almost fully scattered by the compound’s intrinsic distorted rocksalt sublattice, the heat is mainly transported by the longitudinal acoustic phonons. The ultralow thermal conductivity in α -MgAgSb is attributed to its atomic dynamics being altered by the structure distortion, which presents a possible microscopic route to enhance the performance of similar thermoelectric materials. In order to optimize thermoelectric (TE) materials which are used to convert thermal energy and electrical energy, the underlying physics needs to be understood. Here, the authors show that by exploiting static local structure distortion, transverse acoustic phonons can be suppressed resulting in high performing TE materials.

Dislocations are the intrinsic origin of crystal plasticity. However, initial high-density dislocations in work-hardened materials are commonly asserted to be detrimental to ductility according to textbook strengthening theory. Inspired by the self-organized critical states of non-equilibrium complex systems in nature, we explored the mechanical response of an additively manufactured medium entropy alloy with segregation-dislocation self-organized structures (SD-SOS). We show here that when initial dislocations are in the form of SD-SOS, the textbook theory that dislocation hardening inevitably sacrifices ductility can be overturned. Our results reveal that the SD-SOS, in addition to providing dislocation sources by emitting dislocations and stacking faults, also dynamically interacts with gliding dislocations to generate sustainable Lomer-Cottrell locks and jogs for dislocation storage. The effective dislocation multiplication and storage capabilities lead to the continuous refinement of planar slip bands, resulting in high ductility in the work-hardened alloy produced by additive manufacturing. These findings set a precedent for optimizing the mechanical behavior of alloys via tuning dislocation configurations. Textbook theory asserts that dislocation hardening inherently sacrifices ductility. Here, the authors report that high-density dislocations with segregation-modified configurations produced by additive manufacturing increase strength without compromising ductility.

Cardiovascular diseases (CVDs) are the leading causes of death worldwide. Increasing reports demonstrated that non-coding RNAs (ncRNAs) have been crucially involved in the development of CVDs. Piwi-interacting RNAs (piRNAs) are a novel cluster of small non-coding RNAs with strong uracil bias at the 5′ end and 2′-O-methylation at the 3′ end that are mainly present in the mammalian reproductive system and stem cells and serve as potential modulators of developmental and pathophysiological processes. Recently, piRNAs have been reported to be widely expressed in human tissues and can potentially regulate various diseases. Specifically, concomitant with the development of next-generation sequencing techniques, piRNAs have been found to be differentially expressed in CVDs, indicating their potential involvement in the occurrence and progression of heart diseases. However, the molecular mechanisms and signaling pathways involved with piRNA function have not been fully elucidated. In this review, we present the current understanding of the piRNAs from the perspectives of biogenesis, characteristics, biological function, and regulatory mechanisms, and highlight their potential roles and underlying mechanisms in CVDs, which will provide new insights into the potential applications of piRNAs in the clinical diagnosis, prognosis, and therapeutic strategies for heart diseases.

Baicalein is a natural flavonoid extracted from the root of Scutellaria baicalensis that exhibits a variety of pharmacological activities. In this study, we investigated the molecular mechanisms underlying the protective effect of baicalein against cardiac hypertrophy in vivo and in vitro. Cardiac hypertrophy was induced in mice by injection of isoproterenol (ISO, 30 mg·kg −1 ·d −1 ) for 15 days. The mice received caudal vein injection of baicalein (25 mg/kg) on 3rd, 6th, 9th, 12th, and 15th days. We showed that baicalein administration significantly attenuated ISO-induced cardiac hypertrophy and restored cardiac function. The protective effect of baicalein against cardiac hypertrophy was also observed in neonatal rat cardiomyocytes treated with ISO (10 μM). In cardiomyocytes, ISO treatment markedly increased reactive oxygen species (ROS) and inhibited autophagy, which were greatly alleviated by pretreatment with baicalein (30 μM). We found that baicalein pretreatment increased the expression of catalase and the mitophagy receptor FUN14 domain containing 1 (FUNDC1) to clear ROS and promote autophagy, thus attenuated ISO-induced cardiac hypertrophy. Furthermore, we revealed that baicalein bound to the transcription factor FOXO3a directly, promoting its transcription activity, and transactivated catalase and FUNDC1. In summary, our data provide new evidence for baicalein and FOXO3a in the regulation of ISO-induced cardiac hypertrophy. Baicalein has great potential for the treatment of cardiac hypertrophy.

Inorganic nanowires-based organogel, a class of emerging organogel with convenient preparation, recyclability, and excellent mechanical properties, is in its infancy. Solidifying and functionalizing nanowires-based organogels by designing the gelator structure remains challenging. Here, we fabricate Ca 2 -P 2 W 16 and Ca 2 -P 2 W 15 M nanowires utilizing highly charged [Ca 2 P 2 W 16 O 60 ] 10 − and [Ca 2 P 2 W 15 MO 60 ] 14 − /13 − cluster units, respectively, which are then employed for preparing organogels. The mechanical performance and stability of prepared organogels are improved due to the enhanced interactions between nanowires and locked organic molecules. Compressive stress and tensile stress of Ca 2 -P 2 W 16 nanowires-based organogel reach 34.5 and 29.0 kPa, respectively. The critical gel concentration of Ca 2 -P 2 W 16 nanowires is as low as 0.28%. Single-molecule force spectroscopy confirms that the connections between cluster units and linkers can regulate the flexibility of nanowires. Furthermore, the incorporation of fluorophores into the organogels adds fluorescence properties. This work reveals the relationships between the microstructures of inorganic gelators and the properties of organogels, guiding the synthesis of high-performance and functional organogels. Functionalizing inorganic organogels is a challenge and can have a negative impact on their mechanical properties. Here, the authors present nanowire-based organogels based on highly charged polyoxometalate cluster units that are mechanically tuned and loaded with fluorescent dyes.