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Key Points Perturbations of the endoplasmic reticulum (ER) caused by accumulation of unfolded proteins in this organelle trigger signal-transduction responses that assist with restoration of homeostasis during short-term but contribute to pathology when prolonged, including causing cell death. Among the stimuli that trigger ER stress are hypoxia, oxidative injury, a high-fat diet, hypoglycaemia, protein-inclusion bodies and viral infection, thus linking these organelle-initiated responses to a diversity of diseases, including cancer, autoimmunity, diabetes, heart disease, stroke and neurodegeneration. With increasing recognition of ER stress in association with human diseases and with improving understanding of the underlying molecular mechanisms, novel targets for drug discovery and new strategies for therapeutic intervention are beginning to emerge from the study of ER stress. Scenarios in which ER stress contributes to disease are outlined and prospects for drug discovery are discussed. Among the cell death mechanisms addressed are: pro-apoptotic signals resulting from activation of the ER-associated kinase IRE1, an upstream activator of apoptotic signalling kinase 1 (ASK1) that activates a stress kinase pathway affecting the activity or expression of several apoptosis regulators including BCL-2, BIM and CHOP; cytoprotective ER-membrane-associated proteins that modulate ER stress signalling; and the interplay among ER-initiated signal-transduction mechanisms that control apoptosis, necrosis and autophagy. Endoplasmic reticulum (ER) stress is induced following the accumulation of unfolded proteins in the ER. This triggers the unfolded protein response, which initially acts to compensate for damage, but if prolonged or excessive can trigger cell death. Here, Reed and colleagues discuss the role of ER-initiated cell death pathways in diseases including neurodegeneration, hypoxia, heart disease, diabetes and immune disorders, while identifying promising therapeutic targets. The accumulation of unfolded proteins in the endoplasmic reticulum (ER) represents a cellular stress induced by multiple stimuli and pathological conditions. These include hypoxia, oxidative injury, high-fat diet, hypoglycaemia, protein inclusion bodies and viral infection. ER stress triggers an evolutionarily conserved series of signal-transduction events, which constitutes the unfolded protein response. These signalling events aim to ameliorate the accumulation of unfolded proteins in the ER; however, when these events are severe or protracted they can induce cell death. With the increasing recognition of an association between ER stress and human diseases, and with the improved understanding of the diverse underlying molecular mechanisms, novel targets for drug discovery and new strategies for therapeutic intervention are beginning to emerge.

Developing highly efficient, selective and low-overpotential electrocatalysts for carbon dioxide (CO 2 ) reduction is crucial. This study reports an efficient Ni single-atom catalyst coordinated with pyrrolic nitrogen and pyridinic nitrogen for CO 2 reduction to carbon monoxide (CO). In flow cell experiments, the catalyst achieves a CO partial current density of 20.1 mA cm geo −2 at −0.15 V vs. reversible hydrogen electrode (V RHE ). It exhibits a high turnover frequency of over 274,000 site −1 h −1 at −1.0 V RHE and maintains high Faradaic efficiency of CO (FE CO ) exceeding 90% within −0.15 to −0.9 V RHE . Operando synchrotron-based infrared and X-ray absorption spectra, and theoretical calculations reveal that mono CO-adsorbed Ni single sites formed during electrochemical processes contribute to the balance between key intermediates formation and CO desorption, providing insights into the catalyst’s origin of catalytic activity. Overall, this work presents a Ni single-atom catalyst with good selectivity and activity for CO 2 reduction while shedding light on its underlying mechanism. Rational design of electrocatalysts for selective CO 2 conversion is of great interests. Here the authors show that Ni single atom with asymmetric pyrrolic and pyridinic nitrogen for efficient CO 2 electroconversion to CO.

Exosomes are lipid bilayer membrane vesicles and are emerging as competent nanocarriers for drug delivery. The clinical translation of exosomes faces many challenges such as massive production, standard isolation, drug loading, stability and quality control. In recent years, artificial exosomes are emerging based on nanobiotechnology to overcome the limitations of natural exosomes. Major types of artificial exosomes include ‘nanovesicles (NVs)’, ‘exosome-mimetic (EM)’ and ‘hybrid exosomes (HEs)’, which are obtained by top-down, bottom-up and biohybrid strategies, respectively. Artificial exosomes are powerful alternatives to natural exosomes for drug delivery. Here, we outline recent advances in artificial exosomes through nanobiotechnology and discuss their strengths, limitations and future perspectives. The development of artificial exosomes holds great values for translational nanomedicine.

This study tackles the numerical challenges posed by solutions with steep gradients in the Burgers equation, particularly poor stability in high-gradient regions and the ill-posedness of inverse problems in shock wave modeling. We propose a Weak-Form Physics-Informed Neural Network (WF-PINN) that fundamentally enhances both forward and inverse problem solving. Key innovations include: (i) a weak-form integral formulation of the PDE loss, which improves training stability near shocks; (ii) enforcement of an entropy condition to ensure unique and physically consistent shock capture; (iii) a dual-network architecture for inverse problems, where an auxiliary network dedicated to initial condition reconstruction is coupled with the main solver via consistency constraints. Numerical experiments show that WF-PINNs achieve significantly higher accuracy and convergence robustness compared to strong-form PINNs, accurately resolving shock locations and amplitudes while enabling precise identification of unknown initial conditions and viscosity coefficients. The framework offers a unified and generalizable approach for solving conservation laws with discontinuities.

Rock mass deformation and failure are macroscopic manifestations of crack initiation, propagation, and coalescence. However, simulating the transition of rocks from continuous to discontinuous media under cyclic dynamic loading remains challenging. This study proposes a hybrid finite-discrete element method (HFDEM) to model crack propagation, incorporating a frequency-dependent cohesive-zone model. The mechanical properties of standard sandy mudstone under quasi-static and cyclic dynamic loading were simulated using HFDEM, and the method's reliability was verified through experimental comparison. The comparative analysis demonstrates that HFDEM successfully captures crack interaction mechanisms and accurately simulates the overall failure behavior of specimens. Additionally, the effects of pre-existing flaw inclination angle and dynamic loading frequency on rock failure mechanisms were investigated. The numerical results reveal that rock samples exhibit significantly higher compressive strength under dynamic loading compared to quasi-static loading, with compressive strength increasing with higher cyclic dynamic load frequencies. Furthermore, by analyzing the strength characteristics, crack propagation, and failure modes of the samples, insights into the failure mechanisms of rocks under different frequency loads were obtained. This study provides valuable insights into crack development and failure of rocks under seismic loads, offering guidance for engineering practices.

Solution-processable perovskites show highly emissive and good charge transport, making them attractive for low-cost light-emitting diodes (LEDs) with high energy conversion efficiencies. Despite recent advances in device efficiency, the stability of perovskite LEDs is still a major obstacle. Here, we demonstrate stable and bright perovskite LEDs with high energy conversion efficiencies by optimizing formamidinium lead iodide films. Our LEDs show an energy conversion efficiency of 10.7%, and an external quantum efficiency of 14.2% without outcoupling enhancement through controlling the concentration of the precursor solutions. The device shows low efficiency droop, i.e. 8.3% energy conversion efficiency and 14.0% external quantum efficiency at a current density of 300 mA cm −2 , making the device more efficient than state-of-the-art organic and quantum-dot LEDs at high current densities. Furthermore, the half-lifetime of device with benzylamine treatment is 23.7 hr under a current density of 100 mA cm −2 , comparable to the lifetime of near-infrared organic LEDs. Light-emitting diodes based on halide perovskites have made remarkable progress but their stability is the bottleneck. Here Miao et al . show 14% quantum efficiency at a current density of 300 mA cm-2 and 23.7 hr lifetime under a current density of 100 mA cm-2, making it promising for actual application.

Solution-processed metal-halide perovskites are emerging as one of the most promising materials for displays, lighting and energy generation. Currently, the best-performing perovskite optoelectronic devices are based on lead halides and the lead toxicity severely restricts their practical applications. Moreover, efficient white electroluminescence from broadband-emission metal halides remains a challenge. Here we demonstrate efficient and bright lead-free LEDs based on cesium copper halides enabled by introducing an organic additive (Tween, polyethylene glycol sorbitan monooleate) into the precursor solutions. We find the additive can reduce the trap states, enhancing the photoluminescence quantum efficiency of the metal halide films, and increase the surface potential, facilitating the hole injection and transport in the LEDs. Consequently, we achieve warm-white LEDs reaching an external quantum efficiency of 3.1% and a luminance of 1570 cd m −2 at a low voltage of 5.4 V, showing great promise of lead-free metal halides for solution-processed white LED applications. Designing efficient light-emitting diodes with white-light-emission from broadband-emission metal halides remains a challenge. Here, the authors demonstrate bright and efficient lead-free LEDs based on cesium copper halides enabled by introducing Tween organic additive in the precursor.

Dendritic and axonal morphology reflects the input and output of neurons and is a defining feature of neuronal types 1 , 2 , yet our knowledge of its diversity remains limited. Here, to systematically examine complete single-neuron morphologies on a brain-wide scale, we established a pipeline encompassing sparse labelling, whole-brain imaging, reconstruction, registration and analysis. We fully reconstructed 1,741 neurons from cortex, claustrum, thalamus, striatum and other brain regions in mice. We identified 11 major projection neuron types with distinct morphological features and corresponding transcriptomic identities. Extensive projectional diversity was found within each of these major types, on the basis of which some types were clustered into more refined subtypes. This diversity follows a set of generalizable principles that govern long-range axonal projections at different levels, including molecular correspondence, divergent or convergent projection, axon termination pattern, regional specificity, topography, and individual cell variability. Although clear concordance with transcriptomic profiles is evident at the level of major projection type, fine-grained morphological diversity often does not readily correlate with transcriptomic subtypes derived from unsupervised clustering, highlighting the need for single-cell cross-modality studies. Overall, our study demonstrates the crucial need for quantitative description of complete single-cell anatomy in cell-type classification, as single-cell morphological diversity reveals a plethora of ways in which different cell types and their individual members may contribute to the configuration and function of their respective circuits. Sparse labelling and whole-brain imaging are used to reconstruct and classify brain-wide complete morphologies of 1,741 individual neurons in the mouse brain, revealing a dependence on both brain region and transcriptomic profile.

The inferior tolerance with reversible accommodation of large‐sized Na+ ion in electrode materials has plagued the adaptability of sodium‐ion chemistry. The sluggish diffusion kinetics of Na+ also baffles the desirability. Herein, a carbon fiber supported binder‐free electrode consisting of bismuth and carbon composite is designed. Well‐confined bismuth nanodots are synthesized by replacing cobalt in the metal–organic frameworks (MOF)–derived, nitrogen‐doped carbon arrays, which are demonstrated with remarkable reversibility during sodiation and desodiation. Cobalt species in the pristine MOF catalyze the graphitization around organic components in calcination, generating a highly conductive network in which the bismuth is to be embedded. The uniformly dispersed bismuth nanodots provide plenty boundaries and abundant active sites in the carbon arrays, where fast sodium storage kinetics are realized to contribute extra capacity and excellent rate performance. Bismuth nanodots are synthesized by confined replacement reaction with cobalt from metal–organic frameworks (MOF)‐derived templates on carbon fiber substrate. As binder‐free electrode for sodium‐ion batteries, nanosized bismuth can accommodate volume changes during sodiation/desodiation. The carbon arrays are with plenty phase boundaries and abundant active sites, which can contribute to extra capacity and excellent rate performance with fast capacitive sodium storage kinetics.

This paper optimizes the 2D Wadell roundness calculation of particles based on digital image processing methods. An algorithm for grouping corner key points is proposed to distinguish each independent corner. Additionally, the cyclic midpoint filtering method is introduced for corner dealiasing, aiming to mitigate aliasing issues effectively. The relationships between the number of corner pixels ( m ), the central angle of the corner ( α ) and the parameter of the dealiasing degree ( n ) are established. The Krumbein chart and a sandstone thin section image were used as examples to calculate the 2D Wadell roundness. A set of regular shapes is calculated, and the error of this method is discussed. When α  ≥ 30°, the maximum error of Wadell roundness for regular shapes is 5.21%; when 12° ≤  α  < 30°, the maximum error increases. By applying interpolation to increase the corner pixels to the minimum number ( m 0 ) within the allowable range of error, based on the α - m 0 relational expression obtained in this study, the error of the corner circle can be minimized. The results indicate that as the value of m increases, the optimal range interval for n also widens. Additionally, a higher value of α leads to a lower dependence on m . The study's results can be applied to dealiasing and shape analysis of complex closed contours.