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\"Eight light years away, a star has died, creating a supernova event that showers Earth in deadly levels of radiation. Within a year, everyone over the age of thirteen will die. And so the countdown begins. Parents apprentice their children and try to pass on the knowledge needed to keep the world running. But when the world is theirs, the last generation may not want to continue the legacy left to them. And in shaping the future however they want, will the children usher in an era of bright beginnings or final mistakes ?

It is widely known that the low-temperature physical properties, such as the heat capacity and thermal conductivity, of a disordered amorphous solid are markedly different from those of its ordered crystalline counterpart. However, the origin of this discrepancy is not known. One of the universal features of disordered solids is the excess vibrational density of states, known as the ‘boson peak’. Here we study the microscopic origin of the boson peak through numerical investigations of the dynamic structure factor of two-dimensional model glasses over a wide frequency–wavenumber range. We show that the boson peak originates from quasi-localized vibrations of string-like dynamical defects. Furthermore, we reveal that these dynamical defects provide a common structural origin for the three most fundamental dynamic modes of glassy systems: the boson peak, fast β relaxation and slow structural relaxation. The relation between physical properties and structure in amorphous materials is poorly understood. Simulations now show that vibrations of string-like dynamical defects likely govern the low-temperature dynamics in these systems.

The recent discovery of non-classical crystal nucleation pathways has revealed the role of fluctuations in the liquid structural order, not considered in classical nucleation theory. On the other hand, classical crystal growth theory states that crystal growth is independent of interfacial energy, but this is questionable. Here we elucidate the role of liquid structural ordering in crystal nucleation and growth using computer simulations of supercooled liquids. We find that suppressing the crystal-like structural order in the supercooled liquid through a new order-killing strategy can reduce the crystallisation rate by several orders of magnitude. This indicates that crystal-like liquid preordering and the associated interfacial energy reduction play an essential role in nucleation and growth processes, forcing critical modifications of the classical crystal growth theory. Furthermore, we evaluate the importance of this additional factor for different types of liquids. These findings shed new light on the fundamental understanding of crystal growth kinetics. In classical nucleation theory, structural order in the liquid phase is not considered. But simulations of supercooled liquids now show that crystal-like liquid preordering play an essential role in nucleation and growth processes - calling for extensions of the classical theory.

Deep learning for digital pathology is hindered by the extremely high spatial resolution of whole-slide images (WSIs). Most studies have employed patch-based methods, which often require detailed annotation of image patches. This typically involves laborious free-hand contouring on WSIs. To alleviate the burden of such contouring and obtain benefits from scaling up training with numerous WSIs, we develop a method for training neural networks on entire WSIs using only slide-level diagnoses. Our method leverages the unified memory mechanism to overcome the memory constraint of compute accelerators. Experiments conducted on a data set of 9662 lung cancer WSIs reveal that the proposed method achieves areas under the receiver operating characteristic curve of 0.9594 and 0.9414 for adenocarcinoma and squamous cell carcinoma classification on the testing set, respectively. Furthermore, the method demonstrates higher classification performance than multiple-instance learning as well as strong localization results for small lesions through class activation mapping. Deep learning for digital pathology is hindered by the extremely high spatial resolution of whole slide images (WSIs), which requires researchers to adopt patch-based methods and laborious free-hand contouring. Here, the authors develop a whole-slide training method to classify types of lung cancers using slide-level diagnoses with deep learning.

Ferroptosis is a type of iron-dependent cell death pertaining to an excess of lipid peroxidation. It has been suggested that sorafenib—an anti-angiogenic medication for hepatocellular carcinoma (HCC)—induces ferroptosis, but the underlying mechanism for this remains largely unknown. We employed siRNA-mediated gene silencing to investigate the role of Src homology region 2 domain-containing phosphatase-1 (SHP-1), following sorafenib treatment, in cystine/glutamate-antiporter-system-Xc−-regulated cystine uptake. Co-immunoprecipitation was also performed to examine the interactions between MCL1, beclin 1 (BECN1), and solute carrier family 7 member 11 (SLC7A11), which functions as the catalytic subunit of system Xc−. The results of this study showed that sorafenib enhanced the activity of SHP-1, dephosphorylated STAT3, downregulated the expression of MCL1 and, consequently, reduced the association between MCL1 and BECN1. In contrast, increased binding between BECN1 and SLC7A11 was observed following sorafenib treatment. The elevated interaction between BECN1 and SLC7A11 inhibited the activity of system Xc−, whereas BECN1 silencing restored cystine intake and protected cells from ferroptosis. Notably, ectopic expression of MCL1 uncoupled BECN1 from SLC7A11 and rescued cell viability by attenuating lipid peroxidation. The results revealed that ferroptosis could be induced in HCC via SHP-1/STAT3-mediated downregulation of MCL1 and subsequent inhibition of SLC7A11 by increased BECN1 binding.

Dielectric polymers for electrostatic energy storage suffer from low energy density and poor efficiency at elevated temperatures, which constrains their use in the harsh-environment electronic devices, circuits, and systems. Although incorporating insulating, inorganic nanostructures into dielectric polymers promotes the temperature capability, scalable fabrication of high-quality nanocomposite films remains a formidable challenge. Here, we report an all-organic composite comprising dielectric polymers blended with high-electron-affinity molecular semiconductors that exhibits concurrent high energy density (3.0 J cm −3 ) and high discharge efficiency (90%) up to 200 °C, far outperforming the existing dielectric polymers and polymer nanocomposites. We demonstrate that molecular semiconductors immobilize free electrons via strong electrostatic attraction and impede electric charge injection and transport in dielectric polymers, which leads to the substantial performance improvements. The all-organic composites can be fabricated into large-area and high-quality films with uniform dielectric and capacitive performance, which is crucially important for their successful commercialization and practical application in high-temperature electronics and energy storage devices. Dielectric polymers are widely used in electrostatic energy storage but suffer from low energy density and efficiency at elevated temperatures. Here, the authors show that all-organic composites containing high-electron-affinity molecular semiconductors exhibit excellent capacitive performance at 200 °C.

The present study aims to assess the superiority of the metaheuristic evolutionary when compared to the conventional machine learning classification techniques for landslide occurrence estimation. To evaluate and compare the applicability of these metaheuristic algorithms, a real-world problem of landslide assessment (i.e., including 266 records and fifteen landslide conditioning factors) is selected. In the first step, seven of the most common traditional classification techniques are applied. Then, after introducing the elite model, it is optimized using six state-of-the-art metaheuristic evolutionary techniques. The results show that applying the proposed evolutionary algorithms effectively increases the prediction accuracy from 81.6 to the range (87.8–98.3%) and the classification ratio from 58.3% to the range (60.1–85.0%).

Pathological accumulation of microtubule-associated protein tau and lysosome dysfunction are both important pathological events in Alzheimer's disease (AD). It is necessary to study the effects on lysosomes of tau burdened cells systematically. To get the informations of lysosomes in tau overexpressed cultured cells (HEK293 cells, N2a cells and primary neurons) and brains of mice carrying P301S tau, we used the techniques including proteomics, Western blotting, lysosomal fluorescence imaging, ultramicro-scopic imaging, and lysosomal functional imaging. The Lysosome signal was enriched by the differentially expressed proteins in HEK293tau cells, and the disruption of microtubule system and deficiency of lysosomal transporters were also suggested in the proteomic data. The lysosomes in tau burdened cells were larger, less numerous, more perinuclear distributed, deacidified and less active in proteolysis. The number of neuronal residual body type lysosomes (RLs), which contain particles with high electron densities and lipid droplets with low electron densities not been digested, was significantly increased in hippocampi of P301S tau mice. All these data suggested the lysosomal stress response in tau overexpressed cells, and helped to understand the role of tau in the neurodegeneration of AD.

In crystals, defects are well-defined and crucial to their mechanical properties. In contrast, the structural disorder in glasses makes it challenging to directly identify defects at the particle level. However, low-frequency quasi-localised modes (QLMs) in glasses provide valuable insights, acting as mechanical defects associated with shear transformation zones and soft spots. Using molecular dynamics simulations of two-dimensional glasses, we identify a particle-level defect responsible for generating QLMs. The primary QLM originates from a “key-core” square of four particles vibrating in a two-in, two-out pattern, interpretable as a microscopic Eshelby inclusion. The motion of these particles induces nearby volumetric and far-field shear deformations, forming a characteristic four-leaf pattern. Despite the structural isotropy of the glass, these QLMs introduce notable mechanical anisotropy, particularly in nano-sized glasses. Crucially, pinning the key-core particles dramatically reduces shear modulus anisotropy, confirming their role as “localised particle-level defects.” This discovery deepens our understanding of glass defects and offers valuable insights for nanoscale glass applications. Unlike crystals with well-defined defects, glasses exhibit structural disorder that makes identifying particle-level defects difficult. The authors identify a localized defect in 2D glasses-a square of four particles vibrating in a two-in, two-out pattern-as the origin of quasi-localized modes and a microscopic Eshelby inclusion driving mechanical anisotropy.

Supercooled liquids display sluggish dynamics, often attributed to their structural characteristics, yet the underlying mechanism remains elusive. Here we conduct numerical investigations into the structure–dynamics relationship in model glass-forming liquids, with a specific focus on an elementary particle rearrangement mode known as the ‘T1 process’. We discover that the ability of a T1 process to preserve glassy structural order before and after is pivotal towards determining a liquid’s fragility—whether it exhibits super-Arrhenius-like or Arrhenius-like behaviour. If a T1 process disrupts local structural order, it must occur independently without cooperativity, resulting in Arrhenius-like behaviour. By contrast, if it can maintain order, it sequentially propagates from disordered peripheries to the middle of high-structural-order regions, leading to cooperativity and super-Arrhenius-like behaviour. Our study establishes a microscopic link between liquid-structure ordering, dynamic cooperativity and super-Arrhenius-like dynamics, extending the understanding of the structure–dynamics relationships in supercooled liquids. An elementary particle rearrangement mode known as the T1 process links liquid dynamics and local structural ordering to understand the physical mechanisms of super-Arrhenius-like behaviour in glass-forming liquids.