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Oxygen reversibly binds to the redox active iron, a transition metal in human Hemoglobin (Hb), which subsequently undergoes oxidation in air. This process is akin to iron rusting in non-biological systems. This results in the formation of non-oxygen carrying methemoglobin (ferric) (Fe3+) and reactive oxygen species (ROS). In circulating red blood cells (RBCs), Hb remains largely in the ferrous functional form (HbF2+) throughout the RBC's lifespan due to the presence of effective enzymatic and non-enzymatic proteins that keep the levels of metHb to a minimum (1%-3%). In biological systems Hb is viewed as a Fenton reagent where oxidative toxicity is attributed to the formation of a highly reactive hydroxyl radical (OH•) generated by the reaction between Hb's iron (Fe2+) and hydrogen peroxide (H2O2). However, recent research on both cellular and acellular Hbs revealed that the protein engages in enzymatic-like activity when challenged with H2O2, resulting in the formation of a highly reactive ferryl heme (Fe4+) that can target other biological molecules before it self-destructs. Accumulating evidence from several in vitro and in vivo studies are summarized in this review to show that Hb's pseudoperoxidase activity is physiologically more dominant than the Fenton reaction and it plays a pivotal role in the pathophysiology of several blood disorders, storage lesions associated with old blood, and in the toxicity associated with the infusion of Hb-derived oxygen therapeutics.

Biasing the sex ratio of populations of different organisms, including plants, insects, crustacean, and fish, has been demonstrated by genetic and non-genetic approaches. However, biasing the sex ratio of mammalian populations has not been demonstrated genetically. Here, we provide a first proof of concept for such a genetic system in mammals by crossing two genetically engineered mouse lines. The maternal line encodes a functional Cas9 protein on an autosomal chromosome, whereas the paternal line encodes guide RNAs on the Y chromosome targeting vital mouse genes. After fertilization, the presence of both the Y-encoded guide RNAs from the paternal sperm and the Cas9 protein from the maternal egg targets the vital genes in males. We show that these genes are specifically targeted in males and that this breeding consequently self-destructs solely males. Our results pave the way for a genetic system that allows biased sex production of livestock.

The protease kallikrein 4 (KLK4) plays a pivotal role during dental enamel formation by degrading the major enamel protein, amelogenin, prior to the final steps of enamel hardening. KLK4 dysfunction is known to cause some types of developmental defect in enamel but the mechanisms responsible for transient retention of KLK4 in semi-hardened enamel matrix remain unclear. To address contradictory reports about the affinity of KLK4 for enamel hydroxyapatite-like mineral, we used pure components in quasi-physiological conditions and found that KLK4 binds hydroxyapatite directly. Hypothesising KLK4 self-destructs once amelogenin is degraded, biochemical analyses revealed that KLK4 progressively lost activity, became aggregated, and autofragmented when incubated without substrate in both the presence and absence of reducer. However, with non-ionic detergent present as proxy substrate, KLK4 remained active and intact throughout. These findings prompt a new mechanistic model and line of enquiry into the role of KLK4 in enamel hardening and malformation.

The unique optoelectronic properties and smooth, rigid pores of macrocycles with radially oriented π systems render them fascinating candidates for the design of novel mechanically interlocked molecules with new properties. Two high-yielding strategies are used to prepare nanohoop [2]rotaxanes, which owing to the π-rich macrocycle are highly emissive. Then, metal coordination, an intrinsic property afforded by the resulting mechanical bond, can lead to molecular shuttling as well as modulate the observed fluorescence in both organic and aqueous conditions. Inspired by these findings, a self-immolative [2]rotaxane was then designed that self-destructs in the presence of an analyte, eliciting a strong fluorescent turn-on response, serving as proof-of-concept for a new type of molecular sensing material. More broadly, this work highlights the conceptual advantages of combining compact π-rich macrocyclic frameworks with mechanical bonds formed via active-template syntheses.

Many filamentous fungi are capable of undergoing conspecific hyphal fusion with a genetically different individual to form a heterokaryon. However, the viability of such heterokaryons is dependent upon vegetative (heterokaryon) incompatibility (het) loci. If two individuals undergo hyphal anastomosis, but differ in allelic specificity at one or more het loci, the fusion cell is usually compartmentalized and self-destructs. Many of the microscopic features associated with vegetative incompatibility resemble apoptosis in metazoans and plants. To test the hypothesis whether vegetative incompatibility results in nuclear degradation, a characteristic of apoptosis, the cytology of hyphal fusions between incompatible Neurospora crassa strains that differed at three het loci, mat, het-c and het-6, and the cytology of transformants containing incompatible het-c alleles were examined using fluorescent DNA stains and terminal deoxynucleotidyl transferase-mediated dUTP-X nick end labeling (TUNEL). Hyphal fusion cells between het incompatible strains and hyphal segments in het-c incompatible transformants were compartmentalized by septal plugging and contained heavily degraded nuclear DNA. Hyphal fusion cells in compatible self-pairings and hyphal cells in het-c compatible transformants were not compartmentalized and rarely showed TUNEL-positive nuclei. Cell death events also were observed in senescent, older hyphae. Morphological features of hyphal compartmentation and death during vegetative incompatibility and the extent to which it is genetically controlled can best be described as a form of programmed cell death.

Every robot built to date was predesigned by an external process, prior to deployment. Here we show a robot that actively participates in its own design during its lifetime. Starting from a randomly assembled body, and using only proprioceptive feedback, the robot dynamically ``sculpts'' itself into a new design through kinematic self-destruction: identifying redundant links within its body that inhibit its locomotion, and then thrashing those links against the surface until they break at the joint and fall off the body. It does so using a single autoregressive sequence model, a universal controller that learns in simulation when and how to simplify a robot's body through self-destruction and then adaptively controls the reduced morphology. The optimized policy successfully transfers to reality and generalizes to previously unseen kinematic trees, generating forward locomotion that is more effective than otherwise equivalent policies that randomly remove links or cannot remove any. This suggests that self-designing robots may be more successful than predesigned robots in some cases, and that kinematic self-destruction, though reductive and irreversible, could provide a general adap

Self-destructive behaviors are linked to complex psychological states and can be challenging to diagnose. These behaviors may be even harder to identify within subcultural groups due to their unique expressions. As large language models (LLMs) being deployed across various fields, some researchers have begun exploring their application for detecting self-destructive behaviors. Motivated by this, we investigate self-destructive behavior detection within subcultures using current LLM-based methods. However, these methods have two main challenges: (1) Knowledge Lag: Subcultural slang evolves rapidly, faster than LLMs' training cycles; and (2) Semantic Misalignment: it is challenging to grasp the specific and nuanced expressions unique to subcultures. To address these issues, we propose Subcultural Alignment Solver (SAS), a multi-agent framework that incorporates automatic retrieval and subculture alignment, significantly boosting the performance of LLMs in detecting self-destructive behavior. Our experimental results show that SAS outperforms the current advanced multi-agent framework OWL. Notably, it competes well with fine-tuned LLMs. We hope that SAS will advance the field of self-

Multimodal systems are vulnerable to partial or complete loss of input channels at deployment, which undermines reliability in real-world settings. This paper presents ModalImmune, a training framework that enforces modality immunity by intentionally and controllably collapsing selected modality information during training so the model learns joint representations that are robust to destructive modality influence. The framework combines a spectrum-adaptive collapse regularizer, an information-gain guided controller for targeted interventions, curvature-aware gradient masking to stabilize destructive updates, and a certified Neumann-truncated hyper-gradient procedure for automatic meta-parameter adaptation. Empirical evaluation on standard multimodal benchmarks demonstrates that ModalImmune improves resilience to modality removal and corruption while retaining convergence stability and reconstruction capacity.

Biological transport networks adapt through dynamic interactions between material transport and structural modification during growth and development. In this work, we present a model of transport network growth driven by local material concentration. Using an advection-diffusion framework on a metric graph with a tip growth rule, we investigate how successive construction and destruction influence network development. Our results reveal that while repeated cycles of elongation and retraction can facilitate growth enhancement, a network need to have a structure that mitigates material dissipation. This finding suggests that additional regulatory mechanisms are necessary for networks to efficiently redistribute resources following structural retraction.

This paper introduces JiraiBench, the first bilingual benchmark for evaluating large language models' effectiveness in detecting self-destructive content across Chinese and Japanese social media communities. Focusing on the transnational "Jirai" (landmine) online subculture that encompasses multiple forms of self-destructive behaviors including drug overdose, eating disorders, and self-harm, we present a comprehensive evaluation framework incorporating both linguistic and cultural dimensions. Our dataset comprises 10,419 Chinese posts and 5,000 Japanese posts with multidimensional annotation along three behavioral categories, achieving substantial inter-annotator agreement. Experimental evaluations across four state-of-the-art models reveal significant performance variations based on instructional language, with Japanese prompts unexpectedly outperforming Chinese prompts when processing Chinese content. This emergent cross-cultural transfer suggests that cultural proximity can sometimes outweigh linguistic similarity in detection tasks. Cross-lingual transfer experiments with fine-tuned models further demonstrate the potential for knowledge transfer between these language systems w

Harmful fine-tuning attacks pose a major threat to the security of large language models (LLMs), allowing adversaries to compromise safety guardrails with minimal harmful data. While existing defenses attempt to reinforce LLM alignment, they fail to address models' inherent "trainability" on harmful data, leaving them vulnerable to stronger attacks with increased learning rates or larger harmful datasets. To overcome this critical limitation, we introduce SEAM, a novel alignment-enhancing defense that transforms LLMs into self-destructive models with intrinsic resilience to misalignment attempts. Specifically, these models retain their capabilities for legitimate tasks while exhibiting substantial performance degradation when fine-tuned on harmful data. The protection is achieved through a novel loss function that couples the optimization trajectories of benign and harmful data, enhanced with adversarial gradient ascent to amplify the self-destructive effect. To enable practical training, we develop an efficient Hessian-free gradient estimate with theoretical error bounds. Extensive evaluation across LLMs and datasets demonstrates that SEAM creates a no-win situation for adversarie

The structural integrity of self-piercing rivet (SPR) joints is critical in automotive industries, yet its evaluation poses challenges due to the limitations of traditional destructive methods. This research introduces an innovative approach for non-destructive evaluation using micro-CT imaging, Micro-Computed Tomography, combined with machine vision and deep learning techniques, specifically focusing on automated keypoint estimation to assess joint quality. Recognizing the scarcity of real micro-CT data, this study utilizes synthetic data for initial model training, followed by transfer learning to adapt the model for real-world conditions. A UNet-based architecture is employed to localize three keypoints with precision, enabling the measurement of critical parameters such as head height, interlock, and bottom layer thickness. Extensive validation demonstrates that pre-training on synthetic data, complemented by fine-tuning with limited real data, bridges domain gaps and enhances predictive accuracy. The proposed framework not only offers a scalable and cost-efficient solution for evaluating SPR joints but also establishes a foundation for broader applications of machine vision an

Swimming and flying animals demonstrate remarkable adaptations to diverse flow conditions in their environments. In this study, we aim to advance the fundamental understanding of the interaction between flexible bodies and heterogeneous flow conditions. We develop a linear inviscid model of an elastically mounted foil that passively pitches in response to a prescribed heaving motion and an incoming flow that consists of a traveling wave disturbance superposed on a uniform flow. In addition to the well-known resonant response, the wavy flow induces an antiresonant response for non-dimensional phase velocities near unity due to the emergence of non-circulatory forces that oppose circulatory forces. We also find that the wavy flow destructively interferes with itself, effectively rendering the foil a low-pass filter. The net result is that the waviness of the flow always improves thrust and efficiency when the wavy flow is of a different frequency than the prescribed heaving motion. Such a simple statement cannot be made when the wavy flow and heaving motion have the same frequency. Depending on the wavenumber and relative phase, the two may work in concert or in opposition, but they

Simple local interactions can cause primitive building blocks to self-assemble into complex and functional patterns. However, even for a small number of blocks, there exist a vast number of possible configurations that are plausible, stable, and with varying degree of order. The ability to dynamically shift between multi-stable patterns (i.e., reprogram the self-assembly) entails navigating an intractable search space, which remains a challenge. In this paper, we engineer the self-assembly of macroscopic magnetic particles to create metamaterials with dynamically reversible emergent phases. We utilize a boundary composed of magnetic hinges to confine free-floating magnetic disks into different stable assemblies. We exploit the non-destructive nature of the magnetic boundaries to create re-programmable two-dimensional metamaterials that morphs from crystalline to quasi-crystalline to disordered assembly using the same number of disks and boundary. Furthermore, we explore their utility to control the propagation of sound waves in an effectively undamped media with rich nonlinearities. Our findings can expand the metamaterials horizon into functional and tunable devices.

Cavity optomechanics implements a unique platform where moving objects can be probed by quantum fields, either laser light or microwave signals. With a pump tone driving at a frequency above the cavity resonance, self-sustained oscillations can be triggered at large injected powers. These limit cycle dynamics are particularly rich, presenting hysteretic behaviours, broad comb signals and especially large motion amplitudes. All of these features can be exploited for both fundamental quantum research and engineering. Here we present low temperature microwave experiments performed on a high-Q cavity resonance capacitively coupled to the flexure of a beam resonator. We study the limit cycle dynamics phase space as a function of pump parameters (detuning, power). Unexpectedly, we find that in a region of this phase space the microwave resonance is irremediably destroyed: only a dramatic power-reset can restore the dynamics to its original state. The phenomenon can be understood as an optical instability linked to the Kerr nonlinearity of the cavity. A theory supporting this claim is presented, reproducing almost quantitatively the measurement. This remarkable feature might be further op