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Objective: Online patient education materials (OPEMs) are important resources for patients seeking health information. While the National Institutes of Health (NIH) and American Medical Association (AMA) recommend a sixth-grade readability level for OPEMs, commonly available material often exceeds such criteria. Large language models (LLMs), such as ChatGPT and Gemini, have emerged as tools for health education with potential applications in simplification of health material. This study assesses the utility of ChatGPT and Gemini in enhancing the readability of OPEMs for peripheral nerve surgeries. Methods: Eleven common peripheral nerve surgeries were used as online search terms. The first 20 unique search results were assessed; results were excluded if they did not include patient-facing material. ChatGPT and Gemini were instructed to rewrite the text of the OPEM at or below a sixth-grade reading level. Readability metrics were calculated for original OPEMs, alongside ChatGPT and Gemini rewrites. LLM responses were reviewed for accuracy/quality (five-point scale) and comprehensiveness (three-point scale) using predefined criteria. Results: A total of 220 websites were assessed. In total, 155 OPEMs met the inclusion criteria; 65 websites were excluded because they were academic journal articles or other provider-facing materials. The average Flesch-Kincaid grade level (FKGL) of OPEMs was 11.3, significantly greater than the NIH/AMA-sixth grade recommendations (p < 0.001). The average FKGL of ChatGPT rewrites was significantly lower than that of OPEMs (11.3 vs. 7.5, p < 0.001), as was the average FKGL of Gemini rewrites (11.3 vs. 5.6, p < 0.001). ChatGPT rewrites were of higher accuracy/quality (4.5/5.0 vs. 4.0/5.0, p < 0.001) and comprehensiveness (2.0/3.0 vs. 1.0/3.0, p < 0.001) relative to Gemini rewrites. Conclusions: The readability of online patient education materials for peripheral nerve surgery significantly exceeded NIH/AMA recommendations. ChatGPT and Gemini were able to significantly simplify the reading level of these OPEMs. LLMs may serve as tools to improve the readability of peripheral nerve surgery OPEMs.
Mulberry (Morus spp.) includes ecologically important tree species that are highly valued for their exceptional economic and medicinal properties. Among its diverse species, Morus wittiorum and Morus laevigata are particularly valuable genetic resources because of their resistance to Sclerotinia, their relatively high content of specific flavonoids, and elongated fruit morphology. In this study, hybridization experiments were conducted using 10 mulberry accessions spanning three taxonomic sections (Alba, Wittiorum, and Laevigata). All six attempted intersectional crosses successfully yielded hybrid progeny. Using genomic in situ hybridization with blocking DNA, we detected distinct chromosomal signal patterns among the three sections, enabling precise identification of hybrid and wild-type chromosomal constitutions. Notably, this study provides the first documented evidence of 2n gamete formation in the genus Morus, where 2n eggs from M. wittiorum 'W-4' produced a pentaploid hybrid, 'Mp-7'. This discovery not only rewrites the chromosomal inheritance patterns of Morus but also unveils untapped polyploid breeding potential. These findings provide an efficient approach for identifying hybrids and offer novel polyploid breeding strategies, thereby promising to reshape global mulberry breeding and creating new opportunities for the genetic improvement of this agronomically important species.
When we are awake, we pay attention to a specific object in the surrounding world. Feedforward transmission of sensory information is enhanced in the cortico-cortical networks during the inhalation phase of the respiratory cycle for identification and evaluation of sensory information. Memory engrams of the associated scene are recalled by the sensory signal as a search tag. In the mammalian olfactory system, a topographic pattern of activated glomeruli in the olfactory bulb is transferred to the piriform cortex via the anterior olfactory nucleus, and then to higher cognitive areas. The odor map is utilized as a QR code (two-dimensional bar code) for recollection of the associative memory scene. Higher cognitive areas generate imagery of the multisensory cognitive scene, determine the valence of input information, and make behavioral decisions during the exhalation phase of respiration. The cognitive scene information for valence decision is transmitted back to the olfactory cortical areas in the top-down direction through the same set of pyramidal cells as used for the feedforward transmission of sensory information. The top-down information not only directs the output behaviors and generates emotional states but also rewrites the existing memory engrams. Then, How is this attentional switch from the surrounding outer world to the cognitive inner world regulated in a respiration-phase correlated manner? We propose that the basal forebrain inputs to neurogliaform cells in the piriform cortex may play a key role in switching attention by layer-specific blanket inhibition of pyramidal cells.
Fibrous porous media, such as paper and textiles, are extensively used in point-of-care diagnostics and smart sensors, due to capillarity and bio-compatibility. However, the fluid permeation through such substrates defies the simplicity of classical capillarity, owing to interaction between swelling and pore-scale heterogeneity. We hypothesize that a swelling porous matrix becomes an active participant in fluid permeation through evolving local heterogeneity, scripting emergent pathways of transport, thereby limiting classical models, such as Lucas-Washburn and Darcy's model. In the present study, we investigate the effect of swelling induced evolution of pore scale heterogeneity on macroscopic imbibition patterns. We develop a comprehensive mathematical model incorporating the swelling kinetics of fibres and evolving pore scale heterogeneity to modify Darcy's law, thereby accounting for liquid absorption into swelling fibres. The evolution of local permeability K and porosity φ were obtained using capillary-bundle approach, upon upscaling the evolving underlying Pore Size Distribution (PSD), validated with Pore Network Modelling simulations. The overall theoretical model was validated with the lateral flow imbibition experiments through different fibrous porous substrates. We show that the coupled interplay between evolving pore-scale flow pathways and absorption, driven by localized swelling, impedes the imbibition. We demonstrate that the evolving underlying PSD critically affects the microscopic flow distribution, which significantly affects the slowing down of the imbibition front. Our results demonstrate that the resulting feedback between dynamic morphology and flow redistribution gives rise to distinct wicking modes- Continuous, Interrupted and Termination. By mapping the conditions that toggle between these competing regimes, we thus reveal a universal framework for understanding capillary phenomena in living, swelling, or reactive media, where the flow rewrites the medium as much as the medium guides the flow.
The sciences divide into those that discover laws and those that reconstruct histories. We argue that this division does not reflect a difference in subject matter, but a difference in epistemic regime. Law-based sciences operate under episodic closure: systems are idealized so that the outcomes of prior interactions do not alter the rules governing future ones. This regime-defining idealization (distinguished from pragmatic idealization) underlies the predictive successes of physics, but creates a systematic blind spot for evolutionary dynamics. We formalize this distinction using Stability-Driven Assembly (SDA), a minimal non-equilibrium framework in which differential persistence couples episodes into population-level evolutionary dynamics without genes, replication, or predefined fitness functions. Representing compositional objects as λ-calculus terms, we show that episodic science studies isolated λ-reductions under fixed rules, while evolutionary science studies populations of λ-instantiations whose outputs re-enter the space of operators. The resulting dynamics are self-modifying and irreducibly sequential: each step rewrites the conditions for the next. A four-quadrant taxonomy locates episodic science, evolutionary science, and two commonly conflated intermediate cases: formal possibility and constructive potential, within a single framework. From this analysis we derive the "No Free Telos" constraint: in constructive systems where population feedback reshapes the effective dynamics at each step, the cost of predicting future states cannot in general be reduced below the cost of simulating the generative history. The resulting framework bridges episodic and historical sciences, not by reducing one to the other, but by identifying population-level memory as the structural condition that transforms law-governed episodes into open-ended evolutionary processes.
Graph-based pangenome references often misrepresent Copy Number Variations (CNVs) and Variable Number Tandem Repeats (VNTRs) as alternative acyclic paths, which hinders downstream analyses, degrades alignment behavior, and reduces interpretability in graph visualizations. For these reasons, we introduce PANPHORTE, a topology-optimization methodology that detects repeat-driven misrepresentations within superbubbles and rewrites them into structures that more faithfully reflect the underlying biology. Given a pangenome graph annotated with haplotype paths, PANPHORTE identifies repetitive elements inside superbubbles, isolates shared repeat sequences across distinct subpaths, and refactors the graph by splitting nodes and introducing explicit cycles, encoding CNVs and VNTRs without loss of information. We provide a C++ command-line implementation of the proposed specifications, and a complementary pipeline that applies PANPHORTE followed by GFAffix to further reduce redundancy in regions not affected by repeat-induced artifacts. We evaluate PANPHORTE on synthetic and real pangenome graphs, showing reductions in memory footprint of up to 71.69%, improvements in exact read matches of up to 34.4%, and substantially clearer visual identification of repeated loci.
The complement system is traditionally recognized as a major effector of innate immunity, essential for pathogen clearance, inflammation and the maintenance of tissue homeostasis. In recent years, however, its role in cancer has been substantially redefined. Beyond its canonical extracellular activity, complement has emerged as a multifaceted regulator of tumor biology, acting not only within the tumor microenvironment but also intracellularly through the recently described intracellular complement system (complosome). While extracellular complement primarily shapes immune responses and the tumor microenvironment, the complosome directly regulates fundamental cellular processes, including metabolism, proliferation, autophagy, stress responses and cell survival. In this review, we discuss current evidence on the canonical and non-canonical roles of complement in cancer. Importantly, complement signaling exhibits a strong context-dependent duality, exerting either tumor-promoting or tumor-restraining effects depending on the tumor type, disease stage, cellular source, and localization. Taken together, the available evidence indicates that the complosome is not merely an extension of classical complement biology, but a distinct and biologically significant signaling network that rewrites our understanding of complement in cancer. Its growing relevance in tumor development and therapy resistance positions it as a promising target for future mechanistic studies and innovative therapeutic interventions.
Youth addiction often unfolds within landscapes of neglect, yet adult accounts of these histories are commonly gathered through one-off interviews or clinical assessments. This study examined how adults in stable recovery narrate adolescent alcohol and other drug use and associated neglect through handwritten diary-style writing, and what such writing contributes methodologically to qualitative inquiry across time. This paper reports a secondary analysis of community-generated qualitative materials produced through the SIDINL peer-support and mental health recovery network. The available corpus comprised n = 14 handwritten diary-style texts and then participants took part in a participant-led co-reading conversation. Four cases (n = 4) were selected for in-depth vignette-based analysis based on explicit consent for secondary analysis and quotation, sufficient narrative density/legibility of written material, completion of co-reading discussion, and feasibility of de-identification. Data were analyzed using case-centered narrative summaries and reflexive thematic analysis, attending to both content and material features of handwriting. The analysis foregrounded three recurring patterns. (1) "No one was watching": participants described moving through unsupervised spaces where adults were absent or silent; substance use was framed as both relief and proof of invisibility, and neglect appeared as both wound and perceived freedom from scrutiny. (2) "My body on the page": writers portrayed the body as a site of self-neglect and communication, describing hunger, injury, dirt, ignored symptoms, and hygiene or clothing as shields that kept others away or signaled distress that others did not read. (3) "Writing from after": letter and then/now formats staged dialogue between youth and adult selves, redistributing responsibility for neglect across families, institutions, and self while also acknowledging harms caused during use. Handwritten diary-style writing, paired with participant-led co-reading, generated temporally layered narratives of youth addiction and neglect that extend beyond single interview recall. The approach positions writing as an active practice of identity reconstruction in recovery and offers a practical method for qualitative substance-use research where control over disclosure and careful attention to shame, stigma, and omission of care are essential.
Radiation detection technology is critical in medical diagnosis, high-energy physics experiments, nuclear environmental monitoring, and radiation safety protection. Its technological iteration stems from innovations in high-performance radiation detection materials. Traditional materials often have narrow dose-response intervals, insufficient high-precision measurement capability, low spatial resolution, and poor stability, failing to meet high-precision detection requirements. Ag-doped phosphate glass (Ag-PG), based on radio-photoluminescence (RPL), effectively addresses these limitations with its comprehensive advantages: high radiation sensitivity, a wide linear dose-response range, submicron spatial resolution for radiation imaging, write-erase-rewrite capability, and visualized dose monitoring potential, and it also boasts significant fundamental research value and engineering application prospects. Specifically, while existing RPL reviews mainly provide a comprehensive analysis from the perspective of RPL and present typical RPL material systems, this paper systematically analyzes the structural characteristics of the Ag-PG matrix and the coordination configuration and site occupation of Ag ions. It clarifies RPL luminescence properties, dose-response mechanisms, and the evolution of luminescence centers, while reviewing advancements in applications such as radiation dose detection and high-resolution X-ray imaging. By summarizing the current research status, technical advantages and existing challenges of Ag-PG, this study provides theoretical references and conceptual insights to promote breakthroughs in its fundamental research and practical applications in high-precision radiation dose detection, advanced medical imaging, micro-nano-scale radiation detection, and nuclear industry non-destructive testing.
Effective communication about clinical trials is essential, as low enrollment undermines scientific validity and contributes to health care inequities. However, recruitment remains a persistent challenge, particularly among older adults, minority populations, and individuals with limited health literacy. Although large language models (LLMs) show promise in understanding and generating health information, it is unclear whether these generative artificial intelligence (AI) tools can improve the content of hospitals' frequently asked questions (FAQ) pages to enhance public attitudes and intentions toward clinical trial participation. This study aimed to compare clinical trial FAQ from health organizations and hospitals with versions rewritten by LLMs to examine whether the generated content improves public attitudes and intentions toward clinical trial participation and to identify the mechanisms underlying these effects. A total of 308 question-answer pairs were collected from the FAQ pages of 38 health organizations and hospitals, categorizing them into 52 types and selecting the 11 most frequent for testing. A comparative survey experiment was conducted with 440 participants randomly assigned to one of the two survey stimuli: the original FAQ versus the GPT-4o-generated answers emphasizing comprehension and empathy. The study compared the impact of AI-generated versus standard FAQ content on attitudes toward clinical trials and examined Theory of Planned Behavior constructs to determine for whom and how AI information is most effective. Participants were recruited through CloudResearch, yielding a 96.94% completion rate, resulting in 440 valid responses across the 2 types of content exposure. Participants who viewed GPT-4o-generated information (mean 0.26, SD 0.65) showed a marginally greater positive change in outcome evaluation attitudes than those who viewed standard FAQ (mean 0.13, SD 0.70; P=.05; 95% CI 0.00-0.25). Follow-up linear regression analyses revealed that several individual factors significantly moderated the effect of the information type (FAQ vs GPT-4o) on attitude change, including age (mean difference 0.87, SE 0.33; 2-tailed t394=2.62; P=.009); race (mean difference 0.36, SE 0.15; t383=2.47; P=.01); risk aversion (B=0.12; SE 0.06; t383=2.23; P=.03); fear of ineffective treatment (B=0.11; SE 0.05; t383=2.03; P=.04); and fear of unknown treatment effects (B=0.21; SE 0.07; t383=3.10; P=.002). This study is the first to apply the Theory of Planned Behavior to compare LLM-rewritten versus original FAQ content for clinical trial communication. The findings show that the GPT-4o-generated responses improved attitudes among traditionally underrepresented groups, including older adults, Black participants, and those with higher uncertainty avoidance or treatment concerns. These attitude gains were positively linked to participation intentions, suggesting that AI-generated language can enhance public attitudes, perceptions, and engagement with clinical research.
The influential 2019 study by Bycroft et al. reported remarkably pronounced ultrafine-scale genetic structure within a small region of Galicia (northwestern Iberia). Using fineSTRUCTURE clustering of ChromoPainter coancestry profiles, the authors identified multiple internally coherent clusters characterized by high levels of within-group haplotype sharing and reduced sharing with neighboring populations. On a broader national scale, they also reported a marked East-West genetic differentiation and North-South homogeneity across the Iberian Peninsula, patterns which they largely attributed to historical population movements associated with the gradual territorial expansion of Christian kingdoms between the 8th and 15th centuries following the initial Islamic conquest. Several subsequent studies have adopted similar interpretations, but all relied on the same genetic resource, the EPICOLON Phase I cohort. Following a detailed reexamination of Bycroft et al., we identify multiple methodological issues, most notably signals consistent with unaccounted batch effects in EPICOLON, some of which were recognized by the authors in earlier studies. In contrast, we highlight recent analyses based on independent genomic datasets that consistently report high levels of genetic homogeneity within Galicia and across central and southern Iberia, with no evidence supporting a pronounced East-West genetic divide across Iberia. In addition, recent studies have documented North African demographic influences in Iberia, particularly in Galicia, predating the period of Islamic rule and therefore challenging the historical interpretation proposed by Bycroft et al. We also show that while fineSTRUCTURE is a powerful tool for detecting subtle population stratification, it may be highly sensitive to technical confounders, which can magnify or distort inferred clustering patterns. This study demonstrates that technical artifacts can strongly affect fine-scale population structure inferred by fineSTRUCTURE, emphasizing the need for rigorous quality control and cautious interpretation of highly resolved genetic clusters.
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Precise and efficient replacement of large genomic DNA segments without inducing double-strand breaks (DSBs) remains a central challenge in genome engineering. Traditional homologous recombination relies on DSBs and long homologous arms, yet it remains inefficient, while recombinase or integrase systems suffer from residual sequences at integration sites. Prime editing (PE), limited by the processivity of reverse transcriptase, struggles to integrate large fragments (>100 bp). To address this challenge, we introduce Prime Editing-Microhomology-Enabled Replacement (PREMIER), a DSB-free platform by installing single-stranded microhomology arms at donor and genomic junctions via PE. In cell lines, PREMIER achieved a mean efficiency of 63.4% (median 65.2%) in diverse target sites, with peak efficiencies reaching 85.9%, exceeding homology-directed repair by 10-20-fold and reducing off-target integrations by over 100-fold compared to nonhomologous end joining. It bypasses the need for long homology arms, simplifies donor preparation, achieves targeted replacement of sequences up to 10.3 kb. In vivo, PREMIER integrates a 6.2-kb oncogene cassette into the mouse liver. Additionally, PREMIER replaces murine Trp53 with human TP53 CDS, generating functional humanized mice. Altogether, PREMIER provides a precise, high-efficiency, and DSB-free strategy for large-scale genome rewriting, offering a powerful tool for complex modeling and therapeutic genome editing.
Efforts to systematically understand how cell interactions tune tissue-level function have motivated transformative advances in single-cell transcriptomics and spatial profiling. Although these technologies can measure molecular states in individual cells and their spatial mapping within tissues, they also reveal that there exists a fundamental knowledge gap of how cells influence each other in context. In this Perspective, we propose an initiative to map and engineer the human cell-cell interactome: a functional atlas of how all major human cell types communicate. We highlight how recent innovations can make this vision achievable. As a first moonshot, we propose the 'Billion Cell×Cell Project', which systematically characterizes the outcomes of defined cell-cell dyads across diverse cell types and conditions. We envision this multistage initiative will produce progressively deeper insights and unlock additional avenues for therapeutic discovery. We call on the scientific community to join us in building the tools, datasets and models that will decode and rewrite the language of life between cells.
The realization of rewritable and customizable electromagnetic illusions fundamentally hinges upon the ability to achieve precise spatiotemporal control over electromagnetic waves. Conventional metasurfaces are confined to globally stationary, periodic protocols, lacking the information entropy to orchestrate complex illusion patterns. Here, we introduce a modular metasurface time-domain programming framework that organizes discrete temporal modulation waveforms as reusable modulation units within a predefined library. By flexibly selecting and sequencing these units, rather than redesigning the entire control law for each new task, the framework redistributes the spectral components of the scattered field to synthesize diverse electromagnetic illusions. A deep generative model is established to map target illusions directly to specific time-domain modulation sequences. The metasurface executes these signals across distinct pulses to physically realize the user-defined illusion. Validated on a synthetic aperture imaging testbed, the system rewrites periodic baselines and synthesizes representative aperiodic illusion patterns under user-specified inputs, achieving high fidelity between intended objectives and measurements (structural similarity index ≥0.91). This work establishes a practical route from target-scene specification to executable metasurface control and provides a scalable paradigm for task-driven wave manipulation in radar imaging scenarios.
Green-energy technologies (GETs)-including solar cells, batteries, and biomass systems-underpin climate-change mitigation, but their performance increasingly depends on engineered nanoscale interfaces, making the energy transition also an interface transition. The ligand shells, coatings, and conductive networks that optimize a device can be inherited by nanomaterials (NMs) released during manufacturing, operation, aging, accidents, and end-of-life recycling, thereby programming how this debris behaves in the environment and the body. Released fragments are seldom pristine cores with fixed identities; instead, natural organic matter, proteins, and metabolites rewrite their surfaces into eco-coronas (ECs) and protein coronas (PCs) that reset charge, aggregation, dissolution, transport, and biological recognition. These successive coronas form a life-cycle EC-PC continuum linking interfacial evolution to environmental fate, bioaccessibility, and biological effects. Yet mechanistic corona evidence comes largely from model NMs such as silver, gold, and titania, leaving the release states of deployed GET materials-perovskite (PVSK) residues and quantum dots (QDs), high-nickel (high-Ni) cathodes and black-mass residues, carbon conductors, MXenes, and metal-organic framework (MOF)-derived fragments-scattered across disconnected literatures and unable to support prediction. Here we develop a life-cycle eco-to-protein-corona framework that organizes these materials along a single interfacial sequence and recasts the corona as reportable state variables-composition, enrichment, stability, transformation, exposure context, and outcome-that make heterogeneous systems comparable. This framework exposes a structured evidence gap: GET materials are increasingly characterized for environmental transformation, persistence, and ecotoxicity, whereas matched EC/PC and health-outcome evidence remains scarce. Corona fingerprints thus offer a computable bridge from surface history to predictive, scenario-specific, and safer-by-design assessment of green-energy NMs.
In this paper, we develop a class of antisymmetrized geminal power configuration interaction (AGP-CI) wave functions that extend the AGP framework by incorporating inter-geminal correlations through a CI expansion. To make these wavefunctions computationally tractable, we evaluate them by rewriting the AGP-CI ansatz as a linear combination of AGPs (LC-AGP), for which overlaps and Hamiltonian matrix elements can be computed with standard AGP machinery. Motivated by border-rank decompositions, we further reorganize this ansatz into a compact linear combination of AGPs depending on a small deformation parameter τ, which controls how closely the truncated expansion approximates the full AGP-CI state. Benchmark applications to the Hubbard model and to the small molecules H2O and N2 demonstrate that the proposed wavefunctions achieve consistently high accuracy and outperform the LC-AGP, particularly for systems with more electrons and in strongly correlated regimes.
Implementing cryptographic primitives on resource-constrained IoT devices involves tight latency, code-size, and energy budgets. This work proposes a general LLVM backend instruction-selection strategy that recognizes single-bit update idioms-typically expressed as LOAD--(AND/OR)--STORE sequences in SHA-256 and similar bit-oriented code-and lowers them to the most efficient target-specific bit-manipulation primitive when legality and cost conditions are met. As a concrete instantiation, we implement the strategy for the Renesas RL78/G23 ISA by rewriting eligible patterns into SET1/CLR1 instructions when the constant mask targets exactly one bit. We evaluate the resulting backend on an RL78/G23 platform using cycle counts and code size (bytes) across SHA-256-driven workloads motivated by firmware integrity checking, Merkle-tree hashing, HMAC-based authentication, password-based key derivation (PBKDF2), and chunk-level update validation. The observed cycle reductions are also converted to absolute time across the device's supported on-chip oscillator frequencies to quantify latency impact under different clocking modes. The experimental validation in this work is limited to the RL78/G23 backend implementation. The underlying instruction-selection idea may be adaptable to other RL78-family devices or to other embedded architectures that provide equivalent single-bit set/clear or bitfield operations; however, such adaptations require target-specific legality checks, cost modeling, and separate experimental validation.
As a crucial traditional information carrier in human civilization, paper exhibits increasingly significant environmental burdens associated with its manufacturing and recycling processes, prompting exploration into novel paper solutions. Although various materials have been employed to develop rewritable paper, challenges such as chemical toxicity and inadequate color retention frequently remain. Novel rewritable paper with dual advantages of environmental friendliness and long-term color stability is essential. Photonic paper is a type of functional information medium that employs the structural color of photonic crystals as its chromic unit. By integrating stimuli-responsive materials, it achieves a reversible modulation of its photonic bandgap in response to external stimuli. This mechanism enables the complete cycle of information writing, reading, erasing, and rewriting. This review systematically summarizes the fabrication strategies of responsive photonic crystals and the distinct response mechanisms of rewritable photonic paper. It surveys key methods, including capillary force-driven vertical deposition, evaporation-induced self-assembly, and external field-assisted assembly, and details chromogenic processes under various stimuli. Finally, we discuss prospects and challenges, highlighting the potential of this technology as a versatile platform for information encryption and interactive displays through the synergy of structural color and stimulus responsiveness.