Chromatin conformational changes are key to gene regulation in the brain and have recently been shown to be regulated by super-enhancer RNAs (seRNAs). We examine the role of seRNAs in major depressive disorder (MDD) by profiling the prefrontal cortex from 25 MDD subjects and 25 matched non-psychiatric controls. Our analysis reveals 175 differentially expressed seRNAs, of which 140 are upregulated and 35 are downregulated. Our proximity mapping (±50 kb) links 94 seRNAs to nearby protein-coding genes, while long-range analysis (±500 kb) uncovered 584 seRNA-mRNA interaction pairs, most within 400 kb of genomic range. These interactions formed cis-regulatory hubs and hotspots, with notable pairs involving CEBPA, NR3C1, and DUSP1. Additionally, several genes are found to be co-regulated by distinct seRNAs. Spatial mapping confirms that many altered seRNAs are located near super-enhancer regions. Our results for the first time highlight seRNAs as key modulators of proximal and distal gene networks in MDD, offering novel insights and therapeutic avenues.
The gain-of-function allele rol-6 ( su1006 ) has been used as a co-injection marker to visually identify transgenic progeny following microinjection in Caenorhabditis elegans . The rol-6 (gf) allele yields a clear, visual roller phenotype; however, affected worms are twisted along the anterior-posterior axis, obscuring tissues and complicating visual analyses. We deployed CRISPR/Cas9 to "unroll" transgenic worm strains harboring rol-6 (gf) . We discovered that our successfully unrolled strains introduced additional nucleotides into the rol-6 (gf) loci, rendering the rol-6 (gf) copies inactive. Our results indicate that genome engineering can be easily deployed to modify existing transgenic worm strains and could be applied to other gain-of-function co-injection markers.
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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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.
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.
Historical narratives hold that sericulture originated in China and spread along the Silk Road roughly two millennia ago. In questioning this assumption, we conducted micromorphological and proteomics analyses on three cocoons recovered from the Sapalli Tepe (approximately 2000 to 1500 BCE) site in Uzbekistan. We identified them as Bombyx mori, directly dated to 1940 to 1765 cal. BCE, representing the earliest remains of Bombyx cocoons. We further report the earliest mulberry (Morus sp.) charcoal fragments from Central Asia, attesting to mulberry-Bombyx sericulture in the Oxus nearly 2000 years earlier than previously documented. We highlight that early silkworms and mulberry trees may have been introduced to Central Asia along the southern slope of the Himalayas, although other routes could also plausibly explain the observed pattern. This multidisciplinary research rewrites the enigmatic history of early sericulture and its globalization.
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.
This paper introduces an open-source Python package for simplified, customizable computerized adaptive testing (CAT) using Bayesian methods for ability estimation. It addresses the lack of sophisticated packages for CAT in the Python programming language. Moreover, it bridges the gap between the construction and simulation of adaptive tests and their practical application by providing a dedicated API for integration with experiment software. Thereby, it eliminates the need for major code rewrites when transitioning from simulated to real-world adaptive testing. By leveraging Python's object-oriented programming approach, such as abstract classes, protocols, and inheritance, the package allows for easy extension and customization of its functionality. For example, Bayesian estimators can be modified to incorporate custom priors. This paper outlines the relevance and practical use of the adaptivetesting package through a walkthrough example. The package is fully documented, and its source code is published on GitHub. It is also available on the Python Package Index (PyPi) and conda-forge thus it can easily be installed using Python's package manager pip or conda. Leveraging R's reticulate package, adaptivetesting can also be accessed from within RStudio.
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.
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.
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.
Online patient educational materials (PEMs) have poor readability, limiting their intended purposes in improving patient comprehension of health topics. Orthopaedic oncology PEMs are particularly complex. Although ChatGPT has demonstrated limited success in simplifying PEMs to the recommended sixth-grade reading level, other large language models (LLMs) have not been thoroughly evaluated. The goals of this study were to (1) assess baseline readability of online orthopaedic oncology PEMs, (2) evaluate five LLMs (ChatGPT-4o, Google Gemini, DeepSeek AI, Microsoft Copilot, and Meta AI) for improving readability while preserving accuracy and comprehension, and (3) to examine tradeoffs when PEMs were simplified below the sixth-grade level. Seventy-two PEMs were collected from academic and professional sources. Readability metrics included the Flesch-Kincaid Grade Level (FKGL), Gunning Fog Index (GFI), and Flesch Reading Ease (FRE). Each PEM was rewritten by the five LLMs using the prompt: "rewrite this document to a sixth-grade reading level." Two independent graders then evaluated outputs for comprehension and accuracy (F1 score). ANOVA with pairwise comparisons assessed differences among LLMs and versus baseline (PEMs as written). A secondary analysis evaluated the effect on readability, accuracy, and comprehension of prompts to the fifth-grade, fourth-grade, and third-grade reading level. Baseline FKGL (8.7 ± 1.5) was between the eighth-grade and ninth-grade reading level, and GFI (10.5 ± 1.9) was slightly higher. Baseline FRE was 53.9 ± 8.2. All LLMs significantly improved readability (P < 0.001), and ChatGPT-4o, DeepSeek AI, and Google Gemini conversion produced the most readable outputs. Google Gemini achieved the highest F1 score of 0.986 (range: 0.765-0.986) and 100% comprehension. Accuracy and comprehension were compromised for MetaAI when prompted below sixth grade. ChatGPT-4o, Google Gemini, and DeepSeekAI effectively improved readability while preserving comprehension and accuracy. These findings may guide patient use of LLMs and inform healthcare-AI partnerships.
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.
After more than a decade of clinical experience with BTK inhibitors, resistance to BTK targeting has become a moving target, shaped by drug-specific BTK mutations, downstream signaling escape, and disease-dependent adaptive programs. The arrival of BTK degraders introduces a mechanistically distinct modality for lymphoid malignancies, but also raises a timely question: does degrading BTK rewrite the rules of resistance, or simply redraw its boundaries?
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.
Electronic health record (EHR)-linked biobanks generate unprecedented genomic and phenotypic datasets, but their scientific utility is constrained by data fragmentation across institutional silos and incompatible computing infrastructures, forcing researchers to rewrite ad-hoc scripts for each new environment. We present the PMBB Geno-Pheno Toolkit, a suite of modular Nextflow pipelines for biobank-scale association analyses. This note focuses on the toolkit's SAIGE family of pipelines - supporting genome-wide (GWAS), exome-wide (ExWAS), and phenome-wide (PheWAS) association testing - together with the companion GWAMA and ExWAS meta-analysis pipelines that enable cross-biobank replication. All components are containerized (Docker/Apptainer) and orchestrated with Nextflow, allowing the same workflows to run unmodified on local HPC clusters, cloud platforms, and the All of Us Research Workbench. Complementary toolkit pipelines for PLINK-based GWAS, polygenic scoring, LD-based clumping, and phenotype harmonization are also available and briefly noted. The PMBB Geno-Pheno Toolkit is freely available at https://github.com/PMBB-Informatics-and-Genomics/pmbb-geno-pheno-toolkit under MIT open-source license.
Phosphorylated tau at threonine 217 (p-tau217) is generally considered a very specific biomarker of Alzheimer's disease and tau-mediated neurodegeneration in adult patients, where its increase is strongly associated with amyloid, pathology, synaptic dysfunction, and progressive cognitive decline. However, surprisingly, some data report extremely elevated levels of p-tau217 in plasma and cerebrospinal fluid of healthy neonates and infants, i.e., without the presence of neurodegeneration, cognitive impairment, or structural brain damage. The latter paradoxical situation raises a fundamental question that might literally rewrite the interpretations about the tau phosphorylation being a strictly pathological process. This overview aims to bring together the latest clinical, biochemical, and developmental neuroscience literature to unveil the physiological function of p-tau217 during early brain development. We argue that high p-tau217 in infancy may be indicative of ongoing neurodevelopmental processes, such as axonal growth, synaptogenesis, neuronal plasticity, and cytoskeletal remodeling, rather than tau toxicity. Further, we analyze the contribution of the differential expression of tau isoforms, phosphorylation rates, clearance pathways, and BBB permeability in development that could explain the age-dependent biomarker profiles. In addition, this review compares the biology of tau in neonates and adults in a detailed manner, explaining how the context-dependent regulation of tau phosphorylation distinguishes p-tau217 as a developmental marker or a pathological marker. Grasping this difference is essential for the correct reading of tau biomarkers throughout the life span and for preventing the neurodegenerative risk in children from being wrongly diagnosed. In the end, this paradox highlights the need for biomarker frameworks that are specific to different ages and offers new understandings of tau physiology that might lead to novel therapies for tauopathies in the future.