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Eliminating single points of trust: a hybrid quantum and post-quantum blockchain with distributed key generation
Abstract Blockchain systems built on classical cryptography face immediate risks from large-scale quantum computers, while purely quantum-based blockchains often rely on a single Private Key Generator (PKG) and incur heavy resource overheads. To overcome these issues, this paper proposes a hybrid quantum and post-quantum blockchain approach that removes single points of trust by using Distributed Key Generation and a dual-layer signature mechanism. This method integrates quantum digital signatures, rooted in the Fully Flipped Permutation problem, with classical post-quantum (lattice-based) cryptography, enabling users to switch between quantum and classical signatures according to security requirements and channel conditions. Delegated Proof-of-Stake with node behavior and Borda count has been incorporated to manage consensus, ensuring that witness nodes are regularly re-elected and malicious actors are penalized by distributing secret shares among multiple rotating witnesses. We eliminate the central vulnerability of a sole PKG while maintaining rigorous resistance to collusions. Our analytical model indicates that a fraction of transactions can use quantum signatures without system-wide bottlenecks, while the remaining transactions follow classical PQC paths with throughput approaching classical baselines under our modeling assumptions. Consequently, this hybrid method offers higher scalability, robust collusion resistance, and long-term security even under quantum-capable adversaries. This paper presents extensive theoretical analyses, probability models, and algorithmic complexities, demonstrating that our design provides resilient infrastructure that meets the key performance and security requirements of next-generation blockchain systems.
Eco-friendly fabrication of flag blue flower-derived CQD-doped chitosan films with tunable optical and photoluminescent properties
Pilot comparison of monocular and binocular modes of virtual reality field testing in strabismus
Self-assembling multilayer MSC-sheet promotes wound healing increasing M2 macrophage polarization
Abstract Mesenchymal stem cell (MSC) therapy holds promise for treating chronic wounds. However, low cell density in the target tissue and short retention time limits its efficacy. This study investigated the therapeutic effects of the self-assembling multilayer MSC-sheet in a murine wound model, focusing on wound healing and macrophage infiltration. The MSC-sheet was prepared from human subcutaneous adipose tissue and characterized by histology and RNA sequencing. In vivo, the α-MEM (control), MSC-suspension, and MSC-sheet were applied to full-thickness skin defects in C57BL/6 J Jcl mice. On days 7, 14, and 21, the remaining wound area, neoepithelialization, granulation tissue, cell retention time, angiogenesis, and macrophage infiltration were evaluated using hematoxylin and eosin, Azan, anti-human nucleoli (HN), anti-CD31, anti-CD68, and anti-CD163 staining. Macrophage polarization was evaluated by RT-PCR on days 3, 7, and 14. The MSC-sheet formed a multilayer structure (6–7 layers) containing extracellular matrix (ECM). RNA sequencing identified 756 differentially expressed genes compared with MSC-suspension, highlighting pathways related to wound healing, anti-inflammation, angiogenesis, and macrophage polarization. In vivo, both the MSC-sheet and MSC-suspension significantly reduced the remaining wound area (days 7 and 14), increased neoepithelialization (day 14), granulation tissue formation (day 7), angiogenesis (day 7) and macrophage infiltration (days 7 and 14) compared to the control group. The MSC-sheet maintained superior MSC retention and greater M2 macrophage induction than suspension, confirmed by RT-PCR. The MSC-sheet and MSC-suspension accelerated wound healing and promoted M2 macrophage polarization. The MSC-sheet, with its multilayer structure and ECM, outperformed suspension in sustaining MSCs and enhancing M2 infiltration. These findings position the self-assembling multilayer MSC-sheet as a promising, scaffold-free cell delivery platform for chronic wounds, with potential scalability and clinical applicability for future translational use.
Analysis of blasting characteristics in interbedded rock masses based on particle expansion method
Long-term hydrodynamic changes in marginal estuarine seas: the role of sea level rise and freshwater fluxes
Abstract Significant environmental changes have occurred in marginal estuarine seas in the past, driven by changes in sea level and water and salt balances. Predicting the future evolution of these seas is a scientific challenge because adequate scenario studies are currently hindered by the quality of 3D models on millennial timescales. In this paper, we present relatively simple and efficient models of the Black Sea. This sea connects rivers to the ocean and acts as a ‘mixer’, diluting inflowing saline ocean water with riverine freshwater to generate brackish water. For such an ocean system, it is crucial to incorporate mixing within the sea and two-layer exchange within the strait into a single framework. This paper demonstrates that the developed models can be used to (1) estimate past and future ocean states resulting from specific combinations of global sea level and freshwater fluxes; (2) critically address the consistency of used water and salt balances; (3) conceptualizing further studies of major environmental trends. Illustrations are provided to show the evolution of stratification of the Black Sea in the Holocene. Application to other ocean environments controlled by two-layer exchange flows is also discussed.
Exploring the association between pulse wave velocity and CKD progression: a longitudinal study in Mashhad, Iran
Modulation of inter-hemispheric temperature gradients on the Holocene Asian-Australian summer monsoon
Evaluation of the effect of resveratrol on bone healing after tooth extraction in rats treated with zoledronic acid
Synthesizing selection mosaic theory and host-pathogen theory to explain large-scale pathogen coexistence
Proteomic profiling reveals age-related changes in transporter proteins in the human blood–brain barrier
Abstract The Blood–Brain Barrier (BBB) is a selective barrier that regulates the entry of molecules including nutrients, environmental toxins, and therapeutic medications into the brain. Its function continues to evolve postnatally, through aging, and disease states. Here we present a global proteomics analysis focused on the ontogeny and aging of proteins in human brain microvessels (BMVs), predominantly composed of brain endothelial cells. Our proteomic study quantified 6,223 proteins and revealed possible age-related alterations in BBB permeability due to basement membrane component changes through the early developmental stage and age-dependent changes in transporter expression. Age dependent expression changes were observed within nutrient transporters and transporters that play critical roles in drug disposition. This research 1) provides important information on the mechanisms that drive changes in the metabolic content of the brain with age and 2) enables the creation of physiologically based pharmacokinetic models for CNS drug distribution across different life stages.
Expression of antibody-drug conjugate targets in post-mortem samples of breast cancer metastases and normal tissue
Detection and diagnosis of diabetic retinopathy in retinal fundus images using agentic AI approaches
Gut microbiota-derived metabolite isovalerylcarnitine modulates salt sensitivity of blood pressure and incident hypertension: a multicenter dietary salt intervention trial
Catechin hydrate induces GFAP expression and astrocyte branch elongation in rat neural stem cells
Factoring an integer with three oscillators and a qubit
Abstract A common starting point of traditional quantum algorithm design is the notion of a universal quantum computer with a scalable number of qubits. This convenient abstraction mirrors classical computations manipulating bits. It allows for a device-independent development of algorithmic primitives. Here we argue that an alternative approach centered on the physical setup can yield great benefits. As an example, we consider hybrid qubit-oscillator systems with linear optics operations augmented by certain qubit-controlled Gaussian unitaries. The continuous variable Fourier transform and certain arithmetic operations have native realizations in such systems. We put this to algorithmic use and give a polynomial-time quantum factoring algorithm which uses only one qubit and three oscillators, independent of the number being factored.
A novel TaqMan probe-based pentaplex qPCR assay for the simultaneous detection of five pathogenic protozoans
Nano-confinement engineering boosts C–N coupling for urea electrosynthesis
Convolutional neural network approach for automated well zonation in the Lower Bahariya member north Western Desert Egypt
Abstract Accurate well-to-well stratigraphic zonation is fundamental to subsurface reservoir characterization, particularly in geologically complex settings such as tidal channel systems where lithological variability and discontinuous shale barriers pose significant interpretation challenges. This study introduces a novel, image-based workflow leveraging Convolutional Neural Networks (CNNs) to automate zonation in newly drilled wells using conventional well log data. The proposed approach transforms multiple input features—including Gamma Ray (GR), Density (RHOB), Neutron Porosity (NPHI), Photoelectric Effect (PE), interpreted facies, True Vertical Depth Subsea (TVDSS), and three spatial distance features—into images. Each image represents a normalized vertical window of subsurface data and is labeled according to expert-interpreted stratigraphic zones. These labeled images form the training dataset for a supervised CNN classification model. The methodology is applied to the Shahd SE field in the northern Western Desert of Egypt, targeting the Lower Bahariya member. The trained CNN model is evaluated on blind wells demonstrating high prediction accuracy and successful generalization across wells. The proposed workflow introduces a novel image-based transformation of 1D well-log sequences into 2D representations that enables the use of convolutional neural networks traditionally designed for spatial image analysis. Unlike previous zonation studies that rely on raw 1D signals or statistical clustering, our approach (i) applies an optimized pseudo-image encoding of log data, (ii) integrates inter-well spatial distance as an explicit feature to enhance geological continuity, and (iii) incorporates post-processing filtering to refine zone boundaries. This combination provides a more robust and automated zonation methodology with improved generalization across blind wells.