Search Results (1,922)

Prime editing (PE), a novel “search-and-replace” genome editing technology, demonstrates significant potential for crop genetic improvement due to its precision and versatility. However, since its initial application in plants, PE technology has consistently faced challenges of low and variable editing efficiency, representing a major bottleneck hindering its broader application. Therefore, this study conducted a systematic review following the PRISMA 2020 guidelines. We systematically searched databases—Web of Science, PubMed, and Google Scholar—for studies published up to June 2025 focusing on enhancing PE performance in crops. After a rigorous screening process, 38 eligible primary research articles were ultimately included for comprehensive analysis. Our analysis revealed that early PE systems such as PE2 could perform diverse edits, including all 12 base substitutions and small insertions or deletions (indels), but their efficiency was highly variable across species, targets, and edit types. To overcome this bottleneck, researchers developed four major optimization strategies: (1) engineering core components such as Cas9, reverse transcriptase (RT), and editor architecture; (2) enhancing expression and delivery via optimized promoters and vectors; (3) improving reaction processes by modulating DNA repair pathways or external conditions; and (4) enriching edited events through selectable or visual markers. These advancements broadened PE’s targeting scope with novel Cas9 variants and enabled complex, kilobase-scale DNA insertions and rearrangements. The application of PE technology in plants has evolved from basic functional validation, through systematic optimization for enhanced efficiency, to advanced stages of functional expansion. This review charts this trajectory and clarifies the key strategies driving these advancements. We posit that future breakthroughs will increasingly depend on synergistically integrating these strategies to enable the efficient, precise, and predictable application of PE technology across diverse crops and complex breeding objectives. This study provides an important theoretical framework and practical guidance for subsequent research and application in this field. Full article

Zero-day attacks pose a persistent threat to computing infrastructure by exploiting previously unknown software vulnerabilities that evade traditional signature-based network intrusion detection systems (NIDSs). To address this limitation, machine learning (ML) techniques offer a promising approach for enhancing anomaly detection in network traffic. This study evaluates several ML models on a labeled network traffic dataset, with a focus on zero-day attack detection. Ensemble learning methods, particularly eXtreme gradient boosting (XGBoost), achieved perfect classification, identifying all 6231 zero-day instances without false positives and maintaining efficient training and prediction times. While classical support vector machines (SVMs) performed modestly at 64% accuracy, their performance improved to 98% with the use of the borderline synthetic minority oversampling technique (SMOTE) and SMOTE + edited nearest neighbours (SMOTEENN). To explore quantum-enhanced alternatives, a quantum SVM (QSVM) is implemented using three-qubit and four-qubit quantum circuits simulated on the aer_simulator_statevector. The QSVM achieved high accuracy (99.89%) and strong F1-scores (98.95%), indicating that nonlinear quantum feature maps (QFMs) can increase sensitivity to zero-day exploit patterns. Unlike prior work that applies standard quantum kernels, this study introduces a parameterised quantum feature encoding scheme, where each classical feature is mapped using a nonlinear function tuned by a set of learnable parameters. Additionally, a sparse entanglement topology is derived from mutual information between features, ensuring a compact and data-adaptive quantum circuit that aligns with the resource constraints of noisy intermediate-scale quantum (NISQ) devices. Our contribution lies in formalising a quantum circuit design that enables scalable, expressive, and generalisable quantum architectures tailored for zero-day attack detection. This extends beyond conventional usage of QSVMs by offering a principled approach to quantum circuit construction for cybersecurity. While these findings are obtained via noiseless simulation, they provide a theoretical proof of concept for the viability of quantum ML (QML) in network security. Future work should target real quantum hardware execution and adaptive sampling techniques to assess robustness under decoherence, gate errors, and dynamic threat environments. Full article

Plants must contend with oxidative stress, a paradoxical phenomenon in which reactive oxygen species (ROS) can cause cellular damage while also serving as key signaling molecules. Environmental stressors, such as drought, salinity, and temperature extremes, promote ROS accumulation, affecting plant growth and productivity. To maintain redox homeostasis, plants rely on antioxidant systems comprising enzymatic defenses, such as superoxide dismutase, catalase, and ascorbate peroxidase, and non-enzymatic molecules, including ascorbate, glutathione, flavonoids, and emerging compounds such as proline and nano-silicon. This review provides an integrated overview of antioxidant responses and their modulation through recent biotechnological advances, emphasizing the role of emerging technologies in advancing our understanding of redox regulation and translating molecular insights into stress-resilient phenotypes. Omics approaches have enabled the identification of redox-related genes, while genome editing tools, particularly those based on clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated (Cas) proteins, offer opportunities for precise functional manipulation. Artificial intelligence and systems biology are accelerating the discovery of regulatory modules and enabling predictive modeling of antioxidant networks. We also highlight the contribution of synthetic biology to the development of stress-responsive gene circuits and address current regulatory and ethical considerations. Overall, this review aims to provide a comprehensive perspective on molecular, biochemical, and technological strategies to enhance oxidative stress tolerance in plants, thereby contributing to sustainable agriculture and food security in a changing climate. Full article

Cool-season creeping bentgrass (Agrostis stolonifera L., As) is extensively used on golf courses worldwide and is negatively affected by several fungal diseases and abiotic stresses including drought and salinity. CRISPR/Cas9 (clustered regularly interspaced short palindromic repeats/CRISPR-associated) gene editing technology was employed in this project to knock out the AsDREBL (dehydration responsive element binding-like factor) gene, a potential negative regulator in stress tolerance. With our established single guide RNA (sgRNA)-based CRISPR-editing vector and optimized creeping bentgrass tissue culture system using mature seed-derived embryogenic calli of cv. Crenshaw as explant, more than 20 transgenic plants were produced by gene gun bombardment. Fifteen confirmed AsDREBL mutant plants were tested for drought and salinity tolerance by withholding water and applying salt spray in greenhouse settings. Some of the mutants were shown to be more tolerant of drought and salinity stress compared to the non-edited, wild type Crenshaw plants. Our results demonstrate that CRISPR-gene editing technology can be successfully applied to improve the agronomical traits of turfgrass. Full article

Systemic light-chain (AL) amyloidosis is a challenging, complex and heterogeneous disease. AL amyloidosis is classified under the category of plasma cell neoplasms and other diseases with paraproteins in the fifth edition of the World Health Organization classification of lymphoid tumors. Epidemiological information is limited, largely due to its low incidence and the lack of a global network of population-based specific registries. Despite recent advances, AL amyloidosis is still considered an incurable disease. The presence of a precursor disease, particularly monoclonal gammopathy of uncertain significance, is the main consolidated risk factor. Limited knowledge about other risk factors precludes the possibility of establishing preventive measures. A relevant percentage of AL amyloidosis patients fulfill the current diagnostic criteria of multiple myeloma. Incidence should be evaluated in the setting of population-based studies. On the one hand, incidence shows a slightly increasing pattern. On the other hand, survival is progressively increasing. Consequently, prevalence is also rising. Early mortality, commonly associated with advanced heart involvement, remains a serious drawback to improve the outcome. Epidemiology represents the first level of heterogeneity in AL amyloidosis. Both genomic and clinical epidemiological research in systemic AL amyloidosis have a crucial role in the global strategy to combat this multifaceted disease. Full article

In the context of the deep integration of digital art and geometric computing, this paper proposes a digital art pattern generation method with arbitrary quadrilateral tiling. The aim is to break through the limitations of traditional fixed tiling templates in terms of adaptability to irregular tiling shapes, controllability of local deformations, and naturalness of boundary transitions. By decoupling the topological stability of quadrilaterals from deformation parameters and combining the Coons surface interpolation method, a smooth invariant mapping for the fundamental region of arbitrary quadrilaterals is constructed, solving the seamless splicing problem of irregular fundamental region. This method supports real-time editing of quadrilateral shape and colors the fundamental region based on the dynamical system model to generate periodic seamless patterns with global symmetry and controllable local details. Experiments show that the proposed method can be adapted to any quadrilateral structure, from regular rectangles to non-convex polygons. By adjusting the interpolation parameters and dynamical system functions, the symmetry, texture complexity, and visual rhythm of the patterns can be flexibly regulated. The algorithm achieves efficient computation under GPU parallel optimization (with an average generation time of 0.25 s per pattern), providing a new solution for the pattern generation and personalized design of digital art patterns. Full article

Human induced pluripotent stem cells (iPSCs) can be genetically engineered to evade host immune recognition, rendering them hypoimmunogenic and suitable as “universal donor” cells for allogeneic transplantation. Such modifications enable the development of off-the-shelf iPSC-derived therapeutics that are immediately available for clinical use without the need for patient-specific derivation or immunosuppression. This review focuses on recent developments in strategies for generating hypoimmunogenic human iPSCs, with particular emphasis on their applications in central nervous system (CNS) cell therapy and repair. We assess immunomodulatory factors based on their immune functions and potential roles in CNS development and disease, with the goal of identifying strategies to use these factors either individually, in combination, or alongside gene editing to reduce immune rejection without compromising neurogenesis or tissue repair. Full article

Transposable elements (TEs) serve as important drivers mediating polyploidization events and phenotypic diversification in plant genomes. However, the dynamic changes in various TE subclasses post-polyploidization and their mechanisms of influencing phenotypic variation require further investigation. The allopolyploid Gossypium species, originating from two diploid progenitors, provide an ideal model for studying TE dynamics following polyploidization. This study investigated TE dynamics post-polyploidization based on 21 diploid and 7 polyploid cotton genomes. The Tekay subclass of the Gypsy serves as a major driver of Gossypium genome evolution, as it underwent two burst events in the At-subgenome and its progenitor, exhibiting the highest abundance, longest length, and largest proportion among all TE subclasses. In contrast, the Gopia superfamily Tork subclass has lower abundance but greater genic association, facilitating environmental adaptation and phenotypic variation. Additionally, a pan-TE-related structural variation, the pan-TRV map, was constructed by integrating resequencing data from 256 accessions. Genome-wide analysis of 28 cotton genomes identified 142,802 TRVs, among which 72,116 showed polymorphisms in the 256 G. hirsutum accessions. The Gypsy superfamily, particularly the Tekay subclass, has been identified as a major source of TRVs, while Copia-type elements demonstrate significantly greater enrichment in gene-proximal genomic regions. A total of 334 TRVs exhibiting statistically significant associations with 10 key phenotypic traits, including 164 TRVs affecting yield components and 170 TRVs determining fiber quality. This investigation delineates the evolutionary significance of transposable elements in Gossypium genome diversification while simultaneously providing novel functional markers and potential editing targets for genetic dissection and molecular breeding of key agronomic traits in cotton. Full article

Multidrug resistance (MDR) remains a formidable barrier to successful cancer treatment, driven by mechanisms such as efflux pump overexpression, enhanced DNA repair, evasion of apoptosis and the protective characteristics of the tumour microenvironment. Nanoparticle-based delivery systems have emerged as promising platforms capable of addressing these challenges by enhancing intracellular drug accumulation, enabling targeted delivery and facilitating stimuli-responsive and controlled release. This review provides a comprehensive overview of the molecular and cellular mechanisms underlying MDR and critically examines recent advances in nanoparticle strategies developed to overcome it. Various nanoparticle designs are analysed in terms of their structural and functional features, including surface modifications, active targeting ligands and responsiveness to tumour-specific cues. Particular emphasis is placed on the co-delivery of chemotherapeutic agents with gene regulators, such as siRNA, and the use of nanoparticles to deliver CRISPR/Cas9 gene editing tools as a means of re-sensitising resistant cancer cells. While significant progress has been made in preclinical settings, challenges such as tumour heterogeneity, limited clinical translation and immune clearance remain. Future directions include the integration of precision nanomedicine, scalable manufacturing and non-viral genome editing platforms. Collectively, nanoparticle-based drug delivery systems offer a multifaceted approach to combat MDR and hold great promise for improving therapeutic outcomes in resistant cancers. Full article

Educational assessment relies on well-constructed test items to measure student learning accurately, yet traditional item development is time-consuming and demands specialized psychometric expertise. Automatic item generation (AIG) offers template-based scalability, and recent large language model (LLM) advances promise to democratize item creation. However, fully automated approaches risk introducing factual errors, bias, and uneven difficulty. To address these challenges, we propose and evaluate a hybrid human-in-the-loop (HITL) framework for AIG that combines psychometric rigor with the linguistic flexibility of LLMs. In a Spring 2025 case study at Franklin University Switzerland, the instructor collaborated with ChatGPT (o4-mini-high) to generate parallel exam variants for two undergraduate business courses: Quantitative Reasoning and Data Mining. The instructor began by defining “radical” and “incidental” parameters to guide the model. Through iterative cycles of prompt, review, and refinement, the instructor validated content accuracy, calibrated difficulty, and mitigated bias. All interactions (including prompt templates, AI outputs, and human edits) were systematically documented, creating a transparent audit trail. Our findings demonstrate that a HITL approach to AIG can produce diverse, psychometrically equivalent exam forms with reduced development time, while preserving item validity and fairness, and potentially reducing cheating. This offers a replicable pathway for harnessing LLMs in educational measurement without sacrificing quality, equity, or accountability. Full article

The increasing global health crisis of neurodegenerative diseases such as Alzheimer’s, Parkinson’s, amyotrophic lateral sclerosis, and Huntington’s disease is worsening because of a rapidly increasing aging population. Disease-modifying therapies continue to face development challenges due to the blood–brain barrier (BBB), which prevents more than 98% of small molecules and all biologics from entering the central nervous system. The therapeutic landscape for neurodegenerative diseases has recently undergone transformation through advances in targeted drug delivery that include ligand-decorated nanoparticles, bispecific antibody shuttles, focused ultrasound-mediated BBB modulation, intranasal exosomes, and mRNA lipid nanoparticles. This review provides an analysis of the molecular pathways that cause major neurodegenerative diseases, discusses the physiological and physicochemical barriers to drug delivery to the brain, and reviews the most recent drug targeting strategies including receptor-mediated transcytosis, cell-based “Trojan horse” approaches, gene-editing vectors, and spatiotemporally controlled physical methods. The review also critically evaluates the limitations such as immunogenicity, scalability, and clinical translation challenges, proposing potential solutions to enhance therapeutic efficacy. The recent clinical trials are assessed in detail, and current and future trends are discussed, including artificial intelligence (AI)-based carrier engineering, combination therapy, and precision neuro-nanomedicine. The successful translation of these innovations into effective treatments for patients with neurodegenerative diseases will require essential interdisciplinary collaboration between neuroscientists, pharmaceutics experts, clinicians, and regulators. Full article

Retinitis pigmentosa is a group of inherited retinal dystrophies characterized by progressive photoreceptor cell loss leading to irreversible vision loss. Affecting approximately 1 in 4000 individuals worldwide, retinitis pigmentosa exhibits significant genetic heterogeneity, with mutations in genes such as RHO, PRPF31, RPE65, USH2A, and NR2E3, which contribute to its diverse clinical presentation. This review outlines the genetic basis of retinitis pigmentosa and explores cutting-edge gene-based therapeutic strategies. Luxturna (voretigene neparvovec-rzyl), the first FDA-approved gene therapy targeting RPE65 mutations, represents a milestone in precision ophthalmology, while OCU400 is a gene-independent therapy that uses a modified NR2E3 construct to modulate retinal homeostasis across different RP genotypes. Additionally, CRISPR–Cas genome-editing technologies offer future potential for the personalized correction of specific mutations, though concerns about off-target effects and delivery challenges remain. The article also highlights MCO-010, a novel optogenetic therapy that bypasses defective phototransduction pathways, showing promise for patients regardless of their genetic profile. Moreover, QR-1123, a mutation-specific antisense oligonucleotide targeting the P23H variant in the RHO gene, is under clinical investigation for autosomal dominant RP and has shown encouraging preclinical results in reducing toxic protein accumulation and preserving photoreceptors. SPVN06, another promising candidate, is a mutation-agnostic gene therapy delivering RdCVF and RdCVFL via AAV to support cone viability and delay degeneration, currently being evaluated in a multicenter Phase I/II trial for patients with various rod–cone dystrophies. Collectively, these advances illustrate the transition from symptom management toward targeted, mutation-specific therapies, marking a major advancement in the treatment of RP and inherited retinal diseases. Full article

Prompt engineering provides an efficient way to adapt large language models (LLMs) to downstream tasks without retraining model parameters. However, designing effective prompts can be challenging, especially when model gradients are unavailable and human expertise is required. Existing automated methods based on gradient optimization or heuristic search exhibit inherent limitations under black box or limited-query conditions. We propose Domain-Aware Reinforcement Learning for Prompt Optimization (DA-RLPO), which treats prompt editing as a sequential decision process and leverages structured domain knowledge to constrain candidate edits. Our experimental results show that DA-RLPO achieves higher accuracy than baselines on text classification tasks and maintains robust performance with limited API calls, while also demonstrating effectiveness on text-to-image and reasoning tasks. Full article

Cell culture-based influenza vaccines exhibit comparable safety and immunogenicity to traditional egg-based vaccines. However, improving viral yield remains a key challenge in optimizing cell culture-based production systems. Madin–Darby canine kidney (MDCK) cells, the predominant cell line for influenza vaccine production, inherently activate interferon (IFN)-mediated antiviral defenses that restrict viral replication. To overcome this limitation, we employed CRISPR/Cas9 gene-editing technology to generate an IFN alpha/beta receptor subunit 1 (IFNAR1)-knockout (KO) adherent MDCK cell line. Viral titer analysis demonstrated significant enhancements in the yield of multiple vaccine strains (H1N1, H3N2, and type B) in IFNAR1-KO cells compared to wild-type (WT) cells. Transcriptomic profiling revealed marked downregulation of key interferon-stimulated genes (ISGs)—including OAS, MX2, and ISG15—within the IFNAR1-KO cells, indicating a persistent suppression of antiviral responses that established a more permissive microenvironment for influenza virus replication. Collectively, the engineered IFNAR1-KO cell line provides a valuable tool for influenza virus research and a promising strategy for optimizing large-scale MDCK cell cultures to enhance vaccine production efficiency. Full article

This review synthesizes advances in livestock genomics by examining the interplay between candidate genes, molecular markers (MMs), signatures of selection (SSs), and quantitative trait loci (QTLs) in shaping economically vital traits across livestock species. By integrating advances in genomics, bioinformatics, and precision breeding, the study elucidates genetic mechanisms underlying productivity, reproduction, meat quality, milk yield, fibre characteristics, disease resistance, and climate resilience traits pivotal to meeting the projected 70% surge in global animal product demand by 2050. A critical synthesis of 1455 peer-reviewed studies reveals that targeted genetic markers (e.g., SNPs, Indels) and QTL regions (e.g., IGF2 for muscle development, DGAT1 for milk composition) enable precise selection for superior phenotypes. SSs, identified through genome-wide scans and haplotype-based analyses, provide insights into domestication history, adaptive evolution, and breed-specific traits, such as heat tolerance in tropical cattle or parasite resistance in sheep. Functional candidate genes, including leptin (LEP) for feed efficiency and myostatin (MSTN) for double-muscling, are highlighted as drivers of genetic gain in breeding programs. The review underscores the transformative role of high-throughput sequencing, genome-wide association studies (GWASs), and CRISPR-based editing in accelerating trait discovery and validation. However, challenges persist, such as gene interactions, genotype–environment interactions, and ethical concerns over genetic diversity loss. By advocating for a multidisciplinary framework that merges genomic data with phenomics, metabolomics, and advanced biostatistics, this work serves as a guide for researchers, breeders, and policymakers. For example, incorporating DGAT1 markers into dairy cattle programs could elevate milk fat content by 15-20%, directly improving farm profitability. The current analysis underscores the need to harmonize high-yield breeding with ethical practices, such as conserving heat-tolerant cattle breeds, like Sahiwal. Full article