Search Results (100)

Aspergillus fijiensis is an industrially important filamentous fungus, whose genetic analysis has been limited by the absence of species-specific tools. This study establishes an optimized CRISPR–Cas9 genome editing platform for A. fijiensis, from protoplast preparation to DNA repair pathway engineering. Antibiotic screening first identified hygromycin B and 5-FOA (5-fluoroorotic acid) as effective positive and counter-selection markers. A high-efficiency protoplast regeneration protocol was developed depending on specific osmotic stabilization and mycelial competence. Evaluation of a plasmid-based CRISPR system revealed that while autonomous replication was feasible, gene editing was constrained by low efficiency and a predominant bias toward NHEJ (non-homologous end joining). We implemented a Cas9–sgRNA RNP (ribonucleoprotein) delivery approach, with RNP delivery alone producing frequent indels. However, targeted integration remained inefficient when using conventional MMEJ (Microhomology-mediated end joining) donors. By employing donors containing short (5 bp) microhomology arms between cleavage sites, we effectively engaged the MMEJ pathway, enabling precise insertions and large-fragment deletions in 92% of the analyzed transformants. Donor templates containing minimal 5 bp microhomology sequences could effectively shift the predominant repair pathway from NHEJ to MMEJ. These findings demonstrate that MMEJ is the superior pathway with a unique mechanism for genome engineering in A. fijiensis, providing a versatile toolkit for unlocking the biotechnological potential of this recalcitrant species and a successful paradigm for establishing genetic systems in other species. Full article

Phaeodactylum tricornutum is one of the most well-characterized microalgae and serves as a pivotal model diatom in global carbon fixation and the mediation of biogeochemical cycling of essential nutrients. Over the past few decades, the availability of a complete genome assembly, coupled with the development of robust DNA manipulation tools and efficient DNA delivery methodologies, has established P. tricornutum as a promising photosynthetic chassis for the sustainable bioproduction of high-value compounds, including fucoxanthin and eicosapentaenoic acid (EPA). This review systematically summarizes the research progress in the strain improvement toolkit of P. tricornutum, encompassing both genetic and non-genetic engineering strategies. It elaborates on the types and applications of its representative bioactive products, as well as the molecular mechanisms underlying key synthetic pathways. Additionally, this work synthesizes the research findings on the optimization of critical cultivation conditions (e.g., light, temperature, and nutrient composition) that modulate the growth and product synthesis of P. tricornutum. On this basis, the challenges encountered by P. tricornutum in industrial applications are proposed for further discussion, aiming to provide a reference for in-depth exploration of related research directions and facilitate the expansion of its application scope in the field of biomanufacturing. Full article

As the digitalization of construction standards accelerates, the integration of structural analysis results into Building Information Modeling (BIM) environments has become a critical prerequisite for effective BIM-based Automated Code Checking (ACC), particularly for structural code compliance. In current practice, structural analysis results generated by Computer-Aided Engineering (CAE) tools are often manually transferred into IFC-based BIM models, leading to inefficiencies and increased risk of human error. To address this limitation, this study proposes an extended IFC-based representation, termed IFC-KR-Structure, designed to systematically organize and manage section-wise and load combination-dependent structural analysis results required for code compliance within the IFC environment. Based on the proposed schema, an automated CAE-to-BIM integration module was implemented within the IFC-KR Toolkit to enable direct integration of analysis results generated by a commercial CAE tool (midas Civil NX) into IFC models. The approach establishes consistent element correspondence between structural and BIM models through coordinate alignment and spatial mapping procedures and represents multidimensional analysis results using a schema-compliant, tabular data structure embedded within IFC models. The applicability of the proposed framework was validated using a prestressed concrete girder bridge case, confirming that structural analysis results were accurately mapped, stored, visualized, and subsequently utilized within a BIM-based ACC workflow. The results demonstrate that the proposed approach enables systematic reintegration of CAE-generated analysis results into BIM models and significantly improves the efficiency, consistency, and reliability of BIM-based code compliance processes. Full article

Modern TCP/IP networks are increasingly exposed to unpredictable conditions, both from the physical transmission medium and from malicious cyber threats. Traditional stochastic models often fail to capture the non-linear and highly sensitive nature of these disturbances. This work introduces a formal mathematical framework combining classical network modeling with chaos theory to describe perturbations in latency and packet loss, alongside adversarial processes such as denial-of-service, packet injection, or routing attacks. By structuring the problem into four scenarios (quiescent, perturbed, attacked, perturbed-attacked), the model enables a systematic exploration of resilience and emergent dynamics. The integration of artificial intelligence techniques further enhances this approach, allowing automated detection of chaotic patterns, anomaly classification, and predictive analytics. Machine learning models trained on simulation outputs can identify subtle signatures distinguishing chaotic perturbations from cyber attacks, supporting proactive defense and adaptive traffic engineering. This combination of formal modeling, chaos theory, and AI-driven analysis provides network engineers and security specialists with a powerful toolkit to understand, predict, and mitigate complex threats that go beyond conventional probabilistic assumptions. The result is a more robust methodology for safeguarding critical infrastructures in highly dynamic and adversarial environments. Full article

Effective drug delivery in oncology is challenged by a hierarchy of biological barriers—from abnormal vasculature and dense stroma to cellular immunosuppression and specialized interfaces like the blood–brain barrier. This review provides a contemporary analysis of smart nanoformulations through the lens of a rational, stage-gated design pipeline. We first deconstruct the solid tumor microenvironment as a multi-tiered obstacle (systemic, stromal, cellular), establishing a barrier-specific foundation for nanocarrier design. The core of the review articulates an architectural toolkit, detailing how intrinsic nanoparticle properties precondition in vivo identity via the protein corona, which in turn informs the selection of advanced ligands for cellular targeting and programmed intracellular trafficking. This integrated framework sets the stage for exploring sophisticated applications, including endogenous and externally triggered responsive systems, bio-orthogonal activation, immuno-nanoformulations, and combination strategies aimed at overcoming multidrug resistance. By synthesizing these components into a cohesive design philosophy, this review moves beyond a catalog of advances to offer a blueprint for engineering next-generation nanotherapeutics. We critically assess the translational landscape and contend that this hierarchical design approach is essential for developing more effective, personalized, and clinically viable cancer treatments. Full article

The integration of Artificial Intelligence (AI) in higher education offers a potential solution to the scalability of personalized learning, yet empirical frameworks connecting diagnostic data with teacher-mediated interventions remain limited in developing contexts. This study adopts a sequential multi-phase research design to address this gap. Phase 1 comprised a diagnostic quantitative analysis of the National Survey on Access and Permanence in Education (ENAPE 2021), involving a representative sample of 3422 Mexican undergraduate students. Using Exploratory Factor Analysis (KMO = 0.96) and Pearson correlations, the study established a structural baseline. Phase 2 implemented a quasi-experimental exploratory pilot (N = 23) across two academic clusters (Civil Engineering and Nutrition) using “ActivAI”, a custom GPT configured with Retrieval-Augmented Generation (RAG). Results from Phase 1 revealed a strong, statistically significant correlation ($r=0.72$, $p<0.01$) between the perceived impact of education on daily life and the perception of equity, identifying “relevance” as a key driver of accessibility. Phase 2 results demonstrated high student satisfaction with AI-driven personalization (M = 4.49, SD = 0.64), although disciplinary variations in engagement were observed (SD = 0.85 in Nutrition versus 0.45 in Engineering). The study concludes by proposing the Dynamic Integration Model, which leverages AI not as a replacement for instruction but as a scalability toolkit for teacher-led orchestration, ensuring that personalization addresses dynamic student needs rather than static learning styles. Full article

Manipulating the mitochondrial genome remains a significant challenge in genetic engineering, primarily due to the mitochondrial double-membrane structure. While recent advances have expanded the genetic toolkit for nuclear and cytoplasmic targets, precise editing of mitochondrial DNA (mtDNA) has remained elusive. Here we report the first successful mitochondrial import of a catalytically active RNA-guided prokaryotic Argonaute protein from the mesophilic bacterium Alteromonas macleodii (AmAgo). By guiding AmAgo to the single-stranded D- or R-loop region of mtDNA using synthetic RNA guides, we observed a nearly threefold reduction in mtDNA copy number in human cell lines. This proof of concept study demonstrates that a bacterial Argonaute can remain active within the mitochondrial environment and influence mtDNA levels. These findings establish a foundational framework for further development of programmable systems for mitochondrial genome manipulation. Full article

Recent advancements in molecular tools for plant genetic engineering, particularly CRISPR-based technologies, have created new opportunities for targeted genome editing. However, applying these tools remains challenging in crop species such as sunflower (Helianthus annuus) that lack established and effective transformation pipelines, including transient reagent delivery methods for functional screening and validation of genetic engineering tools. To address this gap, three major reagent delivery platforms, namely protoplast transfection, leaf infiltration, and Agrobacterium-mediated tissue co-culture, were systematically adapted and assessed for use in sunflower seedlings. While each method enabled successful reagent delivery, they differed in their levels of scalability and efficiency. With these platforms, delivery by different Agrobacterium strains and the effectiveness of various reporter gene expression cassettes were compared to define the most experimentally suitable components for different applications in sunflowers. Together, these results establish a foundational toolkit for transient functional testing in sunflower and pave the way for more sophisticated genetic engineering approaches in this agriculturally important oilseed, confectionary seed, and horticultural crop. Full article

This paper presents Hy-LIFT (Hybrid LLM-Integrated Fault Diagnosis Toolkit), a multi-stage framework for interpretable and data-efficient fault diagnosis in 5G/6G networks that integrates a high-precision interpretable rule-based engine (IRBE) for known patterns, a semi-supervised classifier (SSC) that leverages scarce labels and abundant unlabeled logs via consistency regularization and pseudo-labeling, and an LLM Augmentation Engine (LAE) that generates operator-ready, context-aware explanations and zero-shot hypotheses for novel faults. Evaluations on a five-class, imbalanced Dataset-A and a simulated production setting with noise and label scarcity show that Hy-LIFT consistently attains higher macro-F1 than rule-only and standalone ML baselines while maintaining strong per-class precision/recall (≈0.85–0.93), including minority classes, indicating robust generalization under class imbalance. IRBE supplies auditable, high-confidence seeds; SSC expands coverage beyond explicit rules without sacrificing precision; and LAE improves operational interpretability and surfaces potential “unknown/novel” faults without altering classifier labels. The paper’s contributions are as follows: (i) a reproducible, interpretable baseline that doubles as a high-quality pseudo-label source; (ii) a principled semi-supervised learning objective tailored to network logs; (iii) an LLM-driven explanation layer with zero-shot capability; and (iv) an open, end-to-end toolkit with scripts to regenerate all figures and tables. Overall, Hy-LIFT narrows the gap between brittle rules and opaque black-box models by combining accuracy, data efficiency, and auditability, offering a practical path toward trustworthy AIOps in next-generation mobile networks. Full article

Cadmium (Cd) is a pervasive and highly toxic heavy metal that severely threatens environmental integrity, agricultural systems, plant metabolism, ecosystem health, and human food safety. Plants have evolved intricate detoxification mechanisms aimed at mitigating heavy metal toxicity, in which ATP-binding cassette (ABC) transporters play pivotal roles. This article contextualizes findings on the functional genetic manipulation of plant ABC transporters in Cd-exposed species, integrating evidence from model plants, crops, and transgenic systems. Key insights reveal how these transporters contribute to Cd distribution through multiple cellular and physiological pathways. We highlight the contribution of ABC transporters both in modulating Cd accumulation in plant tissues for food safety considerations and in regulating Cd-related parameters relevant to environmental cleanup and phytoremediation. Functional studies in different plant species demonstrate differential outcomes depending on transporter specificity and regulatory context. Cross-kingdom engineering further expands the biotechnological toolkit for Cd mitigation. Additionally, we performed a bibliometric analysis that underscores research trends linking ABC transporters with genetic manipulation strategies. The body of evidence highlights the perspective that precise modulation of ABC transporters—through strategies such as multi-gene engineering, tissue-specific expression, or fine-tuned regulatory approaches—offers a promising yet complex route to reconcile scientific and applied Cd management strategies. Full article

Cas9 with specialized temperature adaptations are essential for broadening the application of CRISPR-based genome editing across diverse biological contexts. Although Cas9 orthologs from thermophilic and mesophilic organisms have been characterized for high- and moderate-temperature applications, cold-active variants remain largely unexplored, limiting genome engineering in low-temperature systems such as aquaculture species. Here, we report the functional characterization of Fp2Cas9, a cold-adapted Type II-C nuclease from Flavobacterium psychrophilum. In vitro assays showed that Fp2Cas9 efficiently cleaves double-stranded DNA with a refined PAM requirement of 5′-SNAAAG-3′, and that its engineered sgRNA scaffold (sgRNA-V2) supports programmable DNA targeting. Notably, Fp2Cas9 retains 75% cleavage efficiency at 5 °C, approximately 2.5-fold higher than SpCas9 under the same conditions, but shows a marked reduction in activity at 35 °C. In vivo, a nuclear-localized variant (2NLS-Fp2Cas9) mediated efficient mutagenesis of the zebrafish slc45a2 gene, yielding ~60% indel frequencies and pigmentation-deficient phenotypes in ~43% of injected embryos. Collectively, these findings establish Fp2Cas9 as a cold-adapted Cas9 with reliable activity at low temperatures. This work adds a valuable tool to the CRISPR-Cas9 toolkit and may facilitate genome editing in cold-water organisms and other low-temperature systems. Full article

Modern crop breeding requires efficient tools for genetic identity and parentage verification to manage large-scale programs. To address this, we present GIPA (Genomic Identity and Parentage Analysis), a high-performance toolkit designed for these tasks. GIPA integrates key innovations: a sliding-window algorithm enhances accuracy by correcting genotyping errors, an intelligent system classifies samples by heterozygosity to streamline parentage analysis, and an integrated engine generates intuitive chromosome-level heatmaps. We demonstrate its utility in a soybean backcrossing scenario, where it identified a donor line with 98.02% genomic identity to the recipient, providing a strategy to significantly shorten the breeding program. In maize, its parentage module accurately identified the known parents of commercial hybrids with match scores exceeding 97%, validating its use for variety authentication and quality control. By transforming complex SNP data into clear, quantitative, and visual insights, GIPA provides a robust solution that accelerates data-driven decision-making in plant breeding. Full article

N-Acetylneuraminic acid (Neu5Ac), the predominant form of sialic acids (Sias), is extensively utilized in the food, pharmaceutical, and cosmetic industries. Microbial fermentation serves as a critical production method for its economical, eco-friendly, and scalable production. Escherichia coli and Bacillus subtilis, as primary industrial workhorses for Neu5Ac production, have been extensively investigated owing to their well-characterized genetic frameworks and mature molecular toolkits. Nevertheless, the intricate regulatory networks inherent to microbial systems present formidable obstacles to the high-efficiency biosynthesis of Neu5Ac. This review delineates the genetic and molecular mechanisms underlying Neu5Ac biosynthesis in both E. coli and B. subtilis. Furthermore, the rational and irrational strategies for constructing Neu5Ac microbial cell factories are systematically summarized, including the application of rational metabolic engineering to relieve feedback regulation, reconfigure metabolic networks, implement dynamic regulation, and optimize carbon sources; as well as the use of irrational strategies including directed evolution of key enzymes and high-throughput screening based on biosensors. Finally, this review addresses current challenges in Neu5Ac bioproduction and proposes integrative solutions combining machine learning with systems metabolic engineering to advance the construction of high-titer Neu5Ac microbial cell factory and the refinement of advanced fermentation technologies. Full article

Electronic-waste (e-waste) management is a key challenge in engineering smart cities due to its rapid accumulation, complex composition, sparse data availability, and significant environmental and economic impacts. This study employs a bespoke machine learning infrastructure on an Indian e-waste supply chain network (SCN) focusing on the three pillars of sustainability—environmental, economic, and social. The economic resilience of the SCN is investigated against external perturbations, like market fluctuations or policy changes, by analyzing six stochastically perturbed modules, generated from the optimal point of the original dataset using Monte Carlo Simulation (MCS). In the process, MCS is demonstrated as a powerful technique to deal with sparse statistics in SCN modeling. The perturbed model is then analyzed to uncover “hidden” non-linear relationships between key variables and their sensitivity in dictating economic arbitrage. Two complementary ensemble-based approaches have been used—Feedforward Neural Network (FNN) model and Random Forest (RF) model. While FNN excels in regressing the model performance against the industry-specified target, RF is better in dealing with feature engineering and dimensional reduction, thus identifying the most influential variables. Our results demonstrate that the FNN model is a superior predictor of arbitrage conditions compared to the RF model. The tangible deliverable is a data-driven toolkit for smart engineering solutions to ensure sustainable e-waste management. Full article

The global transition to a circular bioeconomy is accelerating the demand for sustainable, high-performance materials. Filamentous fungi represent a promising solution, as they function as living foundries that transform low-value biomass into advanced, self-assembling materials. While mycelium-based composites have proven potential, progress has been predominantly driven by empirical screening of fungal species and substrates. To unlock their full potential, a paradigm shift from empirical screening to rational design is required. This review introduces a conceptual framework centered on the biochemical programming of the fungal cell wall. Viewed through a materials science lens, the cell wall is a dynamic, hierarchical nanocomposite whose properties can be deliberately tuned. We analyze the contributions of its principal components—the chitin–glucan structural scaffold, the glycoprotein functional matrix, and surface-active hydrophobins—to the bulk characteristics of mycelium-derived materials. We then identify biochemical levers for controlling these properties. External factors such as substrate composition and environmental cues (e.g., pH) modulate cell wall architecture through conserved signaling pathways. Complementing these, an internal synthetic biology toolkit enables direct genetic and chemical intervention. Strategies include targeted engineering of biosynthetic and regulatory genes (e.g., CHS, AGS, GCN5), chemical genetics to dynamically adjust synthesis during growth, and modification of surface chemistry for specialized applications like tissue engineering. By integrating fungal cell wall biochemistry, materials science, and synthetic biology, this framework moves the field from incidental discovery toward the intentional creation of smart, functional, and sustainable mycelium-based materials—aligning material innovation with the imperatives of the circular bioeconomy. Full article