Search Results (2,404)

Lactic acid bacteria (LAB) are central to food, feed, and health biotechnology, yet their genomes have long resisted rapid, precise manipulation. This review charts the evolution of LAB genome-editing strategies from labor-intensive RecA-dependent double-crossovers to state-of-the-art CRISPR and CRISPR-associated transposase systems. Native homologous recombination, transposon mutagenesis, and phage-derived recombineering opened the door to targeted gene disruption, but low efficiencies and marker footprints limited throughput. Recent phage RecT/RecE-mediated recombineering and CRISPR/Cas counter-selection now enable scar-less point edits, seamless deletions, and multi-kilobase insertions at efficiencies approaching model organisms. Endogenous Cas9 systems, dCas-based CRISPR interference, and CRISPR-guided transposases further extend the toolbox, allowing multiplex knockouts, precise single-base mutations, conditional knockdowns, and payloads up to 10 kb. The remaining hurdles include strain-specific barriers, reliance on selection markers for large edits, and the limited host-range of recombinases. Nevertheless, convergence of phage enzymes, CRISPR counter-selection and high-throughput oligo recombineering is rapidly transforming LAB into versatile chassis for cell-factory and therapeutic applications. Full article

In this study, we propose a bidirectional chemical sensor platform based on a reconfigurable ion-sensitive field-effect transistor (R-ISFET) architecture. The device incorporates Ni-silicide Schottky barrier source/drain (S/D) contacts, enabling ambipolar conduction and bidirectional turn-on behavior for both p-type and n-type configurations. Channel polarity is dynamically controlled via the program gate (PG), while the control gate (CG) suppresses leakage current, enhancing operational stability and energy efficiency. A dual-control-gate (DCG) structure enhances capacitive coupling, enabling sensitivity beyond the Nernst limit without external amplification. The extended-gate (EG) architecture physically separates the transistor and sensing regions, improving durability and long-term reliability. Electrical characteristics were evaluated through transfer and output curves, and carrier transport mechanisms were analyzed using band diagrams. Sensor performance—including sensitivity, hysteresis, and drift—was assessed under various pH conditions and external noise up to 5 V_pp (i.e., peak-to-peak voltage). The n-type configuration exhibited high mobility and fast response, while the p-type configuration demonstrated excellent noise immunity and low drift. Both modes showed consistent sensitivity trends, confirming the feasibility of complementary sensing. These results indicate that the proposed R-ISFET sensor enables selective mode switching for high sensitivity and robust operation, offering strong potential for next-generation biosensing and chemical detection. Full article

The European Union promotes decarbonization in energy-intensive industries like glass manufacturing. Collaboration between industry and researchers focuses on reducing CO₂ emissions through hydrogen (H₂) integration as a natural gas substitute. However, to the best of the authors’ knowledge, no updated real-world case studies are available in the literature that consider the on-site implementation of an electrolyzer for autonomous hydrogen production capable of meeting the needs of a glass manufacturing plant within current technological constraints. This study examines a representative hollow glass plant and develops various decarbonization scenarios through detailed process simulations in Aspen Plus. The models provide consistent mass and energy balances, enabling the quantification of energy demand and key cost drivers associated with H₂ integration. These results form the basis for a scenario-specific techno-economic assessment, including both on-grid and off-grid configurations. Subsequently, the analysis estimates the levelized costs of hydrogen (LCOH) for each scenario and compares them to current and projected benchmarks. The study also highlights ongoing research projects and technological advancements in the transition from natural gas to H₂ in the glass sector. Finally, potential barriers to large-scale implementation are discussed, along with policy and infrastructure recommendations to foster industrial adoption. These findings suggest that hybrid configurations represent the most promising path toward industrial H₂ adoption in glass manufacturing. Full article

This study explores entertainment consumption patterns in Romania, a transitional digital culture characterized by high digital connectivity but underdeveloped physical infrastructure. Employing a dual qualitative coding methodology, this research combines inductive analysis of consumer focus groups with deductive analysis of expert interviews, enabling a multi-layered interpretation of both overt behaviors and latent emotional drivers. Seven key thematic dimensions, motivational depth, perceived barriers, emotional needs, clarity of preferences, future behavioral intentions, social connection, and identity construction, were analyzed and compared using a Likert-based scoring framework, supported by a radar chart and comparison matrix. Findings reveal both convergence and divergence between consumer and expert perspectives. While consumers emphasize immediate experiences and logistical constraints, experts uncover deeper emotional motivators such as validation, mentorship, and identity formation. This behavioral–emotional gap suggests that, although digital entertainment dominates due to accessibility, it often lacks the emotional richness associated with physical formats, which are preferred but less accessible. This study underscores the importance of triangulated qualitative inquiry in revealing not only stated preferences but also unconscious psychological needs. It offers actionable insights for designing emotionally intelligent and culturally responsive entertainment strategies in digitally saturated yet infrastructure-limited environments. Full article

As civil engineering projects grow increasingly complex, ensuring pile integrity is essential for pile bearing capacity and structural safety. Pile integrity testing (PIT) has long been a focal point for researchers and engineers. With the rapid development of industrial-level advancements and artificial intelligence technology, PIT methods have undergone significant technological advancements. This paper reviews traditional PIT techniques, including low-strain integrity testing and thermal integrity profiling. The review covers the principles, advantages, limitations, and recent developments of various testing techniques. Additionally, recent advances in artificial intelligence (AI) techniques, particularly in signal processing and data-driven recognition methods, are discussed. Finally, the advantages, limitations, and potential future research directions of existing methods are summarized. This paper aims to offer a systematic reference for researchers and engineers in PIT, synthesizing technical details of traditional methods and their AI-enabled advancements. Furthermore, it explores potential directions for integrating AI with PIT, with a focus on key challenges such as noisy signal interpretation and regulatory barriers in applications. Full article

Background/Objectives: Improving health outcomes for Indigenous people by strengthening the cultural safety of care is a vital challenge for the health sector. University Departments of Rural Health (UDRH), academic centres based in regional, rural, and remote (RRR) locations across Australia, are uniquely positioned to foster a culturally safe rural health workforce through training, education, and engagement with Indigenous communities. This narrative review examines the contributions of UDRHs to health workforce issues through analysis of their publications focused on Indigenous health. Methods: Research articles relating to workforce were identified from an established database of UDRH Indigenous health-related publications published 2010–2021. Results: Of 46 articles identified across the 12 years, 19 focused on developing the understanding and cultural safety skills of university students studying in a health field, including campus-based Indigenous health education and support for students undertaking rural clinical placements. Twelve articles investigated cultural safety skills and recruitment and retention of the rural health workforce. Fifteen articles focused on Indigenous people in the health workforce, examining clinical training and resources, and the enablers and barriers to retaining Indigenous students and workers. Conclusions: This analysis highlights the sustained efforts of UDRHs to improve Indigenous health through multiple areas within their influence, including curriculum design, health student training on campus, and rural placement opportunities to transform understanding of Indigenous strengths and disadvantages and rural health workforce development. A continuing effort is needed on ways UDRHs can support Indigenous health students during their studies and while on placement, how to improve cultural safety in the health workforce, and ways to better support Indigenous health professionals. Full article

This study developed a water-soluble antifouling coating to protect ship hulls against corrosion and fouling without the usage of a primer. The coating retains its adhesion to the steel substrate and reduces corrosion rates compared to those for uncoated specimens. The coating’s protective properties rely on the interaction of conductive polyaniline (PAni) nanorods, magnetite (Fe₃O₄) nanoparticles, and graphene oxide (GO) sheets modified with titanium dioxide (TiO₂) nanoparticles. The PAni/Fe₃O₄ nanocomposite improves the antifouling layer’s out-of-plane conductivity, whereas GO increases its in-plane conductivity. The anisotropy in the conductivity distribution reduces the electrostatic attraction and limits primary bacterial and pathogen adsorption. TiO₂ augments the conductivity of the PAni nanorods, enabling visible light to generate H₂O₂. The latter decomposes into H₂O and O₂, rendering the coating environmentally benign. The coating acts as an effective barrier with limited permeability to the steel surface, demonstrating outstanding durability for naval steel over extended periods. Full article

How do regulatory policies, funding structures, and cross-sector coordination shape knowledge flows and institutional transformation? Focusing on the smart medical device sector in Taiwan, this study explores how governance dynamics accelerate system transformation and foster demand for adaptive and integrative innovation systems. Building on the National Biotechnology Innovation System framework and qualitative system dynamics modeling, the study analyzes institutional interactions through 28 semi-structured interviews and 18 policy documents. Findings identify systemic bottlenecks, including translational gaps, coordination challenges, and barriers for traditional manufacturers. These gaps have enabled tech firms to emerge as system leaders by bridging these institutional gaps. This study extends innovation systems theory by conceptualizing an emergent governance function that addresses institutional gaps. At the policy level, the study highlights the importance of enabling institutional change in governance to address structural fragmentation and support system-wide transformation. Full article

The COVID-19 pandemic catalyzed the rapid adoption of remote patient monitoring (RPM) technologies such as telemedicine and wearable devices (WDs), significantly transforming healthcare delivery. Telemedicine made virtual consultations possible, reducing in-person visits and infection risks, particularly for the management of chronic diseases. Wearable devices enabled the real-time continuous monitoring of health that assisted in condition prediction and management, such as for COVID-19. This narrative review addresses these transformations by uniquely synthesizing findings from 13 diverse studies (sourced from PubMed and Google Scholar, 2020–2024) to analyze the parallel evolution of telemedicine and WDs as interconnected RPM components. It highlights the pandemic’s dual impact, as follows: accelerating RPM innovation and adoption while simultaneously unmasking systemic challenges such as inequities in access and a need for robust integration approaches; while telemedicine usage soared during the pandemic, consumption post-pandemic, as indicated by the reviewed studies, suggests continued barriers to adoption among older adults. Likewise, wearable devices demonstrated significant potential in early disease detection and long-term health management, with promising applications extending beyond COVID-19, including long COVID conditions. Addressing the identified challenges is crucial for healthcare providers and systems to fully embrace these technologies and this would improve efficiency and patient outcomes. Full article

The rapid evolution of Industry 4.0 technologies has resulted in a scenario in which collaborative networks are essential to overcome the challenges related to their implementation. However, the frameworks to guide such collaborations remain underexplored. This study addresses this gap by proposing Business Rules and Goals Models to operationalize Industry 4.0 solutions through enterprise collaboration. Using the For Enterprise Modeling (4EM) method, the research integrates qualitative insights from expert opinions, including interviews with 12 professionals (academics, industry professionals, and consultants) from Brazilian manufacturing sectors. The Goals Model identifies five main objectives—competitiveness, efficiency, flexibility, interoperability, and real-time collaboration—while the Business Rules Model outlines 18 actionable recommendations, such as investing in digital infrastructure, upskilling employees, and standardizing information technology systems. The results reveal that cultural resistance, limited resources, and knowledge gaps are critical barriers, while interoperability and stakeholder integration emerge as enablers of digital transformation. The study concludes that successfully adopting Industry 4.0 requires technological investments, organizational alignment, structured governance, and collaborative ecosystems. These models provide a practical roadmap for companies navigating the complexities of Industry 4.0, emphasizing adaptability and cross-functional synergy. The research contributes to the literature on collaborative networks by connecting theoretical frameworks with actionable enterprise-level strategies. Full article

Cancer metabolic reprogramming plays a critical role in tumor progression and therapeutic resistance, underscoring the need for advanced analytical strategies. Metabolomics, leveraging mass spectrometry and nuclear magnetic resonance (NMR) spectroscopy, offers a comprehensive and functional readout of tumor biochemistry. By enabling both targeted metabolite quantification and untargeted profiling, metabolomics captures the dynamic metabolic alterations associated with cancer. The integration of metabolomics with machine learning (ML) approaches further enhances the interpretation of these complex, high-dimensional datasets, providing powerful insights into cancer biology from biomarker discovery to therapeutic targeting. This review systematically examines the transformative role of ML in cancer metabolomics. We discuss how various ML methodologies—including supervised algorithms (e.g., Support Vector Machine, Random Forest), unsupervised techniques (e.g., Principal Component Analysis, t-SNE), and deep learning frameworks—are advancing cancer research. Specifically, we highlight three major applications of ML–metabolomics integration: (1) cancer subtyping, exemplified by the use of Similarity Network Fusion (SNF) and LASSO regression to classify triple-negative breast cancer into subtypes with distinct survival outcomes; (2) biomarker discovery, where Random Forest and Partial Least Squares Discriminant Analysis (PLS-DA) models have achieved >90% accuracy in detecting breast and colorectal cancers through biofluid metabolomics; and (3) prognostic modeling, demonstrated by the identification of race-specific metabolic signatures in breast cancer and the prediction of clinical outcomes in lung and ovarian cancers. Beyond these areas, we explore applications across prostate, thyroid, and pancreatic cancers, where ML-driven metabolomics is contributing to earlier detection, improved risk stratification, and personalized treatment planning. We also address critical challenges, including issues of data quality (e.g., batch effects, missing values), model interpretability, and barriers to clinical translation. Emerging solutions, such as explainable artificial intelligence (XAI) approaches and standardized multi-omics integration pipelines, are discussed as pathways to overcome these hurdles. By synthesizing recent advances, this review illustrates how ML-enhanced metabolomics bridges the gap between fundamental cancer metabolism research and clinical application, offering new avenues for precision oncology through improved diagnosis, prognosis, and tailored therapeutic strategies. Full article

To enable safe and effective human–robot collaboration (HRC) in smart manufacturing, it is critical to seamlessly integrate sensing, cognition, and prediction into the robot controller for real-time awareness, response, and communication inside a heterogeneous environment (robots, humans, and equipment). The proposed approach takes advantage of the prediction capabilities of nonlinear model predictive control (NMPC) to execute safe path planning based on feedback from a vision system. To satisfy the requirements of real-time path planning, an embedded solver based on a penalty method is applied. However, due to tight sampling times, NMPC solutions are approximate; therefore, the safety of the system cannot be guaranteed. To address this, we formulate a novel safety-critical paradigm that uses an exponential control barrier function (ECBF) as a safety filter. Several common human–robot assembly subtasks have been integrated into a real-life HRC assembly task to validate the performance of the proposed controller and to investigate whether integrating human pose prediction can help with safe and efficient collaboration. The robot uses OptiTrack cameras for perception and dynamically generates collision-free trajectories to the predicted target interactive position. Results for a number of different configurations confirm the efficiency of the proposed motion planning and execution framework, with a 23.2% reduction in execution time achieved for the HRC task compared to an implementation without human motion prediction. Full article

Background: High-intensity gait training (HIT) is an evidence-based intervention recommended for stroke rehabilitation; however, its implementation in routine practice is inconsistent. This study examined the real-world implementation of HIT in an inpatient rehabilitation setting in Norway, focusing on fidelity, barriers, and knowledge translation (KT) strategies. Methods: Using the Knowledge-to-Action (KTA) framework, HIT was implemented in three phases: pre-implementation, implementation, and competency. Fidelity metrics and coverage were assessed in 99 participants post-stroke. Barriers and facilitators were documented and categorized using the Consolidated Framework for Implementation Research. Results: HIT was delivered with improved fidelity during the implementation and competency phases, reflected by increased stepping and heart rate metrics. A coverage rate of 52% was achieved. Barriers evolved over time, beginning with logistical and knowledge challenges and shifting toward decision-making complexity. The KT interventions, developed collaboratively by clinicians and external facilitators, supported implementation. Conclusions: Structured pre-implementation planning, clinician engagement, and external facilitation enabled high-fidelity HIT implementation in a real-world setting. Pragmatic, context-sensitive strategies were critical to overcoming evolving barriers. Future research should examine scalable, adaptive KT strategies that balance theoretical guidance with clinical feasibility to sustain evidence-based practice in rehabilitation. Full article

The current paper presents a comprehensive intelligent system designed to optimize the performance of a barrier eddy current separator (BECS), comprising a conveyor belt, a vibration feeder, and a magnetic drum. This system was trained and validated on real-world industrial data gathered directly from the working separator under 81 different operational scenarios. The intelligent models were used to recommend optimal settings for drum speed, belt speed, vibration intensity, and drum angle, thereby maximizing separation quality and minimizing energy consumption. the smart separation module utilizes YOLOv11n-seg and achieves a mean average precision (mAP) of 0.838 across 7163 industrial instances from aluminum, copper, and plastic materials. For shape classification (sharp vs. smooth), the model reached 91.8% accuracy across 1105 annotated samples. Furthermore, the thermal monitoring unit can detect iron contamination by analyzing temperature anomalies. Scenarios with iron showed a maximum temperature increase of over 20 °C compared to clean materials, with a detection response time of under 2.5 s. The architecture integrates a Digital Twin using Azure Digital Twins to virtually mirror the system, enabling real-time tracking, behavior simulation, and remote updates. A full connection with the PLC has been implemented, allowing the AI-driven system to adjust physical parameters autonomously. This combination of AI, IoT, and digital twin technologies delivers a reliable and scalable solution for enhanced separation quality, improved operational safety, and predictive maintenance in industrial recycling environments. Full article

The environmental burden of conventional plastics has sparked interest in sustainable alternatives such as polyhydroxyalkanoates (PHAs). However, despite ample research in bioprocess development and the use of inexpensive waste streams, production costs remain a barrier to widespread commercialization. Complementary to this, genetic engineering offers another avenue for improved productivity. Cupriavidus necator stands out as a model host for PHA production due to its substrate flexibility, high intracellular polymer accumulation, and tractability to genetic modification. This review delves into metabolic engineering strategies that have been developed to enhance the production of poly(3-hydroxybutyrate) (PHB) and related copolymers in C. necator. Strategies include the optimization of central carbon flux, redox and cofactor balancing, adaptation to oxygen-limiting conditions, and fine-tuning of granule-associated protein expression and the regulatory network. This is followed by outlining engineered pathways improving the synthesis of PHB copolymers, PHBV, PHBHHx, and other emerging variants, emphasizing genetic modifications enabling biosynthesis based on unrelated single-carbon sources. Among these, enzyme engineering strategies and the establishment of novel artificial pathways are widely discussed. In particular, this review offers a comprehensive overview of promising engineering strategies, serving as a resource for future strain development and positioning C. necator as a valuable microbial chassis for biopolymer production at an industrial scale. Full article