Search Results (91)

Increasingly complex epidemics of animal infectious diseases have emerged as a major risk to livestock production and human health. However, current detection methods for animal infectious diseases suffer from shortcomings such as insufficient sensitivity, complicated operation, and reliance on skilled personnel, highlighting the urgent need for novel sensing platforms. CRISPR/Cas systems are adaptive immune systems found in many prokaryotes. Owing to their ability to precisely and reliably target and cleave nucleic acids, the CRISPR/Cas-based nucleic acid detection technology is considered a promising new detection method. When leveraged with a pre-amplification step and established readout methods, CRISPR/Cas-based sensing platforms can achieve a high sensitivity of single-base resolution or attomolar levels on-site. In this review, we first outline the history, working principles, and nucleic acid detection platforms derived from various CRISPR/Cas systems. Next, we evaluate the advantages and limitations of different nucleic acid pre-amplification methods integrated with CRISPR/Cas systems, followed by a discussion of readout methods employed in CRISPR/Cas-based sensing platforms. Additionally, we highlight recent applications of CRISPR/Cas-based sensing platforms in identifying animal infectious diseases. Finally, we address the challenges and prospects of CRISPR/Cas-based sensing platforms for the early and accurate diagnosis of animal infectious diseases. Full article

In increasingly complex Engineer-to-Order (EtO) supply chains, accurately predicting supplier delivery delays is essential for ensuring operational resilience. This study proposes an intelligent Internet of Things (IoT)-enhanced probabilistic framework for early warning and dynamic prediction of supplier lead times in smart manufacturing contexts. Within this framework, three novel Early Warning Systems (EWS) are introduced: the Baseline Probabilistic Alert System (BPAS) based on fixed thresholds, the Smart IoT-Calibrated Alert System (SIoT-CAS) leveraging IoT-driven calibration, and the Adaptive IoT-Driven Risk Alert System (AID-RAS) featuring real-time threshold adaptation. Supplier lead times are modeled using statistical distributions and dynamically adjusted with IoT data to capture evolving disruptions. A comprehensive Monte Carlo simulation was conducted across varying levels of lead time uncertainty (σ), alert sensitivity ($P_{threshold}$), and delivery constraints ($L_{max}$), generating over 1000 synthetic scenarios per configuration. The results highlight distinct trade-offs between predictive accuracy, sensitivity, and robustness: BPAS minimizes false alarms in stable environments, SIoT-CAS improves forecasting precision through IoT calibration, and AID-RAS maximizes detection capability and resilience under high-risk conditions. Overall, the findings advance theoretical understanding of adaptive, data-driven risk modeling in EtO supply chains and provide practical guidance for selecting appropriate EWS mechanisms based on operational priorities. Furthermore, they offer actionable insights for integrating predictive EWS into MES (Manufacturing Execution System) and digital control tower platforms, thereby contributing to both academic research and industrial best practices. Full article

Rapid urbanization has intensified the systemic inequities of resources and infrastructure distribution in informal settlements, particularly in the Global South. Digital Twin Modeling (DTM), as an effective data-driven representation, enables real-time analysis, scenario simulation, and design optimization, making it a promising tool to support urban resilience. This study introduces the Integrated Modification Methodology (IMM), developed by Politecnico di Milano (Italy), to explore how DTM can be systematically structured and transformed into an active instrument, linking theories with practical application. Focusing on Paranoá (Brasília), a case study developed under the NBSouth project in collaboration with the Politecnico di Milano and the University of Brasília, this research integrates advanced spatial mapping with comprehensive key performance indicators (KPIs) analysis to address developmental and environmental challenges during the regeneration process. Key metrics—Green Space Diversity, Ecosystem Service Proximity, and Green Space Continuity—were analyzed by a Geographic Information System (GIS) platform on 30 m by 30 m sampling grids. Additional KPIs across urban structural, environmental, and mobility layers were calculated to support the decision-making process for strategic mapping. This study contributes to theoretical advancements in DTM and broader discourse on urban regeneration under climate stress, offering a systemic and practical approach for multi-dimensional digitalization of urban structure and performance, supporting a more adaptive, data-based, and transferable planning process in the Global South. Full article

Regenerative design (RD) is attracting attention as a concept that goes beyond sustainability. However, RD has been criticized as an overly theoretical and abstract approach. This study constructs a multiscale RD approach in urban areas by combining the theoretical frameworks of an adaptive planning approach based on the complex adaptive systems (CAS) theory and transformative capacities (TC) through the case study of Shimokita-Senrogai. The study’s main contribution is to materialize the process for a multiscale RD approach in urban areas, where it is difficult to reach consensus among diverse stakeholders immediately. The main finding is identifying the necessary conditions for implementing an RD approach that enhances TC by adapting to urban uncertainties from global climate change to local civic dynamics through the agency of more-than-human actor networks. Based on these, this study proposes a methodology to visualize actors, their activity ranges, bases, and ecosystemic flows across multiple territorial scales beyond the development site and its vicinity. Full article

Agrobacterium-mediated transformation systems are extensively applied in japonica rice varieties. However, the adaptability of local rice varieties to existing transformation systems remains limited, owing to their complex genotypes, posing a substantial challenge to transformation. In this study, four local rice varieties were selected to optimize the effects of different culture media on callus induction, browning resistance, contamination resistance, callus tolerance, differentiation, regeneration, and root development, and then two varieties were selected to improve plant architecture and tiller development by CRISPR/Cas9-mediated gene editing, based on constructive transformation systems. The goal was to enhance the transformation efficiency of local varieties and innovate germplasms. The results demonstrated that japonica rice varieties XG293 and WD68 exhibited higher induction rates under the treatment of 2 mg/L 2,4-D (2,4-Dichlorophenoxyacetic acid) + 1 mg/L NAA (Naphthaleneacetic acid), whereas indica rice varieties H128 and E33 performed the best under 3 mg/L 2,4-D + 1 mg/L NAA. Severe browning in H128 was effectively mitigated by a carbon source of 20 g/L maltose supplemented with 40 mg/L ascorbic acid. Contamination after Agrobacterium infection was controlled by 300 mg/L Tmt (Timentin). Under a treatment of 200 µM/L acetosyringone +10 min infection duration, XG293 and WD68 exhibited higher callus tolerance, differentiation rates, and GUS staining rates, achieving transformation efficiencies of 43.24% and 52.38%, respectively. In contrast, H128 and E33 performed better under the treatment of 200 µM/L Acetosyringone + 5 min, with transformation efficiencies of 40.00% and 40.74%, respectively. The mutants after OsCCD7 gene editing in WD68 and H128 displayed a dwarfness of plant height, a significant increase in tiller numbers, and compact architecture. These findings demonstrate that an optimized combination of plant growth regulators and infection durations effectively improves transformation efficiency for local varieties, and the OsCCD7 gene regulates plant architecture and tiller development with variable effects, depending on the rice complex genotypes. This study provides a theoretical basis for the efficient transformation of local rice varieties and germplasm innovation. Full article

The regulation of calcium (Ca²⁺) homeostasis is a critical process in both plant and animal systems, involving complex interplay between various organelles and a diverse network of channels, pumps, and transporters. This review provides a concise overview of inter-organellar Ca²⁺ homeostasis, highlighting key regulators and mechanisms in plant and animal cells. We discuss the roles of key Ca²⁺ channels and transporters, including IP₃Rs, RyRs, TPCs, MCUs, TRPMLs, and P2XRs in animals, as well as their plant counterparts. Here, we explore recent innovations in structural biology and advanced microscopic techniques that have enhanced our understanding of these proteins’ structure, functions, and regulations. We examine the importance of membrane contact sites in facilitating Ca²⁺ transfer between organelles and the specific expression patterns of Ca²⁺ channels and transporters. Furthermore, we address the physiological implications of inter-organellar Ca²⁺ homeostasis and its relevance in various pathological conditions. For extended comparability, a brief excursus into bacterial intracellular Ca²⁺ homeostasis is also made. This meta-analysis aims to bridge the gap between plant and animal Ca²⁺ signaling research, identifying common themes and unique adaptations in these diverse biological systems. Full article

The growing need for adaptable and efficient hardware-based encryption methods has led to increased interest in unconventional models such as cellular automata (CA). This study presents the hardware design and the field programmable gate array (FPGA)-based implementation of a multi-layer symmetric block encryption algorithm built on the principles of reversible cellular automata (RCA). The algorithm operates on 128-bit plaintext blocks processed over iterative rounds and integrates five RCA components, each assigned with specific transformation roles to ensure high data diffusion. A 256-bit secret key that governs the rule configuration yields a vast keyspace, significantly enhancing resistance to brute-force attacks. Key elements such as rule-based evolution, neighborhood radius, and hybrid cellular automata for random state generation are also integrated into the hardware logic. All cryptographic components, including initialization, encryption logic, and control, are built exclusively using CA, ensuring design consistency and low complexity. The cryptosystem takes advantage of the localized interactions and naturally parallel CA structure, which align with the architecture of FPGA devices, making them a suitable platform for implementing such encryption schemes. The results demonstrate the feasibility of deploying multi-layer RCA encryption schemes on reconfigurable devices and provide a viable path toward efficient and secure hardware-level encryption systems. Full article

To address the challenges of high computational cost and energy consumption posed by deep neural networks in embedded systems, this paper presents CaDCR, a lightweight dynamic collaborative reasoning framework. By integrating a feature discrepancy-guided skipping mechanism with a depth-sensitive early exit mechanism, the framework establishes hierarchical decision logic: dynamically selects execution paths of network blocks based on the complexity of input samples and enables early exit for simple samples through shallow confidence assessment, thereby forming an adaptive computational resource allocation strategy. CaDCR can both constantly suppress unnecessary computational cost for simple samples and satisfy hard resource constraints by forcibly terminating the inference process for all samples. Based on this framework, we design a cascaded inference system tailored for embedded system deployment to tackle practical deployment challenges. Experiments on the CIFAR-10/100, SpeechCommands datasets demonstrate that CaDCR maintains accuracy comparable to or higher than baseline models while significantly reducing computational cost by approximately 40–70% within a controllable accuracy loss margin. In deployment tests on the STM32 embedded platform, the framework’s performance matches theoretical expectations, further verifying its effectiveness in reducing energy consumption and accelerating inference speed. Full article

Due to the dynamic and harsh underwater environment, which involves a long propagation delay, high bit error rate, and limited bandwidth, it is challenging to achieve reliable communication in underwater wireless sensor networks (UWSNs) and network support applications, like environmental monitoring and natural disaster prediction, which require energy efficiency and low latency. To tackle these challenges, we introduce AC-RL-based power control (ACRLPC), a novel hybrid MAC protocol that can efficiently integrate Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA)-based MAC and Time Division Multiple Access (TDMA) with Actor–Critic Reinforcement Learning (AC-RL). The proposed framework employs adaptive strategies, utilizing adaptive power control and intelligent access methods, which adjust to fluctuating conditions on the network. Harsh and dynamic underwater environment performance evaluations of the proposed scheme confirm a significant outperformance of ACRLPC compared to the current protocols of FDU-MAC, TCH-MAC, and UW-ALOHA-QM in all major performance measures, like energy consumption, throughput, accuracy, latency, and computational complexity. The ACRLPC is an ultra-energy-efficient protocol since it provides higher-grade power efficiency by maximizing the throughput and limiting the latency. Its overcoming of computational complexity makes it an approach that greatly relaxes the processing requirement, especially in the case of large, scalable underwater deployments. The unique hybrid architecture that is proposed effectively combines the best of both worlds, leveraging TDMA for reliable access, and the flexibility of CSMA/CA serves as a robust and holistic mechanism that meets the desired enablers of the system. Full article

Salt concentration monitoring is crucial for industrial process control and wastewater management, yet existing methods often lack real-time capability or require invasive sampling. This paper presents a novel RFID wireless sensing system for noninvasive solution concentration monitoring, combining physical modeling with advanced estimation algorithms. By combining the Cole–Cole model and the slit cylindrical capacitor (SCC) model, the system establishes physics-based state-space models to characterize concentration-dependent RFID signal variations. The concentration dynamics are modeled as a hidden Markov process and tracked using an adaptive extended Kalman filter (AEKF). The AEKF algorithm avoids computationally expensive inversion of complex observation equations while automatically adjusting noise covariance matrices via innovation sequence. Experimental results demonstrate a mean relative error (MRE) of 2.8% for CaCl₂ solution across 2–10 g/L concentrations. Within the experimentally validated optimal range (2–8 g/L CaCl₂), the system maintains MRE below 3% under artificially introduced measurement noise, confirming its strong robustness. Compared with baseline approaches, the proposed AEKF algorithm shows improved performance in both accuracy and computational efficiency. Full article

(1) Background: CRISPR-Cas systems provide adaptive immunity against mobile genetic elements (MGEs) carrying antimicrobial resistance (AMR) genes. Carbapenem-resistant (CR) Klebsiella pneumoniae is a major public health concern, and the role of CRISPR-Cas in its resistance is understudied. This study explored CRISPR-Cas associations with multidrug resistance in clinical K. pneumoniae. (2) Methods: 400 K. pneumoniae isolates (200 CR and 200 carbapenem susceptible (CS)) were analyzed. Carbapenemase genes (bla_OXA-48, bla_NDM-1, bla_KPC-2), cas1, rpoB, and CRISPR1-3 loci were identified by PCR, while only CRISPR loci were sequenced. Genetic relatedness was assessed via PFGE, MLST, and spacer analysis. Statistical analysis utilized chi-squared and Fisher’s exact tests. (3) Results: CRISPR-Cas was present in 15.8% of isolates, mainly subtypes I-E and I-E* (93.3%), with CRISPR3 loci showing the greatest spacer diversity. Clonal complexes ST14/15/101 (CR) and ST35 (CS) were identified. bla_OXA-48 was linked to CRISPR-Cas-negative strains, while bla_NDM-1 and bla_KPC-2 were more frequent in CRISPR-Cas-positive strains (p < 0.0001). Imipenem/relebactam resistance was higher in CRISPR-Cas-negative isolates. (4) Conclusions: K. pneumoniae CRISPR-Cas systems correlate with specific carbapenemase profiles, suggesting pressure against bla_OXA-48 acquisition. The coexistence of I-E and I-E* subtypes highlight synergies in targeting MGEs. CRISPR loci could be tools for subtyping organisms following MLST. Full article

The yeast metabolic and evolutionary engineering, especially Saccharomyces cerevisiae, plays a significant role in the enhancement of its industrial applications in food, beverage, and biofuel production. This review integrates genetic engineering, systems biology, and evolutionary principles to optimize yeast performance, adaptability, and productivity. The key strategies which enable targeted genome modifications to improve substrate utilization, stress tolerance, and the biosynthesis of valuable metabolites such as flavor compounds, organic acids, vitamins, and antioxidants, including precise gene editing, notably CRISPR-Cas9. The metabolic pathway optimization through gene overexpression, deletion, and heterologous pathway integration, supported by multi-omics analyses and the Subcellular compartmentalization of metabolic pathways, which enhances biosynthetic efficiency. This review then discusses evolutionary engineering and global transcription machinery engineering by leveraging natural selection and global gene regulation to improve complex traits. The exploration of non-Saccharomyces species and genome shuffling expands the genetic toolkit for strain development. Emerging approaches, including machine learning and synthetic biology, are accelerating rational strain design. By critically synthesizing these diverse methodologies, this review highlights current advancements, identifies key challenges, and outlines future directions in engineering robust yeast strains for sustainable food biotechnology. Full article

The CRISPR-Cas system represents an adaptive immune mechanism found across diverse Archaea and Bacteria, allowing them to defend against invading genetic elements such as viruses and plasmids. Despite its broad distribution, the prevalence and complexity of CRISPR-Cas systems differ significantly between these domains. This study aimed to characterize and compare the genomic distribution, structural features, and functional implications of CRISPR-Cas systems and associated antibiotic resistance genes in 30 archaeal and 30 bacterial genomes. Through bioinformatic analyses of CRISPR arrays, cas gene architectures, direct repeats (DRs), and thermodynamic properties, we observed that Archaea exhibit a higher number and greater complexity of CRISPR loci, with more diverse cas gene subtypes exclusively of Class 1. Bacteria, in contrast, showed fewer CRISPR loci, comprising a mix of Class 1 and Class 2 systems, with Class 1 representing the majority (~75%) of the detected systems. Notably, Bacteria lacking CRISPR-Cas systems displayed a higher prevalence of antibiotic resistance genes, suggesting a possible inverse correlation between the presence of these immune systems and the acquisition of such genes. Phylogenetic and thermodynamic analyses further highlighted domain-specific adaptations and conservation patterns. These findings support the hypothesis that CRISPR-Cas systems play a dual role: first, as a defense mechanism preventing the integration of foreign genetic material—reflected in the higher complexity and diversity of CRISPR loci in Archaea—and second, as a regulator of horizontal gene transfer, evidenced by the lower frequency of antibiotic resistance genes in organisms with active CRISPR-Cas systems. Together, these results underscore the evolutionary and functional diversification of CRISPR-Cas systems in response to environmental and selective pressures. Full article

The Ca Mau Peninsula (CMP), the southernmost region of the Mekong Delta, is increasingly threatened by groundwater salinization, posing severe risks to both the freshwater supply and land sustainability. This study develops a three-dimensional, density-dependent groundwater flow and salinity transport model to investigate salinization dynamics across the CMP’s complex multi-aquifer system. Unlike previous studies that largely rely on model calibration, this research introduces a novel approach by systematically deriving the spatial distribution of longitudinal dispersivity based on sediment characteristics. Moreover, detailed land use mapping is integrated to assign spatially and temporally variable Total Dissolved Solids (TDS) values to the uppermost layers, thereby enhancing the model realism in areas where monitoring data are limited. The model was utilized not only to simulate the regional salinity evolution, but also to critically evaluate conceptual hypotheses related to the mechanisms driving groundwater salinization. Results reveal a strong influence of seasonal and land use factors on salinity variability in the upper aquifers, while deeper aquifers remain largely stable, affected primarily by paleosalinity and localized pumping. This integrated modeling approach contributes to a better understanding of regional-scale groundwater salinization and highlights both the potential and the limitations of numerical modeling under data-scarce conditions. The findings provide a valuable scientific basis for adaptive water resource management in vulnerable coastal zones. Full article

The increasing digitalization of the world underscores the critical importance of both social and technical aspects in software engineering practice. While prior research links socio-technical congruence (STC) to positive workstream outcomes, the current convergence of digital products, technologies, and social systems introduces novel and often unpredictable results, driven by the complex interplay of leadership, organizational culture, and software engineering practices operating as a complex adaptive system (CAS). This paper proposes a novel model for adopting socio-cultural practices to bridge the social and technical divide through the lens of STC. The innovation of the model lies in its socio-technical evolutionary journey, built upon dual systems: (1) an analytical System-I focused on enhancing robustness via compliance with Lean and Agile socio-cultural practices, and (2) a holistic System-II emphasizing resilience through an acceptance of interdependence of system actors that requires sense-making techniques. A methodology based on this model was piloted across six case studies: three in an Enterprise IT organization and three in two business units undergoing transformations on Lean and Agile plus DevOps adoption. System-I’s robustness was evaluated through surveys and structured STC maturity assessments (self and guided ones). System-II employed sense-making techniques to foster resilience within the system of work (SoW), laying the groundwork for their evolutionary journeys. The findings reveal a significant need for greater alignment between management (as transformation agents) and software engineering practices. However, the study suggests actionable guidelines, grounded in new principles and mental models for operating within a CAS, to cultivate enhanced resilience and robustness in a VUCA world. Full article