Search Results (2,819)

Phycobiliproteins (PBPs) are a family of pigment-proteins renowned for their exceptional light-harvesting, fluorescent, and antioxidant properties. Among cyanobacteria, Spirulina stands out as one of the richest natural sources of PBPs, particularly phycocyanin (PC) and allophycocyanin (APC), yet the large-scale production of analytical-grade PBPs remains hampered by an inherently complex downstream process that relies on multiple purification steps, compromising both yield and scalability. This work presents a streamlined strategy to obtain analytical-grade PC, combining ultrasound-assisted extraction (UAE) with an aqueous ionic liquid (IL) solution and a single hydrophobic interaction chromatography (HIC) step, integrated within a biorefinery framework. The proposed approach yielded analytical-grade PC with a recovery of up to 50.44% and enhanced APC purity up to 10.57-fold. Furthermore, the IL was successfully reused in both extraction and purification steps without compromising yield or purity. The environmental performance of the proposed process was assessed through a cradle-to-gate life cycle assessment (LCA), with system boundaries encompassing the following biorefinery stages: cultivation, harvesting and drying, PC extraction and purification, post-processing, and spent biomass valorization via anaerobic digestion. The LCA identified the main environmental hotspots and guided the proposal of targeted process improvements—particularly HIC salt substitution and increased IL recovery—which reduced environmental impacts by 65.9–89.8% across most categories. The proposed strategy was further benchmarked against two model scenarios for analytical-grade PC production, one conventional and one innovative, revealing its relative advantages and limitations. Overall, this work demonstrates a viable pathway for producing high-purity PC that balances process efficiency with environmental sustainability, supporting the development of greener microalgae-based bioprocesses. Full article

The rapid adoption of Generative Artificial Intelligence (GenAI) tools is transforming learning practices in higher education, raising important questions about their educational value and impact on student learning. This study examines how university students use GenAI tools in both academic and everyday contexts, with emphasis on usage patterns, applications and motivations. A large-scale voluntary survey was conducted with 788 undergraduate students from a single public university in Greece, with respondents drawn from multiple schools and disciplines. Data were collected through an online questionnaire and analyzed using descriptive and inferential statistical methods to explore frequency of use, application categories and motivations for engagement with GenAI tools. The results indicate a high level of reported GenAI engagement among the participants, with ChatGPT emerging as the most frequently used tool. Students primarily rely on GenAI tools for information searching, understanding academic content and supporting academic tasks, while creative and entertainment-related uses are less frequent. Overall, the findings suggest that students perceive GenAI tools as useful for learning support and efficiency improvement. The results indicate that GenAI tools are becoming integrated into students’ reported learning practices. They also highlight the need for clear pedagogical guidelines and systematic AI literacy integration in teaching and learning. Full article

Road freight logistics is one of the most difficult transport segments to decarbonize. In recent years, battery electric trucks and hydrogen fuel cell electric trucks have emerged as the most promising alternatives to conventional heavy-duty vehicles. However, their integration cannot be reduced to a question of vehicle substitution, as it depends on a broader system of conditions. This paper aims to identify and structure the system-determining parameters that influence the use of battery electric trucks and hydrogen fuel cell electric trucks in road freight logistics. To this end, the study applies a systematic literature review, yielding a final sample of 42 publications. The review shows that drive type suitability depends on parameters across four categories: economic, ecological, performance-related, and external. Accordingly, no single factor determines suitability; rather, outcomes emerge from the interaction of multiple conditions. The reviewed literature does not support a universally superior drive technology. Instead, the suitability of battery electric trucks and hydrogen fuel cell electric trucks depends on the specific configuration of the surrounding system. The paper thus provides a structured framework for future comparative assessments in sustainable road freight logistics. The study is embedded in the Research Campus Mobility2Grid, which provides a practice-oriented context for assessing alternative drive technologies in relation to fleet, depot, energy, and logistics-system requirements. Full article

(1) Background: Dental implant stability is influenced by multiple biomechanical, implant-related, and systemic factors, including bone density, implant geometry, biomechanical loading, smoking, osteoporosis, and diabetes mellitus. Computational bioengineering approaches may facilitate theoretical assessment of implant stability and support future risk-evaluation strategies. The aim of this study was to develop a conceptual computational framework for assessing theoretical implant instability using clinically relevant biomechanical and systemic parameters. (2) Methods: A multivariable computational framework was developed by integrating bone density, implant dimensions, implant mobility indicators, biomechanical loading conditions, smoking status, osteoporosis, and diabetes mellitus into a conceptual Implant Stability Index (ISI). Computational simulations and theoretical risk stratification procedures were used to evaluate framework behavior under different simulated conditions. (3) Results: The framework demonstrated the theoretical ability to differentiate between favorable and unfavorable implant stability conditions. Reduced bone density, increased implant mobility indicators, excessive biomechanical loading, and adverse systemic factors resulted in lower calculated ISI values and a higher theoretical instability risk. The framework further enabled the classification of simulated conditions into high-, moderate-, and increased-instability-risk categories. (4) Conclusions: The proposed Implant Stability Index represents a conceptual computational framework for integrating biomechanical, implant-related, and systemic factors associated with implant stability. Although not clinically validated, the framework may provide a proof-of-concept foundation for future studies involving clinical datasets, biomechanical simulations, and advanced computational modeling approaches. Full article

Cronobacter spp. are opportunistic foodborne pathogens that can cause neonatal meningitis, necrotizing enterocolitis, and sepsis. This study conducted a systematic contamination survey and whole-genome epidemiological analysis of 562 low-water-activity food samples in Hunan Province of China. The results showed an overall Cronobacter spp. detection rate of 41.99% (236/562), with spices exhibiting the highest contamination rate (60.06%), and with high-level contamination samples (>110 MPN/g) concentrated in this category. The 236 isolates comprised 6 species, 120 sequence types, and 39 clonal complexes, with C. sakazakii being the most frequently isolated species (64.83%) and high-risk clones ST4, ST1, ST148, and ST64 prevailing. Multiple virulence genes (TraJ, fur, rcsAB, rpoS) and antimicrobial resistance genes (qnrS1, blaTEM-1, blaCTX-M-55, blaLAP-2, aac(3)-IId, aadA2, tet(A), floR, mcr-9.1, sul2) were detected. Core genome multilocus sequence typing (cgMLST) identified two clustering patterns: Cluster C, whose genetic clustering was consistent with transmission associated with potential common upstream raw materials across different brands and provinces, and Cluster G, whose clustering suggested potential persistent colonization in the production environment across multiple batches of the same brand. This study elucidates the contamination characteristics of Cronobacter spp. in low-water-activity foods from Hunan Province and provides a basis for WGS-based active surveillance and supply chain traceability. Full article

Urban traffic congestion remains a persistent global challenge, contributing to significant economic inefficiencies, elevated greenhouse gas emissions, and diminished quality of life. This paper presents a real-world video-based traffic monitoring study combined with a proposed adaptive signal control framework. In the monitoring component, YOLOv11 object detection was applied directly to footage recorded from an overhead bridge position on a 40 km/h road. The model successfully detected and tracked multiple road-user categories, including cars, trucks, buses, motorcycles, cyclists, and pedestrians, yielding 1041 vehicle detections across 25 unique tracked objects. Vehicle speeds were estimated from inter-frame centroid displacement, and a Region of Interest (ROI) occupancy model was used to classify congestion states as High, Medium, or Free Flow using thresholds grounded in Highway Capacity Manual (HCM) level-of-service criteria. The system detected 11 high-congestion frames (3.8%), 184 medium-congestion frames (63.9%), and 93 free-flow frames (32.3%), consistent with moderate congestion observed during the recording period. In the proposed control component, a Proximal Policy Optimisation (PPO)-based reinforcement learning signal controller is designed around the YOLOv11 detection outputs as its state representation. Based on comparable adaptive traffic signal control studies in the literature, the proposed framework is projected to achieve approximately 25% higher peak-hour throughput, 35% shorter queue lengths, and 32% lower average waiting times relative to a fixed-time signal baseline. The detection accuracy (mAP@0.5 = 93.2%) and inference speed (32 FPS) cited are published YOLOv11 benchmarks used as indicative performance references. This work bridges real-world perception and proposed intelligent control, providing a transparent and reproducible methodology for next-generation smart city traffic management. Full article

We show that a single Hodgkin–Huxley (HH) neuron with Pyragas-type delayed feedback control (DFC) can store multiple symbols as stable periodic orbits, where the specific orbit is selected by tuning the DFC gain K and time delay $\tau$. Sweeping the $(K,\tau )$ parameter plane at fixed bias current $I_{\mathrm{bias}}$ = 10.0 μA/cm² reveals 207 orbit types across 12 topological categories, with inter-spike interval (ISI) means from 5.9 to 56.9 ms. We establish: (i) a write protocol that reliably locks orbits with 13.9 ms median settling time; (ii) a novel Pattern-Oriented Limit-cycle Decoder (POLD) that reads orbits at 100% accuracy from only five observed ISIs (1200 trials across 12 orbits; Wilson 95% CI: 99.7–100%); (iii) a complete single-symbol write–read–erase (W–R–E) cycle with 100% read accuracy, 92% erase verification, and no decay over hold durations up to 50 s; and (iv) a fully validated 12-symbol memory capacity with a read-discriminable upper bound of 67 symbols (11.2× over rate coding; write viability confirmed only for the conservative 12-symbol subset). Reliable orbit addressing needs delay precision of ±2%, which constitutes a write-precision specification and not a fundamental capacity limit. These findings show that parametric delayed feedback is a viable mechanism for limit-cycle-based information storage in conductance-based spiking neurons. The biological interpretation is analogical, not direct: the ±2% delay-precision requirement exceeds what has been demonstrated for biological autaptic variability, and the orbit-coded memory framing is best understood as a computational proof-of-principle aimed at neuromorphic engineering, not as a claim about biological working memory. Full article

Accurate analysis of lesions in colonoscopic images is essential for computer-aided diagnosis. However, most existing methods are designed for single-lesion segmentation and assume a predefined lesion category, limiting their applicability in real-world scenarios where multiple lesion types exhibit similar visual characteristics. To address this issue, we propose a unified framework for the joint classification and segmentation of dual-type lesions in colonoscopic images, enabling simultaneous identification and localization of submucosal lesions and polyps/adenomas. The proposed method integrates joint supervision, context-aware feature enhancement, and ambiguity-aware optimization to improve consistency between semantic recognition and spatial delineation. In particular, a soft-label supervision strategy is introduced to alleviate semantic ambiguity, while an imbalance-aware loss design enhances segmentation accuracy and reduces false negative predictions. Extensive experiments on both private and public datasets demonstrate that the proposed method achieves superior performance compared with representative CNN- and transformer-based approaches. Notably, the method shows clear advantages in segmentation accuracy, localization precision, and robustness under challenging conditions. Ablation studies further confirm the effectiveness of each component in the proposed framework. These results indicate that the proposed approach provides an effective solution for dual-type lesion analysis and has the potential to assist clinical decision-making in gastrointestinal endoscopy. Full article

Hydrogel-based sensors have emerged as transformative soft-sensing platforms, featuring tissue-matched compliance, high water content, stimuli responsiveness, and chemical tunability, properties which are unachievable with conventional rigid sensors. Despite substantial advances, the existing reviews focus on individual polymer categories, discrete transduction mechanisms, or targeted standalone applications, failing to establish an integrated pipeline from material design to final sensing performance. This review fills these crucial gaps by systematically correlating polymer chemistry, crosslinking tactics, and fabrication protocols with the selection of transduction mechanisms and resultant sensing performance across biomedical and environmental fields. We conduct a critical assessment of natural and synthetic polymers together with chemical, physical, and hybrid composite crosslinking methodologies. Multiple sensing modalities, including piezoresistive, capacitive, thermogalvanic, electrochemical, colorimetric, ratiometric fluorescence, and piezoionic sensing are elaborated alongside representative quantitative performance parameters. Emerging platforms, including self-powered thermogalvanic sensors, SERS-integrated biosensors, and MXene/MOF composites, are highlighted as underexplored frontiers. In addition, persistent bottlenecks including dehydration-derived signal drift, inferior long-term operational stability, unsatisfactory target selectivity, and obstacles toward large-scale manufacturability are rigorously analyzed. Ultimately, this review constructs a holistic unified framework bridging polymer molecular design, fabrication engineering, signal transduction, and practical end-use applications, laying a clear developmental roadmap for next-generation flexible and smart hydrogel-based sensing systems. Full article

Background/Objectives: Brain magnetic resonance imaging (MRI) is an important imaging modality for assessing neurological disorders. However, automatic multi-class MRI classification remains challenging because of visual similarity between disease categories, heterogeneous pathological patterns, class imbalance, and the need for reliable confidence estimation. This study aims to develop a comprehensive and well-calibrated deep learning framework for image-level brain MRI classification across multiple neurological categories. Methods: This paper introduces a new deep learning framework, MCND-ComputeNet++, for brain MRI classification into eight image-level categories using the MCND dataset, which comprises 16,400 two-dimensional brain MRI images belonging to eight diagnostic categories: AD-MildDemented, AD-ModerateDemented, AD-VeryMildDemented, BT-glioma, BT-meningioma, BT-pituitary, MS, and Normal. The proposed model uses a single pretrained EfficientNetV2-S backbone to extract hierarchical feature maps from three intermediate stages. These multi-level features are projected into a common latent space, spatially aligned, adaptively fused through learnable gated multi-scale fusion, further refined using convolutional processing, and aggregated using spatial attention pooling before classification. The training strategy combines class-balanced focal loss with label smoothing, MixUp/CutMix regularization, exponential moving average weight smoothing, warmup cosine learning-rate scheduling, temperature scaling, and test-time augmentation to improve generalization and calibration. The framework was evaluated using accuracy, precision, recall, macro-F1, macro-AUC, macro-average precision, expected calibration error, Brier score, bootstrap confidence intervals, ablation analysis, McNemar testing, and comparisons against standard pretrained baseline models. Results: MCND-ComputeNet++ achieved mean accuracy, macro-F1, macro-AUC, and macro-average precision values of 0.9738, 0.9771, 0.9993, and 0.9971, respectively, with narrow bootstrap confidence intervals indicating stable image-level performance. These findings should be interpreted as image-level/slice-level performance on MCND, because patient-level identifiers and subject-wise splitting were not available. These results outperformed most evaluated baselines, including ResNet50, DenseNet121, EfficientNetB0, EfficientNetV2-S with a standard classifier, Swin-Tiny, and ConvNeXt-Tiny, across several discrimination and calibration metrics. Compared with ConvNeXt-Tiny, the proposed model achieved higher macro-AUC and macro-average precision, together with a lower ECE and Brier score, suggesting improved image-level discrimination and confidence reliability. Compared with the EfficientNetV2-S standard classifier, accuracy increased from 0.9308 to 0.9738, while the Brier score decreased from 0.1045 to 0.0400. Conclusions: The results suggest that MCND-ComputeNet++ is a promising image-level brain MRI classification framework for the eight MCND categories. The proposed model integrates hierarchical feature extraction, shared latent projection, gated multi-scale fusion, convolutional refinement, spatial attention pooling, and calibrated inference within a unified architecture. However, because the current evaluation was conducted at the image/slice level without available patient-level identifiers, the findings should not be interpreted as patient-level clinical diagnostic validation. Further studies using subject-wise splitting, external multi-center datasets, 3D volumetric modeling, and multimodal clinical information are required to assess generalizability and potential clinical decision-support applicability. Full article

Background: Chronic hepatitis B virus (HBV) infection remains a major global health challenge and a leading cause of liver cirrhosis and hepatocellular carcinoma. Interleukin-6 (IL-6), a key pro-inflammatory cytokine, plays an important role in immune regulation and hepatic inflammation. However, its relationship with HBV viral load and disease severity remains incompletely understood. Methods: A hospital-based cross-sectional study was conducted among 293 HBsAg-positive patients. Serum IL-6 levels were measured using ELISA, and HBV DNA was quantified using real-time quantitative PCR. Patients were stratified according to viral load. Statistical analyses included non-parametric tests, Spearman correlation, principal component analysis (PCA), and multiple linear regression. Results: The median age was 45 years (IQR: 34–57), among which 54.6% were male. The median HBV DNA was 3.37 log₁₀ IU/mL (IQR: 2.45–3.75), and IL-6 concentration was 2.38 log₁₀ pg/mL (IQR: 2.21–2.49). IL-6 levels increased significantly across viral load categories (p < 0.001) and were higher in HBeAg-positive patients (p = 0.002), with no significant differences across age, sex, or cirrhosis. IL-6 levels correlated with HBV DNA (r = 0.40, p < 0.001). PCA identified distinct viral-inflammatory and biochemical axes. Regression analysis confirmed HBV DNA as the significant independent predictor (β = 0.461, p < 0.001; adjusted R² = 0.206). Conclusion: IL-6 was closely associated with HBV DNA levels, while the association with conventional biochemical markers of hepatocellular injury was significantly less in this cohort, suggesting that IL-6 may serve as an adjunct biomarker of disease activity in patients with chronic hepatitis B. Full article

Flexible strain sensors have attracted considerable attention in gait recognition owing to their ability to adhere directly to the skin near joints and transduce local deformation. In existing work, however, sensor placement and orientation are largely determined by anatomical experience, while multi-channel classification still relies on back-end digital processors, whose power consumption and latency constrain system practicality in wearable scenarios. This paper presents an integrated design path that proceeds from skin-mechanics theory through sensor-layout optimization to analog-domain front-end inference. On the layout side, the lines-of-non-extension (LoNE) theory is employed to convert the selection of sensor attachment angles from empirical judgment into a calculable mechanics problem; guided by the spatial course of LoNE in the ankle and knee regions, the positions and angles of the nine sensors are determined individually—channels perpendicular to the LoNE capture maximum strain, channels offset by 45 degrees supplement non-sagittal-plane information, and a channel aligned along the LoNE provides a near-zero-strain reference. On the circuit side, the mathematical equivalence between the weighted summation of a linear classifier and Kirchhoff’s current law (KCL) nodal current superposition is exploited to map the classification operation onto current aggregation in an analog circuit, yielding an in-sensor computing (ISC) front end in which the nine-channel weighted summation is completed in a single analog step. The sensors are fabricated by screen-printing a liquid-metal–polymer composite conductive ink onto a TPU film substrate, with a gauge factor RSD of $6.8\%$ and a tensile linearity $R^2>0.99$. Using walking, running, and stair descent as verification targets, the analog classifier reaches 99% accuracy at the circuit-level functional-verification stage. On real multi-subject data, it achieves $87.0\%\pm 8.4\%$ accuracy under intra-subject cross-session validation, with an analog-domain inference response faster than $100\mathsf{\mu }\mathrm{s}$. This design path is not bound to a specific joint or sensor material; when the layout methodology is extended to additional joint regions and the circuit architecture incorporates multiple outputs to cover more classification categories, the same workflow remains applicable, offering a promising low-power, lightweight technical solution for wearable motion monitoring. Full article

Background: The rapid expansion of functional ready-to-drink (RTD) beverages—formulated with prebiotic fibers, botanical extracts, and reduced sugar—has outpaced systematic characterization of their small-molecule composition. Methods: We applied dual-mode untargeted high-resolution liquid chromatography–tandem mass spectrometry (LC–MS/MS), integrating hydrophilic interaction (HILIC) and reversed-phase C18 separations, to profile five commercial RTD beverages spanning distinct formulation categories: Coca-Cola^®, Poppi^® Orange, OLIPOP^® Cream Soda, Pure Leaf^® Unsweetened Black Tea, and BeePop™ Peach + Orange Blossom Honey. Results: Across all products, 478 compounds were structurally annotated at Metabolomics Standards Initiative (MSI) Levels 1 and 2, of which 42 matched compounds with reported bioactivity in a curated literature-based reference database. Seventeen compounds—including the NAD+ precursor trigonelline and multiple B vitamins—were detected across all five products. The number and diversity of compounds with reported bioactivity varied substantially by product and correlated with botanical ingredient complexity. Conclusions: This work presents a qualitative molecular survey of the RTD beverage category using standardized, dual-mode untargeted metabolomics, providing a reference dataset for future targeted quantitation studies. Full article

To address the issues of model overfitting under scarce samples and poor segmentation performance on slender objects in the task of semantic segmentation of remote sensing hyperspectral images, this paper proposes a hyperspectral image semantic segmentation framework that integrates edge awareness and multi-scale feature enhancement under extremely few-shot conditions. This architecture effectively integrates orthogonal-direction convolutions, elongated feature enhancement, multi-scale feature fusion, and deep supervision mechanisms, solving challenges such as difficulty in extracting features of slender objects, model overfitting under few-sample conditions, and insufficient generalization ability. The experimental results on multiple public datasets show that the proposed algorithm achieves excellent segmentation performance with just one small-sized sample per labeled category, surpassing existing popular algorithms and thereby confirming the algorithm’s effectiveness and superiority. On the PaviaU dataset, the overall accuracy (OA) and mean intersection over union (mIoU) improved by approximately 9.7% and 15.5% compared to the second-best model; especially for the segmentation of the key elongated feature ‘road’, the intersection over union reached 94.75%, highlighting the effectiveness of the proposed mechanism. This paper provides a novel and efficient solution for fine interpretation of hyperspectral images under few-sample conditions. Full article

Arthrogryposis multiplex congenita (AMC) is a diverse group of conditions characterized by multiple joint contractures. Although individually rare, these disorders are estimated to affect 1 in 3000–5000 live births. Their common pathophysiological mechanism is fetal akinesia, a sustained reduction of fetal movement that may arise from intrinsic disturbances—such as central nervous system malformations, motor neuronopathies, neuropathies, neuromuscular junction defects, congenital myopathies, muscular dystrophies, or metabolic diseases—or from extrinsic factors including uterine constraint, maternal illness, infections, or toxic exposures. Reduced fetal motion leads to relatively uniform clinical manifestations, known as the fetal akinesia deformation sequence (FADS), which is characterized by craniofacial anomalies, pulmonary hypoplasia, growth restriction, and contractures. Currently, AMC is classified by clinical features, such as distal arthrogryposis or lethal congenital contracture syndromes. However, advances in molecular genetics have shown wide variability among conditions classified into the same category. Prognosis is widely variable, ranging from lethal perinatal forms to non-progressive mild conditions. This review discusses AMC etiologies from a topographic standpoint, considering the different levels of the motor system involved, by combining current clinical, genetic, and pathophysiological information. Full article