Search Results (1,687)

Community detection in complex networks plays a pivotal role in modern scientific research, including in social network analysis and protein structure analysis. Traditional community detection methods face challenges in integrating heterogeneous multi-source information, capturing global semantic relationships, and adapting to dynamic network evolution. This paper proposes a novel unsupervised multimodal community detection algorithm (UMM) based on fractal iteration. The core idea is to design a dual-channel encoder that comprehensively considers node semantic features and network topological structures. Initially, node representation vectors are derived from structural information (using feature vectors when available, or singular value decomposition to obtain feature vectors for nodes without attributes). Subsequently, a parameter-free graph convolutional encoder (PFGC) is developed based on fractal iteration principles to extract high-order semantic representations from structural encodings without requiring any training process. Furthermore, a semantic–structural dual-channel encoder (DC-SSE) is designed, which integrates semantic encodings—reduced in dimensionality via UMAP—with structural features extracted by PFGC to obtain the final node embeddings. These embeddings are then clustered using the K-means algorithm to achieve community partitioning. Experimental results demonstrate that the UMM outperforms existing methods on multiple real-world network datasets. Full article

Microalgae, with their unparalleled capabilities for sunlight-driven growth, CO₂ fixation, and synthesis of diverse high-value compounds, represent sustainable cell factories for a circular bioeconomy. However, industrial deployment has been hindered by biological constraints and the inadequacy of conventional genetic tools. The advent of CRISPR-Cas systems initially provided precise gene editing via targeted DNA cleavage. This review argues that the true transformative potential lies in moving decisively beyond cutting to harness CRISPR as a versatile synthetic biology “Swiss Army Knife”. We synthesize the rapid evolution of CRISPR-derived tools—including transcriptional modulators (CRISPRa/i), epigenome editors, base/prime editors, multiplexed systems, and biosensor-integrated logic gates—and their revolutionary applications in microalgal engineering. These tools enable tunable gene expression, stable epigenetic reprogramming, DSB-free nucleotide-level precision editing, coordinated rewiring of complex metabolic networks, and dynamic, autonomous control in response to environmental cues. We critically evaluate their deployment to enhance photosynthesis, boost lipid/biofuel production, engineer high-value compound pathways (carotenoids, PUFAs, proteins), improve stress resilience, and optimize carbon utilization. Persistent challenges—species-specific tool optimization, delivery efficiency, genetic stability, scalability, and biosafety—are analyzed, alongside emerging solutions and future directions integrating AI, automation, and multi-omics. The strategic integration of this CRISPR toolkit unlocks the potential to engineer robust, high-productivity microalgal cell factories, finally realizing their promise as sustainable platforms for next-generation biomanufacturing. Full article

The usage of fossil fuels has resulted in increasingly severe environmental problems, such as climate change, air pollution, water pollution, etc. Hydrogen energy is considered one of the most promising clean energies to replace fossil fuels due to its pollution-free and high-heat properties. However, the oxygen evolution reaction (OER) remains a critical challenge due to its high overpotential and slow kinetics during water electrolysis for hydrogen production. Electrocatalysts play an important role in lowering the overpotential of OER and promoting the kinetics. Co₃O₄-based electrocatalysts have emerged as promising candidates for the oxygen evolution reaction (OER) due to their favorable catalytic activity and good compatibility compared with precious metal-based electrocatalysts. This review presents a summary of the recent developments in the synthesis strategies and mechanisms of Co₃O₄-based electrocatalysts for the OER. Various synthesis strategies have been explored to control the size, morphology, and composition of Co₃O₄ nanoparticles. These strategies enable the fabrication of well-defined nanostructures with enhanced catalytic performance. Additionally, the mechanisms of OER catalysis on Co₃O₄-based electrocatalysts have been elucidated. Coordinatively unsaturated sites, synergistic effects with other elements, surface restructuring, and pH dependency have been identified as crucial factors influencing the catalytic activity. The understanding of these mechanisms provides insights into the design and optimization of Co₃O₄-based electrocatalysts for efficient OER applications. The recent advancements discussed in this review offer valuable perspectives for researchers working on the development of electrocatalysts for the OER, with the goal of achieving sustainable and efficient energy conversion and storage systems. Full article

Antimicrobial Resistance (AMR), i.e., the evolution of microbes to become resistant to chemicals used to control them, is a global public health concern that can make bacterial diseases untreatable. Inputs including antibiotics, metals, and biocides can create an environment in the agrifood chain that selects for AMR. Consumption of food represents a potential exposure route to AMR microbes and AMR genes (ARGs), which may be present in viable bacteria or on free DNA. Ready-to-eat (RTE) foods are of particular interest because they are eaten without further cooking, so AMR bacteria or ARGs that are present may be consumed intact. They also represent varied production systems (fresh produce, cooked meat, dairy, etc.). An evidence gap exists regarding the diversity and consumption of ARGs in RTE food, which this study begins to address. We sampled 1001 RTE products at retail sale in the UK, in proportion to their consumption by the UK population, using National Diet and Nutrition Survey data. Bacterial DNA content of sample extracts was assessed by 16S metabarcoding, and 256 samples were selected for metagenomic sequencing for identification of ARGs based on consumption and likely bacterial DNA content. A total of 477 unique ARGs were identified in the samples, including ARGs that may be involved in resistance to important antibiotics, such as colistin, fluoroquinolones, and carbapenems, although phenotypic AMR was not measured. Based on the incidence of ARGs in food types, ARGs are estimated to be present in a high proportion of average diets. ARGs were detected on almost all RTE food types tested (48 of 52), and some efflux pump genes are consumed in 97% of UK diets. Full article

This paper investigates the problem of tracking targets with unknown but fixed 3D star-convex shapes using point cloud measurements. While existing methods typically model shape parameters as random variables evolving according to predefined prior models, this evolution process is often unknown in practice. We propose a particular approach within the Expectation Conditional Maximization (ECM) framework that circumvents this limitation by treating shape-defining quantities as parameters estimated directly via optimization. The objective is the joint estimation of target kinematics, extent, and orientation in 3D space. Specifically, the 3D shape is modeled using a radial function estimated via double Fourier series (DFS) expansion, and orientation is represented using the compact, singularity-free axis-angle method. The ECM algorithm facilitates this joint estimation: an Unscented Kalman Smoother infers kinematics in the E-step, while the M-step estimates DFS shape parameters and rotation angles by minimizing regularized cost functions, promoting robustness and smoothness. The effectiveness of the proposed algorithm is substantiated through two experimental evaluations. Full article

Objective: To compare the effect of different bone augmentation procedures, namely, autogenous bone blocks (ABBs) versus guided bone regeneration (GBR), on patient-reported outcomes (PROMs). Methods: This systematic review was conducted according to the PRISMA guidelines. A MEDLINE, Embase, and Web of Science search was conducted by two independent reviewers in combination with a free-hand search in relevant journals until June 2025. Outcomes were PROMs to enhance our understanding of the evolution of these procedures. Results: The electronic search yielded 6291 articles. After title screening, 67 articles were further analyzed for abstract review, which resulted in 14 articles eligible for full-text reading. Six articles were finally included based on the exclusion and inclusion criteria with a total of 295 patients. The overall study quality was low, since only two RCTs could be included. One study demonstrated a high risk of bias. Different PROMs were examined and compared such as pain, edema, neurosensory disturbance, Patient-Reported Predominant Symptom, OHIP-14, postoperative analgesic usage, willingness to repeat, and likelihood to recommend. Meta-analysis was not achievable due to a lack of direct comparisons and heterogeneity in terms of PROMs. Evaluation points varied between pretreatment and up to nearly 10-years of follow-up. Conclusions: Despite significant heterogeneity and reporting, this systematic review concluded that ABB and GBR are well-tolerated procedures. Trends such as transient postoperative pain and swelling with a minor occurring of neurosensory disturbances were reported in a few studies. Overall, a good perception of postoperative recovery was reported for both treatment modalities. Good quality of life was noted related to GBR procedures. Patient-reported outcomes were only analyzed for patients who completed the entire follow-up period. This may introduce bias, as patients who dropped out and were more likely to experience complications were not represented, potentially resulting in a more favorable portrayal of the outcomes. Further well-conducted prospective studies with a long follow-up are needed for an evidence-based evaluation and comparison of PROMs for these procedures. Full article

Modern vehicles are rapidly transforming into interconnected cyber–physical systems that rely on advanced sensor technologies and pervasive connectivity to support autonomous functionality. Yet, despite this evolution, standardized methods for quantifying cybersecurity vulnerabilities across critical automotive components remain scarce. This paper introduces a novel hybrid model that integrates expert-driven insights with generative AI tools to adapt and extend the Common Vulnerability Scoring System (CVSS) specifically for autonomous vehicle sensor systems. Following a three-phase methodology, the study conducted a systematic review of 16 peer-reviewed sources (2018–2024), applied CVSS version 4.0 scoring to 15 representative attack types, and evaluated four free source generative AI models—ChatGPT, DeepSeek, Gemini, and Copilot—on a dataset of 117 annotated automotive-related vulnerabilities. Expert validation from 10 domain professionals reveals that Light Detection and Ranging (LiDAR) sensors are the most vulnerable (9 distinct attack types), followed by Radio Detection And Ranging (radar) (8) and ultrasonic (6). Network-based attacks dominate (104 of 117 cases), with 92.3% of the dataset exhibiting low attack complexity and 82.9% requiring no user interaction. The most severe attack vectors, as scored by experts using CVSS, include eavesdropping (7.19), Sybil attacks (6.76), and replay attacks (6.35). Evaluation of large language models (LLMs) showed that DeepSeek achieved an F1 score of 99.07% on network-based attacks, while all models struggled with minority classes such as high complexity (e.g., ChatGPT F1 = 0%, Gemini F1 = 15.38%). The findings highlight the potential of integrating expert insight with AI efficiency to deliver more scalable and accurate vulnerability assessments for modern vehicular systems.This study offers actionable insights for vehicle manufacturers and cybersecurity practitioners, aiming to inform strategic efforts to fortify sensor integrity, optimize network resilience, and ultimately enhance the cybersecurity posture of next-generation autonomous vehicles. Full article

Malignant mediastinal tumors are a group representing some of the most demanding oncological challenges for early, multi-level, and successful management. The timely identification of any suspicious clinical symptomatology is urgent in achieving an accurate, staged histological diagnosis, in order to follow up with an equally detailed medical therapeutic plan (interventional or not) and determine the principal goals regarding efficient overall treatment in these patients. We report a case of a 24-year-old male patient with an incident-free prior medical history. An initial chest X-ray was performed after the patient reported short-term, consistent moderate chest pain symptomatology, early work fatigue, and shortness of breath. The following imaging procedures (chest CT, PET-CT) indicated the presence of an anterior mediastinal mass (meas. ~11 cm × 10 cm × 13 cm, SUV: 8.7), applying additional pressure upon both right heart chambers. The Alpha-Fetoprotein (aFP) blood levels had exceeded at least 50 times their normal range. Two consecutive diagnostic attempts with non-specific histological results, a negative-for-malignancy fine-needle aspiration biopsy (FNA-biopsy), and an additional tumor biopsy, performed via mini anterior (R) thoracotomy with “suspicious” cellular gatherings, were performed elsewhere. After admission to our department, an (R) Video-Assisted Thoracic Surgery (VATS) was performed, along with multiple tumor biopsies and moderate pleural effusion drainage. The tumor’s measurements had increased to DMax: 16 cm × 9 cm × 13 cm, with a severe degree of atelectasis of the Right Lower Lobe parenchyma (RLL) and a pressure-displacement effect upon the Superior Vena Cava (SVC) and the (R) heart sinus, based on data from the preoperative chest MRA. The histological report indicated elements of a combined, non-seminomatous germ-cell mediastinal tumor, posthuberal-type teratoma, and embryonal carcinoma. The imminent chemotherapeutic plan included a “BEP” (Bleomycin^®/Cisplatin^®/Etoposide^®) scheme, which needed to be modified to a “VIP” (Cisplatin^®/Etoposide^®/Ifosfamide^®) scheme, due to an acute pulmonary embolism incident. While the aFP blood levels declined, even reaching normal measurements, the tumor’s size continued to increase significantly (DMax: 28 cm × 25 cm × 13 cm), with severe localized pressure effects, rapid weight loss, and a progressively worsening clinical status. Thus, an emergency surgical intervention took place via median sternotomy, extended with a complementary “T-Shaped” mini anterior (R) thoracotomy. A large, approx. 4 Kg mediastinal tumor was extracted, with additional RML and RUL “en-bloc” segmentectomy and partial mediastinal pleura decortication. The following histological results, apart from verifying the already-known posthuberal-type teratoma, indicated additional scattered small lesions of combined high-grade rabdomyosarcoma, chondrosarcoma, and osteosarcoma, as well as numerous high-grade glioblastoma cellular gatherings. No visible findings of the previously discovered non-seminomatous germ-cell and embryonal carcinoma elements were found. The patient’s postoperative status progressively improved, allowing therapeutic management to continue with six “TIP” (Cisplatin^®/Paclitaxel^®/Ifosfamide^®) sessions, currently under his regular “follow-up” from the oncological team. This report underlines the importance of early, accurate histological identification, combined with any necessary surgical intervention, diagnostic or therapeutic, as well as the appliance of any subsequent multimodality management plan. The diversity of mediastinal tumors, especially for young patients, leaves no place for complacency. Such rare examples may manifest, with equivalent, unpredictable evolution, obliging clinical physicians to stay constantly alert and not take anything for granted. Full article

The rapid evolution of SARS-CoV-2 has underscored the need for a detailed understanding of antibody binding mechanisms to combat immune evasion by emerging variants. In this study, we investigated the interactions between Class I neutralizing antibodies—BD55-1205, BD-604, OMI-42, P5S-1H1, and P5S-2B10—and the receptor-binding domain (RBD) of the SARS-CoV-2 spike protein using multiscale modeling, which combined molecular simulations with the ensemble-based mutational scanning of the binding interfaces and binding free energy computations. A central theme emerging from this work is that the unique binding strength and resilience to immune escape of the BD55-1205 antibody are determined by leveraging a broad epitope footprint and distributed hotspot architecture, additionally supported by backbone-mediated specific interactions, which are less sensitive to amino acid substitutions and together enable exceptional tolerance to mutational escape. In contrast, BD-604 and OMI-42 exhibit localized binding modes with strong dependence on side-chain interactions, rendering them particularly vulnerable to escape mutations at K417N, L455M, F456L and A475V. Similarly, P5S-1H1 and P5S-2B10 display intermediate behavior—effective in some contexts but increasingly susceptible to antigenic drift due to narrower epitope coverage and concentrated hotspots. Our computational predictions show strong agreement with experimental deep mutational scanning data, validating the accuracy of the models and reinforcing the value of binding hotspot mapping in predicting antibody vulnerability. This work highlights that neutralization breadth and durability are not solely dictated by epitope location, but also by how binding energy is distributed across the interface. The results provide atomistic insight into mechanisms driving resilience to immune escape for broadly neutralizing antibodies targeting the ACE2 binding interface—which stems from cumulative effects of structural diversity in binding contacts, redundancy in interaction patterns and reduced vulnerability to mutation-prone positions. Full article

In this paper, we develop a peridynamic computational framework to analyze thermomechanical interactions in fractured thin films subjected to ultrashort-pulsed laser excitation, employing nonlocal discrete material point discretization to eliminate mesh dependency artifacts. The generalized Cattaneo–Fourier thermal flux formulation uncovers contrasting dynamic responses: hyperbolic heat propagation ($F_T=0$) generates intensified temperature localization and elevates transient crack-tip stress concentrations relative to classical Fourier diffusion ($F_T=1$). A GSSSS (Generalized Single Step Single Solve) i-Integration temporal scheme achieves oscillation-free numerical solutions across picosecond-level laser–matter interactions, effectively resolving steep thermal fronts through adaptive stabilization. These findings underscore hyperbolic conduction’s essential influence on stress-mediated fracture evolution during ultrafast laser processing, providing critical guidelines for thermal management in micro-/nano-electromechanical systems. Full article

Nanomotion sensors have emerged as a pivotal technology in microbiology and biology, leveraging advances in nanotechnology, microelectronics, and optics to provide a highly sensitive, label-free detection of biological activity and interactions. These sensors were first limited to nanomechanical oscillators like atomic force microscopy cantilevers, but now they are expanding into new, more intriguing setups. The idea is to convert the inherent nanoscale movements of living organisms—a direct manifestation of their metabolic activity—into measurable signals. This review highlights the evolution and diverse applications of nanomotion sensing. Key methodologies include Atomic Force Microscopy-based sensors, optical nanomotion detection, graphene drum sensors, and optical fiber-based sensors, each offering unique advantages in sensitivity, cost, and applicability. The analysis of complex nanomotion data is increasingly supported by advanced modeling and the integration of artificial intelligence and machine learning, enhancing pattern recognition and automation. The versatility and real-time, label-free nature of nanomotion sensing position it as a transformative tool that could revolutionize diagnostics, therapeutics, and fundamental biological research. Full article

The colour evolution of malvidin-3-O-glucoside (Mv-3-O-glc) elicited by caffeic acid (CAF), (+)-catechin (CA), or syringic acid (SI) was spectrophotometrically monitored in model wine solution, modulating the malvidin-to-polyphenol molar ratio (1:1 to 1:20) and the pH (2.8–3.8). The spectral features provided the thermodynamic parameters Gibbs free energy (ΔG⁰) and equilibrium constant (K_eq), showing that the copigmentation extent is maximized at pH 3.6 and a higher molar ratio (1:20), and that copigments have different efficiency. In a long-term evolution (12 months), transient complexes evolved into different colour characteristics. Spectrophotometry and colorimetry (chroma C*, hue H*, and lightness L*) revealed the formation of stable pigments with peculiar orange-reddish colour when CAF was present; however, in the case of CA, an accentuated yellow tone was observed. SI showed minimum impact in the long-term evolution of Mv-3-O-glc. This study expands knowledge on oenological copigmentation, further exploring its potential implication in the colour of aged red wines. Full article

This article examines the institutional evolution of university data governance in China through the lens of historical institutionalism, offering a novel perspective on this critical topic. This framework provides a structured approach to analyzing the role of institutional factors, power dynamics, and path dependence in shaping university data governance. Since the onset of the information age, Chinese university data governance has evolved through three distinct phases: functional departmentalism, cross-departmental collaborative governance with hierarchical structures, and governance focused on data openness and application. At a deeper level, shifts in governmental data governance serve as key indicators of transformations in university data governance, demonstrating the interplay between institutional frameworks and power structures. Path dependence is evident, with rational choices made by both the government and universities driving the persistence of existing governance models. Legitimacy emerges as the core driving force behind these institutional changes, while efficiency acts as an accelerator, contingent on legitimacy. To advance data governance, Chinese universities must break free from path dependence, reform institutional frameworks, and adapt data power structures to meet the evolving demands of data openness and effective application. Full article

The escalating threat of infectious diseases necessitates the development of diagnostic technologies that are not only rapid and sensitive but also deployable at the point of care. Electrochemical impedance spectroscopy (EIS) has emerged as a leading technique for the label-free detection of pathogens, offering a unique combination of sensitivity, non-invasiveness, and adaptability. This review provides a comprehensive overview of the design and application of EIS-based biosensors tailored for pathogen detection, focusing on critical components such as biorecognition elements, electrode materials, nanomaterial integration, and surface immobilization strategies. Special emphasis is placed on the mechanisms of signal generation under Faradaic and non-Faradaic modes and how these underpin performance characteristics such as the limit of detection, specificity, and response time. The application spectrum spans bacterial, viral, fungal, and parasitic pathogens, with case studies highlighting detection in complex matrices such as blood, saliva, food, and environmental water. Furthermore, integration with microfluidics and point-of-care systems is explored as a pathway toward real-world deployment. Emerging strategies for multiplexed detection and the utilization of novel nanomaterials underscore the dynamic evolution of the field. Key challenges—including non-specific binding, matrix effects, the inherently low ΔR_ct/decade sensitivity of impedance transduction, and long-term stability—are critically evaluated alongside recent breakthroughs. This synthesis aims to support the future development of robust, scalable, and user-friendly EIS-based pathogen biosensors with the potential to transform diagnostics across healthcare, food safety, and environmental monitoring. Full article

Oral cancer, the most common form of head and neck cancer, is worldwide a serious public health problem. Most patients present a locally advanced disease, and face poor prognosis, even with multimodality treatment. They may also develop second primary tumors in the entirety of their upper aerodigestive tract. The most altered signaling pathways are the PI3K/AKT/mTOR, TP53, RB, and the WNT/β-catenin pathways. Genomic and molecular cytogenetic analyses have revealed frequent losses at 3p, 8p, 9p, and 18q, along with gains at 3q, 7p, 8q, and 11q, and several genes frequently affected have been identified, such as TP53, CCND1, CTTN, CDKN2A, EGFR, HRAS, PI3K, ADAM9, MGAM, SIRPB1, and FAT1, among others. Various epigenetic alterations were also found, such as the global hypomethylation and hypermethylation of CDKN2A, APC, MGMT, PTEN, CDH1, TFP12, SOX17, GATA4, ECAD, MGMT, and DAPK. Several microRNAs are upregulated in oral cancer, including miR-21, miR-24, miR-31, miR-184, miR-211, miR-221, and miR-222, while others are downregulated, such as miR-203, miR-100, miR-200, miR-133a, miR-133b, miR-138, and miR-375. The knowledge of this molecular pathogenesis has not yet been translated into clinical practice, apart from the use of cetuximab, an EGFR antibody. Oral tumors are also genetically heterogenous and affect several pathways, which means that, due to the continuous evolution of these genetic alterations, a single biopsy is not sufficient to fully evaluate the most adequate molecular targets when more drugs become available. Liquid biopsies, either resorting to circulating tumor cells, extracellular vesicles or cell-free nucleic acids, have the potential to bypass this problem, and have potential prognostic and staging value. We critically review the current knowledge on the molecular, genetic and epigenetic alterations in oral cancer, as well as the applications and challenges of liquid biopsies in its diagnosis, follow-up, and prognostic stratification. Full article