Search Results (337)

Human Papillomavirus (HPV), particularly high-risk strains such as HPV16 and HPV18, is a leading cause of cervical cancer and a significant risk factor for several other epithelial malignancies. While the oncogenic mechanisms of viral proteins E6 and E7 are well characterized, the broader effects of HPV infection on host transcriptional regulation remain less clearly defined. This study explores the hypothesis that conserved genomic motifs within the HPV genome may act as molecular decoys, sequestering human transcription factors (TFs) and thereby disrupting normal gene regulation in host cells. Such interactions could contribute to oncogenesis by altering the transcriptional landscape and promoting malignant transformation.We conducted a computational analysis of the genomes of high-risk HPV types using MEME-ChIP for de novo motif discovery, followed by Tomtom for identifying matching human TFs. Protein–protein interactions among the predicted TFs were examined using STRING, and biological pathway enrichment was performed with Enrichr. The analysis identified conserved viral motifs with the potential to interact with host transcription factors (TFs), notably those from the FOX, HOX, and NFAT families, as well as various zinc finger proteins. Among these, SMARCA1, DUX4, and CDX1 were not previously associated with HPV-driven cell transformation. Pathway enrichment analysis revealed involvement in several key biological processes, including modulation of Wnt signaling pathways, transcriptional misregulation associated with cancer, and chromatin remodeling. These findings highlight the multifaceted strategies by which HPV may influence host cellular functions and contribute to pathogenesis. In this context, the study underscores the power of in silico approaches for elucidating viral–host interactions and reveals promising therapeutic targets in computationally predicted regulatory network changes. Full article

Autonomous vehicles and intelligent traffic transportation are widely investigated for road networks. Context-aware traffic light scheduling algorithms determine signal phases by analyzing the real-time characteristics and contextual information of competing traffic flows. The context of traffic flows mainly considers the existence of regular, emergency, or heavy vehicles. This is an important factor in setting the phases of the traffic light schedule and assigning a high priority for emergency vehicles to pass through the signalized intersection first. VANET technology, through its communication capabilities and the exchange of data packets among moving vehicles, is utilized to collect real-time traffic information for the analyzed road scenarios. This introduces an attractive environment for hackers, intruders, and criminals to deceive drivers and intelligent infrastructure by manipulating the transmitted packets. This consequently leads to the deployment of less efficient traffic light scheduling algorithms. Therefore, ensuring secure communications between traveling vehicles and verifying the integrity of transmitted data are crucial. In this work, we investigate the possible attacks on the integrity of transferred messages and vehicles’ identities and their effects on the traffic light schedules. Then, a new secure context-aware traffic light scheduling system is proposed that guarantees the integrity of transmitted messages and verifies the vehicles’ identities. Finally, a comprehensive series of experiments were performed to assess the proposed secure system in comparison to the absence of security mechanisms within a simulated road intersection. We can infer from the experimental study that attacks on the integrity of vehicles have different effects on the efficiency of the scheduling algorithm. The throughput of the signalized intersection and the waiting delay time of traveling vehicles are highly affected parameters. Full article

Influence-based data selection methods, such as TracIn, aim to estimate the impact of individual training samples on model predictions and are increasingly used for dataset curation and reduction. This study investigates whether selecting the most positively influential training examples can be used to create compressed yet effective training datasets for transfer learning in plastic waste classification. Using a ResNet-18 model trained on a custom dataset of plastic waste images, TracIn was applied to compute influence scores across multiple training checkpoints. The top 50 influential samples per class were extracted and used to train a new model. Contrary to expectations, models trained on these highly influential subsets significantly underperformed compared to models trained on either the full dataset or an equally sized random sample. Further analysis revealed that many top-ranked influential images originated from different classes, indicating model biases and potential label confusion. These findings highlight the limitations of using influence scores for dataset compression. However, TracIn proved valuable for identifying problematic or ambiguous samples, class imbalance issues, and issues with fuzzy class boundaries. Based on the results, the utilized TracIn approach is recommended as a diagnostic instrument rather than for dataset curation. Full article

Rainbow table attacks utilize a time-memory trade-off to efficiently crack passwords by employing precomputed tables containing chains of passwords and hash values. Generating these tables is computationally intensive, and several researchers have proposed utilizing parallel computing to speed up the generation process. This paper introduces a modification to the traditional master-slave parallelization model using the MPI framework, where, unlike previous approaches, the generation of starting points is decentralized, allowing each process to generate its own tasks independently. This design is proposed to reduce communication overhead and improve the efficiency of rainbow table generation. We reduced the number of inter-process communications by letting each process generate chains independently. We conducted three experiments to evaluate the performance of the parallel rainbow tables generation algorithm for four cryptographic hash functions: NTLMv2, MD5, SHA-256 and SHA-512. The first experiment assessed parallel performance, showing near-linear speedup and 95–99% efficiency across varying numbers of nodes. The second experiment evaluated scalability by increasing the number of processed chains from 100 to 100,000, revealing that higher workloads significantly impacted execution time, with SHA-512 being the most computationally intensive. The third experiment evaluated the effect of chain length on execution time, confirming that longer chains increase computational cost, with SHA-512 consistently requiring the most resources. The proposed approach offers an efficient and practical solution to the computational challenges of rainbow tables generation. The findings of this research can benefit key stakeholders, including cybersecurity professionals, ethical hackers, digital forensics experts and researchers in cryptography, by providing an efficient method for generating rainbow tables to analyze password security. Full article

In increasingly competitive digital markets, software firms must strategically balance cybersecurity investments and pricing decisions to attract consumers while safeguarding their platforms. This study develops a game-theoretic model in which two competing firms choose among three cybersecurity strategies—no action, bug bounty programs, and in-house protection—before setting prices. We demonstrate that cybersecurity efforts and pricing are interdependent: investment choices significantly alter market outcomes by influencing consumer trust and competitive dynamics. Our analysis reveals that a bug bounty program is preferable when consumer sensitivity to security and the probability of ethical vulnerability disclosures are high, while in-house protection becomes optimal when firms must rebuild credibility from a weaker competitive position. Furthermore, initial service quality gaps between firms critically shape both investment intensity and pricing behavior. By jointly endogenizing security efforts and prices, this study offers new insights into strategic cybersecurity management and provides practical guidance for software firms seeking to integrate security initiatives with competitive pricing strategies. Full article

As Advanced Persistent Threats (APTs) continue to evolve, constructing a dynamic cybersecurity knowledge graph requires precise extraction of entity–relationship triples from unstructured threat intelligence. Existing approaches, however, face significant challenges in modeling low-frequency threat associations, extracting multi-relational entities, and resolving overlapping entity scenarios. To overcome these limitations, we propose the Symmetry-Aware Prototype Contrastive Learning (SAPCL) framework for joint entity and relation extraction. By explicitly modeling syntactic symmetry in attack-chain dependency structures and its interaction with asymmetric adversarial semantics, SAPCL integrates dependency relation types with contextual features using a type-enhanced Graph Attention Network. This symmetry–asymmetry fusion facilitates a more effective extraction of multi-relational triples. Furthermore, we introduce a triple prototype contrastive learning mechanism that enhances the robustness of low-frequency relations through hierarchical semantic alignment and adaptive prototype updates. A non-autoregressive decoding architecture is also employed to globally generate multi-relational triples while mitigating semantic ambiguities. SAPCL was evaluated on three publicly available CTI datasets: HACKER, ACTI, and LADDER. It achieved F1-scores of 56.63%, 60.21%, and 53.65%, respectively. Notably, SAPCL demonstrated a substantial improvement of 14.5 percentage points on the HACKER dataset, validating its effectiveness in real-world cyber threat extraction scenarios. By synergizing syntactic–semantic multi-feature fusion with symmetry-driven dynamic representation learning, SAPCL establishes a symmetry–asymmetry adaptive paradigm for cybersecurity knowledge graph construction, thus enhancing APT attack tracing, threat hunting, and proactive cyber defense. Full article

Anion exchange membrane fuel cells (AEMFCs) have garnered significant attention for their potential to advance fuel cell technology. In this study, we developed and characterized anion exchange membranes (AEMs) composed of quaternized poly(vinyl alcohol) (QPVA) electrospun nanofiber mats with poly(acrylamide-co-diallyldimethylammonium chloride) (PAADDA) as a matrix filler for interfiber voids. The objective was to investigate the effect of varying PAADDA concentrations as a matrix filler for interfiber voids on the structural, mechanical, and electrochemical properties of QPVA-based electrospun AEMs. Membranes with various concentrations of PAADDA were fabricated and extensively characterized using FTIR, SEM, tensile strength, water uptake, swelling degree, ion exchange capacity (IEC), and hydroxide ion conductivity (σ). FTIR confirmed the successful incorporation of PAADDA into the membrane structure, while SEM images showed that PAADDA effectively filled the voids between the QPVA fibers, resulting in denser membranes. The results indicated that the eQPAD5.0 membrane, with the highest PAADDA content, exhibited the best overall performance. The incorporation of PAADDA into QPVA-based electrospun AEMs significantly enhanced their mechanical strength, achieving a tensile strength of 23.9 MPa, an IEC of 1.25 mmol g⁻¹, and hydroxide conductivity of 19.49 mS cm⁻¹ at 30 °C and 29.29 mS cm⁻¹ at 80 °C, making them promising candidates for fuel cell applications. Full article

A platform of two-component cross-linked hydrogel (cGEL) based on gelatinous peptides and anhydride-containing cross-linkers (oPNMA, oPDMA) is extended for use in peripheral nerve regeneration. Hybrid composites with bio-/chemical cues for enhanced biophysical and biochemical properties were fabricated by covalently grafting small molecular, heterobifunctional amines including the nerve growth factor mimetic LM11A-31 to the oligomeric cross-linkers prior to hydrogel formation. The cytocompatibility and growth-supportive conditions within the matrix are confirmed for pristine and modified hydrogels using L929 mouse fibroblasts and human adipose-derived stem cells (hASCs). For hASCs, cell behavior depends on the type of cross-linker and integrated amine. In a subsequent step, neonatal rat Schwann cells (SCs) are seeded on pristine and functionalized cGEL to investigate the materials’ capabilities to support SC growth and morphology. Within all formulations, cell viability, adherence, and cell extension are maintained though the cell elongation and orientation vary compared to the two-dimensional control. It is possible to merge adjustable two-component hydrogels with amines as biochemical signals, leading to improved nervous cell proliferation and activity. This indicates the potential of tunable bioactive cGEL as biomaterials in nerve implants, suggesting their use as a foundational component for nerve conduits. Full article

This paper investigates, applies, and evaluates state-of-the-art Large Language Models (LLMs) for the classification of posts from a dark web hackers’ forum into four cyber-security categories. The LLMs applied included Mistral-7B-Instruct-v0.2, Gemma-1.1-7B, Llama-3-8B-Instruct, and Llama-2-7B, with zero-shot learning, few-shot learning, and fine-tuning. The four cyber-security categories consisted of “Access Control and Management”, “Availability Protection and Security by Design Mechanisms”, “Software and Firmware Flaws”, and “not relevant”. The hackers’ posts were also classified and labelled by a human cyber-security expert, allowing a detailed evaluation of the classification accuracy per each LLM and customization/learning method. We verified LLM fine-tuning as the most effective mechanism to enhance the accuracy and reliability of the classifications. The results include the methodology applied and the labelled hackers’ posts dataset. Full article

Background/Objectives: Fibroblast activation protein (FAP) has gained tremendous traction as a target for tumor imaging and cancer treatment, while also playing a key role in fibrosis. Our study aimed to evaluate [⁶⁸Ga]Ga-DATA^5m.SA.FAPi for PET imaging of replacement fibrosis following myocardial infarction (MI) or interstitial fibrosis associated with hypertrophy. Methods: MI or transverse aortic constriction (TAC)-induced hypertrophy was induced in C57BL/6 mice, with sham-operated animals serving as controls. At multiple time points during disease progression (1, 2, and 6 weeks post-surgery), [⁶⁸Ga]Ga-DATA^5m.SA.FAPi PET/CT scans were performed, followed by ex vivo investigations. Additionally, in vitro cell uptake experiments simulating hypertrophy were conducted. Results: Cardiac uptake of [⁶⁸Ga]Ga-DATA5m.SA.FAPi significantly increased two weeks after MI induction (MI: 2.1 ± 0.2%ID/g, n = 7 vs. SHAM: 1.1 ± 0.2%ID/g, n = 5; p = 0.002), confirmed by ex vivo autoradiography. No significant difference was observed at six weeks post-MI (MI: 1.1 ± 0.1%ID/g, n = 4 vs. SHAM: 0.8 ± 0.0%ID/g, n = 3), indicating infarct healing completion. In contrast, TAC mice showed increased uptake after six weeks (TAC: 1.8 ± 0.2%ID/g, n = 6; p = 0.007), related to interstitial fibrosis progression. Consistently, high-stretched cardiac fibroblasts demonstrated a higher uptake compared to low-stretched conditioned ones, suggesting the stretch mediates regulation of FAP. Conclusions: This study demonstrated the efficacy of [⁶⁸Ga]Ga-DATA^5m.SA.FAPi for longitudinal imaging of cardiac fibrosis in response to different cardiac injuries. In vivo FAP imaging during cardiac remodeling may serve as a valuable tool for diagnosing and predicting disease progression, ultimately aiding in the clinical management of patients. Full article

Background/Objectives: Patients with transthyretin amyloid cardiomyopathy (ATTR-CM) often experience significantly reduced functional capacity due to myocardial involvement. Cardiopulmonary exercise testing (CPET) is the gold standard to quantify functional capacity, and ^99mTc-DPD scintigraphy and SPECT/CT have proven to be highly effective tools for diagnostic and disease monitoring. We aimed to investigate the complementary role and correlation between both methods, focusing on their combined potential as a strong prognostic framework for monitoring disease progression and evaluating treatment efficacy. Methods: A total of 44 patients with diagnosed ATTR-CM, who underwent ^99mTc-DPD scintigraphy and SPECT/CT imaging as well as CPET, were included. All patients were divided into two groups based on the median DPD retention index (low DPD uptake: ≤5.0, n = 22; high DPD uptake: >5.0, n = 22). Results: The mean age was 78 years, with 82% of participants being male. Significant correlations were observed between peak VO₂ and DPD retention index (r = −0.355, p = 0.018) as well as between peak VO₂ at anaerobic threshold with DPD retention index (r = −0.391, p = 0.009). Interestingly, there was no strong correlation between VE/VCO₂ slope and the retention index. A strong association was identified between cardiac biomarkers and peak VO₂, specifically for NT-proBNP (r = −0.530, p < 0.001) and Troponin T (r = −0.431, p < 0.001). Conclusions: In ATTR-CM, significant correlations were observed between key CPET parameters and quantitative cardiac DPD uptake, which further reflects on disease severity and functional impairment. Our findings highlight the utility of integrating CPET and SPECT/CT for comprehensive patient assessment in ATTR-CM. Full article

Agents that target the amyloid-β (Aβ) peptide associated with Alzheimer’s disease have seen renewed interest following the clinical success of antibody therapeutics. Small molecules, specifically metal-based complexes, are excellent candidates for advancement, given their relative ease of preparation and modular scaffold. Herein, several ruthenium–arene complexes containing 2,2-bipyridine (bpy) ligands were prepared and evaluated for their respective ability to modulate the aggregation of Aβ. This was carried out using the three sequential methods of thioflavin T (ThT) fluorescence, dynamic ligand scattering (DLS), and transmission electron microscopy (TEM). Overall, it was observed that RuBA, the complex with a 4,4-diamino-2,2-bipyridine ligand, had the greatest impact on Aβ aggregation. Further evaluation of the complexes was performed to determine their relative affinity for serum albumin and biocompatibility towards two neuronal cell lines. Ultimately, RuBA outperformed the other Ru complexes, where the structure–activity relationship codified the importance of the amino groups on the bpy for anti-Aβ activity. Full article

Hackers usually launch cyberattacks through several stepping-stone hosts to reduce the chance of being detected. With stepping-stone intrusion (SSI), the attacker’s identity is hidden behind a long interactive connection chain of stepping stones and thus is very difficult to reveal. Many algorithms for detecting SSI have been proposed since 1995. Most of these known detection algorithms for SSI only work for network traffic without intruders’ session manipulation. These known SSID algorithms are either weak to resisting intruders’ chaff-perturbation manipulation or have a very limited capability in resisting attacker’s session manipulation. This paper proposes an innovative SSID algorithm resistant to intruders’ chaff perturbation through matching TCP packets by using the crossover of packets. Our proposed SSID algorithm is verified by well-designed network experiments. Our experimental results show that the proposed SSID algorithm works effectively in detecting network intrusion as well as resisting intruders’ chaff perturbation. Full article

Metal anodes, such as those based on Ca, Mg, Na and Li, are considered to be one of the keys to the further development of high-energy-density rechargeable batteries. The thickness of these metal anodes directly affects the energy density of the battery. However, the fabrication of thin anodes poses technical challenges which often result in using excessively thick metal anodes in batteries. Here we present, for the first time, a study on the development of a thin Ca battery anode fabricated by electrodeposition. The battery anode with a thickness of approximately 10 µm corresponds to a charge density of 4.0 mAh cm⁻². This study systematically investigates the electrodeposition behavior of Ca using a 1.0 M Ca(BH₄)₂ in THF as the electrolyte. A systematic evaluation of electrodeposition parameters—including substrate pretreatment, current density, hydrodynamics and charge density by area—is conducted. Scanning electron microscopy (SEM) and complementary image analysis provide detailed insights into these parameters. Electrodeposition offers a promising route to achieve a defined battery cell balance with minimal excess of metal at the anode. This will improve overall battery performance and efficiency. The findings contribute to the advancement of fundamental aspects of rechargeable batteries, particularly Ca-based batteries. Full article

The proliferation of Internet of Things (IoT) devices presents significant challenges for cybersecurity and digital forensics, particularly as these devices have become increasingly weaponised for malicious activities. This research focuses on the forensic analysis capabilities of Raspberry Pi devices configured with Kali Linux, comparing their forensic capabilities to conventional PC-based forensic investigations. The study identifies key gaps in existing IoT forensic methodologies, including limited tool compatibility, constrained data retention, and difficulties in live memory analysis due to architectural differences. The research employs a testbed-based approach to simulate cyberattacks on both platforms, capturing and analysing forensic artefacts such as system logs, memory dumps, and network traffic. The research findings reveal that while traditional PCs offer extensive forensic capabilities due to superior storage, tool support, and system logging, Raspberry Pi devices present significant forensic challenges, primarily due to their ARM architecture and limited forensic readiness. The study emphasises the need for specialised forensic tools tailored to IoT environments and suggests best practices to enhance forensic investigation capabilities in weaponised IoT scenarios. This research contributes to the field by bridging the gap between theoretical frameworks and real-world forensic investigations, offering insights into the evolving landscape of IoT forensics and its implications for digital evidence collection, analysis, and forensic readiness. Full article