Search Results (330)

We propose the Clustered Link-Prediction SEIRS model with Temporal Node Activation (CLP-SEIRS-T), a novel epidemiological framework that integrates community structure, link prediction, and temporal activation schedules to simulate malware propagation in urban communication networks. Unlike traditional static or homogeneous models, our approach captures the heterogeneous community structure of the network (modular connectivity), along with evolving connectivity (emergent links) and periodic device-usage patterns (online/offline cycles), providing a more realistic portrayal of how computer viruses spread. Simulation results demonstrate that strong community modularity and intermittent connectivity significantly slow and localize outbreaks. For instance, when devices operate on staggered duty cycles (asynchronous online schedules), malware transmission is fragmented into multiple smaller waves with lower peaks, often confining infections to isolated communities. In contrast, near-continuous and synchronized connectivity produces rapid, widespread contagion akin to classic epidemic models, overcoming community boundaries and infecting the majority of nodes in a single wave. Furthermore, by incorporating a common-neighbor link-prediction mechanism, CLP-SEIRS-T accounts for future connections that can bridge otherwise disconnected clusters. This inclusion significantly increases the reach and persistence of malware spread, suggesting that ignoring evolving network topology may underestimate outbreak risk. Our findings underscore the importance of considering temporal usage patterns and network evolution in malware epidemiology. The proposed model not only elucidates how timing and community structure can flatten or exacerbate infection curves, but also offers practical insights for enhancing the resilience of urban communication networks—such as staggering device online schedules, limiting inter-community links, and anticipating new connections—to better contain fast-spreading cyber threats. Full article

Cervical lymph node metastasis is the strongest prognostic factor in oral tongue carcinoma, yet current clinical guidelines rely primarily on depth of invasion to guide elective neck dissection. This approach results in unnecessary surgery in up to 70% of patients. Cancer-testis antigens (CTAs) are a family of genes associated with tumor aggressiveness and may serve as predictive biomarkers for nodal spread. A multi-step analysis integrating large-scale public datasets, including microarray (GSE78060), bulk RNA-seq emerging from the cancer genome atlas (TCGA), and single-cell RNA-seq (GSE103322), was employed to identify CTA genes active in oral tongue cancer. Selected genes were validated using NanoString nCounter RNA profiling of 16 patients undergoing curative glossectomy with elective neck dissection. Machine learning algorithms, including decision trees, t-distributed stochastic neighbor embedding (t-SNE), and convolutional neural networks (CNN), were applied to assess predictive power for nodal metastasis. Computational analysis initially identified 40 cancer-active CTA genes, of which four genes (LY6K, MAGEA3, CEP55, and ATAD2) were most indicative of nodal spread. In our patient cohort, NanoString nCounter profiling combined with machine learning confirmed these four genes as highly predictive. We present a proof-of-concept CTA-based genetic diagnostic tool capable of discriminating nodal involvement in oral tongue cancer. This approach may reduce unnecessary neck dissections, minimizing surgical morbidity. Full article

Gray mold is one of the major diseases affecting tomato production. Its early symptoms are often inconspicuous, yet the disease spreads rapidly, leading to severe economic losses. Therefore, the development of efficient and non-destructive early detection technologies is of critical importance. At present, multispectral imaging-based detection methods are constrained by two major bottlenecks: limited sample size and single modality, which hinder precise recognition at the early stage of infection. To address these challenges, this study explores a detection approach integrating multispectral fluorescence and reflectance imaging, combined with machine learning algorithms, to enhance early recognition of tomato gray mold. Particular emphasis is placed on evaluating the effectiveness of multimodal information fusion in extracting early disease features, and on elucidating the quantitative relationships between disease progression and key physiological indicators such as chlorophyll content, water content, malondialdehyde levels, and antioxidant enzyme activities. Furthermore, an improved WGAN-GP (Wasserstein Generative Adversarial Network with Gradient Penalty) is employed to alleviate data scarcity under small-sample conditions. The results demonstrate that multimodal data fusion significantly improves model sensitivity to early-stage disease detection, while WGAN-GP-based data augmentation effectively enhances learning performance with limited samples. The Random Forest model achieved an early recognition precision of 97.21% on augmented datasets, and transfer learning models attained an overall precision of 97.56% in classifying different disease stages. This study provides an effective approach for the early prediction of tomato gray mold, with potential application value in optimizing disease management strategies and reducing environmental impact. Full article

In this study, a prototype system was developed as a potential alternative to lockdown measures against the spread of airborne infectious diseases such as COVID-19. The system integrates real-time estimation functions for respiratory flow and mask leakage into a low-cost powered air-purifying respirator (PAPR) designed for the general public. Using only a single differential-pressure sensor (SDP810) and a controller (Arduino UNO R4 WiFi), the respiratory flow (Q_3e) is estimated from the differential pressure (ΔP) and battery voltage (V_b), and both the wearing status and leak status are transmitted to and displayed on a smartphone application. For evaluation, a testbench called the Respiratory Airflow Testbench was constructed by connecting a cylinder–piston drive to a mannequin head to simulate realistic wearing conditions. The estimated respiratory flow Q_3e, calculated solely from ΔP and V_b, showed high agreement with the measured flow Q_3m obtained from a reference flow sensor, confirming the effectiveness of the estimation algorithm. Furthermore, an automatic leak detection method based on the time-integrated value of Q_3e was implemented, enabling the detection of improper wearing. This system thus achieves respiratory flow estimation and leakage detection based only on ΔP and V_b. In the future, it is expected to be extended to applications such as pressure control synchronized with breathing activity and health monitoring based on respiratory and coughing analysis. This platform also has the potential to serve as the foundation of a PAPR Wearing Status Network Management System, which will contribute to societal-level infection control through the networked sharing of wearing status information. Full article

Rumors have become a serious issue in today’s modern era, particularly in view of increased activity in social and online platforms. False information can go viral almost instantaneously through social networks, which immediately affect society and people’s minds. The form of rumor it develops within, whether fabricated intentionally or not, impacts public perspectives through manipulation of emotion and cognition. We propose and analyze a mathematical model describing how rumors can spread through an online social media (OSM) platform. Our model focuses on two coexisting rumors (two strains). The results provide some conditions under which rumors die out or become persistent, and they show the influence of delays, skepticism levels, and incidence rates on the dynamics of information spread. We combine analytical tools (Routh–Hurwitz tests and delay-induced stability switches) with MATLAB/Python simulations to validate the theoretical predictions. Full article

The growing demand for scalability and efficiency in Low Power Wide Area Networks (LPWANs) presents significant challenges, particularly due to the increasing number of connected devices and the inherent limitations of the ALOHA protocol, which is widely used in LoRaWAN networks. In this context, this work proposes the design and development of a low-cost dual-channel gateway tailored for Internet of Things (IoT) networks based on LoRa technology. To address the aforementioned challenges, this study explores approaches such as channel activity detection (CAD) and dynamic channel allocation, aiming to reduce collisions and optimize spectrum utilization. Experimental tests were conducted in environments subject to interference from coexisting networks to evaluate the performance of the proposed gateway. The results demonstrated significant improvements in packet delivery rate (PDR) and loss reduction, with PDR exceeding 94% for spreading factors (SFs) ranging from SF7 to SF12. In comparison, the single-channel gateway operating under the same conditions achieved a PDR between 80% and 85%. These results highlight the feasibility of the dual-channel gateway for small- and medium-scale IoT applications in scenarios with multiple coexisting networks. Full article

Natural hydrogen is generated by fairly deep processes and/or in low-permeability rocks. In such contexts, fluids circulate mainly through the network of faults and fractures. However, hydrogen flows from these hydrogen-generating layers can reach sedimentary rocks with more typical permeability and porosity, allowing H₂ flows to spread out rather than be concentrated in fractures. In that case, three different H₂ transport modes exist: advection (displacement of water carrying dissolved gas), diffusion, and free gas Darcy flow. Numerical models have been run to compare the efficiency of these different modes and the pathway they imply for the H₂ in a sedimentary basin with active aquifers. The results show the key roles of these aquifers but also the competition between free gas flow and the dissolved gas displacement which can go in opposite directions. Even with a conservative hypothesis on the H₂ charge, a gaseous phase exists at few kilometers deep as well as free gas accumulation. Gaseous phase displacement could be the faster and diffusion is neglectable. The modeling also allows us to predict where H₂ is expected in the soil: in fault zones, eventually above accumulations, and, more likely, due to exsolution, above shallow aquifers. Full article

Background: Faced with the profound impact of major infectious diseases on public life and economic development, humans have long sought to understand disease transmission and intervention strategies. To better explore the impact of individuals’ different coping behaviors—triggered by changes in their psychological security due to public information and external environmental changes—on the spread to infectious diseases, the model will place greater emphasis on quantifying psychological factors to make it more aligned with real-world situations. Methods: To better understand the interplay between information dissemination and disease transmission, we propose a two-layer network model that incorporates psychological safety factors. Results: Our model reveals key insights into disease transmission dynamics: (1) active defense behaviors help reduce both disease spread and information diffusion; (2) passive resistance behaviors expand disease transmission and may trigger recurrence but enhance information spread; (3) high-timeliness, low-fuzziness information reduces the peak of the initial infection but does not significantly curb overall disease spread, and the rapid dissemination of disease-related information is most effective in limiting the early stages of transmission; and (4) community structures in information networks can effectively curb the spread of infectious diseases. Conclusions: These findings offer valuable theoretical support for public health strategies and disease prevention after government information release. Full article

Viral oncolysis is considered a promising cancer treatment method because of its good tolerability and durable anti-tumor effects. Compared with other oncolytic viruses, Newcastle disease virus (NDV) has some distinct advantages. As an RNA virus, NDV does not recombine with the host genome, making it safer compared with DNA viruses and retroviruses; NDV can induce syncytium formation, allowing the virus to spread among cells without exposure to host neutralizing antibodies; and its genome adheres to the hexamer genetic code rule (genome length as a multiple of six nucleotides), ensuring accurate replication, low recombination rates, and high genetic stability. Although wild-type NDV has a killing effect on various tumor cells, its oncolytic effect and working mechanism are diverse, increasing the complexity of generating engineered oncolytic viruses with NDV. This study aims to employ whole-genome CRISPR-Cas9 knockout screening and RNA sequencing to identify putative key regulatory factors involved in the interaction between NDV and human colon cancer HCT116 cells and map their global interaction networks. The results suggests that NDV infection disrupts cellular homeostasis, thereby exerting oncolytic effects by inhibiting cell metabolism and proliferation. Meanwhile, the antiviral immune response triggered by NDV infection, along with the activation of anti-apoptotic signaling pathways, may be responsible for the limited oncolytic efficacy of NDV against HCT116 cells. These findings not only enhance our understanding of the oncolytic mechanism of NDV against colonic carcinoma but also provide potential strategies and targets for the development of NDV-based engineered oncolytic viruses. Full article

We present a neural-network-based approach to solve the image-to-image translation problem in microscale droplets squeeze flow. A residual convolutional neural network is proposed to address the inverse problem: reconstructing a low-resolution (LR) droplet pattern image from a high-resolution (HR) liquid film thickness imprint. This enables the prediction of initial droplet configurations that evolve into target HR imprints after a specified spreading time. The developed neural network architecture aims at learning to tune the refinement level of its residual convolutional blocks by using function approximators that are trained to map a given film thickness to an appropriate refinement level indicator. We use multiple stacks of convolutional layers, the output of which is translated according to the refinement level indicators provided by the directly connected function approximators. Together with a non-linear activation function, the translation mechanism enables the HR imprint image to be refined sequentially in multiple steps until the target LR droplet pattern image is revealed. We believe that this work holds value for the semiconductor manufacturing and packaging industry. Specifically, it enables desired layouts to be imprinted on a surface by squeezing strategically placed droplets with a blank surface, eliminating the need for customized templates and reducing manufacturing costs. Additionally, this approach has potential applications in data compression and encryption. Full article

White-tailed deer (Odocoileus virginianus) in the wild and on cervid farms have drawn the attention of state wildlife agencies and animal health agencies as Chronic Wasting Disease (CWD) has spread across North America. Deer farm regulations have been implemented to reduce direct contact between wild and farmed cervids; however, evidence suggests that indirect contact to infectious prions passed through the alimentary tracts of scavengers may be an important transmission pathway. The objective of this study was to characterize mammalian scavenger and wild deer activities associated with deer farms and link these activities with site-specific spatial covariates utilizing a network of camera traps, mounted to farm perimeter fences. We monitored each of 14 farms in Minnesota, Wisconsin, and Pennsylvania for two weeks during the summer, with a subset of farms also monitored in the winter and fall. Across all sites and seasons, we captured 749 observations of wildlife. In total, nine species were captured, with wild white-tailed deer accounting for over three quarters of observations. Despite the large number of wild deer observed, we found that interactions between wild and farmed deer at the fence line were infrequent (six direct contacts observed). In contrast, mammalian scavengers were frequently observed inside and outside of the fence. Supplementary cameras placed on deer feeders revealed higher observation rates of scavengers than those placed along fence lines, highlighting the potential for transmission of CWD through indirect contact via scavenger excreta. To evaluate associations between the number of observations of focal species with land-cover characteristics, two mixed-effects regression models were fitted, one model for scavengers and one for wild deer. Contrary to our hypothesis, landscape context did not have a strong impact on wildlife visitation. This suggests that farm location is less important than management practices, highlighting the need for future research into how farming practices impact rates of wildlife visitation onto cervid farms. Full article

Background/Objective: The global spread of Monkeypox (Mpox) has highlighted the urgent need for rapid, accurate diagnostic tools. Traditional methods like polymerase chain reaction (PCR) are resource-intensive, while skin image-based detection offers a promising alternative. This study evaluates the effectiveness of vision transformers (ViTs) for automated Mpox detection. Methods: By fine-tuning a pre-trained ViT model on an Mpox lesion image dataset, a robust ViT-based transfer learning (TL) model was created. Performance was assessed relative to convolutional neural network (CNN)-based TL models and ViT models trained from scratch across key metrics: accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC). Furthermore, a transferability measure was utilized to assess the effectiveness of feature transfer to Mpox images. Results: The results show that the ViT model outperformed a CNN, achieving an AUC of 0.948 and an accuracy of 0.942 with a p-value of less than 0.05 across all metrics, highlighting its potential for accurate and scalable Mpox detection. Moreover, the ViT models yielded a better hypothesis margin-based transferability measure, highlighting its effectiveness in transferring useful learning weights to Mpox images. Gradient-weighted Class Activation Mapping (Grad-CAM) visualizations also confirmed that the ViT model attends to clinically relevant features, supporting its interpretability and reliability for diagnostic use. Conclusions: The results from this study suggest that ViT offers superior accuracy, making it a valuable tool for Mpox early detection in field settings, especially where conventional diagnostics are limited. This approach could support faster outbreak response and improved resource allocation in public health systems. Full article

Clusterin (CLU) is a heterodimeric, ATP-independent molecular chaperone that exhibits high expression in the brain. While CLU primarily functions in the extracellular environment, its chaperone activity in the intracellular compartment under different stress conditions, as well as its involvement in various signaling networks, has been demonstrated. CLU has been extensively associated with Alzheimer’s Disease; however, increasing evidence links this chaperone to Parkinson’s Disease (PD) as well. Thus, in this review we will discuss evidence concerning the involvement of CLU in the pathogenesis of PD with a particular focus on molecular mechanisms leading to the formation and the spreading of alpha-Synuclein (α-Syn) aggregates. Specifically, the role of CLU will be discussed in neurons and in glial cells, taking into account that the neuron–glia cross-talk is an essential and dynamic interplay that is compromised in neurodegenerative disorders. Moreover, the possible role of CLU as a biomarker in different biological fluids, such as cerebrospinal fluid, plasma, and serum, and its therapeutic potential will be addressed. In this regard, the past years have seen huge efforts to discover molecules able to mitigate α-Syn burden and its related toxicity. Overall, this overview highlights CLU as an intriguing target that can affect biochemical events underlying PD pathology. Full article

Alzheimer’s disease (AD), a progressive neurodegenerative disorder, is marked by the pathological accumulation of amyloid-β plaques and tau neurofibrillary tangles, both of which disrupt neuronal communication and function. Emerging evidence highlights the role of extracellular vesicles (EVs) as key mediators of intercellular communication, particularly in the propagation of pathological proteins in AD. Among the regulatory factors influencing EV composition and function, neuraminidase 1 (NEU1), a lysosomal sialidase responsible for desialylating glycoproteins has gained attention for its involvement in EV glycosylation. This review explores the role of NEU1 in modulating EV glycosylation, with particular emphasis on its influence on immune modulation and intracellular trafficking pathways and the subsequent impact on intercellular signaling and neurodegenerative progression. Altered NEU1 activity has been associated with abnormal glycan profiles on EVs, which may facilitate the enhanced spread of amyloid-β and tau proteins across neural networks. By regulating glycosylation, NEU1 influences EV stability, targeting and uptake by recipient cells, primarily through the desialylation of surface glycoproteins and glycolipids, which alters the EV charge, recognition and receptor-mediated interactions. Targeting NEU1 offers a promising therapeutic avenue to restore EV homeostasis and reduces pathological protein dissemination. However, challenges persist in developing selective NEU1 inhibitors and effective delivery methods to the brain. Furthermore, altered EV glycosylation patterns may serve as potential biomarkers for early AD diagnosis and monitoring. Overall, this review highlights the importance of NEU1 in AD pathogenesis and advocates for deeper investigation into its regulatory functions, with the aim of advancing therapeutic strategies and biomarker development for AD and related neurological disabilities. Full article

This study investigates the occurrence and distribution of geomagnetic pulsations (Pc2–Pc5) over South America during 2014, analyzing their dependence on magnetic latitude, local time, and geomagnetic activity. Geomagnetic field data were obtained from the Embrace magnetometer network, which spans Brazil and Argentina and includes regions influenced by the Equatorial Electrojet (EEJ) and the South Atlantic Magnetic Anomaly (SAMA). Both continuous and discrete wavelet transforms (CWT and DWT) were employed to analyze non-stationary signals and reconstruct pulsation activity during quiet and disturbed geomagnetic periods. The results reveal that Pc5 and Pc3 pulsations exhibit a pronounced diurnal peak around local noon, with significantly stronger and more widespread activity under storm conditions. Spatial analyses highlight localized enhancements near the dip equator during quiet times and broader latitudinal spread during geomagnetic disturbances. These findings underscore the strong modulation of pulsation activity by geomagnetic conditions and offer new insights into wave behavior at low and mid-latitudes. This work contributes to understanding magnetosphere–ionosphere coupling and has implications for space weather prediction and geomagnetically induced current (GIC) risk assessment in the South American sector. Full article