Search Results (118)

Type 1 interferons (IFN-Is) exhibit significant antiviral, antitumor, and immunoregulatory properties, demonstrating substantial therapeutic potential. However, IFN-Is are pleiotropic cytokines, and the available data on their effect under specific pathological conditions are inconclusive. Furthermore, the systemic administration of IFN-Is can result in side effects. Generating cells that can migrate to the pathological focus and provide regulated local production of IFN-Is could overcome this limitation and provide a model for an in-depth analysis of the biological and therapeutic effects of IFN-Is. Induced pluripotent stem cells (iPSCs) are a valuable source of various differentiated cell types, including human immune cells. In this study, we describe the generation of genetically modified human iPSCs with doxycycline-controlled overexpression of interferon β (IFNB1). Three IFNB1-overexpressing iPSC lines (IFNB-iPSCs) and one control line expressing the transactivator M2rtTA (TA-iPSCs) were generated using the CRISPR/Cas9 technology. The pluripotency of the generated cell lines has been confirmed by the following: (i) cell morphology; (ii) the expression of the pluripotency markers OCT4, SOX2, TRA 1-60, and NANOG; and (iii) the ability to spontaneously differentiate into the derivatives of the three germ layers. Upon the addition of doxycycline, all IFNB-iPSCs upregulated IFNB1 expression at RNA (depending on the iPSC line, 126-816-fold) and protein levels. The IFNB-iPSCs and TA-iPSCs generated here represent a valuable cellular model for studying the effects of IFN-β on the activity and differentiation trajectories of different cell types, as well as for generating different types of cells with controllable IFN-β expression. Full article

E-waste, a form of solid waste, contains many recyclable metals, but improper disposal can make it very harmful. Therefore, the recycling of e-waste is very important, and the willingness of residents to participate is crucial in e-waste recycling. Taking Jiangsu Province, China as an example, we used the theory of reasoned action (TRA) to construct a research model to investigate the factors influencing residents’ willingness to participate in e-waste recycling. The paper introduces impression management motivation and further reveals the application of the Hawthorne effect in e-waste recycling. The paper also introduces the awareness of benefits, which encompasses personal economic benefits, physical health benefits, and environmental benefits, with physical health benefits being ignored by most of the previous literature. In addition, knowledge and convenience are also introduced in this paper. A total of 400 valid responses were used to test the hypotheses of the structural equation model. It was found that all factors positively influenced residents’ willingness to engage in e-waste recycling. Attitude has a mediating role in the effects of convenience, knowledge, and awareness of benefits on willingness, and subjective norms have a mediating role in the effects of impression management motivation on willingness. The model explains 82.9% of the variance in residents’ willingness to recycle e-waste, surpassing the original TRA model’s explanatory power and confirming the strength of the extended framework. The study provides valuable policy implications for the government to promote e-waste recycling. Full article

This study explores the application of spiking neural networks (SNNs) for click-through rate (CTR) prediction in personalized online advertising systems, introducing a novel hybrid model, the Temporal Rate Spike with Attention Neural Network (TRA–SNN). By leveraging the biological plausibility and energy efficiency of SNNs, combined with attention-based mechanisms, the TRA–SNN model captures temporal dynamics and rate-based patterns to achieve performance comparable to state-of-the-art Artificial Neural Network (ANN)-based models, such as Deep & Cross Network v2 (DCN-V2) and FinalMLP. The models were trained and evaluated on the Avazu and Digix datasets, using standard metrics like AUC-ROC and accuracy. Through rigorous hyperparameter tuning and standardized preprocessing, this study ensures fair comparisons across models, highlighting SNNs’ potential for scalable, sustainable deployment in resource-constrained environments like mobile devices and large-scale ad platforms. This work is the first to apply SNNs to CTR prediction, setting a new benchmark for energy-efficient predictive modeling and opening avenues for future research in hybrid SNN–ANN architectures across domains like finance, healthcare, and autonomous systems. Full article

The increasing number of natural and man-made disasters registered at the global level is causing a significant amount of damage. This represents one of the main sustainability challenges at the global level. The collapse of the Twin Towers, Hurricane Katrina, and the nuclear accident at the Fukushima power plant are some of the most representative disaster events that occurred at the beginning of the third millennium. These relevant disasters need an enhanced level of preparedness to reduce the gaps between the plan and its implementation. Among these actions, training and exercises play a relevant role because they increase the capability of planners, managers, and the people involved. By focusing on the exposure risk component, the general objective of the research is to obtain quantitative evaluations of the exercise’s contribution to risk reduction through evacuation. The paper aims to analyze serious games using a set of methods and models that simulate an urban risk reduction plan. In particular, the paper proposes a transparent framework that merges transport risk analysis (TRA) and transport system models (TSMs), developing serious game activities with the support of emerging information and communication technologies (e-ICT). Transparency is possible through the explicitation of reproducible analytical formulations and linked parameters. The core framework of serious games is constituted by a set of models that reproduce the effects of players’ choices, including planned actions of decisionmakers and travel users’ choices. The framework constitutes the prototype of a digital platform in a “non-stressful” context aimed at providing more insights about the effects of planned actions. The proposed framework is characterized by transparency, a feature that allows other analysts and planners to reproduce each risk scenario, by applying TRA and relative effects simulations in territorial contexts by means of TSMs and parameters updated by e-ICT. A basic experimentation is performed by using a game, presenting the main results of a prototype test based on a reproducible exercise. The prototype experiment demonstrates the efficacy of increasing preparedness levels and reducing exposure by designing and implementing a serious game. The paper’s methodology and results are useful for policymakers, emergency managers, and the community for increasing the preparedness level. Full article

This study explores the factors driving employees’ sustainability-switching behaviors (SSBs) by integrating the Push, Pull, and Mooring (PPM) model with the Theory of Reasoned Action (TRA). A quantitative, cross-sectional survey was conducted with a convenience sample of 132 professionals actively involved in organizational sustainability initiatives across diverse industries and global regions. The findings reveal that leadership commitment significantly fosters both affective and normative employee commitments, with normative commitment positively influencing SSB. Surprisingly, organizational trust showed a negative impact on SSB, suggesting that employees may delegate responsibility for sustainability to the organization when trust is high. Inertia emerged as a strong barrier to behavioral change, independently inhibiting sustainability efforts. The study highlights the complex dynamics among leadership, trust, and inertia, offering practical insights for organizations aiming to foster sustainability. Addressing inertia directly and promoting shared responsibility for sustainability are critical for successful organizational transitions. Future research should explore the psychological mechanisms behind inertia and further investigate the paradoxical role of trust in sustainability initiatives. Full article

The Quercus genus is found across a broad latitudinal range, and its spread in heterogeneous ecosystems is influenced by environmental, genetic, and anthropogenic factors. However, Mediterranean oak ecosystems, in particular, have been significantly impacted by climate-driven shifts. These shifts reshape the composition and spatial configuration of a great number of species. Here, this study evaluates the impact of climate change on the habitat suitability of Valonia oak (Quercus ithaburensis subsp. macrolepis (Kotschy) Hedge & Yalt.) and particularly focuses on understanding whether its population is native or was introduced to the Karagüney Mountains, Türkiye. Using ecological niche modeling with MaxEnt and climate data from CHELSA-TraCE21k (a 1 km climate time series), we built 120 models to analyze the habitat suitability of Valonia oak across different climatic periods from the Last Glacial Maximum (LGM) (21 ka BP) to the present. The results indicate that habitat suitability is primarily influenced by temperature- and precipitation-related variables. In fact, temperature fluctuations clearly affect the target species of this study. The most significant factors are the mean diurnal temperature range (bio2; 33.1%), precipitation in the wettest month (bio13; 19%), and mean annual temperature (bio1; 16.7%). Paleoclimatic predictions show that suitable habitats contracted during the early Holocene but expanded afterward, with current distributions aligning more closely with the natural range. In other words, it can be stated that Valonia oak’s habitat suitability has gradually improved from the LGM to the present, with both the total and natural ranges expanding over time. The results indicate that the species has demonstrated long-term stability, resilience, and adaptability to climate change, making it a potential alternative species for future climate scenarios. In addition, the data support the hypothesis that the species’ population in the Karagüney Mountains is relict, but was previously unrecognized as native. This study improves our knowledge about the distribution and environmental preferences of Valonia oak, which is important for underpinning its conservation strategies. Full article

Next point-of-interest (POI) recommendation aims to predict users’ future visitation intentions based on historical check-in trajectories. However, this task faces significant challenges, including coarse-grained user interest representation, insufficient social modeling, sparse check-in data, and the insufficient learning of contextual patterns. To address this, we propose a model that combines check-in trajectory information with user friendship relationships and uses a Transformer architecture for prediction (TraFriendFormer). Our approach begins with the construction of trajectory flow graphs using graph convolutional networks (GCNs) to globally capture POI correlations across both spatial and temporal dimensions. In parallel, we design an integrated social graph that combines explicit friendships with implicit interaction patterns, in which GraphSAGE aggregates neighborhood information to generate enriched user embeddings. Finally, we fuse the POI embeddings, user embeddings, timestamp embeddings, and category embeddings and input them into the Transformer architecture. Through the self-attention mechanism, the model captures the complex temporal relationships in the check-in sequence. We validate the effectiveness of TraFriendFormer on two real-world datasets (FourSquare and Gowalla). The experimental results show that TraFriendFormer achieves an average improvement of 10.3% to 37.2% in metrics such as Acc@k and MRR compared to the selected state-of-the-art baselines. Full article

Hierarchy analysis of the knowledge graphs aims to discover the latent structure inherent in knowledge base data. Drawing inspiration from topic modeling, which identifies latent themes and content patterns in text corpora, our research seeks to adapt these analytical frameworks to the hierarchical exploration of knowledge graphs. Specifically, we adopt a non-parametric probabilistic model, the nested hierarchical Dirichlet process, to the field of knowledge graphs. This model discovers latent subject-specific distributions along paths within the tree. Consequently, the global tree can be viewed as a collection of local subtrees for each subject, allowing us to represent subtrees for each subject and reveal cross-thematic topics. We assess the efficacy of this model in analyzing the topics and word distributions that form the hierarchical structure of complex knowledge graphs. We quantitatively evaluate our model using four common datasets: Freebase, Wikidata, DBpedia, and WebRED, demonstrating that it outperforms the latest neural hierarchical clustering techniques such as TraCo, SawETM, and HyperMiner. Additionally, we provide a qualitative assessment of the induced subtree for a single subject. Full article

The limited availability of primary normal human epidermal keratinocyte (NHEK) has hampered the large-scale implementation of skin models in biomedical, toxicological, and pharmaceutical research. Therefore, in this study, we aimed to establish an induced pluripotent stem cell (iPSC)-derived epidermal skin model that is not limited by donor type and cell lifespan, and evaluate whether it is equivalent to the primary NHEK-derived reconstructed epidermal skin model (RHE) for skin irritation testing. The results show that high expression of OCT4, SOX2, KLF4, c-MYC, and SSEA-4, TRA-1-60, TRA-1-81 indicated that iPSCs were successfully generated from human fibroblasts in vitro. The expression levels of ectoderm or KC marker genes CGB, IVL, KRT10, KRT14, TP63, and TBP were close to those of NHEKs. This result confirms that iPSCs were successfully differentiated into iPSC-KCs. The expression levels of iPSC-derived-RHE in FLG (60), AQP3 (151), CLDN1 (30.6), IVL (209), KRT5 (39.3), KRT10 (39.2), TSLP (99), IL-6 (53.1), IL-8 (79.4), and TNF-a (91.5) were significantly higher than those in RHE. These results indicate that iPSC-derived RHE has extremely strong vitality and renewal capacity. Meanwhile, there was no significant difference between iPSC-derived RHE and SkinEthic in predicting skin irritation, which means that our iPSC-derived RHE performed well in the test. iPSC-derived RHE can replace other skin models for skin irritation testing related to cosmetics. This technology has the potential to generate an unlimited number of genetically identical skin models and improve the reproducibility of experiments. Full article

The concept of the sustainable development of the world economy is currently aimed at achieving carbon neutrality, and this is due to the global warming of the planet. Energy and construction make a significant contribution to the release of carbon emissions into the environment and atmosphere. According to statistics, simply burning one ton of Portland cement clinker provokes the release of at least half a ton of carbon dioxide. In this study, the prepared samples were subjected to electron diffraction studies, as well as the X-ray phase analysis of the zone (XRF) using an ARLX’TRA diffractometer. Studies of macro- and microstructures were carried out using a Quanta 3D 200i scanning microscope. The obtained spectra were processed using EDAX TEAM software. The study of the microstructure of the samples showed that the bulk of the heterogeneous systems consisted of volumetric aggregates and intergrowths, i.e., small accumulations on their surfaces with pronounced cleavage, features of the microstructure indicating mineral formation processes. Therefore, the development of low-carbon construction models will make it possible to make a contribution and open an effective path to the implementation of climate policy through the rational use of natural resources and the involvement of industrial waste and nature-like technologies in the production process. In this regard, one of the options for solving the identified problems is to revise existing technologies and develop low-carbon, low-clinker binders using industrial waste and substandard raw materials. Full article

X-adrenoleukodystrophy (X-ALD) is a peroxisomal metabolic disorder caused by mutations in the ABCD1 gene encoding the peroxisomal ABC transporter adrenoleukodystrophy protein (ALDP). Similar mutations in ABCD1 may result in a spectrum of phenotypes in males with slow progressing adrenomyeloneuropathy (AMN) and fatal cerebral adrenoleukodystrophy (cALD) dominating most cases. Mouse models of X-ALD do not capture the phenotype differences and an appropriate model to investigate the mechanism of disease onset and progress remains a critical need. Here, we generated induced pluripotent stem cell (iPSC) lines from skin fibroblasts of two each of apparently healthy control, AMN, and cALD patients with non-integrating mRNA-based reprogramming. iPSC lines expanded normally and expressed pluripotency markers Oct4, SOX2, NANOG, SSEA, and TRA-1–60. Expression of markers SOX17, Brachyury, Desmin, OXT2, and beta tubulin III demonstrated the ability of the iPSCs to differentiate into all three germ layers. iPSC-derived lines from CTL, AMN, and cALD male patients were differentiated into astrocytes. Differentiated AMN and cALD astrocytes lacked ABCD1 expression and accumulated saturated very long chain fatty acids (VLCFAs), a hallmark of X-ALD, and demonstrated differential mitochondrial bioenergetics, cytokine gene expression, and differences in STAT3 and AMPK signaling between AMN and cALD astrocytes. These patient astrocytes provide disease-relevant tools to investigate the mechanism of differential neuroinflammatory response in X-ALD and will be valuable cell models for testing new therapeutics. Full article

This paper focuses on one of the instruments provided by the municipal sphere to solve problems in integrated territorial public policies, namely the Strategic Urban Development Plan (SUDP). Considering the starting question, “What lessons can we draw for the construction of governance models in the territorial development processes of historic city centres in low-density cities?”, the following objectives were defined: understanding territorial and multilevel governance; capturing the contribution of governance types to the SUDP in Trás-os-Montes, Bragança; analysing the case study through the anchor projects carried out; and understanding the involvement of municipal technical managers in its implementation. Full article

Understanding the behavior of carbon dioxide (CO₂) under varying thermodynamic conditions is essential for optimizing processes such as Carbon Capture and Storage (CCS) and supercritical fluid extraction. This study employs molecular dynamics (MD) simulations with the EPM2 and TraPPE-small force fields to examine CO₂ phase behavior, structural characteristics, and transport properties across a temperature range of 228–500 K and pressures from 1 to 150 atm. Our findings indicate a good agreement between simulated and experimental liquid–vapor coexistence curves, validating the capability of both force fields to model CO₂ accurately in a wide range of thermodynamical conditions. Radial distribution functions (RDFs) reveal distinct interaction patterns in liquid and supercritical phases, while mean squared displacement (MSD) analyses show diffusivity increasing from $5.2\times {10}^{-9}$ m²/s at 300 K to $1.8\times {10}^{-8}$ m²/s at 500 K. Additionally, response functions such as the heat capacity effectively capture phase transitions. These findings provide quantitative insights into CO₂ phase behavior and transport properties, enhancing the predictive reliability of simulations for CCS and related industrial technologies. This work bridges gaps in the CO₂ modeling literature and highlights the potential of MD simulations in advancing sustainable applications. Full article

Attention is one of many human cognitive functions that are essential in everyday life. Given our limited processing capacity, attention helps us focus only on what matters. Focusing attention on one speaker in an environment with many speakers is a critical ability of the human auditory system. This paper proposes a new end-to-end method based on the combined transformer and graph convolutional neural network (TraGCNN) that can effectively detect auditory attention from electroencephalograms (EEGs). This approach eliminates the need for manual feature extraction, which is often time-consuming and subjective. Here, the first EEG signals are converted to graphs. We then extract attention information from these graphs using spatial and temporal approaches. Finally, our models are trained with these data. Our model can detect auditory attention in both the spatial and temporal domains. Here, the EEG input is first processed by transformer layers to obtain a sequential representation of EEG based on attention onsets. Then, a family of graph convolutional layers is used to find the most active electrodes using the spatial position of electrodes. Finally, the corresponding EEG features of active electrodes are fed into the graph attention layers to detect auditory attention. The Fuglsang 2020 dataset is used in the experiments to train and test the proposed and baseline systems. The new TraGCNN approach, as compared with state-of-the-art attention classification methods from the literature, yields the highest performance in terms of accuracy (80.12%) as a classification metric. Additionally, the proposed model results in higher performance than our previously graph-based model for different lengths of EEG segments. The new TraGCNN approach is advantageous because attenuation detection is achieved from EEG signals of subjects without requiring speech stimuli, as is the case with conventional auditory attention detection methods. Furthermore, examining the proposed model for different lengths of EEG segments shows that the model is faster than our previous graph-based detection method in terms of computational complexity. The findings of this study have important implications for the understanding and assessment of auditory attention, which is crucial for many applications, such as brain–computer interface (BCI) systems, speech separation, and neuro-steered hearing aid development. Full article

Studying the factors affecting machining accuracy, surface quality, and machining efficiency in the powerful honing machining process system, analyzing the basic law between various errors and machining quality, exploring the method of evaluating the quality of honing, and improving the machining quality and transmission performance of hardened gears has important engineering application value. Firstly, this paper establishes an effective abrasive grains micro-edge honing quality evaluation model, proposes a method based on the Trapezoidal Fuzzy Analytic Hierarchy Process (Tra-FAHP) and Set Pair Analysis (SPA) to comprehensively evaluate the quality of the honing process, and obtains the influence weights of each factor on the quality of honing. Secondly, the paper analyzes the influence rules of three types of abrasive grain sizes on helix error, tooth pitch error, tooth profile error, surface roughness, and honing efficiency. Finally, the correctness of the established comprehensive evaluation model of honing quality was verified with the threshold method and weights. The research results show that the model can correctly evaluate the quality of hardened gear honing and can be applied to studying the influence of abrasive grain micro-edge honing on machining characteristics. Full article