Search Results (20,188)

Macroautophagy, commonly referred to as autophagy, is an essential cytoprotective mechanism that plays a significant role in cellular homeostasis. It has emerged as a promising target for drug development aimed at treating various cancers and infectious diseases. However, the scientific community has yet to reach a consensus on the most effective approach to manipulating autophagy, with ongoing debates about whether its inhibition or stimulation is preferable for managing these complex conditions. One critical factor contributing to the variability in treatment responses for both cancers and infectious diseases is estrogen, a hormone known for its diverse biological effects. Given the strong correlations observed between estrogen signaling and autophagy, this review seeks to summarize the intricate molecular mechanisms that underlie the dual cytoprotective effects of estrogen signaling in conjunction with autophagy. We highlight recent findings from studies that involve various ligands, disease contexts, and cell types, including immune cells. Furthermore, we discuss several factors that regulate autophagy in the context of estrogen’s influence. Ultimately, we propose a hypothetical model to elucidate the regulatory effects of the estrogen–autophagy axis on cell fate. Understanding these interactions is crucial for advancing our knowledge of related diseases and facilitating the development of innovative treatment strategies. Full article

Since multi-view learning leverages complementary information from multiple feature sets to improve model performance, a tensor-based data fusion layer for neural networks, called Multi-View Data Tensor Fusion (MV-DTF), is used. It fuses M feature spaces ${\mathcal{X}}_1,\cdots ,{\mathcal{X}}_M$, referred to as views, in a new latent tensor space, $\mathcal{S}$, of order P and dimension $J_1\times \cdots \times J_P$, defined in the space of affine mappings composed of a multilinear map $\mathcal{T}:{\mathcal{X}}_1\times \cdots \times {\mathcal{X}}_M\to \mathcal{S}$—represented as the Einstein product between a $(P+M)$-order tensor $\mathcal{A}$ anda rank-one tensor, $\mathcal{X}={\mathrm{x}}^{\left(1\right)}\otimes \cdots \otimes {\mathrm{x}}^{\left(M\right)}$, where ${\mathrm{x}}^{\left(m\right)}\in {\mathcal{X}}_m$ is the m-th view—and a translation. Unfortunately, as the number of views increases, the number of parameters that determine the MV-DTF layer grows exponentially, and consequently, so does its computational complexity. To address this issue, we enforce low-rank constraints on certain subtensors of tensor $\mathcal{A}$ using canonical polyadic decomposition, from which M other tensors ${\mathcal{U}}^{\left(1\right)},\cdots ,{\mathcal{U}}^{\left(M\right)}$, called here Hadamard factor tensors, are obtained. We found that the Einstein product $\mathcal{A}{⊛}_M\mathcal{X}$ can be approximated using a sum of R Hadamard products of M Einstein products encoded as ${\mathcal{U}}^{\left(m\right)}{⊛}_1{\mathrm{x}}^{\left(m\right)}$, where R is related to the decomposition rank of subtensors of $\mathcal{A}$. For this relationship, the lower the rank values, the more computationally efficient the approximation. To the best of our knowledge, this relationship has not previously been reported in the literature. As a case study, we present a multitask model of vehicle traffic surveillance for occlusion detection and vehicle-size classification tasks, with a low-rank MV-DTF layer, achieving up to $92.81\%$ and $95.10\%$ in the normalized weighted Matthews correlation coefficient metric in individual tasks, representing a significant $6\%$ and $7\%$ improvement compared to the single-task single-view models. Full article

Microglia, the brain immune cells, support neurons by producing several established neurotrophic molecules including glial cell line-derived neurotrophic factor (GDNF) and brain-derived neurotrophic factor (BDNF). Modern analytical techniques have identified numerous phenotypic states of microglia, each associated with the secretion of a diverse set of substances, which likely include not only canonical neurotrophic factors but also other less-studied molecules that can interact with neurons and provide trophic support. In this review, we consider the following eight such candidate cytokines: oncostatin M (OSM), leukemia inhibitory factor (LIF), activin A, colony-stimulating factor (CSF)-1, interleukin (IL)-34, growth/differentiation factor (GDF)-15, fibroblast growth factor (FGF)-2, and insulin-like growth factor (IGF)-2. The available literature provides sufficient evidence demonstrating murine cells produce these cytokines and that they exhibit neurotrophic activity in at least one neuronal model. Several distinct types of neurotrophic activity are identified that only partially overlap among the cytokines considered, reflecting either their distinct intrinsic properties or lack of comprehensive studies covering the full spectrum of neurotrophic effects. The scarcity of human-specific studies is another significant knowledge gap revealed by this review. Further studies on these potential microglia-derived neurotrophic factors are warranted since they may be used as targeted treatments for diverse neurological disorders. Full article

Commonsense question answering (CSQA) is a challenging task in the field of knowledge graph question answering. It combines the context of the question with the relevant knowledge in the knowledge graph to reason and give an answer to the question. Existing CSQA models combine pretrained language models and graph neural networks to process question context and knowledge graph information, respectively, and obtain each other’s information during the reasoning process to improve the accuracy of reasoning. However, the existing models do not fully utilize the textual representation and graph representation after reasoning to reason about the answer, and they do not give enough semantic representation to the edges during the reasoning process of the knowledge graph. Therefore, we propose a novel parallel fusion framework for text and knowledge graphs, using the fused global graph information to enhance the semantic information of reasoning answers. In addition, we enhance the relationship embedding by enriching the initial semantics and adjusting the initial weight distribution, thereby improving the reasoning ability of the graph neural network. We conducted experiments on two public datasets, CommonsenseQA and OpenBookQA, and found that our model is competitive when compared with other baseline models. Additionally, we validated the generalizability of our model on the MedQA-USMLE dataset. Full article

Neuro-symbolic models combine deep learning and symbolic reasoning to produce better-performing hybrids. Not only do neuro-symbolic models perform better, but they also deal better with data scarcity, enable the direct incorporation of high-level domain knowledge, and are more explainable. However, these benefits come at the cost of increased complexity, which may deter the uninitiated from using these models. In this work, we present a framework to simplify the creation of neuro-symbolic models for tree crown delineation and tree species classification via the use of object-oriented programming and hyperparameter tuning algorithms. We show that models created using our framework outperform their non-neuro-symbolic counterparts by as much as two F1 points for crown delineation and three F1 points for species classification. Furthermore, our use of hyperparameter tuning algorithms allows users to experiment with multiple formulations of domain knowledge without the burden of manual tuning. Full article

Based on their ability to efficiently and intuitively represent real-world relationships and structures, graph databases are gaining increasing popularity. In this context, this paper proposes an innovative integration of a Large Language Model into NoSQL databases and Knowledge Graphs to bridge the gap in field of Text-to-Cypher queries, focusing on Neo4j. Using the Design Science Research Methodology, we developed a Natural Language Interface which can receive user queries in real time, convert them into Cypher Query Language (CQL), and perform targeted queries, allowing users to choose from different graph databases. In addition, the user interaction is expanded by an additional chat function based on the chat history, as well as an error correction module, which elevates the precision of the generated Cypher statements. Our findings show that the chatbot is able to accurately and efficiently solve the tasks of database selection, chat history referencing, and CQL query generation. The developed system therefore makes an important contribution to enhanced interaction with graph databases, and provides a basis for the integration of further and multiple database technologies and LLMs, due to its modular pipeline architecture. Full article

Is learning more knowledge always better for vision-and-language models? In this paper, we study knowledge transferability in multi-modal tasks. The current tendency in machine learning is to assume that by joining multiple datasets from different tasks, their overall performance improves. However, we show that not all knowledge transfers well or has a positive impact on related tasks, even when they share a common goal. We conducted an exhaustive analysis based on hundreds of cross-experiments on twelve vision-and-language tasks categorized into four groups. While tasks in the same group are prone to improve each other, results show that this is not always the case. In addition, other factors, such as dataset size or the pre-training stage, may have a great impact on how well the knowledge is transferred. Full article

Fractured-porous carbonate reservoirs, mainly composed of dolomites and crystalline rocks with various rock types and extremely poor initial porosity and permeability, are dominated by tectonic fractures and exhibit extreme heterogeneity. The fracture system plays a predominant role in hydrocarbon fluid transport. Compared with conventional sandstone reservoirs, fracture geometry and topological structure parameters are key factors for the accuracy and computational efficiency of numerical simulation history matching in fractured reservoirs. To address the matching issue, this paper introduces an artificial intelligence history matching method combining the Monte Carlo experimental planning method with an artificial neural network and a particle swarm optimization algorithm. Taking reservoir geological parameters and phase infiltration properties as the objective function, this method performs reservoir production history matching to correct the geological model. Through case studies, it is verified that this method can accurately correct the geological model of fractured-porous reservoirs and match the observed production data. This research represents a collaborative effort among multiple disciplines, integrating advanced algorithms and geological knowledge with the expertise of computer scientists, geologists, and engineers. Currently the world’s major oilfields history fitting is mainly based on reservoir engineers’ experience to fit; the method is applicable to major oilfields, but the fitting accuracy and fitting efficiency is severely limited, the fitting accuracy is less than 75%, while the artificial intelligence history fitting method shows a stronger applicability; intelligent history fitting is mainly based on the integrity of the field data, and as far as the theory is concerned, the accuracy of the intelligent history fitting can be up to 100%. Therefore, AI history fitting can provide a significant foundation for mine field research. Future research could further explore interdisciplinary collaboration to address other challenges in reservoir characterization and management. Full article

Modeling peatland hydraulic processes in cold regions requires defining near-surface hydraulic parameters. The current study aims to determine the soil freezing and water characteristic curve parameters for organic soils from peatland-dominated permafrost mires. The three research objectives are as follows: (i) Setting up an in situ soil freezing characteristic curve experiment by installing sensors for measuring volumetric water content and temperature in Storflaket mire, Abisko region, Sweden; (ii) Conducting laboratory evaporation experiments and inverse numerical modeling to determine soil water characteristic curve parameters and comparing three soil water characteristic curve models to the laboratory data; (iii) Deriving a relationship between soil freezing and water characteristic curves and optimizing this equation with sensor data from (i). A long-lasting in situ volumetric water content station has been successfully set up in sub-Arctic Sweden. The soil water characteristic curve experiments showed that bimodality also exists for the investigated peat soils. The optimization results of the bimodal relationship showed excellent agreement with the soil freezing cycle measurements. To the best of our knowledge, this is one of the first studies to establish and test bimodality for frozen peat soils. The estimated hydraulic parameters could be used to better simulate permafrost dynamics in peat soils. Full article

Event causality identification (ECI) focuses on detecting causal relationships between events within a document. Existing approaches typically treat each event-mention pair independently, overlooking the relational dynamics and potential conflicts among event causalities. To tackle this challenge, we propose the Event-induced Graph with Constraints (EiGC), which models the complex event-level causal structures in a more realistic manner, facilitating comprehensive causal relation identification. To be more specific, we construct a graph based on diverse event-driven knowledge sources, such as coreference and co-occurrence relations. A graph convolutional network (GCN) is then employed to encode these structural features, effectively capturing both local and global dependencies between nodes. Additionally, we implement event-aware constraints through integer linear programming, incorporating the principles of uniqueness, non-reflexivity, and coreference consistency in event-causal relationships. This approach ensures logical consistency and prevents conflicts in the prediction outcomes. Experimental results on three widely used datasets illustrate that our proposed EiGC approach achieves excellent performance among all the baseline models. Full article

Organisations need dynamic capabilities in the ongoing digital transformation to reconfigure knowledge and learning. There is a need to define new concepts and explain mechanisms of relevant factors to build dynamic capabilities. Organisations acting in healthcare experience a dilemmatic situation. New digital processes and business models are promising benefits for cost-containment measures, improved patient-centric care, and digital services. However, investments are needed to benefit. The critical question is the following: How can individual actors in healthcare be motivated to engage in this transformational process to build and reconfigure relevant competences and establish new learning routines? Founded on the essence of the existing literature, we assume sustained learning to be a relevant dynamic capability to seize and sense competences and reconfigure human capital. This paper answers the call for deeper investigations into the mechanisms in new digitally transformed environments and sectors focussing less on performance and competitive advantages, like public administration or the healthcare sector. Based on previous research, validated in qualitative interviews and quantitative testing, we define the new construct of sustained learning with its subdimensions. By providing measures, we build the grounds for further quantitative research. Full article

This article presents the issue of sustainably managing of the technical maintenance of multi-family residential buildings with diverse characteristics. The results of this research conducted on entities managing real estate in a real estate market in the city of Poznan (0.5 million inhabitants) are presented along with comments, with attention drawn to the commonness of the approaches used in building maintenance management. Of particular note is the reactive approach (which, in extreme cases, turns out to be unreliable and does not guarantee the absence of restrictions in the possibility of using housing resources) and the practical lack of application of the proactive-predictive approach. The main features of the approaches used in management are presented, and selected examples are used to demonstrate the advantage of proactive approaches in the economic, social, and environmental aspects. This article presents arguments for the implementation of proactive management strategies, including predictive maintenance. Due to financial and technical limitations, our model is presented in support of the technical management of buildings with predictive elements. The model is based on case-based reasoning (CBR). An important element of the model is the knowledge acquisition system, which uses mandatory technical condition inspections resulting from the applicable law and inspection protocols created as a result. The proposed model is not as expensive as using full predictive maintenance using sensor infrastructure devices, and at the same time allows advanced reasoning and solves the practical problems of maintaining housing resources. Full article

A low-carbon society aims to achieve sustainable social development through innovative technologies and mechanisms, promoting low-carbon economic models and lifestyles. In light of China’s commitment to achieving carbon neutrality and transitioning to a low-carbon society, it is crucial to control waste generation at its source, as the waste management sector is highly polluting and contributes substantially to carbon emissions. Adopting the 3R (reduce, reuse, and recycle) approach, reducing the quantity of waste is the priority in waste management. Therefore, exploring rural residents’ willingness to adopt the “pay as you throw” (PAYT) policy in underdeveloped mountainous areas and the factors influencing this willingness is highly valuable. This paper adopts the Contingent Valuation Method (CVM) with a face-to-face questionnaire survey, involving 1429 farmers from six cities around the underdeveloped mountainous area in Northwestern China. It measures their willingness to pay (WTP) and preferred payment levels for the PAYT policy. Based on the theory of planned behavior, the paper finds that farmers’ environmental knowledge, environmental awareness and social trust positively influence their WTP, while farmers’ perception of pollution and daily waste disposal do not significantly impact their WTP. Additionally, social trust negatively moderates the relationship between environmental knowledge and WTP. This paper provides empirical results that can support the implementation of a nationwide waste fee management system and the promotion of volume-based waste fee management. It also offers targeted suggestions for the government to establish PAYT and improve the efficiency of rural household waste management in rural China. Full article

Acquiring knowledge of aircraft flight dynamics is crucial for simulation, control, mission performance and safety assurance analysis. In the fast-paced UAV market, long flight testing campaigns are hard to achieve, leaving limited controlled flight data and a significant amount of unplanned flight data. This work delves into the application of system identification techniques on unplanned flight data when faced with a shortage of dedicated flight test data. Based on a medium-sized, fixed-wing UAV, it focuses on the system identification of longitudinal dynamics using structural routine flight test data of pitch down and pitch up manoeuvres with no specific guidelines for the control inputs given. The proposed solution uses first- and second-order parameter-based models to build a non-linear dynamic model which, using a least square error optimisation algorithm in a time domain formulation, has its parameters tuned to converge the model behaviour with the real aircraft dynamics. The optimisation uses a combination of pitch, altitude, airspeed and pitch rate responses as a measure of model accuracy. Very significant improvements regarding the UAV model response are found when trimmed flight manoeuvres are used, resulting in proper estimation of important aerodynamic and control derivatives. Pitching moment and control derivatives are shown to be the crucial parameters. However, difficulties in estimation are shown for untrimmed flight manoeuvres. Better results were obtained when using multiple manoeuvres simultaneously in the optimisation error metric, as opposed to single manoeuvres that led to system bias. The proposed system identification procedure can be applied to any fixed-wing UAV without the need for specific flight testing campaigns. Full article

Artificial intelligence (AI), specifically Generative Pre-trained Transformer (GPT) technology, has revolutionized various fields, including medicine and dentistry. The AI model ChatGPT, developed by OpenAI, mimics human language on a large scale, generating coherent and contextually appropriate responses. ChatGPT serves as an auxiliary resource for diagnosis and decision-making across various medical disciplines. This comprehensive narrative review aims to explore how ChatGPT can assist the dental sector, highlighting its potential to enhance various aspects of the discipline. This review includes a literature search on the application of ChatGPT in dentistry, with a focus on the differences between the free version, ChatGPT 3.5, and the more advanced subscription-based version, ChatGPT 4. Specifically, ChatGPT has proven to be effective in enhancing user interaction, providing fast and accurate information and improving the accessibility of knowledge. However, despite these advantages, several limitations are identified, including concerns regarding the accuracy of responses in complex scenarios, ethical considerations surrounding its use, and the need for improved training to handle highly specialized queries. In conclusion, while ChatGPT offers numerous benefits in terms of efficiency and scalability, further research and development are needed to address these limitations, particularly in areas requiring greater precision, ethical oversight, and specialized expertise. Full article