Search Results (1,261)

Oxidative stress is a defining and pervasive driver of neurodegenerative diseases, including Alzheimer’s disease (AD), Parkinson’s disease (PD), and amyotrophic lateral sclerosis (ALS). As a molecular accelerant, reactive oxygen species (ROS) and reactive nitrogen species (RNS) compromise mitochondrial function, amplify lipid peroxidation, induce protein misfolding, and promote chronic neuroinflammation, creating a positive feedback loop of neuronal damage and cognitive decline. Despite its centrality in promoting disease progression, attempts to neutralize oxidative stress with monotherapeutic antioxidants have largely failed owing to the multifactorial redox imbalance affecting each patient and their corresponding variation. We are now at the threshold of precision redox medicine, driven by advances in syndromic multi-omics integration, Artificial Intelligence biomarker identification, and the precision of patient-specific therapeutic interventions. This paper will aim to reveal a mechanistically deep assessment of oxidative stress and its contribution to diseases of neurodegeneration, with an emphasis on oxidatively modified proteins (e.g., carbonylated tau, nitrated α-synuclein), lipid peroxidation biomarkers (F2-isoprostanes, 4-HNE), and DNA damage (8-OHdG) as significant biomarkers of disease progression. We will critically examine the majority of clinical trial studies investigating mitochondria-targeted antioxidants (e.g., MitoQ, SS-31), Nrf2 activators (e.g., dimethyl fumarate, sulforaphane), and epigenetic reprogramming schemes aiming to re-establish antioxidant defenses and repair redox damage at the molecular level of biology. Emerging solutions that involve nanoparticles (e.g., antioxidant delivery systems) and CRISPR (e.g., correction of mutations in SOD1 and GPx1) have the potential to transform therapeutic approaches to treatment for these diseases by cutting the time required to realize meaningful impacts and meaningful treatment. This paper will argue that with the connection between molecular biology and progress in clinical hyperbole, dynamic multi-targeted interventions will define the treatment of neurodegenerative diseases in the transition from disease amelioration to disease modification or perhaps reversal. With these innovations at our doorstep, the future offers remarkable possibilities in translating network-based biomarker discovery, AI-powered patient stratification, and adaptive combination therapies into individualized/long-lasting neuroprotection. The question is no longer if we will neutralize oxidative stress; it is how likely we will achieve success in the new frontier of neurodegenerative disease therapies. Full article

The abandoned goaf resulting from coal resource integration in China poses a significant threat to coal mine safety. The transient electromagnetic method (TEM) has emerged as a crucial technology for detecting goafs in coal mines due to its adaptable equipment and efficient implementation. In recent years, small-loop TEM has demonstrated high resolution and adaptability in challenging terrains with vegetation, such as coal mine ponding areas, karst regions, and reservoir seepage scenarios. By considering the sedimentary characteristics of coal seams and addressing the resistivity changes encountered in single-point inversion, a joint optimization inversion process incorporating lateral weighting factors and vertical roughness constraints has been developed to enhance the connectivity between adjacent survey points and improve the continuity of inversion outcomes. Through an OCCAM inversion approach, the regularization factor is dynamically determined by evaluating the norms of the data objective function and model objective function in each iteration, thereby reducing the reliance of inversion results on the initial model. Using the Xiaolong Coal Mine as a geological context, the impact of lateral and vertical weighting factors on the inversion outcomes of high- and low-resistivity structural models is examined through a control variable method. The analysis reveals that optimal inversion results are achieved with a combination of a lateral weighting factor of 0.5 and a vertical weighting factor of 0.1, ensuring both result continuity and accurate depiction of vertical and lateral electrical interfaces. The practical application of this approach validates its effectiveness, offering theoretical support and technical assurance for old goaf detection in coal mines, thereby holding significant engineering value. Full article

With the accelerating globalization and rapid development of science and technology, scientific collaboration has become a key driver of knowledge production, yet its patterns vary significantly across disciplines. This study aims to explore the disciplinary differences in scholars’ scientific collaboration patterns and their underlying mechanisms. Data were collected from the China National Knowledge Infrastructure (CNKI) database, covering papers from four disciplines: mathematics, mechanical engineering, philosophy, and sociology. Using social network analysis, we examined core network metrics (degree centrality, neighbor connectivity, clustering coefficient) in collaboration networks, analyzed collaboration patterns across scholars of different academic ages, and compared the academic age distribution of collaborators and network characteristics across career stages. Key findings include the following. (1) Mechanical engineering exhibits the highest and most stable clustering coefficient (mean 0.62) across all academic ages, reflecting tight team collaboration, with degree centrality increasing fastest with academic age (3.2 times higher for senior vs. beginner scholars), driven by its reliance on experimental resources and skill division. (2) Philosophy shows high degree centrality in early career stages (mean 0.38 for beginners) but a sharp decline in clustering coefficient in senior stages (from 0.42 to 0.17), indicating broad early collaboration but loose later ties due to individualized knowledge production. (3) Mathematics scholars prefer collaborating with high-centrality peers (higher neighbor connectivity, mean 0.51), while sociology shows more inclusive collaboration with dispersed partner centrality. Full article

We investigated the dispersal ability of Euphydryas aurinia provincialis in a local-scale analysis within a single habitat patch of the Colfiorito highlands metapopulation. Our findings indicate that inside a single node, the organization of nesting patches can be conceptualized as a metapopulation itself, where reproductive sites, despite their spatial proximity, can act as either source or sink habitats depending on environmental conditions. We conducted fieldwork in six nesting patches inside a single node, capturing, marking, and recapturing individuals to assess their spatial distribution and movement tendencies at a large landscape scale. We found a high degree of site fidelity among individuals, with many recaptures occurring within the original marking site, but also a sex-based difference in movement patterns; females dispersed farther than males, likely driven by reproductive strategies, while males remained more localized, prioritizing mate-searching. Our findings suggest a complex dynamic in habitat connectivity: pastures and abandoned fields, despite being open, seem to act like sink areas, while breeding sites with shrub and tree cover act as source habitats, offering optimal conditions for reproduction. Individuals, especially females, from these source areas were later compelled to disperse into open habitats, highlighting a nuanced interaction between landscape structure and population dynamics. These results highlight the importance of maintaining habitat corridors to support metapopulation dynamics and prevent genetic isolation; the abandonment of traditional grazing practices is leading to the rapid closure of these source habitats, posing a severe risk of local extinction. Conservation efforts should prioritize the preservation of these source habitats to ensure the long-term viability of E. a. provincialis populations in fragmented landscapes. Full article

The sedimentary characteristics and model of the shallow-water delta front are of great significance for the development of oil and gas reservoirs. At present, there are great differences in the understanding of the distribution patterns of estuary dams in the shallow-water delta front. Therefore, this paper reveals the distribution characteristics of estuary dams through the detailed dissection of the Qing 1 Member in the Daqingzijing area and establishes a completely new distribution pattern of estuary dams. By using geological data such as logging and core measurements, sedimentary microfacies at the shallow-water delta front are classified and logging facies identification charts for each sedimentary microfacies are developed. Based on the analysis of single-well and profile facies, the sedimentary evolution laws of the Qing 1 Member reservoirs are analyzed. On this basis, the sedimentary characteristics and model of the lacustrine shallow-water delta front are established. The results indicate that the Qing 1 Member in the Daqingzijing area exhibits a transitional sequence from a delta front to pro-delta facies and finally to deep lacustrine facies, with sediments continuously retrograding upward. Subaqueous distributary channels and estuary dams constitute the skeletal sand bodies of the retrogradational shallow-water delta. The estuary dam sand bodies are distributed on both sides of the subaqueous distributary channels, with sand body development gradually decreasing in scale from bottom to top. These bodies are intermittently distributed, overlapping, and laterally connected in plan view, challenging the conventional understanding that estuary dams only occur at the bifurcation points of underwater distributary channels. Establishing the sedimentary characteristics and model of the shallow-water delta front is of great significance for the exploration and development of reservoirs with similar sedimentary settings. Full article

Research on helicopter stability is essential for the design of flotation systems and serves as a primary basis for evaluating wind and wave resistance. The drainage volume method and fluid–solid coupling method are commonly used for calculating floating characteristics. However, the drainage volume method ignores the flexibility of airbags and their interaction with the helicopter, while the fluid–solid coupling method is computationally intensive. In contrast, the analysis of a helicopter’s hydrostatic floating characteristics is a static problem. It suffices to obtain relevant results when the helicopter reaches a stationary state, without the need to accurately simulate the dynamic process of achieving that state. Therefore, this paper proposes an equivalent calculation method, in which the hydrostatic effect of water on the helicopter is represented by the hydrostatic pressure applied across the entire flotation system. The finite element method (FEM) is then employed to determine the final static state, and the results are compared with those from the drainage volume method and available experimental data to validate the reliability of the proposed approach. To elucidate the influence mechanism of airbags and flexible connecting straps on the lateral static stability of helicopters, this paper analyzes airbag positions at various heeling angles and examines the impact of different internal airbag pressures. The results indicate that the main factor affecting lateral static stability is the displacement of the airbags. This displacement causes variations in the airbag’s buoyancy and center of buoyancy, thereby reducing the lateral heeling moment. Full article

Hydraulic fracturing is a crucial technology for developing unconventional oil and gas resources. However, conventional geophysical methods struggle to efficiently and accurately image proppant-connected channels created by hydraulic fracturing. The borehole-to-surface electromagnetic imaging method (BSEM) overcomes this limitation by utilizing a controlled cased well source. Placing the source close to the target reservoir and deploying multi-component receivers on the surface enable high-precision lateral monitoring, providing an effective approach for dynamic monitoring of hydraulic fracturing operations. This study focuses on key aspects of forward modeling for BSEM. A three-dimensional finite-volume method based on the Yee grid was used to simulate the borehole-to-surface electromagnetic system incorporating metal casings, validating the method of simulating metal casing using multiple line sources. The simulation of the observation system and the frequency-domain electromagnetic monitoring simulation based on actual well data confirm BSEM’s high sensitivity for monitoring deep subsurface formations. Critically, well casing exerts a substantial influence on surface electromagnetic responses, while the electromagnetic contribution from line sources emulating perforation zones necessitates explicit incorporation within data processing workflows. Full article

Renewable energy sources (RES) are the foundation of the ongoing energy transition in Poland and worldwide. However, increased use of RES has brought several challenges, as most of these sources are dependent on weather conditions. The instability and lack of control over electricity production lead to both overloads and power shortages in transmission and distribution networks. A significant advantage of biogas plants over sources such as photovoltaics or wind turbines is their ability to control electricity generation and align it with actual demand. Biogas produced during fermentation can be temporarily stored in a biogas tank above the digester and later used in an enlarged CHP unit to generate electricity and heat during peak demand periods. While demand-driven biogas plants operate similarly to traditional installations, their development requires navigating regulatory and administrative procedures, particularly those related to the grid connection of the generated electricity. In Poland, it has only recently become possible to obtain grid connection conditions for such installations, following the adoption of the Act of 28 July 2023, which amended the Energy Law and certain other acts. However, the biogas sector still faces challenges, particularly the need for effective incentive mechanisms and the removal of regulatory and economic barriers, especially given its estimated potential of up to 7.4 GW. Full article

Based on the need to enhance the seismic performance of point-supported steel frame precast cladding panel systems, this study proposes a novel friction-energy-dissipating connection joint. Through establishing refined finite element models, low-cycle reversed loading analyses and elastoplastic time-history analyses were conducted on three frame systems. These included a benchmark bare frame and two cladding-panel-equipped frame structures configured with energy-dissipating joints using different specifications of high-strength bolts (M14 and M20, respectively). The low-cycle reversed loading results demonstrate that the friction energy dissipation of the novel joints significantly improved the seismic performance of the frame structures. Compared to the bare frame, the frames equipped with cladding panels using M14 bolts demonstrated 10.9% higher peak lateral load capacity, 17.6% greater lateral stiffness, and 45.6% increased cumulative energy dissipation, while those with M20 bolts showed more substantial improvements of 22.8% in peak load capacity, 32.0% in lateral stiffness, and 64.2% in cumulative energy dissipation. The elastoplastic time-history analysis results indicate that under seismic excitation, the maximum inter-story drift ratios of the panel-equipped frames with M14 and M20 bolts were reduced by 42.7% and 53%, respectively, compared to the bare frame. Simultaneously, the equivalent plastic strain in the primary structural members significantly decreased. Finally, based on the mechanical equilibrium conditions, a calculation formula was derived to quantify the contribution of joint friction to the horizontal load-carrying capacity of the frame. Full article

Aside from a few passages addressing animals or the environment, Confucian philosophy appears to lack an environmental ethics perspective. Li Zhehou’s (李澤厚) contemporary work in Confucian philosophy continues this lacuna by limiting his understanding of community to the human realm. Using the common liberal humanism that limits moral actions to the interpersonal human realm misses the importance of inclusive moralities such as animal rights and environmental ethics. I propose that if we return to the original shared common cultural roots of Confucian and Daoist philosophy that a Confucian understanding of the natural world can embrace the non-human environment within the scope of Confucian morality. Extricating ideas from the Yijing, the Shijing, Xunzi, Dong Zhongshu, Wang Chong, and later scholars, the concept of the mutual resonance and response (ganying 感應) between the natural world and humans developed into the unity of heaven and humanity (tianren heyi 天人合一). An inclusive Confucian depth ecology opens new ways of thinking that can be deployed to envision deeper dimensions for understanding the self’s inner life, its connections to the outer life of the self–other relationship, and its extension to a kin relationship with the environment. This paper explores how these old and new ways of thinking can change our behavior and change our moral interactions with others including the environment and thereby enhancing freedom as an achievement concept derived from graceful moral action. Full article

This study explores key factors influencing Aging-in-Place Attachment (AiPA) among older adults in Macau’s high-density community spaces, emphasizing interactions between the built environment, behavior, and psychology. A multidimensional framework evaluates environmental, behavioral, human-factor, and psychological contributions. A mixed-methods, multisource approach was employed. This study measured spatial characteristics of nine public spaces, conducted systematic behavioral observations, and collected questionnaire data on place attachment and aging intentions. Eye-tracking and galvanic skin response (GSR) captured visual attention and emotional arousal. Hierarchical regression analysis tested the explanatory power of each variable group, supplemented by semi-structured interviews for qualitative depth. The results showed that the physical environment had a limited direct impact but served as a critical foundation. Behavioral variables increased explanatory power (~15%), emphasizing community engagement. Human-factor data added ~4%, indicating that sensory and habitual interactions strengthen bonds. Psychological factors contributed most (~59%), confirming AiPA as a multidimensional construct shaped primarily by emotional and social connections, supported by physical and behavioral contexts. In Macau’s dense urban context, older adults’ desire to age in place is mainly driven by emotional connection and social participation, with spatial design serving as an enabler. Effective age-friendly strategies must extend beyond infrastructure upgrades to cultivate belonging and interaction. This study advances environmental gerontology and architecture theory by explaining the mechanisms of attachment in later life. Future work should explore how physical spaces foster psychological well-being and examine emerging factors such as digital and intergenerational engagement. Full article

Accurate prediction of Air Quality Index (AQI) concentrations remains a critical challenge in environmental monitoring and public health management due to the complex nonlinear relationships among multiple atmospheric factors. To address this challenge, we propose a novel prediction model that integrates an adaptive-weight particle swarm optimization (AWPSO) algorithm with a back propagation neural network (BPNN). First, the random forest (RF) algorithm is used to scree the influencing factors of AQI concentration. Second, the inertia weights and learning factors of the standard PSO are improved to ensure the global search ability exhibited by the algorithm in the early stage and the ability to rapidly obtain the optimal solution in the later stage; we also introduce an adaptive variation algorithm in the particle search process to prevent the particles from being caught in local optima. Finally, the BPNN is optimized using the AWPSO algorithm, and the final values of the optimized particle iterations serve as the connection weights and thresholds of the BPNN. The experimental results show that the RFAWPSO-BP model reduces the root mean square error and mean absolute error by 9.17 μg/m³, 5.7 μg/m³, 2.66 μg/m³; and 9.12 μg/m³, 5.7 μg/m³, 2.68 μg/m³ compared with the BP, PSO-BP, and AWPSO-BP models, respectively; furthermore, the goodness of fit of the proposed model was 14.8%, 6.1%, and 2.3% higher than that of the aforementioned models, respectively, demonstrating good prediction accuracy. Full article

Background/Objectives: Early and accurate prediction of stroke severity is crucial for optimizing guided therapeutic decisions and improving outcomes. This study investigates the predictive value of lesion size and functional connectivity for neurological deficits, assessed by the National Institutes of Health Stroke Scale (NIHSS score), in patients with acute or subacute subcortical ischemic stroke. Methods: Forty-four patients (mean age: 68.11 years, 23 male, and admission NIHSS score 4.30 points) underwent high-resolution anatomical and resting-state functional Magnetic Resonance Imaging (rs-fMRI) within seven days of stroke onset. Lesion size was volumetrically quantified, while functional connectivity within the motor, default mode, and frontoparietal networks was analyzed using seed-based correlation methods. Multiple linear regression and cross-validation were applied to develop predictive models for stroke severity. Results: Our results showed that lesion size explained 48% of the variance in NIHSS scores (R² = 0.48, cross-validated R² = 0.49). Functional connectivity metrics alone were less predictive but enhanced model performance when combined with lesion size (achieving an R² = 0.71, cross-validated R² = 0.73). Additionally, left hemisphere connectivity features were particularly informative, as models based on left-hemispheric connectivity outperformed those using right-hemispheric or bilateral predictors. This suggests that the inclusion of contralateral hemisphere data did not enhance, and in some configurations, slightly reduced, model performance—potentially due to lateralized functional organization and lesion distribution in our cohort. Conclusions: The findings highlight lesion size as a reliable early marker of stroke severity and underscore the complementary value of functional connectivity analysis. Integrating rs-fMRI into clinical stroke imaging protocols offers a potential approach for refining prognostic models. Future research efforts should prioritize establishing this approach in larger cohorts and analyzing additional biomarkers to improve predictive models, advancing personalized therapeutic strategies for stroke management. Full article

The spatial orientation of mammals and birds has been intensively studied for many years, but the cognitive mechanism of spatial orientation and memory used by squamates remains poorly understood. Our study evaluated the learning and memory abilities of leopard geckos (Eublepharis macularius) in an adapted Morris water maze. The animals learned during the training phase consisted of 20 trials. To assess long-term memory, we retested geckos twice after several months. The geckos remembered the learned information in a short re-test after two months, but after four months, they required retraining to find the platform. We hypothesise that the duration of memory corresponds with short-term changes in semi-desert environments within one season, while disruption of memory performance after a six-month gap may simulate the more extensive seasonal change in spatial relationships in their natural environment. Moreover, during the winter period, geckos exhibit low activity, which can be connected with decreased frequency of foraging trips. Therefore, the memory loss after four months may reflect the low level of memory jogging. The motivation during the experiment was the crucial parameter of learning and memory processes. In later phases, geckos were less motivated to perform the task. Finally, they relearned the spatial orientation task, but they moved more slowly as the experiment progressed. Full article

The lateral hypothalamic area (LHA) serves as a central integrative hub for the regulation of energy homeostasis and motivational behaviors, including feeding and arousal. Recent advances in single-cell transcriptomics have revealed remarkable molecular heterogeneity within the LHA, identifying more than 30 distinct neuronal subtypes, such as GABAergic (LHA^Vgat), glutamatergic (LHA^Vglut2), orexin, melanin-concentrating hormone (MCH), and leptin receptor-expressing (LHA^Lepr) neurons. These neuronal populations sense peripheral metabolic signals—such as leptin, insulin, and glucose—both directly and indirectly, and they coordinate appropriate physiological and behavioral responses through local circuits and reciprocal connections with other hypothalamic nuclei. Furthermore, the LHA interfaces with extrahypothalamic regions, including the ventral tegmental area (VTA), nucleus accumbens (NAc), and lateral habenula (LHb), thereby linking metabolic state to reward processing and behavioral prioritization. In this review, we summarize and integrate recent molecular and functional findings to present a comprehensive view of the LHA as a dynamic, multifunctional center in the central regulation of metabolism. A deeper understanding of these mechanisms may offer new therapeutic avenues for addressing obesity and related metabolic disorders. Full article