Search Results (310)

Sporadic Parkinson’s Disease (PD) affects 3% of people over 65 years of age. People are living longer, thanks in large part to improvements in global health technology and health access for non-neurological diseases. Consequently, neurological diseases of senescence, such as PD, are representing an ever-increasing share of global disease burden. There is an intensifying research focus on the processes that underlie these conditions in the hope that neurological decay may be arrested at the earliest time point. The concept of neuronal death linked to ageing- neural senescence- first emerged in the 1800s. By the late 20th century, it was recognized that neurodegeneration was common to all ageing human brains, but in most cases, this process did not lead to clinical disease during life. Conditions such as PD are the result of accelerated neurodegeneration in particular brain foci. In the case of PD, degeneration of the substantia nigra pars compacta (SNpc) is especially implicated. Why neural degeneration accelerates in these particular regions remains a point of contention, though current evidence implicates a complex interplay between a vast array of neuronal cell functions, bioenergetic failure, and a dysfunctional brain immunological response. Their complexity is a considerable barrier to disease modification trials, which seek to intercept these maladaptive cell processes. This paper reviews current evidence in the domain of neurodegeneration in Parkinson’s disease, focusing on alpha-synuclein accumulation and deposition and the role of oxidative stress and inflammation in progressive brain changes. Recent approaches to disease modification are discussed, including the prevention or reversal of alpha-synuclein accumulation and deposition, modification of oxidative stress, alteration of maladaptive innate immune processes and reactive cascades, and regeneration of lost neurons using stem cells and growth factors. The limitations of past research methodologies are interrogated, including the difficulty of recruiting patients in the clinically quiescent prodromal phase of sporadic Parkinson’s disease. Recommendations are provided for future studies seeking to identify novel therapeutics with disease-modifying properties. Full article

Object detection technology enables the automatic identification of photovoltaic (PV) panel locations and conditions, significantly enhancing operational efficiency for maintenance teams while reducing the time and cost associated with manual inspections. Challenges arise due to the low resolution of remote sensing images combined with small-sized targets—PV panels intertwined with complex urban or natural backgrounds. To address this, a parallel architecture model based on YOLOv5 was designed, substituting traditional residual connections with parallel convolution structures to enhance feature extraction capabilities and information transmission efficiency. Drawing inspiration from the bottleneck design concept, a primary feature extraction module framework was constructed to optimize the model’s deep learning capacity. The improved model achieved a 4.3% increase in mAP, a 0.07 rise in F1 score, a 6.55% enhancement in recall rate, and a 6.2% improvement in precision. Additionally, the study validated the model’s performance and examined the impact of different loss functions on it, explored learning rate adjustment strategies under various scenarios, and analyzed how individual factors affect learning rate decay during its initial stages. This research notably optimizes detection accuracy and efficiency, holding promise for application in large-scale intelligent PV power station maintenance systems and providing reliable technical support for clean energy infrastructure management. Full article

The architecture of contemporary museums often emphasizes visual aesthetics, such as large volumes, open-plan layouts, and highly reflective finishes, resulting in acoustic challenges, such as excessive reverberation, poor speech intelligibility, elevated background noise, and reduced privacy. This study quantified the impact of surface—specific absorption treatments on acoustic metrics across eight gallery spaces. Room impulse responses calibrated virtual models, which simulated nine absorption scenarios (low, medium, and high on ceilings, floors, and walls) and evaluated reverberation time (T₂₀), speech transmission index (STI), clarity (C₅₀), distraction distance (r_D), Spatial Decay Rate of Speech (D_2,S), and Speech Level at 4 m (L_p,A,S,4m). The results indicate that going from concrete to a wooden floor yields the most rapid T₂₀ reductions (up to −1.75 s), ceiling treatments deliver the greatest STI and C₅₀ gains (e.g., STI increases of +0.16), and high-absorption walls maximize privacy metrics (D_2,S and L_p,A,S,4m). A linear regression model further predicted the STI from T₂₀, total absorption (Sabins), and room volume, with an 84.9% conditional R², enabling ±0.03 accuracy without specialized testing. These findings provide empirically derived, surface-specific “first-move” guidelines for architects and acousticians, underscoring the necessity of integrating acoustics early in museum design to balance auditory and visual objectives and enhance the visitor experience. Full article

Traditional numerical models have been widely employed to simulate the transport of multispecies reactive contaminants in groundwater systems; however, their high computational cost limits their applicability in real-time or large-scale scenarios. Recent advances in artificial intelligence (AI) offer promising alternatives, particularly data-driven machine learning techniques, for accelerating such simulations. This study presents the development of a surrogate model based on artificial neural networks (ANNs) to simulate the transport and decay of interacting multispecies contaminants in groundwater. High-fidelity training datasets are generated through finite difference-based reactive transport simulations across a wide range of environmental and geochemical conditions. The ANN model is trained to learn the complex nonlinear relationships governing the multispecies transport and transformation processes. Model validation reveals that the ANN surrogate accurately reproduces the spatial–temporal concentration profiles of both original and degradation species, capturing key dynamic behaviors with high precision. Notably, the ANN model achieves up to a 100-fold reduction in computational time compared to traditional analytical or semi-analytical solutions. These results highlight the ANN’s potential as an efficient and accurate surrogate modeling tool for groundwater contamination assessment, offering a valuable advancement for decision-making in environmental risk analysis and remediation planning. Full article

With the rapid development of the Internet, the speed and scope of sudden public events disseminating in cyberspace have grown significantly. Current methods of quantifying public opinion heat often neglect emotion-driven factors and user interaction behaviors, making it difficult to accurately capture fluctuations during dissemination. To address these issues, first, this study addressed the complexity of interaction behaviors by introducing an approach that employs the information gain ratio as a weighting indicator to measure the “interaction heat” contributed by different interaction attributes during event evolution. Second, this study built on SnowNLP and expanded textual features to conduct in-depth sentiment mining of large-scale opinion texts, defining the variance of netizens’ emotional tendencies as an indicator of emotional fluctuations, thereby capturing “emotional heat”. We then integrated interactive behavior and emotional conflict assessment to achieve comprehensive heat index to quantification and dynamic evolution analysis of online public opinion heat. Subsequently, we used Hodrick–Prescott filter to separate long-term trends and short-term fluctuations, extract six key quantitative features (number of peaks, time of first peak, maximum amplitude, decay time, peak emotional conflict, and overall duration), and applied K-means clustering algorithm (K-means) to classify events into three propagation patterns, which are extreme burst, normal burst, and long-tail. Finally, this study conducted ablation experiments on critical external intervention nodes to quantify the distinct contribution of each intervention to the propagation trend by observing changes in the model’s goodness-of-fit ($R^2$) after removing different interventions. Through an empirical analysis of six representative public opinion events from 2024, this study verified the effectiveness of the proposed framework and uncovered critical characteristics of opinion dissemination, including explosiveness versus persistence, multi-round dissemination with recurring emotional fluctuations, and the interplay of multiple driving factors. Full article

Mangrove-associated fungi represent a diverse but understudied group of eukaryotic organisms, especially in the Neotropics. The Colombian Pacific region, with approximately 1300 km of coastline covered with 194,880 ha of mangrove forests that remain largely unexplored for macrofungal diversity, is recognized as a global biodiversity hotspot. This study aimed to catalog the macrofungi associated with mangrove ecosystems in Colombia, integrating morphological characterization and molecular phylogenetics, focusing on three Valle del Cauca Pacific coast localities. A total of 81 specimens were collected from both living trees and decaying wood. Detailed macroscopic and microscopic analyses were conducted, and DNA sequences from two ribosomal DNA barcode regions (ITS and LSU) were generated for 43 specimens. Three new species—Neohypochnicium manglarense, Phlebiopsis colombiana, and Porogramme bononiae—were documented. In addition, eight species were reported as new records for both Colombia and mangrove ecosystems, including Microporus affinis, Paramarasmius palmivorus, Phlebiopsis flavidoalba, Porogramme brasiliensis, Resinicium grandisporum, Trametes ellipsospora, T. menziesii, and T. polyzona. Although previously recorded in Colombian terrestrial ecosystems, Lentinus scleropus and Oudemansiella platensis are globally reported here for the first time from mangrove habitats. Furthermore, Fomitopsis nivosella and Punctularia strigosozonata were documented for the first time in Colombia. This study addresses the first exploration of mangrove-associated macrofungi in the country and provides new insights into the hidden fungal diversity and potential of mangrove ecosystems as a latent niche for basidiomycete dispersal along Colombia’s Pacific coast. Full article

We analyze long-range dependence (LRD) for word time series, understood as a slower than exponential decay of the two-point Shannon mutual information. We achieve this by examining the decay of the cosine correlation, a proxy object defined in terms of the cosine similarity between word2vec embeddings of two words, computed by an analogy to the Pearson correlation. By the Pinsker inequality, the squared cosine correlation between two random vectors lower bounds the mutual information between them. Using the Standardized Project Gutenberg Corpus, we find that the cosine correlation between word2vec embeddings exhibits a readily visible stretched exponential decay for lags roughly up to 1000 words, thus corroborating the presence of LRD. By contrast, for the Human vs. LLM Text Corpus entailing texts generated by large language models, there is no systematic signal of LRD. Our findings may support the need for novel memory-rich architectures in large language models that exceed not only hidden Markov models but also Transformers. Full article

The homotrimeric P2X7 receptor (P2X7R) contains three ATP⁴⁻ binding sites in its ectodomain. Here, we investigated the role of individual ATP⁴⁻ activation sites in rat P2X7R (rP2X7R) using trimeric concatemers consisting of either three wild-type subunits (7-7-7) or one to three subunits with ATP binding sites knocked out by the K64A mutation. Following expression in Xenopus laevis oocytes, ATP⁴⁻-elicited ion currents were recorded using the two-microelectrode voltage clamp technique. The 7-7-7 concatamer exhibited a biphasic ATP⁴⁻ concentration dependence, best fit by the sum of two Hill functions, confirming the existence of functionally distinct ATP⁴⁻ activation sites. The activation time course of the 7-7-7 was best approximated by the sum of a fast and a slow exponential saturating activation component. Similarly, deactivation exhibited both fast and slow exponential decay. Only one Hill function was required to best fit the ATP⁴⁻ concentration dependence of concatamers with only two or one ATP⁴⁻ binding sites, and their deactivation time courses largely lacked the slowly deactivating components. We conclude that the binding of one ATP⁴⁻ is sufficient for partial activation of the rP2X7R and that allosteric effects occur when all three ATP⁴⁻ binding sites are occupied, leading to distinct functional activation sites. Full article

The magnetic field of the Sun is formed by the mechanism of hydromagnetic dynamo. In this mechanism, the flow of the conducting medium (plasma) of the convective zone generates a magnetic field, and this field corrects the flow using the Lorentz force, creating feedback. An important role in dynamo is played by memory (hereditary), when a change in the current state of a physical system depends on its states in the past. Taking these effects into account may provide a more accurate description of the generation of the Sun’s magnetic field. This paper generalizes classical dynamo models by including hereditary feedback effects. The feedback parameters such as the presence or absence of delay, delay duration, and memory duration are additional degrees of freedom. This can provide more diverse dynamic modes compared to classical memoryless models. The proposed model is based on the kinematic dynamo problem, where the large-scale velocity field is predetermined. The field in the model is represented as a linear combination of two stationary predetermined modes with time-dependent amplitudes. For these amplitudes, equations are obtained based on the kinematic dynamo equations. The model includes two generators of a large-scale magnetic field. In the first, the field is generated due to large-scale flow of the medium. The second generator has a turbulent nature; in it, generation occurs due to the nonlinear interaction of small-scale pulsations of the magnetic field and velocity. Memory in the system under study is implemented in the form of feedback distributed over all past states of the system. The feedback is represented by an integral term of the type of convolution of a quadratic form of phase variables with a kernel of a fairly general form. The quadratic form models the influence of the Lorentz force. This integral term describes the turbulent generator quenching. Mathematically, this model is written with a system of integro-differential equations for amplitudes of modes. The model was applied to a real space object, namely, the solar dynamo. The model representation of the Sun’s velocity field was constructed based on helioseismological data. Free field decay modes were chosen as components of the magnetic field. The work considered cases when hereditary feedback with the system arose instantly or with a delay. The simulation results showed that the model under study reproduces dynamic modes characteristic of the solar dynamo, if there is a delay in the feedback. Full article

Green technological innovation, government policies, and the low-carbon transformation of the manufacturing industry are critical for promoting high-quality development in the Yangtze River Economic Belt. This study, based on input–output and total factor productivity theories, selects relevant variables and utilizes a PVAR model to analyze data from 11 provinces in the region from 2011 to 2023. The empirical results indicate that (1) green technological innovation and the low-carbon transformation of the manufacturing industry exhibit significant bidirectional causality, with the low-carbon transformation exerting a stronger positive impact on green innovation, underscoring the “demand–pull” effect. (2) Government policies provide initial impetus for both green innovation and low-carbon transformation but show signs of self-restriction and diminishing returns over time, reflecting a typical “policy lag–decay” pattern. (3) Variance decomposition highlights the dominant role of green technological innovation in driving long-term low-carbon transformation, while the direct impact of government policies remains limited, indicating that policy effectiveness is largely mediated through technological channels. These findings emphasize the importance of enhancing regional green innovation capacity, establishing dynamic policy feedback mechanisms, and fostering sustained technological advancement as key pathways to deepening the low-carbon transformation. Full article

Recent research on the use of heptazine-based polymeric carbon nitride materials as potential photocatalysts for hydrogen evolution has made significant progress. However, the impact of the water cluster’s size on the time-dependent photochemical mechanisms during the water splitting process of heptazine–water clusters remains largely unexplored. Here, we present a Landau–Zener trajectory surface hopping dynamics calculation for heptazine–(H₂O)₄ clusters at the ADC(2) level. The electron-driven proton transfer (EDPT) mechanism reaction from water to hydrogen-bonded heptazine–water clusters was confirmed using this method, yielding a heptazinyl radical and an OH biradical as products. The calculated quantum yield of the EDPT for the heptazine–(H₂O)₄ complex was 6.5%, which was slightly lower than that of the heptazine–H₂O complex (9%), suggesting that increasing the water cluster size does not significantly enhance the efficiency of hydrogen transfer. Interestingly, our results show that the de-excitation of the heptazine–water complex from the excited state to the ground state via the EDPT process follows both fast and slow decay modes, which govern population relaxation and facilitate the photochemical water splitting reaction. This newly identified differential decay behavior offers valuable insights that could help deepen our understanding of the EDPT process, potentially improving the efficiency of water splitting under sunlight. Full article

Short-term observations suggest that environmental changes affect the diversity and composition of soil fungi, significantly influencing forest resilience, plant diversity, and soil processes. However, time-series experiments should be supplemented with geobiological archives to capture the long-term effects of environmental changes on fungi–soil–plant interactions, particularly in undersampled, floristically diverse tropical forests. We recently conducted trnL-P6 amplicon sequencing to generate a sedimentary ancient DNA (sedaDNA) record of the regional catchment vegetation of the tropical waterbody Lake Towuti (Sulawesi, Indonesia), spanning over one million years (Myr) of the lake’s developmental history. In this study, we performed 18SV9 amplicon sequencing to create a parallel paleofungal record to (a) infer the composition, origins, and functional guilds of paleofungal community members and (b) determine the extent to which downcore changes in fungal community composition reflect the late Pleistocene evolution of the Lake Towuti catchment. We identified at least 52 members of Ascomycota (predominantly Dothiodeomycetes, Eurotiomycetes, and Leotiomycetes) and 12 members of Basidiomycota (primarily Agaricales and Polyporales). Spearman correlation analysis of the relative changes in fungal community composition, geochemical parameters, and paleovegetation assemblages revealed that the overwhelming majority consisted of soil organic matter and wood-decaying saprobes, except for a necrotrophic phytopathogenic association between Mycosphaerellaceae (Cadophora) and wetland herbs (Alocasia) in more-than-1-Myr-old silts and peats deposited in a pre-lake landscape, dominated by small rivers, wetlands, and peat swamps. During the lacustrine stage, vegetation that used to grow on ultramafic catchment soils during extended periods of inferred drying showed associations with dark septate endophytes (Ploettnerulaceae and Didymellaceae) that can produce large quantities of siderophores to solubilize mineral-bound ferrous iron, releasing bioavailable ferrous iron needed for several processes in plants, including photosynthesis. Our study showed that sedaDNA metabarcoding paired with the analysis of geochemical parameters yielded plausible insights into fungal-plant-soil interactions, and inferred changes in the paleohydrology and catchment evolution of tropical Lake Towuti, spanning more than one Myr of deposition. Full article

Small, forested catchments are prototypes of terrestrial ecosystems and have been studied in several disciplines of environmental science over several decades. Time series of water and matter fluxes and nutrient concentrations from these systems exhibit a bewildering diversity of spatiotemporal patterns, indicating the intricate nature of processes acting on a large range of time scales. Nonlinear dynamics is an obvious framework to investigate catchment time series. We analyzed selected long-term data from three headwater catchments in the Bramke valley, Harz mountains, Lower Saxony in Germany at common biweekly resolution for the period 1991 to 2023. For every time series, we performed gap filling, detrending, and removal of the annual cycle using singular system analysis (SSA), and then calculated metrics based on ordinal pattern statistics: the permutation entropy, permutation complexity, and Fisher information, as well as their generalized versions (q-entropy and α-entropy). Further, the position of each variable in Tarnopolski diagrams is displayed and compared to reference stochastic processes, like fractional Brownian motion, fractional Gaussian noise, and β noise. Still another way of distinguishing deterministic chaos and structured noise, and quantifying the latter, is provided by the complexity from ordinal pattern positioned slopes (COPPS). We also constructed horizontal visibility graphs and estimated the exponent of the decay of the degree distribution. Taken together, the analyses create a characterization of the dynamics of these systems which can be scrutinized for universality, either across variables or between the three geographically very close catchments. Full article

Reconstituted high-density lipoprotein nanoparticles (NPs), which mimic the structure and function of endogenous human plasma HDL, hold promise as a robust drug delivery system. These nanoparticles, when loaded with appropriate agents, serve as powerful tools for targeted drug delivery. The fundamental challenge lies in controlling and estimating the actual drug load and the efficiency of drug release at the target. In this report, we present a novel approach based on enhanced Förster Resonance Energy Transfer (FRET) to assess particle load and monitor payload release. The NPs are labeled with donor molecules embedded in the lipid phase, while the spherical core volume is filled with acceptor molecules. Highly enhanced FRET efficiency to multiple acceptors in the NP core has been observed at distances significantly larger than the characteristic Förster distance (R₀). To confirm that the observed changes in donor and acceptor emissions are a result of FRET, we developed a theoretical model for nonradiative energy transfer from a single donor to multiple acceptors enclosed in a spherical core volume. The load-dependent shortening of the fluorescence lifetime of the donor correlated with the presence of a negative component in the intensity decay of the acceptor clearly demonstrates that FRET can occur at a large distance comparable to the nanoparticle size (over 100 Å). Comparison of theoretical simulations with the measured intensity decays of the donor and acceptor fluorophores constitute a new method for evaluating particle load. The observed FRET efficiency depends on the number of acceptors in the core, providing a simple way to estimate the nanoparticle load efficiency. Particle disintegration and load release result in a distinct change in donor and acceptor emissions. This approach constitutes a novel strategy for assessing NP core load, monitoring NP integrity, and evaluating payload release efficiency to target cells. Significants: In the last decade, nanoparticles have emerged as a promising strategy for targeted drug delivery, with applications ranging from cancer therapy to ocular neurodegenerative disease treatments. Despite their potential, a significant issue has been the real-time monitoring of these drug delivery vehicles within biological systems. Effective strategies for monitoring NP payload loading, NP integrity, and payload release are needed to assess the quality of new drug delivery systems. In our study, we have found that FRET-enabled NPs function as an improved method for monitoring these aspects currently missing from current drug delivery efforts. Full article

Living and dying well together in the Anthropocene, in the context of intensifying climate crises, global pandemics, and fast-paced hustle culture, is an increasingly daunting task. While many wellness movements call for strict regimes and vigorous activity, striving for largely unattainable bodily norms and longevity, an emerging trend centres on embracing natural processes and temporalities of resistance focused on relaxation, rest, and even decay. So-called ‘girl mossing’ and ‘girl rotting’ encourage women to be intentionally unproductive, and to spend time instead lying on a forest floor, staring up at a canopy of trees, caressing moss. Similarly, members of the ‘death positive’ and ‘new death’ movements advocate for sensorial connection with nature at the end of life, and for an embrace of practices of decay and decomposition. Both trends are dominated by women and influenced by Buddhist and Pagan traditions. They also exemplify spiritual complexity, particularly relating to biomedicine and consumerism. Examining these interconnected lifestyle and deathstyle movements, this article considers the uptake of ‘relational naturalism’ in contemporary societies as an antidote to the personal and planetary harms of neoliberal capitalism. Full article