Ceratophyllum demersum alleviates microplastics uptake and physiological stress responses in aquatic organisms, an overlooked ability.

It has been demonstrated that microplastics (MPs) may be inadvertently ingested by aquatic animals, causing harm to their physiological functions and potentially entering the food chain, thereby posing risks to human food safety. To achieve an environmentally friendly and efficient reduction of MPs in freshwater environments, this experiment investigates the depuration effect of C. demersum on MPs using three common aquatic animals: Macrobrachium nipponense, Corbicula fluminea, and Bellamya aeruginosa as research subjects. The amounts of MPs, digestive enzyme activity, oxidative stress index, and energy metabolism enzyme activity in the digestive and non-digestive systems of three aquatic animals were measured on exposure days 1, 3, and 7 and on depuration days 1 and 3. The results indicated that the depuration effect of C. demersum and the species interaction were significant for the whole individual. Concerning digestive tissue, C. demersum was the most effective in purifying B. aeruginosa. When subjected to short-term exposure to MPs, C. demersum displayed a superior depuration effect. Among non-digestive tissues, C. demersum exhibited the earliest purifying effect on C. fluminea. Additionally, C. demersum alleviated physiological responses caused by MPs. In conclusion, this study underscores C. demersum as a promising new method for removing MPs from aquatic organisms.

Strategic assessment model of smart stadiums based on genetic algorithms and literature visualization analysis: A case study from Chengdu, China.

This paper leverages Citespace and VOSviewer software to perform a comprehensive bibliometric analysis on a corpus of 384 references related to smart sports venues, spanning from 1998 to 2022. The analysis encompasses various facets, including author network analysis, institutional network analysis, temporal mapping, keyword clustering, and co-citation network analysis. Moreover, this paper constructs a smart stadiums strategic assessment model (SSSAM) to compensate for confusion and aimlessness by genetic algorithms (GA). Our findings indicate an exponential growth in publications on smart sports venues year over year. Arizona State University emerges as the institution with the highest number of collaborative publications, Energy and Buildings becomes the publication with the most documents. While, Wang X stands out as the scholar with the most substantial contribution to the field. In scrutinizing the betweenness centrality indicators, a paradigm shift in research hotspots becomes evident-from intelligent software to the domains of the Internet of Things (IoT), intelligent services, and artificial intelligence (AI). The SSSAM model based on artificial neural networks (ANN) and GA algorithms also reached similar conclusions through a case study of the International University Sports Federation (FISU), building Information Modeling (BIM), cloud computing and artificial intelligence Internet of Things (AIoT) are expected to develop in the future. Three key themes developed over time. Finally, a comprehensive knowledge system with common references and future hot spots is proposed.

Explaining deep learning-based representations of resting state functional connectivity data: focusing on interpreting nonlinear patterns in autism spectrum disorder.

Background: Resting state Functional Magnetic Resonance Imaging fMRI (rs-fMRI) has been used extensively to study brain function in psychiatric disorders, yielding insights into brain organization. However, the high dimensionality of the rs-fMRI data presents significant challenges for data analysis. Variational autoencoders (VAEs), a type of neural network, have been instrumental in extracting low-dimensional latent representations of resting state functional connectivity (rsFC) patterns, thereby addressing the complex nonlinear structure of rs-fMRI data. Despite these advances, interpreting these latent representations remains a challenge. This paper aims to address this gap by developing explainable VAE models and testing their utility using rs-fMRI data in autism spectrum disorder (ASD). Methods: One-thousand one hundred and fifty participants (601 healthy controls [HC] and 549 patients with ASD) were included in the analysis. RsFC correlation matrices were extracted from the preprocessed rs-fMRI data using the Power atlas, which includes 264 regions of interest (ROIs). Then VAEs were trained in an unsupervised manner. Lastly, we introduce our latent contribution scores to explain the relationship between estimated representations and the original rs-fMRI brain measures. Results: We quantified the latent contribution scores for both the ASD and HC groups at the network level. We found that both ASD and HC groups share the top network connectivitives contributing to all estimated latent components. For example, latent 0 was driven by rsFC within ventral attention network (VAN) in both the ASD and HC. However, we found significant differences in the latent contribution scores between the ASD and HC groups within the VAN for latent 0 and the sensory/somatomotor network for latent 2. Conclusion: This study introduced latent contribution scores to interpret nonlinear patterns identified by VAEs. These scores effectively capture changes in each observed rsFC feature as the estimated latent representation changes, enabling an explainable deep learning model that better understands the underlying neural mechanisms of ASD.

Astragaloside IV inhibits angiotensin II-induced atrial fibrosis and atrial fibrillation by SIRT1/PGC-1α/FNDC5 pathway.

Aims and objectives: Astragaloside IV (AS-IV) has been found to possess anti-oxidative, anti-inflammatory, and anti-apoptotic properties, but its effect on atrial fibrosis is yet to be determined. This research investigates the protective role of AS-IV in angiotensin II (Ang II)-induced atrial fibrosis and atrial fibrillation (AF). Methods: C57BL/6 male mice aged 8-10 weeks (n = 40) were subcutaneously administered Ang II (2.0 mg/kg/day) or saline, with AS-IV (80 mg/kg) intraperitoneally administered 2 h before Ang II infusion for 4 weeks. Biochemical, histological, and morphological analyses were carried out. Using transesophageal burst pacing, AF was generated in vivo. Results: Here, we report that AS-IV treatment inhibited Ang II-induced AF development in mice (58 ± 5.86 vs 15.13 ± 2.16 %, p < 0.001). Ang II + AS-IV therapy was effective in reducing the atrial fibrotic area and decreasing the increase in smooth muscle alpha-actin (α-SMA)-positive myofibroblasts brought on by Ang II treatment (fibrotic area: 26.25 ± 3.81 vs 8.62 ± 1.83 %, p < 0.001 and α-SMA: 65.62 ± 10.63 vs 17.25 ± 1.78 %, p < 0.001). The reactive oxygen species (ROS) production was reduced by pretreatment with Ang II + AS-IV (9.20 ± 0.92 vs 2.63 ± 0.22 %/sec, p < 0.001). In addition, Ang II + AS-IV treatment suppressed oxidative stress in Ang II-induced atrial fibrosis (malondialdehyde: 701.78 ± 85.01 vs 504.07 ± 25.62 pmol/mg protein, p < 0.001; superoxide dismutase: 13.82 ± 1.25 vs 29.54 ± 2.45 U/mg protein, p < 0.001 and catalase: 11.43 ± 1.19 vs 20.83 ± 3.29 U/mg protein, p < 0.001, respectively). Moreover, Ang II + AS-IV decreased the expression of α-SMA, collagen III and collagen I (3.32 ± 0.53 vs 1.41 ± 0.20 fold, p < 0.001; 3.41 ± 0.55 vs 1.48 ± 0.18 fold, p < 0.001; 2.34 ± 0.55 vs 0.99 ± 0.17 fold, p < 0.001, respectively) while increasing the protein expression of sirtuin 1 (SIRT1), peroxisome proliferator-activated receptor-gamma coactivator-1 alpha (PGC-1α), and fibronectin type III domain-containing protein 5 (FNDC5) in Ang II-treated mice (0.22 ± 0.02 vs 0.57 ± 0.08 fold, p < 0.001; 0.28 ± 0.04 vs 0.72 ± 0.05 fold, p < 0.001; 0.38 ± 0.03 vs 0.68 ± 0.06 fold, p < 0.001, respectively). Conclusion: Our data led us to speculate that AS-IV may protect against Ang II-induced atrial fibrosis and AF via upregulation of the SIRT1/PGC-1α/FNDC5 pathway.

Association between household size and risk of incident dementia in the UK Biobank study.

Currently, the relationship between household size and incident dementia, along with the underlying neurobiological mechanisms, remains unclear. This prospective cohort study was based on UK Biobank participants aged ≥ 50 years without a history of dementia. The linear and non-linear longitudinal association was assessed using Cox proportional hazards regression and restricted cubic spline models. Additionally, the potential mechanisms driven by brain structures were investigated by linear regression models. We included 275,629 participants (mean age at baseline 60.45 years [SD 5.39]). Over a mean follow-up of 9.5 years, 6031 individuals developed all-cause dementia. Multivariable analyses revealed that smaller household size was associated with an increased risk of all-cause dementia (HR, 1.06; 95% CI 1.02-1.09), vascular dementia (HR, 1.08; 95% CI 1.01-1.15), and non-Alzheimer's disease non-vascular dementia (HR, 1.09; 95% CI 1.03-1.14). No significant association was observed for Alzheimer's disease. Restricted cubic splines demonstrated a reversed J-shaped relationship between household size and all-cause and cause-specific dementia. Additionally, substantial associations existed between household size and brain structures. Our findings suggest that small household size is a risk factor for dementia. Additionally, brain structural differences related to household size support these associations. Household size may thus be a potential modifiable risk factor for dementia.

Dual Information Enhanced Multiview Attributed Graph Clustering.

Multiview attributed graph clustering is an important approach to partition multiview data based on the attribute characteristics and adjacent matrices from different views. Some attempts have been made in using graph neural network (GNN), which have achieved promising clustering performance. Despite this, few of them pay attention to the inherent specific information embedded in multiple views. Meanwhile, they are incapable of recovering the latent high-level representation from the low-level ones, greatly limiting the downstream clustering performance. To fill these gaps, a novel dual information enhanced multiview attributed graph clustering (DIAGC) method is proposed in this article. Specifically, the proposed method introduces the specific information reconstruction (SIR) module to disentangle the explorations of the consensus and specific information from multiple views, which enables graph convolutional network (GCN) to capture the more essential low-level representations. Besides, the contrastive learning (CL) module maximizes the agreement between the latent high-level representation and low-level ones and enables the high-level representation to satisfy the desired clustering structure with the help of the self-supervised clustering (SC) module. Extensive experiments on several real-world benchmarks demonstrate the effectiveness of the proposed DIAGC method compared with the state-of-the-art baselines.

StMAPK10 gene functional identification and analysis in drought resistance of potato crop (Solanum tuberosum L.).

All over the world, potato (Solanum tuberosum L.) production is constrained by several biotic and abiotic factors. Many techniques and mechanisms have been used to overcome these hurdles and increase food for the rising population. In crop plants, the mitogen-activated protein kinase (MAPK) cascade, a significant regulator of the MAPK pathway under various biotic and abiotic stress conditions, is one of the targets to increase productivity. MAPK plays a significant role under drought stress in potato. However, the function of MAPK in drought resistance in potato is poorly understood. In this study, we wanted to identify the function of StMAPK10 in the drought resistance in potato. StMAPK10 was up-regulated under drought conditions and dynamically modulated by abiotic stresses. Over-expression and down-regulation of StMAPK10 revealed that StMAPK10 stimulated potato growth under drought conditions, as demonstrated by changes in SOD, CAT, and POD activity, as well as H2O2, proline, and MDA content. StMAPK10 up-regulation exaggerated the drought resistance of the potato plant by uplifting antioxidant activities and photosynthetic indices. Overexpressed-StMAPK10 potato lines showed highly significant results for physiological and photosynthetic indices in response to drought stress, while knockdown expression showed opposite outcomes. Additionally, subcellular localization and phenotypic analysis of transgenic and non-transgenic plants substantiated the role of the increased expression of StMAPK10 against drought stress. The results could provide novel insights into the functionality of StMAPK10 in drought responses and conceivable mechanisms.

GhMYB52 Like: A Key Factor That Enhances Lint Yield by Negatively Regulating the Lignin Biosynthesis Pathway in Fibers of Upland Cotton (Gossypium hirsutum L.).

In the context of sustainable agriculture and biomaterial development, understanding and enhancing plant secondary cell wall formation are crucial for improving crop fiber quality and biomass conversion efficiency. This is especially critical for economically important crops like upland cotton (Gossypium hirsutum L.), for which fiber quality and its processing properties are essential. Through comprehensive genome-wide screening and analysis of expression patterns, we identified a particularly high expression of an R2R3 MYB transcription factor, GhMYB52 Like, in the development of the secondary cell wall in cotton fiber cells. Utilizing gene-editing technology to generate a loss-of-function mutant to clarify the role of GhMYB52 Like, we revealed that GhMYB52 Like does not directly contribute to cellulose synthesis in cotton fibers but instead represses a subset of lignin biosynthesis genes, establishing it as a lignin biosynthesis inhibitor. Concurrently, a substantial decrease in the lint index, a critical measure of cotton yield, was noted in parallel with an elevation in lignin levels. This study not only deepens our understanding of the molecular mechanisms underlying cotton fiber development but also offers new perspectives for the molecular improvement of other economically important crops and the enhancement of biomass energy utilization.

StMAPKK5 responds to heat stress by regulating potato growth, photosynthesis, and antioxidant defenses.

Backgrounds: As a conserved signaling pathway, mitogen-activated protein kinase (MAPK) cascade regulates cellular signaling in response to abiotic stress. High temperature may contribute to a significant decrease in economic yield. However, research into the expression patterns of StMAPKK family genes under high temperature is limited and lacks experimental validation regarding their role in supporting potato plant growth. Methods: To trigger heat stress responses, potato plants were grown at 35°C. qRT-PCR was conducted to analyze the expression pattern of StMAPKK family genes in potato plants. Plant with StMAPKK5 loss-of-function and gain-of-function were developed. Potato growth and morphological features were assessed through measures of plant height, dry weight, and fresh weight. The antioxidant ability of StMAPKK5 was indicated by antioxidant enzyme activity and H2O2 content. Cell membrane integrity and permeability were suggested by relative electrical conductivity (REC), and contents of MDA and proline. Photosynthetic capacity was next determined. Further, mRNA expression of heat stress-responsive genes and antioxidant enzyme genes was examined. Results: In reaction to heat stress, the expression profiles of StMAPKK family genes were changed. The StMAPKK5 protein is located to the nucleus, cytoplasm and cytomembrane, playing a role in controlling the height and weight of potato plants under heat stress conditions. StMAPKK5 over-expression promoted photosynthesis and maintained cell membrane integrity, while inhibited transpiration and stomatal conductance under heat stress. Overexpression of StMAPKK5 triggered biochemical defenses in potato plant against heat stress, modulating the levels of H2O2, MDA and proline, as well as the antioxidant activities of CAT, SOD and POD. Overexpression of StMAPKK5 elicited genetic responses in potato plants to heat stress, affecting heat stress-responsive genes and genes encoding antioxidant enzymes. Conclusion: StMAPKK5 can improve the resilience of potato plants to heat stress-induced damage, offering a promising approach for engineering potatoes with enhanced adaptability to challenging heat stress conditions.

Pangenome analyses of Clostridium butyricum provide insights into its genetic characteristics and industrial application.

Clostridium butyricum is a Gram-positive anaerobic bacterium known for its ability to produce butyate. In this study, we conducted whole-genome sequencing and assembly of 14C. butyricum industrial strains collected from various parts of China. We performed a pan-genome comparative analysis of the 14 assembled strains and 139 strains downloaded from NCBI. We found that the genes related to critical industrial production pathways were primarily present in the core and soft-core gene categories. The phylogenetic analysis revealed that strains from the same clade of the phylogenetic tree possessed similar antibiotic resistance and virulence factors, with most of these genes present in the shell and cloud gene categories. Finally, we predicted the genes producing bacteriocins and botulinum toxins as well as CRISPR systems responsible for host defense. In conclusion, our research provides a desirable pan-genome database for the industrial production, food application, and genetic research of C. butyricum.

General Aqueous System Simulation through an AI-Embedded Metaverse Chemistry Laboratory.

Recent decades have witnessed the rapid development of autonomous laboratories and artificial intelligence, where experiments can be automatically run and optimized. Although human work is reduced, the total time of experimental optimization is still consuming due to limitations of the current ab metaverse framework, which accurately predicts the future state of the system by receiving and analyzing in situ experimental data. To substitute for traditional simulation methods, we designed a physically endorsed deep learning model to predict the future system picture ranging from atomic image to bulk appearance, intensively using the correlations between properties of the system. Through this framework, we studied the general aqueous system, covering 100+ common ionic solutions. We can accurately simulate properties for a general aqueous system as well as predict the time of solvation of ionic compounds ahead of real experiments. In this way, the experiments can be optimized more efficiently without waiting for the end of a bad iteration. We hope our work offers a fresh direction for the digitization of chemical information, enhancing access to and use of experimental data in advancing the field of physical chemistry.

Enhanced Light Absorption and Photo-Generated Charge Separation Efficiency for Boosting Photocatalytic H2 Evolution through TiO2 Quantum Dots with N-Doping and Concomitant Oxygen Vacancy.

Low-range light absorption and rapid recombination of photo-generated charge carriers have prevented the occurrence of effective and applicable photocatalysis for decades. Quantum dots (QDs) offer a solution due to their size-controlled photon properties and charge separation capabilities. Herein, well-dispersed interstitial nitrogen-doped TiO2 QDs with stable oxygen vacancies (N-TiO2-x-VO) are fabricated by using a low-temperature, annealing-assisted hydrothermal method. Remarkably, electrostatic repulsion prevented aggregation arising from negative charges accumulated in situ on the surface of N-TiO2-x-VO, enabling complete solar spectrum utilization (200-800 nm) with a 2.5 eV bandgap. Enhanced UV-vis photocatalytic H2 evolution rate (HER) reached 2757 µmol g-1 h-1, 41.6 times higher than commercial TiO2 (66 µmol g-1 h-1). Strikingly, under visible light, HER rate was 189 µmol g-1 h-1. Experimental and simulated studies of mechanisms reveal that VO can serve as an electron reservoir of photo-generated charge carriers on N-doped active sites, and consequently, enhance the separation rate of exciton pairs. Moreover, the negative free energy (-0.35 V) indicates more favorable thermodynamics for HER as compared with bulk TiO2 (0.66 V). This research work paves a new way of developing efficient photocatalytic strategies of HER that are applicable in the sustainable carbon-zero energy supply.

Foxq1 Promotes Alveolar Epithelial Cell Death through Tle1-mediated Inhibition of the NFκB Signaling Pathway.

Acute lung injury is a common respiratory disease characterized by diffuse alveolar injury and interstitial edema, as well as a hyperinflammatory response, lung cell damage and oxidative stress. Foxq1, a member of the FOX family of transcription factors, is expressed in various tissues, such as the lungs, liver, and kidneys, and contributes to various biological processes, such as stress, metabolism, cell cycle arrest, and aging-related apoptosis. However, the role of Foxq1 in acute lung injury is unknown. We constructed ex vivo and in vivo acute lung injury models by lipopolysaccharide tracheal perfusion of ICR mice and conditioned medium stimulation of injured MLE-12 cells. Foxq1 expression was increased, and its localization was altered in our acute lung injury model. In normal or injured MLE-12 cells, knockdown of Foxq1 promoted cell survival, and overexpression had the opposite effect. This regulatory effect was likely mediated by Tle1 and the NFκB/Bcl2/Bax signaling pathway. These data suggest a potential link between Foxq1 and acute lung injury, indicating that Foxq1 can be used as a biomarker for the diagnosis of acute lung injury. Targeted inhibition of Foxq1 expression could promote alveolar epithelial cell survival and may provide a strategy for mitigating acute lung injury.

Using Virtual Reality to Study Spatial Mapping and Threat Learning.

Using spatial mapping processes to learn about threat and safety in an environment is crucial for survival. Research using conditioning paradigms has explored the effects of state (transient arousal) and trait anxiety (anxiety as an aspect of personality) on threat learning and acquisition. However, results are mixed, and little is known regarding why some individuals do not learn to discriminate between threat and safety during contextual conditioning. We used a virtual reality (VR) contextual threat conditioning paradigm to elucidate the effects of state and trait anxiety on contextual threat learning. 70 healthy participants (46 female) navigated and "picked" flowers in a VR environment. Flowers picked in the dangerous zone (half of the environment) were paired with an electric shock (or "bee sting") to the hand; flowers picked in the safe zone were never paired with a shock. Participants also collected and returned neutral objects as a measure of spatial memory. Galvanic skin response (GSR) was measured throughout the task and anxiety was assessed via the State Trait Anxiety Inventory (STAI). Participants were categorized as learners if they correctly identified the two zones after the task. Non-learners, compared to learners, performed significantly worse during the spatial memory task and demonstrated significantly higher state anxiety scores and GSR levels throughout the task. Learners showed higher skin conductance response (SCR) in the dangerous zone compared to the safe zone while non-learners showed no SCR differences between zones. These results indicate that state anxiety may impair spatial mapping, disrupting contextual threat learning.

Deep learning-assisted diagnosis of benign and malignant parotid tumors based on ultrasound: a retrospective study.

BACKGROUND: To develop a deep learning(DL) model utilizing ultrasound images, and evaluate its efficacy in distinguishing between benign and malignant parotid tumors (PTs), as well as its practicality in assisting clinicians with accurate diagnosis. METHODS: A total of 2211 ultrasound images of 980 pathologically confirmed PTs (Training set: n = 721; Validation set: n = 82; Internal-test set: n = 89; External-test set: n = 88) from 907 patients were retrospectively included in this study. The optimal model was selected and the diagnostic performance evaluation is conducted by utilizing the area under curve (AUC) of the receiver-operating characteristic(ROC) based on five different DL networks constructed at varying depths. Furthermore, a comparison of different seniority radiologists was made in the presence of the optimal auxiliary diagnosis model. Additionally, the diagnostic confusion matrix of the optimal model was calculated, and an analysis and summary of misjudged cases' characteristics were conducted. RESULTS: The Resnet18 demonstrated superior diagnostic performance, with an AUC value of 0.947, accuracy of 88.5%, sensitivity of 78.2%, and specificity of 92.7% in internal-test set, and with an AUC value of 0.925, accuracy of 89.8%, sensitivity of 83.3%, and specificity of 90.6% in external-test set. The PTs were subjectively assessed twice by six radiologists, both with and without the assisted of the model. With the assisted of the model, both junior and senior radiologists demonstrated enhanced diagnostic performance. In the internal-test set, there was an increase in AUC values by 0.062 and 0.082 for junior radiologists respectively, while senior radiologists experienced an improvement of 0.066 and 0.106 in their respective AUC values. CONCLUSIONS: The DL model based on ultrasound images demonstrates exceptional capability in distinguishing between benign and malignant PTs, thereby assisting radiologists of varying expertise levels to achieve heightened diagnostic performance, and serve as a noninvasive imaging adjunct diagnostic method for clinical purposes.

Contrastive Multiview Attribute Graph Clustering With Adaptive Encoders.

Multiview attribute graph clustering aims to cluster nodes into disjoint categories by taking advantage of the multiview topological structures and the node attribute values. However, the existing works fail to explicitly discover the inherent relationships in multiview topological graph matrices while considering different properties between the graphs. Besides, they cannot well handle the sparse structure of some graphs in the learning procedure of graph embeddings. Therefore, in this article, we propose a novel contrastive multiview attribute graph clustering (CMAGC) with adaptive encoders method. Within this framework, the adaptive encoders concerning different properties of distinct topological graphs are chosen to integrate multiview attribute graph information by checking whether there exists high-order neighbor information or not. Meanwhile, the number of layers of the GCN encoders is selected according to the prior knowledge related to the characteristics of different topological graphs. In particular, the feature-level and cluster-level contrastive learning are conducted on the multiview soft assignment representations, where the union of the first-order neighbors from the corresponding graph pairs is regarded as the positive pairs for data augmentation and the sparse neighbor information problem in some graphs can be well dealt with. To the best of our knowledge, it is the first time to explicitly deal with the inherent relationships from the interview and intraview perspectives. Extensive experiments are conducted on several datasets to verify the superiority of the proposed CMAGC method compared with the state-of-the-art methods.

Altered hierarchical gradients of intrinsic neural timescales in mild cognitive impairment and Alzheimer's disease.

Alzheimer's disease (AD) is a devastating neurodegenerative disease that affects millions of seniors in the US. Resting-state functional magnetic resonance imaging (rs-fMRI) is widely used to study neurophysiology in AD and its prodromal condition, mild cognitive impairment (MCI). The intrinsic neural timescale (INT), which can be estimated through the magnitude of the autocorrelation of neural signals from rs-fMRI, is thought to quantify the duration that neural information is stored in a local circuit. Such heterogeneity of the timescales forms a basis of the brain functional hierarchy and captures an aspect of circuit dynamics relevant to excitation/inhibition balance, which is broadly relevant for cognitive functions. Given that, we applied rs-fMRI to test whether distinct changes of INT at different hierarchies are present in people with MCI, those progressing to AD (called Converter), and AD patients of both sexes. Linear mixed effect model was implemented to detect altered hierarchical gradients across populations followed by pairwise comparisons to identify regional differences. High similarities between AD and Converter were observed. Specifically, the inferior temporal, caudate, pallidum areas exhibit significant alterations in both AD and Converter. Distinct INT related pathological changes in MCI and AD were found. For AD/Converter, neural information is stored for a longer time in lower hierarchical areas, while higher levels of hierarchy seem to be preferentially impaired in MCI leading to a less pronounced hierarchical gradients. These results inform that the INT holds great potential as an additional measure for AD prediction, even a stable biomarker for clinical diagnosis.Significance Statement We observed high similarities of intrinsic neural timescales (INT) between patients with Alzheimer's Disease (AD) and people that will later progress to AD (called Converter), deviating from cognitively normal individuals. This indicates that pathological excitation/inhibition imbalance already started before the conversion to AD. We also revealed distinct pathophysiological changes in stable mild cognitive impairment (MCI) and AD/Converter. For the AD and Converter, neural information is stored for a longer time in lower brain hierarchical areas; while higher levels of the hierarchy seem to be preferentially impaired in stable MCI. These results suggest the potential for INT as an additional measure for AD prediction, even a stable biomarker for clinical diagnosis.

Modulation of evapotranspiration and stream runoff by weathered bedrock in arid and semi-arid mountains.

Earth's Critical Zone exhibits remarkable heterogeneity and complexity. Hence, further investigation is required to examine the composition of Earth's Critical Zone as well as the diverse eco-hydrological patterns they exhibit under varying climatic and geological circumstances. This exploration should primarily be conducted through the investigation and experiments of the hillslope unit, where the topography and weathered bedrock are representative, with particular emphasis on semi-arid regions where water resources serve as the primary limiting factor. Here, we have determined that the structure of the weathering profile displays systematic variation across the topography and heterogeneous landscape on uninterrupted slopes. Differences in the structure of the subsurface critical zone led to differences in its water storage capacity at the same time. Runoff in alpine shrubs and forests was dominated by subsurface runoff, and grassland was dominated by surface runoff. In the alpine shrub immediately adjacent to the watershed, an estimated quantity of 129 mm of water is stored within the unsaturated zone of the soil, serving as exchange water to replenish moisture in the underlying bedrock. In contrast to alpine shrubs, an estimated quantity of 62.7 mm of water originates from the unsaturated zone of soil and weathered bedrock in the forest. However, approximately 21.1 mm of moisture is unavailable to plants. The soil water storage in grasslands exhibits a decline throughout the growing season, with a subsequent augmentation occurring solely after substantial precipitation events exceeding 20 mm. In wet years, dynamic storage predominantly manifests as groundwater saturation throughout the entire ground and high subsurface runoff. In dry years, the limited runoff response indicates that the catchment's dynamic water storage primarily comprises "indirect" water storage, which predominantly resides within the soil, saprolite, and weathered rock below the "field capacity", subsequently being released into the atmosphere through evapotranspiration.