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Location prediction based on social media information is highly valuable in human mobility research and has multiple real-life applications. However, existing research methods often ignore social influences, largely ignoring implicit information regarding interactions between users and geographical locations. Additionally, they generally employ single modeling structures, which restricts the effective integration of complex spatiotemporal characteristics and factors influencing user mobility. In this context, we propose a novel network with physical and social awareness that expresses both physical and social influences of user mobility from a global perspective based on a heterogeneous graph constructed using users and spatial locations as nodes and relationships between them as edges. This graph enables the model to leverage information from connected nodes and edges to infer missing or unobserved data. The model predicts future locations of users by effectively integrating the temporal and spatial features of user trajectory series. The proposed model is validated using three social media datasets. The experimental results demonstrate that the proposed method outperforms the state-of-the-art baseline models. This indicates the importance of considering complex interactions between users and locations, as well as the various influences of physical and social spaces. [ABSTRACT FROM AUTHOR]

Frequent flooding seriously affects people's safety and economic construction, and assessing the inundation probability can help to strengthen the capacity of emergency management of floods. There are currently two general means of flood sensing: physical and social. Remote sensing data feature high reliability but are often unavailable in disasters caused by persistent heavy rainfall. Social media is characterized by high timeliness and a large data volume but has high redundancy and low reliability. The existing studies have primarily relied on physical sensing data and have not fully exploited the potential of social media data. This paper combines traditional physical sensing data with social media and proposes an integrated physical and social sensing (IPS) method to estimate the probability distribution of flood inundation. Taking the "7·20" Henan rainstorm in 2021 and the study area of Xinxiang, China, as a case study, more than 60,000 messages and 1900 images about this occurrence were acquired from the Weibo platform. Taking filtered water depth points with their geographic location and water depth information as the main input, the inverse distance attenuation function was used to calculate the inundation potential layer of the whole image. Then, the Gaussian kernel was used to weight the physical sensing data based on each water depth point, and finally, the submergence probability layer of the whole image was enhanced. In the validation of the results using radar and social media points, accuracies of 88.77% and 75% were obtained by setting up a threshold classification, demonstrating the effectiveness and usefulness of the method. The significance of this study lies in obtaining discrete social media flood points and achieving space-continuous flood inundation probability mapping, providing decision-making support for urban flood diagnosis and mitigation. [ABSTRACT FROM AUTHOR]

Over the past decades, accumulating evidence suggests that the gut microbiome exerts a key role in Alzheimer's disease (AD). The Alzheimer's Association Workgroup is updating the diagnostic criteria for AD, which changed the profiles and categorization of biomarkers from "AT(N)" to "ATNIVS." Previously, most of studies focus on the correlation between the gut microbiome and amyloid beta deposition ("A"), the initial AD pathological feature triggering the "downstream" tauopathy and neurodegeneration. However, limited research investigated the interactions between the gut microbiome and other AD pathogenesis ("TNIVS"). In this review, we summarize current findings of the gut microbial characteristics in the whole spectrum of AD. Then, we describe the association of the gut microbiome with updated biomarker categories of AD pathogenesis. In addition, we outline the gut microbiome‐related therapeutic strategies for AD. Finally, we discuss current key issues of the gut microbiome research in the AD field and future research directions. Highlights: The new revised criteria for Alzheimer's disease (AD) proposed by the Alzheimer's Association Workgroup have updated the profiles and categorization of biomarkers from "AT(N)" to "ATNIVS."The associations of the gut microbiome with updated biomarker categories of AD pathogenesis are described.Current findings of the gut microbial characteristics in the whole spectrum of AD are summarized.Therapeutic strategies for AD based on the gut microbiome are proposed. [ABSTRACT FROM AUTHOR]

Objective: To explore the association between the apolipoprotein E (APOE) genotype and objectively assessed cognitive function. Methods: In this cross-sectional study, 537 participants underwent a neuropsychological assessment for cognitive function and blood testing for APOE genotype. Based on cognitive test results, participants were stratified into two cohorts: Cognitively Unimpaired participants (CU) and Cognitively Impaired participants (CI). The CI group was further divided into Mild Cognitive Impairment (MCI) and Alzheimer's Disease (AD). Furthermore, we conducted age stratification, categorizing participants into three age groups: age 1: <65 years, age 2: 65–75 years, and age 3: >75 years. We assessed the disparities in cognitive function associated with ε4 carrier status across different age brackets. Plasma amyloid-β levels were measured in a cohort of 294 participants to investigate potential interactions involving ε4 carrier status, diagnosis, sex, or plasma markers. Results: The APOE genotypic distribution among the 537 participants was characterized as follows: ε2/ε2 (5 participants), ε2/ε3 (67), ε2/ε4 (13), ε3/ε3 (330), ε3/ε4 (113), and ε4/ε4 (9). Allele frequencies were: ε3 at 78.21%, ε4 at 13.41%, and ε2 at 8.38%. Notably, the ε4 carrier frequency was markedly elevated in the AD group at 81.8% when compared to MCI at 32.8% and CU at 21.3% (p < 0.05). Within the Cognitively Unimpaired (CU) cohort, the sole discernible contrast between ε4+ and ε4− emerged in STT-B (p < 0.05). Within the CI group, ε4 carriers showed statistically poorer scores as compared to non-ε4 carriers in several cognitive tests (p < 0.05). Age stratification result revealed that, among ε4 carriers, cognitive function scores within the age 3 group were significantly inferior to those of age 1 and age 2 groups (p < 0.05). Plasma amyloid-β detection was applied to the 294 participants. We tested plasma amyloid-β (Aβ42) and plasma amyloid-β (Aβ40) levels and calculated the Aβ42/Aβ40 ratio. We found that among female ε4 carriers, both Aβ42 and the Aβ42/Aβ40 ratio were notably lower than their male counterparts (p < 0.05). Conclusions: The ε3/ε3 was the most prevalent among participants, succeeded by ε3/ε4 and ε2/ε3. The least prevalent were ε2/ε4, ε4/ε4, and ε2/ε2 genotypes. The ε3 was predominant, followed by the ε4 and ε2. Individuals with the ε4 allele exhibited significant cognitive impairment, with an especially high prevalence in AD group at 81.8%. The study unveils a pronounced correlation between the ε4 allele and cognitive deficits, implying its potential role in the advancement and severity of cognitive disorders, notably Alzheimer's disease. Cognitive function declines with age in individuals carrying the ε4, and women are more affected by ε4. [ABSTRACT FROM AUTHOR]

The current work proposes a novel super-resolution convolutional transposed network (SRCTN) deep learning architecture for downscaling daily climatic variables. The algorithm was established based on a super-resolution convolutional neural network with transposed convolutions. This study designed synthetic experiments to downscale daily reference evapotranspiration (ET0) data, which are a key indicator for climate change, from low resolutions (2°, 1°, and 0.5°) to a fine resolution (0.25°). The entire time period was divided into two major parts, i.e., training–validation (80%) and test periods (20%), and the training–validation period was further divided into training (80%) and validation (20%) parts. In the comparison of the downscaling performance between the SRCTN and Q-M models, the root-mean-squared error (RMSE) values indicated the accuracy of the models. For the SRCTN model, the RMSE values were reported for different scaling ratios: 0.239 for a ratio of 8, 0.077 for a ratio of 4, and 0.015 for a ratio of 2. In contrast, the RMSE values for the Q-M method were 0.334, 0.208, and 0.109 for scaling ratios of 8, 4, and 2, respectively. Notably, the RMSE values in the SRCTN model were consistently lower than those in the Q-M method across all scaling ratios, suggesting that the SRCTN model exhibited better downscaling performance in this evaluation. The results exhibited that the SRCTN method could reproduce the spatiotemporal distributions and extremes for the testing period very well. The trained SRCTN model in one study area performed remarkably well in a different area via transfer learning without re-training or calibration, and it outperformed the classic downscaling approach. The good performance of the SRCTN algorithm can be primarily attributed to the incorporation of transposed convolutions, which can be partially seen as trainable upsampling operations. Therefore, the proposed SRCTN method is a promising candidate tool for downscaling daily ET0 and can potentially be employed to conduct downscaling operations for other variables. [ABSTRACT FROM AUTHOR]

Water quality prediction, a well-established field with broad implications across various sectors, is thoroughly examined in this comprehensive review. Through an exhaustive analysis of over 170 studies conducted in the last five years, we focus on the application of machine learning for predicting water quality. The review begins by presenting the latest methodologies for acquiring water quality data. Categorizing machine learning-based predictions for water quality into two primary segments—indicator prediction and water quality index prediction—further distinguishes between single-indicator and multi-indicator predictions. A meticulous examination of each method's technical details follows. This article explores current cutting-edge research trends in machine learning algorithms, providing a technical perspective on their application in water quality prediction. It investigates the utilization of algorithms in predicting water quality and concludes by highlighting significant challenges and future research directions. Emphasis is placed on key areas such as hydrodynamic water quality coupling, effective data processing and acquisition, and mitigating model uncertainty. The paper provides a detailed perspective on the present state of application and the principal characteristics of emerging technologies in water quality prediction. [ABSTRACT FROM AUTHOR]

Land use/land cover change (LUCC) constitutes a significant contributor to variations in the storage of carbon within ecosystems and holds substantial significance within the context of the carbon cycling process. This study analyzed land use data from the Nanchang urban agglomeration in 2000 and 2020 to investigate changes in land use and carbon storage using the PLUS model and GIS. The results show the following: (1) From 2000 to 2020, the Nanchang urban agglomeration experienced reductions in the extents of croplands, woodlands, grasslands, and unused lands. The predominant trend in land transformation involved the conversion of cropland into built-up land. (2) Between 2000 and 2020, there was a declining trajectory observed in carbon storage for the Nanchang urban agglomeration, with an overall decrease of 1.13 × 107 t. The space is characterized by a high-altitude perimeter and a low-altitude center. Urbanization's encroachment on cropland is the main reason for declining carbon storage. (3) The predictive outcomes reveal that, in 2040, carbon storage in the Nanchang urban agglomeration will be reduced by 1.00 × 107 t under the natural development scenario, and reduced by 3.90 × 106 t and increased by 2.29 × 105 t, respectively, under the cropland protection and ecological protection scenarios. The risk of carbon loss is significantly reduced by ecological protection policy interventions. Our analysis of the land use patterns and carbon storage distribution in the Nanchang urban agglomeration over the past 20 years and our exploration of the land use change trend over the next 20 years under the conservation policy provide a reference basis for increasing the carbon sink in the core area of the ecological city cluster of Poyang Lake and realizing the sustainable development of the city. [ABSTRACT FROM AUTHOR]

Surface soil moisture (SSM) and root-zone soil moisture (RZSM) are key hydrological variables for the agricultural water cycle and vegetation growth. Accurate SSM and RZSM forecasting at sub-seasonal scales would be valuable for agricultural water management and preparations. Currently, weather model-based soil moisture predictions are subject to large uncertainties due to inaccurate initial conditions and empirical parameterization schemes, while the data-driven machine learning methods have limitations in modeling long-term temporal dependences of SSM and RZSM because of the lack of considerations in the soil water process. Thus, here, we innovatively integrate the model-based soil moisture predictions from a sub-seasonal-to-seasonal (S2S) model into a data-driven stacked deep learning model to construct a hybrid SSM and RZSM forecasting framework. The hybrid forecasting model is evaluated over the Yangtze River Basin and parts of Europe from 1- to 46-day lead times and is compared with four baseline methods, including the support vector regression (SVR), random forest (RF), convolutional long short-term memory (ConvLSTM) and the S2S model. The results indicate substantial skill improvements in the hybrid model relative to baseline models over the two study areas spatiotemporally, in terms of the correlation coefficient, unbiased root mean square error (ubRMSE) and RMSE. The hybrid forecasting model benefits from the long-lead predictive skill from S2S and retains the advantages of data-driven soil moisture memory modeling at short-lead scales, which account for the superiority of hybrid forecasting. Overall, the developed hybrid model is promising for improved sub-seasonal SSM and RZSM forecasting over global and local areas. [ABSTRACT FROM AUTHOR]

Volunteered geographic information (VGI) plays an increasingly crucial role in flash floods. However, topic classification and spatiotemporal analysis are complicated by the various expressions and lengths of social media textual data. This paper conducted applicability analysis on bidirectional encoder representation from transformers (BERT) and four traditional methods, TextRank, term frequency–inverse document frequency (TF-IDF), maximal marginal relevance (MMR), and linear discriminant analysis (LDA), and the results show that for user type, BERT performs best on the Government Affairs Microblog, whereas LDA-BERT performs best on the We Media Microblog. As for text length, TF-IDF-BERT works better for texts with a length of <70 and length >140 words, and LDA-BERT performs best with a text length of 70–140 words. For the spatiotemporal evolution pattern, the study suggests that in a Henan rainstorm, the textual topics follow the general pattern of "situation-tips-rescue". Moreover, this paper detected the hotspot of "Metro Line 5" related to a Henan rainstorm and discovered that the topical focus of the Henan rainstorm spatially shifts from Zhengzhou, first to Xinxiang, and then to Hebi, showing a remarkable tendency from south to north, which was the same as the report issued by the authorities. We integrated multi-methods to improve the overall topic classification accuracy of Sina microblogs, facilitating the spatiotemporal analysis of flooding. [ABSTRACT FROM AUTHOR]

From a process perspective, a flood includes several phases with distinguishable features. Fine-grained multisource data for different flood phases can be used to inform decision-making as flooding progresses. Therefore, the aim of this study was to develop an integrated framework based on human perceptions to progressively profile floods, including flood process segmentation rules (FPSR), flood severity index (FSI) and flood process perception ontology (FPPO). FPSR identifies flood phases based on specific signals in multisource data and provides spatiotemporal process information to FPPO consistent with flood perception. FSI follows FPSR to evaluate flooding throughout its evolution process. The comparison between FPSR and the flood monitoring index (IF) demonstrates that FPSR can detect flood events and segment the flooding process into latency, onset, development and recovery phases. The correlations between the standardized antecedent precipitation index (SAPI) and FSI show that FSI can assess flood severity with both natural and social effects in every flooding phase (R2 = 0.726 and 0.673 for the 2016 and 2020 floods, respectively). An experiment finds that flood events in Wuhan, China, usually begin in mid-to-late June and are the most severe in July, when more caution is needed for flood prevention and mitigation. [ABSTRACT FROM AUTHOR]

Ocean primary productivity generated by phytoplankton is critical for ocean ecosystems and the global carbon cycle. Accurate ocean primary productivity forecasting months in advance is beneficial for marine management. Previous persistence-based prediction studies ignore the temporal memories of multiple relevant factors and the seasonal forecasting skill drops quickly with increasing lead time. On the other hand, the emerging ensemble climate forecasts are not well considered as new predictability sources of ocean conditions. Here we proposed a joint forecasting model by combining the seasonal climate predictions from ten heterogeneous models and the temporal memories of relevant factors to examine the monthly predictability of ocean productivity from 0.5- to 11.5-month lead times. The results indicate that a total of ~90% and ~20% productive oceans are expected to be skillfully predicted by the combination of seasonal SST predictions and local memory at 0.5- and 4.5-month leads, respectively. The joint forecasting model improves by 10% of the skillfully predicted areas at 6.5-month lead relative to the prediction by productivity persistence. The hybrid data-driven and model-driven forecasting approach improves the predictability of ocean productivity relative to individual predictions, of which the seasonal climate predictions contribute largely to the skill improvement over the equatorial Pacific and Indian Ocean. These findings highlight the advantages of the integration of climate predictions and temporal memory for ocean productivity forecasting and may provide useful seasonal forecasting information for ocean ecosystem management. [ABSTRACT FROM AUTHOR]

One of the major barriers to hindering the sustainable development of the terrestrial environment is the desertification process, and revegetation is one of the most significant duties in anti-desertification. Desertification deteriorates land ecosystems through species decline, and remote sensing is becoming the most effective way to monitor desertification. Mu Us Sandy Land is the fifth largest desert and the representative area under manmade vegetation restorations in China. Therefore, it is essential to understand the spatiotemporal characteristics of artificial desert transformation for seeking the optimal revegetation location for future restoration planning. However, there are no previous studies focusing on exploring regular patterns between the spatial distribution of vegetation restoration and human-related geographical features. In this study, we use Landsat satellite data from 1986 to 2020 to achieve annual monitoring of vegetation change by a threshold segmentation method, and then use spatiotemporal analysis with Open Street Map (OSM) data to explore the spatiotemporal distribution pattern between vegetation occurrence and human-related features. We construct an artificial vegetation restoration suitability index (AVRSI) by considering human-related features and topographical factors, and we assess artificial suitability for vegetation restoration by mapping methods based on that index and the vegetation distribution pattern. The AVRSI can be commonly used for evaluating restoration suitability in Sandy areas and it is tested acceptable in Mu Us Sandy Land. Our results show during this period, the segmentation threshold and vegetation area of Mu Us Sandy Land increased at rates of 0.005/year and 264.11 km2/year, respectively. Typically, we found the artificial restoration vegetation suitability in Mu Us area spatially declines from southeast to northwest, but eventually increases in the most northwest region. This study reveals the revegetation process in Mu Us Sandy Land by figuring out its spatiotemporal vegetation change with human-related features and maps the artificial revegetation suitability. [ABSTRACT FROM AUTHOR]

Background: Sleep appears to be a sensitive biomarker that facilitates early detection and effective intervention for Alzheimer's disease, while subjective cognitive decline (SCD) is a risk factor for Alzheimer's disease. Prefrontal cortex atrophy is associated with both sleep disruption and cognitive decline. Transcranial brain photobiomodulation (PBM) therapy can enhance frontal cortex oxygen consumption, increasing frontal cortex mediated memory function.Objective: This study aimed to test whether PBM therapy targeting the frontal cortex could improve sleep and cognitive function in SCD.Methods: Fifty-eight SCDs were divided into the PBM group (N = 32) in which real light therapy was administered and a sham light therapy group (N = 26). All the participants received either real light or sham light therapy for 6 days consecutively, while the sleep data were recorded. The n-back task was employed to measure each participant's working memory.Results: We found no differences in sleep efficiency change (F = 211, p = 0.279), REM stage percent change (F = 420, p = 0.91), and wake-up time (F = 212, p = 0.277) between the two groups. The sleep efficiency and REM were improved within the true light group on the fifth day. The true light group perform better than the control group in the n-back test, the accuracy was higher in the 2-back test (88.6% versus 79.6%, p = 0.001), and the reaction time in 1-back was shorter (544.80±202.00 versus 592.87±222.05, p = 0.003).Conclusion: After five days of PBM therapy targeting the prefrontal cortex, sleep efficiency and N-back cognitive performance were improved on the fifth day. [ABSTRACT FROM AUTHOR]

Background: Subjective cognitive decline (SCD) is considered as the first symptomatic manifestation of Alzheimer's disease (AD), which is also affected by different cultural backgrounds. Establishing cross-cultural prediction models of SCD is challenging.Objective: To establish prediction models of SCD available for both the Chinese and European populations.Methods: In this project, 330 SCD from China and 380 SCD from Germany are intended to be recruited. For all participants, standardized assessments, including clinical, neuropsychological, apolipoprotein E (APOE) genotype, blood, and multi-parameter magnetic resonance imaging (MRI) at baseline will be conducted. Participants will voluntarily undergo amyloid positron emission tomography (PET) and are classified into amyloid-β (Aβ) positive SCD (SCD+) and Aβ negative SCD (SCD-). First, baseline data of all SCD individuals between the two cohorts will be compared. Then, key features associated with brain amyloidosis will be extracted in SCD+ individuals, and the diagnosis model will be established using the radiomics method. Finally, the follow-up visits will be conducted every 12 months and the primary outcome is the conversion to mild cognitive impairment or dementia. After a 4-year follow-up, we will extract factors associated with the conversion risk of SCD using Cox regression analysis.Results: At present, 141 SCD from China and 338 SCD from Germany have been recruited. Initial analysis showed significant differences in demographic information, neuropsychological tests, and regional brain atrophy in SCD compared with controls in both cohorts.Conclusion: This project may be of great value for future implications of SCD studies in different cultural backgrounds.Trial Registration: ClinicalTrials.gov, NCT04696315. Registered 3 January 2021. [ABSTRACT FROM AUTHOR]

Background: Evidence suggests that subjective cognitive decline (SCD) individuals with worry have a higher risk of cognitive decline. However, how SCD-related worry influences the functional brain network is still unknown.Objective: In this study, we aimed to explore the differences in functional brain networks between SCD subjects with and without worry.Methods: A total of 228 participants were enrolled from the Sino Longitudinal Study on Cognitive Decline (SILCODE), including 39 normal control (NC) subjects, 117 SCD subjects with worry, and 72 SCD subjects without worry. All subjects completed neuropsychological assessments, APOE genotyping, and resting-state functional magnetic resonance imaging (rs-fMRI). Graph theory was applied for functional brain network analysis based on both the whole brain and default mode network (DMN). Parameters including the clustering coefficient, shortest path length, local efficiency, and global efficiency were calculated. Two-sample T-tests and chi-square tests were used to analyze differences between two groups. In addition, a false discovery rate-corrected post hoc test was applied.Results: Our analysis showed that compared to the SCD without worry group, SCD with worry group had significantly increased functional connectivity and shortest path length (p = 0.002) and a decreased clustering coefficient (p = 0.013), global efficiency (p = 0.001), and local efficiency (p < 0.001). The above results appeared in both the whole brain and DMN.Conclusion: There were significant differences in functional brain networks between SCD individuals with and without worry. We speculated that worry might result in alterations of the functional brain network for SCD individuals and then result in a higher risk of cognitive decline. [ABSTRACT FROM AUTHOR]

The discovery of preclinical Alzheimer's disease (preAD) provides a wide time window for the early intervention of AD. The coupling relationships between glucose and oxygen metabolisms from hybrid PET/MRI can provide complementary information on the brain's physiological state for preAD. In this study, we purpose to explore the change of coupling relationship among 27 normal controls (NCs), 20 preADs, and 15 cognitive impairments (CIs). For each subject, we calculated the Spearman partial correlation between the fractional amplitude of low‐frequency fluctuations (fALFF) and the regional homogeneity (ReHo) from functional image (fMRI), and the standard uptake value ratio (SUVR) from [18F] fluorodeoxyglucose positron emission tomography (18F‐FDG PET), in the whole‐brain and default mode network (DMN) as a novel potential biomarker. The diagnostic performance of this biomarker was evaluated by the receiver operating characteristic analysis. Significant Spearman correlations between the FDG SUVR and the fALFF/ReHo were found in 98% of subjects. For the DMN‐based biomarker, there was a significant decreasing trend for the preAD and CI groups compared to the NC group, whereas no significant difference in preAD based on whole‐brain. The correlation ρ value for the FDG SUVR/ReHo showed the highest area under curve of the preAD classification (0.787). The results imply the coupling relationship changed during the preAD stage in the DMN area. [ABSTRACT FROM AUTHOR]

Background: Mounting evidence suggests that sex differences exist in cognitive reserve (CR) for cognitively unimpaired (CU) elderly individuals. Global left frontal connectivity (gLFC connectivity) is a reliable neural substrate of CR.Objective: The purpose of this study was to explore sex differences in gLFC connectivity among CU elderly individuals.Methods: One hundred thirteen normal controls (NCs) (women = 66) and 132 individuals with subjective cognitive decline (SCD) (women = 92) were recruited from the Sino Longitudinal Study on Cognitive Decline (SILCODE) (data 1). Among them, 88 subjects underwent amyloid-β (Aβ) imaging, including 32 Aβ+ and 56 Aβ-subjects. Forty-six subjects underwent another rs-fMRI examination (data 2) to validate the repeatability of the calculation of gLFC connectivity, which was determined through seed-based functional connectivity between the LFC and voxels throughout the whole brain. Independent-sample t-tests were used to evaluate the sex differences in gLFC connectivity across different subgroups (NC versus SCD, Aβ+ versus Aβ-). Partial correlation analysis was used to calculate the correlations between gLFC connectivity and cognitive assessments.Results: Women exhibited lower gLFC connectivity in both the NC (p = 0.001) and SCD (p = 0.020) subgroups than men. Women also exhibited lower gLFC connectivity in both the Aβ-(p = 0.006) and Aβ+ (p = 0.025) groups. However, the significant difference disappeared in the Aβ+ group when considering the covariates of age, education, total intracranial volume, and APOE4-carrying status. In addition, gLFC connectivity values were negatively correlated with Geriatric Depression Scale scores in the SCD group (r = -0.176, p = 0.047).Conclusion: Women showed lower gLFC connectivity among CU elderly individuals. [ABSTRACT FROM AUTHOR]

Background: Visual rating scales for medial temporal lobe atrophy (MTA) and posterior atrophy (PA) have been reported to be useful for Alzheimer's disease diagnosis in routine clinical practice.Objective: To investigate the efficacy of combined MTA and PA visual rating scales to discriminate amnestic mild cognitive impairment (aMCI) patients from healthy controls.Methods: This study included T1-weighted MRI images from two different cohorts. In the first cohort, we recruited 73 patients with aMCI and 48 group-matched cognitively normal controls for training and validation. Visual assessments of MTA and PA were carried out for each participant. Global gray matter volume and density were estimated using voxel-based morphometry analysis as the objective reference. We investigated the discriminative power of a single visual rating scale and the combination of the MTA and PA rating scales for identifying aMCI. The second cohort, consisting of 33 aMCI patients and 45 controls, was used to verify the reliability of the visual assessments.Results: Compared with the single visual rating scale, the combination of the MTA and PA exhibited the best discriminative power, with an AUC of 0.818±0.041, which was similar to the diagnostic accuracy of the gray matter volumetric measures. The discriminative power of the combined MTA and PA was verified in the second cohort (AUC 0.824±0.058).Conclusion: The combined MTA and PA rating scales demonstrated practical diagnostic value for distinguishing aMCI patients from controls, suggesting its potential to serve as a convenient and reproducible method to assess the degree of atrophy in clinical settings. [ABSTRACT FROM AUTHOR]

Background: Subjective cognitive decline (SCD) refers to the self-perception of cognitive decline among cognitively normal elderly individuals. SCD related worry confers a higher risk of developing cognitive decline. However, the clinical characteristics of SCD patients with worry are not clear.Objective: To explore the clinical characteristics of SCD patients with worry.Methods: A cross-sectional study was carried out, with 270 consecutive participants of the Sino Longitudinal Study on Cognitive Decline (SILCODE) study. Participants were classified as normal controls (n = 36), SCD patients without worry (n = 91), or SCD patients with worry (n = 143) and were comprehensively compared on 1) their self-perception of cognitive decline, 2) multiple cognitive domains, 3) neuropsychiatric symptoms, and 4) sleep status.Results: SCD patients with worry had significantly more self-perception of cognitive decline (p < 0.001); increased depression (p < 0.001) and anxiety (p < 0.001); decreased sleep quality (p < 0.001), sleep latency (p < 0.05), sleep time (p < 0.01), and sleep efficiency (p < 0.05); more sleep disorders (p < 0.05) and daytime dysfunction (p < 0.05); and a higher global score on the Pittsburgh Sleep Quality Index (p < 0.001) than normal controls. Although there was a significant increase only in self-perception of cognitive decline (p < 0.001), anxiety (p < 0.001), and Pittsburgh Sleep Quality Index scores (p < 0.05), the severity of the increase in those without worry was between that in SCD patients with worry and normal controls.Conclusion: Our findings show that participants who had SCD with worry showed distinct clinical characteristics compared with normal controls and SCD patients without worry, which could be useful for understanding the higher risk in SCD patients with worry of subsequently developing Alzheimer's disease. [ABSTRACT FROM AUTHOR]

Flood Detection and Service (FD&S) is of great significance to flood management and decision-making. But most of the state-of-art FD&S methods are weak in timeliness and extensibility, and could not reflect flood processes. To resolve the abovementioned problems, this paper proposed the process-based FD&S (PFD&S) method based on Sensor Web. PFD&S is a four-layer architecture the core components of which are the access adapter and process-based detection rules and it is capable of heterogeneous sensor access, flood detection, and flood phase adaptive services. A prototype was developed based on the PFD&S method. Two floods occurring in the Huanghan basin (Hubei, China, with the area of 5.04 × 104 km2) during July 2016, was selected as the case studies to validate the PFD&S method and prototype. The results demonstrated that the proposed PFD&S method and prototype could achieve the instant flood process detection and services in 2.7 min. • The PFD&S method was proposed to achieve the rapid flood process detection and flood phase adaptive services. • The PFD&S prototype was designed and implemented based on the PFD&S method. • The method and prototype were sensor web - based, with universality and extensibility. [ABSTRACT FROM AUTHOR]

A functional brain network, termed the parietal memory network (PMN), has been shown to reflect the familiarity of stimuli in both memory encoding and retrieval. The function of this network has been separated from the commonly investigated default mode network (DMN) in both resting-state fMRI and task-activations. This study examined the deficit of the PMN in Alzheimer's disease (AD) patients using resting-state fMRI and independent component analysis (ICA) and investigated its diagnostic value in identifying AD patients. The DMN was also examined as a reference network. In addition, the robustness of the findings was examined using different types of analysis methods and parameters. Our results showed that the integrity as an intrinsic connectivity network for the PMN was significantly decreased in AD and this feature showed at least equivalent predictive ability to that for the DMN. These findings were robust to varied methods and parameters. Our findings suggest that the intrinsic connectivity of the PMN is disrupted in AD and further call for considering the PMN and the DMN separately in clinical neuroimaging studies. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is one of the most common progressive and irreversible neurodegenerative diseases. The study of the pathological mechanism of AD and early-stage diagnosis is essential and important. Subjective cognitive decline (SCD), the first at-risk stage of AD occurring prior to amnestic mild cognitive impairment (aMCI), is of great research value and has gained our interest. To investigate the entire pathological development of AD pathology efficiently, we proposed a machine learning classification method based on a multimodal support vector machine (SVM) to investigate the structural and functional connectivity patterns of the three stages of AD (SCD, aMCI, and AD). Our experiments achieved an accuracy of 98.58% in the AD group, 97.76% in the aMCI group, and 80.24% in the SCD group. Moreover, in our experiments, we identified the most discriminating brain regions, which were mainly located in the default mode network and subcortical structures (SCS). Notably, with the development of AD pathology, SCS regions have become increasingly important, and structural connectivity has shown more discriminative power than functional connectivity. The current study may shed new light on the pathological mechanism of AD and suggests that whole-brain connectivity may provide potential effective biomarkers for the early-stage diagnosis of AD. [ABSTRACT FROM AUTHOR]

Background: Subjective cognitive decline (SCD) is a high risk for Alzheimer's disease. The physiological mechanisms of SCD is largely unknown. Most of the existing studies made their observations during the resting state of the brain. How the SCD group performs differently from the normal controls (NC) during functional tasks is less studied. This study compared the responses of SCD and NC evoked by loaded working memory tasks. The results of this study help clarify the underlying hemodynamic mechanisms of SCD. Methods: 15 NC (8 females) and 23 SCD subjects (14 females) participated in the study. Table 1 shows the biographical and behavioral data of the two groups. All subjects participated block‐designed 0‐back, 1‐back, 2‐back and 3‐back working memory tasks. Followed by the N‐back tasks, each subject underwent a hypercapnic challenge during which the subject breathed a gas mixture (5% CO2, 20% O2, 75%N2) through a non‐rebreather mask. A pulse sequence which simultaneously acquires cerebral blood volume (CBV), cerebral blood flow (CBF), blood oxygen‐level dependent (BOLD)‐weighted signals was implemented on a 3.0 T GE MR750 system. At the group level, holding sex, age and education as covariates, a general linear model was applied to compare the above parameters between the two groups, and a partial correlation test was performed to relate the imaging results with the behavior data. P < 0.05 was considered statistically significant. Results: The group averages of δCBV, δCBF, δBOLD and δCMRO2 evoked by 1‐, 2‐ and 3‐back, with respect to 0‐back, in the frontal cortex were displayed in Figure 1. In general, all parameters show a load‐dependent effect, where 1‐back caused minimum signal changes. Among all parameters, δCBF evoked by 3‐back, with respect to 0‐back, was significantly lower (p = 0.04) in the SCD group compared to the NC group. None of the other parameters showed statistical differences between the two groups. In addition, δCBF at 3‐back was found to be significantly correlated with the MoCA (p = 0.04) and AFT (p 0.03) scores of the subjects. Conclusion: This study may help understand the physiological mechanisms and develop early diagnostic strategies of SCD. [ABSTRACT FROM AUTHOR]

Background: Recent criteria have unveiled the importance of biomarkers in the continuum of Alzheimer's disease, which greatly enhances the progression of clinical trials of prevention and treatment. However, the expanding cost and resources limit the first‐line brain amyloid measurement techniques. Objective: We aimed to develop an inexpensive and sensitive method to predict brain amyloid‐β burden by integrating machine learning and multimodal MRI. Methods: Eighty‐three elders were enrolled from the Xuanwu Hospital. Brain amyloid‐β burden, structural and functional changes were respectively assessed with (18)F‐AV45 amyloid PET, structural and diffusion MRI, functional MRI. Using regression machine implementations, we predicted brain amyloid‐β level by voxel‐based morphometry, fractional anisotropy and fractional amplitude of low frequency fluctuations (fALFF). Results: We found that extracted features could predict brain amyloid‐β burden, using voxel‐based morphometry (r=0.55, p=0.001) or/and fractional anisotropy (r=0.36, p=0.002), but not fALFF(r=0.07, p=0.2388). These results were replicated using cross‐validation procedures and these models were statistical significance determined by permutation testing. The brain regions that showed most important to predict amyloid‐β burden were superior temporal gyrus, parahippocampal gyrus, inferior frontal gyrus and hippocampus. Conclusion: The current study showed high correlation between the structural and diffusion MRI indexes and amyloid burden in the elders which opens a new possibility for early diagnosis of AD. [ABSTRACT FROM AUTHOR]