A systematic review and meta-analysis of Comaneci/Cascade temporary neck bridging devices for the treatment of intracranial aneurysms

BackgroundThe temporary neck bridging devices represented by Comaneci and Cascade are a type of promising endovascular device for the treatment of intracranial bifurcation or wide-necked aneurysms. This systematic review and meta-analysis aim to assess the efficacy and safety of Comaneci/Cascade devices for the treatment of intracranial aneurysms.MethodsWe performed a systematic literature search on articles in PubMed, Embase, and Web of Science that evaluated the efficacy and safety of Comaneci/Cascade devices for endovascular treatment of intracranial aneurysms, based on the Preferred Reporting Items for Systematic Reviews and Meta Analytics (PRISMA) guideline. We extracted the characteristics and treatment related information of patients included in the study, recorded the rate of technical success, procedural related complications, and angiographic outcomes. The angiographic outcome was evaluated based on Raymond Roy classification, and adequate occlusion was defined as Raymond Ray I + II.ResultsNine studies comprising 253 patients with 255 aneurysms were included. Among them, eight studies were conducted in Europe, one study was conducted in the USA. All these studies were retrospective. 206 aneurysms (80.78%) were ruptured. The vast majority of patients with ruptured aneurysms did not receive antiplatelet therapy. The rate of technical success was 97.1% (95% CI, 94.9 to 99.3%, I2 = 0%). The rate of periprocedural clinical complications was 10.9% (95% CI, 5.4 to 22.1%, I2 = 54%). The rate of complete occlusion (RR1) and adequate occlusion (RR1 + RR2) on immediate angiography after the procedure were 77.7% (95% CI, 72.7 to 83.2%, I2 = 35%) and 98% (95% CI, 95.9 to 100%, I2 = 0%) respectively. The rate of complete occlusion (RR1) and adequate occlusion (RR1 + RR2) on the last follow-up angiography were 81.2% (95% CI, 69.2 to 95.2%, I2 = 81%) and 93.7% (95% CI, 85.6 to 100%, I2 = 69%) respectively, with follow-up range from 3 to 18 months. 22/187 (11.76%) cases of aneurysms progressed during the follow-up period. 39/187 (20.86%) cases of aneurysms received additional treatment during the follow-up period. No fatal complications occurred during the treatment.ConclusionThe Comaneci/Cascade device can be used as an auxiliary treatment for intracranial aneurysms, with a good occlusion effect, but the incidence of complications still needs to be monitored

Time correlation of success recanalization for endovascular recanalization of medically refractory non-acute intracranial arterial occlusions

Background and purposeThe management of patients with symptomatic non-acute atherosclerotic intracranial artery occlusion (sNAA-ICAO), which is a special subset with high morbidity and a high probability of recurrent serious ischemic events despite standard medical therapy, has been clinically challenging. A number of small-sample clinical studies have discussed endovascular recanalization for sNAA-ICAO and the lack of a uniform standard of operation time. The purpose of this study was to investigate the time correlation of successful recanalization.MethodsFrom January 2013 to August 2021, 69 consecutive patients who underwent endovascular recanalization for sNAA-ICAO were analyzed retrospectively in the First Affiliated Hospital of Harbin Medical University. The technical success rate, periprocedural complications, and rate of TIA/ischemic stroke during follow-up were evaluated.ResultsThe overall technical success rate was 73.91% (51/69), and the rate of perioperative complications was 37.68% (26/69). The percentage of patients with perioperative symptoms was 27.53% (19/69). The rate of serious symptomatic perioperative complications was 8.70% (6/69). After adjusting for age, sex, and BMI, the effect of the time from the last symptom to operation on successful recanalization was 0.42 (IQR, 0.20, 0.88, P = 0.021), before the inflection point (51 days).ConclusionsEndovascular recanalization for sNAA-ICAO is technically feasible in reasonably selected patients. The perioperative safety is within the acceptable range. Before 51 days, the last symptoms to operation time, for every 10 days of delay, the probability of successful recanalization is reduced by 57%

Remote Sensing-Supported Flood Forecasting of Urbanized Watersheds—A Case Study in Southern China

Urbanization has significant impacts on watershed hydrology, but previous studies have been confirmatory and not comprehensive; in particular, few studies have addressed the impact of urbanization on flooding in highly urbanized watersheds. In this study, this effect is studied in Chebei Creek, a highly urbanized watershed in the Pearl River Delta, southern China. Landsat satellite images acquired in 2015 were used to estimate land use and cover changes using the Decision Tree (DT) C4.5 classification algorithm, while the Liuxihe model, a physically based distributed hydrological model (PBDHM), is employed to simulate watershed flooding and hydrological processes. For areas with high degrees of urbanization, the duration of the flood peak is only 1 h, and the flood water level shows steep rises and falls. These characteristics increase the difficulty of flood modeling and forecasting in urbanized areas. At present, hydrological research in urbanized watersheds generally focuses on the quantitative simulation of runoff from urban areas to the watershed, flood flows, peak flood flow, and runoff depth. Few studies have involved real-time flood forecasting in urbanized watersheds. To achieve real-time flood forecasting in urbanized watersheds, PBDHMs and refined underlying surface data based on remote sensing technology are necessary. The Liuxihe model is a PBDHM that can meet the accuracy requirements of inflow flood forecasting for reservoir flood control operations. The accuracies of the two flood forecasting methods used in this study were 83.95% and 97.06%, showing the excellent performance of the Liuxihe model for the real-time flood forecasting of urbanized rivers such as the Chebei Creek watershed

A machine learning-based approach for generating high-resolution soil moisture from SMAP products

The coarse resolutions of passive microwave surface soil moisture (SSM) products often hamper the applications of such products in regional or local studies. This study developed a machine learning-based approach for the downscaling of Soil Moisture Active and Passive (SMAP) SSM products at both ascending and descending passes based on the relationships between SSM and environmental variables, such as the land surface temperature (LST), enhanced vegetation index (EVI), surface albedo, cumulative precipitation, digital elevation model (DEM), and soil texture. Two machine learning algorithms—random forest (RF) and long short-term memory network (LSTM)—were applied to downscale the SMAP product from a coarse resolution (36 km) to a fine resolution (1 km). The downscaled SSM results were validated against ground observations. The contributions of environmental variables to the established model were also discussed. The results showed that both RF and LSTM models demonstrated satisfactory performance in SMAP SSM prediction, indicating that the downscaling models were able to learn the relationships between the environmental variables and SMAP SSM well at coarse resolution. The RF and LSTM-based downscaled SSM maps not only realized full spatial coverage but also reasonably maintained the spatiotemporal evolution trends of the original SMAP SSM products. Both downscaled results correlated well with the ground observations and indicated good temporal consistency with the daily precipitation. In addition, the variable importance analysis reveals that the DEM, precipitation, EVI, and surface albedo were dominant to establishing SSM regression models, particularly for DEM in regions with large height differences. Overall, RF and LSTM-based models are promising downscaling approaches for generating full spatial coverage and fine-resolution SSM data from passive microwave SSM

Accelerate spatiotemporal fusion for large-scale applications

Spatiotemporal fusion (STF) can provide dense satellite image series with high spatial resolution. However, most spatiotemporal fusion approaches are time-consuming, which seriously limits their applicability in large-scale areas. To address this problem, some efforts have been paid for accelerating STF approaches with help of graphics processing units (GPUs), whose effect is dramatic. However, this strategy is hardware dependent, which may not be always satisfied. In this paper, we develop a hardware independent accelerating strategy, named AcSTF. The proposed AcSTF consists of two steps, which are medium resolution STF (MSTF) and local normalization-based fast fusion (LNFM). The MSTF utilizes STF methods to improve the coarse spatial resolution images to a medium spatial resolution, while the LNFM further refines the medium spatial resolution images to provide fine spatial resolution images. To test the AcSTF, the experiments are conducted using five commonly used STF approaches on two public Landsat-MODIS datasets. The experimental results indicate that AcSTF can not only reduce 87%–95% running time of current STF approaches, but also preserve their qualitative and quantitative performance well. After that, we apply the AcSTF to produce an intact 30 m image of the whole Ukraine mainland. Without any hardware which can speed up computing,the time for reconstructing the 30 m image is 5.42 h just using an unremarkable central processing unit (CPU). Compared to the real Landsat image, the reconstructed image achieves remarkable qualitative and quantitative performance, which demonstrates the practicability of the AcSTF

Integrating DMSP-OLS and NPP-VIIRS Nighttime Light Data to Evaluate Poverty in Southwestern China

Poverty alleviation is one of the most important tasks facing human social development. It is necessary to make accurate monitoring and evaluations for areas with poverty to improve capability of implementing poverty alleviation policies. Here, this study introduced nighttime light (NTL) data to estimate county-level poverty in southwest China. First, this study used particle swarm optimization-back propagation hybrid algorithm to explore the potential relationship between two NTL data (the Defense Meteorological Satellite Program’s Operational Line Scan System data and the Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite data). Then, we integrated two NTL data at the pixel level to establish a consistent time-series of NTL dataset from 2000 to 2019. Next, an actual comprehensive poverty index (ACPI) was employed as an indicator of multidimensional poverty at county level based on 11 socioeconomic and natural variables, and which could be the reference to explore the poverty evaluation using NTL data. Based on the correlation between the ACPI and NTL characteristic variables, a poverty evaluation model was developed to evaluate the poverty situation. The result showed the great matching relationship between DMSP-OLS and NPP-VIIRS data (R2 = 0.84). After calibration, the continuity and comparability of DMSP-OLS data were significantly improved. The integrated NTL data also reflected great consistency with socioeconomic development (r = 0.99). The RMSE between ACPI and the estimated comprehensive poverty index (ECPI) based on the integrated NTL data is approximately 0.19 (R2 = 0.96), which revealed the poverty evaluation model was feasible and reliable. According to the ECPI, we found that the magnitude of poverty eradication increased in southwest China until 2011, but slowed down from 2011 to 2019. Regarding the spatial scale, geographic barriers are a key factor for poverty, with high altitude and mountainous areas typically having a high incidence of poverty. Our approach offers an effective model for evaluation poverty based on the NTL data, which can contribute a more reliable and efficient monitoring of poverty dynamic and a better understanding of socioeconomic development

A new avenue to improve the performance of integrated modeling for flash flood susceptibility assessment: Applying cluster algorithms

Flash flood is one of the most severe natural disasters around the world, and has caused sizeable economic losses and countless death. Assessing flash flood susceptibility by hybrid models of statistical and machine learning methods is essential for flood mitigation strategies and disaster preparedness. Although classifying the flash flood conditioning factors becomes a crucial step before applying these hybrid models, their impact on the accuracy of integrated modeling is still unclear. Most previous studies used natural break classification (NBC) and quantile classification methods by default to conduct the classification, but more classification methods have not been tried. In this context, this study introduced three clustering algorithms of K-Means, Expectation Maximization, and ISOMaximum likelihood algorithm (ISOMax) into the classification of factors, and compared them to NBC and quantile classification. To test the impact of classification methods on integrated modeling, these classification results were applied into the construction of three hybrid models (i.e., the integrating of frequency ratio with support vector machines, random forest, and bayesian-regularization neural networks). Then, the accuracy of these hybrid models was evaluated by using ROC curves and statistical indicators. The classification results show that the clustering intervals in the same factor varied with classification algorithms. It can be found from the model performance evaluation results that different classification algorithms will lead to discrepancies in accuracy of integrated modeling. Compared to NBC, the ISOMax allows a better fitting and prediction ability of hybrid models in this study. The application of clustering algorithm provides a new perspective for improving the accuracy of integrated modeling

Vegetation Change and Its Response to Climate Change in Yunnan Province, China

The impact of global climate change on vegetation has become increasingly prominent over the past several decades. Understanding vegetation change and its response to climate can provide fundamental information for environmental resource management. In recent years, the arid climate and fragile ecosystem have led to great changes in vegetation in Yunnan Province, so it is very important to further study the relationship between vegetation and climate. In this study, we explored the temporal changes of normalized difference vegetation index (NDVI) in different seasons based on MOD13Q1 NDVI by the maximum value composite and then analyzed spatial distribution characteristics of vegetation using Sen’s tendency estimation, Mann–Kendall significance test, and coefficient of variation model (CV) combined with terrain factors. Finally, the concurrent and lagged effects of NDVI on climate factors in different seasons and months were discussed using the Pearson correlation coefficient. The results indicate that (1) the temporal variation of the NDVI showed that the NDVI values of different vegetation types increased at different rates, especially in growing season, spring, and autumn; (2) for spatial patterns, the NDVI, CV, and NDVI trends had strong spatial heterogeneity owning to the influence of altitudes, slopes, and aspects; and (3) the concurrent effect of vegetation on climate change indicates that the positive effect of temperature on NDVI was mainly in growing season and autumn, whereas spring NDVI was mainly influenced by precipitation. In addition, the lag effect analysis results revealed that spring precipitation has a definite inhibition effect on summer and autumn vegetation, but spring and summer temperature can promote the growth of vegetation. Meanwhile, the precipitation in the late growing season has a lag effect of 1-2 months on vegetation growth, and air temperature has a lag effect of 1 month in the middle of the growing season. Based on the above results, this study provided valuable information for ecosystem degradation and ecological environment protection in the Yunnan Province

Dynamics and Drivers of Vegetation Phenology in Three-River Headwaters Region Based on the Google Earth Engine

Phenology shifts over time are known as the canary in the mine when studying the response of terrestrial ecosystems to climate change. Plant phenology is a key factor controlling the productivity of terrestrial vegetation under climate change. Over the past several decades, the vegetation in the three-river headwaters region (TRHR) has been reported to have changed greatly owing to the warming climate and human activities. However, uncertainties related to the potential mechanism and influence of climatic and soil factors on the plant phenology of the TRHR are poorly understood. In this study, we used harmonic analysis of time series and the relative and absolute change rate on Google Earth Engine to calculate the start (SOS), end (EOS), and length (LOS) of the growing season based on MOD09A1 datasets; the results were verified by the observational data from phenological stations. Then, the spatiotemporal patterns of plant phenology for different types of terrain and basins were explored. Finally, the potential mechanism involved in the influence of climatic and soil factors on the phenology of plants in the TRHR were explored based on the structural equation model and Pearson’s correlation coefficients. The results show the remotely sensed monitoring data of SOS (R2 = 0.84, p < 0.01), EOS (R2 = 0.72, p < 0.01), and LOS (R2 = 0.86, p < 0.01) were very similar to the observational data from phenological stations. The SOS and LOS of plants possessed significant trends toward becoming advanced (Slope < 0) and extended (Slope > 0), respectively, from 2001 to 2018. The SOS was the earliest and the LOS was the longest in the Lancang River Basin, while the EOS was the latest in the Yangtze River Basin owing to the impact of climate change and soil factors. Meanwhile, the spatial patterns of SOS, EOS, and LOS have strong spatial heterogeneity at different elevations, slopes, and aspects. In addition, the results show that the drivers of plant phenology have basin-wide and stage differences. Specifically, the influence of soil factors on plant phenology in the Yangtze River Basin was greater than that of climatic factors, but climatic factors were key functional indicators of LOS in the Yellow and Lancang river basins, which directly or indirectly affect plant LOS through soil factors. This study will be helpful for understanding the relationship between the plant phenology of the alpine wetland ecosystem and climate change and improving the level of environmental management

Hybrid Models Incorporating Bivariate Statistics and Machine Learning Methods for Flash Flood Susceptibility Assessment Based on Remote Sensing Datasets

Flash floods are considered to be one of the most destructive natural hazards, and they are difficult to accurately model and predict. In this study, three hybrid models were proposed, evaluated, and used for flood susceptibility prediction in the Dadu River Basin. These three hybrid models integrate a bivariate statistical method of the fuzzy membership value (FMV) and three machine learning methods of support vector machine (SVM), classification and regression trees (CART), and convolutional neural network (CNN). Firstly, a geospatial database was prepared comprising nine flood conditioning factors, 485 flood locations, and 485 non-flood locations. Then, the database was used to train and test the three hybrid models. Subsequently, the receiver operating characteristic (ROC) curve, seed cell area index (SCAI), and classification accuracy were used to evaluate the performances of the models. The results reveal the following: (1) The ROC curve highlights the fact that the CNN-FMV hybrid model had the best fitting and prediction performance, and the area under the curve (AUC) values of the success rate and the prediction rate were 0.935 and 0.912, respectively. (2) Based on the results of the three model performance evaluation methods, all three hybrid models had better prediction capabilities than their respective single machine learning models. Compared with their single machine learning models, the AUC values of the SVM-FMV, CART-FMV, and CNN-FMV were 0.032, 0.005, and 0.055 higher; their SCAI values were 0.05, 0.03, and 0.02 lower; and their classification accuracies were 4.48%, 1.38%, and 5.86% higher, respectively. (3) Based on the results of the flood susceptibility indices, between 13.21% and 22.03% of the study area was characterized by high and very high flood susceptibilities. The three hybrid models proposed in this study, especially CNN-FMV, have a high potential for application in flood susceptibility assessment in specific areas in future studies