Search Results (369)

Background: Systemic-to-pulmonary collaterals (SPCs) are common in congenital heart disease (CHD). Particularly in single ventricle anatomy and Fontan circulation, SPC can both complicate the postoperative course and lead to clinical deterioration in the long term. The treatment of SPC is controversial. The aim of our study was (1) to retrospectively analyse patients who underwent SPC embolization using Concerto™ Helix nylon-fibred microcoils (CHMs) and (2) to describe the interventional technique. Methods: In this single-centre retrospective observational cohort study, we analysed clinical and imaging data of all patients who underwent transcatheter embolization of SPCs using CHMs from January 2016 to December 2023. Results: In 38 consecutive patients (65.8% male, median age 41 months, range 2–490), a total number of 141 CHMs had been implanted into 64 SPCs in 49 procedures. The majority were arterial SPCs (n = 59/64) originating from the thoracic aorta or its branches; 5/64 were veno-venous SPCs. Primary closure succeeded in all procedures. The CHM diameters ranged from 3 to 8 mm, with 5 mm being the most commonly used diameter. The mean coil/SPC ratio was 2.6 (range 1.3–5.3). CHM implantation was performed via four French sheaths. Both detachment and stable positioning were simple and safe. Neither non-target embolization nor coil migration occurred. One complication was a vascular injury with resulting extravasation of contrast medium. In 18/49 procedures (36.7%), coils other than CHMs or vascular plugs were additionally inserted into separate SPCs. Conclusions: CHMs are appropriate for SPC embolization in all age groups, including infants, with a low complication rate. The coils are particularly suitable for the closure of collaterals with a small diameter or tortuous course. They can be used in combination with other embolization devices to achieve comprehensive collateral closure. Full article

Accurate estimates of forest aboveground biomass (AGB) are necessary for the accurate tracking of forest carbon stock. Gaofen-7 (GF-7) is the first civilian sub-meter three-dimensional (3D) mapping satellite from China. It is equipped with a laser altimeter system and a dual-line array stereoscopic mapping camera, which enables it to synchronously generate full-waveform LiDAR data and stereoscopic images. The bulk of existing research has examined how accurate GF-7 is for topographic measurements of bare land or canopy height. The measurement of forest aboveground biomass has not received as much attention as it deserves. This study aimed to assess the GF-7 stereo imaging capability, displayed as topographic features for aboveground biomass estimation in forests. The aboveground biomass model was constructed using the random forest machine learning technique, which was accomplished by combining the use of in situ field measurements, pairs of GF-7 stereo images, and the corresponding generated canopy height model (CHM). Findings showed that the biomass estimation model had an accuracy of R² = 0.76, RMSE = 7.94 t/ha, which was better than the inclusion of forest canopy height (R² = 0.30, RMSE = 21.02 t/ha). These results show that GF-7 has considerable application potential in gathering large-scale high-precision forest aboveground biomass using a restricted amount of field data. Full article

Background: An estimated 10–15% of all genetic diseases are attributable to variants in noncanonical splice sites, auxiliary splice sites and deep-intronic variants. Most of these unstudied variants are classified as variants of uncertain significance (VUS), which are not clinically actionable. This study investigated two novel splice-altering variants, CHM NM_000390.4:c.941-11T>G and CACNA1F NM_005183.4:c.2576+4_2576+5del implicated in choroideremia and cone dystrophy (COD), respectively, resulting in significant visual loss. Methods: Next-generation sequencing was employed to identify the candidate variants in CHM and CACNA1F, which were confirmed using Sanger sequencing. Cascade analysis was undertaken when additional family members were available. Functional analysis was conducted by cloning genomic regions of interest into gateway expression vectors, creating variant and wildtype midigenes, which were subsequently transfected into HEK293 cells. RNA was harvested and amplified by RT-PCR to investigate the splicing profile for each variant compared to the wildtype. Novel variants were reclassified according to ACMG/AMP and ClinGen SVI guidelines. Results: Midigene functional analysis confirmed that both variants disrupted splicing. The CHM NM_000390.4:c.941-11T>G variant caused exon 8 skipping, leading to a frameshift and the CACNA1F NM_005183.4:c.2576+4_2576+5del variant caused a multimodal splice defect leading to an in-frame insertion of seven amino acids and a frameshift. With this evidence, the former was upgraded to likely pathogenic and the latter to a hot VUS. Conclusions: This study adds to the mutational spectrum of splicing defects implicated in retinal degenerations by identifying and characterising two novel variants in CHM and CACNA1F. Our results highlight the importance of conducting functional analysis to investigate the consequences of intronic splice-altering variants and the significance of reclassifying VUS to confirm a genetic diagnosis. Full article

Reactive oxygen species (ROS) within the retina play a key role in maintaining function and cell survival. However, excessive ROS can lead to oxidative stress, inducing dysregulation of metabolic and inflammatory pathways. The chm^ru848 zebrafish models choroideremia (CHM), an X-linked chorioretinal dystrophy, which predominantly affects the photoreceptors, retinal pigment epithelium (RPE), and choroid. In this study, we examined the transcriptomic signature of the chm^ru848 zebrafish retina to reveal the upregulation of cytokine pathways and glia migration, upregulation of oxidative, ER stress and apoptosis markers, and the dysregulation of glucose metabolism with the downregulation of glycolysis and the upregulation of the oxidative phase of the pentose phosphate pathway. Glucose uptake was impaired in the chm^ru848 retina using the 2-NBDG glucose uptake assay. Following the overexpression of human PFKM, partial rescue was seen with the preservation of photoreceptors and RPE and increased glucose uptake, but without modifying glycolysis and oxidative stress markers. Therapies targeting glucose metabolism in CHM may represent a potential remedial approach. Full article

Forests are crucial for biodiversity, climate regulation, and hydrological cycles, requiring sustainable management due to threats like deforestation and climate change. Traditional forest monitoring methods are labor-intensive and limited, whereas UAV LiDAR offers detailed three-dimensional data on forest structure and extensive coverage. This study primarily assesses individual tree segmentation algorithms in two forest ecosystems with different levels of complexity using high-density LiDAR data captured by the Zenmuse L1 sensor on a DJI Matrice 300RTK platform. The processing methodology for LiDAR data includes preliminary preprocessing steps to create Digital Elevation Models, Digital Surface Models, and Canopy Height Models. A comprehensive evaluation of the most effective techniques for classifying ground points in the LiDAR point cloud and deriving accurate models was performed, concluding that the Triangular Irregular Network method is a suitable choice. Subsequently, the segmentation step is applied to enable the analysis of forests at the individual tree level. Segmentation is crucial for monitoring forest health, estimating biomass, and understanding species composition and diversity. However, the selection of the most appropriate segmentation technique remains a hot research topic with a lack of consensus on the optimal approach and metrics to be employed. Therefore, after the review of the state of the art, a comparative assessment of four common segmentation algorithms (Dalponte2016, Silva2016, Watershed, and Li2012) was conducted. Results demonstrated that the Li2012 algorithm, applied to the normalized 3D point cloud, achieved the best performance with an F1-score of 91% and an IoU of 83%. Full article

For point cloud data compiled over larger spatial domains, the rasterization of features is effectively streamlined by means of structured query language (SQL). This comprises enhanced control with filtering data and implementing specific metrics for summarization to derive environmental covariates. LiDAR (light detection and ranging) point cloud data were analyzed via SQL to generate rasterized covariates of the digital terrain model (DTM), canopy height model (CHM), and a gap fraction for a boreal study region in Northern Ontario, Canada. These features, along with topographic covariates computed from the DTM, were later ascertained as important for subsequent tree species classification research. Full article

Silvicultural operations such as planting, pruning, and thinning are vital for the forest value chain, requiring efficient monitoring to prevent value loss. While effective, traditional field plots are time-consuming, costly, spatially limited, and rely on assumptions that they adequately represent a wider area. Alternatively, unmanned aerial vehicles (UAVs) can cover large areas while keeping operators safe from hazards including steep terrain. Despite their utility, optimal flight parameters to ensure flight efficiency and data quality remain under-researched. This study evaluated the impact of forward and side overlap and flight altitude on the quality of two- and three-dimensional spatial data products from UAV photogrammetry (UAV-SfM) for assessing stand density in a recently thinned Pinus radiata D. Don plantation. A contemporaneously acquired UAV laser scanner (ULS) point cloud provided reference data. The results indicate that the optimal UAV-SfM flight parameters are 90% forward and 85% side overlap at a 120 m altitude. Flights at an 80 m altitude offered marginal resolution improvement (2.2 cm compared to 3.2 cm ground sample distance/GSD) but took longer and were more error-prone. Individual tree detection (ITD) for stand density assessment was then applied to both UAV-SfM and ULS canopy height models (CHMs). Manual cleaning of the detected ULS tree peaks provided ground truth for both methods. UAV-SfM had a lower recall (0.85 vs. 0.94) but a higher precision (0.97 vs. 0.95) compared to ULS. Overall, the F-score indicated no significant difference between a prosumer-grade photogrammetric UAV and an industrial-grade ULS for stand density assessments, demonstrating the efficacy of affordable, off-the-shelf UAV technology for forest managers. Furthermore, in addressing the knowledge gap regarding optimal UAV flight parameters for conducting operational forestry assessments, this study provides valuable insights into the importance of side overlap for orthomosaic quality in forest environments. Full article

The kinetic stability provided by the sterically demanding {SiN^Dipp}²⁻ dianion (SiN^Dipp = {CH₂SiMe₂NDipp}₂; Dipp = 2,6-i-Pr₂C₆H₃) is intrinsic to the isolation of not only the group 1 alumanyl reagents ([{SiN^Dipp}AlM]₂; M = K, Rb, Cs) but also facilitates the completely selective oxidative addition of a C-H bond of 1,2-C₂B₁₀H₁₂ to the aluminium centre. In each case, the resultant compounds comprise a four-coordinate o-carboranyl (hydrido)aluminate anion, [(SiN^Dipp)Al(H)(1,2-C₂B₁₀H₁₁)]⁻, in which the carboranyl cage is bonded to aluminium by an Al-C σ bond. Although the anions further assemble as extended network structures based on Al-H∙∙∙M, B-H∙∙∙M, and C-H∙∙∙M interactions, each structure is unique due to the significant variation in M⁺ ionic radius as group 1 is descended. The potassium derivative crystallises as a one-dimensional polymer, its rubidium analogue is a dimer due to the polyhapto-sequestration of a molecule of benzene solvent within the alkali metal coordination sphere, and the caesium species is a two-dimensional assembly of hexameric aggregates. Full article

Background: Choroideremia is a monogenic inherited retinal dystrophy that manifests in males with night blindness, progressive loss of peripheral vision, and ultimately profound sight loss, commonly by middle age. It is caused by genetic defects of the CHM gene, which result in a deficiency in Rab-escort protein-1, a key element for intracellular trafficking of vesicles, including those carrying melanin. As choroideremia primarily affects the retinal pigment epithelium, fundus autofluorescence, which focuses on the fluorescent properties of pigments within the retina, is an established imaging modality used for the assessment and monitoring of affected patients. Methods and Results: In this manuscript, we demonstrate the use of both short-wavelength blue and near-infrared autofluorescence and how these imaging modalities reveal distinct disease patterns in choroideremia. In addition, we show how these structural measurements relate to retinal functional measures, namely microperimetry, and discuss the potential role of these retinal imaging modalities in clinical practice and research studies. Moreover, we discuss the mechanisms underlying retinal autofluorescence patterns by imaging with a particular focus on melanin pigment. Conclusions: This could be of particular significance given the current progress in therapeutic options, including gene replacement therapy. Full article

In hydrological modeling, the accuracy of precipitation data and the reflection of the model’s physical mechanisms are crucial for accurately describing hydrological processes. Identifying reliable data sources and exploring reasonable hydrological evolution mechanisms for hydrology and water resources research in high-altitude mountainous regions with sparse stations and limited data constitute a significant challenge and focus in the field of hydrology. This study focuses on the Yarkant River Basin in Xinjiang, which originates from glaciers and contains a substantial amount of meltwater runoff. A dynamic glacier melt module considering the synergistic effects of multiple meteorological factors was developed and integrated into the original Soil and Water Assessment Tool (SWAT) model. Four precipitation datasets (ERA5-land, MSWEP, CMA V2.0, and CHM-PRE) were selected to train the model, including remote sensing precipitation products and station-interpolated precipitation data. The applicability of the improved SWAT model and precipitation datasets in the source region of the Yarkant River was evaluated and analyzed using statistical indicators, hydrological characteristic values, and watershed runoff simulation effectiveness. The optimal dataset was further used to analyze glacier evolution characteristics in the basin. The results revealed the following: (1) The improved model fills the gap in glacier runoff simulation with respect to the original SWAT model, with the simulation results more closely aligning with the actual runoff variation patterns in the study area, better describing the meltwater runoff process. (2) CMA V2.0 precipitation data has the best applicability in the study area. This is specifically reflected in the rationality of the spatial and temporal distribution patterns of the inverted precipitation, the accuracy observed in capturing precipitation events and actual precipitation characteristics, the goodness of fit in driving hydrological models, and the observed precision in reflecting the composition of watershed runoff, all of which are superior to those pertaining to other precipitation products. (3) The glacier melt calculated using the improved SWAT model informed by CMA V2.0 shows that during the study period, the basin formed a pattern with a positive–negative glacier balance demarcation at 36.5° N, featuring melting at higher latitudes and accumulation at lower latitudes. The results of this study are of significant importance for hydrometeorological applications and hydrological and water resources research in this region. Full article

Coastal mangrove forests provide numerous ecosystem services, which can be disrupted by natural disturbances, mainly hurricanes. Canopy height (CH) is a key parameter for estimating carbon storage. Airborne Light Detection and Ranging (LiDAR) is widely viewed as the most accurate method for estimating CH but data are often limited in spatial coverage and are not readily available for rapid impact assessment after hurricane events. Hence, we evaluated the use of systematically acquired space-based Synthetic Aperture Radar (SAR) and optical observations with airborne LiDAR to predict CH across expansive mangrove areas in South Florida that were severely impacted by Category 3 Hurricane Irma in 2017. We used pre- and post-Irma LiDAR-derived canopy height models (CHMs) to train Random Forest regression models that used features of Sentinel-1 SAR time series, Landsat-8 optical, and classified mangrove maps. We evaluated (1) spatial transfer learning to predict regional CH for both time periods and (2) temporal transfer learning coupled with species-specific error correction models to predict post-Irma CH using models trained by pre-Irma data. Model performance of SAR and optical data differed with time period and across height classes. For spatial transfer, SAR data models achieved higher accuracy than optical models for post-Irma, while the opposite was the case for the pre-Irma period. For temporal transfer, SAR models were more accurate for tall trees (>10 m) but optical models were more accurate for short trees. By fusing data of both sensors, spatial and temporal transfer learning achieved the root mean square errors (RMSEs) of 1.9 m and 1.7 m, respectively, for absolute CH. Predicted CH losses were comparable with LiDAR-derived reference values across height and species classes. Spatial and temporal transfer learning techniques applied to readily available spaceborne satellite data can enable conservation managers to assess the impacts of disturbances on regional coastal ecosystems efficiently and within a practical timeframe after a disturbance event. Full article

Accurate estimation of forest above-ground biomass (AGB) is significant for understanding changes in global carbon storage and addressing climate change. This study focuses on 53 samples of natural forests at the Greater Khingan Ecological Station, exploring the potential of integrating Canopy Height Model (CHM) with multi-source remote sensing (RS) data—airborne LiDAR, Landsat 8 OLI, and hyperspectral data to estimate forest AGB. Firstly, RS features with strong horizontal and vertical correlation with the forests AGB are optimized by a partial least squares algorithm (PLSR). Then, multivariate linear stepwise regression (MLSR) and K-nearest neighbor with fast iterative features selection (KNN-FIFS) are applied to estimate forest AGB using seven different data combinations. Finally, the leave-one-out cross-validation method is selected for the validation of the estimation results. The results are as follows: (1) When forest AGB is estimated using a single data source, the inversion results of using LiDAR are better, with R² = 0.76 and RMSE = 21.78 Mg/ha. (2) The estimation accuracy of two models showed obvious improvement after using fused CHM into RS information. The MLSR model showed the best performance, with R² increased by 0.41 and RMSE decreased to 14.15 Mg/ha. (3) The estimation results based on the KNN-FIFS model using the combined data of LiDAR, CHM + Landsat 8 OLI, and CHM + Hyperspectral imaging were the best in this study, with R² = 0.85 and RMSE = 18.17 Mg/ha. The results of the study show that fusing CHM into multi-spectral data and hyperspectral data can improve the estimation accuracy a lot; the forest AGB estimation accuracies of the multi-source RS data are better than the single data source. This study provides an effective method for estimating forest AGB using multi-source data integrated with CHM to improve estimation accuracy. Full article

High-quality water availability is substantial for sustaining life, so its contamination presents a serious problem that has been the focus of several studies. The presence of heavy metals, such as cadmium, is frequently studied due to the increase in the contamination levels caused by fast industrial expansion. Cadmium ions were removed from aqueous solutions at pH 7.0 by chitosan–magnetite (ChM) xerogel beads and chitosan–FeO (ChF) xerogel beads in batch systems. Kinetic studies were best modeled by the Elovich model. The adsorption isotherms obtained showed an inflection point suggesting the formation of a second layer, and the BET model adjusted to liquid–solid systems was adequate for the description of the experimental data. Maximum uptake capacities of 36.97 ± 0.77 and 28.60 ± 2.09 mg Cd/g xerogel were obtained for ChM and ChF, respectively. The studied composites are considered promising adsorbent materials for removing cadmium ions from aqueous systems. Full article

Saponins are an important class of active components in Chinese herbal medicines (CHMs), which are present in large quantities in Ginseng Radix et Rhizoma, Notoginseng Radix et Rhizoma, Polygonati Rhizoma, etc., and have immune regulation, anti-tumor, anti-inflammatory, anti-cardiovascular disease, and hypoglycemic activities. Storage and processing are essential processes in the production process of CHMs which affect the stability of saponin components and then reduce the medicinal and economic value. Therefore, it is of great importance to investigate the effects of storage and processing conditions on the content of saponin components in CHMs. In this paper, the effects of various storage and processing factors, including temperature, pH, enzymes, meta lions, extraction methods, etc., on the saponin content of CHMs are investigated and the underlying mechanisms for the quantitative changes of saponin are summarized. These findings may provide technical guidance for the production and processing of saponin-rich CHMs. Full article

Accurate and timely crop growth prediction is crucial for efficient farm management and food security, particularly given challenges like labor shortages and climate change. This study presents a novel method for the early prediction of Kimchi cabbage heights using drone imagery and a long short-term memory (LSTM) model. High-resolution drone images were used to generate a canopy height model (CHM) for estimating plant heights at various growth stages. Missing height data were interpolated using a logistic growth curve, and an LSTM model was trained on this time series data to predict the final height at harvest well before the actual harvest date. The model trained on data from 44 days after planting (DAPs) demonstrated the highest accuracy (R² = 0.83, MAE = 2.48 cm, and RMSE = 3.26 cm). Color-coded maps visualizing the predicted Kimchi cabbage heights revealed distinct growth patterns between different soil types, highlighting the model’s potential for site-specific management. Considering the trade-off between accuracy and prediction timing, the model trained on DAP 36 data (MAE = 2.77 cm) was deemed most suitable for practical applications, enabling timely interventions in cultivation management. This research demonstrates the feasibility and effectiveness of integrating drone imagery, logistic growth curves, and LSTM models for the early and accurate prediction of Kimchi cabbage heights, facilitating data-driven decision-making in precision agriculture for improved crop management and yield optimization. Full article