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Our proposed architecture, IBA-U-Net, has been compared with the U-Net and state-of-the-art segmentation methods on three publicly available datasets, the lung image segmentation dataset, skin lesion image dataset, and retinal blood vessel image segmentation dataset, each with their unique challenges, and it has improved the prediction performance even with slightly less calculation expense and fewer network parameters. By devising a deep convolutional neural network with a multiscale feature fusion and Attentive BConvLSTM mechanism, medical image segmentation of different tasks can be completed effectively and accurately with only 45% of U-Net parameters.Kidney stones are a common complaint worldwide, causing many people to admit to emergency rooms with severe pain. Various imaging techniques are used for the diagnosis of kidney stone disease. Specialists are needed for the interpretation and full diagnosis of these images. Computer-aided diagnosis systems are the practical approaches that can be used as auxiliary tools to assist the clinicians in their diagnosis. In this study, an automated detection of kidney stone (having stone/not) using coronal computed tomography (CT) images is proposed with deep learning (DL) technique which has recently made significant progress in the field of artificial intelligence. A total of 1799 images were used by taking different cross-sectional CT images for each person. Our developed automated model showed an accuracy of 96.82% using CT images in detecting the kidney stones. We have observed that our model is able to detect accurately the kidney stones of even small size. Our developed DL model yielded superior results with a larger dataset of 433 subjects and is ready for clinical application. This study shows that recently popular DL methods can be employed to address other challenging problems in urology.Zebrafish is a powerful and widely-used model system for a host of biological investigations, including cardiovascular studies and genetic screening. Zebrafish are readily assessable during developmental stages; however, the current methods for quantifying and monitoring cardiac functions mainly involve tedious manual work and inconsistent estimations. find more In this paper, we developed and validated a Zebrafish Automatic Cardiovascular Assessment Framework (ZACAF) based on a U-net deep learning model for automated assessment of cardiovascular indices, such as ejection fraction (EF) and fractional shortening (FS) from microscopic videos of wildtype and cardiomyopathy mutant zebrafish embryos. Our approach yielded favorable performance with accuracy above 90% compared with manual processing. We used only black and white regular microscopic recordings with frame rates of 5-20 frames per second (fps); thus, the framework could be widely applicable with any laboratory resources and infrastructure. Most importantly, the automatic feature holds promise to enable efficient, consistent, and reliable processing and analysis capacity for large amounts of videos, which can be generated by diverse collaborating teams.High-fidelity patient-specific modeling of cardiovascular flows and hemodynamics is challenging. Direct blood flow measurement inside the body with in-vivo measurement modalities such as 4D flow magnetic resonance imaging (4D flow MRI) suffer from low resolution and acquisition noise. In-vitro experimental modeling and patient-specific computational fluid dynamics (CFD) models are subject to uncertainty in patient-specific boundary conditions and model parameters. Furthermore, collecting blood flow data in the near-wall region (e.g., wall shear stress) with experimental measurement modalities poses additional challenges. In this study, a computationally efficient data assimilation method called reduced-order modeling Kalman filter (ROM-KF) was proposed, which combined a sequential Kalman filter with reduced-order modeling using a linear model provided by dynamic mode decomposition (DMD). The goal of ROM-KF was to overcome low resolution and noise in experimental and uncertainty in CFD modeling of cardiovascular flows. The accuracy of the method was assessed with 1D Womersley flow, 2D idealized aneurysm, and 3D patient-specific cerebral aneurysm models. Synthetic experimental data were used to enable direct quantification of errors using benchmark datasets. The accuracy of ROM-KF in reconstructing near-wall hemodynamics was assessed by applying the method to problems where near-wall blood flow data were missing in the experimental dataset. The ROM-KF method provided blood flow data that were more accurate than the computational and synthetic experimental datasets and improved near-wall hemodynamics quantification.Radioactive borate waste containing a high concentration of boron (B) is problematic to be solidified using cement because soluble borate such as boric acid hinders the hydration reaction. In this study, borate waste was used as a raw material for metakaolin-based geopolymer according to the characteristic that B replaces a part of Si. Geopolymers using KOH alkaline activator (K-geopolymers) showed higher compressive strength than geopolymers using NaOH alkaline activator (Na-geopolymer). In addition, the compressive strength increased proportionally to the Si/(Al+B) ratio regardless of the alkaline cation species. These variations in compressive strength might be due to the viscosity of the geopolymer mixture, atomic size of alkaline cations, and the increase in Si content. The characteristic analyses (XRD, FT-IR, and solid state 11B MAS NMR) indicated that B was incorporated into the geopolymer structure. Thus, the K-geopolymer has a dense and homogeneous microstructure. In a semi-dynamic leaching test, less B leached from the geopolymers compared to the cement waste form. Consequently, borate waste can be solidified using metakaolin-based geopolymer, and the use of a KOH alkaline activator is advantageous in terms of mechanical property and structural durability.Iron plaques have been found to limit the phytoremediation efficiency by reducing iron solubility, while chelating agents can increase the bioavailability of iron from Fe plaques to numerous terrestrial plants. However, the effects of chelating agents on Fe plaques along the As accumulation in aquatic plants remain unknown. In this study, the effects of five chelating agents (EDTA, DTPA, NTA, GLDA, and CA) on the As (As(III) or As(V)), phosphate, and iron uptake by iron plaques and duckweed (Lemna minor) were examined. The results showed that the chelating agents increased the As accumulation in L. minor plants by desorbing and mobilizing As from Fe plaques. The desorption rates of As(V) (As(III)) from the Fe plaques by the chelating agents were 5.26-8.77% (8.70-15.02%), and the plants/DCB extract ratios of As(V) (As(III)) increased from 2.63 ± 0.13 (1.97 ± 0.06) to the peak value of 3.38 ± 0.21 (2.70 ± 0.14) upon adding chelating agents. Besides, the addition of chelating agents increased the uptake of P and Fe by L.