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All four candidates were capable to trigger the immune response and stimulate the production of antiserum in mice. Furthermore, top-ranked proteins including FliK and BcsZ provided wide antigenic coverage among the multi-serotype of Salmonella. The S. Typhimurium LT2 challenge model used in mice immunized with FliK and BcsZ showed a high relative percentage survival (RPS) of 52.74 % and 64.71 % respectively. In conclusion, this study constructed an in-silico pipeline able to successfully pre-screen the vaccine targets characterized by high immunogenicity and protective immunity. We show that reverse vaccinology allowed screening of appropriate broad-spectrum vaccines for Salmonella.Ground-penetrating radar (GPR) is an established geophysical technique used extensively for the accurate reconstruction of the shallow ( less then 10 m) subsurface. Reconstructions have largely been completed and presented as 2D vertical and horizontal planes, leaving limited visualization of subsurface 3D shapes and their spatial relationships. With technological advancements, particularly the availability and integration of various software platforms, 3D modelling of GPR data is now emerging as the new standard. However, despite these developments, there remains an inadequate examination and testing of these techniques, particularly in determining if their application is beneficial and warranted. In this study we conducted a GPR grid survey on a churchyard cemetery to generate and evaluate 2D and 3D-modelled reconstructions of the cemetery burial sites. Data collection and processing was completed using a Sensors and Software Incorporated pulseEKKO™ Pro SmartCart GPR system and EKKO_Project™ software, respectively. The modelling component was achieved using Schlumberger's Petrel™ E & P software platform, which is tailored to the petroleum industry. The subsurface patterns present in the 2D and 3D models closely matched the cemetery plot plan, validating our data collection, processing, and modelling methods. Both models were adequate for 2D horizontal visualization of reflection patterns at any specific depth. The 3D model was used to identify the presence of a companion burial plot (stacked caskets) and possible leachate plumes below and encircling burial sites, both of which were not evident in the 2D model, highlighting the benefits of 3D modelling when discerning subsurface objects. We expect our findings to be of value to similar GPR studies, with particular significance to geoforensic studies and criminal investigations.The detection of biomarkers in body fluids plays a great role in the diagnosis, treatment, and prognosis of diseases. Here, we present novel aptamer-decorated porous microneedles (MNs) arrays to realize the extraction and detection of biomarkers in skin interstitial fluid (ISF) in situ. The porous MNs arrays are fabricated by replicating the negative molds comprising glass microspheres with a UV-curable ethoxylated trimethylolpropane triacrylate (ETPTA). As the MNs arrays combine the superiorities of porous structure and aptamers, their specific surface area increased significantly to 6.694 m2/g, thus vast of stable aptamer probes with a concentration of 0.9459 μM could be immobilized. In addition, the MNs arrays could extract skin ISF into their porous structure on the basis of the capillarity principle, and subsequently capture and detect skin ISF biomarkers without sample post-process. Taking advantage of these features, we further demonstrated a highly sensitive and rapid detection of ISF endotoxin in the concentration ranges of 0.0342 EU/mL to 8.2082 EU/mL from rats model injected with endotoxin via tail vein by using such aptamer-decorated porous MNs arrays, with the limit of detection (LOD) of 0.0064 EU/mL. These results indicated that the aptamer-decorated porous MNs arrays possess great potential for non-invasive extraction and detection of biomarkers in clinical applications.Accurate modeling of diffusion-weighted magnetic resonance imaging measurements is necessary for accurate brain connectivity analysis. Existing methods for estimating the number and orientations of fascicles in an imaging voxel either depend on non-convex optimization techniques that are sensitive to initialization and measurement noise, or are prone to predicting spurious fascicles. In this paper, we propose a machine learning-based technique that can accurately estimate the number and orientations of fascicles in a voxel. Our method can be trained with either simulated or real diffusion-weighted imaging data. Our method estimates the angle to the closest fascicle for each direction in a set of discrete directions uniformly spread on the unit sphere. This information is then processed to extract the number and orientations of fascicles in a voxel. On realistic simulated phantom data with known ground truth, our method predicts the number and orientations of crossing fascicles more accurately than several classical and machine learning methods. It also leads to more accurate tractography. On real data, our method is better than or compares favorably with other methods in terms of robustness to measurement down-sampling and also in terms of expert quality assessment of tractography results.Accurate cardiac segmentation of multimodal images, e.g., magnetic resonance (MR), computed tomography (CT) images, plays a pivot role in auxiliary diagnoses, treatments and postoperative assessments of cardiovascular diseases. However, training a well-behaved segmentation model for the cross-modal cardiac image analysis is challenging, due to their diverse appearances/distributions from different devices and acquisition conditions. For instance, a well-trained segmentation model based on the source domain of MR images is often failed in the segmentation of CT images. BVD-523 supplier In this work, a cross-modal images-oriented cardiac segmentation scheme is proposed using a symmetric full convolutional neural network (SFCNN) with the unsupervised multi-domain adaptation (UMDA) and a spatial neural attention (SNA) structure, termed UMDA-SNA-SFCNN, having the merits of without the requirement of any annotation on the test domain. Specifically, UMDA-SNA-SFCNN incorporates SNA to the classic adversarial domain adaptation network to highlight the relevant regions, while restraining the irrelevant areas in the cross-modal images, so as to suppress the negative transfer in the process of unsupervised domain adaptation.
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