Crystal plane engineering of MAPbI3 in epoxy-based materials for superior gamma-ray shielding performance

The rapid development of the aerospace and nuclear industries is accompanied by increased exposure to high-energy ionising radiation. Thus, the performance of radiation shielding materials needs to be improved to extend the service life of detectors and ensure the safety of personnel. The development of novel lightweight materials with high electron density has therefore become urgent to alleviate radiation risks. In this work, new MAPbI3/epoxy (CH3NH3PbI3/epoxy) composites were prepared via a crystal plane engineering strategy. These composites delivered excellent radiation shielding performance against 59.5 keV gamma rays. A high linear attenuation coefficient (1.887 cm−1) and mass attenuation coefficient (1.352 cm2 g−1) were achieved for a representative MAPbI3/epoxy composite, which was 10 times higher than that of the epoxy. Theoretical calculations indicate that the electron density of MAPbI3/epoxy composites significantly increases when the content ratio of the (110) plane in MAPbI3 increases. As a result, the chances of collision between the incident gamma rays and electrons in the MAPbI3/epoxy composites were enhanced. The present work provides a novel strategy for designing and developing high-efficiency radiation shielding materials

Modality-based attention and dual-stream multiple instance convolutional neural network for predicting microvascular invasion of hepatocellular carcinoma

Background and purposeThe presence of microvascular invasion (MVI) is a crucial indicator of postoperative recurrence in patients with hepatocellular carcinoma (HCC). Detecting MVI before surgery can improve personalized surgical planning and enhance patient survival. However, existing automatic diagnosis methods for MVI have certain limitations. Some methods only analyze information from a single slice and overlook the context of the entire lesion, while others require high computational resources to process the entire tumor with a three-dimension (3D) convolutional neural network (CNN), which could be challenging to train. To address these limitations, this paper proposes a modality-based attention and dual-stream multiple instance learning(MIL) CNN.Materials and methodsIn this retrospective study, 283 patients with histologically confirmed HCC who underwent surgical resection between April 2017 and September 2019 were included. Five magnetic resonance (MR) modalities including T2-weighted, arterial phase, venous phase, delay phase and apparent diffusion coefficient images were used in image acquisition of each patient. Firstly, Each two-dimension (2D) slice of HCC magnetic resonance image (MRI) was converted into an instance embedding. Secondly, modality attention module was designed to emulates the decision-making process of doctors and helped the model to focus on the important MRI sequences. Thirdly, instance embeddings of 3D scans were aggregated into a bag embedding by a dual-stream MIL aggregator, in which the critical slices were given greater consideration. The dataset was split into a training set and a testing set in a 4:1 ratio, and model performance was evaluated using five-fold cross-validation.ResultsUsing the proposed method, the prediction of MVI achieved an accuracy of 76.43% and an AUC of 74.22%, significantly surpassing the performance of the baseline methods.ConclusionOur modality-based attention and dual-stream MIL CNN can achieve outstanding results for MVI prediction

Development of a colloidal gold immunochromatographic assay strip using monoclonal antibody for rapid detection of porcine deltacoronavirus

Porcine deltacoronavirus (PDCoV) cause diarrhea and dehydration in newborn piglets and has the potential for cross-species transmission. Rapid and early diagnosis is important for preventing and controlling infectious disease. In this study, two monoclonal antibodies (mAbs) were generated, which could specifically recognize recombinant PDCoV nucleocapsid (rPDCoV-N) protein. A colloidal gold immunochromatographic assay (GICA) strip using these mAbs was developed to detect PDCoV antigens within 15 min. Results showed that the detection limit of the GICA strip developed in this study was 103 TCID50/ml for the suspension of virus-infected cell culture and 0.125 μg/ml for rPDCoV-N protein, respectively. Besides, the GICA strip showed high specificity with no cross-reactivity with other porcine pathogenic viruses. Three hundred and twenty-five fecal samples were detected for PDCoV using the GICA strip and reverse transcription-quantitative real-time PCR (RT-qPCR). The coincidence rate of the GICA strip and RT-qPCR was 96.9%. The GICA strip had a diagnostic sensitivity of 88.9% and diagnostic specificity of 98.5%. The specific and efficient detection by the strip provides a convenient, rapid, easy to use and valuable diagnostic tool for PDCoV under laboratory and field conditions

Current landscape of fecal microbiota transplantation in treating depression

Depression, projected to be the predominant contributor to the global disease burden, is a complex condition with diverse symptoms including mood disturbances and cognitive impairments. Traditional treatments such as medication and psychotherapy often fall short, prompting the pursuit of alternative interventions. Recent research has highlighted the significant role of gut microbiota in mental health, influencing emotional and neural regulation. Fecal microbiota transplantation (FMT), the infusion of fecal matter from a healthy donor into the gut of a patient, emerges as a promising strategy to ameliorate depressive symptoms by restoring gut microbial balance. The microbial-gut-brain (MGB) axis represents a critical pathway through which to potentially rectify dysbiosis and modulate neuropsychiatric outcomes. Preclinical studies reveal that FMT can enhance neurochemicals and reduce inflammatory markers, thereby alleviating depressive behaviors. Moreover, FMT has shown promise in clinical settings, improving gastrointestinal symptoms and overall quality of life in patients with depression. The review highlights the role of the gut-brain axis in depression and the need for further research to validate the long-term safety and efficacy of FMT, identify specific therapeutic microbial strains, and develop targeted microbial modulation strategies. Advancing our understanding of FMT could revolutionize depression treatment, shifting the paradigm toward microbiome-targeting therapies

Hardware-assisted view-dependent isosurface extraction using spherical partition

Extracting only the visible portion of an isosurface can improve both the computation efficiency and the rendering speed. However, the visibility test overhead can be quite high for large scale data sets. In this paper, we present a view-dependent isosurface extraction algorithm utilizing occlusion query hardware to accelerate visible isosurface extraction. A spherical partition scheme is proposed to traverse the data blocks in a layered front-to-back order. Such traversal order helps our algorithm to identify the visible isosurface blocks more quickly with fewer visibility queries. Our algorithm can compute a more complete isosurface in a smaller amount of time, and thus is suitable for time-critical visualization applications

Parallel View-Dependent Isosurface Extraction Using Multi-Pass Occlusion Culling

Presents a parallel algorithm that can effectively extract only the visible portion of isosurfaces. The main focus of our research is to devise a load-balanced and output-sensitive algorithm, that is, each processor will generate approximately the same amount of triangles, and cells that do not contain the visible isosurface will not be visited. A multi-pass algorithm is proposed to achieve these goals. In the algorithm, we first use an octree data structure to rapidly skip the empty cells. An image space visibility culling technique is then used to identify the visible isosurface cells in a progressive manner. To distribute the workload, we use a binary image space partitioning method to ensure that each processor will generate approximately the same amount of triangles. Isosurface extraction and visibility update are performed in parallel to reduce the total computation time. In addition to reducing the size of output geometry and accelerating the process of isosurface extraction, the multi-pass nature of our algorithm can also be used to perform time-critical computation

Visibility culling for timevarying volume rendering using temporal occlusion coherence

Typically there is a high coherence in data values between neighboring time steps in an iterative scientific software simulation; this characteristic similarly contributes to a corresponding coherence in the visibility of volume blocks when these consecutive time steps are rendered. Yet traditional visibility culling algorithms were mainly designed for static data, without consideration of such potential temporal coherency. In this paper, we explore the use of Temporal Occlusion Coherence (TOC) to accelerate visibility culling for time-varying volume rendering. In our algorithm, the opacity of volume blocks is encoded by means of Plenoptic Opacity Functions (POFs). A coherence-based block fusion technique is employed to coalesce time-coherent data blocks over a span of time steps into a single, representative block. Then POFs need only be computed for these representative blocks. To quickly determine the subvolumes that do not require updates in their visibility status for each subsequent time step, a hierarchical “TOC tree ” data structure is constructed to store the spans of coherent time steps. To achieve maximal culling potential, while remaining conservative, we have extended our previous POF into an Optimized POF (OPOF) encoding scheme for this specific scenario. To test our general TOC and OPOF approach, we have designed a parallel time-varying volume rendering algorithm accelerated by visibility culling. Results from experimental runs on a 32-processor cluster confirm both the effectiveness and scalability of our approach