Facile synthesis of a rod-like Ni/TiO2/C nanocomposite for enhanced electromagnetic wave absorption.

In this study, we utilized a simple calcination method to prepare a Ni/TiO2/C composite, which was synchronously grown from magnetic, semiconductor, and conductive materials. XRD, SEM, Raman, and XPS characterization methods were used to analyze the crystal structure, graphitization degree, morphology size, and valence state of Ni/TiO2/C, and its electromagnetic wave absorption performance was tested. It was revealed that rod-like Ni/TiO2/C had good electromagnetic wave absorption performance at a thickness of 1-5.5 mm; in particular, its reflectance reached -40 dB at 3.5 mm and its absorption bandwidth (reflectivity < -10 dB) reached 4.4 GHz (6.0-10.4 GHz) at a thickness of 4.0 mm. It was thus revealed that its electromagnetic wave absorption rate and absorption bandwidth can be regulated by its thickness. Compared with Ni/TiO2, it was proven that the conductive materials (carbon), magnetic materials (Ni), and semiconductor materials (TiO2) in the rod-like Ni/TiO2/C composite can synergistically absorb electromagnetic wave energy through dielectric and magnetic losses.

NTM-Based Skill-Aware Knowledge Tracing for Conjunctive Skills.

Knowledge tracing (KT) is the task of modelling students' knowledge state based on their historical interactions on intelligent tutoring systems. Existing KT models ignore the relevance among the multiple knowledge concepts of a question and characteristics of online tutoring systems. This paper proposes a neural Turing machine-based skill-aware knowledge tracing (NSKT) for conjunctive skills, which can capture the relevance among the knowledge concepts of a question to model students' knowledge state more accurately and to discover more latent relevance among knowledge concepts effectively. We analyze the characteristics of the three real-world KT datasets in depth. Experiments on real-world datasets show that NSKT outperforms the state-of-the-art deep KT models on the AUC of prediction. This paper explores details of the prediction process of NSKT in modelling students' knowledge state, as well as the relevance of knowledge concepts and conditional influences between exercises.

A 3D reconstruction method based on multi-views of contours segmented with CNN-transformer for long bones.

PURPOSE: In computer-assisted diagnosis for orthopedic treatment, 3D reconstruction of bones is critical. Traditional 3D imaging technologies like CT and MRI have been proposed, but their high radiation dose and the requirements for lying postures could impact the accuracy of reconstructed bones and diagnosis results. Meanwhile, methods based on bone contours always depend on prior knowledge and lack precise bone segmentation methods. To address these issues, a bone reconstruction method based on multi-views of contours is proposed, as well as a hybrid CNN-Transformer approach for bone contours segmentation. METHODS: A four-step strategy is introduced including segmenting bone contours from X-ray images, calculating 3D sparse, dense point clouds based on contours, and reconstructing surface. The Trans-DetSeg approach for interest regions detection and bone segmentation is proposed for accurate contours. Besides, the mathematical description of mapping relationships between contours in different views of X-ray images is provided. Then, bone sparse and dense point clouds are generated subsequently. Based on dense point clouds and the power crust method, realistic bone models are reconstructed. RESULTS: Evaluated on 301 bone X-ray images and by considering p-value < 0.05, the proposed Trans-Detseg approach performed better with Dice Similarity Coefficient of 0.949 and Hausdorff Distance of 26.17 than three state-of-the-art models. Furthermore, the accuracy of the bone 3D reconstruction was investigated in three tibia cases and the proposed method was verified based on comparisons of results and CT data. It was proved that increased views of X-ray images could reduce the Average Surface Distance and perfect the structure information of reconstructed bones. CONCLUSION: A new method for bone 3D reconstruction based on segmented bone contours on multi-views of X-ray images has been developed. Besides, a hybrid CNN-Transformer approach is introduced to segment bone contours. Evaluations proved the efficiency and accuracy of the proposed bone 3D reconstruction method.

Grayscale medical image segmentation method based on 2D&3D object detection with deep learning.

BACKGROUND: Grayscale medical image segmentation is the key step in clinical computer-aided diagnosis. Model-driven and data-driven image segmentation methods are widely used for their less computational complexity and more accurate feature extraction. However, model-driven methods like thresholding usually suffer from wrong segmentation and noises regions because different grayscale images have distinct intensity distribution property thus pre-processing is always demanded. While data-driven methods with deep learning like encoder-decoder networks always are always accompanied by complex architectures which require amounts of training data. METHODS: Combining thresholding method and deep learning, this paper presents a novel method by using 2D&3D object detection technologies. First, interest regions contain segmented object are determined with fine-tuning 2D object detection network. Then, pixels in cropped images are turned as point cloud according to their positions and grayscale values. Finally, 3D object detection network is applied to obtain bounding boxes with target points and boxes' bottoms and tops represent thresholding values for segmentation. After projecting to 2D images, these target points could composite the segmented object. RESULTS: Three groups of grayscale medical images are used to evaluate the proposed image segmentation method. We obtain the IoU (DSC) scores of 0.92 (0.96), 0.88 (0.94) and 0.94 (0.94) for segmentation accuracy on different datasets respectively. Also, compared with five state of the arts and clinically performed well models, our method achieves higher scores and better performance. CONCLUSIONS: The prominent segmentation results demonstrate that the built method based on 2D&3D object detection with deep learning is workable and promising for segmentation task of grayscale medical images.

Overexpression of OsLCT2, a Low-Affinity Cation Transporter Gene, Reduces Cadmium Accumulation in Shoots and Grains of Rice.

Cadmium (Cd)-contaminated rice is a serious issue affecting food safety. Understanding the molecular regulatory mechanisms of Cd accumulation in rice grains is crucial to minimizing Cd concentrations in grains. We identified a member of the low-affinity cation transporter family, OsLCT2 in rice. It was a membrane protein. OsLCT2 was expressed in all tissues of the elongation and maturation zones in roots, with the strongest expression in pericycle and stele cells adjacent to the xylem. When grown in Cd-contaminated paddy soils, rice plants overexpressing OsLCT2 significantly reduced Cd concentrations in the straw and grains. Hydroponic experiment demonstrated its overexpression decreased the rate of Cd translocation from roots to shoots, and reduced Cd concentrations in xylem sap and in shoots of rice. Moreover, its overexpression increased Zn concentrations in roots by up-regulating the expression of OsZIP9, a gene responsible for Zn uptake. Overexpression of OsLCT2 reduces Cd accumulation in rice shoots and grains by limiting the amounts of Cd loaded into the xylem and restricting Cd translocation from roots to shoots of rice. Thus, OsLCT2 is a promising genetic resource to be engineered to reduce Cd accumulation in rice grains.

Characterization and fine-mapping of a novel premature leaf senescence mutant yellow leaf and dwarf 1 in rice.

Leaves are the main organs in which photosynthates are produced. Leaf senescence facilitates the translocation of photosynthates and nutrients from source to sink, which is important for plant development and especially for crop yield. However, the molecular mechanism of leaf senescence is unknown. Here, we identified a mutant, yellow leaf and dwarf 1 (yld1), which exhibited decreased plant height and premature leaf senescence. Nitroblue tetrazolium and diamiobenzidine staining analyses revealed that the concentrations of reactive oxygen species were higher in yld1 leaves than in wild type leaves. The photosynthetic pigment contents were significantly decreased in yld1. The yld1 chloroplasts had collapsed and were filled with abnormal starch granules. Combining bulk segregant and MutMap gene mapping approaches, the mutation responsible for the yld1 phenotype was mapped to a 7.3 Mb centromeric region, and three non-synonymous single nucleotide polymorphisms located in three novel genes were identified in this region. The expression patterns of the three candidate genes indicated that LOC_Os06g29380 had the most potential for functional verification. Plant hormone measurements showed that salicylic acid was highly accumulated in yld1 leaves when compared with wild type leaves, and yld1 was more sensitive to salicylic acid than wild type. This work lays the foundation for understanding the molecular regulatory mechanism of leaf senescence, and may reveal new connections among the molecular pathways related to leaf senescence, starch metabolism and salicylic acid signaling.

Protection effect of cosmetics on human skin under simulated rigorous environment.

BACKGROUND/PURPOSE: The efficacy of cosmetics on human skin measured under normal mild laboratory environment might be discounted by exterior environment factors such as wind, UV exposure, etc. Few studies have focused on the 'genuine' efficacy of cosmetics on human skin during exposure to external rigorous environment. The aim of this study was to develop a model for the evaluation of the efficacy of cosmetics on human skin under simulated rigorous environment. METHODS: We measured skin water content and transepidermal water loss (TEWL) before and after products application under simulated windy and sub-erythema UV exposure treatment in a constant temperature and humidity laboratory. RESULTS: The results showed that the products had higher water content and lower TEWL at 2, 4 and 8 h of products application, and ameliorated the skin moisturization situation after a 3-week continuous use of the products. In addition, the products might protect the skin to maintain the normal water content and TEWL under the simulated windy or sub-erythema UV exposure treatment in our laboratory when compared with untreated area. CONCLUSION: Our data indicate that this model may provide a more accurate evaluation for the genuine moisturizing effect of cosmetics under external natural climate.

Moisturizing and antisebum effect of cosmetic application on facial skin.

BACKGROUND: Water content of the stratum corneum and skin surface lipids are important factors in the appearance and function of skin. High water content and low sebum secretion are considered main features of fair skin. Aim This paper aims to study the change of skin physiological parameters after cosmetic application. METHODS: The skin water content, transepidermal water loss, and skin sebum secretion on different regions of the facial skin before and after the cosmetic application were measured using Corneometer, Tewameter, and Sebumeter, respectively. RESULTS: The cosmetics kept higher water content and lower transepidermal water loss, at the same time lower sebum secretion 4 and 8 h after the cosmetic application, compared with those before it. The situation was maintained in the following 3-week continuous use of the cosmetics. CONCLUSION: The cosmetic application on human facial skin could provide some moisturizing effect and at the same time some antisebum effect according to different regions on facial skin, which favored the maintenance of good skin physiological function after applying skin care products.