An Anatomy-aware Framework for Automatic Segmentation of Parotid Tumor from Multimodal MRI

Magnetic Resonance Imaging (MRI) plays an important role in diagnosing the parotid tumor, where accurate segmentation of tumors is highly desired for determining appropriate treatment plans and avoiding unnecessary surgery. However, the task remains nontrivial and challenging due to ambiguous boundaries and various sizes of the tumor, as well as the presence of a large number of anatomical structures around the parotid gland that are similar to the tumor. To overcome these problems, we propose a novel anatomy-aware framework for automatic segmentation of parotid tumors from multimodal MRI. First, a Transformer-based multimodal fusion network PT-Net is proposed in this paper. The encoder of PT-Net extracts and fuses contextual information from three modalities of MRI from coarse to fine, to obtain cross-modality and multi-scale tumor information. The decoder stacks the feature maps of different modalities and calibrates the multimodal information using the channel attention mechanism. Second, considering that the segmentation model is prone to be disturbed by similar anatomical structures and make wrong predictions, we design anatomy-aware loss. By calculating the distance between the activation regions of the prediction segmentation and the ground truth, our loss function forces the model to distinguish similar anatomical structures with the tumor and make correct predictions. Extensive experiments with MRI scans of the parotid tumor showed that our PT-Net achieved higher segmentation accuracy than existing networks. The anatomy-aware loss outperformed state-of-the-art loss functions for parotid tumor segmentation. Our framework can potentially improve the quality of preoperative diagnosis and surgery planning of parotid tumors.Comment: under revie

Segmentation of Parotid Gland Tumors Using Multimodal MRI and Contrastive Learning

Parotid gland tumor is a common type of head and neck tumor. Segmentation of the parotid glands and tumors by MR images is important for the treatment of parotid gland tumors. However, segmentation of the parotid glands is particularly challenging due to their variable shape and low contrast with surrounding structures. Recently deep learning has developed rapidly, which can handle complex problems. However, most of the current deep learning methods for processing medical images are still based on supervised learning. Compared with natural images, medical images are difficult to acquire and costly to label. Contrastive learning, as an unsupervised learning method, can more effectively utilize unlabeled medical images. In this paper, we used a Transformer-based contrastive learning method and innovatively trained the contrastive learning network with transfer learning. Then, the output model was transferred to the downstream parotid segmentation task, which improved the performance of the parotid segmentation model on the test set. The improved DSC was 89.60%, MPA was 99.36%, MIoU was 85.11%, and HD was 2.98. All four metrics showed significant improvement compared to the results of using a supervised learning model as a pre-trained model for the parotid segmentation network. In addition, we found that the improvement of the segmentation network by the contrastive learning model was mainly in the encoder part, so this paper also tried to build a contrastive learning network for the decoder part and discussed the problems encountered in the process of building

Integrating environmental and satellite data to estimate county-level cotton yield in Xinjiang Province

Accurate and timely estimation of cotton yield over large areas is essential for precision agriculture, facilitating the operation of commodity markets and guiding agronomic management practices. Remote sensing (RS) and crop models are effective means to predict cotton yield in the field. The satellite vegetation indices (VIs) can describe crop yield variations over large areas but can’t take the exact environmental impact into consideration. Climate variables (CVs), the result of the influence of spatial heterogeneity in large regions, can provide environmental information for better estimation of cotton yield. In this study, the most important VIs and CVs for estimating county-level cotton yield across Xinjiang Province were screened out. We found that the VIs of canopy structure and chlorophyll contents, and the CVs of moisture, were the most significant factors for cotton growth. For yield estimation, we utilized four approaches: least absolute shrinkage and selection operator regression (LASSO), support vector regression (SVR), random forest regression (RFR) and long short-term memory (LSTM). Due to its ability to capture temporal features over the long term, LSTM performed best, with an R2 of 0.76, root mean square error (RMSE) of 150 kg/ha and relative RMSE (rRMSE) of 8.67%; moreover, an additional 10% of the variance could be explained by adding CVs to the VIs. For the within-season yield estimation using LSTM, predictions made 2 months before harvest were the most accurate (R2 = 0.65, RMSE = 220 kg/ha, rRMSE = 15.97%). Our study demonstrated the feasibility of yield estimation and early prediction at the county level over large cotton cultivation areas by integrating satellite and environmental data

Exotic single-photon and enhanced deep-level emissions in hBN strain superlattice

The peculiar defect-related photon emission processes in 2D hexagonal boron nitride (hBN) have become a topic of intense research due to their potential applications in quantum information and sensing technologies. Recent efforts have focused on activating and modulating the defect energy levels in hBN by methods that can be integrated on a chip, and understanding the underlying physical mechanism. Here, we report on exotic single photon and enhanced deep-level emissions in 2D hBN strain superlattice, which is fabricated by transferring multilayer hBN onto hexagonal close-packed silica spheres on silica substrate. We realize effective activation of the single photon emissions (SPEs) in the multilayer hBN at the positions that are in contact with the apex of the SiO2 spheres. At these points, the local tensile strain induced blue-shift of the SPE is found to be up to 12 nm. Furthermore, high spatial resolution cathodoluminescence measurments show remarkable strain-enhanced deep-level (DL) emissions in the multilayer hBN with the emission intensity distribution following the periodic hexagonal pattern of the strain superlattice. The maximum DL emission enhancement is up to 350% with a energy redshift of 6 nm. Our results provide a simple on-chip compatible method for activating and tuning the defect-related photon emissions in multilayer hBN, demonstrating the potential of hBN strain superlattice as a building block for future on-chip quantum nanophotonic devices

NaoXinTong Inhibits the Development of Diabetic Retinopathy in d

Buchang NaoXinTong capsule (NXT) is a Chinese Materia Medica standardized product extracted from 16 Chinese traditional medical herbs and widely used for treatment of patients with cerebrovascular and cardiovascular diseases in China. Formation of microaneurysms plays an important role in the development of diabetic retinopathy. In this study, we investigated if  NXT can protect diabetic mice against the development of diabetic retinopathy. The db/db mice (~6 weeks old), a diabetic animal model, were divided into two groups and fed normal chow or plus NXT for 14 weeks. During the treatment, fasting blood glucose levels were monthly determined. After treatment, retinas were collected to determine retinal thickness, accumulation of carbohydrate macromolecules, and caspase-3 (CAS-3) expression. Our results demonstrate that administration of NXT decreased fasting blood glucose levels. Associated with the decreased glucose levels, NXT blocked the diabetes-induced shrink of multiple layers, such as photoreceptor layer and outer nuclear/plexiform layers, in the retina. NXT also inhibited the diabetes-induced expression of CAS-3 protein and mRNA, MMP-2/9 and TNFα mRNA, accumulation of carbohydrate macromolecules, and formation of acellular capillaries in the retina. Taken together, our study shows that NXT can inhibit the development of diabetic retinopathy and suggests a new potential application of NXT in clinic

Preparation and electromechanical properties of PVDF matrix piezo-electric composites containing highly oriented BaTiO3 whiskers

The piezoelectric composites containing highly oriented BaTiO3 whiskers as active phase and PVDF as matrix have been prepared by micro-hole extrusion and orientation in carried fibers. The morphology of oriented BaTiO3 whiskers and microstructure of the composites were observed by SEM. As for its electromechanical properties, it is found that the dielectric constant, piezoelectric constant and remnant of polarization in the BaTiO3 whisker-PVDF composite are considerably higher than that in the BaTiO3 powders-PVDF composite, while the loss factors follow the opposite trend. For the BaTiO3 whisker-PVDF composite, the values of epsilon, d(33) and P-r parallel to the whisker orientation (normal specimen) are much higher than that perpendicular to the whisker orientation (parallel specimen). The significant effects of the connective passages of active phase on electromechanical properties of the piezoelectric composites has also been investigated

Cluster analysis for the overall health status of elderly, multimorbid patients with diabetes

PurposeTo evaluate the overall health status and health-related abilities and problems of elderly patients with diabetes and multimorbidity compared with those with diabetes only. Additionally, we aimed to identify different subgroups of elderly, multimorbid patients with diabetes.MethodsThis cross-sectional study included 538 elderly patients with diabetes. The participants completed a series of questionnaires on self-rated health (SRH), diabetes self-management, self-efficacy, health literacy, depression, and diabetes distress. Differences in health-related abilities and problems were compared between elderly patients with diabetes and multimorbidity and those with diabetes only, with adjustments for covariates using propensity score matching. A cluster analysis was also performed to identify the overall health status subgroups of elderly, multimorbid patients with diabetes. Additionally, we conducted a multinomial logistic regression analysis to examine the predictors of health-related abilities and problem-cluster group membership.ResultsElderly patients with diabetes and multimorbidity experienced more health-related abilities and problems than those with diabetes only, particularly within the domains of depression (p < 0.001), and diabetes distress. The level of health literacy (p < 0.001) and self-management (p = 0.013) in elderly, multimorbid patients with diabetes was also significantly higher than that in elderly patients with diabetes only. Cluster analysis of elderly, multimorbid patients with diabetes revealed three distinct overall health status clusters. Multinomial logistic regression analysis indicated that age (OR = 1.090, p = 0.043), sex (OR = 0.503, p = 0.024), living situation (OR = 2.769, p = 0.011), BMI (OR = 0.838, p = 0.034), regular exercise (OR = 2.912, p = 0.041 in poor vs. good; OR = 3.510, p < 0.001 in intermediate vs. good), and cerebral infarction (OR = 26.280, p < 0.001) independently and significantly predicted cluster membership.ConclusionCompared with elderly patients with diabetes only, those with diabetes and multimorbidity experienced more health-related abilities and problems within the domains of depression, and diabetes distress. Additionally, the level of health literacy and self-management in elderly, multimorbid patients with diabetes was significantly higher than that in those with diabetes only. Among the multimorbid diabetes group, old age, male sex, living without a partner, slightly lower BMIs, not exercising regularly, and experiencing cerebral infarctions were all positively correlated with worse overall health status

Say What You Are Looking At: An Attention-Based Interactive System for Autistic Children

Gaze-following is an effective way for intention understanding in human–robot interaction, which aims to follow the gaze of humans to estimate what object is being observed. Most of the existing methods require people and objects to appear in the same image. Due to the limitation in the view of the camera, these methods are not applicable in practice. To address this problem, we propose a method of gaze following that utilizes a geometric map for better estimation. With the help of the map, this method is competitive for cross-frame estimation. On the basis of this method, we propose a novel gaze-based image caption system, which has been studied for the first time. Our experiments demonstrate that the system follows the gaze and describes objects accurately. We believe that this system is competent for autistic children’s rehabilitation training, pension service robots, and other applications.</jats:p

Processing and characterization of cobalt silicide nanoparticle-containing silicon carbide fibers through a colloidal method and their underlying mechanism

National Natural Science Foundation of China [51002127, 51072169]Cobalt-containing silicon carbide (Co-SiC) fibers were synthesized through a colloidal method. Dicobalt octacarbonyl [Co-2(CO)(8)] was employed to react with low-molecular weight liquid polycarbosilane (LPCS) to prepare a stable Co-containing colloid (Co-colloid), which was subsequently added to high-molecular weight solid polycarbosilane to obtain the precursor. FTIR, GPC, XRD, and TEM were employed to further understand and develop the mechanism for the formation of the Co-colloid. Results show that active Co intermediates derived from the incomplete decomposition of Co-2(CO)(8) promoted LPCS cross-linkage. The effects of the Co-colloid on the oxidation-curing nature of the green fiber were also investigated. Under heat treatment at higher temperature, carbonyls in the fibers completely decomposed and further crystallized in the morphology of cobalt silicide (CoSi) domains. The effects of Co on the electrical resistivity, magnetic properties, dielectric properties, microwave absorption properties and tensile strength of SiC fibers were also studied

FECTS: A Facial Emotion Cognition and Training System for Chinese Children with Autism Spectrum Disorder

Traditional training methods such as card teaching, assistive technologies (e.g., augmented reality/virtual reality games and smartphone apps), DVDs, human-computer interactions, and human-robot interactions are widely applied in autistic rehabilitation training in recent years. In this article, we propose a novel framework for human-computer/robot interaction and introduce a preliminary intervention study for improving the emotion recognition of Chinese children with an autism spectrum disorder. The core of the framework is the Facial Emotion Cognition and Training System (FECTS, including six tasks to train children with ASD to match, infer, and imitate the facial expressions of happiness, sadness, fear, and anger) based on Simon Baron-Cohen's E-S (empathizing-systemizing) theory. Our system may be implemented on PCs, smartphones, mobile devices such as PADs, and robots. The training record (e.g., a tracked record of emotion imitation) of the Chinese autistic children interacting with the device implemented using our FECTS will be uploaded and stored in the database of a cloud-based evaluation system. Therapists and parents can access the analysis of the emotion learning progress of these autistic children using the cloud-based evaluation system. Deep-learning algorithms of facial expressions recognition and attention analysis will be deployed in the back end (e.g., devices such as a PC, a robotic system, or a cloud system) implementing our FECTS, which can perform real-time tracking of the imitation quality and attention of the autistic children during the expression imitation phase. In this preliminary clinical study, a total of 10 Chinese autistic children aged 3-8 are recruited, and each of them received a single 20-minute training session every day for four consecutive days. Our preliminary results validated the feasibility of the developed FECTS and the effectiveness of our algorithms based on Chinese children with an autism spectrum disorder. To verify that our FECTS can be further adapted to children from other countries, children with different cultural/sociological/linguistic contexts should be recruited in future studies