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

Janus Monolayer Transition Metal Dichalcogenides

A novel crystal configuration of sandwiched S-Mo-Se structure (Janus SMoSe) at the monolayer limit has been synthesized and carefully characterized in this work. By controlled sulfurization of monolayer MoSe2 the top layer of selenium atoms are substituted by sulfur atoms while the bottom selenium layer remains intact. The peculiar structure of this new material is systematically investigated by Raman, photoluminescence and X-ray photoelectron spectroscopy and confirmed by transmission-electron microscopy and time-of-flight secondary ion mass spectrometry. Density-functional theory calculations are performed to better understand the Raman vibration modes and electronic structures of the Janus SMoSe monolayer, which are found to correlate well with corresponding experimental results. Finally, high basal plane hydrogen evolution reaction (HER) activity is discovered for the Janus monolayer and DFT calculation implies that the activity originates from the synergistic effect of the intrinsic defects and structural strain inherent in the Janus structure.Comment: 22 pages, 12 figure

Прочность болтового фланцевого соединения стальной конструкции при землетрясении

Болтовое фланцевое соединение является одним из наиболее широко используемых видов соединения стальных конструкций. Его прочность при землетрясении обеспечивает сейсмостойкость стального каркаса. Предложены метод расчета прочности болтового фланцевого соединения стальных конструкций и конечноэлементная модель для расчета прочности фланцевого соединения стальной конструкции при землетрясении. При приложении односторонней статической и динамической нагрузки моделируется и рассчитывается прочность болтового фланцевого соединения. Результаты расчета показывают, что под действием землетрясения толщина фланца незначительно влияет на площадь контакта, но оказывает большое влияние на напряжение в зоне контакта. Под действием землетрясения, когда стенка колонны и балка деформируются одновременно, деформации изгиба являются самыми полными, коэффициент рассеивания энергии и параметры сейсмических воздействий оказываются наибольшими. Аналитические результаты использованных методов хорошо согласуются с реальными значениями, а коэффициент совпадения превышает значение 0.9900

Enzyme structure dynamics of xylanase I from Trichoderma longibrachiatum

BACKGROUND: Enzyme dynamics has recently been shown to be crucial for structure-function relationship. Among various structure dynamics analysis platforms, HDX (hydrogen deuterium exchange) mass spectrometry stands out as an efficient and high-throughput way to analyze protein dynamics upon ligand binding. Despite the potential, limited research has employed the HDX mass spec platform to probe regional structure dynamics of enzymes. In particular, the technique has never been used for analyzing cell wall degrading enzymes. We hereby used xylanase as a model to explore the potential of HDX mass spectrometry for studying cell wall degrading enzymes. RESULTS: HDX mass spectrometry revealed significant intrinsic dynamics for the xylanase enzyme. Different regions of the enzymes are differentially stabilized in the apo enzyme. The comparison of substrate-binding enzymes revealed that xylohexaose can significantly stabilize the enzyme. Several regions including those near the reaction centres were significantly stabilized during the xylohexaose binding. As compared to xylohexaose, xylan induced relatively less protection in the enzyme, which may be due to the insolubility of the substrate. The structure relevance of the enzyme dynamics was discussed with reference to the three dimensional structure of the enzyme. HDX mass spectrometry revealed strong dynamics-function relevance and such relevance can be explored for the future enzyme improvement. CONCLUSION: Ligand-binding can lead to the significant stabilization at both regional and global level for enzymes like xylanase. HDX mass spectrometry is a powerful high-throughput platform to identify the key regions protected during the ligand binding and to explore the molecular mechanisms of the enzyme function. The HDX mass spectrometry analysis of cell wall degrading enzymes has provided a novel platform to guide the rational design of enzymes

HCV 6a Prevalence in Guangdong Province Had the Origin from Vietnam and Recent Dissemination to Other Regions of China: Phylogeographic Analyses

Recently in China, HCV 6a infection has shown a fast increase among patients and blood donors, possibly due to IDU linked transmission.We recruited 210 drug users in Shanwei city, Guangdong province. Among them, HCV RNA was detected in 150 (71.4%), both E1 and NS5B genes were sequenced in 136, and 6a genotyped in 70. Of the 6a sequences, most were grouped into three clusters while 23% represent emerging strains. For coalescent analysis, additional 6a sequences were determined among 21 blood donors from Vietnam, 22 donors from 12 provinces of China, and 36 IDUs from Liuzhou City in Guangxi Province. Phylogeographic analyses indicated that Vietnam could be the origin of 6a in China. The Guangxi Province, which borders Vietnam, could be the first region to accept 6a for circulation. Migration from Yunnan, which also borders Vietnam, might be equally important, but it was only detected among IDUs in limited regions. From Guangxi, 6a could have further spread to Guangdong, Yunnan, Hainan, and Hubei provinces. However, evidence showed that only in Guangdong has 6a become a local epidemic, making Guangdong the second source region to disseminate 6a to the other 12 provinces. With a rate of 2.737×10⁻³ (95% CI: 1.792×10⁻³ to 3.745×10⁻³), a Bayesian Skyline Plot was portrayed. It revealed an exponential 6a growth during 1994-1998, while before and after 1994-1998 slow 6a growths were maintained. Concurrently, 1994-1998 corresponded to a period when contaminated blood transfusion was common, which caused many people being infected with HIV and HCV, until the Chinese government outlawed the use of paid blood donations in 1998.With an origin from Vietnam, 6a has become a local epidemic in Guangdong Province, where an increasing prevalence has subsequently led to 6a spread to many other regions of China

Comparative genome analysis of lignin biosynthesis gene families across the plant kingdom

<p>Abstract</p> <p>Background</p> <p>As a major component of plant cell wall, lignin plays important roles in mechanical support, water transport, and stress responses. As the main cause for the recalcitrance of plant cell wall, lignin modification has been a major task for bioenergy feedstock improvement. The study of the evolution and function of lignin biosynthesis genes thus has two-fold implications. First, the lignin biosynthesis pathway provides an excellent model to study the coordinative evolution of a biochemical pathway in plants. Second, understanding the function and evolution of lignin biosynthesis genes will guide us to develop better strategies for bioenergy feedstock improvement.</p> <p>Results</p> <p>We analyzed lignin biosynthesis genes from fourteen plant species and one symbiotic fungal species. Comprehensive comparative genome analysis was carried out to study the distribution, relatedness, and family expansion of the lignin biosynthesis genes across the plant kingdom. In addition, we also analyzed the comparative synteny map between rice and sorghum to study the evolution of lignin biosynthesis genes within the <it>Poaceae </it>family and the chromosome evolution between the two species. Comprehensive lignin biosynthesis gene expression analysis was performed in rice, poplar and <it>Arabidopsis</it>. The representative data from rice indicates that different fates of gene duplications exist for lignin biosynthesis genes. In addition, we also carried out the biomass composition analysis of nine <it>Arabidopsis </it>mutants with both MBMS analysis and traditional wet chemistry methods. The results were analyzed together with the genomics analysis.</p> <p>Conclusion</p> <p>The research revealed that, among the species analyzed, the complete lignin biosynthesis pathway first appeared in moss; the pathway is absent in green algae. The expansion of lignin biosynthesis gene families correlates with substrate diversity. In addition, we found that the expansion of the gene families mostly occurred after the divergence of monocots and dicots, with the exception of the C4H gene family. Gene expression analysis revealed different fates of gene duplications, largely confirming plants are tolerant to gene dosage effects. The rapid expansion of lignin biosynthesis genes indicated that the translation of transgenic lignin modification strategies from model species to bioenergy feedstock might only be successful between the closely relevant species within the same family.</p

Research on fully mechanized mining equipment removal planning during sequencing working face

The current fully mechanized mining equipment removal plan during sequencing working face mainly depends on manual preparation. The large workload and low efficiency lead to the extension of the construction period. The quick removal mainly depends on a high degree of mechanized operations. There is little research on optimizing the fully mechanized mining equipment removal plan during sequencing working face between different mines or different working faces in the same mine. In order to solve this problem, by investigating the mining conditions of Shendong Group's fully mechanized mining equipment in recent three years, the key parameters such as working face, equipment, personnel, and time are defined, which characterize the fully mechanized mining equipment removal during sequencing working face. Taking minimizing the maximum completion time as the objective function, a mathematical model for the fully mechanized mining equipment removal planning during sequencing working face is established. A genetic algorithm is designed to solve the mathematical model. The three-segment coding method considering the selection of working face, fully mechanized mining equipment and construction team is adopted, and the fitness function is built. The chromosomes of working face, fully mechanized mining equipment and construction team are selected, crossed and mutated. Considering the latest mining time, the legitimacy of chromosomes is judged and adjusted. By setting the number of iterations, search process of the algorithm is terminated and outputs the results. Based on the genetic algorithm for the fully mechanized mining equipment removal planning during sequencing working face, a management system of the fully mechanized mining equipment removal plan during sequencing working face based on B/S architecture is developed. It has realized the functions of basic information management of fully mechanized working face removal during sequence working face, and fully mechanized mining equipment removal planning during sequencing working face. The example shows that the application of genetic algorithm can shorten the construction period of fully mechanized mining equipment removal of 11 fully mechanized working faces in Shendong Group in 2021 from 103 days to 91 days. The method effectively improves the fully mechanized mining equipment removal planning efficiency and engineering efficiency