Design of Intelligent Health Care Glasses Based on the Concept of Traditional Chinese Medicine and Health

With the gushing development of electronic products, electronic products cover all aspects of modern people's life. In the face of ubiquitous electronic screens, modern people are trapped in it, I have to say that this is an era of excessive eyes. The arrival of this era means that the number of people suffering from eye diseases will also rise sharply, and the "eye" has become one of the problems that modern people can not ignore. This plan takes the regular Chinese medicinal drug as the primary look up theory, combines the standard Chinese acupuncture science with the present day electric powered acupuncture science, fills the surrounding eye muscles and blood, and realizes the purpose of health and beauty. This smart health care glasses choose different ways to wear according to the functional attributes of professional use, and use the fragmented time to create the value of health care prevention

Improved Convergence Rate of Nested Simulation with LSE on Sieve

Nested simulation encompasses the estimation of functionals linked to conditional expectations through simulation techniques. In this paper, we treat conditional expectation as a function of the multidimensional conditioning variable and provide asymptotic analyses of general Least Squared Estimators on sieve, without imposing specific assumptions on the function's form. Our study explores scenarios in which the convergence rate surpasses that of the standard Monte Carlo method and the one recently proposed based on kernel ridge regression. We also delve into the conditions that allow for achieving the best possible square root convergence rate among all methods. Numerical experiments are conducted to support our statements

A Context Model for Service Composition Based on Dynamic Description Logic

Abstract: A service composition task for service broker is to discovery and compose provider's services to satisfy user's request. Many researchers model the context utilizing ontology-based or attribute-based method to assist service composition. We propose a new context model by combining the context logic with the dynamic description logic (DDL), where user' context, provider's context and broker's context are described by DDL separately and reasoned under the context logic. The reasoning results finally can be used to discovery and compose services intelligently. We evaluate this model on a simple, yet realistic example, and the results show that our context model provides a practical solution

Deep Learning for Multivariate Time Series Imputation: A Survey

The ubiquitous missing values cause the multivariate time series data to be partially observed, destroying the integrity of time series and hindering the effective time series data analysis. Recently deep learning imputation methods have demonstrated remarkable success in elevating the quality of corrupted time series data, subsequently enhancing performance in downstream tasks. In this paper, we conduct a comprehensive survey on the recently proposed deep learning imputation methods. First, we propose a taxonomy for the reviewed methods, and then provide a structured review of these methods by highlighting their strengths and limitations. We also conduct empirical experiments to study different methods and compare their enhancement for downstream tasks. Finally, the open issues for future research on multivariate time series imputation are pointed out. All code and configurations of this work, including a regularly maintained multivariate time series imputation paper list, can be found in the GitHub repository~\url{https://github.com/WenjieDu/Awesome\_Imputation}.Comment: 9 pages, 1 figure, 5 tables, 58 referred paper

TBX 5 gene mutation analysis among Tanzanian children with congenital heart diseases using high-resolution melting assays

Early cardiac development is governed by transcription factor genes. TBX5, a T-box transcription factor gene, plays an important role in the development  of the second heart field during cardiac septation by promoting cell cycle progression through the enhancement of Cdk6 and hedgehog signaling  pathways. TBX5 binds to the promoter region of genes, enhancing the expression of alpha cardiac myosin heavy chain 6 (MYH6), which is a predominant  isoform found in human cardiac tissue. TBX5 gene mutations are postulated to cause congenital heart diseases. A casecontrol TBX5 mutational analysis  was performed to provide insight into the etiology of sporadic congenital heart diseases in our setting. We used a magnetic induction cycler (mic-PCR),  which is a next-generation tool for polymerase chain reaction-high resolution melting assays, to detect mutations in children with sporadic isolated  congenital heart diseases. A retrospective casecontrol study was conducted at the Jakaya Kikwete Cardiac Institute. The peripheral blood samples were  collected, and DNA was extracted using the Quick-DNA Miniprep Kit. The primers were designed using Primer 3 software, validated using the program  BLAST, and checked for hairpin and homo-hetero-dimerization using the IDT oligo analyzer. Real-time polymerase chain reaction (PCR)-high-resolution  melting assays for screening TBX5 gene mutations were done using a magnetic induction cycler. We found two (2) TBX5 mutations in exon 5, among  patients with Atrial-Ventral Septal Defects (ASVD) and Atrial-Septal Defects (ASD) and none among controls. TBX5 exon 5 is a molecular hotspot for  isolated congenital heart diseases.&nbsp

Long-term liver lesion tracking in contrast-enhanced ultrasound videos via a siamese network with temporal motion attention

Propose: Contrast-enhanced ultrasound has shown great promises for diagnosis and monitoring in a wide range of clinical conditions. Meanwhile, to obtain accurate and effective location of lesion in contrast-enhanced ultrasound videos is the basis for subsequent diagnosis and qualitative treatment, which is a challenging task nowadays.Methods: We propose to upgrade a siamese architecture-based neural network for robust and accurate landmark tracking in contrast-enhanced ultrasound videos. Due to few researches on it, the general inherent assumptions of the constant position model and the missing motion model remain unaddressed limitations. In our proposed model, we overcome these limitations by introducing two modules into the original architecture. We use a temporal motion attention based on Lucas Kanade optic flow and Karman filter to model the regular movement and better instruct location prediction. Moreover, we design a pipeline of template update to ensure timely adaptation to feature changes.Results: Eventually, the whole framework was performed on our collected datasets. It has achieved the average mean IoU values of 86.43% on 33 labeled videos with a total of 37,549 frames. In terms of tracking stability, our model has smaller TE of 19.2 pixels and RMSE of 27.6 with the FPS of 8.36 ± 3.23 compared to other classical tracking models.Conclusion: We designed and implemented a pipeline for tracking focal areas in contrast-enhanced ultrasound videos, which takes the siamese network as the backbone and uses optical flow and Kalman filter algorithm to provide position prior information. It turns out that these two additional modules are helpful for the analysis of CEUS videos. We hope that our work can provide an idea for the analysis of CEUS videos

Morphological changes in the cerebellum during aging: evidence from convolutional neural networks and shape analysis

The morphology and function of the cerebellum are associated with various developmental disorders and healthy aging. Changes in cerebellar morphology during the aging process have been extensively investigated, with most studies focusing on changes in cerebellar regional volume. The volumetric method has been used to quantitatively demonstrate the decrease in the cerebellar volume with age, but it has certain limitations in visually presenting the morphological changes of cerebellar atrophy from a three-dimensional perspective. Thus, we comprehensively described cerebellar morphological changes during aging through volume measurements of subregions and shape analysis. This study included 553 healthy participants aged 20–80 years. A novel cerebellar localized segmentation algorithm based on convolutional neural networks was utilized to analyze the volume of subregions, followed by shape analysis for localized atrophy assessment based on the cerebellar thickness. The results indicated that out of the 28 subregions in the absolute volume of the cerebellum, 15 exhibited significant aging trends, and 16 exhibited significant sex differences. Regarding the analysis of relative volume, only 11 out of the 28 subregions of the cerebellum exhibited significant aging trends, and 4 exhibited significant sex differences. The results of the shape analysis revealed region-specific atrophy of the cerebellum with increasing age. Regions displaying more significant atrophy were predominantly located in the vermis, the lateral portions of bilateral cerebellar hemispheres, lobules I-III, and the medial portions of the posterior lobe. This atrophy differed between sexes. Men exhibited slightly more severe atrophy than women in most of the cerebellar regions. Our study provides a comprehensive perspective for observing cerebellar atrophy during the aging process

Exploring the relationship between abnormally high expression of NUP205 and the clinicopathological characteristics, immune microenvironment, and prognostic value of lower-grade glioma

Nuclear pore complex (NPC) is a major transport pivot for nucleocytoplasmic molecule exchange. Nucleoporin 205 (NUP205)—a main component of NPC—plays a key regulatory role in tumor cell proliferation; however, few reports document its effect on the pathological progression of lower-grade glioma (LGG). Therefore, we conducted an integrated analysis using 906 samples from multiple public databases to explore the effects of NUP205 on the prognosis, clinicopathological characteristics, regulatory mechanism, and tumor immune microenvironment (TIME) formation in LGG. First, multiple methods consistently showed that the mRNA and protein expression levels of NUP205 were higher in LGG tumor tissue than in normal brain tissue. This increased expression was mainly noted in the higher WHO Grade, IDH-wild type, and 1p19q non-codeleted type. Second, various survival analysis methods showed that the highly expressed NUP205 was an independent risk indicator that led to reduced survival time of patients with LGG. Third, GSEA analysis showed that NUP205 regulated the pathological progress of LGG via the cell cycle, notch signaling pathway, and aminoacyl-tRNA biosynthesis. Ultimately, immune correlation analysis suggested that high NUP205 expression was positively correlated with the infiltration of multiple immune cells, particularly M2 macrophages, and was positively correlated with eight immune checkpoints, particularly PD-L1. Collectively, this study documented the pathogenicity of NUP205 in LGG for the first time, expanding our understanding of its molecular function. Furthermore, this study highlighted the potential value of NUP205 as a target of anti-LGG immunotherapy