Notes

NeuroGrasp: Real-Time EEG Classification of High-Level Generator Image Jobs By using a Dual-Stage Deep Understanding Platform.
Image-guided radiation therapy (IGRT) is the most effective treatment for head and neck cancer. The successful implementation of IGRT requires accurate delineation of organ-at-risk (OAR) in the computed tomography (CT) images. In routine clinical practice, OARs are manually segmented by oncologists, which is time-consuming, laborious, and subjective. To assist oncologists in OAR contouring, we proposed a three-dimensional (3D) lightweight framework for simultaneous OAR registration and segmentation. The registration network was designed to align a selected OAR template to a new image volume for OAR localization. A region of interest (ROI) selection layer then generated ROIs of OARs from the registration results, which were fed into a multiview segmentation network for accurate OAR segmentation. To improve the performance of registration and segmentation networks, a centre distance loss was designed for the registration network, an ROI classification branch was employed for the segmentation network, and further, context information was incorporated to iteratively promote both networks' performance. The segmentation results were further refined with shape information for final delineation. We evaluated registration and segmentation performances of the proposed framework using three datasets. On the internal dataset, the Dice similarity coefficient (DSC) of registration and segmentation was 69.7% and 79.6%, respectively. In addition, our framework was evaluated on two external datasets and gained satisfactory performance. These results showed that the 3D lightweight framework achieved fast, accurate and robust registration and segmentation of OARs in head and neck cancer. The proposed framework has the potential of assisting oncologists in OAR delineation.Unsupervised domain adaptation without accessing expensive annotation processes of target data has achieved remarkable successes in semantic segmentation. However, most existing state-of-the-art methods cannot explore whether semantic representations across domains are transferable or not, which may result in the negative transfer brought by irrelevant knowledge. To tackle this challenge, in this paper, we develop a novel Knowledge Aggregation-induced Transferability Perception (KATP) for unsupervised domain adaptation, which is a pioneering attempt to distinguish transferable or untransferable knowledge across domains. Specifically, the KATP module is designed to quantify which semantic knowledge across domains is transferable, by incorporating transferability information propagation from global category-wise prototypes. Based on KATP, we design a novel KATP Adaptation Network (KATPAN) to determine where and how to transfer. The KATPAN contains a transferable appearance translation module T_A() and a transferable representation augmentation module T_R(), where both modules construct a virtuous circle of performance promotion. T_A() develops a transferability-aware information bottleneck to highlight where to adapt transferable visual characterizations and modality information; T_R() explores how to augment transferable representations while abandoning untransferable information, and promotes the translation performance of T_A() in return. Experiments on several representative datasets and a medical dataset support the state-of-the-art performance of our model.This paper aims at proposing an unsupervised hierarchical nonparametric Bayesian framework for modeling axial data (i.e. observations are axes of direction) that can be partitioned into multiple groups, where each observation within a group is sampled from a mixture of Watson distributions with an infinite number of components that are allowed to be shared across different groups. First, we propose a hierarchical nonparametric Bayesian model for modeling grouped axial data based on the hierarchical Pitman-Yor process mixture model of Watson distributions. Then, we demonstrate that by setting the discount parameters of the proposed model to 0, another hierarchical nonparametric Bayesian model based on hierarchical Dirichlet process can be derived for modeling axial data. To learn the proposed models, we systematically develop a closed-form optimization algorithm based on the collapsed variational Bayes (CVB) inference. Furthermore, to ensure the convergence of the proposed learning algorithm, an annealing mechanism is introduced to the framework of CVB inference, leading to an averaged collapsed variational Bayes inference strategy. The merits of the proposed models for modeling grouped axial data are demonstrated through experiments on both synthetic data and real-world applications involving gene expression data clustering and depth image analysis.A little knowledge can be a dangerous thing. Most of us have heard of this admonition, and it applies directly to engineering education. It turns out that people who know just a little about a subject greatly overestimate their understanding and abilities. "The Dunning-Kruger effect is a cognitive bias in which people wrongly overestimate their knowledge or ability in a specific area. This tends to occur because a lack of self-awareness prevents them from accurately assessing their own skills" [5].One of the core missions of the IEEE Engineering in Medicine and Biology Society (EMBS) is to be a platform for enhancing the personal and professional development of its members. This month we focus on two related priority areas of the IEEE EMBS Student Activities Committee (SAC) [1], namely Leadership Development and Professional Development Portfolios, and bring you up close to the student and professional leaders actively building these programs. The Leadership Development Portfolio, currently led by Agnieszka Łach from Silesian University of Technology, Gliwice, Poland, focuses on nurturing and supporting student leaders of the EMBS globally. The Professional Development Portfolio, currently led by Josée Rosset from the University of Manitoba, Winnipeg, MB, Canada, aims to help EMBS student members develop their skills and experiences in the practice of biomedical engineering.Circular RNA (circRNA) is a key regulator of tumor progression. However, the role of circFOXM1 in glioblastoma (GBM) progression is unclear. The aim of this study was to investigate the role of circFOXM1 in GBM progression. The expression levels of circFOXM1, miR-577 and E2F transcription factor 5 (E2F5) were examined by real-time quantitative PCR. Cell counting kit 8 assay, EdU staining and transwell assay were used to detect cell proliferation, migration, and invasion. The levels of glutamine, glutamate and α-ketoglutarate were determined to evaluate the glutaminolysis ability of cells. Protein expression was tested by western blot analysis. Dual-luciferase reporter assay, RNA pull-down assay and RNA immunoprecipitation assay were employed to verify the interaction between miR-577 and circFOXM1 or E2F5. Mice xenograft model for GBM was constructed to perform in vivo experiments. Our results showed that circFOXM1 was highly expressed in GBM tumor tissues and cells. Silencing of circFOXM1 inhibited GBM cell proliferation, migration, invasion, glutaminolysis, as well as tumor growth. MiR-577 could be sponged by circFOXM1, and its inhibitor could reverse the suppressive effect of circFOXM1 downregulation on GBM progression. E2F5 was a target of miR-577, and the effect of its knockdown on GBM progression was consistent with that of circFOXM1 silencing. find more CircFOXM1 positively regulated E2F5 expression, while miR-577 negatively regulated E2F5 expression. In conclusion, our data confirmed that circFOXM1 could serve as a sponge of miR-577 to enhance the progression of GBM by targeting E2F5, which revealed that circFOXM1 might be a biomarker for GBM treatment.Dyslipidemia has recently been identified as an important factor in modulating the progression of several health conditions, grouped as cardio-metabolic syndrome and including obesity,insulin resistance, and atherosclerosis. Among multiple factors which regulate the development of cardio-metabolic syndrome, sortilin has been found in multiple cell types, such as adipocyte, hepatocyte, and macrophage, suggesting that sortilin is correlated to the development and the severity of cardio-metabolic syndrome. Consistently, several genome-wide association (GWAS) and basic experimental research studies are being conducted to find novel gene loci involved in regulating the pathological progression of cardio-metabolic syndrome. According to these data, both SORT1 gene and sortilin protein have an important function in regulating the circulating lipid and glucose metabolism resulting in modulation of disease progression. In this comprehensive review, we summarize the recent research results in regards to sortilin function in modulating the circulating lipid and glucose metabolism. Moreover, we also discuss and analyze the emerging evidence elucidating the potential mechanisms by which sortilin affects synthesis and secretion of lipid and glucose.According to the previous reports, hypothyroidism has been shown to be strongly correlated with increased circulating concentrations of total cholesterol, low-density lipoprotein cholesterol (LDL-C), and triglycerides (TG). Notably, thyroid hormones are confirmed to modulate the production, clearance, and transformation process of cholesterol within circulation of mammals. Moreover, emerging evidence suggests that the thyroid-stimulating hormone could also participate in modulating serum lipid metabolism independently of thyroid hormones, which further induces the pathological development of dyslipidemia. However, the underlying mechanism is still not fully elucidated. Recently, several research studies have demonstrated that the pathogenic progression of hypothyroidism-related dyslipidemia might be correlated with the decreased serum concentrations of thyroid hormones and the increased serum concentrations of thyroid-stimulating hormones. Thus, this indicates that hypothyroidism could induce dyslipidemia and its related cardio-metabolic disorder diseases. In addition, several newly identified modulatory biomarkers, such as proprotein convertase subtilisin/kexin type 9 (PCSK9), angiopoietin-like protein (ANGPTLs), and fibroblast growth factors (FGFs), might play an important role in the regulation of dyslipidemia induced by hypothyroidism. Furthermore, under the status of hypothyroidism, significantly dysfunctional HDL particles could also be observed. In the current review, we summarized the recent knowledge of the relationship between the development of hypothyroidism with dyslipidemia. We also discussed the updated understanding of the mechanisms whereby hypothyroidism induces the risk and the development of dyslipidemia and cardio-metabolic diseases.Acute myeloid leukemia (AML) is a highly heterogeneous hematopoietic malignancy that strongly correlates with poor clinical outcomes. Ferroptosis is an iron-dependent, non-apoptotic form of regulated cell death which plays an important role in various human cancers. Nevertheless, the prognostic significance and functions of ferroptosis-related genes (FRGs) in AML have not received sufficient attention. The aim of this article was to evaluate the association between FRGs levels and AML prognosis using publicly available RNA-sequencing datasets. The univariate Cox regression analysis identified 20 FRGs that correlate with patient overall survival. The LASSO Cox regression model was used to construct a prognostic 12-gene risk model using a TCGA cohort, and internal and external validation proved the signature efficient. The 12-FRGs signature was then used to assign patients into high- and low-risk groups, with the former exhibiting markedly reduced overall survival, compared to the low-risk group. ROC curve analysis verified the predictive ability of the risk model.
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