Learning-based tumor segmentation using metabolic imaging features

The ultimate objective of brain tumor imaging is to distill patient-specific knowledge that guides therapy planning and medical care. Magnetic resonance imaging (MRI) is a prevailing technique to visualize tumors due to its excellent contrast of soft tissue and non-invasiveness. Decades of research have helped brain tumor segmentation performance dramatically. However, precise segmentation is still considered hard partly due to the limitation in resolution, signal-to-noise ratio, and possible artifacts. While some tumors are easier to delineate, more infiltrating ones like gliomas have ragged and obscure boundaries that are harder to define. In recognition of this hardship, researchers have started exploring the use of Proton Magnetic Resonance Spectroscopic Imaging (MRSI) for better tumor prognosis, diagnosis, and characterization. MRSI investigates the spatial distribution of metabolic changes by leveraging its unique temporal information. The wealth of this spectroscopic information is beneficial in classifying tumor subregions and aiding ongoing research investigations in tumor heterogeneity and related topics. Several studies have reported an increase in choline-containing compounds level and a reduced N-acetyl-aspartate level in brain tumors. Spectroscopic techniques can pick up these metabolic changes, and they might be the missing pieces of better MRI-based tumor segmentation solutions. This study shows a successful application of deep learning and MRSI to identify tumor and edema regions of human brains with glioblastomas. The deep learning framework of choice is nnU-Net. Most specialized solutions in applying deep neural models in the medical image domain depend on dataset properties and hardware constraints. nnU-Net is a framework that automatically adapts itself to various medical image segmentation tasks. Therefore, it ensures a fair comparison of experiments. This work shows an improved segmentation result after incorporating high-resolution metabolite maps derived from MRSI data acquired by the SPICE sequence. The high resolution, rapidness, and near whole-brain performance of SPICE should assist radiologists and oncologists in delimiting the pathological area better and providing more appropriate medical help

Semantic Communication for Cooperative Perception based on Importance Map

Cooperative perception, which has a broader perception field than single-vehicle perception, has played an increasingly important role in autonomous driving to conduct 3D object detection. Through vehicle-to-vehicle (V2V) communication technology, various connected automated vehicles (CAVs) can share their sensory information (LiDAR point clouds) for cooperative perception. We employ an importance map to extract significant semantic information and propose a novel cooperative perception semantic communication scheme with intermediate fusion. Meanwhile, our proposed architecture can be extended to the challenging time-varying multipath fading channel. To alleviate the distortion caused by the time-varying multipath fading, we adopt explicit orthogonal frequency-division multiplexing (OFDM) blocks combined with channel estimation and channel equalization. Simulation results demonstrate that our proposed model outperforms the traditional separate source-channel coding over various channel models. Moreover, a robustness study indicates that only part of semantic information is key to cooperative perception. Although our proposed model has only been trained over one specific channel, it has the ability to learn robust coded representations of semantic information that remain resilient to various channel models, demonstrating its generality and robustness.Comment: 13 pages,22 figures;journal;submitted for possible publicatio

Strategic weight manipulation in multiple attribute decision making

In some real-world multiple attribute decision making (MADM) problems, a decision maker can strategically set attribute weights to obtain her/his desired ranking of alternatives, which is called the strategic weight manipulation of the MADM. In this paper, we define the concept of the ranking range of an alternative in the MADM, and propose a series of mixed 0-1 linear programming models (MLPMs) to show the process of designing a strategic attribute weight vector. Then, we reveal the conditions to manipulate a strategic attribute weight based on the ranking range and the proposed MLPMs. Finally, a numerical example with real background is used to demonstrate the validity of our models, and simulation experiments are presented to show the better performance of the ordered weighted averaging operator than the weighted averaging operator in defending against the strategic weight manipulation of the MADM problems

Multiple Attribute Strategic Weight Manipulation With Minimum Cost in a Group Decision Making Context With Interval Attribute Weights Information

Abstract—In multiple attribute decision making (MADM), strategic weight manipulation is understood as a deliberate manipulation of attribute weight setting to achieve a desired ranking of alternatives. In this paper, we study the strategic weight manipulation in a group decision making (GDM) context with interval attribute weight information. In GDM, the revision of the decision makers’ original attribute weight information implies a cost. Driven by a desire to minimize the cost, we propose the minimum cost strategic weight manipulation model, which is achieved via optimization approach, with the mixed 0-1 linear programming model being proved appropriate in this context. Meanwhile, some desired properties to manipulate a strategic attribute weight based on the ranking range under interval attribute weight information are proposed. Finally, numerical analysis and simulation experiments are provided with a twofold aim: 1) to verify the validity of the proposed models and 2) to show the effects of interval attribute weights information and the unit cost, respectively, on the cost to manipulate strategic weights in the MADM in a group decision context.This work was supported in part by National Science Foundation of China under Grant 71571124, Grant 71871149, and Grant 71601133; in part by Sichuan University under Grant sksyl201705 and Grant 2018hhs-58; and in part by FEDER Funds under Grant TIN2016- 75850-R

Dynamics of Uncertain Opinion Formation: An Agent-Based Simulation

Abstract: Opinion formation describes the dynamics of opinions in a group of interaction agents and is a powerful tool for predicting the evolution and di usion of the opinions. The existing opinion formation studies assume that the agents express their opinions by using the exact number, i.e., the exact opinions. However, when people express their opinions, sentiments, and support emotions regarding di erent issues, such as politics, products, and events, they o en cannot provide the exact opinions but express uncertain opinions. Furthermore, due to the di erences in culture backgrounds and characters of agents, people who encounter uncertain opinions o en show di erent uncertainty tolerances. The goal of this study is to investigate the dynamics of uncertain opinion formation in the framework of bounded confidence. By taking di erent uncertain opinions and di erent uncertainty tolerances into account, we use an agent-based simulation to investigate the influences of uncertain opinions in opinion formation from two aspects: the ratios of the agents that express uncertain opinions and the widths of the uncertain opinions, and also provide the explanations of the observations obtained

Inhibition of cyclin-dependent kinase 7 down-regulates yes-associated protein expression in mesothelioma cells.

Cyclin-dependent kinase 7 (CDK7) is a protein kinase that plays a major role in transcription initiation. Yes-associated protein (YAP) is a main effector of the Hippo/YAP signalling pathway. Here, we investigated the role of CDK7 on YAP regulation in human malignant pleural mesothelioma (MPM). We found that in microarray samples of human MPM tissue, immunohistochemistry staining showed correlation between the expression level of CDK7 and YAP (n = 70, r = .513). In MPM cells, CDK7 expression level was significantly correlated with GTIIC reporter activity (r = .886, P = .019). Inhibition of CDK7 by siRNA decreased the YAP protein level and the GTIIC reporter activity in the MPM cell lines 211H, H290 and H2052. Degradation of the YAP protein was accelerated after CDK7 knockdown in 211H, H290 and H2052 cells. Inhibition of CDK7 reduced tumour cell migration and invasion, as well as tumorsphere formation ability. Restoration of the CDK7 gene rescued the YAP protein level and GTIIC reporter activity after siRNA knockdown in 211H and H2052 cells. Finally, we performed a co-immunoprecipitation analysis using an anti-YAP antibody and captured the CDK7 protein in 211H cells. Our results suggest that CDK7 inhibition reduces the YAP protein level by promoting its degradation and suppresses the migration and invasion of MPM cells. Cyclin-dependent kinase 7 may be a promising therapeutic target for MPM

Asynchronous Opinion Dynamics with Online and Offline Interactions in Bounded Confidence Model

Open Access journalNowadays, in the world, about half of the population can receive information and exchange their opinions with others in online environments (e.g. the Internet), while the other half obtain information and exchange their opinions in offline environments (e.g. face to face) (see eMarketer Report, 2016). The speed at which information is received and opinions are exchanged in online environments is much faster than in offline environments. To model this phenomenon, in this paper we consider online and offline as two subsystems in opinion dynamics, and there is asynchronization when the agents in these two subsystems update their opinions. We show that asynchronization strongly impacts the steady-state time of the opinion dynamics, the opinion clusters and the interactions between the online subsystem and offline subsystem. Furthermore, these effects are often enhanced the larger the size of the online subsystem