Physical Constraint Finite Element Model for Medical Image Registration

Due to being derived from linear assumption, most elastic body based non-rigid image registration algorithms are facing challenges for soft tissues with complex nonlinear behavior and with large deformations. To take into account the geometric nonlinearity of soft tissues, we propose a registration algorithm on the basis of Newtonian differential equation. The material behavior of soft tissues is modeled as St. Venant-Kirchhoff elasticity, and the nonlinearity of the continuum represents the quadratic term of the deformation gradient under the Green- St.Venant strain. In our algorithm, the elastic force is formulated as the derivative of the deformation energy with respect to the nodal displacement vectors of the finite element; the external force is determined by the registration similarity gradient flow which drives the floating image deforming to the equilibrium condition. We compared our approach to three other models: 1) the conventional linear elastic finite element model (FEM); 2) the dynamic elastic FEM; 3) the robust block matching (RBM) method. The registration accuracy was measured using three similarities: MSD (Mean Square Difference), NC (Normalized Correlation) and NMI (Normalized Mutual Information), and was also measured using the mean and max distance between the ground seeds and corresponding ones after registration. We validated our method on 60 image pairs including 30 medical image pairs with artificial deformation and 30 clinical image pairs for both the chest chemotherapy treatment in different periods and brain MRI normalization. Our method achieved a distance error of 0.320±0.138 mm in x direction and 0.326±0.111 mm in y direction, MSD of 41.96±13.74, NC of 0.9958±0.0019, NMI of 1.2962±0.0114 for images with large artificial deformations; and average NC of 0.9622±0.008 and NMI of 1.2764±0.0089 for the real clinical cases. Student's t-test demonstrated that our model statistically outperformed the other methods in comparison (p-values <0.05)

From Skepticism to Acceptance: Simulating the Attitude Dynamics Toward Fake News

In the digital era, the rapid propagation of fake news and rumors via social networks brings notable societal challenges and impacts public opinion regulation. Traditional fake news modeling typically forecasts the general popularity trends of different groups or numerically represents opinions shift. However, these methods often oversimplify real-world complexities and overlook the rich semantic information of news text. The advent of large language models (LLMs) provides the possibility of modeling subtle dynamics of opinion. Consequently, in this work, we introduce a Fake news Propagation Simulation framework (FPS) based on LLM, which studies the trends and control of fake news propagation in detail. Specifically, each agent in the simulation represents an individual with a distinct personality. They are equipped with both short-term and long-term memory, as well as a reflective mechanism to mimic human-like thinking. Every day, they engage in random opinion exchanges, reflect on their thinking, and update their opinions. Our simulation results uncover patterns in fake news propagation related to topic relevance, and individual traits, aligning with real-world observations. Additionally, we evaluate various intervention strategies and demonstrate that early and appropriately frequent interventions strike a balance between governance cost and effectiveness, offering valuable insights for practical applications. Our study underscores the significant utility and potential of LLMs in combating fake news

Association of chemerin mRNA expression in human epicardial adipose tissue with coronary atherosclerosis

<p>Abstract</p> <p>Background</p> <p>Growing evidence suggests that epicardial adipose tissue (EAT) may play a key role in the pathogenesis and development of coronary artery disease (CAD) by producing several inflammatory adipokines. Chemerin, a novel adipokine, has been reported to be involved in regulating immune responses and glucolipid metabolism. Given these properties, chemerin may provide an interesting link between obesity, inflammation and atherosclerosis. In this study, we sought to determine the relationship of chemerin expression in EAT and the severity of coronary atherosclerosis in Han Chinese patients.</p> <p>Methods</p> <p>Serums and adipose tissue biopsies (epicardial and thoracic subcutaneous) were obtained from CAD (n = 37) and NCAD (n = 16) patients undergoing elective cardiac surgery. Gensini score was used to assess the severity of CAD. Serum levels of chemerin, adiponectin and insulin were measured by ELISA. Chemerin protein expression in adipose tissue was detected by immunohistochemistry. The mRNA levels of chemerin, chemR23, adiponectin and TNF-alpha in adipose tissue were detected by RT-PCR.</p> <p>Results</p> <p>We found that EAT of CAD group showed significantly higher levels of chemerin and TNF-alpha mRNA, and significantly lower level of adiponectin mRNA than that of NCAD patients. In CAD group, significantly higher levels of chemerin mRNA and protein were observed in EAT than in paired subcutaneous adipose tissue (SAT), whereas such significant difference was not found in NCAD group. Chemerin mRNA expression in EAT was positively correlated with Gensini score (r = 0.365, <it>P </it>< 0.05), moreover, this correlation remained statistically significant (r = 0.357, <it>P </it>< 0.05) after adjusting for age, gender, BMI and waist circumference. Chemerin mRNA expression in EAT was also positively correlated with BMI (r = 0.305, <it>P </it>< 0.05), waist circumference (r = 0.384, <it>P </it>< 0.01), fasting blood glucose (r = 0.334, <it>P </it>< 0.05) and negatively correlated with adiponectin mRNA expression in EAT (r = -0.322, <it>P </it>< 0.05). However, there were no significant differences in the serum levels of chemerin or adiponectin between the two groups. Likewise, neither serum chemerin nor serum adiponectin was associated with Gensini score (<it>P </it>> 0.05).</p> <p>Conclusions</p> <p>The expressions of chemerin mRNA and protein are significantly higher in EAT from patients with CAD in Han Chinese patients. Furthermore, the severity of coronary atherosclerosis is positive correlated with the level of chemerin mRNA in EAT rather than its circulating level.</p

Improving the Robustness of Summarization Systems with Dual Augmentation

A robust summarization system should be able to capture the gist of the document, regardless of the specific word choices or noise in the input. In this work, we first explore the summarization models' robustness against perturbations including word-level synonym substitution and noise. To create semantic-consistent substitutes, we propose a SummAttacker, which is an efficient approach to generating adversarial samples based on language models. Experimental results show that state-of-the-art summarization models have a significant decrease in performance on adversarial and noisy test sets. Next, we analyze the vulnerability of the summarization systems and explore improving the robustness by data augmentation. Specifically, the first brittleness factor we found is the poor understanding of infrequent words in the input. Correspondingly, we feed the encoder with more diverse cases created by SummAttacker in the input space. The other factor is in the latent space, where the attacked inputs bring more variations to the hidden states. Hence, we construct adversarial decoder input and devise manifold softmixing operation in hidden space to introduce more diversity. Experimental results on Gigaword and CNN/DM datasets demonstrate that our approach achieves significant improvements over strong baselines and exhibits higher robustness on noisy, attacked, and clean datasets.Comment: 10 pages, 6 figures, ACL 2023 main coferenc

Abstractive Text Summarization by Incorporating Reader Comments

In neural abstractive summarization field, conventional sequence-to-sequence based models often suffer from summarizing the wrong aspect of the document with respect to the main aspect. To tackle this problem, we propose the task of reader-aware abstractive summary generation, which utilizes the reader comments to help the model produce better summary about the main aspect. Unlike traditional abstractive summarization task, reader-aware summarization confronts two main challenges: (1) Comments are informal and noisy; (2) jointly modeling the news document and the reader comments is challenging. To tackle the above challenges, we design an adversarial learning model named reader-aware summary generator (RASG), which consists of four components: (1) a sequence-to-sequence based summary generator; (2) a reader attention module capturing the reader focused aspects; (3) a supervisor modeling the semantic gap between the generated summary and reader focused aspects; (4) a goal tracker producing the goal for each generation step. The supervisor and the goal tacker are used to guide the training of our framework in an adversarial manner. Extensive experiments are conducted on our large-scale real-world text summarization dataset, and the results show that RASG achieves the state-of-the-art performance in terms of both automatic metrics and human evaluations. The experimental results also demonstrate the effectiveness of each module in our framework. We release our large-scale dataset for further research.Comment: Accepted by AAAI 201