Effect of the location and size of thyroid nodules on the diagnostic performance of ultrasound elastography: A retrospective analysis

OBJECTIVES: Ultrasound-guided fine-needle aspiration biopsies are recommended for the detection of suspicious thyroid nodules. However, the best approach regarding suspicious ultrasound features for thyroid nodules is still unclear. This study aimed to evaluate the effect of location and size of thyroid nodules on the diagnostic performance of strain ultrasound elastography. In addition, this study evaluated whether ultrasound elastography predicts malignancy in thyroid nodules. METHODS: Data regarding the size, depth, and distance from the carotid artery of nodules, the elasticity contrast index, and the nature of nodules were analyzed. RESULTS: There was no significant difference in the depth (p=0.092) and the distance from the carotid artery (p=0.061) between benign and suspicious nodules. Suspicious nodules were smaller than benign nodules (po0.0001, q=23.84) and had a higher elasticity contrast index (po0.0001, q=21.05). The depth of nodules and the size of the nodule were not associated with the correct value of the elasticity contrast index (p40.05 for both). The diagnostic performance of ultrasound elastography was not affected by the distance of the nodules from the carotid artery if they were located X15 mm from the carotid artery (p=0.5960). However, if the suspicious nodules were located o15 mm from the carotid artery, the diagnostic accuracy was hampered (p=0.006). CONCLUSIONS: The strain ultrasound elastography should be carefully evaluated when small thyroid nodules are located near the carotid artery

Special values of spectral zeta functions of graphs and Dirichlet LL-functions

In this paper, we establish relations between special values of Dirichlet $L$-functions and that of spectral zeta functions or $L$-functions of cycle graphs. In fact, they determine each other in a natural way. These two kinds of special values were bridged together by a combinatorial derivative formula obtained from studying spectral zeta functions of the first order self-adjoint differential operators on the unit circle

Wettability Alteration Process at Pore-Scale during Engineered Waterflooding using Computational Fluid Dynamics

Engineered waterflooding modifies chemistry of injected brine to efficiently and environmentally friendly enhance oil recovery. The common practice of engineered waterflooding includes low salinity waterflooding (LSW) and carbonated waterflooding. Among these oil recovery methods, wettability alteration has been perceived as a critical physicochemical process for additional oil recovery. While extensive work has been conducted to characterize the wettability alteration, the existing theory cannot explain the conflict oil recovery between secondary mode (injecting engineered water at the very beginning of flooding) and tertiary mode (injecting engineered water after conventional waterflooding), where secondary engineered waterflooding always gives a greater incremental oil recovery than tertiary mode. To explain this recovery difference, a preferential flow channel was hypothesized to be created by secondary flooding, which likely reduces sweep efficiency of tertiary flooding. To test this hypothesis, computational fluid dynamic simulations were performed with finite volume method coupled with dynamic contact angles in OpenFOAM to represent wettability characteristics (from strongly oil-wet to strongly water-wet) at pore scale to quantify the role of pre-existing flow channel in the oil recovery at different flooding modes. The simulation results showed that secondary engineered waterflooding indeed generates a preferential flow pathway, which reduces recovery efficiency of subsequent tertiary waterflooding. Streamline analysis confirms that tertiary engineered waterflooding transports faster than secondary engineered waterflooding, implying that sweep efficiency of tertiary engineered waterflooding is lower than secondary engineered waterflooding. This work provides insights for a greater oil recovery at secondary mode than tertiary mode during engineered waterflooding at pore scale

Positional Information Matters for Invariant In-Context Learning: A Case Study of Simple Function Classes

In-context learning (ICL) refers to the ability of a model to condition on a few in-context demonstrations (input-output examples of the underlying task) to generate the answer for a new query input, without updating parameters. Despite the impressive ICL ability of LLMs, it has also been found that ICL in LLMs is sensitive to input demonstrations and limited to short context lengths. To understand the limitations and principles for successful ICL, we conduct an investigation with ICL linear regression of transformers. We characterize several Out-of-Distribution (OOD) cases for ICL inspired by realistic LLM ICL failures and compare transformers with DeepSet, a simple yet powerful architecture for ICL. Surprisingly, DeepSet outperforms transformers across a variety of distribution shifts, implying that preserving permutation invariance symmetry to input demonstrations is crucial for OOD ICL. The phenomenon specifies a fundamental requirement by ICL, which we termed as ICL invariance. Nevertheless, the positional encodings in LLMs will break ICL invariance. To this end, we further evaluate transformers with identical positional encodings and find preserving ICL invariance in transformers achieves state-of-the-art performance across various ICL distribution shiftsComment: Ongoing work; preliminary versio

Does Invariant Graph Learning via Environment Augmentation Learn Invariance?

Invariant graph representation learning aims to learn the invariance among data from different environments for out-of-distribution generalization on graphs. As the graph environment partitions are usually expensive to obtain, augmenting the environment information has become the de facto approach. However, the usefulness of the augmented environment information has never been verified. In this work, we find that it is fundamentally impossible to learn invariant graph representations via environment augmentation without additional assumptions. Therefore, we develop a set of minimal assumptions, including variation sufficiency and variation consistency, for feasible invariant graph learning. We then propose a new framework Graph invAriant Learning Assistant (GALA). GALA incorporates an assistant model that needs to be sensitive to graph environment changes or distribution shifts. The correctness of the proxy predictions by the assistant model hence can differentiate the variations in spurious subgraphs. We show that extracting the maximally invariant subgraph to the proxy predictions provably identifies the underlying invariant subgraph for successful OOD generalization under the established minimal assumptions. Extensive experiments on datasets including DrugOOD with various graph distribution shifts confirm the effectiveness of GALA.Comment: NeurIPS 2023, 34 pages, 35 figure

TCEIP: Text Condition Embedded Regression Network for Dental Implant Position Prediction

When deep neural network has been proposed to assist the dentist in designing the location of dental implant, most of them are targeting simple cases where only one missing tooth is available. As a result, literature works do not work well when there are multiple missing teeth and easily generate false predictions when the teeth are sparsely distributed. In this paper, we are trying to integrate a weak supervision text, the target region, to the implant position regression network, to address above issues. We propose a text condition embedded implant position regression network (TCEIP), to embed the text condition into the encoder-decoder framework for improvement of the regression performance. A cross-modal interaction that consists of cross-modal attention (CMA) and knowledge alignment module (KAM) is proposed to facilitate the interaction between features of images and texts. The CMA module performs a cross-attention between the image feature and the text condition, and the KAM mitigates the knowledge gap between the image feature and the image encoder of the CLIP. Extensive experiments on a dental implant dataset through five-fold cross-validation demonstrated that the proposed TCEIP achieves superior performance than existing methods.Comment: MICCAI 202

A new framework for the integrative analytics of intravascular ultrasound and optical coherence tomography images

Abstract:The integrative analysis of multimodal medical images plays an important role in the diagnosis of coronary artery disease by providing additional comprehensive information that cannot be found in an individual source image. Intravascular ultrasound (IVUS) and optical coherence tomography (IV-OCT) are two imaging modalities that have been widely used in the medical practice for the assessment of arterial health and the detection of vascular lumen lesions. IV-OCT has a high resolution and poor penetration, while IVUS has a low resolution and high detection depth. This paper proposes a new approach for the fusion of intravascular ultrasound and optical coherence tomography pullbacks to significantly improve the use of those two types of medical images. It also presents a new two-phase multimodal fusion framework using a coarse-to-fine registration and a wavelet fusion method. In the coarse-registration process, we define a set of new feature points to match the IVUS image and IV-OCT image. Then, the improved quality image is obtained based on the integration of the mutual information of two types of images. Finally, the matched registered images are fused with an approach based on the new proposed wavelet algorithm. The experimental results demonstrate the performance of the proposed new approach for significantly enhancing both the precision and computational stability. The proposed approach is shown to be promising for providing additional information to enhance the diagnosis and enable a deeper understanding of atherosclerosis

Photothermal hydrogels for infection control and tissue regeneration

In this review, we report investigating photothermal hydrogels, innovative biomedical materials designed for infection control and tissue regeneration. These hydrogels exhibit responsiveness to near-infrared (NIR) stimulation, altering their structure and properties, which is pivotal for medical applications. Photothermal hydrogels have emerged as a significant advancement in medical materials, harnessing photothermal agents (PTAs) to respond to NIR light. This responsiveness is crucial for controlling infections and promoting tissue healing. We discuss three construction methods for preparing photothermal hydrogels, emphasizing their design and synthesis, which incorporate PTAs to achieve the desired photothermal effects. The application of these hydrogels demonstrates enhanced infection control and tissue regeneration, supported by their unique photothermal properties. Although research progress in photothermal hydrogels is promising, challenges remain. We address these issues and explore future directions to enhance their therapeutic potential