SAM-U: Multi-box prompts triggered uncertainty estimation for reliable SAM in medical image

Recently, Segmenting Anything has taken an important step towards general artificial intelligence. At the same time, its reliability and fairness have also attracted great attention, especially in the field of health care. In this study, we propose multi-box prompts triggered uncertainty estimation for SAM cues to demonstrate the reliability of segmented lesions or tissues. We estimate the distribution of SAM predictions via Monte Carlo with prior distribution parameters, which employs different prompts as formulation of test-time augmentation. Our experimental results found that multi-box prompts augmentation improve the SAM performance, and endowed each pixel with uncertainty. This provides the first paradigm for a reliable SAM

Bus arrival time prediction using mixed multi-route arrival time data at previous stop

The primary objective of this paper is to develop models to predict bus arrival time at a target stop using actual multi-route bus arrival time data from previous stop as inputs. In order to mix and fully utilize the multiple routes bus arrival time data, the weighted average travel time and three Forgetting Factor Functions (FFFs) – F1, F2 and F3 – are introduced. Based on different combinations of input variables, five prediction models are proposed. Three widely used algorithms, i.e. Support Vector Machine (SVM), Artificial Neutral Network (ANN) and Linear Regression (LR), are tested to find the best for arrival time prediction. Bus location data of 11 road segments from Yichun (China), covering 12 bus stops and 16 routes, are collected to evaluate the performance of the proposed approaches. The results show that the newly introduced parameters, the weighted average travel time, can significantly improve the prediction accuracy: the prediction errors reduce by around 20%. The algorithm comparison demonstrates that the SVM and ANN outperform the LR. The FFFs can also affect the performance errors: F1 is more suitable for ANN algorithm, while F3 is better for SVM and LR algorithms. Besides, the virtual road concept in this paper can slightly improve the prediction accuracy and halve the time cost of predicted arrival time calculation. First published online 02 May 201

Uncertainty-informed Mutual Learning for Joint Medical Image Classification and Segmentation

Classification and segmentation are crucial in medical image analysis as they enable accurate diagnosis and disease monitoring. However, current methods often prioritize the mutual learning features and shared model parameters, while neglecting the reliability of features and performances. In this paper, we propose a novel Uncertainty-informed Mutual Learning (UML) framework for reliable and interpretable medical image analysis. Our UML introduces reliability to joint classification and segmentation tasks, leveraging mutual learning with uncertainty to improve performance. To achieve this, we first use evidential deep learning to provide image-level and pixel-wise confidences. Then, an Uncertainty Navigator Decoder is constructed for better using mutual features and generating segmentation results. Besides, an Uncertainty Instructor is proposed to screen reliable masks for classification. Overall, UML could produce confidence estimation in features and performance for each link (classification and segmentation). The experiments on the public datasets demonstrate that our UML outperforms existing methods in terms of both accuracy and robustness. Our UML has the potential to explore the development of more reliable and explainable medical image analysis models. We will release the codes for reproduction after acceptance.Comment: 13 page

The Interactions Between Candida albicans and Mucosal Immunity

Mucosa protects the body against external pathogen invasion. However, pathogen colonies on the mucosa can invade the mucosa when the immunosurveillance is compromised, causing mucosal infection and subsequent diseases. Therefore, it is necessary to timely and effectively monitor and control pathogenic microorganisms through mucosal immunity. Candida albicans is the most prevalent fungi on the mucosa. The C. albicans colonies proliferate and increase their virulence, causing severe infectious diseases and even death, especially in immunocompromised patients. The normal host mucosal immune defense inhibits pathogenic C. albicans through stepwise processes, such as pathogen recognition, cytokine production, and immune cell phagocytosis. Herein, the current advances in the interactions between C. albicans and host mucosal immune defenses have been summarized to improve understanding on the immune mechanisms against fungal infections

The Ubiquitin Peptidase UCHL1 Induces G0/G1 Cell Cycle Arrest and Apoptosis Through Stabilizing p53 and Is Frequently Silenced in Breast Cancer

Background: Breast cancer (BrCa) is a complex disease driven by aberrant gene alterations and environmental factors. Recent studies reveal that abnormal epigenetic gene regulation also plays an important role in its pathogenesis. Ubiquitin carboxyl- terminal esterase L1 (UCHL1) is a tumor suppressor silenced by promoter methylation in multiple cancers, but its role and alterations in breast tumorigenesis remain unclear. Methodology/Principal Findings: We found that UCHL1 was frequently downregulated or silenced in breast cancer cell lines and tumor tissues, but readily expressed in normal breast tissues and mammary epithelial cells. Promoter methylation of UCHL1 was detected in 9 of 10 breast cancer cell lines (90%) and 53 of 66 (80%) primary tumors, but rarely in normal breast tissues, which was statistically correlated with advanced clinical stage and progesterone receptor status. Pharmacologic demethylation reactivated UCHL1 expression along with concomitant promoter demethylation. Ectopic expression of UCHL1 significantly suppressed the colony formation and proliferation of breast tumor cells, through inducing G0/G1 cell cycle arrest and apoptosis. Subcellular localization study showed that UCHL1 increased cytoplasmic abundance of p53. We further found that UCHL1 induced p53 accumulation and reduced MDM2 protein level, and subsequently upregulated the expression of p21, as well as cleavage of caspase3 and PARP, but not in catalytic mutant UCHL1 C90Sexpressed cells

Feasibility of Penetrant Testing on Surface Axial-Radial Cracks of GH4169 Super Alloy Turbine Disk

The post emulsifiable and water-washable fluorescent penetrant testing were carried out with ZL-27A and ZL67 respectively. Ultrasonic cleaning by detergent were used for 30 minutes before penetrant. The parts were immersed and drained for 60 minutes. The macroscopic and microscopic characteristics of cracks were researched using the split mirror and scanning electron microscope. The results show that the outgrowth of high temperature oxidation plugs up the forging cracks. Thus the penetrant testing is not effective in detecting this type of cracks

Extract Descriptors for Point Cloud Registration by Graph Clustering Attention Network

Extracting geometric descriptors in 3D vision is the first step. It plays an important role in 3D registration, 3D reconstruction, and other applications. The success of many 3D tasks is closely related to whether the geometric descriptor has accurate characteristics. Today, the main methods are divided into manual production and neural network learning. The applicability of descriptors is limited to a low-level point, corner, edge, and fixed neighborhood features. For this, we use the class attention of the point cloud. In order to extract class attention, the graph clustering approach is utilized. It can collect points with similar structures and divide regions dynamically. While maintaining rotation invariance, features can enhance their fit to the original data. Point attention and edge attention are used to describe the structural characteristics of point clouds. We combine the three attentions indicated before to improve the features obtained by the PointNet decoder. This feature can dynamically reflect the structure of the point cloud, which includes both soft shape information and rich detail information. Finally, the 3D descriptors are extracted with the FoldingNet decoder. Our method is validated on both indoor and outdoor datasets. The accuracy of the final result is improved by two percentage points

Study on thermal anomalies of earthquake process by using tidal-force and outgoing-longwave-radiation

Four earthquakes above magnitude 5.0 in Yunnan and Tibet, China occurred from 2010 to 2011. By calculating the tidal-force changes induced by celestial bodies in this region, we found that the earthquakes occurred when tidal-forces continuous¬ly grew from low to peak levels and approached the maximum amplitude phase, which indicated a tidal-force that had a trigger or inducing effect of active tectonic earthquakes when the ground stress reached a critical point. At the same time analyzing the abnormal changes of outgoing longwave radiation (OLR), along with the tidal cycle, indicated that the regional distribution of the enhancement region of OLR anomalies was closely related to geologic structure, especially ac¬tive faults. The OLR radiation anomaly evolved: an initial infrared rise, followed by an enhancement reaching peak, attenuation, and then a return to normal. The entire process was similar to changes observed in rock-breaking process under stress loads. Our investigation showed that the tidal-force changes caused by ce¬lestial bodies could trigger an earthquake when tectonic stress reached its critical breaking point, and the OLR anomaly was the radiation signature of the change in seismic tectonic stress. Therefore, the method of combining measurements of the tidal-force changes induced by celestial bodies with those of thermal-anomaly changes has some practical value for detecting the precursor state of impending earthquakes