Clappblum7376

Tumor classification and segmentation are two important tasks for computer-aided diagnosis (CAD) using 3D automated breast ultrasound (ABUS) images. However, they are challenging due to the significant shape variation of breast tumors and the fuzzy nature of ultrasound images (e.g., low contrast and signal to noise ratio). Considering the correlation between tumor classification and segmentation, we argue that learning these two tasks jointly is able to improve the outcomes of both tasks. In this paper, we propose a novel multi-task learning framework for joint segmentation and classification of tumors in ABUS images. The proposed framework consists of two sub-networks an encoder-decoder network for segmentation and a light-weight multi-scale network for classification. To account for the fuzzy boundaries of tumors in ABUS images, our framework uses an iterative training strategy to refine feature maps with the help of probability maps obtained from previous iterations. Experimental results based on a clinical dataset of 170 3D ABUS volumes collected from 107 patients indicate that the proposed multi-task framework improves tumor segmentation and classification over the single-task learning counterparts.Accurate liver tumor segmentation without contrast agents (non-enhanced images) avoids the contrast-agent-associated time-consuming and high risk, which offers radiologists quick and safe assistance to diagnose and treat the liver tumor. However, without contrast agents enhancing, the tumor in liver images presents low contrast and even invisible to naked eyes. Thus the liver tumor segmentation from non-enhanced images is quite challenging. We propose a Weakly-Supervised Teacher-Student network (WSTS) to address the liver tumor segmentation in non-enhanced images by leveraging additional box-level-labeled data (labeled with a tumor bounding-box). WSTS deploys a weakly-supervised teacher-student framework (TCH-ST), namely, a Teacher Module learns to detect and segment the tumor in enhanced images during training, which facilitates a Student Module to detect and segment the tumor in non-enhanced images independently during testing. To detect the tumor accurately, the WSTS proposes a Dual-strategy DRL (DDRL), which develops two tumor detection strategies by creatively introducing a relative-entropy bias in the DRL. To accurately predict a tumor mask for the box-level-labeled enhanced image and thus improve tumor segmentation in non-enhanced images, the WSTS proposes an Uncertainty-Sifting Self-Ensembling (USSE). The USSE exploits the weakly-labeled data with self-ensembling and evaluates the prediction reliability with a newly-designed Multi-scale Uncertainty-estimation. WSTS is validated with a 2D MRI dataset, where the experiment achieves 83.11% of Dice and 85.12% of Recall in 50 patient testing data after training by 200 patient data (half amount data is box-level-labeled). Such a great result illustrates the competence of WSTS to segment the liver tumor from non-enhanced images. Thus, WSTS has excellent potential to assist radiologists by liver tumor segmentation without contrast-agents.The main goal of this work is to improve the quality of simultaneous multi-slice (SMS) reconstruction for diffusion MRI. We accomplish this by developing an image domain method that reaps the benefits of both SENSE and GRAPPA-type approaches and enables image regularization in an optimization framework. We propose a new approach termed regularized image domain split slice-GRAPPA (RI-SSG), which establishes an optimization framework for SMS reconstruction. Within this framework, we use a robust forward model to take advantage of both the SENSE model with explicit sensitivity estimations and the SSG model with implicit kernel relationship among coil images. The proposed approach also allows combining of coil images to increase the SNR and enables image domain regularization on estimated coil-combined single slices. We compare the performance of RI-SSG with that of SENSE and SSG using in-vivo diffusion EPI datasets with simulated and actual SMS acquisitions collected on a 3T MR scanner. Reconstructed diffusion-wr compared to SSG.Intraoperative tracking of laparoscopic instruments is often a prerequisite for computer and robotic-assisted interventions. While numerous methods for detecting, segmenting and tracking of medical instruments based on endoscopic video images have been proposed in the literature, key limitations remain to be addressed Firstly, robustness, that is, the reliable performance of state-of-the-art methods when run on challenging images (e.g. in the presence of blood, smoke or motion artifacts). Secondly, generalization; algorithms trained for a specific intervention in a specific hospital should generalize to other interventions or institutions. In an effort to promote solutions for these limitations, we organized the Robust Medical Instrument Segmentation (ROBUST-MIS) challenge as an international benchmarking competition with a specific focus on the robustness and generalization capabilities of algorithms. For the first time in the field of endoscopic image processing, our challenge included a task on binary segmentation and also addressed multi-instance detection and segmentation. The challenge was based on a surgical data set comprising 10,040 annotated images acquired from a total of 30 surgical procedures from three different types of surgery. The validation of the competing methods for the three tasks (binary segmentation, multi-instance detection and multi-instance segmentation) was performed in three different stages with an increasing domain gap between the training and the test data. The results confirm the initial hypothesis, namely that algorithm performance degrades with an increasing domain gap. While the average detection and segmentation quality of the best-performing algorithms is high, future research should concentrate on detection and segmentation of small, crossing, moving and transparent instrument(s) (parts).The chicken egg yolk, which is abundant with lipids, proteins, and minerals, is the major nutrient resource for the embryonic development. In fact, the magnitude and type of yolk nutrients are dynamically changed during the chicken embryogenesis to meet the developmental and nutritional requests at different stages. The yolk nutrients are metabolized and absorbed by the yolk sac membrane and then used by the embryo or other extraembryonic tissues. Thus, understanding the metabolites in the yolk helps to unveil the developmental nutritional requirements for the chicken embryo. In this study, we performed ultra high performance liquid chromatography/tandem mass spectrometry (UHPLC-MS/MS) analysis to investigate the change of metabolites in the egg yolk at embryonic (E) 07, E09, E11, E15, E17, and E19. The results showed that 1) the egg yolk metabolites at E07 and E09 were approximately similar, but E09, E11, E15, E17, and E19 were different from each other, indicating the developmental and metabolic change of tens.Previously, a fungus was isolated from a diseased pigeon group clinically suspected of being infected with Candida. The fungus was subsequently identified as Candida glabrata using morphology, physiology, biochemistry, and molecular biology testing methods. In the present study, to determine the controlling effects of Chinese herbal medicine for C. glabrata, the bacteriostatic effects of the ethanol extracts Acorus gramineus, Sophora flavescens, Polygonum hydropiper, Cassia obtusifolia, Pulsatilla chinensis, Dandelion, and Cortex phellodendri on C. glabrata in vitro were analyzed. The results showed that the minimum inhibitory concentrations (MIC80) of Cortex phellodendri was 0.25 μg/μL. Meanwhile, that of S. flavescens was 32 μg/μL; C. obtusifolia was 56 μg/μL; A. gramineus and Polygonum hydropiper was 64 μg/μL; and P. chinensis was 112 μg/μL. However, MIC80 for Dandelion was undetectable. In addition, improved drug sensitivity tests revealed that colonies had grown after 24 h in the blank group, as well as he future, these Chinese herbal medicines are expected to be used to treat the fungal infections related to C. glabrata in poultry to improve production performance.PA-X is a novel discovered accessory protein encoded by the PA mRNA of the influenza A virus. Accumulated studies have demonstrated the crucial role of this protein in regulating the virulence of various subtypes of influenza virus, including H1N1, H5N1, H9N2, H1N2, H3N8 and H3N2 virus. However, the role of PA-X protein in regulating the virulence of the highly pathogenic avian H7N9 virus was unknown. In this study, we firstly generated two recombinant H7N9 viruses which have lower PA-X expression level than the parental H7N9 virus. We then systematically compared their difference in virus replication, polymerase activity, virulence and virus-induced host immune responses in mice. The results showed that the PA-X deficient viruses significantly increased viral replication in madin darby canine kidney cells and slightly increased viral replication in mouse lung. In addition, loss of PA-X expression significantly increased viral polymerase activity and alleviated the host-shutoff activity mediated by the parental PA protein. However, in contrast with the usual function of PA-X in regulating the virulence in different subtype influenza virus, no obvious effect on viral virulence in mice was observed by H7N9 PA-X protein. Furthermore, among the 12 kinds of cytokines and 2 kinds of complement derived components that we tested, the PA-X deficiency viruses only induced significantly higher expression levels of MX1 than the parental virus. Altogether, these results showed that PA-X has little effect on viral virulence and viral induced innate immune response of the H7N9 subtype virus. Our study adds further information for the growing understanding of the complexity of PA-X in regulating viral virulence and host innate immune response of different influenza virus.Mycoplasma bovis (M. bovis) is a small bacterium that lacks a cell wall. M. bovis infection can result in chronic pneumonia and polyarthritis syndrome (CPPS), otitis media, conjunctivitis, and meningitis in feedlot cattle and mastitis in dairy cattle. To gain more understanding of the mechanism of M. Rimiducid in vivo bovis and host interaction, this study focused on P48, an important membrane protein involved in M. bovis adhesion, proliferation and virulence. In this study, exogenous P48 protein was introduced to explore its function in embryonic bovine lung (EBL) cells by recombinant vector and protein purification. We found that M. bovis infection inhibited EBL cells growth and enhanced apoptosis. Both intracellular and extracellular P48 protein treatment also induce apoptosis. Moreover, P48 activates endoplasmic reticulum (ER) stress response via increasing ER stress markers expression. To further explore the underlying mechanism, we performed inhibition experiments using ER stress inhibitor 4-PBA and specific siRNA interference against GRP78, and found that P48 protein modulated EBL cells apoptosis in an ER stress signaling-dependent manner. This study provided more data to further understand M. bovis infection mechanism and develop effective anti-mycoplasma strategy.