Contrastive Image Synthesis and Self-supervised Feature Adaptation for Cross-Modality Biomedical Image Segmentation

This work presents a novel framework CISFA (Contrastive Image synthesis and Self-supervised Feature Adaptation)that builds on image domain translation and unsupervised feature adaptation for cross-modality biomedical image segmentation. Different from existing works, we use a one-sided generative model and add a weighted patch-wise contrastive loss between sampled patches of the input image and the corresponding synthetic image, which serves as shape constraints. Moreover, we notice that the generated images and input images share similar structural information but are in different modalities. As such, we enforce contrastive losses on the generated images and the input images to train the encoder of a segmentation model to minimize the discrepancy between paired images in the learned embedding space. Compared with existing works that rely on adversarial learning for feature adaptation, such a method enables the encoder to learn domain-independent features in a more explicit way. We extensively evaluate our methods on segmentation tasks containing CT and MRI images for abdominal cavities and whole hearts. Experimental results show that the proposed framework not only outputs synthetic images with less distortion of organ shapes, but also outperforms state-of-the-art domain adaptation methods by a large margin

The design and realization of flexible wearable wireless music controller

By using flexible sensors and micro-control units, a wireless controller that can be integrated into clothing for cross-platform music control has been designed and implemented, providing a new idea for making a simple and low-cost flexible wearable sensing system. The design and implementation of a wireless controller that can be integrated into clothing for cross-platform music control provides a new idea for the preparation of a simple and low-cost flexible wearable sensing system. Based on the flexible fabric sensing material, a simple structured sensor piece is proposed as a button for the controller with good wearing comfort. The sensor element is capable of sensing finger presses up to 15kPa. ESP32 is used as a micro-control unit for sensing signal acquisition and data processing. Using the Bluetooth chip integrated inside the ESP32, the controller can be connected with terminal devices of different platforms for wireless data transmission. The results show that the prepared wireless music controller can be stably connected with both Windows computer terminal and Android cell phone terminal, and the sensor recognition accuracy of finger press is 99.7%, which indicates that the flexible fabric sensor has a broad application prospect in the field of wearable devices

Additional Positive Enables Better Representation Learning for Medical Images

This paper presents a new way to identify additional positive pairs for BYOL, a state-of-the-art (SOTA) self-supervised learning framework, to improve its representation learning ability. Unlike conventional BYOL which relies on only one positive pair generated by two augmented views of the same image, we argue that information from different images with the same label can bring more diversity and variations to the target features, thus benefiting representation learning. To identify such pairs without any label, we investigate TracIn, an instance-based and computationally efficient influence function, for BYOL training. Specifically, TracIn is a gradient-based method that reveals the impact of a training sample on a test sample in supervised learning. We extend it to the self-supervised learning setting and propose an efficient batch-wise per-sample gradient computation method to estimate the pairwise TracIn to represent the similarity of samples in the mini-batch during training. For each image, we select the most similar sample from other images as the additional positive and pull their features together with BYOL loss. Experimental results on two public medical datasets (i.e., ISIC 2019 and ChestX-ray) demonstrate that the proposed method can improve the classification performance compared to other competitive baselines in both semi-supervised and transfer learning settings.Comment: 8 page

Conditional Diffusion Models for Weakly Supervised Medical Image Segmentation

Recent advances in denoising diffusion probabilistic models have shown great success in image synthesis tasks. While there are already works exploring the potential of this powerful tool in image semantic segmentation, its application in weakly supervised semantic segmentation (WSSS) remains relatively under-explored. Observing that conditional diffusion models (CDM) is capable of generating images subject to specific distributions, in this work, we utilize category-aware semantic information underlied in CDM to get the prediction mask of the target object with only image-level annotations. More specifically, we locate the desired class by approximating the derivative of the output of CDM w.r.t the input condition. Our method is different from previous diffusion model methods with guidance from an external classifier, which accumulates noises in the background during the reconstruction process. Our method outperforms state-of-the-art CAM and diffusion model methods on two public medical image segmentation datasets, which demonstrates that CDM is a promising tool in WSSS. Also, experiment shows our method is more time-efficient than existing diffusion model methods, making it practical for wider applications

Clonality Analysis of Synchronous Lesions of Cervical Carcinoma Based on X Chromosome Inactivation Polymorphism, Human Papillomavirus Type 16 Genome Mutations, and Loss of Heterozygosity

One of the most common forms of carcinoma in women, cervical invasive squamous cell carcinoma (CIC), often coexists with multiple lesions of cervical intraepithelial neoplasia (CIN). CIC and CIN show heterogeneity with respect to both histopathology and biology. To understand the causes, origin, and model of progression of cervical carcinoma, we assessed the clonality of a case with multiple synchronous lesions by analyzing X chromosome inactivation polymorphism, human papillomavirus type 16 (HPV16) sequence variation/mutations, and loss of heterozygosity (LOH). Microdissection was performed on 24 samples from this case, representing the entire lesional situation. The combination of different X chromosome inactivation patterns, two HPV16 point mutations, and LOH at three genomic microsatellite loci, led to the identification of five different “monoclonal” lesions (CIN II, CIN III, and invasive carcinoma nests) and five different “polyclonal” areas (CIN II and normal squamous epithelium). This finding indicated that CIC can originate from multiple precursor cells, from which some clones might progress via multiple steps, namely via CIN II and CIN III, whereas others might develop independently and possibly directly from the carcinoma precursor cells. Our results also supported the view that HPV16 as a “field factor” causes cervical carcinoma, which is probably promoted by the loss of chromosomal material as indicated by the LOH

Radiation fluxes in a business district of Shanghai, China

Radiative fluxes are key drivers of surface-atmosphere heat exchanges in cities. Here the first year-long (December 2012 – November 2013) measurements of the full radiation balance for a dense urban site in Shanghai are presented, collected with a net radiometer CNR4 mounted 80 m above ground. Clear sky incoming shortwave radiation (K↓) (median daytime maxima) ranges from 575 W m-2 in winter to 875 W m-2 in spring, with cloud cover reducing the daily maxima by about 160 W m-2. The median incoming longwave radiation daytime maxima is 305 and 468 W m-2 in winter and summer, respectively, with increases of 30 and 15 W m-2 for cloudy conditions. The effect of air quality is evident: ‘haze’ conditions decrease hourly median K↓ by 11.3%. The midday (11:00 -13:00 LST) clear sky surface albedo (α) is 0.128, 0.141, 0.143 and 0.129 for winter, spring, summer and autumn, respectively. α varies with solar elevation and azimuth angle due to heterogeneity of the urban surface. In winter, shadows play an important role in decreasing α in the late afternoon. For the site, the bulk α is 0.14. The NARP/SUEWS land surface model reproduces the radiation components at this site well, a promising result for applications elsewhere. These observations help to fill the gap of long-term radiation measurements in East Asian and low-latitude cities quantifying the effects of season, cloud cover and air quality

HPV E6 induces eIF4E transcription to promote the proliferation and migration of cervical cancer

AbstractIncreasing evidence has placed eukaryotic translation initiation factor 4E (eIF4E) at the hub of tumor development and progression. Several studies have reported that eIF4E is over-expressed in cervical cancer; however, the mechanism remains elusive. The results of this study further confirm over-expression of eIF4E in cervical cancer tumors and cell lines, and we have discovered that the transcription of eIF4E is induced by protein E6 of the human papillomavirus (HPV). Moreover, regulation of eIF4E by E6 significantly influences cell proliferation, the cell cycle, migration, and apoptosis. Therefore, eIF4E emerges as a key player in tumor development and progression and a potential target for CC treatment and prevention

Pre-Operative Prediction of Mediastinal Node Metastasis Using Radiomics Model Based on 18F-FDG PET/CT of the Primary Tumor in Non-Small Cell Lung Cancer Patients

Purpose: We investigated whether a fluorine-18-fluorodeoxy glucose positron emission tomography/computed tomography (18F-FDG PET/CT)-based radiomics model (RM) could predict the pathological mediastinal lymph node staging (pN staging) in patients with non-small cell lung cancer (NSCLC) undergoing surgery.Methods: A total of 716 patients with a clinicopathological diagnosis of NSCLC were included in this retrospective study. The prediction model was developed in a training cohort that consisted of 501 patients. Radiomics features were extracted from the 18F-FDG PET/CT of the primary tumor. Support vector machine and extremely randomized trees were used to build the RM. Internal validation was assessed. An independent testing cohort contained the remaining 215 patients. The performances of the RM and clinical node staging (cN staging) in predicting pN staging (pN0 vs. pN1 and N2) were compared for each cohort. The area under the curve (AUC) of the receiver operating characteristic curve was applied to assess the model's performance.Results: The AUC of the RM [0.81 (95% CI, 0.771–0.848); sensitivity: 0.794; specificity: 0.704] for the predictive performance of pN1 and N2 was significantly better than that of cN in the training cohort [0.685 (95% CI, 0.644–0.728); sensitivity: 0.804; specificity: 0.568], (P-value = 8.29e-07, as assessed by the Delong test). In the testing cohort, the AUC of the RM [0.766 (95% CI, 0.702–0.830); sensitivity: 0.688; specificity: 0.704] was also significantly higher than that of cN [0.685 (95% CI, 0.619–0.747); sensitivity: 0.799; specificity: 0.568], (P = 0.0371, Delong test).Conclusions: The RM based on 18F-FDG PET/CT has a potential for the pN staging in patients with NSCLC, suggesting that therapeutic planning could be tailored according to the predictions

Advances of MnO2 nanomaterials as novel agonists for the development of cGAS-STING-mediated therapeutics

As an essential micronutrient, manganese plays an important role in the physiological process and immune process. In recent decades, cGAS-STING pathway, which can congenitally recognize exogenous and endogenous DNA for activation, has been widely reported to play critical roles in the innate immunity against some important diseases, such as infections and tumor. Manganese ion (Mn2+) has been recently proved to specifically bind with cGAS and activate cGAS-STING pathway as a potential cGAS agonist, however, is significantly restricted by the low stability of Mn2+ for further medical application. As one of the most stable forms of manganese, manganese dioxide (MnO2) nanomaterials have been reported to show multiple promising functions, such as drug delivery, anti-tumor and anti-infection activities. More importantly, MnO2 nanomaterials are also found to be a potential candidate as cGAS agonist by transforming into Mn2+, which indicates their potential for cGAS-STING regulations in different diseased conditions. In this review, we introduced the methods for the preparation of MnO2 nanomaterials as well as their biological activities. Moreover, we emphatically introduced the cGAS-STING pathway and discussed the detailed mechanisms of MnO2 nanomaterials for cGAS activation by converting into Mn2+. And we also discussed the application of MnO2 nanomaterials for disease treatment by regulating cGAS-STING pathway, which might benefit the future development of novel cGAS-STING targeted treatments based on MnO2 nanoplatforms