Identification of Topological Features in Renal Tumor Microenvironment Associated with Patient Survival

Motivation As a highly heterogeneous disease, the progression of tumor is not only achieved by unlimited growth of the tumor cells, but also supported, stimulated, and nurtured by the microenvironment around it. However, traditional qualitative and/or semi-quantitative parameters obtained by pathologist’s visual examination have very limited capability to capture this interaction between tumor and its microenvironment. With the advent of digital pathology, computerized image analysis may provide a better tumor characterization and give new insights into this problem. Results We propose a novel bioimage informatics pipeline for automatically characterizing the topological organization of different cell patterns in the tumor microenvironment. We apply this pipeline to the only publicly available large histopathology image dataset for a cohort of 190 patients with papillary renal cell carcinoma obtained from The Cancer Genome Atlas project. Experimental results show that the proposed topological features can successfully stratify early- and middle-stage patients with distinct survival, and show superior performance to traditional clinical features and cellular morphological and intensity features. The proposed features not only provide new insights into the topological organizations of cancers, but also can be integrated with genomic data in future studies to develop new integrative biomarkers

Class Incremental Learning with Pre-trained Vision-Language Models

With the advent of large-scale pre-trained models, interest in adapting and exploiting them for continual learning scenarios has grown. In this paper, we propose an approach to exploiting pre-trained vision-language models (e.g. CLIP) that enables further adaptation instead of only using zero-shot learning of new tasks. We augment a pre-trained CLIP model with additional layers after the Image Encoder or before the Text Encoder. We investigate three different strategies: a Linear Adapter, a Self-attention Adapter, each operating on the image embedding, and Prompt Tuning which instead modifies prompts input to the CLIP text encoder. We also propose a method for parameter retention in the adapter layers that uses a measure of parameter importance to better maintain stability and plasticity during incremental learning. Our experiments demonstrate that the simplest solution -- a single Linear Adapter layer with parameter retention -- produces the best results. Experiments on several conventional benchmarks consistently show a significant margin of improvement over the current state-of-the-art

Multi-wavelength coherent random laser in bio-microfibers

In this paper, pure silk protein was extracted from Bombyx mori silks and fabricated into a new kind of disordered bio-microfiber structure using electrospinning technology. Coherent random lasing emission with low threshold was achieved in the silk fibroin fibers. The random lasing emission wavelength can be tuned in the range of 33 nm by controlling the pump location with different scattering strengths. Therefore, the bio-microfiber random lasers can be a wide spectral light source when the system is doped with a gain or energy transfer medium with a large fluorescence emission band. Application of the random lasers of the bio-microfibers as a low-coherence light source in speckle-free imaging had also been studied

Integrative Analysis of Histopathological Images and Genomic Data Predicts Clear Cell Renal Cell Carcinoma Prognosis

In cancer, both histopathologic images and genomic signatures are used for diagnosis, prognosis, and subtyping. However, combining histopathologic images with genomic data for predicting prognosis, as well as the relationships between them, has rarely been explored. In this study, we present an integrative genomics framework for constructing a prognostic model for clear cell renal cell carcinoma. We used patient data from The Cancer Genome Atlas (n = 410), extracting hundreds of cellular morphologic features from digitized whole-slide images and eigengenes from functional genomics data to predict patient outcome. The risk index generated by our model correlated strongly with survival, outperforming predictions based on considering morphologic features or eigengenes separately. The predicted risk index also effectively stratified patients in early-stage (stage I and stage II) tumors, whereas no significant survival difference was observed using staging alone. The prognostic value of our model was independent of other known clinical and molecular prognostic factors for patients with clear cell renal cell carcinoma. Overall, this workflow and the shared software code provide building blocks for applying similar approaches in other cancers

The transition from incoherent to coherent random laser in defect waveguide based on organic/inorganic hybrid laser dye

This paper systematically demonstrated a variety of experimental phenomena of random lasers (RLs) of N,N′-di-(3-(isobutyl polyhedral oligomeric silsesquioxanes)propyl) perylene diimide (DPP) organic/inorganic hybrid laser dye, which is composed of perylene diimide (PDI) as gain media and polyhedral oligomeric silsesquioxanes (POSS) as scattering media at a mole ratio of 1:2. In this work, we observe the transition from incoherent RL in the DPP-doped solutions and polymer membrane systems using dip-coating method to coherent RL in the polymer membrane system with defect waveguide using semi-polymerization (SP) coating method. Meanwhile, we found that the hybrid dye-DPP has a long lasing lifetime compared with the traditional laser dyes, which indicates that the POSS group can suppress the photo-bleaching effect to extend the working life of laser dyes

Developing a class of dual atom materials for multifunctional catalytic reactions

Dual atom catalysts, bridging single atom and metal/alloy nanoparticle catalysts, offer more opportunities to enhance the kinetics and multifunctional performance of oxygen reduction/evolution and hydrogen evolution reactions. However, the rational design of efficient multifunctional dual atom catalysts remains a blind area and is challenging. In this study, we achieved controllable regulation from Co nanoparticles to CoN4 single atoms to Co2N5 dual atoms using an atomization and sintering strategy via an N-stripping and thermal-migrating process. More importantly, this strategy could be extended to the fabrication of 22 distinct dual atom catalysts. In particular, the Co2N5 dual atom with tailored spin states could achieve ideally balanced adsorption/desorption of intermediates, thus realizing superior multifunctional activity. In addition, it endows Zn-air batteries with long-term stability for 800 h, allows water splitting to continuously operate for 1000 h, and can enable solar-powered water splitting systems with uninterrupted large-scale hydrogen production throughout day and night. This universal and scalable strategy provides opportunities for the controlled design of efficient multifunctional dual atom catalysts in energy conversion technologies

Prevention of hyperglycemia-induced myocardial apoptosis by gene silencing of Toll-like receptor-4

<p>Abstract</p> <p>Background</p> <p>Apoptosis is an early event involved in cardiomyopathy associated with diabetes mellitus. Toll-like receptor (TLR) signaling triggers cell apoptosis through multiple mechanisms. Up-regulation of TLR4 expression has been shown in diabetic mice. This study aimed to delineate the role of TLR4 in myocardial apoptosis, and to block this process through gene silencing of TLR4 in the myocardia of diabetic mice.</p> <p>Methods</p> <p>Diabetes was induced in C57/BL6 mice by the injection of streptozotocin. Diabetic mice were treated with 50 μg of TLR4 siRNA or scrambled siRNA as control. Myocardial apoptosis was determined by TUNEL assay.</p> <p>Results</p> <p>After 7 days of hyperglycemia, the level of TLR4 mRNA in myocardial tissue was significantly elevated. Treatment of TLR4 siRNA knocked down gene expression as well as diminished its elevation in diabetic mice. Apoptosis was evident in cardiac tissues of diabetic mice as detected by a TUNEL assay. In contrast, treatment with TLR4 siRNA minimized apoptosis in myocardial tissues. Mechanistically, caspase-3 activation was significantly inhibited in mice that were treated with TLR4 siRNA, but not in mice treated with control siRNA. Additionally, gene silencing of TLR4 resulted in suppression of apoptotic cascades, such as Fas and caspase-3 gene expression. TLR4 deficiency resulted in inhibition of reactive oxygen species (ROS) production and NADPH oxidase activity, suggesting suppression of hyperglycemia-induced apoptosis by TLR4 is associated with attenuation of oxidative stress to the cardiomyocytes.</p> <p>Conclusions</p> <p>In summary, we present novel evidence that TLR4 plays a critical role in cardiac apoptosis. This is the first demonstration of the prevention of cardiac apoptosis in diabetic mice through silencing of the TLR4 gene.</p