Learning Social Image Embedding with Deep Multimodal Attention Networks

Learning social media data embedding by deep models has attracted extensive research interest as well as boomed a lot of applications, such as link prediction, classification, and cross-modal search. However, for social images which contain both link information and multimodal contents (e.g., text description, and visual content), simply employing the embedding learnt from network structure or data content results in sub-optimal social image representation. In this paper, we propose a novel social image embedding approach called Deep Multimodal Attention Networks (DMAN), which employs a deep model to jointly embed multimodal contents and link information. Specifically, to effectively capture the correlations between multimodal contents, we propose a multimodal attention network to encode the fine-granularity relation between image regions and textual words. To leverage the network structure for embedding learning, a novel Siamese-Triplet neural network is proposed to model the links among images. With the joint deep model, the learnt embedding can capture both the multimodal contents and the nonlinear network information. Extensive experiments are conducted to investigate the effectiveness of our approach in the applications of multi-label classification and cross-modal search. Compared to state-of-the-art image embeddings, our proposed DMAN achieves significant improvement in the tasks of multi-label classification and cross-modal search

Efficient Privacy-Preserving Machine Learning with Lightweight Trusted Hardware

In this paper, we propose a new secure machine learning inference platform assisted by a small dedicated security processor, which will be easier to protect and deploy compared to today's TEEs integrated into high-performance processors. Our platform provides three main advantages over the state-of-the-art: (i) We achieve significant performance improvements compared to state-of-the-art distributed Privacy-Preserving Machine Learning (PPML) protocols, with only a small security processor that is comparable to a discrete security chip such as the Trusted Platform Module (TPM) or on-chip security subsystems in SoCs similar to the Apple enclave processor. In the semi-honest setting with WAN/GPU, our scheme is 4X-63X faster than Falcon (PoPETs'21) and AriaNN (PoPETs'22) and 3.8X-12X more communication efficient. We achieve even higher performance improvements in the malicious setting. (ii) Our platform guarantees security with abort against malicious adversaries under honest majority assumption. (iii) Our technique is not limited by the size of secure memory in a TEE and can support high-capacity modern neural networks like ResNet18 and Transformer. While previous work investigated the use of high-performance TEEs in PPML, this work represents the first to show that even tiny secure hardware with really limited performance can be leveraged to significantly speed-up distributed PPML protocols if the protocol can be carefully designed for lightweight trusted hardware.Comment: IEEE S&P'24 submitte

Exosomes as drug delivery system in gastrointestinal cancer

Gastrointestinal cancer is one of the most common malignancies with relatively high morbidity and mortality. Exosomes are nanosized extracellular vesicles derived from most cells and widely distributed in body fluids. They are natural endogenous nanocarriers with low immunogenicity, high biocompatibility, and natural targeting, and can transport lipids, proteins, DNA, and RNA. Exosomes contain DNA, RNA, proteins, lipids, and other bioactive components, which can play a role in information transmission and regulation of cellular physiological and pathological processes during the progression of gastrointestinal cancer. In this paper, the role of exosomes in gastrointestinal cancers is briefly reviewed, with emphasis on the application of exosomes as drug delivery systems for gastrointestinal cancers. Finally, the challenges faced by exosome-based drug delivery systems are discussed

Hierarchical aesthetic quality assessment using deep convolutional neural networks

Aesthetic image analysis has attracted much attention in recent years. However, assessing the aesthetic quality and assigning an aesthetic score are challenging problems. In this paper, we propose a novel framework for assessing the aesthetic quality of images. Firstly, we divide the images into three categories: “scene”, “object” and “texture”. Each category has an associated convolutional neural network (CNN) which learns the aesthetic features for the category in question. The object CNN is trained using the whole images and a salient region in each image. The texture CNN is trained using small regions in the original images. Furthermore, an A & C CNN is developed to simultaneously assess the aesthetic quality and identify the category for overall images. For each CNN, classification and regression models are developed separately to predict aesthetic class (high or low) and to assign an aesthetic score. Experimental results on a recently published large-scale dataset show that the proposed method can outperform the state-of-the-art methods for each category

G-quadruplex structures trigger RNA phase separation

Liquid–liquid phase separation plays an important role in a variety of cellular processes, including the formation of membrane-less organelles, the cytoskeleton, signalling complexes, and many other biological supramolecular assemblies. Studies on the molecular basis of phase separation in cells have focused on protein-driven phase separation. In contrast, there is limited understanding on how RNA specifically contributes to phase separation. Here, we described a phase-separation-like phenomenon that SHORT ROOT (SHR) RNA undergoes in cells. We found that an RNA G-quadruplex (GQ) forms in SHR mRNA and is capable of triggering RNA phase separation under physiological conditions, suggesting that GQs might be responsible for the formation of the SHR phase-separation-like phenomenon in vivo. We also found the extent of GQ-triggered-phase-separation increases on exposure to conditions which promote GQ. Furthermore, GQs with more G-quartets and longer loops are more likely to form phase separation. Our studies provide the first evidence that RNA can adopt structural motifs to trigger and/or maintain the specificity of RNA-driven phase separation

Metformin ameliorates ionizing irradiation-induced long-term hematopoietic stem cell injury in mice

AbstractExposure to ionizing radiation (IR) increases the production of reactive oxygen species (ROS) not only by the radiolysis of water but also through IR-induced perturbation of the cellular metabolism and disturbance of the balance of reduction/oxidation reactions. Our recent studies showed that the increased production of intracellular ROS induced by IR contributes to IR-induced late effects, particularly in the hematopoietic system, because inhibition of ROS production with an antioxidant after IR exposure can mitigate IR-induced long-term bone marrow (BM) injury. Metformin is a widely used drug for the treatment of type 2 diabetes. Metformin also has the ability to regulate cellular metabolism and ROS production by activating AMP-activated protein kinase. Therefore, we examined whether metformin can ameliorate IR-induced long-term BM injury in a total-body irradiation (TBI) mouse model. Our results showed that the administration of metformin significantly attenuated TBI-induced increases in ROS production and DNA damage and upregulation of NADPH oxidase 4 expression in BM hematopoietic stem cells (HSCs). These changes were associated with a significant increase in BM HSC frequency, a considerable improvement in in vitro and in vivo HSC function, and complete inhibition of upregulation of p16Ink4a in HSCs after TBI. These findings demonstrate that metformin can attenuate TBI-induced long-term BM injury at least in part by inhibiting the induction of chronic oxidative stress in HSCs and HSC senescence. Therefore, metformin has the potential to be used as a novel radioprotectant to ameliorate TBI-induced long-term BM injury

Machine Learning in Cardio-Oncology: New Insights from an Emerging Discipline

A growing body of evidence on a wide spectrum of adverse cardiac events following oncologic therapies has led to the emergence of cardio-oncology as an increasingly relevant interdisciplinary specialty. This also calls for better risk-stratification for patients undergoing cancer treatment. Machine learning (ML), a popular branch discipline of artificial intelligence that tackles complex big data problems by identifying interaction patterns among variables, has seen increasing usage in cardio-oncology studies for risk stratification. The objective of this comprehensive review is to outline the application of ML approaches in cardio-oncology, including deep learning, artificial neural networks, random forest and summarize the cardiotoxicity identified by ML. The current literature shows that ML has been applied for the prediction, diagnosis and treatment of cardiotoxicity in cancer patients. In addition, role of ML in gender and racial disparities for cardiac outcomes and potential future directions of cardio-oncology are discussed. It is essential to establish dedicated multidisciplinary teams in the hospital and educate medical professionals to become familiar and proficient in ML in the future.</p