MiR-497 decreases cisplatin resistance in ovarian cancer cells by targeting mTOR/P70S6K1.

The mechanism of cisplatin resistance in ovarian cancer is not clearly understood. In the present investigation, we found that the expression levels of miR-497 were reduced in chemotherapy-resistant ovarian cancer cells and tumor tissues due to hypermethylation of miR-497 promoter. Low miR-497 expression levels were associated with chemo-resistant phonotype of ovarian cancer. By analyzing the expression levels of miR-497, mTOR and p70S6K1 in a clinical gene-expression array dataset, we found that mTOR and p70S6K1, two proteins correlated to chemotherapy-resistance in multiple types of human cancers, were inversely correlated with miR-497 levels in ovarian cancer tissues. By using an orthotopic ovarian tumor model and a Tet-On inducible miR-497 expression system, our results demonstrated that overexpression of miR-497 sensitizes the resistant ovarian tumor to cisplatin treatment. Therefore, we suggest that miR-497 might be used as a therapeutic supplement to increase ovarian cancer treatment response to cisplatin

Sentiment analysis with adaptive multi-head attention in Transformer

We propose a novel framework based on the attention mechanism to identify the sentiment of a movie review document. Previous efforts on deep neural networks with attention mechanisms focus on encoder and decoder with fixed numbers of multi-head attention. Therefore, we need a mechanism to stop the attention process automatically if no more useful information can be read from the memory.In this paper, we propose an adaptive multi-head attention architecture (AdaptAttn) which varies the number of attention heads based on length of sentences. AdaptAttn has a data preprocessing step where each document is classified into any one of the three bins small, medium or large based on length of the sentence. The document classified as small goes through two heads in each layer, the medium group passes four heads and the large group is processed by eight heads. We examine the merit of our model on the Stanford large movie review dataset. The experimental results show that the F1 score from our model is on par with the baseline model.Comment: Accepted by the 4th International Conference on Signal Processing and Machine Learnin

Evolution and Efficiency in Neural Architecture Search: Bridging the Gap Between Expert Design and Automated Optimization

The paper provides a comprehensive overview of Neural Architecture Search (NAS), emphasizing its evolution from manual design to automated, computationally-driven approaches. It covers the inception and growth of NAS, highlighting its application across various domains, including medical imaging and natural language processing. The document details the shift from expert-driven design to algorithm-driven processes, exploring initial methodologies like reinforcement learning and evolutionary algorithms. It also discusses the challenges of computational demands and the emergence of efficient NAS methodologies, such as Differentiable Architecture Search and hardware-aware NAS. The paper further elaborates on NAS's application in computer vision, NLP, and beyond, demonstrating its versatility and potential for optimizing neural network architectures across different tasks. Future directions and challenges, including computational efficiency and the integration with emerging AI domains, are addressed, showcasing NAS's dynamic nature and its continued evolution towards more sophisticated and efficient architecture search methods.Comment: 7 Pages, Double Colum

Decentralized Multi-Robot Navigation for Autonomous Surface Vehicles with Distributional Reinforcement Learning

Collision avoidance algorithms for Autonomous Surface Vehicles (ASV) that follow the Convention on the International Regulations for Preventing Collisions at Sea (COLREGs) have been proposed in recent years. However, it may be difficult and unsafe to follow COLREGs in congested waters, where multiple ASVs are navigating in the presence of static obstacles and strong currents, due to the complex interactions. To address this problem, we propose a decentralized multi-ASV collision avoidance policy based on Distributional Reinforcement Learning, which considers the interactions among ASVs as well as with static obstacles and current flows. We evaluate the performance of the proposed Distributional RL based policy against a traditional RL-based policy and two classical methods, Artificial Potential Fields (APF) and Reciprocal Velocity Obstacles (RVO), in simulation experiments, which show that the proposed policy achieves superior performance in navigation safety, while requiring minimal travel time and energy. A variant of our framework that automatically adapts its risk sensitivity is also demonstrated to improve ASV safety in highly congested environments.Comment: The 2024 IEEE International Conference on Robotics and Automation (ICRA 2024

Thermal management performances of PCM/water cooling-plate using for lithium-ion battery module based on non-uniform internal heat source

In order to improve the working performance of the lithium-ion battery, the battery module with Phase change material/water cooling-plate was designed and numerically analyzed based on the energy conservation and fluid dynamics. The non-uniform internal heat source based on 2D electro-thermal model for battery LiFePO4/C was used to simulate the heat generation of each battery. Then factors such as height of water cooling-plate, space between adjacent batteries, inlet mass flow rate, flow direction, thermal conductivity and melting point of PCM were discussed to research their influences on the cooling performance of module. And the 5 continuous charge-discharge cycles was used to research the effect of PCM/water cooling plate on preventing thermal runaway. The results showed that the water cooling plate set close to the near-electrode area of battery removed the majority of heat generated during discharging and decreased the maximum temperature efficiently. The PCM between the adjacent batteries could improve the uniformity of temperature field. In addition, the PCM/water cooling plate could limit the maximum temperature effectively and improve the uniformity of temperature field during the 5 continuous charge-discharge cycles. As a result, it prevented the emergence of thermal runaway and increased the safety of module. (C) 2017 Elsevier Ltd. All rights reserved

Genome Sequencing Reveals Unique Mutations in Characteristic Metabolic Pathways and the Transfer of Virulence Genes between V. mimicus and V. cholerae

Vibrio mimicus, the species most similar to V. cholerae, is a microbe present in the natural environmental and sometimes causes diarrhea and internal infections in humans. It shows similar phenotypes to V. cholerae but differs in some biochemical characteristics. The molecular mechanisms underlying the differences in biochemical metabolism between V. mimicus and V. cholerae are currently unclear. Several V. mimicus isolates have been found that carry cholera toxin genes (ctxAB) and cause cholera-like diarrhea in humans. Here, the genome of the V. mimicus isolate SX-4, which carries an intact CTX element, was sequenced and annotated. Analysis of its genome, together with those of other Vibrio species, revealed extensive differences within the Vibrionaceae. Common mutations in gene clusters involved in three biochemical metabolism pathways that are used for discrimination between V. mimicus and V. cholerae were found in V. mimicus strains. We also constructed detailed genomic structures and evolution maps for the general types of genomic drift associated with pathogenic characters in polysaccharides, CTX elements and toxin co-regulated pilus (TCP) gene clusters. Overall, the whole-genome sequencing of the V. mimicus strain carrying the cholera toxin gene provides detailed information for understanding genomic differences among Vibrio spp. V. mimicus has a large number of diverse gene and nucleotide differences from its nearest neighbor, V. cholerae. The observed mutations in the characteristic metabolism pathways may indicate different adaptations to different niches for these species and may be caused by ancient events in evolution before the divergence of V. cholerae and V. mimicus. Horizontal transfers of virulence-related genes from an uncommon clone of V. cholerae, rather than the seventh pandemic strains, have generated the pathogenic V. mimicus strain carrying cholera toxin genes

SAMPLE-BASED DYNAMIC HIERARCHICAL TRANSFORMER WITH LAYER AND HEAD FLEXIBILITY VIA CONTEXTUAL BANDIT

Transformer requires a fixed number of layers and heads which makes them inflexible to the complexity of individual samples and expensive in training and inference. To address this, we propose a sample-based Dynamic Hierarchical Transformer (DHT) model whose layers and heads can be dynamically configured with single data samples via solving contextual bandit problems. To determine the number of layers and heads, we use the Uniform Confidence Bound algorithm while we deploy combinatorial Thompson Sampling in order to select specific head combinations given their number. Different from previous work that focuses on compressing trained networks for inference only, DHT is not only advantageous for adaptively optimizing the underlying network architecture during training but also has a flexible network for efficient inference. To the best of our knowledge, this is the first comprehensive data-driven dynamic transformer without any additional auxiliary neural networks that implement the dynamic system. According to the experiment results, we achieve up to 74% computational savings for both training and inference with a minimal loss of accuracy

Investigation of thermal management for lithium-ion pouch battery module based on phase change slurry and mini channel cooling plate

In this paper, the thermal management based on phase change slurry (PCS) and mini channel cooling plate for the lithium-ion pouch battery module was proposed. The three-dimensional thermal model was established and the optimum structure of the cooling plate with mini channel was designed with the orthogonal matrix experimental method to balance the cooling performance and energy consumption. The simulation results showed that the cooling performance of PCS consisting of 20% n-octadecane microcapsules and 80% water was better than that of pure water, glycol solution and mineral oil, when the mass flow rate was less than 3 x 10(-4) kg s(-1). For different concentrations of PCS, if the mass flow rate exceeded the critical value, its cooling performance was worse than that of pure water. When the cooling target for battery maximum temperature was higher than 309 K, the PCS cooling with appropriate microcapsule concentration had the edge over in energy consumption compared with water cooling. At last, the dimensionless empirical formula was obtained to predict the effect of the PCS's physical parameters and flow characteristics on the heat transfer and cooling performance. The simulation results will be useful for the design of PCS based battery thermal management systems. (C) 2018 Elsevier Ltd. All rights reserved

FEDEMB: A VERTICAL AND HYBRID FEDERATED LEARNING ALGORITHM USING NETWORK AND FEATURE EMBEDDING AGGREGATION

Federated learning (FL) is an emerging paradigm for decentralized training of machine learning models on distributed clients, without revealing the data to the central server. The learning scheme may be horizontal, vertical or hybrid (both vertical and horizontal). Most existing research work with deep neural network (DNN) modeling is focused on horizontal data distributions, while vertical and hybrid schemes are much less studied. In this paper, we propose a generalized algorithm FedEmb, for modeling vertical and hybrid DNN-based learning. The idea of our algorithm is characterized by higher inference accuracy, stronger privacy-preserving properties, and lower client-server communication bandwidth demands as compared with existing work. The experimental results show that FedEmb is an effective method to tackle both split feature & subject space decentralized problems. To be specific, there are 0.3% to 4.2% improvement on inference accuracy and 88.9 % time complexity reduction over baseline method