Effects of abiotic stressors on lutein production in the green microalga Dunaliella salina.

BackgroundRecent years have witnessed a rising trend in exploring microalgae for valuable carotenoid products as the demand for lutein and many other carotenoids in global markets has increased significantly. In green microalgae lutein is a major carotenoid protecting cellular components from damage incurred by reactive oxygen species under stress conditions. In this study, we investigated the effects of abiotic stressors on lutein accumulation in a strain of the marine microalga D. salina which had been selected for growth under stress conditions of combined blue and red lights by adaptive laboratory evolution.ResultsNitrate concentration, salinity and light quality were selected as three representative influencing factors and their impact on lutein production in batch cultures of D. salina was evaluated using response surface analysis. D. salina was found to be more tolerant to hyper-osmotic stress than to hypo-osmotic stress which caused serious cell damage and death in a high proportion of cells while hyper-osmotic stress increased the average cell size of D. salina only slightly. Two models were developed to explain how lutein productivity depends on the stress factors and for predicting the optimal conditions for lutein productivity. Among the three stress variables for lutein production, stronger interactions were found between nitrate concentration and salinity than between light quality and the other two. The predicted optimal conditions for lutein production were close to the original conditions used for adaptive evolution of D. salina. This suggests that the conditions imposed during adaptive evolution may have selected for the growth optima arrived at.ConclusionsThis study shows that systematic evaluation of the relationship between abiotic environmental stresses and lutein biosynthesis can help to decipher the key parameters in obtaining high levels of lutein productivity in D. salina. This study may benefit future stress-driven adaptive laboratory evolution experiments and a strategy of applying stress in a step-wise manner can be suggested for a rational design of experiments

Emergency Computing: An Adaptive Collaborative Inference Method Based on Hierarchical Reinforcement Learning

In achieving effective emergency response, the timely acquisition of environmental information, seamless command data transmission, and prompt decision-making are crucial. This necessitates the establishment of a resilient emergency communication dedicated network, capable of providing communication and sensing services even in the absence of basic infrastructure. In this paper, we propose an Emergency Network with Sensing, Communication, Computation, Caching, and Intelligence (E-SC3I). The framework incorporates mechanisms for emergency computing, caching, integrated communication and sensing, and intelligence empowerment. E-SC3I ensures rapid access to a large user base, reliable data transmission over unstable links, and dynamic network deployment in a changing environment. However, these advantages come at the cost of significant computation overhead. Therefore, we specifically concentrate on emergency computing and propose an adaptive collaborative inference method (ACIM) based on hierarchical reinforcement learning. Experimental results demonstrate our method's ability to achieve rapid inference of AI models with constrained computational and communication resources

Treatment of Unruptured Vertebral Artery Aneurysm Involving Posterior Inferior Cerebellar Artery With Pipeline Embolization Device

Background: Treatment of unruptured vertebral artery aneurysm involving posterior inferior cerebellar artery (PICA) is challenging. The experience of pipeline embolization device (PED) therapy for these lesions is still limited.Objective: To evaluate the safety and efficacy of the PED for unruptured vertebral artery aneurysm involving PICA.Methods: Thirty-two patients with unruptured vertebral artery aneurysm involving PICA underwent treatment with PED were retrospectively identified. Procedure-related complications, PICA patency, clinical, and angiographic outcomes were analyzed.Results: Thirty-two aneurysms were successfully treated without any procedure-related complications. Images were available in 30 patients (93.8%) during a period of 3–26 months follow-up (average 8.4 months), which confirmed complete occlusion in 17 patients (56.5%), near-complete occlusion in 9 patients (30%), and incomplete occlusion in one patient (3.3%). Parent artery occlusion (PAO) was occurred in 3 patients (10%). Twenty-eight of 30 PICA remained patent. The two occlusions of PICA were secondary to PAO. At a mean of 20.7 months (range 7–50 months) clinical follow-up, all the patients achieved a favorable outcome without any new neurological deficit.Conclusion: PED seems to be a safe and effective alternative endovascular option for patients with unruptured vertebral artery aneurysm involving PICA

Adaptation and synthetic biology of the model cyanobacterium Synechococcus elongatus for sustainable development: a review

Synechococcus elongatus is a model cyanobacterium with remarkable adaptability to diverse environmental stresses, making it a promising candidate for the photoautotrophic conversion of carbon dioxide into valuable chemicals. This review explores the adaptive mechanisms that allow S. elongatus to survive under various abiotic stresses, such as changes in CO2 levels, heavy metals, and light conditions. We also highlight recent advancements in synthetic biology that have enabled the engineering of S. elongatus to produce biofuels and other value-added compounds, including fatty acids, alcohols, and carotenoids. Additionally, we discuss the applications of modern omics techniques to elucidate the genetic basis of stress tolerance and metabolic regulation. Despite the promising potential of S. elongatus for industrial applications, challenges remain in scaling up production, enhancing genetic stability, and optimizing bioreactor systems. Finally, we provide insights into future directions, including the integration of genome engineering, system-level modeling, and co-culture strategies, to improve the efficiency of cyanobacterial cell factories for sustainable biotechnology applications

Few-shot learning for image-based bridge damage detection

Autonomous bridge visual inspection is a real-world challenge due to various materials, surface coatings, and changing light and weather conditions. Traditional supervised learning relies on massive annotated data to establish a robust model, which requires a time-consuming data acquisition process. This work proposes a few-shot learning (FSL) approach based on improved ProtoNet for damage detection with just a few labeled examples. Feature embedding is achieved through cross-domain transfer learning from ImageNet instead of episodic training. The ProtoNet is improved with embedding normalization to enhance transduction performance based on Euclidean distance and a linear classifier for classification. The approach is explored on a public dataset through different ablation experiments and achieves over 94% mean accuracy for 2-way 5-shot classification via the pre-trained GoogleNet after fine-tuning. Moreover, the proposed fine-tuning methods based on a fully connected layer (FCN) and Hadamard product are demonstrated with better performance than the previous method. Finally, the approach is validated using real bridge inspection images, demonstrating its capability of fast implementation for practical damage inspection with weakly supervised information

A deep learning framework for intelligent fault diagnosis using AutoML-CNN and image-like data fusion

Intelligent fault diagnosis (IFD) is essential for preventative maintenance (PM) in Industry 4.0. Data-driven approaches have been widely accepted for IFD in smart manufacturing, and various deep learning (DL) models have been developed for different datasets and scenarios. However, an automatic and unified DL framework for developing IFD applications is still required. Hence, this work proposes an efficient framework integrating popular convolutional neural networks (CNNs) for IFD based on time-series data by leveraging automated machine learning (AutoML) and image-like data fusion. After normalisation, uniaxial or triaxial signals are reconstructed into -channel pseudo-images to satisfy the input requirements for CNNs and achieve data-level fusion simultaneously. Then, the model training, hyperparameter optimisation, and evaluation can be taken automatically based on AutoML. Finally, the selected model can be deployed on a cloud server or an edge device (via tiny machine learning). The proposed framework and method were validated via two case studies, demonstrating the framework’s availability for the automatic development of IFD applications and the effectiveness of the proposed data-level fusion method

Damage volumetric assessment and digital twin synchronization based on LiDAR point clouds

Point clouds are widely used for structure inspection and can provide damage spatial information. However, how to update a digital twin (DT) with local damage based on point clouds has not been sufficiently studied. This research presents an efficient framework for assessing and DT synchronizing local damage on a planar surface using point clouds. The pipeline starts from damage detection via DeepLabV3+ on the pseudo grayscale images from the point depth. It avoids the drawbacks of image and point cloud fusion. The target point cloud is separated according to the detected damage. Then, it can be converted into a 3D binary matrix through voxelization and binarization, which is highly lightweight and can be losslessly compressed for DT synchronization. The framework is validated via two case studies, demonstrating that the proposed voxel-based method can be easily applied to real-world damage with non-convex geometry instead of convex-hull fitting; finite-element (FE) models and BIM models can be updated automatically through the framework

Multimodal generative semantic communication based on latent diffusion model

In emergencies, the ability to quickly and accurately gather environmental data and command information, and to make timely decisions, is particularly critical. Traditional semantic communication frameworks, primarily based on a single modality, are susceptible to complex environments and lighting conditions, thereby limiting decision accuracy. To this end, this paper introduces a multimodal generative semantic communication framework named mm-GESCO. The framework ingests streams of visible and infrared modal image data, generates fused semantic segmentation maps, and transmits them using a combination of one-hot encoding and zlib compression techniques to enhance data transmission efficiency. At the receiving end, the framework can reconstruct the original multimodal images based on the semantic maps. Additionally, a latent diffusion model based on contrastive learning is designed to align different modal data within the latent space, allowing mm-GESCO to reconstruct latent features of any modality presented at the input. Experimental results demonstrate that mm-GESCO achieves a compression ratio of up to 200 times, surpassing the performance of existing semantic communication frameworks and exhibiting excellent performance in downstream tasks such as object classification and detection