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

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

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

Fluorescent Probes for Molecular Imaging of ROS/RNS Species in Living Systems

Reactive Oxygen Species (ROS) and Reactive Nitrogen Species (RNS) are highly reactive species which play crucial roles in many fundamental physiological processes including cellular signalling pathways. Over-production of these reactive species by various stimuli leads to cellular oxidative stress which is linked to various disease conditions. Therefore, the development of novel detection methods for ROS and RNS is of great interest and indispensable for monitoring the dynamic changes of ROS and RNS in cells and for elucidating their mechanisms of trafficking and connections to diseases. We have been recently developing various fluorescent sensors which can selectively detect metal ions, ROS or RNS species in live cells or animals. Our turn-on profluorescent sensors are capable of imaging oxidative stress promoted by metal and H2O2 (i.e. the Fenton Reaction conditions) in living cells (Chem Commun 2010); our highly selective and sensitive iron sensors can image the endogenous exchangeable iron pools and their dynamic changes with subcellular resolution in living neuronal cells (ChemBioChem 2012 and unpublished data), and so do our superoxide sensors (ChemBioChem 2012 and unpublished data). Moreover, we have recently developed nitric oxide (NO) sensors for molecular imaging of stimulated NO production in live cells with subcellular resolution as well as novel near infra red (NIR) sensors for NO imaging in live animals

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

Epigenetic hypomethylation and upregulation of GD3s in triple negative breast cancer.

Background: Breast cancer remains a major health problem in the world. Triple-negative breast cancer (TNBC) is an aggressive subtype with very poor prognosis. Up to now, the mechanism behind TNBC\u27s activity is still unclear and no candidate drug target has been identified. Thus, it is of critical importance to elucidate the pathways in TNBC and identify the relevant biomarkers. Recent studies showed that ganglioside D3 synthase (GD3s) played a very important role in development of cancers. However, the physiological functions and associated pathways of GD3s in TNBC are still unclear. Methods: Results: Conclusions: In summary, these results suggest that GD3s may be a potential biomarker and drug target in treatment of TNBC

miR-29c plays a suppressive role in breast cancer by targeting the TIMP3/STAT1/FOXO1 pathway.

Background: miR-29c has been associated with the progression of many cancers. However, the function and mechanism of miR-29c have not been well investigated in breast cancers. Methods: Real-time quantitative PCR was used to assess expression of miR-29c and DNMT3B mRNA. Western blot and immunochemistry were used to examine the expression of DNA methyltransferase 3B (DNMT3B) protein in breast cancer cells and tissues. The functional roles of miR-29c in breast cancer cells such as proliferation, migration, invasion, colony formation, and 3D growth were evaluated using MTT, transwell chambers, soft agar, and 3D Matrigel culture, respectively. In addition, the luciferase reporter assay was used to check if miR-29c binds the 3\u27UTR of DNMT3B. The effects of miR-29c on the DNMT3B/TIMP3/STAT1/FOXO1 pathway were also examined using Western blot and methyl-specific qPCR. The specific inhibitor of STAT1, fludarabine, was used to further check the mechanism of miR-29c function in breast cancer cells. Studies on cell functions were carried out in DNMT3B siRNA cell lines. Results: The expression of miR-29c was decreased with the progression of breast cancers and was closely associated with an overall survival rate of patients. Overexpression of miR-29c inhibited the proliferation, migration, invasion, colony formation, and growth in 3D Matrigel while knockdown of miR-29c promoted these processes in breast cancer cells. In addition, miR-29c was found to bind 3\u27UTR of DNMT3B and inhibits the expression of DNMT3B, which was elevated in breast cancers. Moreover, the protein level of TIMP3 was reduced whereas methylation of TIMP3 was increased in miR-29c knockdown cells compared to control. On the contrary, the protein level of TIMP3 was increased whereas methylation of TIMP3 was reduced in miR-29c-overexpressing cells compared to control. Knockdown of DNMT3B reduced the proliferation, migration, and invasion of breast cancer cell lines. Finally, our results showed that miR-29c exerted its function in breast cancers by regulating the TIMP3/STAT1/FOXO1 pathway. Conclusion: The results suggest that miR-29c plays a significant role in suppressing the progression of breast cancers and that miR-29c may be used as a biomarker of breast cancers

Sugar-stimulated CO2 sequestration by the green microalga Chlorella vulgaris

Post-print (lokagerð höfundar) opið á: https://systemsbiology.hi.is/wp-content/uploads/2018/11/Sugar-stimulated-CO2-sequestration-by-the-green-microalga-Chlorella-vulgaris-draft.pdfTo convert waste CO2 from flue gases of power plants into value-added products, bio-mitigation technologies show promise. In this study, we cultivated a fast-growing species of green microalgae, Chlorella vulgaris, in different sizes of photobioreactors (PBRs) and developed a strategy using small doses of sugars for enhancing CO2 sequestration under light-emitting diode illumination. Glucose supplementation at low levels resulted in an increase of photoautotrophic growth-driven biomass generation as well as CO2 capture by 10% and its enhancement corresponded to an increase of supplied photon flux. The utilization of urea instead of nitrate as the sole nitrogen source increased photoautotrophic growth by 14%, but change of nitrogen source didn't compromise glucose-induced enhancement of photoautotrophic growth. The optimized biomass productivity achieved was 30.4% higher than the initial productivity of purely photoautotrophic culture. The major pigments in the obtained algal biomass were found comparable to its photoautotrophic counterpart and a high neutral lipids productivity of 516.6 mg/(L·day) was achieved after optimization. A techno-economic model was also developed, indicating that LED-based PBRs represent a feasible strategy for converting CO2 into value-added algal biomass.This research was supported by the Icelandic Technology Development Fund, the Geothermal Research Group (GEORG) Fund and NYUAD faculty research funds (AD060).Peer Reviewe