Expression of fatty acid synthesis genes and fatty acid accumulation in haematococcus pluvialis under different stressors

<p>Abstract</p> <p>Background</p> <p>Biofuel has been the focus of intensive global research over the past few years. The development of 4^thgeneration biofuel production (algae-to-biofuels) based on metabolic engineering of algae is still in its infancy, one of the main barriers is our lacking of understanding of microalgal growth, metabolism and biofuel production. Although fatty acid (FA) biosynthesis pathway genes have been all cloned and biosynthesis pathway was built up in some higher plants, the molecular mechanism for its regulation in microalgae is far away from elucidation.</p> <p>Results</p> <p>We cloned main key genes for FA biosynthesis in <it>Haematococcus pluvialis</it>, a green microalga as a potential biodiesel feedstock, and investigated the correlations between their expression alternation and FA composition and content detected by GC-MS under different stress treatments, such as nitrogen depletion, salinity, high or low temperature. Our results showed that high temperature, high salinity, and nitrogen depletion treatments played significant roles in promoting microalgal FA synthesis, while FA qualities were not changed much. Correlation analysis showed that acyl carrier protein (ACP), 3-ketoacyl-ACP-synthase (KAS), and acyl-ACP thioesterase (FATA) gene expression had significant correlations with monounsaturated FA (MUFA) synthesis and polyunsaturated FA (PUFA) synthesis.</p> <p>Conclusions</p> <p>We proposed that ACP, KAS, and FATA in <it>H. pluvialis </it>may play an important role in FA synthesis and may be rate limiting genes, which probably could be modified for the further study of metabolic engineering to improve microalgal biofuel quality and production.</p

Investigation on thermal management performance of PCM-fin structure for Li-ion battery module in high-temperature environment

The safety, performance and durability of the Li-ion battery module are limited by the operating temperature especially in the hot temperature regions, hence the thermal management system is essential for battery module. In this paper a novel phase change material (PCM) and fin structure was proposed for the thermal management system of LiFePO4 battery module to reduce the maximum temperature and improve the temperature uniformity in high-temperature environment (40 °C). Carefully designed experiments were performed for model validation. The effects of PCM species, fin thickness, fin spacing and PCM thickness on the cooling performance of battery module were investigated numerically. The results showed that PCM-fin structure thermal management system with optimized design exhibited good thermal performance, keeping the maximum temperature of the battery surface under 51 °C at relatively high discharge rate of 3C. Moreover, by investigating the thermal behavior of PCM during discharge process and cycle test, it has been found that PCM-fin structure has the advantage of improving natural convection and heat conduction within the PCM structure, and as a result enhances heat dissipation efficiency and reduces failure risk in passive thermal management systems using PCMs

Super nucleation and orientation of poly (butylene terephthalate) crystals in nanocomposites containing highly reduced graphene oxide

The ring opening polymerization of cyclic butylene terephthalate into poly (butylene terephthalate) (pCBT) in the presence of reduced graphene oxide (RGO) is an effective method for the preparation of polymer nanocomposites. The inclusion of RGO nanoflakes dramatically affects the crystallization of pCBT, shifting crystallization peak temperature to higher temperatures and, overall, increasing the crystallization rate. This was due to a super nucleating effect caused by RGO, which is maximized by highly reduced graphene oxide. Furthermore, combined analyses by differential scanning calorimetry (DSC) experiments and wide angle X-ray diffraction (WAXS) showed the formation of a thick {\alpha}-crystalline form pCBT lamellae with a melting point of ~250 {\deg}C, close to the equilibrium melting temperature of pCBT. WAXS also demonstrated the pair orientation of pCBT crystals with RGO nanoflakes, indicating a strong interfacial interaction between the aromatic rings of pCBT and RGO planes, especially with highly reduced graphene oxide. Such surface self-organization of the polymer onto the RGO nanoflakes may be exploited for the enhancement of interfacial properties in their polymer nanocomposites

A novel method for purifying bluetongue virus with high purity by co-immunoprecipitation with agarose protein A

<p>Abstract</p> <p>Background</p> <p>Bluetongue virus (BTV) is an icosahedral non-enveloped virus within the genus <it>Orbivirus </it>of <it>Reoviridae </it>and exists as 24 distinct serotypes. BTV can infect all ruminant species and causes severe sickness in sheep. Recently, it was reported that BTV can infect some human cancer cells selectively. Because of the important oncolysis of this virus, we developed a novel purifying method for large-scale production. The purifying logic is simple, which is picking out all the components unwanted and the left is what we want. The process can be summarized in 4 steps: centrifugation, pulling down cell debrises and soluble proteins by co-immunoprecipitation with agarose Protein A, dialysis and filtration sterilization after concentration.</p> <p>Results</p> <p>The result of transmission electron microscope (TEM) observation showed that the sample of purified virus has a very clear background and the virions still kept intact. The result of 50% tissue culture infective dose (TCID₅₀) assay showed that the bioactivity of purified virus is relatively high.</p> <p>Conclusions</p> <p>This method can purify BTV-10 with high quality and high biological activity on large-scale production. It also can be used for purifying other BTV serotypes.</p

RESEARCH ON DATA MANAGEMENT MODEL OF NATIONAL DEFENSE MOBILIZATION POTENTIAL BASED ON GEO-SPATIAL FRAMEWORK

At present, the national defense mobilization potential data is mainly unstructured data composed of text, images, report forms, lacking space attribute and location information. Therefore, a large study of national defense mobilization potential database has focused on data collection, reporting and information system construction, etc. To solve national defense mobilization potential data application problems in the construction of informatization, taking advantage of the characteristics of geographical spatial framework, this paper discusses national defense mobilization potential data management model based on geographical spatial framework

Role of DNA Methylation and Adenosine in Ketogenic Diet for Pharmacoresistant Epilepsy: Focus on Epileptogenesis and Associated Comorbidities

Epilepsy is a neurological disorder characterized by a long term propensity to produce unprovoked seizures and by the associated comorbidities including neurological, cognitive, psychiatric, and impairment the quality of life. Despite the clinic availability of several novel antiepileptic drugs (AEDs) with different mechanisms of action, more than one-third of patients with epilepsy suffer with pharmacoresistant epilepsy. Until now, no AEDs have been proven to confer the efficacy in alteration of disease progression or inhibition of the development of epilepsy. The ketogenic diet, the high-fat, low-carbohydrate composition is an alternative metabolic therapy for epilepsy, especially for children with drug-resistant epilepsy. Recently clinical and experimental results demonstrate its efficacy in ameliorating both seizures and comorbidities associated with epilepsy, such as cognitive/psychiatric concerns for the patients with refractory epilepsy. Of importance, ketogenic diet demonstrates to be a promising disease-modifying or partial antiepileptogenesis therapy for epilepsy. The mechanisms of action of ketogenic diet in epilepsy have been revealed recently, such as epigenetic mechanism for increase the adenosine level in the brain and inhibition of DNA methylation. In the present review, we will focus on the mechanisms of ketogenic diet therapies underlying adenosine system in the prevention of epileptogenesis and disease modification. In addition, we will review the role of ketogenic diet therapy in comorbidities associated epilepsy and the underlying mechanisms of adenosine

Numerical simulation for optimizing the nozzle of moist-mix shotcrete based on orthogonal test

The nozzles of moist-mix shotcrete are the key parts of forming a steady jet flow field and ensuring the uniform mixing of water and other ingredients. In this paper, for optimizing the nozzle of moist-mix shotcrete, both the internal and external field of a variety of spray nozzles were simulated and analyzed by adopting orthogonal test method with Fluent simulation software combined. Then the phase volume fraction and single-phase velocity of the outlet section of flow field inside the nozzles and cloud pictures including single-phase velocity and volume of different sections lengthways in the external flow field of nozzle were obtained. The results demonstrated that the change of different factors and different levels of the same factor affected the shotcreting performance of spray nozzle, but the effect degree is different. Additionally, compared with the traditional nozzle, the rationality of new-type nozzle structure was verified, which provided a basis for the improvement and optimization of the nozzle structure in the future

Hydrogen jet diffusion modeling by using physics-informed graph neural network and sparsely-distributed sensor data

Efficient modeling of jet diffusion during accidental release is critical for operation and maintenance management of hydrogen facilities. Deep learning has proven effective for concentration prediction in gas jet diffusion scenarios. Nonetheless, its reliance on extensive simulations as training data and its potential disregard for physical laws limit its applicability to unseen accidental scenarios. Recently, physics-informed neural networks (PINNs) have emerged to reconstruct spatial information by using data from sparsely-distributed sensors which are easily collected in real-world applications. However, prevailing approaches use the fully-connected neural network as the backbone without considering the spatial dependency of sensor data, which reduces the accuracy of concentration prediction. This study introduces the physics-informed graph deep learning approach (Physic_GNN) for efficient and accurate hydrogen jet diffusion prediction by using sparsely-distributed sensor data. Graph neural network (GNN) is used to model the spatial dependency of such sensor data by using graph nodes at which governing equations describing the physical law of hydrogen jet diffusion are immediately solved. The computed residuals are then applied to constrain the training process. Public experimental data of hydrogen jet is used to compare the accuracy and efficiency between our proposed approach Physic_GNN and state-of-the-art PINN. The results demonstrate our Physic_GNN exhibits higher accuracy and physical consistency of centerline concentration prediction given sparse concentration compared to PINN and more efficient compared to OpenFOAM. The proposed approach enables accurate and robust real-time spatial consequence reconstruction and underlying physical mechanisms analysis by using sparse sensor data