OCT1 regulates the migration of colorectal cancer cells by acting on LDHA

Colorectal cancer is one of the most common cancers with high morbidity and mortality. Effective treatments to improve the prognosis are still lacking. The results of online analysis tools showed that OCT1 and LDHA were highly expressed in colorectal cancer, and the high expression of OCT1 was associated with poor prognosis. Immunofluorescence demonstrated that OCT1 and LDHA co-localized in colorectal cancer cells. In colorectal cancer cells, OCT1 and LDHA were upregulated by OCT1 overexpression, but downregulated by OCT1 knockdown. OCT1 overexpression promoted cell migration. OCT1 or LDHA knockdown inhibited the migration, and the downregulation of LDHA restored the promoting effect of OCT1 overexpression. OCT1 upregulation increased the levels of HK2, GLUT1 and LDHA proteins in colorectal cancer cells. Consequently, OCT1 promoted the migration of colorectal cancer cells by upregulating LDHA

Filling the Missing: Exploring Generative AI for Enhanced Federated Learning over Heterogeneous Mobile Edge Devices

Distributed Artificial Intelligence (AI) model training over mobile edge networks encounters significant challenges due to the data and resource heterogeneity of edge devices. The former hampers the convergence rate of the global model, while the latter diminishes the devices' resource utilization efficiency. In this paper, we propose a generative AI-empowered federated learning to address these challenges by leveraging the idea of FIlling the MIssing (FIMI) portion of local data. Specifically, FIMI can be considered as a resource-aware data augmentation method that effectively mitigates the data heterogeneity while ensuring efficient FL training. We first quantify the relationship between the training data amount and the learning performance. We then study the FIMI optimization problem with the objective of minimizing the device-side overall energy consumption subject to required learning performance constraints. The decomposition-based analysis and the cross-entropy searching method are leveraged to derive the solution, where each device is assigned suitable AI-synthesized data and resource utilization policy. Experiment results demonstrate that FIMI can save up to 50% of the device-side energy to achieve the target global test accuracy in comparison with the existing methods. Meanwhile, FIMI can significantly enhance the converged global accuracy under the non-independently-and-identically distribution (non-IID) data.Comment: 13 pages, 5 figures. Submitted to IEEE for possible publicatio

FAST: Fidelity-Adjustable Semantic Transmission over Heterogeneous Wireless Networks

In this work, we investigate the challenging problem of on-demand semantic communication over heterogeneous wireless networks. We propose a fidelity-adjustable semantic transmission framework (FAST) that empowers wireless devices to send data efficiently under different application scenarios and resource conditions. To this end, we first design a dynamic sub-model training scheme to learn the flexible semantic model, which enables edge devices to customize the transmission fidelity with different widths of the semantic model. After that, we focus on the FAST optimization problem to minimize the system energy consumption with latency and fidelity constraints. Following that, the optimal transmission strategies including the scaling factor of the semantic model, computing frequency, and transmitting power are derived for the devices. Experiment results indicate that, when compared to the baseline transmission schemes, the proposed framework can reduce up to one order of magnitude of the system energy consumption and data size for maintaining reasonable data fidelity.Comment: 6 pages, 4 figures. Accepted by ICC 202

AnycostFL: Efficient On-Demand Federated Learning over Heterogeneous Edge Devices

In this work, we investigate the challenging problem of on-demand federated learning (FL) over heterogeneous edge devices with diverse resource constraints. We propose a cost-adjustable FL framework, named AnycostFL, that enables diverse edge devices to efficiently perform local updates under a wide range of efficiency constraints. To this end, we design the model shrinking to support local model training with elastic computation cost, and the gradient compression to allow parameter transmission with dynamic communication overhead. An enhanced parameter aggregation is conducted in an element-wise manner to improve the model performance. Focusing on AnycostFL, we further propose an optimization design to minimize the global training loss with personalized latency and energy constraints. By revealing the theoretical insights of the convergence analysis, personalized training strategies are deduced for different devices to match their locally available resources. Experiment results indicate that, when compared to the state-of-the-art efficient FL algorithms, our learning framework can reduce up to 1.9 times of the training latency and energy consumption for realizing a reasonable global testing accuracy. Moreover, the results also demonstrate that, our approach significantly improves the converged global accuracy.Comment: Accepted to IEEE INFOCOM 202

Federated Learning-Empowered AI-Generated Content in Wireless Networks

Artificial intelligence generated content (AIGC) has emerged as a promising technology to improve the efficiency, quality, diversity and flexibility of the content creation process by adopting a variety of generative AI models. Deploying AIGC services in wireless networks has been expected to enhance the user experience. However, the existing AIGC service provision suffers from several limitations, e.g., the centralized training in the pre-training, fine-tuning and inference processes, especially their implementations in wireless networks with privacy preservation. Federated learning (FL), as a collaborative learning framework where the model training is distributed to cooperative data owners without the need for data sharing, can be leveraged to simultaneously improve learning efficiency and achieve privacy protection for AIGC. To this end, we present FL-based techniques for empowering AIGC, and aim to enable users to generate diverse, personalized, and high-quality content. Furthermore, we conduct a case study of FL-aided AIGC fine-tuning by using the state-of-the-art AIGC model, i.e., stable diffusion model. Numerical results show that our scheme achieves advantages in effectively reducing the communication cost and training latency and privacy protection. Finally, we highlight several major research directions and open issues for the convergence of FL and AIGC.Comment: 8 pages, 3 figures and 2 tables. Submitted to IEEE Networ

The Light Regime Effect on Triacylglycerol Accumulation of Isochrysis zhangjiangensis

Stress state of microalgal cells is caused under unfavorable conditions such as disordered light regime and depleted nitrogen. The stress state can impair photosynthetic efficiency, inhibit cell growth and result in the accumulation of triacylglycerol (TAG) from protective mechanisms. Continuous light or nitrogen starvation was applied on microalgae and performed effectively on inducing TAG production. To evaluate the light regime effect on inducing TAG production, the effect of different light regimes on nitrogen-starved Isochrysis zhangjiangensis was investigated in this work. The continuous light and nitrogen starvation elevated TAG content of biomass by 73% and 193%, respectively. Furthermore, the TAG accumulation of I. zhangjiangensis cell under nitrogen starvation decreased under aggravated stress from continuous illumination. Our results demonstrated that culturing the cells with 14L: 10D light regime under nitrogen starvation is the optimal mode to achieve maximal accumulation of TAG. A recovery in light regime was necessary for I. zhangjiangensis cultivation

Сравнение ростовых характеристик тихоокеанской трески Gadus Macrocephalus российских вод Японского моря : выпускная бакалаврская работа по направлению подготовки: 060301 - Биология биоразнообразие

Triacylglycerols are considered one of the most promising feedstocks for biofuels. Phospholipid:diacylglycerol acyltransferase (PDAT), responsible for the last step of triacylglycerol synthesis in the acyl-CoA-independent pathway, has attracted much attention by catalyzing membrane lipid transformation. However, due to lack of biochemical and enzymatic studies, PDAT has not carried forward in biocatalyst application. Here, the PDAT from Saccharomyces cerevisiae was expressed in Pichia pastoris. The purified enzymes were studied using different acyl donors and acceptors by thin layer chromatography and gas chromatography. In addition of the preferred acyl donor of PE and PC, the results identified that ScPDAT was capable of using broad acyl donors such as PA, PS, PG, MGDG, DGDG, and acyl-CoA, and ScPDAT was more likely to use unsaturated acyl donors comparing 18:0/18:1 to 18:0/18:0 phospholipids. With regard to acyl acceptors, ScPDAT preferred 1,2 to 1,3-diacylglycerol (DAG), while 12:0/12:0 DAG was identified as the optimal acyl acceptor, followed by 18:1/18:1 and 18:1/16:0 DAG. Additionally, ScPDAT reveals esterification activity that can utilize methanol as acyl acceptor to generate fatty acid methyl esters. The results fully expand the enzymatic selectivity of ScPDAT and provide fundamental knowledge for synthesis of triacylglycerol-derived biofuels

thelightregimeeffectontriacylglycerolaccumulationofisochrysiszhangjiangensis

Stress state of microalgal cells is caused under unfavorable conditions such as disordered light regime and depleted nitrogen.The stress state can impair photosynthetic efficiency,inhibit cell growth and result in the accumulation of triacylglycerol(TAG)from protective mechanisms.Continuous light or nitrogen starvation was applied on microalgae and performed effectively on inducing TAG production.To evaluate the light regime effect on inducing TAG production,the effect of different light regimes on nitrogen-starved Isochrysis zhangjiangensis was investigated in this work.The continuous light and nitrogen starvation elevated TAG content of biomass by 73%and 193%,respectively.Furthermore,the TAG accumulation of I.zhangjiangensis cell under nitrogen starvation decreased under aggravated stress from continuous illumination.Our results demonstrated that culturing the cells with 14L:10D light regime under nitrogen starvation is the optimal mode to achieve maximal accumulation of TAG.A recovery in light regime was necessary for I.zhangjiangensis cultivation

thelightregimeeffectontriacylglycerolaccumulationofisochrysiszhangjiangensis

Stress state of microalgal cells is caused under unfavorable conditions such as disordered light regime and depleted nitrogen.The stress state can impair photosynthetic efficiency,inhibit cell growth and result in the accumulation of triacylglycerol(TAG)from protective mechanisms.Continuous light or nitrogen starvation was applied on microalgae and performed effectively on inducing TAG production.To evaluate the light regime effect on inducing TAG production,the effect of different light regimes on nitrogen-starved Isochrysis zhangjiangensis was investigated in this work.The continuous light and nitrogen starvation elevated TAG content of biomass by 73%and 193%,respectively.Furthermore,the TAG accumulation of I.zhangjiangensis cell under nitrogen starvation decreased under aggravated stress from continuous illumination.Our results demonstrated that culturing the cells with 14L:10D light regime under nitrogen starvation is the optimal mode to achieve maximal accumulation of TAG.A recovery in light regime was necessary for I.zhangjiangensis cultivation

The ΔF/F m′-guided supply of nitrogen in culture medium facilitates sustainable production of TAG in Nannochloropsis oceanica IMET1

Abstract Background Triacylglycerol (TAG) from photosynthetic microalgae is a sustainable feedstock for biodiesel production. Physiological stress triggers microalgal TAG accumulation. However excessive physiological stress will impair the photosynthesis system seriously thus decreasing TAG productivity because of the low biomass production. Hence, it is critical to quantitatively and timely monitor the degree of the stress while the microalgal cells growing so that the optimal TAG productivity can be obtained. Results The lack of an on-line monitored indicator has limited our ability to gain knowledge of cellular “health status” information regarding high TAG productivity. Therefore, to monitor the degree of nitrogen stress of the cells, we investigated the correlation between the photosynthetic system II (PS II) quantum yield and the degree of stress based on the high relevancy between photosynthetic reduction and nitrogen stress-induced TAG accumulation in microalgal cells. ΔF/F m′, which is the chlorophyll fluorescence parameter that reflects the effective capability of PS II, was identified to be a critical factor to indicate the degree of stress of the cells. In addition, the concept of a nitrogen stress index has been defined to quantify the degree of stress. Based on this index and by monitoring ΔF/F m′ and guiding the supply of nitrogen in culture medium to maintain a stable degree of stress, a stable and efficient semi-continuous process for TAG production has been established. Conclusion The results indicate that the semi-continuous cultivation process with a controlled degree of stress by monitoring the ΔF/F m′ indicator will have a significant impact on microalgal TAG production, especially for the outdoor controllable cultivation of microalgae on a large scale