28 research outputs found
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
Π‘ΡΠ°Π²Π½Π΅Π½ΠΈΠ΅ ΡΠΎΡΡΠΎΠ²ΡΡ Ρ Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊ ΡΠΈΡ ΠΎΠΎΠΊΠ΅Π°Π½ΡΠΊΠΎΠΉ ΡΡΠ΅ΡΠΊΠΈ Gadus Macrocephalus ΡΠΎΡΡΠΈΠΉΡΠΊΠΈΡ Π²ΠΎΠ΄ Π―ΠΏΠΎΠ½ΡΠΊΠΎΠ³ΠΎ ΠΌΠΎΡΡ : Π²ΡΠΏΡΡΠΊΠ½Π°Ρ Π±Π°ΠΊΠ°Π»Π°Π²ΡΡΠΊΠ°Ρ ΡΠ°Π±ΠΎΡΠ° ΠΏΠΎ Π½Π°ΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ ΠΏΠΎΠ΄Π³ΠΎΡΠΎΠ²ΠΊΠΈ: 060301 - ΠΠΈΠΎΠ»ΠΎΠ³ΠΈΡ Π±ΠΈΠΎΡΠ°Π·Π½ΠΎΠΎΠ±ΡΠ°Π·ΠΈΠ΅
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 ΞF/F mβ²-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 βhealth statusβ 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. ΞF/F mβ², 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 ΞF/F mβ² 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 ΞF/F mβ² indicator will have a significant impact on microalgal TAG production, especially for the outdoor controllable cultivation of microalgae on a large scale