Translational assessment of primary tumor-derived cells

Only a few individual cells within less than 5% of all primary tumors form the cell lines commonly used in cancer research. These growth bottlenecks result in cell lines that are often poor models of primary tumors. Co-culture of primary tumor-derived cells with an irradiated mouse fibroblast feeder layer and ROCK inhibitor, known as the Georgetown Method, offers a way to culture over 80% of tumor-derived cells in vitro to create more representative tumor cell models. In our studies, we optimized the Georgetown Method to culture head and neck cancer cells, including oropharyngeal squamous cell carcinoma, and investigated its mechanism of conditionally immortalizing cells in culture. Differential trypsinization and regular feeder layer replacement were found to significantly improve the efficacy of immortalizing co-cultured cells at both atmospheric and physiological oxygen levels. Medium conditioned by irradiated fibroblasts can also substitute for direct co-culture with a feeder layer. The Georgetown Method was found to maintain low levels of p16 in co-cultured cells, suggesting a potential mechanism by which the Georgetown Method prevents differentiation and senescence. Our ability to culture over 80% of primary tumor-derived cells allows us to test the translational value of tumor-derived cell cultures and xenografts using BH3 profiling. Conditioned medium simplifies maintenance of cell cultures and will also allow us to perform high-throughput screens without the need to separate tumor-derived cells from the fibroblast feeder layer. The Georgetown Method provides opportunities to expand small tissue specimens for future diagnostics, therapeutics, and biobanking

Dynamics of plant metal uptake and metal changes in whole soil and soil particle fractions during repeated phytoextraction

Phytoextration of metal polluted soils using hyperaccumulators is a promising technology but requires long term successive cropping. This study investigated the dynamics of plant metal uptake and changes in soil metals over a long remediation time. A soil slightly polluted with metals (S1) was mixed with highly polluted soil (S4) to give two intermediate pollution levels (S2, S3). The four resulting soils were repeatedly phyto-extracted using nine successive crops of Cd/Zn-hyperaccumulator Sedum plumbizincicola over a period of 4 years. Shoot Cd concentration decreased with harvest time in all soils but shoot Zn declined in S1 only. Similar shoot Zn concentrations were found in S2, S3 and S4 although these soils differed markedly in metal availability, and their available metals decreased during phytoextraction. A possible explanation is that plant active acquisition ability served to maintain plant metal uptake. Plant uptake resulted in the largest decrease in the acid-soluble metal fraction followed by reducible metals. Oxidisable and residual fractions were less available to plants. The coarse soil particle fractions made the major contribution to metal decline overall than the fine fractions. Sedum plumbizincicola maintained long term metal uptake and the coarse soil particles played the most important role in phytoextraction.Phytoextration of metal polluted soils using hyperaccumulators is a promising technology but requires long term successive cropping. This study investigated the dynamics of plant metal uptake and changes in soil metals over a long remediation time

Joint Learning-based Causal Relation Extraction from Biomedical Literature

Causal relation extraction of biomedical entities is one of the most complex tasks in biomedical text mining, which involves two kinds of information: entity relations and entity functions. One feasible approach is to take relation extraction and function detection as two independent sub-tasks. However, this separate learning method ignores the intrinsic correlation between them and leads to unsatisfactory performance. In this paper, we propose a joint learning model, which combines entity relation extraction and entity function detection to exploit their commonality and capture their inter-relationship, so as to improve the performance of biomedical causal relation extraction. Meanwhile, during the model training stage, different function types in the loss function are assigned different weights. Specifically, the penalty coefficient for negative function instances increases to effectively improve the precision of function detection. Experimental results on the BioCreative-V Track 4 corpus show that our joint learning model outperforms the separate models in BEL statement extraction, achieving the F1 scores of 58.4% and 37.3% on the test set in Stage 2 and Stage 1 evaluations, respectively. This demonstrates that our joint learning system reaches the state-of-the-art performance in Stage 2 compared with other systems.Comment: 15 pages, 3 figure

A Secure Mechanism for Big Data Collection in Large Scale Internet of Vehicle

As an extension for Internet of Things (IoT), Internet of Vehicles (IoV) achieves unified management in smart transportation area. With the development of IoV, an increasing number of vehicles are connected to the network. Large scale IoV collects data from different places and various attributes, which conform with heterogeneous nature of big data in size, volume, and dimensionality. Big data collection between vehicle and application platform becomes more and more frequent through various communication technologies, which causes evolving security attack. However, the existing protocols in IoT cannot be directly applied in big data collection in large scale IoV. The dynamic network structure and growing amount of vehicle nodes increases the complexity and necessary of the secure mechanism. In this paper, a secure mechanism for big data collection in large scale IoV is proposed for improved security performance and efficiency. To begin with, vehicles need to register in the big data center to connect into the network. Afterwards, vehicles associate with big data center via mutual authentication and single sign-on algorithm. Two different secure protocols are proposed for business data and confidential data collection. The collected big data is stored securely using distributed storage. The discussion and performance evaluation result shows the security and efficiency of the proposed secure mechanism

Metal(loid) Uptake and Physiological Response of Coix lacryma-jobi L to Soil Potentially Toxic Elements in a Polluted Metal-Mining Area

Coix lacryma-jobi L. is a traditional medicinal plant in east Asia and is an important crop in Guizhou province, southwest China, where there are elevated levels of soil mercury and arsenic (As). Exposure to multiple potentially toxic elements (PTEs) may affect plant accumulation of metal(loid)s and food safety in regions with high geological metal concentrations. Field experiments were conducted to study the effects of PTEs on metal(loid) accumulation and physiological response of C. lacryma in different plant parts at three pollution levels. Total root length, number of root tips, number of branches, and number of root crosses increased with increasing pollution level, with increases in highly polluted areas of 44.2, 57.0, 79.6, and 97.2%, respectively, compared to lightly polluted areas. Under multi-element stress the activity of C. lacryma antioxidant oxidase showed an increase at low and medium PTE concentrations and inhibition at high concentrations. The As contents were all below the maximum limit of cereal food contaminants in China (GB 2762-2022, As \u3c 0.5 mg kg-1). The stems had high Tl bioconcentration factors but the translocation factors from stem to grain were very low, indicating that the stems may be a key plant part restricting Tl transport to the grains. C. lacryma increased root retention and reduced the transport effect, thus reducing metal accumulation in the grains. C. lacryma adapted to PTE stress through root remodeling and enhanced antioxidant enzyme activities

Application of catastrophe theory in comprehensive ecological security assessment of plastic greenhouse soil contaminated by phthalate esters

Large amount of phthalate esters (PAEs) used as plasticizers in polyvinyl chloride (PVC) products has caused ubiquitous contamination to the environment and potential ecology security risk all around the world, especially in places plastic films were indispensably utilized due to the widely proposing of facility agriculture in China. A case of PAEs contamination in four suburb areas of Nanjing was analyzed and discussed in this study. A new frame work has been put forward based on multi-criteria evaluation model and mathematical method of catastrophe theory, using farming work, laboratory determination and relevant environmental standards to measure the ecology security risk of PAEs in study areas. The factors were selected based on the availability of the data and the local conditions. The assessment model involves the contamination status of PAEs in soil and vegetables, the contamination effects of PAEs to human and soil organisms and the contamination source of PAEs from plastic films and other products in the four study facility agriculture areas. An evaluation system of the model was composed of thirteen mesosphere indicators and twenty-five underlying indicators including total PAEs concentration in soils, single PAE concentration in soils, total PAEs concentrations in roots, leafy, solanaceous and stem vegetables, PAE human risks, soil microbial counts, microorganism diversity indices, atmospheric deposition of PAEs, whether sewage wastewater irrigation, planting mode of the facility agriculture areas and climate condition of study areas. The modified evaluation system was used in the assessment of ecology security of the same place based on the data of 2012, and the results suggested that the ecology security indicators were reliable and were agree well with the practical situation of the study areas. The results could provide guidance for the application of health risk assessment of soil environment for the strong objectivity of catastrophe theory compared with other evaluation methods

Cross-Domain Fine-Grained Data Usage Control Service for Industrial Wireless Sensor Networks

In an industrial system, wireless sensor networks (WSNs) are usually adapted to industrial applications. Industrial system is a novel scenario to apply WSNs. Industrial WSNs are the base to establish a supervisory control and data acquisition system with the benefits of extending the network boundaries and enhancing the network scalability of the WSNs. The integration of industrial systems, such as smart grids and social networks, is an important trend for new network technologies. In many application scenarios of industrial systems, WSNs are controlled by different authorities. The network nodes that belong to different domains can share the sensor data by standard protocols. Moreover, in an applications, scenario that has high security requirements, the nodes of social networking WSNs could belong to different security levels; thus, these data can be controlled only by specific types of users. Therefore, the cross-domain fine-grained data usage is the core problem for this approach. To address this problem, this paper focuses on the cross-domain fine-grained data usage control mechanism of social networking WSNs in industrial systems, which includes cross-domain fine-grained access control and fuzzy clustering for sensing data for efficient analysis. In addition, dynamic service composition is proposed for data usage. The simulation results verify the feasibility and data usage effectiveness of the proposed scheme

Event-Oriented Dynamic Security Service for Demand Response in Smart Grid Employing Mobile Networks

Equipped with millions of sensors and smart meters in smart gird, a reliable and resilient wireless communication technology is badly needed. Mobile networks are among the major energy communication networks which contribute to global energy consumption increase rapidly. As one of core technologies of smart grid employing mobile networks, Demand Response (DR) helps improving efficiency, reliability and security for electric power grid infrastructure. Security of DR events is one of the most important issues in DR. However, the security requirements of different DR events are dynamic for variousactual demands. To address this, an event-oriented dynamic security service mechanism is proposed for DR. Three kinds of security services including security access service, security communication service and security analysis service for DR event are composited dynamically by the fine-grained sub services. An experiment prototype of the network of State Grid Corporation of China (SGCC) is established. Experiment and evaluations shows the feasibility and effectiveness of the proposed scheme in smart grid employing mobile network

Death effector domain-containing protein induces vulnerability to cell cycle inhibition in triple-negative breast cancer

Lacking targetable molecular drivers, triple-negative breast cancer (TNBC) is the most clinically challenging subtype of breast cancer. In this study, we reveal that Death Effector Domain-containing DNA-binding protein (DEDD), which is overexpressed in > 60% of TNBCs, drives a mitogen-independent G1/S cell cycle transition through cytoplasm localization. The gain of cytosolic DEDD enhances cyclin D1 expression by interacting with heat shock 71 kDa protein 8 (HSC70). Concurrently, DEDD interacts with Rb family proteins and promotes their proteasome-mediated degradation. DEDD overexpression renders TNBCs vulnerable to cell cycle inhibition. Patients with TNBC have been excluded from CDK 4/6 inhibitor clinical trials due to the perceived high frequency of Rb-loss in TNBCs. Interestingly, our study demonstrated that, irrespective of Rb status, TNBCs with DEDD overexpression exhibit a DEDD-dependent vulnerability to combinatorial treatment with CDK4/6 inhibitor and EGFR inhibitor in vitro and in vivo. Thus, our study provided a rationale for the clinical application of CDK4/6 inhibitor combinatorial regimens for patients with TNBC