17 research outputs found

    Wymiar terytorialny w perspektywie nowej polityki spójności w odniesieniu do obszarów wiejskich – przykład województwa dolnośląskiego

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    Summary and evaluation of implemented, in the last financial perspective of EU, policies should be the basis for development of new directions and principles of European spending. This article presents an issue of implementation of the territorial dimension in cohesion policy. The spatial concentration of resources allocated to rural development under the Regional Operational Programme for the Lower Silesia 2007-2013 was analysed. Furthermore, the documents’ analysis to determine development opportunities for these areas in the new financial perspective were undertaken. The research shows inadequate recognition of the territorial dimension in the current implementation of EU policies. However, the results highlights the possibility of using a new instrument, which are Integrated Territorial Investments, in order to strengthen the geographical factors in the allocation of support

    THE TERRITORIAL DIMENSION IN NEW COHESION POLICY ACCORDING TO RURAL AREAS – CASE STUDY OF LOWER SILESIA

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    Summary and evaluation of implemented, in the last financial perspective of EU, policies should be the basis for development of new directions and principles of European spending. This article presents an issue of implementation of the territorial dimension in cohesion policy. The spatial concentration of resources allocated to rural development under the Regional Operational Programme for the Lower Silesia 2007-2013 was analysed. Furthermore, the documents’ analysis to determine development opportunities for these areas in the new financial perspective were undertaken. The research shows inadequate recognition of the territorial dimension in the current implementation of EU policies. However, the results highlights the possibility of using a new instrument, which are Integrated Territorial Investments, in order to strengthen the geographical factors in the allocation of support

    Active Disturbance Rejection Control of a 2DOF manipulator with significant modeling uncertainty

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    Abstract This paper presents a practical verification of an Active Disturbance Rejection Control (ADRC) method in governing a multidimensional system. The experiments were conducted on a two degrees of freedom planar manipulator with only partial knowledge about the mathematical model of the plant. This multi input multi output system was controlled with a set of two, independent, single input single output ADRC controllers, each regulating one of the manipulator degree of freedom. Modeling uncertainty (nonlinearities, cross-coupling effects, etc.) and external disturbances were assumed to be a part of the disturbance, to be estimated with an observer and canceled on-line in the control loop. The ADRC robustness was experimentally compared with the results obtained from using two decentralized, classic PID controllers. Both control methods were tested under various conditions, e.g. changing the inertial parameters of the plant. Significantly better results, in terms of parametric robustness, have been reported for the ADRC approach

    Tumor microenvironment – Unknown niche with powerful therapeutic potential

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    Head and neck squamous cell carcinomas (HNSCC) are in a group of cancers that are the most resistant to treatment. The survival rate of HNSCC patients has been still very low since last 20 years. The existence of relationship between oncogenic and surrounding cells is probably the reason for a poor response to treatment. Fibroblasts are an important element of tumor stroma which increases tumor cells ability to proliferate. Another highly resistance, tumorigenic and metastatic cell population in tumor microenvironment are cancer initiating cells (CICs). The population of cancer initiating cells can be found regardless of differentiation status of cancer and they seem to be crucial for HNSCC development.In this review, we describe the current state of knowledge about HNSCC biological and physiological tumor microenvironment

    Global Proteome Profiling of the Temporal Cortex of Female Rats Exposed to Chronic Stress and the Western Diet

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    The increasing consumption of highly processed foods with high amounts of saturated fatty acids and simple carbohydrates is a major contributor to the burden of overweight and obesity. Additionally, an unhealthy diet in combination with chronic stress exposure is known to be associated with the increased prevalence of central nervous system diseases. In the present study, the global brain proteome approach was applied to explore protein alterations after exposure to the Western diet and/or stress. Female adult rats were fed with the Western diet with human snacks and/or subjected to chronic stress induced by social instability for 12 weeks. The consumption of the Western diet resulted in an obese phenotype and induced changes in the serum metabolic parameters. Consuming the Western diet resulted in changes in only 5.4% of the proteins, whereas 48% of all detected proteins were affected by chronic stress, of which 86.3% were down-regulated due to this exposure to chronic stress. However, feeding with a particular diet modified stress-induced changes in the brain proteome. The down-regulation of proteins involved in axonogenesis and mediating the synaptic clustering of AMPA glutamate receptors (Nptx1), as well as proteins related to metabolic processes (Atp5i, Mrps36, Ndufb4), were identified, while increased expression was detected for proteins involved in the development and differentiation of the CNS (Basp1, Cend1), response to stress, learning and memory (Prrt2), and modulation of synaptic transmission (Ncam1, Prrt2). In summary, global proteome analysis provides information about the impact of the combination of the Western diet and stress exposure on cerebrocortical protein alterations and yields insight into the underlying mechanisms and pathways involved in functional and morphological brain alterations as well as behavioral disturbances described in the literature

    Different levels of let-7d expression modulate response of FaDu cells to irradiation and chemotherapeutics.

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    The implication of the let-7 family in cancer development is multifaceted. The family acts as tumor suppressor miRNA although overexpression of let-7 has also been described in many types of cancer, including head and neck squamous cell carcinoma (HNSCC). The aim of this study includes whether different expression levels of let-7d has an influence on chemo- and radiosensitivity. FaDu cell line models with a gradually increased level of let-7d (models from A to E) were generated with the lentiviral system. Expression levels of pluripotency, chemo-radioresistance/apoptosis, and targets of mRNAs were analyzed by real-time reverse transcription-PCR (qRT-PCR). Radiosensitivity was analyzed using a clonogenic assay after irradiation. Response to cisplatin, 5-FU, doxorubicin, and paclitaxel was done with MTT assay. Statistically significant decrease of K-RAS (p = 0.0369) and CASPASE3 (p = 0.0342) were observed with the growing expression level of let-7d. Cisplatin, 5-FU and doxorubicin caused similar decreased of cell survival with the increase of let-7d level (p = 0.004, post-trend p = 0.046; p = 0.004, post trend p = 0.0005 and p<0.0001, post trend p = 0.0001, respectively). All models were resistant to paclitaxel, irrespective of let-7d expression levels. Only two of the generated models (A and C) were radiosensitive (p = 0.0002). CONCLUSION:the above results indicated that the level of let-7d expression is an important factor for cell response to irradiation and chemotherapeutics

    Response of FaDu let-7d models to chemo-and radiotherapy.

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    <p>A) The cell line models (A-E) were exposed to chemotherapeutics: cisplatin (1.12 μg/mL); 5-FU (0.86 μg/mL); paclitaxel (0.54 μM), and doxorubicin (0.06 μM) were compared to FaDu-GFP B) Survival fractions [SF%] of the cell models were assessed according to a dose of 2 Gy. The differences in survival were statistical significant (<i>p</i> = 0.0002) for the models A and C.</p
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