Environmental Impact of Customs Union Agreement with EU on Turkey’s Trade in Manufacturing Industry

In this study, we analyze Turkey’s manufacturing industry trade by estimating sectoral import and export demand equations for 1980-2000. The study aims to understand whether the trade in the manufacturing industry complies with pollution haven hypothesis, and whether the free trade environment provided by the customs union (CU) agreement altered the trade pattern of the clean and dirty industries. Results of our econometric models have shown that while CU positively affects the import demand, it does not have any significant impact on the export demand of Turkish manufacturing industry. In terms of the environmental impact, distinction between clean and dirty industries turns out to be significant for both import and export demand. In general, our findings suggest that both clean and dirty industries’ import demand increase during the study period. In terms of export demand, clean industries’ export demand declines whereas dirty industries’ export demand increases compared to the total demand.Environmental impact analysis, EU, Turkey, manufacturing industry

CO2 Emissions vs. CO2 Responsibility: An Input-Output Approach for the Turkish Economy

Recently, global warming (greenhouse effect) and its effects have become one of the hottest topics in the world agenda. There have been several international attempts to reduce the negative effects of global warming. Kyoto Protocol can be cited as the most important agreement which tries to limit the countries’ emissions within a time horizon. For this reason it becomes important to calculate the greenhouse gas emissions of countries. The aim of this study is to estimate the amount of CO2 -the most important greenhouse gas- emissions, for the Turkish economy. An extended input-output model is estimated by using 1996 data in order to identify the sources of CO2 emissions and to discuss the share of sectors in total emission. Besides ‘CO2 responsibility’, which takes into account the CO2 content of imports, is estimated for the Turkish economy. The sectoral CO2 emissions and CO2 responsibilities are compared and these two notions are linked to foreign trade volume. One of the main conclusions is that the manufacturing industry has the first place in both of the rankings for CO2 emissions and CO2 responsibilities; while agriculture and husbandry has the last place.CO2 responsibility, Turkey, input-output analysis

Label-free nanometer-resolution imaging of biological architectures through surface enhanced raman scattering

Label free imaging of the chemical environment of biological specimens would readily bridge the supramolecular and the cellular scales, if a chemical fingerprint technique such as Raman scattering can be coupled with super resolution imaging. We demonstrate the possibility of label-free super-resolution Raman imaging, by applying stochastic reconstruction to temporal fluctuations of the surface enhanced Raman scattering (SERS) signal which originate from biomolecular layers on large-area plasmonic surfaces with a high and uniform hot-spot density (>1011/cm2, 20 to 35 nm spacing). A resolution of 20 nm is demonstrated in reconstructed images of self-assembled peptide network and fibrilated lamellipodia of cardiomyocytes. Blink rate density is observed to be proportional to the excitation intensity and at high excitation densities (>10 kW/cm2) blinking is accompanied by molecular breakdown. However, at low powers, simultaneous Raman measurements show that SERS can provide sufficient blink rates required for image reconstruction without completely damaging the chemical structure

Label-Free Nanometer-Resolution Imaging of Biological Architectures through Surface Enhanced Raman Scattering

Label free imaging of the chemical environment of biological specimens would readily bridge the supramolecular and the cellular scales, if a chemical fingerprint technique such as Raman scattering can be coupled with super resolution imaging. We demonst

Reciprocal Encoding of Signal Intensity and Duration in a Glucose-Sensing Circuit

Cells continuously adjust their behavior in response to changing environmental conditions. Both intensity and duration of external signals are critical factors in determining what response is initiated. To understand how intracellular signaling networks process such multidimensional information, we studied the AtRGS1-mediated glucose response system of Arabidopsis. By combining experiments with mathematical modeling, we discovered a reciprocal dose and duration response relying on the orchestrated action of three kinases (AtWNK1, AtWNK8, AtWNK10) acting on distinct time scales and activation thresholds. Specifically, we find that high concentrations of D-glucose rapidly signal through AtWNK8 and AtWNK10, whereas low, sustained sugar concentration slowly activate the pathway through AtWNK1, allowing the cells to respond similarly to transient, high-intensity signals, and sustained low-intensity signals. This “dose-duration reciprocity” allows encoding of both the intensity and persistence of glucose as an important energy resource and signaling molecule

Psychometric properties of Greek versions of the Modified Corah Dental Anxiety Scale (MDAS) and the Dental Fear Survey (DFS)

Background: A growing body of literature describes the performance of dental fear questionnaires in various countries. We describe the psychometric properties of Greek versions of the Modified Dental Anxiety Scale (MDAS) and the Dental Fear Survey (DFS) in adult Greek patients. Methods: Greek versions of the MDAS and DFS were administered to two samples of adult dental patients. In the first sample, 195 patients attending one of three private practice dental offices in a large city in Greece completed the questionnaires in the waiting room before dental treatment. After treatment, their dentists (who did not know how the patients had answered the questionnaire) rated their anxiety during dental treatment. In the second sample, 41 patients attending a Greek university dental school clinic completed the questionnaire twice at two separate visits, in order to provide test-retest data. Cronbach's alpha was used to compute the internal consistencies, while Spearman's rho was used to compute the testretest reliabilities. Construct validity was assessed by correlating the responses to the MDAS and DFS by Spearman's rho. Spearman's rho was also used to examine the criterion validities, by comparing the questionnaire responses with the dentists' ratings of anxiety. Results: The internal consistencies for the MDAS were 0.90 and 0.92 in the two samples; for the DFS, the internal consistencies were 0.96 in both samples. The test-retest reliabilities were 0.94 for the MDAS and 0.95 for the DFS. The correlation between the two questionnaires was 0.89. The patients' responses to both questionnaires were significantly related to the dentists' ratings of their anxiety during dental treatment (both p values less than 0.001). Conclusion: The results indicate that the Greek versions of the MDAS and DFS have good internal consistencies and test-retest reliabilities, as well as good construct and criterion validities. The psychometric properties of the Greek versions of these questionnaires appear to be similar to those previously reported in other countries.This research was supported by NIH/NIDCR grant T32DE07132

Reviews and syntheses: Remotely sensed optical time series for monitoring vegetation productivity

Vegetation productivity is a critical indicator of global ecosystem health and is impacted by human activities and climate change. A wide range of optical sensing platforms, from ground-based to airborne and satellite, provide spatially continuous information on terrestrial vegetation status and functioning. As optical Earth observation (EO) data are usually routinely acquired, vegetation can be monitored repeatedly over time, reflecting seasonal vegetation patterns and trends in vegetation productivity metrics. Such metrics include gross primary productivity, net primary productivity, biomass, or yield. To summarize current knowledge, in this paper we systematically reviewed time series (TS) literature for assessing state-of-the-art vegetation productivity monitoring approaches for different ecosystems based on optical remote sensing (RS) data. As the integration of solar-induced fluorescence (SIF) data in vegetation productivity processing chains has emerged as a promising source, we also include this relatively recent sensor modality. We define three methodological categories to derive productivity metrics from remotely sensed TS of vegetation indices or quantitative traits: (i) trend analysis and anomaly detection, (ii) land surface phenology, and (iii) integration and assimilation of TS-derived metrics into statistical and process-based dynamic vegetation models (DVMs). Although the majority of used TS data streams originate from data acquired from satellite platforms, TS data from aircraft and unoccupied aerial vehicles have found their way into productivity monitoring studies. To facilitate processing, we provide a list of common toolboxes for inferring productivity metrics and information from TS data. We further discuss validation strategies of the RS data derived productivity metrics: (1) using in situ measured data, such as yield; (2) sensor networks of distinct sensors, including spectroradiometers, flux towers, or phenological cameras; and (3) inter-comparison of different productivity metrics. Finally, we address current challenges and propose a conceptual framework for productivity metrics derivation, including fully integrated DVMs and radiative transfer models here labelled as “Digital Twin”. This novel framework meets the requirements of multiple ecosystems and enables both an improved understanding of vegetation temporal dynamics in response to climate and environmental drivers and enhances the accuracy of vegetation productivity monitoring