A klímaváltozás közösségökológiai hatásainak elemzései

A klímaváltozás ökológiai hatásainak elemzésével kapcsolatos tudományos eredményeink megközelítési módjukat tekintve az alábbi hat fő csoportba oszthatók: 1. Az elméleti várakozások tisztázása stratégiai modellezéssel. 2. Lehetséges hatások mértékének behatárolása Magyarország vonatkozásában, földrajzi analógiai megközelítéssel. 3. Nagy monitoring adatbázisok elemzései a már bekövetkezett változási tendenciák feltárására. 4. Természetközeli populációk és ökoszisztémák várható változásainak elemzése taktikai modellezéssel és statisztikai elemzésekkel. 5. A klímaváltozás agroökoszisztémákra gyakorolt hatásai 6. Új, hatékony és a korábbiaknál általánosabban használható bioindikátor-rendszer kidolgozása. A továbbiakban eredményeinket ezen felosztás szerinti csoportosításban ismertetjük

Sustainable technology solutions for reuse of process wastewaters from fine chemical industries

In the fine chemical industries, especially in the pharmaceutical industry, production technology generates large amounts of liquid waste and industrial waste solvents. Separation of various organic substances used in industry, such as adsorbable organic halides (AOX), from industrial wastewater is an important task of environmental protection. In this work, two technologies were compared to investigate the recycling/reuse of organic material of process wastewaters. The analysis was based on real case study from fine chemical industry. The separation efficiency, operational parameters and cost analysis were carried out to examine stripping and distillation technologies. The calculation was achieved in professional flowsheet simulator environment. According to the results, it can be determined there is no significant difference in separation efficiency of wastewater output streams. However, in the case of distillation technology, the reuse of halides can be possible inside the factory, so this is the recommended procedure for environmental protection. The cost of recovery technologies is also compared with waste incineration. These calculations also demonstrate the effectiveness of the treatment methods, because with recovery technologies it is possible to obtain a reduction of up to 85% compared to incineration

Simulation modeling of phytoplankton dynamics in a large eutrophic river, Hungary – Danubian Phytoplankton Growth Model (DPGM)

Ecological models have often been used in order to answer questions that are in the limelight of recent researches such as the possible effects of climate change. The methodology of tactical models is a very useful tool comparison to those complex models requiring relatively large set of input parameters. In this study, a theoretical strategic model (TEGM ) was adapted to the field data on the basis of a 24-year long monitoring database of phytoplankton in the Danube River at the station of G¨od, Hungary (at 1669 river kilometer – hereafter referred to as “rkm”). The Danubian Phytoplankton Growth Model (DPGM) is able to describe the seasonal dynamics of phytoplankton biomass (mg L−1) based on daily temperature, but takes the availability of light into consideration as well. In order to improve fitting, the 24-year long database was split in two parts in accordance with environmental sustainability. The period of 1979–1990 has a higher level of nutrient excess compared with that of the 1991–2002. The authors assume that, in the above-mentioned periods, phytoplankton responded to temperature in two different ways, thus two submodels were developed, DPGM-sA and DPGMsB. Observed and simulated data correlated quite well. Findings suggest that linear temperature rise brings drastic change to phytoplankton only in case of high nutrient load and it is mostly realized through the increase of yearly total biomass

Frequent Constriction-Like Echocardiographic Findings in Elite Athletes Following Mild COVID-19: A Propensity Score-Matched Analysis

Background: The cardiovascular effects of SARS-CoV-2 in elite athletes are still a matter of debate. Accordingly, we sought to perform a comprehensive echocardiographic characterization of post-COVID athletes by comparing them to a non-COVID athlete cohort. Methods: 107 elite athletes with COVID-19 were prospectively enrolled (P-CA; 23 ± 6 years, 23% female) 107 healthy athletes were selected as a control group using propensity score matching (N-CA). All athletes underwent 2D and 3D echocardiography. Left (LV) and right ventricular (RV) end-diastolic volumes (EDVi) and ejection fractions (EF) were quantified. To characterize LV longitudinal deformation, 2D global longitudinal strain (GLS) and the ratio of free wall vs. septal longitudinal strain (FWLS/SLS) were also measured. To describe septal flattening (SF—frequently seen in P-CA), LV eccentricity index (EI) was calculated. Results: P-CA and N-CA athletes had comparable LV and RVEDVi (P-CA vs. N-CA; 77 ± 12 vs. 78 ± 13mL/m2; 79 ± 16 vs. 80 ± 14mL/m2). P-CA had significantly higher LVEF (58 ± 4 vs. 56 ± 4%, p < 0.001), while LVGLS values did not differ between P-CA and N-CA (−19.0 ± 1.9 vs. −18.8 ± 2.2%). EI was significantly higher in P-CA (1.13 ± 0.16 vs. 1.01 ± 0.05, p < 0.001), which was attributable to a distinct subgroup of P-CA with a prominent SF (n = 35, 33%), further provoked by inspiration. In this subgroup, the EI was markedly higher compared to the rest of the P-CA (1.29 ± 0.15 vs. 1.04 ± 0.08, p < 0.001), LVEDVi was also significantly higher (80 ± 14 vs. 75 ± 11 mL/m2, p < 0.001), while RVEDVi did not differ (82 ± 16 vs. 78 ± 15mL/m2). Moreover, the FWLS/SLS ratio was significantly lower in the SF subgroup (91.7 ± 8.6 vs. 97.3 ± 8.2, p < 0.01). P-CA with SF experienced symptoms less frequently (1.4 ± 1.3 vs. 2.1 ± 1.5 symptom during the infection, p = 0.01). Conclusions: Elite athletes following COVID-19 showed distinct morphological and functional cardiac changes compared to a propensity score-matched control athlete group. These results are mainly driven by a subgroup, which presented with some echocardiographic features characteristic of constrictive pericarditis

The Effect of Grapevine Variety and Wine Region on the Primer Parameters of Wine Based on 1H NMR-Spectroscopy and Machine Learning Methods

Nuclear magnetic resonance (NMR) spectroscopy is an innovative method for wine analysis. Every grapevine variety has a unique structural formula, which can be considered as the genetic fingerprint of the plant. This specificity appears in the composition of the final product (wine). In the present study, the originality of Hungarian wines was investigated with 1H NMR-spectroscopy considering 861 wine samples of four varieties (Cabernet Sauvignon, Blaufr&auml;nkisch, Merlot, and Pinot Noir) that were collected from two wine regions (Vill&aacute;ny, Eger) in 2015 and 2016. The aim of our analysis was to classify these varieties and region and to select the most important traits from the observed 22 ones (alcohols, sugars, acids, decomposition products, biogene amines, polyphenols, fermentation compounds, etc.) in order to detect their effect in the identification. From the tested four classification methods&mdash;linear discriminant analysis (LDA), neural networks (NN), support vector machines (SVM), and random forest (RF)&mdash;the last two were the most successful according to their accuracy. Based on 1000 runs for each, we report the classification results and show that NMR analysis completed with machine learning methods such as SVM or RF might be a successfully applicable approach for wine identification

Patient Attitudes Toward Transplantation as Preferred Treatment Modality in Different Stages of Renal Disease

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