Effects of Temperature–Climate Patterns on the Production of Some Competitive Species on Grounds of Modelling

Climate change has serious effects on the setting up and the operation of natural ecosystems. Small increase in temperature could cause rise in the amount of some species or potential disappearance of others. During our researches, the dispersion of the species and biomass production of a theoretical ecosystem were examined on the effect of the temperature–climate change. The answers of the ecosystems which are given to the climate change could be described by means of global climate modelling and dynamic vegetation models. The examination of the operation of the ecosystems is only possible in huge centres on supercomputers because of the number and the complexity of the calculation. The number of the calculation could be decreased to the level of a PC by considering the temperature and the reproduction during modelling a theoretical ecosystem, and several important theoretical questions could be answered

Comparative Assessment of Climate Change Scenarios Based on Aquatic Food Web Modeling

In the years 2004 and 2005, we collected samples of phytoplankton, zooplankton, and macroinvertebrates in an artificial small pond in Budapest (Hungary). We set up a simulation model predicting the abundances of the cyclopoids, Eudiaptomus zachariasi, and Ischnura pumilio by considering only temperature and the abundance of population of the previous day. Phytoplankton abundance was simulated by considering not only temperature but the abundances of the three mentioned groups. When we ran the model with the data series of internationally accepted climate change scenarios, the different outcomes were discussed. Comparative assessment of the alternative climate change scenarios was also carried out with statistical methods

Distinct innate immune responses to different murine mammary carcinoma are evident within the first 72 hours of tumor injection

Abstract We used three different murine mammary carcinoma, each syngeneic in Balb/c mice, to study the early anti-tumor immune response. Initial analysis showed that the number of cells at the tumor sites almost doubled between 12–72 hours going from about 3e5 cells at 12 hours to about 6e5 cells at 72 hours. Surface staining revealed that the immune response during this time period was predominated by Gr1+ and F4/80+ cells. Strikingly, similar cell numbers and cell types were recruited to saline injected sites suggesting that these cells may be recruited non-specifically. However, closer examination revealed significant differences not only between the saline and tumor injected sites, but also among different tumor sites. For instance, at 12 hours post injection there were significantly more Gr1+ cells at the EMT6 tumor site than at the 4T1 tumor site, and at 48 hours there were significantly more Gr1+ cells at the 168 tumor site than at the saline and 4T1 tumor sites. At 24 hours post injection there were significantly more F4/80+ cells at the EMT6 tumor site than at the 4T1, 168 and saline injected sites, and at 72 hours post injection there were significantly more F4/80+ cells at the EMT6 tumor site compared to the 4T1 and 168 tumor sites. Next, we collected the Gr1+ and F4/80+ cells from the sites 24 hours after tumor delivery and using qRT-PCR we found several additional differences. For instance, the Gr1+ cells from the EMT6 tumor site expressed more arginase and less CCL2 than the Gr1+ cells from the 4T1 and 168 tumor sites, and the F4/80+ cells from the EMT6 site expressed more MMP-9 and TNF-a than F4/80+ cells from the 4T1 and 168 tumor sites. Collectively, these data indicated that a unique anti-tumor immune response was evident within hours of tumor delivery.</jats:p

An in silico design tool for Fe(II) spin crossover and light-induced excited spin state-trapped complexes

The discovery of new coordination complexes that can support spin crossover (SCO) or light-induced excited spin state trapping (LIESST) could be radically improved by better computational tools. While methods such as density functional theory (DFT) are capable of high accuracy, they are too slow for molecular discovery, where millions of individual calculations may be required. In contrast, empirical ligand-field molecular mechanics (LFMM) captures the d-electron effects implicit in DFT and thus can be as accurate, but LFMM is up to 4 orders of magnitude faster. We demonstrate for simple Fe(II) am(m)ines how LFMM can be used to redesign "old" systems to generate novel, potential SCO and LIESST complexes