Growth responses to irradiance regime along an ecological gradient of <Emphasis Type="Italic">Quercus</Emphasis> <Emphasis Type="Italic">castaneifolia</Emphasis> seedlings of different provenance

Understory light is known as one of the most important environmental predictors of growth response of woody species. Hence, the primary objective of most forest management practices is optimizing that resource for understory seedlings. In this study, growth responses of Quercus castaneifolia seedlings from five different provenances from west to east of the Hyrcanian forest were investigated along their ecological gradients (from high to low precipitation). An experimental design was executed under controlled conditions at eight different irradiance levels (10, 20, 30, 40, 50, 60, 70 and 100 % full irradiance). Results showed that the irradiance is probably the most important determinant of variation in seedling characteristics. Among all investigated variables, variability in seedling size was affected significantly by provenance, while seedling morphology and their architectural response was affected by different levels of irradiance in a curvilinear manner. The biggest changes were observed at lowest irradiance levels (10–20 %) while at higher irradiance (70–100 %) the curves flatten. It was shown that, unlike at low irradiance levels, there is little capacity in seedling morphology to acclimatize with high irradiance intensity. Attaining maximal biomass varies across provenances and irradiance gradient. The highest biomass for the five provenances could be ranked as follows: 20–60 % and 50–60 % for the wetter and drier provenances, respectively. These results demonstrated that the light requirement increases from wetter to drier provenances, with a negative relationship between light requirement and precipitation regime. Different responses to irradiance levels may be the result of genetic adaptation to the ecological conditions prevailing in native habitat, especially precipitation regime.     

Analysis and identification of beta-turn types using multinomial logistic regression and artificial neural network

MOTIVATION: So far various statistical and machine learning techniques applied for prediction of beta-turns. The majority of these techniques have been only focused on the prediction of beta-turn location in proteins. We developed a hybrid approach for analysis and prediction of different types of beta-turn. RESULTS: A two-stage hybrid model developed to predict the beta-turn Types I, II, IV and VIII. Multinomial logistic regression was initially used for the first time to select significant parameters in prediction of beta-turn types using a self-consistency test procedure. The extracted parameters were consisted of 80 amino acid positional occurrences and 20 amino acid percentages in beta-turn sequence. The most significant parameters were then selected using multinomial logistic regression model. Among these, the occurrences of glutamine, histidine, glutamic acid and arginine, respectively, in positions i, i + 1, i + 2 and i + 3 of beta-turn sequence had an overall relationship with five beta-turn types. A neural network model was then constructed and fed by the parameters selected by multinomial logistic regression to build a hybrid predictor. The networks have been trained and tested on a non-homologous dataset of 565 protein chains by 9-fold cross-validation. It has been observed that the hybrid model gives a Matthews correlation coefficient (MCC) of 0.235, 0.473, 0.103 and 0.124, respectively, for beta-turn Types I, II, IV and VIII. Our model also distinguished the different types of beta-turn in the embedded binary logit comparisons which have not carried out so far. AVAILABILITY: Available on request from the authors.     

A radiobiological comparison of hypo-fractionation versus conventional fractionation for breast cancer 3D-conformal radiation therapy

BackgroundThe present research was aimed to compare the toxicity and effectiveness of conventional fractionated radiotherapy versus hypo-fractionated radiotherapy in breast cancer utilizing a radiobiological model.Materials and methodsThirty-five left-sided breast cancer patients without involvement of the supraclavicular and axillary lymph nodes (with the nodal stage of N0) that had been treated with conventional or hypo-fractionated were incorporated in this study. A radiobiological model was performed to foretell normal tissue complication probability (NTCP) and tumor control probability (TCP).ResultsThe data represented that TCP values for conventional and hypo-fractionated regimens were 99.16 ± 0.09 and 95.96 ± 0.48, respectively (p = 0.00). Moreover, the NTCP values of the lung for conventional and hypo-fractionated treatment were 0.024 versus 0.13 (p = 0.035), respectively. Also, NTCP values of the heart were equal to zero for both regimens.ConclusionIn summary, hypo-fractionated regimens had comparable efficacy to conventional fraction radiation therapy in the case of dosimetry parameters for patients who had left breast cancer. But, utilizing the radiobiological model, conventional fractionated regimens presented better results compared to hypo-fractionated regimens.     

The plant traits that drive ecosystems: Evidence from three continents

Question: A set of easily‐measured (‘soft’) plant traits has been identified as potentially useful predictors of ecosystem functioning in previous studies. Here we aimed to discover whether the screening techniques remain operational in widely contrasted circumstances, to test for the existence of axes of variation in the particular sets of traits, and to test for their links with ‘harder’ traits of proven importance to ecosystem functioning. Location: central‐western Argentina, central England, northern upland Iran, and north‐eastern Spain. Recurrent patterns of ecological specialization: Through ordination of a matrix of 640 vascular plant taxa by 12 standardized traits, we detected similar patterns of specialization in the four floras. The first PCA axis was identified as an axis of resource capture, usage and release. PCA axis 2 appeared to be a size‐related axis. Individual PCA for each country showed that the same traits remained valuable as predictors of resource capture and utilization in all of them, despite their major differences in climate, biogeography and land‐use. The results were not significantly driven by particular taxa: the main traits determining PCA axis 1 were very similar in eudicotyledons and monocotyledons and Asteraceae, Fabaceae and Poaceae. Links between recurrent suites of ‘soft’ traits and ‘hard’ traits: The validity of PCA axis 1 as a key predictor of resource capture and utilization was tested by comparisons between this axis and values of more rigorously established predictors (‘hard’ traits) for the floras of Argentina and England. PCA axis 1 was correlated with variation in relative growth rate, leaf nitrogen content, and litter decomposition rate. It also coincided with palatability to model generalist herbivores. Therefore, location on PCA axis 1 can be linked to major ecosystem processes in those habitats where the plants are dominant. Conclusion: We confirm the existence at the global scale of a major axis of evolutionary specialization, previously recognised in several local floras. This axis reflects a fundamental trade‐off between rapid acquisition of resources and conservation of resources within well‐protected tissues. These major trends of specialization were maintained across different environmental situations (including differences in the proximate causes of low productivity, i.e. drought or mineral nutrient deficiency). The trends were also consistent across floras and major phylogenetic groups, and were linked with traits directly relevant to ecosystem processes.