Changes in plant species richness and productivity in response to decreased nitrogen inputs in grassland in southern England

Biomass production and plant species diversity in grassland in southern England was monitored before and after a change from conventional to organic farming. Our 18-year study, part of the UK's Environmental Change Network long-term monitoring programme, showed that the cessation of artificial fertiliser use on grassland after conversion to organic farming resulted in a decrease in biomass production and an increase in plant species richness. Grassland productivity decreased immediately after fertiliser application ceased, and after two years the annual total biomass production had fallen by over 50%. In the subsequent decade, total annual grassland productivity did not change significantly, and yields reached 31–66% of the levels recorded pre-management change. Plant species richness that had remained stable during the first 5 years of our study under conventional farming, increased by 300% over the following 13 years under organic farm management. We suggest that the change in productivity is due to the altered composition of species within the plots. In the first few years after the change in farming practice, high yielding, nitrogen-loving plants were outcompeted by lower yielding grasses and forbs, and these species remained in the plots in the following years. This study shows that grassland can be converted from an environment lacking in plant species diversity to a relatively species-rich pasture within 10–15 years, simply by stopping or suspending nitrogen additions. We demonstrate that the trade-off for increasing species richness is a decrease in productivity. Grassland in the UK is often not only managed from a conservation perspective, but to also produce a profitable yield. By considering the species composition and encouraging specific beneficial species such as legumes, it may be possible to improve biomass productivity and reduce the trade-off.     

Focus: A Multifaceted Battle Against Cancer: Extracting Knowledge from Chemical Imaging Data Using Computational Algorithms for Digital Cancer Diagnosis

Fourier transform infrared (FTIR) spectroscopic imaging is an emerging microscopy modality for clinical histopathologic diagnoses as well as for biomedical research. Spectral data recorded in this modality are indicative of the underlying, spatially resolved biochemical composition but need computerized algorithms to digitally recognize and transform this information to a diagnostic tool to identify cancer or other physiologic conditions. Statistical pattern recognition forms the backbone of these recognition protocols and can be used for highly accurate results. Aided by biochemical correlations with normal and diseased states and the power of modern computer-aided pattern recognition, this approach is capable of combating many standing questions of traditional histology-based diagnosis models. For example, a simple diagnostic test can be developed to determine cell types in tissue. As a more advanced application, IR spectral data can be integrated with patient information to predict risk of cancer, providing a potential road to precision medicine and personalized care in cancer treatment. The IR imaging approach can be implemented to complement conventional diagnoses, as the samples remain unperturbed and are not destroyed. Despite high potential and utility of this approach, clinical implementation has not yet been achieved due to practical hurdles like speed of data acquisition and lack of optimized computational procedures for extracting clinically actionable information rapidly. The latter problem has been addressed by developing highly efficient ways to process IR imaging data but remains one that has considerable scope for progress. Here, we summarize the major issues and provide practical considerations in implementing a modified Bayesian classification protocol for digital molecular pathology. We hope to familiarize readers with analysis methods in IR imaging data and enable researchers to develop methods that can lead to the use of this promising technique for digital diagnosis of cancer.     

Frequency-dependent conspecific attraction to food patches

In many ecological situations, resources are difficult to find but become more apparent to nearby searchers after one of their numbers discovers and begins to exploit them. If the discoverer cannot monopolize the resources, then others may benefit from joining the discoverer and sharing their discovery. Existing theories for this type of conspecific attraction have often used very simple rules for how the decision to join a discovered resource patch should be influenced by the number of individuals already exploiting that patch. We use a mechanistic, spatially explicit model to demonstrate that individuals should not necessarily simply join patches more often as the number of individuals exploiting the patch increases, because those patches are likely to be exhausted soon or joining them will intensify future local competition. Furthermore, we show that this decision should be sensitive to the nature of the resource patches, with individuals being more responsive to discoveries in general and more tolerant of larger numbers of existing exploiters on a patch when patches are resource-rich and challenging to locate alone. As such, we argue that this greater focus on underlying joining mechanisms suggests that conspecific attraction is a more sophisticated and flexible tactic than currently appreciated.     

Structural variability of C3larvin toxin. Intrinsic dynamics of the α/β fold of the C3-like group of mono-ADP-ribosyltransferase toxins

C3larvin toxin is a new member of the C3 class of the mono-ADP-ribosyltransferase toxin family. The C3 toxins are known to covalently modify small G-proteins, e.g. RhoA, impairing their function, and serving as virulence factors for an offending pathogen. A full-length X-ray structure of C3larvin (2.3 Å) revealed that the characteristic mixed α/β fold consists of a central β-core flanked by two helical regions. Topologically, the protein can be separated into N and C lobes, each formed by a β-sheet and an α-motif, and connected by exposed loops involved in the recognition, binding, and catalysis of the toxin/enzyme, i.e. the ADP-ribosylation turn–turn and phosphate–nicotinamide PN loops. Herein, we provide two new C3larvin X-ray structures and present a systematic study of the toxin dynamics by first analyzing the experimental variability of the X-ray data-set followed by contrasting those results with theoretical predictions based on Elastic Network Models (GNM and ANM). We identify residues that participate in the stability of the N-lobe, putative hinges at loop residues, and energy-favored deformation vectors compatible with conformational changes of the key loops and 3D-subdomains (N/C-lobes), among the X-ray structures. We analyze a larger ensemble of known C3bot1 conformations and conclude that the characteristic ‘crab-claw’ movement may be driven by the main intrinsic modes of motion. Finally, via computational simulations, we identify harmonic and anharmonic fluctuations that might define the C3larvin ‘native state.’ Implications for docking protocols are derived.     

一种复合神经元网络结构的并行动力学及快速学习算法

提出两种功能互相不同的神经细胞组成的复合神经元网络(CNN)模型;导出一种特殊结构的CNN的并行动力学;而且证明了它的稳定性。在这些结果基础上,得到快速的假逆矩阵学习算法。计算机仿真试验证实学习算法与动力学稳定性的正确性,并表现出良好的容错性能与存储容量。     

The results of 2-year pollen monitoring of an urban network in Perugia,Central Italy

Continuous pollen monitoring of an urban network consisting of three stations has been undertaken for a period of 2 years in Perugia, central Italy. The aim has been to establish whether the Perugia pollen trap, active since 1983, is still representative of the area following recent urbanisation. Quantitative differences were found between the stations, reflecting different vegetational areas, but only slight differences were detected in relation to the timing of the principal period of pollination. Therefore, although individual pollen traps are necessary to characterize fully the different areas, one trap is sufficient to determine the key allergenic thresholds in the studied area.