Comparison of microbially induced calcium carbonate precipitation eligibility using sporosarcina pasteurii and bacillus licheniformis on two different sands

The use of biological means for ground improvement have become popular, which generally works through the process called microbially-induced calcium carbonate precipitation (MICP). Many studies indicate successful application of MICP based improvement with multiple bacteria and on several soils. Given the proven performance of MICP, this study aims to examine the MICP process by comparing the calcium carbonate precipitation ability of widely studied bacteria, i.e., Sporosarcina pasteurii and relatively under-recognized bacteria, i.e., Bacillus licheniformis to outline the formation success. For this purpose, two different sands were tested for observing precipitation behavior using a series of syringe tests. Furthermore, the effect of concentration and inclusion of calcium chloride for nutrition of bacteria, saturation with water, and hybrid use of two bacteria were investigated in some tests for diversification. X-ray diffraction (XRD), scanning electron microscopy (SEM), and energy dispersive x-ray spectroscopy (EDS) were used for the interpretation of results. Results indicated that Sporosarcina pasteurii had performed superior over Bacillus licheniformis when achieving calcium carbonate precipitation in tests for both sands. In addition, many intriguing SEM images contributed to the literature of MICP monitoring, highlighting the effects of the variables investigated.     

Artificial sowing of endangered dry grassland species into disused basalt quarries

Steppe-like dry (semi) natural grasslands are valuable and endangered habitats in central Europe. In the study area (the ?eské st?edoho?í Hills, Czech Republic), they occur in fragments on southern slopes of volcanic hills, often in the vicinity of quarries, which are numerous there. We performed sowing experiments and observed seedling recruitment of six species, typical for the dry grasslands, in 9 basalt quarries located in 3 different climatic regions over 3 yr. The sowing experiments were established in young successional stages, 5–12 yr after quarrying was stopped. The objectives of the experiments were to evaluate: (i) if seedling recruitment and survival of the target species are possible regarding different climatic regions and (ii) what is the influence of weather fluctuations among years on seedling survival. Seedlings of all studied species were able to recruit and survive in the early successional stages at least in some quarries. The species, except one, showed significant differences in recruitment among the climatic regions, with the best recruitment and survival in the driest and warmest region. Seedlings of two species did not recruit in the wettest and coldest region. All species survived in the driest and warmest region, while only one did so in the coldest and wettest region. Different weather conditions in the studied years significantly influenced recruitment of two species. The number of localities (floristic records) of particular species in the regions was the best predictor of species germination and survival. Thus a traditional floristic survey may help to predict success of species in restoration projects. It emerged that artificial sowing can be considered in restoration programs as a way of contributing to restoration of dry grasslands in disused quarries.     

Predicting impacts of Arctic climate change: Past lessons and future challenges

General circulation models predict increases in temperature and precipitation in the Arctic as the result of increases in atmospheric carbon dioxide concentrations. Arctic ecosystems are strongly constrained by temperature, and may be expected to be markedly influenced by climate change. Perturbation experiments have been used to predict how Arctic ecosystems will respond to global climatic change, but these have often simulated individual perturbations (e.g. temperature alone) and have largely been confined to the short Arctic summer. The importance of interactions between global change variables (e.g. CO₂, temperature, precipitation) has rarely been examined, and much experimentation has been short-term. Similarly, very little experimentation has occurred in the winter when General circulation models predict the largest changes in climate will take place. Recent studies have clearly demonstrated that Arctic ecosystems are not dormant during the winter and thus much greater emphasis on experimentation during this period is essential to improve our understanding of how these ecosystems will respond to global change. This, combined with more long-term experimentation, direct observation of natural vegetation change (e.g. at the tundra/taiga boundary) and improvements in model predictions is necessary if we are to understand the future nature and extent of Arctic ecosystems in a changing climate.     

Gradient enhanced selective experiments in the 1H NMR chemical shift assignment of the skeleton and side-chain resonances of stigmasterol, a phytosterol derivative

The applicability of homonuclear gradient enhanced NMR experiments is demonstrated in the structure determination of steroid derivatives using stigmasterol as a model compound. High resolution 1H NMR spectra of steroids very often display well resolved multiplets usually in the low-field region, and these signals can be used as starting points in several selective NMR experiments to study scalar (J coupling), and dipolar (NOE) interactions. Selective excitation was carried out using a double pulsed-field gradient spin-echo sequence (DPFGSE) in which 180 degrees Gaussian pulses are sandwiched between sine shaped z-gradients. Scalar interactions were studied by homonuclear DPFGSE-COSY, DPFGSE-relay-COSY and DPFGSE-TOCSY experiments, while DPFGSE-NOESY was used to monitor spatial environment of the selectively excited proton. These methods provided unambiguous assignments for signals of the main skeleton and the side-chain of the steroid molecule. In addition, they allowed determination of the conformationally important homonuclear proton-proton coupling constants (J).     

The value of biodiversity experiments

Recent biodiversity experiments have investigated the relationship between diversity and ecosystem functioning by synthesizing plant communities from pools of species that have been experimentally manipulated to vary numbers and types of species present while holding abiotic factors constant. Biodiversity experiments therefore focus on a previously under-explored aspect of global change: the feedback from diversity to environment. Consequences of random manipulation of species communities may not correspond well to those of specific extinction sequences observed in the past in response to extinction drivers that cause highly non-random loss. However, random manipulation provides a good starting point given that existing communities could undergo many alternative orders of species loss in the future in response to a variety of different potential extinction drivers. Further, the effects of some extinction drivers are currently poorly understood and therefore difficult to predict (e.g. climate change) and it may be premature to dismiss the predictions of random scenarios as irrelevant to all real examples of species loss. The first generations of biodiversity experiments have provided valuable, and sometimes unexpected, discoveries about the general nature of the relationship between diversity and ecosystem functioning. These discoveries could not have been made using observational studies. We propose that different examples of extinction loss in the real or a potential future world form a continuum from situations where the results of the first-generation biodiversity experiments will be highly relevant to less relevant. At the one extreme are examples where the effects of biodiversity on ecosystem functioning will be overwhelmed by direct effects of the extinction driver on processes (e.g. chronic eutrophication). At the other extreme are situations where ecosystem processes are not strongly affected by direct effects of the extinction driver and where the effects of species loss on functioning may be more important (e.g. habitat fragmentation). Given the unprecedented uncertainty about the future of biodiversity and the functioning of ecosystems, a general approach with randomly varying species pools was the right place to start in order to provide a general foundation. The new challenge is to test for effects of biodiversity on functioning in real-world examples of species loss.

Zusammenfassung

Biodiversitätsversuche zeichnen sich dadurch aus, dass natürliche Artenpools experimentell reduziert werden und anschließend der Zusammenhang zwischen der Artenzahl und Ökosystemfunktionen unter konstanten abiotischen Umweltbedingungen untersucht wird. Dadurch unterscheiden sich Biodiversitätsexperimente grundsätzlich von anderen Versuchen, die die Biodiversität als Zielvariable behandeln und stattdessen die abiotische Umwelt manipulieren. Die Auswahl der Arten für die reduzierten Artenpools in Biodiversitätsexperimenten erfolgte bisher meist zufällig, während natürliche Aussterbefaktoren wie Eutrophierung nicht alle Arten gleichermassen gefährden. Für verschiedene Aussterbefaktoren ist aber so wenig bekannt, dass ein zufälliges Aussterbeszenario die beste gegenwärtig verfügbare Option ist. Dies trifft insbesondere für mögliche zukünftige Aussterbeprozesse zu, die durch globale Umweltveränderungen (Klima, biologische Invasionen) oder Habitatsfragmentierung ausgelöst werden könnten. Die erste Generation von Biodiversitätsexperimenten mit zufälligen Aussterbeszenarien hat wertvolle, teilweise unerwartete, generelle Zusammenhänge zwischen Artenzahl und Ökosystemfunktionen aufgedeckt. Diese Zusammenhänge ließen sich durch vergleichende Studien nicht erkennen. In Zukunft sollten Biodiversitätsexperimente dennoch vermehrt Aussterbeszenarien simulieren, die in der realen Umwelt mit größter Wahrscheinlichkeit auftreten.     

Analyzing a randomized trial on breast self-examination with noncompliance and missing outcomes

Recently, instrumental variables methods have been used to address non-compliance in randomized experiments. Complicating such analyses is often the presence of missing data. The standard model for missing data, missing at random (MAR), has some unattractive features in this context. In this paper we compare MAR-based estimates of the complier average causal effect (CACE) with an estimator based on an alternative, nonignorable model for the missing data process, developed by Frangakis and Rubin (1999, Biometrika, 86, 365-379). We also introduce a new missing data model that, like the Frangakis-Rubin model, is specially suited for models with instrumental variables, but makes different substantive assumptions. We analyze these issues in the context of a randomized trial of breast self-examination (BSE). In the study two methods of teaching BSE, consisting of either mailed information about BSE (the standard treatment) or the attendance of a course involving theoretical and practical sessions (the new treatment), were compared with the aim of assessing whether teaching programs could increase BSE practice and improve examination skills. The study was affected by the two sources of bias mentioned above: only 55% of women assigned to receive the new treatment complied with their assignment and 35% of the women did not respond to the post-test questionnaire. Comparing the causal estimand of the new treatment using the MAR, Frangakis-Rubin, and our new approach, the results suggest that for these data the MAR assumption appears least plausible, and that the new model appears most plausible among the three choices.     

Metabolic isotopomer labeling systems. Part II: structural flux identifiability analysis

Metabolic flux analysis using carbon labeling experiments (CLEs) is an important tool in metabolic engineering where the intracellular fluxes have to be computed from the measured extracellular fluxes and the partially measured distribution of 13C labeling within the intracellular metabolite pools. The relation between unknown fluxes and measurements is described by an isotopomer labeling system (ILS) (see Part I [Math. Biosci. 169 (2001) 173]). Part II deals with the structural flux identifiability of measured ILSs in the steady state. The central question is whether the measured data contains sufficient information to determine the unknown intracellular fluxes. This question has to be decided a priori, i.e. before the CLE is carried out. In structural identifiability analysis the measurements are assumed to be noise-free. A general theory of structural flux identifiability for measured ILSs is presented and several algorithms are developed to solve the identifiability problem. In the particular case of maximal measurement information, a symbolical algorithm is presented that decides the identifiability question by means of linear methods. Several upper bounds of the number of identifiable fluxes are derived, and the influence of the chosen inputs is evaluated. By introducing integer arithmetic this algorithm can even be applied to large networks. For the general case of arbitrary measurement information, identifiability is decided by a local criterion. A new algorithm based on integer arithmetic enables an a priori local identifiability analysis to be performed for networks of arbitrary size. All algorithms have been implemented and flux identifiability is investigated for the network of the central metabolic pathways of a microorganism. Moreover, several small examples are worked out to illustrate the influence of input metabolite labeling and the paradox of information loss due to network simplification.     

On Parametric Stability of Gene Networks Controlling Ontogenetic Processes

The problem of evaluation of parametric stability of three models of pro- and eukaryotic gene networks controlling ontogenetic processes has been defined and solved. Experimental schemes of testing gene networks for parametric stability based on the method of generalized threshold models were developed and realized as a software application. We studied the sensitivity of the functioning modes to random variations of the parameters in three model systems: phage development control system, Arabidopsis thaliana flower morphogenesis control subsystem, and gene subnetwork controlling early ontogeny of Drosophila melanogaster. The parametric stability was quantitatively assessed for these models.