Can the Results of Biodiversity-Ecosystem Productivity Studies Be Translated to Bioenergy Production?

Biodiversity experiments show that increases in plant diversity can lead to greater biomass production, and some researchers suggest that high diversity plantings should be used for bioenergy production. However, many methods used in past biodiversity experiments are impractical for bioenergy plantings. For example, biodiversity experiments often use intensive management such as hand weeding to maintain low diversity plantings and exclude unplanted species, but this would not be done for bioenergy plantings. Also, biodiversity experiments generally use high seeding densities that would be too expensive for bioenergy plantings. Here we report the effects of biodiversity on biomass production from two studies of more realistic bioenergy crop plantings in southern Michigan, USA. One study involved comparing production between switchgrass (Panicum virgatum) monocultures and species-rich prairie plantings on private farm fields that were managed similarly to bioenergy plantings. The other study was an experiment where switchgrass was planted in monoculture and in combination with increasingly species-rich native prairie mixtures. Overall, we found that bioenergy plantings with higher species richness did not produce more biomass than switchgrass monocultures. The lack of a positive relationship between planted species richness and production in our studies may be due to several factors. Non-planted species (weeds) were not removed from our studies and these non-planted species may have competed with planted species and also prevented realized species richness from equaling planted species richness. Also, we found that low seeding density of individual species limited the biomass production of these individual species. Production in future bioenergy plantings with high species richness may be increased by using a high density of inexpensive seed from switchgrass and other highly productive species, and future efforts to translate the results of biodiversity experiments to bioenergy plantings should consider the role of seeding density.     

Crop Residue Removal for Bioenergy Reduces Soil Carbon Pools: How Can We Offset Carbon Losses?

Crop residue removal for bioenergy can deplete soil organic carbon (SOC) pools. Management strategies to counteract the adverse effects of residue removal on SOC pools have not been, however, widely discussed. This paper reviews potential practices that can be used to offset the SOC lost with residue removal. Literature indicates that practices including no-till cover crops, manure and compost application, and return of biofuel co-products increase SOC pools and may thus be used to offset some SOC loss. No-till rotations that include semi-perennial grasses or legumes also offer a promise to promote soil-profile C sequestration and improve soil resilience after residue removal. No-till cover crops can sequester between 0.10 and 1 Mg ha^?1 per year of SOC relative to no-till without cover crops, depending on cover crop species, soil type, and precipitation input. Animal manure and compost contain about 15 % of C and thus their addition to soil can enhance SOC pools and boost soil biological activity. Similarly, application of biofuel co-products such as biochar, which contain between 45 % and 85 % of C depending on the feedstock source and processing method, can enhance long-term C sequestration. These mitigation strategies may maintain SOC pools under partial residue removal in no-till soils but are unlikely to replace all the SOC lost if residue is removed at excessive rates. More field research and modeling efforts are needed to assess the magnitude at which the different mitigation strategies can overcome SOC loss with crop residue removal.     

From Experiment to Theory: What Can We Learn from Growth Curves?

Finding an appropriate functional form to describe population growth based on key properties of a described system allows making justified predictions about future population development. This information can be of vital importance in all areas of research, ranging from cell growth to global demography. Here, we use this connection between theory and observation to pose the following question: what can we infer about intrinsic properties of a population (i.e., degree of heterogeneity, or dependence on external resources) based on which growth function best fits its growth dynamics? We investigate several nonstandard classes of multi-phase growth curves that capture different stages of population growth; these models include hyperbolic–exponential, exponential–linear, exponential–linear–saturation growth patterns. The constructed models account explicitly for the process of natural selection within inhomogeneous populations. Based on the underlying hypotheses for each of the models, we identify whether the population that it best fits by a particular curve is more likely to be homogeneous or heterogeneous, grow in a density-dependent or frequency-dependent manner, and whether it depends on external resources during any or all stages of its development. We apply these predictions to cancer cell growth and demographic data obtained from the literature. Our theory, if confirmed, can provide an additional biomarker and a predictive tool to complement experimental research.     

What Can We Conclude from Death Registration? Improved Methods for Evaluating Completeness

Background

One of the fundamental building blocks for determining the burden of disease in populations is to reliably measure the level and pattern of mortality by age and sex. Where well-functioning registration systems exist, this task is relatively straightforward. Results from many civil registration systems, however, remain uncertain because of a lack of confidence in the completeness of death registration. Incomplete registration systems mean not all deaths are counted, and resulting estimates of death rates for the population are then underestimated. Death distribution methods (DDMs) are a suite of demographic methods that attempt to estimate the fraction of deaths that are registered and counted by the civil registration system. Although widely applied and used, the methods have at least three types of limitations. First, a wide range of variants of these methods has been applied in practice with little scientific literature to guide their selection. Second, the methods have not been extensively validated in real population conditions where violations of the assumptions of the methods most certainly occur. Third, DDMs do not generate uncertainty intervals.

Methods and Findings

In this paper, we systematically evaluate the performance of 234 variants of DDM methods in three different validation environments where we know or have strong beliefs about the true level of completeness of death registration. Using these datasets, we identify three variants of the DDMs that generally perform the best. We also find that even these improved methods yield uncertainty intervals of roughly ± one-quarter of the estimate. Finally, we demonstrate the application of the optimal variants in eight countries.

Conclusions

There continues to be a role for partial vital registration data in measuring adult mortality levels and trends, but such results should only be interpreted alongside all other data sources on adult mortality and the uncertainty of the resulting levels, trends, and age-patterns of adult death considered. Please see later in the article for the Editors'' Summary     

Host-Directed Therapeutics for Tuberculosis: Can We Harness the Host?

SUMMARY

Treatment of tuberculosis (TB) remains challenging, with lengthy treatment durations and complex drug regimens that are toxic and difficult to administer. Similar to the vast majority of antibiotics, drugs for Mycobacterium tuberculosis are directed against microbial targets. Although more effective drugs that target the bacterium may lead to faster cure of patients, it is possible that a biological limit will be reached that can be overcome only by adopting a fundamentally new treatment approach. TB regimens might be improved by including agents that target host pathways. Recent work on host-pathogen interactions, host immunity, and host-directed interventions suggests that supplementing anti-TB therapy with host modulators may lead to shorter treatment times, a reduction in lung damage caused by the disease, and a lower risk of relapse or reinfection. We undertook this review to identify molecular pathways of the host that may be amenable to modulation by small molecules for the treatment of TB. Although several approaches to augmenting standard TB treatment have been proposed, only a few have been explored in detail or advanced to preclinical and clinical studies. Our review focuses on molecular targets and inhibitory small molecules that function within the macrophage or other myeloid cells, on host inflammatory pathways, or at the level of TB-induced lung pathology.     

Religion and Bioethics: Can We Talk?

Religious voices were important in the early days of the contemporary field of bioethics but have now become decidedly less prominent. This is unfortunate because religious elements are essential parts of the most foundational aspects of bioethics. The problem is that there is an incommensurability between religious language and languages of public discourse such as the “public reason” of John Rawls. To eliminate what is unique in religious language is to lose something essential. This paper examines the reasons for the marginalization of religion in bioethics, shows the limitations of Rawls’s notion of public reason, and argues for a more robust role for theology in articulating a new language for public discourse in bioethics.     

To the Final Goal: Can We Predict and Suggest Mutations for Protein to Develop Desired Phenotype?

Directed evolution of proteins is a good approach to develop desired phenotypes from existing proteins. Fully experimental protein evolution usually utilizes randomization of a given protein sequence by error-prone PCR or gene shuffling followed by high-throughput selection or timeconsuming screening method. However, these random methods create mutant library full of deleterious mutations. In addition, they need high-throughput screening or selection method to search for positive clones from an enormous size of mutant library. Construction of a mutant library while retaining the original function is important for efficient protein evolution because it greatly reduces time and effort for the identification of positive mutants. Therefore, researchers have tried to reduce the size of mutant library by minimizing the occurrence of deleterious mutants. Such efforts have led to the creation of a concept of ‘small but smart library’. For this goal, neutral drift theory has been applied. Although smart library greatly reduces the library size, it is still the beyond the capacity of low-throughput assay. In parallel, computational analysis of protein structure and efforts to discriminate mutatable residues from all residues of a given protein have been consistently pursued. Accumulated knowledge of protein evolution through random mutation and selection has improved our understanding of functions of amino acids in protein structure. Protein evolution by rational design is being developed based on such understanding. In this review, we describe how the use of semi-rationally designed library rather than completely random one has impacted the overall procedure of directed evolution. We also describe efforts made to evaluate the effect of single mutation. Such efforts will bring lazy boys to the final goal - computational mutation suggestion system.     

Can We Use Antibodies to Chlamydia trachomatis as a Surveillance Tool for National Trachoma Control Programs? Results from a District Survey

Background

Trachoma is targeted for elimination by 2020. World Health Organization advises districts to undertake surveillance when follicular trachoma (TF) <5% in children 1–9 years and mass antibiotic administration has ceased. There is a question if other tools could be used for surveillance as well. We report data from a test for antibodies to C. trachomatis antigen pgp3 as a possible tool.

Methodology

We randomly sampled 30 hamlets in Kilosa district, Tanzania, and randomly selected 50 children ages 1–9 per hamlet. The tarsal conjunctivae were graded for trachoma (TF), tested for C. trachomatis infection (Aptima Combo2 assay: Hologic, San Diego, CA), and a dried blood spot processed for antibodies to C. trachomatis pgp3 using a multiplex bead assay on a Luminex 100 platform.

Principal findings

The prevalence of trachoma (TF) was 0.4%, well below the <5% indicator for re-starting a program. Infection was also low, 1.1%. Of the 30 hamlets, 22 had neither infection nor TF. Antibody positivity overall was low, 7.5% and increased with age from 5.2% in 1–3 year olds, to 9.3% in 7–9 year olds (p = 0.015). In 16 of the 30 hamlets, no children ages 1–3 years had antibodies to pgp3.

Conclusions

The antibody status of the 1–3 year olds indicates low cumulative exposure to infection during the surveillance period. Four years post MDA, there is no evidence for re-emergence of follicular trachoma.     

Can We Achieve Clinical Breakpoints for the Triazoles in Aspergillosis?

The azoles are first-line agents for the treatment of aspergillosis. A number of recent studies have shown increasing rates of resistance in A. fumigatus. Consequently, reliable in vitro susceptibility testing and breakpoints that appropriately classify resistant isolates are of paramount importance. The European Committee on Antimicrobial Susceptibility Testing (EUCAST) and the Clinical Laboratory Standards Institute (CLSI) have developed susceptibility testing standards, but so far no breakpoints have been defined. The following aspects are evaluated during the process of developing EUCAST breakpoints: the most common dosage, the definition of the wild-type population and epidemiologic cutoff values, pharmacokinetic and pharmacodynamic properties, and the correlation between the minimum inhibitory concentration (MIC) and clinical outcome. This article reviews the issues to be considered before breakpoints can be established for azole drugs and Aspergillus and describes how MICs can be interpreted until clinical breakpoints have been defined.     

What Can We Learn from Global Sensitivity Analysis of Biochemical Systems?

Most biological models of intermediate size, and probably all large models, need to cope with the fact that many of their parameter values are unknown. In addition, it may not be possible to identify these values unambiguously on the basis of experimental data. This poses the question how reliable predictions made using such models are. Sensitivity analysis is commonly used to measure the impact of each model parameter on its variables. However, the results of such analyses can be dependent on an exact set of parameter values due to nonlinearity. To mitigate this problem, global sensitivity analysis techniques are used to calculate parameter sensitivities in a wider parameter space. We applied global sensitivity analysis to a selection of five signalling and metabolic models, several of which incorporate experimentally well-determined parameters. Assuming these models represent physiological reality, we explored how the results could change under increasing amounts of parameter uncertainty. Our results show that parameter sensitivities calculated with the physiological parameter values are not necessarily the most frequently observed under random sampling, even in a small interval around the physiological values. Often multimodal distributions were observed. Unsurprisingly, the range of possible sensitivity coefficient values increased with the level of parameter uncertainty, though the amount of parameter uncertainty at which the pattern of control was able to change differed among the models analysed. We suggest that this level of uncertainty can be used as a global measure of model robustness. Finally a comparison of different global sensitivity analysis techniques shows that, if high-throughput computing resources are available, then random sampling may actually be the most suitable technique.     

Species concepts for trypanosomes: from morphological to molecular definitions?

The way species and subspecies names are applied in African trypanosomes of subgenera Trypanozoon and Nannomonas is reviewed in the light of data from molecular taxonomy. In subgenus Trypanozoon the taxonomic importance of pathogenicity, host range and distribution appear to have been inflated relative to actual levels of genetic divergence. The opposite is true for subgenus Nannomonas, where current taxonomic usage badly underrepresents genetic diversity. Data from molecular characterisation studies are revealing a growing number of genotypes, which may represent distinct taxa. Unfortunately few of these genotypes are yet supported by sufficient biological data to be recognized taxonomically. But we may be missing fundamental epidemiological information, because of our inability to distinguish these trypanosomes in host blood morphologically or in tsetse by their developmental cycle. Molecular taxonomy has led the way in identifying these new genotypes and now offers the key to elucidating the biology of these organisms.     

Closing Yield Gaps: How Sustainable Can We Be?

Global food production needs to be increased by 60–110% between 2005 and 2050 to meet growing food and feed demand. Intensification and/or expansion of agriculture are the two main options available to meet the growing crop demands. Land conversion to expand cultivated land increases GHG emissions and impacts biodiversity and ecosystem services. Closing yield gaps to attain potential yields may be a viable option to increase the global crop production. Traditional methods of agricultural intensification often have negative externalities. Therefore, there is a need to explore location-specific methods of sustainable agricultural intensification. We identified regions where the achievement of potential crop calorie production on currently cultivated land will meet the present and future food demand based on scenario analyses considering population growth and changes in dietary habits. By closing yield gaps in the current irrigated and rain-fed cultivated land, about 24% and 80% more crop calories can respectively be produced compared to 2000. Most countries will reach food self-sufficiency or improve their current food self-sufficiency levels if potential crop production levels are achieved. As a novel approach, we defined specific input and agricultural management strategies required to achieve the potential production by overcoming biophysical and socioeconomic constraints causing yield gaps. The management strategies include: fertilizers, pesticides, advanced soil management, land improvement, management strategies coping with weather induced yield variability, and improving market accessibility. Finally, we estimated the required fertilizers (N, P₂O₅, and K₂O) to attain the potential yields. Globally, N-fertilizer application needs to increase by 45–73%, P₂O₅-fertilizer by 22–46%, and K₂O-fertilizer by 2–3 times compared to the year 2010 to attain potential crop production. The sustainability of such agricultural intensification largely depends on the way management strategies for closing yield gaps are chosen and implemented.     

What Can We Learn from the Evolution of Protein-Ligand Interactions to Aid the Design of New Therapeutics?

Efforts to increase affinity in the design of new therapeutic molecules have tended to lead to greater lipophilicity, a factor that is generally agreed to be contributing to the low success rate of new drug candidates. Our aim is to provide a structural perspective to the study of lipophilic efficiency and to compare molecular interactions created over evolutionary time with those designed by humans. We show that natural complexes typically engage in more polar contacts than synthetic molecules bound to proteins. The synthetic molecules also have a higher proportion of unmatched heteroatoms at the interface than the natural sets. These observations suggest that there are lessons to be learnt from Nature, which could help us to improve the characteristics of man-made molecules. In particular, it is possible to increase the density of polar contacts without increasing lipophilicity and this is best achieved early in discovery while molecules remain relatively small.