Agronomic Management of Indigenous Mycorrhizas

Many of the advantages conferred to plants by arbuscular mycorrhiza (AM) are associated to the ability of AM plants to explore a greater volume of soil through the extraradical mycelium. Sieverding (1991) estimates that for each centimetre of colonized root there is an increase of 15 cm3 on the volume of soil explored, this value can increase to 200 cm3 depending on the circumstances. Due to the enhancement of the volume of soil explored and the ability of the extraradical mycelium to absorb and translocate nutrients to the plant, one of the most obvious and important advantages resulting from mycorrhization is the uptake of nutrients. Among of which the ones that have immobilized forms in soil, such as P, assume particular significance. Besides this, many other benefits are recognized for AM plants (Gupta et al, 2000): water stress alleviation (Augé, 2004; Cho et al, 2006), protection from root pathogens (Graham, 2001), tolerance to toxic heavy metals and phytoremediation (Audet and Charest, 2006; Göhre and Paszkowski, 2006), tolerance to adverse conditions such as very high or low temperature, high salinity (Sannazzaro et al, 2006), high or low pH (Yano and Takaki, 2005) or better performance during transplantation shock (Subhan et al, 1998). The extraradical hyphae also stabilize soil aggregates by both enmeshing soil particles (Miller e Jastrow, 1992) and producing a glycoprotein, golmalin, which may act as a glue-like substance to adhere soil particles together (Wright and Upadhyaya, 1998). Despite the ubiquous distribution of mycorrhizal fungi (Smith and Read, 2000) and only a relative specificity between host plants and fungal isolates (McGonigle and Fitter, 1990), the obligate nature of the symbiosis implies the establishment of a plant propagation system, either under greenhouse conditions or in vitro laboratory propagation. These techniques result in high inoculum production costs, which still remains a serious problem since they are not competitive with production costs of phosphorus fertilizer. Even if farmers understand the significance of sustainable agricultural systems, the reduction of phosphorus inputs by using AM fungal inocula alone cannot be justified except, perhaps, in the case of high value crops (Saioto and Marumoto, 2002). Nurseries, high income horticulture farmers and no-agricultural application such as rehabilitation of degraded or devegetated landscapes are examples of areas where the use of commercial inoculum is current. Another serious problem is quality of commercial available products concerning guarantee of phatogene free content, storage conditions, most effective application methods and what types to use. Besides the information provided by suppliers about its inoculum can be deceiving, as from the usually referred total counts, only a fraction may be effective for a particular plant or in specific soil conditions. Gianinazzi and Vosátka (2004) assume that progress should be made towards registration procedures that stimulate the development of the mycorrhizal industry. Some on-farm inoculum production and application methods have been studied, allowing farmers to produce locally adapted isolates and generate a taxonomically diverse inoculum (Mohandas et al, 2004; Douds et al, 2005). However the inocula produced this way are not readily processed for mechanical application to the fields, being an obstacle to the utilization in large scale agriculture, especially row crops, moreover it would represent an additional mechanical operation with the corresponding economic and soil compaction costs. It is well recognized that inoculation of AM fungi has a potential significance in not only sustainable crop production, but also environmental conservation. However, the status quo of inoculation is far from practical technology that can be widely used in the field. Together a further basic understanding of the biology and diversity of AM fungi is needed (Abbott at al, 1995; Saito and Marumoto, 2002). Advances in ecology during the past decade have led to a much more detailed understanding of the potential negative consequences of species introductions and the potential for negative ecological consequences of invasions by mycorrhizal fungi is poorly understood. Schwartz et al, (2006) recommend that a careful assessment documenting the need for inoculation, and the likelihood of success, should be conducted prior to inoculation because inoculations are not universally beneficial. Agricultural practices such as crop rotation, tillage, weed control and fertilizer apllication all produce changes in the chemical, physical and biological soil variables and affect the ecological niches available for occupancy by the soil biota, influencing in different ways the symbiosis performance and consequently the inoculum development, shaping changes and upset balance of native populations. The molecular biology tools developed in the latest years have been very important for our perception of these changes, ensuing awareness of management choice implications in AM development. In this context, for extensive farming systems and regarding environmental and economic costs, the identification of agronomic management practices that allow controlled manipulation of the fungal community and capitalization of AM mutualistic effect making use of local inoculum, seem to be a wise option for mycorrhiza promotion and development of sustainable crop production

Comparing Artificial Neural Networks, General Linear Models and Support Vector Machines in Building Predictive Models for Small Interfering RNAs

Exogenous short interfering RNAs (siRNAs) induce a gene knockdown effect in cells by interacting with naturally occurring RNA processing machinery. However not all siRNAs induce this effect equally. Several heterogeneous kinds of machine learning techniques and feature sets have been applied to modeling siRNAs and their abilities to induce knockdown. There is some growing agreement to which techniques produce maximally predictive models and yet there is little consensus for methods to compare among predictive models. Also, there are few comparative studies that address what the effect of choosing learning technique, feature set or cross validation approach has on finding and discriminating among predictive models.Three learning techniques were used to develop predictive models for effective siRNA sequences including Artificial Neural Networks (ANNs), General Linear Models (GLMs) and Support Vector Machines (SVMs). Five feature mapping methods were also used to generate models of siRNA activities. The 2 factors of learning technique and feature mapping were evaluated by complete 3x5 factorial ANOVA. Overall, both learning techniques and feature mapping contributed significantly to the observed variance in predictive models, but to differing degrees for precision and accuracy as well as across different kinds and levels of model cross-validation.The methods presented here provide a robust statistical framework to compare among models developed under distinct learning techniques and feature sets for siRNAs. Further comparisons among current or future modeling approaches should apply these or other suitable statistically equivalent methods to critically evaluate the performance of proposed models. ANN and GLM techniques tend to be more sensitive to the inclusion of noisy features, but the SVM technique is more robust under large numbers of features for measures of model precision and accuracy. Features found to result in maximally predictive models are not consistent across learning techniques, suggesting care should be taken in the interpretation of feature relevance. In the models developed here, there are statistically differentiable combinations of learning techniques and feature mapping methods where the SVM technique under a specific combination of features significantly outperforms all the best combinations of features within the ANN and GLM techniques

Reconsideration of In-Silico siRNA Design Based on Feature Selection: A Cross-Platform Data Integration Perspective

RNA interference via exogenous short interference RNAs (siRNA) is increasingly more widely employed as a tool in gene function studies, drug target discovery and disease treatment. Currently there is a strong need for rational siRNA design to achieve more reliable and specific gene silencing; and to keep up with the increasing needs for a wider range of applications. While progress has been made in the ability to design siRNAs with specific targets, we are clearly at an infancy stage towards achieving rational design of siRNAs with high efficacy. Among the many obstacles to overcome, lack of general understanding of what sequence features of siRNAs may affect their silencing efficacy and of large-scale homogeneous data needed to carry out such association analyses represents two challenges. To address these issues, we investigated a feature-selection based in-silico siRNA design from a novel cross-platform data integration perspective. An integration analysis of 4,482 siRNAs from ten meta-datasets was conducted for ranking siRNA features, according to their possible importance to the silencing efficacy of siRNAs across heterogeneous data sources. Our ranking analysis revealed for the first time the most relevant features based on cross-platform experiments, which compares favorably with the traditional in-silico siRNA feature screening based on the small samples of individual platform data. We believe that our feature ranking analysis can offer more creditable suggestions to help improving the design of siRNA with specific silencing targets. Data and scripts are available at http://csbl.bmb.uga.edu/publications/materials/qiliu/siRNA.html

Computational Design of Artificial RNA Molecules For Gene Regulation

This volume provides an overview of RNA bioinformatics methodologies, including basic strategies to predict secondary and tertiary structures, and novel algorithms based on massive RNA sequencing. Interest in RNA bioinformatics has rapidly increased thanks to the recent high-throughput sequencing technologies allowing scientists to investigate complete transcriptomes at single nucleotide resolution. Adopting advanced computational technics, scientists are now able to conduct more in-depth studies and present them to you in this book. Written in the highly successful Methods of Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and equipment, step-by-step, readily reproducible bioinformatics protocols, and key tips to avoid known pitfalls.Authoritative and practical, RNA Bioinformatics seeks to aid scientists in the further study of bioinformatics and computational biology of RNA

Serum indoleamine 2,3-dioxygenase activity is associated with reduced immunogenicity following vaccination with MVA85A.

BackgroundThere is an urgent need for improved vaccines to protect against tuberculosis. The currently available vaccine Bacille Calmette-Guerin (BCG) has varying immunogenicity and efficacy across different populations for reasons not clearly understood. MVA85A is a modified vaccinia virus expressing antigen 85A from Mycobacterium tuberculosis which has been in clinical development since 2002 as a candidate vaccine to boost BCG-induced protection. A recent efficacy trial in South African infants failed to demonstrate enhancement of protection over BCG alone. The immunogenicity was lower than that seen in UK trials.The enzyme Indoleamine 2,3-dioxygenase (IDO) catalyses the first and rate-limiting step in the breakdown of the essential amino acid tryptophan. T cells are dependent on tryptophan and IDO activity suppresses T-cell proliferation and function.MethodsUsing samples collected during phase I trials with MVA85A across the UK and South Africa we have investigated the relationship between vaccine immunogenicity and IDO using IFN-¿ ELISPOT, qPCR and liquid chromatography mass spectrometry.ResultsWe demonstrate an IFN-¿ dependent increase in IDO mRNA expression in peripheral blood mononuclear cells (PBMC) following MVA85A vaccination in UK subjects. IDO mRNA correlates positively with the IFN-¿ ELISPOT response indicating that vaccine specific induction of IDO in PBMC is unlikely to limit the development of vaccine specific immunity. IDO activity in the serum of volunteers from the UK and South Africa was also assessed. There was no change in serum IDO activity following MVA85A vaccination. However, we observed higher baseline IDO activity in South African volunteers when compared to UK volunteers. In both UK and South African serum samples, baseline IDO activity negatively correlated with vaccine-specific IFN-¿ responses, suggesting that IDO activity may impair the generation of a CD4+ T cell memory response.ConclusionsBaseline IDO activity was higher in South African volunteers when compared to UK volunteers, which may represent a potential mechanism for the observed variation in vaccine immunogenicity in South African and UK populations and may have important implications for future vaccination strategies.Trial registrationTrials are registered at ClinicalTrials.gov; UK cohort NCT00427830, UK LTBI cohort NCT00456183, South African cohort NCT00460590, South African LTBI cohort NCT00480558

Immunogenicity of standard and extended dosing intervals of BNT162b2 mRNA vaccine

Extension of the interval between vaccine doses for the BNT162b2 mRNA vaccine was introduced in the United Kingdom to accelerate population coverage with a single dose. At this time, trial data were lacking, and we addressed this in a study of United Kingdom healthcare workers. The first vaccine dose induced protection from infection from the circulating alpha (B.1.1.7) variant over several weeks. In a substudy of 589 individuals, we show that this single dose induces severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) neutralizing antibody (NAb) responses and a sustained B and T cell response to the spike protein. NAb levels were higher after the extended dosing interval (6–14 weeks) compared with the conventional 3- to 4-week regimen, accompanied by enrichment of CD4+ T cells expressing interleukin-2 (IL-2). Prior SARS-CoV-2 infection amplified and accelerated the response. These data on dynamic cellular and humoral responses indicate that extension of the dosing interval is an effective immunogenic protocol

Evaluation of appendicitis risk prediction models in adults with suspected appendicitis

Background Appendicitis is the most common general surgical emergency worldwide, but its diagnosis remains challenging. The aim of this study was to determine whether existing risk prediction models can reliably identify patients presenting to hospital in the UK with acute right iliac fossa (RIF) pain who are at low risk of appendicitis. Methods A systematic search was completed to identify all existing appendicitis risk prediction models. Models were validated using UK data from an international prospective cohort study that captured consecutive patients aged 16–45 years presenting to hospital with acute RIF in March to June 2017. The main outcome was best achievable model specificity (proportion of patients who did not have appendicitis correctly classified as low risk) whilst maintaining a failure rate below 5 per cent (proportion of patients identified as low risk who actually had appendicitis). Results Some 5345 patients across 154 UK hospitals were identified, of which two‐thirds (3613 of 5345, 67·6 per cent) were women. Women were more than twice as likely to undergo surgery with removal of a histologically normal appendix (272 of 964, 28·2 per cent) than men (120 of 993, 12·1 per cent) (relative risk 2·33, 95 per cent c.i. 1·92 to 2·84; P < 0·001). Of 15 validated risk prediction models, the Adult Appendicitis Score performed best (cut‐off score 8 or less, specificity 63·1 per cent, failure rate 3·7 per cent). The Appendicitis Inflammatory Response Score performed best for men (cut‐off score 2 or less, specificity 24·7 per cent, failure rate 2·4 per cent). Conclusion Women in the UK had a disproportionate risk of admission without surgical intervention and had high rates of normal appendicectomy. Risk prediction models to support shared decision‐making by identifying adults in the UK at low risk of appendicitis were identified