Assessing the role of EO in biodiversity monitoring: options for integrating in-situ observations with EO within the context of the EBONE concept

The European Biodiversity Observation Network (EBONE) is a European contribution on terrestrial monitoring to GEO BON, the Group on Earth Observations Biodiversity Observation Network. EBONE’s aims are to develop a system of biodiversity observation at regional, national and European levels by assessing existing approaches in terms of their validity and applicability starting in Europe, then expanding to regions in Africa. The objective of EBONE is to deliver: 1. A sound scientific basis for the production of statistical estimates of stock and change of key indicators; 2. The development of a system for estimating past changes and forecasting and testing policy options and management strategies for threatened ecosystems and species; 3. A proposal for a cost-effective biodiversity monitoring system. There is a consensus that Earth Observation (EO) has a role to play in monitoring biodiversity. With its capacity to observe detailed spatial patterns and variability across large areas at regular intervals, our instinct suggests that EO could deliver the type of spatial and temporal coverage that is beyond reach with in-situ efforts. Furthermore, when considering the emerging networks of in-situ observations, the prospect of enhancing the quality of the information whilst reducing cost through integration is compelling. This report gives a realistic assessment of the role of EO in biodiversity monitoring and the options for integrating in-situ observations with EO within the context of the EBONE concept (cfr. EBONE-ID1.4). The assessment is mainly based on a set of targeted pilot studies. Building on this assessment, the report then presents a series of recommendations on the best options for using EO in an effective, consistent and sustainable biodiversity monitoring scheme. The issues that we faced were many: 1. Integration can be interpreted in different ways. One possible interpretation is: the combined use of independent data sets to deliver a different but improved data set; another is: the use of one data set to complement another dataset. 2. The targeted improvement will vary with stakeholder group: some will seek for more efficiency, others for more reliable estimates (accuracy and/or precision); others for more detail in space and/or time or more of everything. 3. Integration requires a link between the datasets (EO and in-situ). The strength of the link between reflected electromagnetic radiation and the habitats and their biodiversity observed in-situ is function of many variables, for example: the spatial scale of the observations; timing of the observations; the adopted nomenclature for classification; the complexity of the landscape in terms of composition, spatial structure and the physical environment; the habitat and land cover types under consideration. 4. The type of the EO data available varies (function of e.g. budget, size and location of region, cloudiness, national and/or international investment in airborne campaigns or space technology) which determines its capability to deliver the required output. EO and in-situ could be combined in different ways, depending on the type of integration we wanted to achieve and the targeted improvement. We aimed for an improvement in accuracy (i.e. the reduction in error of our indicator estimate calculated for an environmental zone). Furthermore, EO would also provide the spatial patterns for correlated in-situ data. EBONE in its initial development, focused on three main indicators covering: (i) the extent and change of habitats of European interest in the context of a general habitat assessment; (ii) abundance and distribution of selected species (birds, butterflies and plants); and (iii) fragmentation of natural and semi-natural areas. For habitat extent, we decided that it did not matter how in-situ was integrated with EO as long as we could demonstrate that acceptable accuracies could be achieved and the precision could consistently be improved. The nomenclature used to map habitats in-situ was the General Habitat Classification. We considered the following options where the EO and in-situ play different roles: using in-situ samples to re-calibrate a habitat map independently derived from EO; improving the accuracy of in-situ sampled habitat statistics, by post-stratification with correlated EO data; and using in-situ samples to train the classification of EO data into habitat types where the EO data delivers full coverage or a larger number of samples. For some of the above cases we also considered the impact that the sampling strategy employed to deliver the samples would have on the accuracy and precision achieved. Restricted access to European wide species data prevented work on the indicator ‘abundance and distribution of species’. With respect to the indicator ‘fragmentation’, we investigated ways of delivering EO derived measures of habitat patterns that are meaningful to sampled in-situ observations

The 18 kDa Translocator Protein (Peripheral Benzodiazepine Receptor) Expression in the Bone of Normal, Osteoprotegerin or Low Calcium Diet Treated Mice

The presence of the translocator protein (TSPO), previously named as the mitochondrial or peripheral benzodiazepine receptor, in bone cells was studied in vitro and in situ using RT-qPCR, and receptor autoradiography using the selective TSPO ligand PK11195

Pioglitazone administration alters ovarian gene expression in aging obese lethal yellow mice

<p>Abstract</p> <p>Background</p> <p>Women with polycystic ovary syndrome (PCOS) are often treated with insulin-sensitizing agents, e.g. thiazolidinediones (TZD), which have been shown to reduce androgen levels and improved ovulatory function. Acting via peroxisome proliferator-activated receptor (PPAR) gamma, TZD alter the expression of a large variety of genes. Lethal yellow (LY; C57BL/6J Ay/a) mice, possessing a mutation (Ay) in the agouti gene locus, exhibit progressive obesity, reproductive dysfunction, and altered metabolic regulation similar to women with PCOS. The current study was designed to test the hypothesis that prolonged treatment of aging LY mice with the TZD, pioglitazone, alters the ovarian expression of genes that may impact reproduction.</p> <p>Methods</p> <p>Female LY mice received daily oral doses of either 0.01 mg pioglitazone (n = 4) or an equal volume of vehicle (DMSO; n = 4) for 8 weeks. At the end of treatment, ovaries were removed and DNA microarrays were used to analyze differential gene expression.</p> <p>Results</p> <p>Twenty-seven genes showed at least a two-fold difference in ovarian expression with pioglitazone treatment. These included leptin, angiopoietin, angiopoietin-like 4, Foxa3, PGE1 receptor, resistin-like molecule-alpha (RELM), and actin-related protein 6 homolog (ARP6). For most altered genes, pioglitazone changed levels of expression to those seen in untreated C57BL/6J(a/a) non-mutant lean mice.</p> <p>Conclusion</p> <p>TZD administration may influence ovarian function via numerous diverse mechanisms that may or may not be directly related to insulin/IGF signaling.</p

Addressing statistical biases in nucleotide-derived protein databases for proteogenomic search strategies

[Image: see text] Proteogenomics has the potential to advance genome annotation through high quality peptide identifications derived from mass spectrometry experiments, which demonstrate a given gene or isoform is expressed and translated at the protein level. This can advance our understanding of genome function, discovering novel genes and gene structure that have not yet been identified or validated. Because of the high-throughput shotgun nature of most proteomics experiments, it is essential to carefully control for false positives and prevent any potential misannotation. A number of statistical procedures to deal with this are in wide use in proteomics, calculating false discovery rate (FDR) and posterior error probability (PEP) values for groups and individual peptide spectrum matches (PSMs). These methods control for multiple testing and exploit decoy databases to estimate statistical significance. Here, we show that database choice has a major effect on these confidence estimates leading to significant differences in the number of PSMs reported. We note that standard target:decoy approaches using six-frame translations of nucleotide sequences, such as assembled transcriptome data, apparently underestimate the confidence assigned to the PSMs. The source of this error stems from the inflated and unusual nature of the six-frame database, where for every target sequence there exists five “incorrect” targets that are unlikely to code for protein. The attendant FDR and PEP estimates lead to fewer accepted PSMs at fixed thresholds, and we show that this effect is a product of the database and statistical modeling and not the search engine. A variety of approaches to limit database size and remove noncoding target sequences are examined and discussed in terms of the altered statistical estimates generated and PSMs reported. These results are of importance to groups carrying out proteogenomics, aiming to maximize the validation and discovery of gene structure in sequenced genomes, while still controlling for false positives

GIT2 Acts as a Potential Keystone Protein in Functional Hypothalamic Networks Associated with Age-Related Phenotypic Changes in Rats

The aging process affects every tissue in the body and represents one of the most complicated and highly integrated inevitable physiological entities. The maintenance of good health during the aging process likely relies upon the coherent regulation of hormonal and neuronal communication between the central nervous system and the periphery. Evidence has demonstrated that the optimal regulation of energy usage in both these systems facilitates healthy aging. However, the proteomic effects of aging in regions of the brain vital for integrating energy balance and neuronal activity are not well understood. The hypothalamus is one of the main structures in the body responsible for sustaining an efficient interaction between energy balance and neurological activity. Therefore, a greater understanding of the effects of aging in the hypothalamus may reveal important aspects of overall organismal aging and may potentially reveal the most crucial protein factors supporting this vital signaling integration. In this study, we examined alterations in protein expression in the hypothalami of young, middle-aged, and old rats. Using novel combinatorial bioinformatics analyses, we were able to gain a better understanding of the proteomic and phenotypic changes that occur during the aging process and have potentially identified the G protein-coupled receptor/cytoskeletal-associated protein GIT2 as a vital integrator and modulator of the normal aging process

A new tool to assess Clinical Diversity In Meta‐analyses (CDIM) of interventions

OBJECTIVE: To develop and validate Clinical Diversity In Meta-analyses (CDIM), a new tool for assessing clinical diversity between trials in meta-analyses of interventions.STUDY DESIGN AND SETTING: The development of CDIM was based on consensus work informed by empirical literature and expertise. We drafted the CDIM tool, refined it, and validated CDIM for interrater scale reliability and agreement in three groups.RESULTS: CDIM measures clinical diversity on a scale that includes four domains with 11 items overall: setting (time of conduct/country development status/units type); population (age, sex, patient inclusion criteria/baseline disease severity, comorbidities); interventions (intervention intensity/strength/duration of intervention, timing, control intervention, cointerventions); and outcome (definition of outcome, timing of outcome assessment). The CDIM is completed in two steps: first two authors independently assess clinical diversity in the four domains. Second, after agreeing upon scores of individual items a consensus score is achieved. Interrater scale reliability and agreement ranged from moderate to almost perfect depending on the type of raters.CONCLUSION: CDIM is the first tool developed for assessing clinical diversity in meta-analyses of interventions. We found CDIM to be a reliable tool for assessing clinical diversity among trials in meta-analysis.</p

The role of GDNF family ligand signalling in the differentiation of sympathetic and dorsal root ganglion neurons

The diversity of neurons in sympathetic ganglia and dorsal root ganglia (DRG) provides intriguing systems for the analysis of neuronal differentiation. Cell surface receptors for the GDNF family ligands (GFLs) glial cell-line-derived neurotrophic factor (GDNF), neurturin and artemin, are expressed in subpopulations of these neurons prompting the question regarding their involvement in neuronal subtype specification. Mutational analysis in mice has demonstrated the requirement for GFL signalling during embryonic development of cholinergic sympathetic neurons as shown by the loss of expression from the cholinergic gene locus in ganglia from mice deficient for ret, the signal transducing subunit of the GFL receptor complex. Analysis in mutant animals and transgenic mice overexpressing GFLs demonstrates an effect on sensitivity to thermal and mechanical stimuli in DRG neurons correlating at least partially with the altered expression of transient receptor potential ion channels and acid-sensitive cation channels. Persistence of targeted cells in mutant ganglia suggests that the alterations are caused by differentiation effects and not by cell loss. Because of the massive effect of GFLs on neurite outgrowth, it remains to be determined whether GFL signalling acts directly on neuronal specification or indirectly via altered target innervation and access to other growth factors. The data show that GFL signalling is required for the specification of subpopulations of sensory and autonomic neurons. In order to comprehend this process fully, the role of individual GFLs, the transduction of the GFL signals, and the interplay of GFL signalling with other regulatory pathways need to be deciphered

Galantamine improves olfactory learning in the Ts65Dn mouse model of Down syndrome

Down syndrome (DS) is the most common form of congenital intellectual disability. Although DS involves multiple disturbances in various tissues, there is little doubt that in terms of quality of life cognitive impairment is the most serious facet and there is no effective treatment for this aspect of the syndrome. The Ts65Dn mouse model of DS recapitulates multiple aspects of DS including cognitive impairment. Here the Ts65Dn mouse model of DS was evaluated in an associative learning paradigm based on olfactory cues. In contrast to disomic controls, trisomic mice exhibited significant deficits in olfactory learning. Treatment of trisomic mice with the acetylcholinesterase inhibitor galantamine resulted in a significant improvement in olfactory learning. Collectively, our study indicates that olfactory learning can be a sensitive tool for evaluating deficits in associative learning in mouse models of DS and that galantamine has therapeutic potential for improving cognitive abilities