195 research outputs found

    Increased Hippocampal ProBDNF Contributes to Memory Impairments in Aged Mice

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    Memory decline during aging or accompanying neurodegenerative diseases, represents a major health problem. Neurotrophins have long been considered relevant to the mechanisms of aging-associated cognitive decline and neurodegeneration. Mature Brain-Derived Neurotrophic Factor (BDNF) and its precursor (proBDNF) can both be secreted in response to neuronal activity and exert opposing effects on neuronal physiology and plasticity. In this study, biochemical analyses revealed that increased levels of proBDNF are present in the aged mouse hippocampus relative to young and that the level of hippocampal proBDNF inversely correlates with the ability to perform in a spatial memory task, the water radial arm maze (WRAM). To ascertain the role of increased proBDNF levels on hippocampal function and memory we performed infusions of proBDNF into the CA1 region of the dorsal hippocampus in male mice trained in the WRAM paradigm: In well-performing aged mice, intra-hippocampal proBDNF infusions resulted in a progressive and significant impairment of memory performance. This impairment was associated with increased p-cofilin levels, an important regulator of dendritic spines and synapse physiology. On the other hand, in poor performers, intra-hippocampal infusions of TAT-Pep5, a peptide which blocks the interaction between the p75 Neurotrophin Receptor (p75NTR) and RhoGDI, significantly improved learning and memory, while saline infusions had no effect. Our results support a role for proBDNF and its receptor p75NTR in aging-related memory impairments

    Down syndrome-recent progress and future prospects

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    Down syndrome (DS) is caused by trisomy of chromosome 21 (Hsa21) and is associated with a number of deleterious phenotypes, including learning disability, heart defects, early-onset Alzheimer's disease and childhood leukaemia. Individuals with DS are affected by these phenotypes to a variable extent; understanding the cause of this variation is a key challenge. Here, we review recent research progress in DS, both in patients and relevant animal models. In particular, we highlight exciting advances in therapy to improve cognitive function in people with DS and the significant developments in understanding the gene content of Hsa21. Moreover, we discuss future research directions in light of new technologies. In particular, the use of chromosome engineering to generate new trisomic mouse models and large-scale studies of genotype-phenotype relationships in patients are likely to significantly contribute to the future understanding of DS

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

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    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

    Assessing stimulus–stimulus (semantic) conflict in the Stroop task using saccadic two-to-one color response mapping and preresponse pupillary measures

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    © 2015, The Psychonomic Society, Inc. Conflict in the Stroop task is thought to come from various stages of processing, including semantics. Two-to-one response mappings, in which two response-set colors share a common response location, have been used to isolate stimulus–stimulus (semantic) from stimulus–response conflict in the Stroop task. However, the use of congruent trials as a baseline means that the measured effects could be exaggerated by facilitation, and recent research using neutral, non-color-word trials as a baseline has supported this notion. In the present study, we sought to provide evidence for stimulus–stimulus conflict using an oculomotor Stroop task and an early, preresponse pupillometric measure of effort. The results provided strong (Bayesian) evidence for no statistical difference between two-to-one response-mapping trials and neutral trials in both saccadic response latencies and preresponse pupillometric measures, supporting the notion that the difference between same-response and congruent trials indexes facilitation in congruent trials, and not stimulus–stimulus conflict, thus providing evidence against the presence of semantic conflict in the Stroop task. We also demonstrated the utility of preresponse pupillometry in measuring Stroop interference, supporting the idea that pupillary effects are not simply a residue of making a response

    The Relationship Between Homework Compliance and Therapy Outcomes: An Updated Meta-Analysis

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    The current study was an updated meta-analysis of manuscripts since the year 2000 examining the effects of homework compliance on treatment outcome. A total of 23 studies encompassing 2,183 subjects were included. Results indicated a significant relationship between homework compliance and treatment outcome suggesting a small to medium effect (r = .26; 95% CI = .19–.33). Moderator analyses were conducted to determine the differential effect size of homework on treatment outcome by target symptoms (e.g., depression; anxiety), source of homework rating (e.g., client; therapist), timing of homework rating (e.g., retroactive vs. contemporaneous), and type of homework rating (e.g., Likert; total homeworks completed). Results indicated that effect sizes were robust across target symptoms, but differed by source of homework rating, timing of homework rating, and type of homework rating. Specifically, studies utilizing combined client and therapist ratings of compliance had significantly higher mean effect size relative to those using therapist only assessments and those using objective assessments. Further, studies that rated the percentage of homeworks completed had a significantly lower mean effect size compared to studies using Likert ratings, and retroactive assessments had higher effect size than contemporaneous assessments

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

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    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

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

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    [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
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