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Biodiversity, a multidimensional property of natural systems, is difficult to quantify partly because of the multitude of indices proposed for this purpose. Indices aim to describe general properties of communities that allow us to compare different regions, taxa, and trophic levels. Therefore, they are of fundamental importance for environmental monitoring and conservation, although there is no consensus about which indices are more appropriate and informative. We tested several common diversity indices in a range of simple to complex statistical analyses in order to determine whether some were better suited for certain analyses than others. We used data collected around the focal plant Plantago lanceolata on 60 temperate grassland plots embedded in an agricultural landscape to explore relationships between the common diversity indices of species richness (S), Shannon's diversity (H'), Simpson's diversity (D-1), Simpson's dominance (D-2), Simpson's evenness (E), and Berger-Parker dominance (BP). We calculated each of these indices for herbaceous plants, arbuscular mycorrhizal fungi, aboveground arthropods, belowground insect larvae, and P.lanceolata molecular and chemical diversity. Including these trait-based measures of diversity allowed us to test whether or not they behaved similarly to the better studied species diversity. We used path analysis to determine whether compound indices detected more relationships between diversities of different organisms and traits than more basic indices. In the path models, more paths were significant when using H', even though all models except that with E were equally reliable. This demonstrates that while common diversity indices may appear interchangeable in simple analyses, when considering complex interactions, the choice of index can profoundly alter the interpretation of results. Data mining in order to identify the index producing the most significant results should be avoided, but simultaneously considering analyses using multiple indices can provide greater insight into the interactions in a system.
The WHO has recently classified Neisseria gonorrhoeae as a super-bacterium due to the rapid spread of antibiotic resistant derivatives and an overall dramatic increase in infection incidences. Genome sequencing has identified potential genes, however, little is known about the transcriptional organization and the presence of non-coding RNAs in gonococci. We performed RNA sequencing to define the transcriptome and the transcriptional start sites of all gonococcal genes and operons. Numerous new transcripts including 253 potentially non-coding RNAs transcribed from intergenic regions or antisense to coding genes were identified. Strikingly, strong antisense transcription was detected for the phase-variable opa genes coding for a family of adhesins and invasins in pathogenic Neisseria, that may have regulatory functions. Based on the defined transcriptional start sites, promoter motifs were identified. We further generated and sequenced a high density Tn5 transposon library to predict a core of 827 gonococcal essential genes, 133 of which have no known function. Our combined RNA-Seq and Tn-Seq approach establishes a detailed map of gonococcal genes and defines the first core set of essential gonococcal genes.
Brain-computer interfaces (BCIs) translate oscillatory electroencephalogram (EEG) patterns into action. Different mental activities modulate spontaneous EEG rhythms in various ways. Non-stationarity and inherent variability of EEG signals, however, make reliable recognition of modulated EEG patterns challenging. Able-bodied individuals who use a BCI for the first time achieve - on average - binary classification performance of about 75%. Performance in users with central nervous system (CNS) tissue damage is typically lower. User training generally enhances reliability of EEG pattern generation and thus also robustness of pattern recognition. In this study, we investigated the impact of mental tasks on binary classification performance in BCI users with central nervous system (CNS) tissue damage such as persons with stroke or spinal cord injury (SCI). Motor imagery (MI), that is the kinesthetic imagination of movement (e.g. squeezing a rubber ball with the right hand), is the "gold standard" and mainly used to modulate EEG patterns. Based on our recent results in able-bodied users, we hypothesized that pair- wise combination of "brain-teaser" (e.g. mental subtraction and mental word association) and "dynamic imagery" (e. g. hand and feet MI) tasks significantly increases classification performance of induced EEG patterns in the selected end-user group. Within- day (How stable is the classification within a day?) and between-day (How well does a model trained on day one perform on unseen data of day two?) analysis of variability of mental task pair classification in nine individuals confirmed the hypothesis. We found that the use of the classical MI task pair hand vs. feed leads to significantly lower classification accuracy - in average up to 15% less - in most users with stroke or SCI. User-specific selection of task pairs was again essential to enhance performance. We expect that the gained evidence will significantly contribute to make imagery-based BCI technology become accessible to a larger population of users including individuals with special needs due to CNS damage.
Major molecular remission (MMR) is an important therapy goal in chronic myeloid leukemia (CML). So far, MMR is not a failure criterion according to ELN management recommendation leading to uncertainties when to change therapy in CML patients not reaching MMR after 12 months. At monthly landmarks, for different molecular remission status Hazard ratios (HR) were estimated for patients registered to CML study IV who were divided in a learning and a validation sample. The minimum HR for MMR was found at 2.5 years with 0.28 (compared to patients without remission). In the validation sample, a significant advantage for progression-free survival (PFS) for patients in MMR could be detected (p-value 0.007). The optimal time to predict PFS in patients with MMR could be validated in an independent sample at 2.5 years. With our model we provide a suggestion when to define lack of MMR as therapy failure and thus treatment change should be considered. The optimal response time for 1% BCR-ABL at about 12-15 months was confirmed and for deep molecular remission no specific time point was detected. Nevertheless, it was demonstrated that the earlier the MMR is achieved the higher is the chance to attain deep molecular response later.
Within the EURO-SKI trial, 132 chronic phase CML patients discontinued imatinib treatment. RNA was isolated from peripheral blood in order to analyze the expression of MDR1, ABCG2 and OCT1. ABCG2 was predictive for treatment-free remission in Cox regression analysis. High transcript levels of the ABCG2 efflux transporter (>4.5 parts per thousand) were associated with a twofold higher risk of relapse. Introduction: Tyrosine kinase inhibitors (TKIs) can safely be discontinued in chronic myeloid leukemia (CML) patients with sustained deep molecular response. ABCG2 (breast cancer resistance protein), OCT1 (organic cation transporter 1), and ABCB1 (multidrug resistance protein 1) gene products are known to play a crucial role in acquired pharmacogenetic TKI resistance. Their influence on treatment-free remission (TFR) has not yet been investigated. Materials and Methods: RNA was isolated on the last day of TKI intake from peripheral blood leukocytes of 132 chronic phase CML patients who discontinued TKI treatment within the European Stop Tyrosine Kinase Inhibitor Study trial. Plasmid standards were designed including subgenic inserts of OCT1, ABCG2, and ABCB1 together with GUSB as reference gene. For expression analyses, quantitative real-time polymerase chain reaction was used. Multiple Cox regression analysis was performed. In addition, gene expression cutoffs for patient risk stratification were investigated. Results: The TFR rate of 132 patients, 12 months after TKI discontinuation, was 54% (95% confidence interval [CI], 46%-62%). ABCG2 expression (parts per thousand) was retained as the only significant variable (P=.02; hazard ratio, 1.04; 95% CI, 1.01-1.07) in multiple Cox regression analysis. Only for the ABCG2 efflux transporter, a significant cutoff was found (P=.04). Patients with an ABCG2/GUSB transcript level >4.5 parts per thousand (n=93) showed a 12-month TFR rate of 47% (95% CI, 37%-57%), whereas patients with low ABCG2 expression (<= 4.5 parts per thousand; n=39) had a 12-month TFR rate of 72% (95% CI, 55%-82%). Conclusion: In this study, we investigated the effect of pharmacogenetics in the context of a CML treatment discontinuation trial. The transcript levels of the efflux transporter ABCG2 predicted TFR after TKI discontinuation. (C) 2018 The Authors. Published by Elsevier Inc.