Spatial filters selection towards a rehabilitation BCI

Introducing BCI technology in supporting motor imagery (MI) training has revealed the rehabilitative potential of MI, contributing to significantly better motor functional outcomes in stroke patients. To provide the most accurate and personalized feedback during the treatment, several stages of the electroencephalographic signal processing have to be optimized, including spatial filtering. This study focuses on data-independent approaches to optimize spatial filtering step. Specific aims were: i) assessment of spatial filters' performance in relation to the hand and foot scalp areas; ii) evaluation of simultaneous use of multiple spatial filters; iii) minimization of the number of electrodes needed for training. Our findings indicate that different spatial filters showed different performance related to the scalp areas considered. The simultaneous use of EEG signals conditioned with different spatial filters could either improve classification performance or, at same level of performance could lead to a reduction of the number of electrodes needed for successive training, thus improving usability of BCIs in clinical rehabilitation context

Age, sex, adult and larval diet shape starvation resistance in the Mediterranean fruit fly: an ecological and gerontological perspective.

The ability of an animal to withstand periods of food deprivation is a key driver of invasion success (biodiversity), adaptation to new conditions, and a crucial determinant of senescence in populations. Starvation resistance (SR) is a highly plastic trait and varies in relation to environmental and genetic variables. However, beyond Drosophila, SR has been studied poorly. Exploiting an interesting model species in invasion and ageing studies-the Mediterranean fruit fly (Ceratitis capitata)- we investigated how age, food and gender, shape SR in this species. We measured SR in adults feeding in rich and poor dietary conditions, which had been reared either on natural hosts or artificial larval diet, for every single day across their lifespan. We defined which factor is the most significant determinant of SR and we explored potential links between SR and ageing. We found that SR declines with age, and that age-specific patterns are shaped in relation to adult and larval diet. Females exhibited higher SR than males. Age and adult diet were the most significant determinants of SR, followed by gender and the larval diet. Starvation resistance proved to be a weak predictor of functional ageing. Possible underlying mechanisms, ecological and gerontological significance and potential applied benefits are discussed

Effective Alu repeat based RT-qPCR normalization in cancer cell perturbation experiments

Background: Measuring messenger RNA (mRNA) levels using the reverse transcription quantitative polymerase chain reaction (RT-qPCR) is common practice in many laboratories. A specific set of mRNAs as internal control reference genes is considered as the preferred strategy to normalize RT-qPCR data. Proper selection of reference genes is a critical issue, especially in cancer cells that are subjected to different in vitro manipulations. These manipulations may result in dramatic alterations in gene expression levels, even of assumed reference genes. In this study, we evaluated the expression levels of 11 commonly used reference genes as internal controls for normalization of 19 experiments that include neuroblastoma, T-ALL, melanoma, breast cancer, non small cell lung cancer (NSCL), acute myeloid leukemia (AML), prostate cancer, colorectal cancer, and cervical cancer cell lines subjected to various perturbations. Results: The geNorm algorithm in the software package qbase+ was used to rank the candidate reference genes according to their expression stability. We observed that the stability of most of the candidate reference genes varies greatly in perturbation experiments. Expressed Alu repeats show relatively stable expression regardless of experimental condition. These Alu repeats are ranked among the best reference assays in all perturbation experiments and display acceptable average expression stability values (M<0.5). Conclusions: We propose the use of Alu repeats as a reference assay when performing cancer cell perturbation experiments

Water bathing alters the speed-accuracy trade-off of escape flights in European starlings

Birds of most species regularly bathe in water, but the function of this behaviour is unknown. We tested the hypothesis that water bathing is important in feather maintenance, and hence should enhance flight performance. We manipulated European starlings', Sturnus vulgaris, access to bathing water in a 2 × 2 design: birds were housed in aviaries either with or without water baths for a minimum of 3 days (long-term access) before being caught and placed in individual cages either with or without water baths for a further 24 h (short-term access). We subsequently assessed the speed and accuracy of escape flights through an obstacle course of vertical strings. Birds that had bathed in the short-term flew more slowly and hit fewer strings than birds that were deprived of bathing water in the short term, whereas long-term access to bathing water had no significant effect on flight performance. Thus recent access to bathing water alters flight performance by altering the trade-off between escape flight speed and accuracy. We hypothesize that lack of bathing water provision could increase anxiety in captive starlings because of an increase in their perceived vulnerability to predation. This study therefore potentially provides an important functional link between the expression of natural behaviours in captivity and welfare considerations. © 2009 The Association for the Study of Animal Behaviour

Costless metabolic secretions as drivers of interspecies interactions in microbial ecosystems

Metabolic exchange mediates interactions among microbes, helping explain diversity in microbial communities. As these interactions often involve a fitness cost, it is unclear how stable cooperation can emerge. Here we use genome-scale metabolic models to investigate whether the release of “costless” metabolites (i.e. those that cause no fitness cost to the producer), can be a prominent driver of intermicrobial interactions. By performing over 2 million pairwise growth simulations of 24 species in a combinatorial assortment of environments, we identify a large space of metabolites that can be secreted without cost, thus generating ample cross-feeding opportunities. In addition to providing an atlas of putative interactions, we show that anoxic conditions can promote mutualisms by providing more opportunities for exchange of costless metabolites, resulting in an overrepresentation of stable ecological network motifs. These results may help identify interaction patterns in natural communities and inform the design of synthetic microbial consortia.We thank Dr. Niels Klitgord for pioneering ideas that inspired launch of this work. We are also grateful to David Bernstein, Joshua E. Goldford, Meghan Thommes, Demetrius DiMucci, and all members of the Segre Lab for helpful discussions. A.R.P. is supported by a National Academies of Sciences, Engineering, and Medicine Ford Foundation Predoctoral Fellowship and a Howard Hughes Medical Institute Gilliam Fellowship. This work was supported by funding from the Defense Advanced Research Projects Agency (purchase request no. HR0011515303, contract no. HR0011-15-C-0091), the U.S. Department of Energy (grants DE-SC0004962 and DE-SC0012627), the NIH (grants 5R01DE024468, R01GM121950, and Sub_P30DK036836_P&F), the National Science Foundation (grants 1457695 and NSFOCE-BSF 1635070), MURI Grant W911NF-12-1-0390, the Human Frontiers Science Program (grant RGP0020/2016), and the Boston University Inter-disciplinary Biomedical Research Office. (National Academies of Sciences, Engineering, and Medicine Ford Foundation Predoctoral Fellowship; Howard Hughes Medical Institute Gilliam Fellowship; HR0011515303 - Defense Advanced Research Projects Agency; HR0011-15-C-0091 - Defense Advanced Research Projects Agency; DE-SC0004962 - U.S. Department of Energy; DE-SC0012627 - U.S. Department of Energy; 5R01DE024468 - NIH; R01GM121950 - NIH; Sub_P30DK036836_PF - NIH; 1457695 - National Science Foundation; NSFOCE-BSF 1635070 - National Science Foundation; W911NF-12-1-0390 - MURI Grant; RGP0020/2016 - Human Frontiers Science Program; Boston University Inter-disciplinary Biomedical Research Office)Published versio

Identification and validation of reference genes for RT-qPCR normalization in wheat meiosis

Meiosis is a specialized type of cell division occurring in sexually reproducing organisms to generate haploid cells known as gametes. In flowering plants, male gametes are produced in anthers, being encased in pollen grains. Understanding the genetic regulation of meiosis key events such as chromosome recognition and pairing, synapsis and recombination, is needed to manipulate chromosome associations for breeding purposes, particularly in important cereal crops like wheat. Reverse transcription-quantitative PCR (RT-qPCR) is widely used to analyse gene expression and to validate the results obtained by other transcriptomic analyses, like RNA-seq. Selection and validation of appropriate reference genes for RT-qPCR normalization is essential to obtain reproducible and accurate expression data. In this work, twelve candidate reference genes were evaluated using the mainstream algorithms geNorm, Normfinder, BestKeeper and ΔCt, then ranked from most to least suitable for normalization with RefFinder. Different sets of reference genes were recommended to normalize gene expression data in anther meiosis of bread and durum wheat, their corresponding genotypes in the absence of the Ph1 locus and for comparative studies among wheat genotypes. Comparisons between meiotic (anthers) and somatic (leaves and roots) wheat tissues were also carried out. To the best of our knowledge, our study provides the first comprehensive list of reference genes for robust RT-qPCR normalization to study differentially expressed genes during male meiosis in wheat in a breeding framework

Bayesian Approximate Kernel Regression with Variable Selection

Nonlinear kernel regression models are often used in statistics and machine learning because they are more accurate than linear models. Variable selection for kernel regression models is a challenge partly because, unlike the linear regression setting, there is no clear concept of an effect size for regression coefficients. In this paper, we propose a novel framework that provides an effect size analog of each explanatory variable for Bayesian kernel regression models when the kernel is shift-invariant --- for example, the Gaussian kernel. We use function analytic properties of shift-invariant reproducing kernel Hilbert spaces (RKHS) to define a linear vector space that: (i) captures nonlinear structure, and (ii) can be projected onto the original explanatory variables. The projection onto the original explanatory variables serves as an analog of effect sizes. The specific function analytic property we use is that shift-invariant kernel functions can be approximated via random Fourier bases. Based on the random Fourier expansion we propose a computationally efficient class of Bayesian approximate kernel regression (BAKR) models for both nonlinear regression and binary classification for which one can compute an analog of effect sizes. We illustrate the utility of BAKR by examining two important problems in statistical genetics: genomic selection (i.e. phenotypic prediction) and association mapping (i.e. inference of significant variants or loci). State-of-the-art methods for genomic selection and association mapping are based on kernel regression and linear models, respectively. BAKR is the first method that is competitive in both settings.Comment: 22 pages, 3 figures, 3 tables; theory added; new simulations presented; references adde