dataesr/scientific-paragraphs-categorization huggingface dataset:
Class IV proteins are the most similar to the animal FMRPs (Fig. 3 ). To investigate possible developmental processes in which the distinct classes of Agenet/Tudor genes in Arabidopsis could participate, their expression pattern was searched in silico through Genevestigator database  [40]. In general, members of the Agenet/Tudor family were highly expressed in reproductive tissues as seed and embryo (Fig. 4 ). The different Agenet/Tudor family classes showed some particularities in the expression profiles of their members (Fig. 4 ). Class I genes were highly expressed in seed and embryo tissues. Class II were likewise found in seed and embryo, but were also highly expressed in shoot apex and flower female tissues (as carpel and ovules). The expression of Class III members was distributed among different plant organs and tissues, with some genes being more expressed in pollen and seed.
From the five members of Class IV, two genes were not represented in microarray data experiments, invalidating analysis of patterns. The temporal expression of Agenet/Tudor domain proteins during development was also analyzed in silico through Genevestigator database (Additional file 3). Class I members showed moderate levels of expression with almost no variation during development, and increased mRNA levels were observed in late maturation of seeds and senescence of leaves. Class II members also exhibited moderate expression levels, peaking during bolting phase and embryo maturation phase. Expression profile of Class III members was again very diverse. Interestingly, the Arabidopsis Agenet/Tudor genes were highly expressed in reproductive tissues and evolutionary analysis showed a dramatic increase of members and domains diversity of Agenet/Tudor family in the flowering plants (Fig.
1b ). All together, the data suggests a possible role of Agenet/Tudor domain proteins during flower development and embryo formation. Arabidopsis showing the canonical conserved residues analyzed by WebLogo. The highly conserved residues are represented as larger letters in the sequence. Although very diverse, some key-positions contain conserved amino acids and possibly maintain the conserved secondary structure observed. b Multiple sequence alignment of Agenet/Tudor domain sequences from plant proteins: the alignment was performed using Multalin and the result submitted to ESPript server to plot the secondary structure information of the conserved domains over their primary sequence. On the secondary structure displayed, 3 10 -helices are represented as small squiggles (Ƞ), β-strands are rendered as arrows, and strict β-turns (TT).
AIP1 is highly expressed in reproductive tissues To identify possible functions for AIP1 during plant development, its gene expression profile was analyzed in silico in open access microarray databases and by qRT-PCR assays (Fig. 7 ). High AIP1 expression was observed in various reproductive tissues, such as carpel and in seed tissues such as chalazal seed coat of globular and heart shape embryos  [43]  and in the suspensor, where its expression was about 4,5 times higher than in others tissues  [44]  (Fig. 4 ). qRT-PCR confirmed a high expression of AIP1 in siliques and flower buds in comparison to developed flowers and leafs (Fig. 7a ). The shoot apex meristem also showed high AIP1 mRNA levels  [45]  (Fig. 4 ). A peak of AIP1 expression was observed during bolting (Fig. 7b ), the timing of development that marks the transition from vegetative to reproductive phase, and a second peak of expression was observed during seed development.
Acknowledgments The research was supported by  Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ) ,  Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) ,  Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)  and  Instituto Nacional de Ciência de Tecnologia (INCT) in Biological Nitrogen Fixation. JNB was supported by  CNPq  and by the international exchange program  Science without Borders (CNPq)  for PhD fellowships. LPPG is supported by a  CAPES Master  fellowship. ASH and PCGF receive support from a  CNPq  research grant.
Galanin receptor ligands Ülo Langel Stockholm University, Sweden SpringerPlus 2015, 4(Suppl 1):L18 The eff ect of galanin is mediated through three GPCR subtypes, GalR1-3. The limited number of specifi c ligands to the galanin receptor subtypes has hindered the understanding of the individual eff ects of each receptor subtype. This review summarizes the current data of the importance of the galanin receptor subtypes and receptor subtype specifi c agonists and antagonists and their involvement in diff erent biological and pathological functions. through the cell membrane in response to various extracellular stimuli. GPCR have become important targets of many drugs for treatments of very diff erent diseases. During the last decade several fl uorescencebased methods have been implemented for the characterization of signal transduction via GPCRs, starting from ligand binding and including several steps leading up to a response on the level of gene regulation.
INSERM-France SpringerPlus 2015, 4(Suppl 1):L41 The transcription factor Nrf2 and its downstream target heme oxygenase-1 (HO-1) are ubiquitous protective systems against oxidative stress, infl ammation and tissue injury. The products of HO-1 enzymatic activity, biliverdin and carbon monoxide (CO), possess interesting anti-oxidant and anti-infl ammatory properties suggesting that exploitation of this pathway as a target for drug discovery may off er therapeutic avenues in a variety of disorders  [1]. In this context we have developed carbon monoxide-releasing molecules (CO-RMs), a class of compounds that deliver CO in a controlled fashion and exert a variety of pharmacological eff ects  [2]. Data will be presented showing that CO-RMs modulate neuroinfl ammation and neuroprotection in vitro and in vivo.
We used PC12 cells and primary neurons to investigate the potential role of NGF in modulating neuronal autophagy, a cellular process whose deregulation has been linked to the loss of neuronal. The mammalian target of rapamycin has been recently proposed as a therapeutic target for AD due to the role of the autophagy pathway in improving cognitive function by reducing Aβ and Tau pathology. We here show that NGF treatment induces LC3 conversion (LC3-I to LC3-II), a marker of autophagy, through the activation of AMP-activated protein kinase (AMPK). We show that NGF increases the autophagic fl ux (the dynamic process of autophagosome synthesis, delivery to the lysosome and degradation) as indicated by western blot and fl uorescence microscopy analyses in the presence of autophagy inhibitors. These data were confi rmed by RT-PCR array analysis and RNA interference-mediated knockdown of autophagyrelated genes: Atg9b, Atg12 and AMBRA1, a positive regulator of the BECLIN 1-dependent program of autophagy.
Collectively these fi ndings suggest that GBA2 may represent a possible neuronal marker and demonstrate for the fi rst time its direct involvement in the neuronal diff erentiation. Keywords Sphingolipids, neuronal diff erentiation, GBA2 Tianeptine is an atypical antidepressant drug with proven effi cacy, but still not fully understood mechanism of action. Recently it has been suggested that tianeptine may modulate infl ammatory processes, however there is a lack of data on its infl uence on microglia -the main source of pro-infl ammatory cytokines in the brain. Therefore this project aimed to investigate whether tianeptine can infl uence activation of microglial cells. We conducted our study in two experimental models: in vivo -in the hippocampus and frontal cortex of adult rats and in vitro in microglial cultures.
Acknowledgements EA is supported by the  National Epilepsy Fund -"Power of the Small" , the  Hersenstichting Nederland  ( NEF 013-1 ) and  KIKA (Stichting Kinderen Kankervrij ). Acknowledgements Studies supported by a grant  2012/04 /A/ Acknowledgements This work has been fi nanced by Estonian Ministry Acknowledgements Supported by the  EU "Normolife"-LSHC -Acknowledgements Funded by AMN,  ISF , and the  Israeli Ministry of Science. Keywords ADNP, Autism, Alzheimer's disease Acknowledgements Supported by by  Estonian Research Council  (institutional research funding  IUT19-18 ),  National R&D program  " Biotechnology " (grant  AR12030 ),  Norwegian Financial Mechanism  (grant  EMP128 ) and  Estonian Academy of Sciences. Keywords BDNF , synaptic plasticity, gene regulation Acknowledgements The work was supported by  Russian Foundation for Basic Research  (grant No.
No autopsy was conducted. The unidentified gram-negative bacillus was an oxidase-positive strict aerobe. The 16S rRNA and gyrB genes were amplified and sequenced  (4, 5). The 897-bp 16S rRNA sequence obtained for the bacterium was 99% identical to sequences from I. larvae type strain L1/68T (GenBank accession no. AJ252143) and I. ureiclastica type strain FFA3T (GenBank accession no. EU008089). The 973-bp gyrB sequence of the isolate was 96% similar to the sequence of I. ureiclastica type strain FFA3T (Gen-Bank accession no. FJ966120) and 92% with I. larvae type strain L1/68T (GenBank accession no. FJ966121). The 16S rRNA and gyrB sequences (GenBank accession nos. KR184134 and KR184135) were compared with those of all members of the genus Ignatzschineria and with those of several species belonging to the class Gammaproteobacteria.
Figure. Phylogenetic trees showing relationships between the clinical isolate identified in this study ("Tours strain") and type strains of members of the genus Ignatzschineria. A) Relationships among 16S rRNA sequences of "Tours strain" (GenBank accession no. KR184134) and Ignatzschineria strains; scale bar represents 2% differences in nucleotide sequence. B) Relationships among gyrB sequences of "Tours strain" (GenBank accession no. KR184135) and Ignatzschineria strains; scale bars represent 5% differences in nucleotide sequence. Bacillus subtilis was included as an outgroup organism. Numbers at branch nodes are bootstrap values.
Flow cytometry For all assays described below, we acquired fluorescence data for 10,000 events on a FACSCalibur flow cytometer (BD Biosciences, Oxford, UK) and analyzed the data with the use of CellQuest software (BD Biosciences) or FlowJo (Treestar). The experiments were repeated at least three times. In some experiments, cells were analyzed on a FACScan (Becton Dickinson, Franklin Lakes, NJ, USA).
Acknowledgements Fertilised GFP chicken eggs were supplied by  Prof. Helen Sang , The  Roslin Institute, and University of Edinburgh, UK. The  Roslin Transgenic Chicken Facility  is funded by the  Wellcome Trust  and by the  Biotechnology and Biological Sciences Research Council (BBSRC). The work was partly funded, and NT supported, by  Great Ormond Street Hospital Children's Charity, London, UK. The authors thank  Ben Jevans ,  UCL Institute of Child Health , for help with preparing embryos for grafting.
This paper focuses on observations made with the Infrared Array Camera (IRAC), which has been the most widely used Spitzer instrument for observing exoplanets. Specifically, we analyse ten transits and eleven eclipses that have been measured for HD 209458b, most of which have already been published  (Knutson et al. 2008; Beaulieu et al. 2010; Zellem et al. 2014; Diamond-Lowe et al. 2014). By providing a uniform analysis of these datasets, we aim to re-evaluate a number of claims that have been made in the literature. In particular,  Beaulieu et al. (2010)  measured significantly larger effective radii for the planet in the 5. 8µm and 8. 0µm channels relative to the 3. 6µm and 4. 5µm channels in transmission, and interpreted this as evidence for water absorption. However,  Deming et al. (2013)  have since resolved the water absorption band centred at 1.
Furthermore,  Hansen et al. (2014)  have suggested that the emission data for HD 209458b are consistent with radiation from an isothermal blackbody. The conflicting results obtained by different authors analysing the same datasets is likely due to the various methods that have been used to account for the instrumental systematics that dominate IRAC lightcurves. The main contribution of the current study is to apply the machinery of Gaussian processes (GPs) to the task of treating these systematics. This work follows similar applications of GP models to transit lightcurves published by  Gibson et al. (2012b Gibson et al. ( ,a, 2013a,b),b) ,  Evans et al. (2013) , and  Gibson (2014). One of the primary advantages of GP models is that they allow us to naturally handle correlations in the data that may be poorly understood from a first principles standpoint, by specifying only high-level properties of the covariance.
OBSERVATIONS We have analysed ten primary transits and eleven secondary eclipses for HD209458b, made across all four IRAC channels. Details of the relevant Spitzer observing programs are given in Table  1 , along with references to previously published analyses. More specific information for the individual lightcurves is given in Table  2. For this study, we did not model the complete half-and full-phase datasets acquired for Programs 30825, 40280, and 60021. Instead, for these lightcurves only ∼5 hr subsections centred on the transits and eclipses were analysed. Most observations were made in stare mode. The only exceptions were those made as part of Program 20523, which have been published in  Knutson et al. (2008). For the latter, four sets of 64 frames were acquired in a given channel before the telescope was repointed to be centred on the next channel, and another four sets of 64 frames were taken.
This process was cycled through each of the four channels, and repeated for the duration of the observations. Knutson et al. discarded the first set of 64 frames in each set of four, as the star was still drifting significantly during this time following the repointing. In addition, those authors discarded the first ten frames and the 58th frame from each set of 64 for the 5. 8µm and 8. 0µm channels, as these exhibited count levels consistently below the median. We performed two separate analyses for these lightcurves: one with this culling applied, and one without. However, we obtained consistent results in both cases, and only present results for the unculled dataset below.
DATA REDUCTION The Basic Calibrated Data (BCD) frames for each lightcurve were reduced using a custom pipeline written in the Python programming language. 1 The first step performed by the pipeline is to calculate the background level and locate the stellar centroid in each BCD frame. The background was estimated by taking the median pixel value from the four 8 × 8 pixel subarrays at the corners of each frame, and then subtracted from each pixel in the array. The centroid coordinates were then determined by taking the flux-weighted mean of a 7×7 pixel subarray centred on the approximate location of the star. An initial guess was provided for the stellar centroid coordinates in the first frame, and coordinates determined for the previous frame were used as the initial guess in subsequent frames.
Mid-times were computed for each exposure in Barycentric Julian Date Coordinated Universal Time (BJDUTC) using the BMJD OBS and FRAM-TIME header entries. Bad frames were flagged by identifiying those with outlying centroid coordinates or pixel counts. This was done by comparing against the median and standard deviation of the 30 frames immediately preceding and following each frame. If the centroid coordinates or any pixel counts within a subarray spanning the photometric aperture centred at the stellar centroid differed from the median by > 5σ, the frame was discarded from the analysis. This process was iterated twice, resulting in 0. 2-4. 4% of the frames being discarded depending on the dataset (Table  2 ). Photometry was performed for each remaining frame by summing the pixel counts within circular apertures.
GAUSSIAN PROCESSES FOR IRAC SYSTEMATICS In this paper, we present an alternative method for treating intrapixel sensitivity variations and the ramp effect in IRAC lightcurves based on Gaussian processes (GPs). The first application of GP models to transit lightcurves was made by  Formally, a GP is defined as a collection of data points, any subset of which has a multivariate normal distribution (e. g. Rasmussen & Williams 2006). Indeed, this assumption is regularly made for transit lightcurve analyses published in the literature, even if it is not stated explicitly. It is equivalent to assuming that what we measure is some underlying signal, which is the combination of the astrophysical signal of interest and additional systematic terms, plus uncorrelated Gaussian noise. If we are in possession of a model µ that describes the underlying signal, the probability of measuring a specific dataset d = {d1, d2,.
This is because increasing the com- plexity of a model is equivalent to assigning similar probabilities to an increasing diversity of functions. In other words, as the model complexity increases, the likelihood function becomes less sharply peaked near the mean µ and the probability mass of the model becomes more diffusely spread throughout the function space. This is precisely what happens as the term -1 2 ln |K + Σ| decreases, in effect penalising model complexity. Finally, the third term, -1 2 N ln 2π, remains constant for a given dataset. The balance between the first two terms of Equation 4 therefore ensures that the probability mass of the model is distributed over the parameter space in a manner that optimises the trade-off between goodness-of-fit and model complexity.
This is a valuable property of GPs given that the ramp effect is not necessarily strictly monotonic, with an overshoot effect identified in a number of datasets (e. g. Knutson et al. 2012). As with the other channels, we also include a time-dependent Matérn ν = 3/2 kernel kt to account for residual correlations in the lightcurve that may not be related to the ramp. Therefore, the final kernel is given by: k ( v i , v j ) = kτ + kt , (9) with covariance parameters γ = { Aτ , Lτ , h , At , Lt }. The covariance kernels outlined above allow for systematics treatments that are at least as versatile as others used in the literature. For instance, the kxy component of Equation 5 is similar in concept to the 2D Gaussian correction developed by  Ballard et al. (Section 4. 1). Similarly, the kτ component of Equation 9 is reminiscent of the logarithmic time polynomials used in other published studies (Section 4.
Lightcurve binning GP models become computationally intractable for datasets with N 10 3 data points. This is due to the need to factorise the N × N covariance matrix when evaluating the -1 2 r T (K + Σ) -1 r term and computing the determinant -1 2 ln |K + Σ| for each log likelihood evaluation (Equation  4 ). Our code implements this using Cholesky factorisation, which has a computational cost scaling as O N 3. To apply GPs to IRAC datasets, most of which consist of N > 10 4 data points, we bin the fluxes and centroid xy coordinates in time prior to fitting. Binning factors were chosen according to the format of individual lightcurves, such that the time interval between successive binned points was 15 sec and the number of binned points per lightcurve was N b = 1000-1500. Lightcurves obtained in full array mode for Program 461 were binned by a factor of two, giving a median cadence of about 17 sec; lightcurves obtained in subarray mode for Programs 40280 and 60021 with frame times of 0.
By using covariance parameters that optimise the GP likelihood, this continuum is narrowed in a principled manner. To compare with the bilinear interpolation method for pixel mapping used by  Stevenson et al. (2012)  for instance, optimising the covariance length scales Lx and Ly is similar to choosing the optimal grid spacing for the interpolation knots. Treating our dataset as a GP, we have the advantage of being able to do this in the context of a self-consistent probabilistic model, by maximising the likelihood function with respect to the unknown parameters using a numerical optimiser. Having fixed the covariance parameters, an initial chain of 10 5 steps was run with the planet parameters allowed to vary and step sizes again pretuned to ensure acceptance rates of 20-40 %. The first 5 × 10 4 steps were discarded as burn-in.
Marginalisation of the GP model posterior distributions therefore allows us to more exhaustively explore possible degeneracies between the planet signal and systematics, and incorporate these into the uncertainties associated with the inferred planet properties. However, recall from Section 6 that we fixed the covariance parameters to their MLE values before marginalising over the planet parameters with MCMC, which means the uncertainties quoted in Table  4  should in fact be regarded as lower limits to the true uncertainties. Further verification of the reliability of GP models for inferring planet parameters from IRAC lightcurves could be obtained by systematically applying the method to synthetic datasets, similar to what was done by  Gibson (2014). This would allow us to directly compare inferred values for the planet parameters with their known values.
The acceptable fit provided by the isothermal blackbody for our data supports this hypothesis in the case of HD 209458b, especially consid-ering the reduced χ 2 could be even lower if we marginalised over the covariance parameters and obtained larger uncertainties (Section 6). One possibility is that the emission measurements are probing an isothermal layer of the atmosphere above a cloud deck that extends across the dayside hemisphere. This could simultaneously explain the lack of absorption features detected in the IRAC transmission data, and the muted H2O feature detected in the WFC3 transmission data, discussed above. We stress, however, that this is a speculative scenario, poorly constrained by the existing observations.
This can largely be attributed to the flexibility of the GP models, which allow complex correlations to be handled with a small number of free parameters. The latter point is important, as it means that marginalisation over the model parameter space remains computationally tractable, allowing uncertainties that realistically quantify the degeneracies between the planet signal and instrumental systematics to be derived. We obtain an overall improvement in consistency for the normalised semimajor axes a/R and orbital inclinations i across different epochs and wavelength channels, compared to results that have been previously published for these datasets. This provides evidence that the GP models are effectively accounting for the systematics, and typically less prone to underestimating uncertainties compared with other lightcurve fitting approaches used in the literature.
0 2008 Jul 22 full, 2. 0 4. 42 1286 2 16. 8 4. 0 60021 Eclipse (1st) 3. 6 2011 Jan 12 sub, 0. 1 0. 25 1285 128 16. 8 2. 5 Transit 3. 6 2011 Jan 14 sub, 0. 1 0. 25 1286 128 16. 8 2. 5 Eclipse (2nd) 3. 6 2011 Jan 16 sub, 0. 1 0. 29 1286 128 16. 8 3. 0 Eclipse (1st) 3. 6 2014 Feb 13 sub, 0. 1 0. 23 1286 128 16. 8 3. 0 Transit 3. 6 2014 Feb 15 sub, 0. 1 0. 23 1286 128 16. 8 3. 0 Eclipse (2nd) 3. 6 2014 Feb 17 sub, 0. 1 0. 18 1285 128 16. 8 2. 5 Eclipse (1st) 4. 5 2010 Jan 18 sub, 0. 4 0. 62 1577 32 13. 6 2. 5 Transit 4. 5 2010 Jan 19 sub, 0. 4 0. 73 1575 32 13. 6 3. 0 Eclipse (2nd) 4. 5 2010 Jan 21 sub, 0. 4 0. 62 1577 32 13. 6 2. 5 a Readout mode and frame time in seconds. b Fraction of frames flagged as bad. c Number of frames in the binned dataset used for the GP lightcurve fit. d Number of consecutive frames per bin.