Deep RNA-Seq profile reveals biodiversity, plant-microbe interactions and a large family of NBS-LRR resistance genes in walnut (Juglans regia) tissues.

Deep RNA-Seq profiling, a revolutionary method used for quantifying transcriptional levels, often includes non-specific transcripts from other co-existing organisms in spite of stringent protocols. Using the recently published walnut genome sequence as a filter, we present a broad analysis of the RNA-Seq derived transcriptome profiles obtained from twenty different tissues to extract the biodiversity and possible plant-microbe interactions in the walnut ecosystem in California. Since the residual nature of the transcripts being analyzed does not provide sufficient information to identify the exact strain, inferences made are constrained to the genus level. The presence of the pathogenic oomycete Phytophthora was detected in the root through the presence of a glyceraldehyde-3-phosphate dehydrogenase. Cryptococcus, the causal agent of cryptococcosis, was found in the catkins and vegetative buds, corroborating previous work indicating that the plant surface supported the sexual cycle of this human pathogen. The RNA-Seq profile revealed several species of the endophytic nitrogen fixing Actinobacteria. Another bacterial species implicated in aerobic biodegradation of methyl tert-butyl ether (Methylibium petroleiphilum) is also found in the root. RNA encoding proteins from the pea aphid were found in the leaves and vegetative buds, while a serine protease from mosquito with significant homology to a female reproductive tract protease from Drosophila mojavensis in the vegetative bud suggests egg-laying activities. The comprehensive analysis of RNA-seq data present also unraveled detailed, tissue-specific information of ~400 transcripts encoded by the largest family of resistance (R) genes (NBS-LRR), which possibly rationalizes the resistance of the specific walnut plant to the pathogens detected. Thus, we elucidate the biodiversity and possible plant-microbe interactions in several walnut (Juglans regia) tissues in California using deep RNA-Seq profiling

Dynamic clustering of time series with Echo State Networks

In this paper we introduce a novel methodology for unsupervised analysis of time series, based upon the iterative implementation of a clustering algorithm embedded into the evolution of a recurrent Echo State Network. The main features of the temporal data are captured by the dynamical evolution of the network states, which are then subject to a clustering procedure. We apply the proposed algorithm to time series coming from records of eye movements, called saccades, which are recorded for diagnosis of a neurodegenerative form of ataxia. This is a hard classification problem, since saccades from patients at an early stage of the disease are practically indistinguishable from those coming from healthy subjects. The unsupervised clustering algorithm implanted within the recurrent network produces more compact clusters, compared to conventional clustering of static data, and provides a source of information that could aid diagnosis and assessment of the disease.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec

The unique regulation of iron-sulfur cluster biogenesis in a Gram-positive bacterium

Iron-sulfur clusters function as cofactors of a wide range of proteins, with diverse molecular roles in both prokaryotic and eukaryotic cells. Dedicated machineries assemble the clusters and deliver them to the final acceptor molecules in a tightly regulated process. In the prototypical Gram-negative bacterium Escherichia coli, the two existing iron-sulfur cluster assembly systems, iron-sulfur cluster (ISC) and sulfur assimilation (SUF) pathways, are closely interconnected. The ISC pathway regulator, IscR, is a transcription factor of the helix-turn-helix type that can coordinate a [2Fe-2S] cluster. Redox conditions and iron or sulfur availability modulate the ligation status of the labile IscR cluster, which in turn determines a switch in DNA sequence specificity of the regulator: cluster-containing IscR can bind to a family of gene promoters (type-1) whereas the clusterless form recognizes only a second group of sequences (type-2). However, iron-sulfur cluster biogenesis in Gram-positive bacteria is not so well characterized, and most organisms of this group display only one of the iron-sulfur cluster assembly systems. A notable exception is the unique Gram-positive dissimilatory metal reducing bacterium Thermincola potens, where genes from both systems could be identified, albeit with a diverging organization from that of Gram-negative bacteria. We demonstrated that one of these genes encodes a functional IscR homolog and is likely involved in the regulation of iron-sulfur cluster biogenesis in T. potens. Structural and biochemical characterization of T. potens and E. coli IscR revealed a strikingly similar architecture and unveiled an unforeseen conservation of the unique mechanism of sequence discrimination characteristic of this distinctive group of transcription regulators.We thank Jorge Vieira for help with Automatic Detection of Positively Selected Sites. We acknowledge the European Synchrotron Radiation Facility (ESRF) for provision of synchrotron radiation facilities and thank the ESRF staff for help with data collection. Microscale thermophoresis data collection was carried out at the Campus Science Support Facilities Protein Technologies Facility (www.csf.ac.at). This work was funded by Fundo Europeu de Desenvolvimento Regional through the Operational Competitiveness Programme-COMPETE and by national funds through Fundacao para a Ciencia e a Tecnologia under project FCOMP-01-0124-FEDER-028116 (PTDC/BBB - BEP/2127/2012) and PhD Fellowship SFRH/BD/66461/2009 (to J.A.S.). The research leading to these results has received funding from the European Community's Seventh Framework Programme (FP7/2007-2013) under BioStruct-X (Grant Agreement 283570)

Robust Fuzzy Clustering via Trimming and Constraints

Producción CientíficaA methodology for robust fuzzy clustering is proposed. This methodology can be widely applied in very different statistical problems given that it is based on probability likelihoods. Robustness is achieved by trimming a fixed proportion of “most outlying” observations which are indeed self-determined by the data set at hand. Constraints on the clusters’ scatters are also needed to get mathematically well-defined problems and to avoid the detection of non-interesting spurious clusters. The main lines for computationally feasible algorithms are provided and some simple guidelines about how to choose tuning parameters are briefly outlined. The proposed methodology is illustrated through two applications. The first one is aimed at heterogeneously clustering under multivariate normal assumptions and the second one migh be useful in fuzzy clusterwise linear regression problems.Ministerio de Economía, Industria y Competitividad (MTM2014-56235-C2-1-P)Junta de Castilla y León (programa de apoyo a proyectos de investigación – Ref. VA212U13

A magnetically collimated jet from an evolved star

Planetary nebulae often have asymmetric shapes, which could arise due to collimated jets from evolved stars before evolution to the planetary nebula phase. The source of jet collimation in these stars is unknown. Magnetic fields are thought to collimate outflows that are observed in many other astrophysical sources, such as active galactic nuclei and proto-stars, although hitherto there are no direct observations of both the magnetic field direction and strength in any collimated jet. Theoretical models have shown that magnetic fields could also be the dominant source of collimation of jet in evolved stars. Here we report measurements of the polarization of water vapour masers that trace the precessing jet emanating from the asymptotic giant branch star W43A at 2.6 kpc from the Sun, which is undergoing rapid evolution into a planetary nebula. The masers occur in two clusters at opposing tips of the jets, ~1,000 AU from the star. We find direct evidence that the magnetic field is collimating the jet.Comment: Published in Nature 440 (March 2nd 2006). High-res figures can be found at http://www.jb.man.ac.uk/~wouter/papers/w43a/w43a.htm

Comparison of 1-repetition-maximum performance across 3 weightlifting overhead pressing exercises and sport groups

Objective: This study aimed to (I) compare the one repetition maximum (1RM) performance between the push press (PP), push jerk (PJ) and split jerk (SJ), and (II) explore these differences between weightlifters, CrossFit® athletes and a mixed group of athletes. Method: Forty-six resistance trained male (age: 28.8 ± 6.4 years; height: 180.0 ± 6.0 cm; body mass: 84.1 ± 10.2 kg; weightlifting training experience: 3.64 ± 3.14 years) participated in this study. The 1RM performance of the PP, PJ and SJ were assessed during the same session in a sequential order (i.e. combined 1RM assessment method). Thirty-six participants were re-tested to determine between-session reliability of the 1RM values. Results: Intraclass correlation coefficients (ICC) and associated 95% confidence intervals showed a high between-session reliability for the PP (ICC = 0.98 [0.95-0.99]), PJ (ICC = 0.99 [0.98-1.00]) and SJ (ICC = 0.99 [0.98-1.00]). There was a significant main effect of exercise (2 = 0.662) and exercise x group interaction (2 = 0.066) on the 1RM performance (p<0.0001), while the main effect of group did not reach statistical significance (p=0.072). Conclusion: This study provides evidence that the weightlifting overhead pressing derivatives impact the 1RM performance. In addition, the interaction of exercise and sport group was caused by the higher differences in the 1RM performance between-exercises for weightlifters compared to CrossFit® and a mixed group of athletes. Therefore, strength and conditioning professionals should be aware that the differences in 1RM performance between weightlifting overhead pressing derivatives may be affected by the sport group

Multiple Imputation Ensembles (MIE) for dealing with missing data

Missing data is a significant issue in many real-world datasets, yet there are no robust methods for dealing with it appropriately. In this paper, we propose a robust approach to dealing with missing data in classification problems: Multiple Imputation Ensembles (MIE). Our method integrates two approaches: multiple imputation and ensemble methods and compares two types of ensembles: bagging and stacking. We also propose a robust experimental set-up using 20 benchmark datasets from the UCI machine learning repository. For each dataset, we introduce increasing amounts of data Missing Completely at Random. Firstly, we use a number of single/multiple imputation methods to recover the missing values and then ensemble a number of different classifiers built on the imputed data. We assess the quality of the imputation by using dissimilarity measures. We also evaluate the MIE performance by comparing classification accuracy on the complete and imputed data. Furthermore, we use the accuracy of simple imputation as a benchmark for comparison. We find that our proposed approach combining multiple imputation with ensemble techniques outperform others, particularly as missing data increases

Missing Slice Imputation in Population CMR Imaging via Conditional Generative Adversarial Nets

Accurate ventricular volume measurements depend on complete heart coverage in cardiac magnetic resonance (CMR) from where most immediate indicators of normal/abnormal cardiac function are available non-invasively. However, incomplete coverage, especially missing basal or apical slices in CMR sequences is insufficiently addressed in population imaging and current clinical research studies yet has important impact on volume calculation accuracy. In this work, we propose a new deep architecture, coined Missing Slice Imputation Generative Adversarial Network (MSIGAN), to learn key features of cardiac short-axis (SAX) slices across different positions, and use them as conditional variables to effectively infer missing slices in the query volumes. In MSIGAN, the slices are first mapped to latent vectors with position features through a regression net. The latent vector corresponding to the desired position is then projected onto the slice manifold conditional on slice intensity through a generator net. The latent vector along with the slice features (i.e., intensity) and desired position control the generation vs. regression. Two adversarial networks are imposed on the regressor and generator, encouraging more realistic slices. Experimental results show that our method outperforms the previous state-of-the-art in missing slice imputation for cardiac MRI

Holocene geochemical footprint from Semiarid alpine wetlands in southern Spain

Here we provide the geochemical dataset that our research group has collected after 10 years of investigation in the Sierra Nevada National Park in southern Spain. These data come from Holocene sedimentary records from four alpine sites (ranging from ∼2500 to ∼3000 masl): two peatlands and two shallow lakes. Different kinds of organic and inorganic analyses have been conducted. The organic matter in the bulk sediment was characterised using elemental measurements and isotope-ratio mass spectrometry (EA-IRMS). Leaf waxes in the sediment were investigated by means of chromatography with flame-ionization detection and mass spectrometry (GC-FID, GC-MS). Major, minor and trace elements of the sediments were analysed with atomic absorption (AAS), inductively coupled plasma mass spectrometry (ICP-MS), as well as X-ray scanning fluorescence. These data can be reused by environmental researchers and soil and land managers of the Sierra Nevada National Park and similar regions to identify the effect of natural climate change, overprinted by human impact, as well as to project new management policies in similar protected areas.Universidad de Granada. Departamento de Estratigrafía y PaleontologíaJunta de Andalucía: Grupos de investigación RNM190 y RNM309Junta de Andalucía: Proyecto P11-RNM-7332España, Ministerio de Economía y Competitividad: Proyecto CGL2013-47038-RRamón y Cajal Fellowship: RYC-2015-18966Small Research Grant by the Carnegie Trust for the Universities of ScotlandMarie Curie Intra-European Fellowship of the 7th Framework Programme for Research, Technological Development and Demonstration of the European Commission: NAOSIPUK. Grant Number: PIEF-GA-2012-62302