tRNA functional signatures classify plastids as late-branching cyanobacteria.

BackgroundEukaryotes acquired the trait of oxygenic photosynthesis through endosymbiosis of the cyanobacterial progenitor of plastid organelles. Despite recent advances in the phylogenomics of Cyanobacteria, the phylogenetic root of plastids remains controversial. Although a single origin of plastids by endosymbiosis is broadly supported, recent phylogenomic studies are contradictory on whether plastids branch early or late within Cyanobacteria. One underlying cause may be poor fit of evolutionary models to complex phylogenomic data.ResultsUsing Posterior Predictive Analysis, we show that recently applied evolutionary models poorly fit three phylogenomic datasets curated from cyanobacteria and plastid genomes because of heterogeneities in both substitution processes across sites and of compositions across lineages. To circumvent these sources of bias, we developed CYANO-MLP, a machine learning algorithm that consistently and accurately phylogenetically classifies ("phyloclassifies") cyanobacterial genomes to their clade of origin based on bioinformatically predicted function-informative features in tRNA gene complements. Classification of cyanobacterial genomes with CYANO-MLP is accurate and robust to deletion of clades, unbalanced sampling, and compositional heterogeneity in input tRNA data. CYANO-MLP consistently classifies plastid genomes into a late-branching cyanobacterial sub-clade containing single-cell, starch-producing, nitrogen-fixing ecotypes, consistent with metabolic and gene transfer data.ConclusionsPhylogenomic data of cyanobacteria and plastids exhibit both site-process heterogeneities and compositional heterogeneities across lineages. These aspects of the data require careful modeling to avoid bias in phylogenomic estimation. Furthermore, we show that amino acid recoding strategies may be insufficient to mitigate bias from compositional heterogeneities. However, the combination of our novel tRNA-specific strategy with machine learning in CYANO-MLP appears robust to these sources of bias with high accuracy in phyloclassification of cyanobacterial genomes. CYANO-MLP consistently classifies plastids as late-branching Cyanobacteria, consistent with independent evidence from signature-based approaches and some previous phylogenetic studies

The OPERA trial : a protocol for the process evaluation of a randomised trial of an exercise intervention for older people in residential and nursing accommodation

Background: The OPERA trial is large cluster randomised trial testing a physical activity intervention to address depression amongst people living in nursing and residential homes for older people. A process evaluation was commissioned alongside the trial and we report the protocol for this process evaluation. Challenges included the cognitive and physical ability of the participants, the need to respect the privacy of all home residents, including study non-participants, and the physical structure of the homes. Evaluation activity had to be organised around the structured timetable of homes, leaving limited opportunities for data collection. The aims of this process evaluation are to provide findings that will assist in the interpretation of the clinical trial results, and to inform potential implementation of the physical activity intervention on a wider scale. Methods/design: Quantitative data on recruitment of homes and individuals is being collected. For homes in the intervention arm, data on dose and fidelity of the intervention delivered; including individual rates of participation in exercise classes are collected. In the control homes, uptake and delivery of depression awareness training is monitored. These data will be combined with qualitative data from an in-depth study of a purposive sample of eight homes (six intervention and two control). Discussion: Although process evaluations are increasingly funded alongside trials, it is still rare to see the findings published, and even rarer to see the protocol for such an evaluation published. Process evaluations have the potential to assist in interpreting and understanding trial results as well as informing future roll-outs of interventions. If such evaluations are funded they should also be reported and reviewed in a similar way to the trial outcome evaluation

Thermogravimetric Quantification of Biodiesel Produced via Alkali Catalyzed Transesterification of Soybean oil

The aim of this study was to demonstrate the use of thermogravimetric analysis (TGA) as a potential screening method for monitoring biodiesel production by transesterification of soybean oil with methanol. Soybean oil and commercially available biodiesel were mixed in varying proportions by weight as standards. In addition, mixtures of different biodiesel/soybean oil ratios were also created by periodically interrupting base-catalyzed transesterification of soybean oil with methanol. The mixtures produced by both approaches were analyzed with TGA over a temperature range of 25−500 °C. The results were then compared with analytical data obtained by proton nuclear magnetic resonance spectroscopy (1H NMR spectroscopy), an industry standard for biodiesel quantification. It was found in the TGA experiments that a significant weight loss at ca. 150 °C correlated to the volatilization of biodiesel. The relative weight losses in both sets of mixtures correlated well to the proportion of biodiesel present in the transesterification samples, and the results from both analytical methods were in good agreement (±1.5%). Thus, TGA is a simple, convenient, and economical method for monitoring biodiesel production

An Improved Quantum Molecular Dynamics Model and its Applications to Fusion Reaction near Barrier

An improved Quantum Molecular Dynamics model is proposed. By using this model, the properties of ground state of nuclei from $^{6}$Li to $^{208}$Pb can be described very well with one set of parameters. The fusion reactions for $^{40}$Ca+$^{90}$Zr, $^{40}$Ca+$^{96}$Zr and $^{48}$Ca+$^{90}$Zr at energy near barrier are studied by this model. The experimental data of the fusion cross sections for $^{40}$Ca+$^{90,96}$Zr at the energy near barrier can be reproduced remarkably well without introducing any new parameters. The mechanism for the enhancement of fusion probability for fusion reactions with neutron-rich projectile or target is analyzed.Comment: 20 pages, 12 figures, 3 table

Efecto de la ablación unilateral del pedúnculo ocular sobre el tiempo de maduración de los ovocitos y fecundidad de Penaeus vannamei

Los estudios de los procesos de maduración tendientes a optimizar el cultivo de P. vannamei, son necesarios para poder planificar adecuadamente la producción y satisfacer la demanda de poslarvas de mejor calidad a escala comercial que exige hoy en día la industria langostinera en el Perú. La inducción a la maduración y desove en P. vannamei por ablación unilateral del pedúnculo ocular ha sido documentada por diferentes investigadores. Hidalgo (1997), reportó que los centros endocrinos que controlan la reproducción, en crustáceos están compuestos, entre otros, por el sistema neurosecretor y el órgano X-glándula sinusal, ambos localizados en el pedúnculo ocular, en donde se desarrollan las Hormonas Inhibidoras de la Gónada (GIH) y la Hormona Estimulante de la Gónada (GSH). Otras áreas como, el cerebro, el ganglio torácico, el órgano mandibular, son reguladores directos de la reproducción al producir hormonas o factores de estimulación o inhibición del desarrollo gonadal (Vaca, 1999; Huberman, 2000; Diwan, 2005)

The Semi-Classical Relativistic Darwin Potential for Spinning Particles in the Rest-Frame Instant Form: 2-Body Bound States with Spin 1/2 Constituents

In the semiclassical approximation of Grassmann-valued electric charges for regularizing Coulomb self-energies, we extract the unique acceleration-independent interaction hidden in any Lienard-Wiechert solution for the system of N positive-energy spinning particles plus the electromagnetic field in the radiation gauge of the rest-frame instant form. With the help of a semiclassical Foldy-Wouthuysen transformation, this allows us to find the relativistic semiclassical Darwin potential. In the 2-body case the quantization of the lowest order reproduces exactly the results from the reduction of the Bethe-Salpeter equation.Comment: 102 pages, revtex fil

The R Coronae Borealis stars - carbon abundances from forbidden carbon lines

Spectra of several R Coronae Borealis (RCB) stars at maximum light were examined for the [C I] 9850 A and 8727 A absorption lines. The 9850 A line is variously blended with a Fe II and CN lines but positive identifications of the [C I] line are made for R CrB and SU Tau. The 8727 A line is detected in the spectrum of the five stars observed in this wavelength region. Carbon abundances are derived from the [C I] lines using the model atmospheres and atmospheric parameters used by Asplund et al. (2000). Although the observed strength of a C I line is constant from cool to hot RCB stars, the strength is weaker than predicted by an amount equivalent to a factor of four reduction of a line's gf-value. Asplund et al. dubbed this 'the carbon problem' and discussed possible solutions. The [C I] 9850 A line seen clearly in R CrB and SU Tau confirms the magnitude of the carbon problem revealed by the C I lines. The [C I] 8727 A line measured in five stars shows an enhanced carbon problem. The gf-value required to fit the observed [C I] 8727 A line is a factor of 15 less than the well-determined theoretical gf-value. We suggest that the carbon problem for all lines may be alleviated to some extent by a chromospheric-like temperature rise in these stars. The rise far exceeds that predicted by our non-LTE calculations, and requires a substantial deposition of mechanical energy.Comment: 11 pages (embedded 5 figures and 3 tables), accepted for publication in MNRA

Quantum machine learning: a classical perspective

Recently, increased computational power and data availability, as well as algorithmic advances, have led machine learning techniques to impressive results in regression, classification, data-generation and reinforcement learning tasks. Despite these successes, the proximity to the physical limits of chip fabrication alongside the increasing size of datasets are motivating a growing number of researchers to explore the possibility of harnessing the power of quantum computation to speed-up classical machine learning algorithms. Here we review the literature in quantum machine learning and discuss perspectives for a mixed readership of classical machine learning and quantum computation experts. Particular emphasis will be placed on clarifying the limitations of quantum algorithms, how they compare with their best classical counterparts and why quantum resources are expected to provide advantages for learning problems. Learning in the presence of noise and certain computationally hard problems in machine learning are identified as promising directions for the field. Practical questions, like how to upload classical data into quantum form, will also be addressed.Comment: v3 33 pages; typos corrected and references adde