Improvements in the fossil record may largely resolve current conflicts between morphological and molecular estimates of mammal phylogeny

Phylogenies of mammals based on morphological data continue to show several major areas of conflict with the current consensus view of their relationships, which is based largely on molecular data. This raises doubts as to whether current morphological character sets are able to accurately resolve mammal relationships. We tested this under a hypothetical ‘best case scenario’ by using ancestral state reconstruction (under both maximum parsimony and maximum likelihood) to infer the morphologies of fossil ancestors for all clades present in a recent comprehensive DNA sequencebased phylogeny of mammals, and then seeing what effect the subsequent inclusion of these predicted ancestors had on unconstrained phylogenetic analyses of morphological data. We found that this resulted in topologies that are highly congruent with the current consensus phylogeny, at least when the predicted ancestors are assumed to be well preserved and densely sampled. Most strikingly, several analyses recovered the monophyly of clades that have never been found in previous morphology-only studies, such as Afrotheria and Laurasiatheria. Our results suggest that, at least in principle, improvements in the fossil record—specifically the discovery of fossil taxa that preserve the ancestral or near-ancestral morphologies of the nodes in the current consensus—may be sufficient to largely reconcile morphological and molecular estimates of mammal phylogeny, even using current morphological character set

Genetic markers associated with resistance to beta-lactam and quinolone antimicrobials in non-typhoidal Salmonella isolates from humans and animals in central Ethiopia

Abstract Background Beta-lactam and quinolone antimicrobials are commonly used for treatment of infections caused by non-typhoidal Salmonella (NTS) and other pathogens. Resistance to these classes of antimicrobials has increased significantly in the recent years. However, little is known on the genetic basis of resistance to these drugs in Salmonella isolates from Ethiopia. Methods Salmonella isolates with reduced susceptibility to beta-lactams ( n \u2009=\u200943) were tested for genes encoding for beta-lactamase enzymes, and those resistant to quinolones ( n \u2009=\u200929) for mutations in the quinolone resistance determining region (QRDR) as well as plasmid mediated quinolone resistance (PMQR) genes using PCR and sequencing. Results Beta-lactamase genes ( bla ) were detected in 34 (79.1%) of the isolates. The dominant bla gene was bla TEM, recovered from 33 (76.7%) of the isolates, majority being TEM-1 (24, 72.7%) followed by TEM-57, (10, 30.3%). The bla OXA-10 and bla CTX-M-15 were detected only in a single S. Concord human isolate. Double substitutions in gyr A (Ser83-Phe\u2009+\u2009Asp87-Gly) as well as par C (Thr57-Ser\u2009+\u2009Ser80-Ile) subunits of the quinolone resistance determining region (QRDR) were detected in all S. Kentucky isolates with high level resistance to both nalidixic acid and ciprofloxacin. Single amino acid substitutions, Ser83-Phe ( n \u2009=\u20094) and Ser83-Tyr ( n \u2009=\u20091) were also detected in\ua0the gyr A gene. An isolate of S . Miami susceptible to nalidixic acid but intermediately resistant to ciprofloxacin had Thr57-Ser and an additional novel mutation (Tyr83-Phe) in the par C gene. Plasmid mediated quinolone resistance (PMQR) genes investigated were not detected in any of the isolates. In some isolates with decreased susceptibility to ciprofloxacin and/or nalidixic acid, no mutations in QRDR or PMQR genes were detected. Over half of the quinolone resistant isolates in the current study 17 (58.6%) were also resistant to at least one of the beta-lactam antimicrobials. Conclusion Acquisition of bla TEM was the principal beta-lactamase resistance mechanism and mutations within QRDR of gyr A and par C were the primary mechanism for resistance to quinolones. Further study on extended ..

Prime Focus Spectrograph (PFS) for the Subaru telescope: Ongoing integration and future plans

PFS (Prime Focus Spectrograph), a next generation facility instrument on the 8.2-meter Subaru Telescope, is a very wide-field, massively multiplexed, optical and near-infrared spectrograph. Exploiting the Subaru prime focus, 2394 reconfigurable fibers will be distributed over the 1.3 deg field of view. The spectrograph has been designed with 3 arms of blue, red, and near-infrared cameras to simultaneously observe spectra from 380nm to 1260nm in one exposure at a resolution of ∼ 1.6-2.7Å. An international collaboration is developing this instrument under the initiative of Kavli IPMU. The project recently started undertaking the commissioning process of a subsystem at the Subaru Telescope side, with the integration and test processes of the other subsystems ongoing in parallel. We are aiming to start engineering night-sky operations in 2019, and observations for scientific use in 2021. This article gives an overview of the instrument, current project status and future paths forward

Lumbar spine discs labeling using axial view MRI based on the pixels coordinate and gray level features

Disc herniation is a major reason for lower back pain (LBP), a health issue that affects a very high proportion of the UK population and is costing the UK government over £1.3 million per day in health care cost. Currently, the process to diagnose the cause of LBP involves examining a large number of Magnetic Resonance Images (MRI) but this process is both expensive in terms time and effort. Automatic labeling of lumbar disc pixels in the MRI to detect the herniation area will reduce the time to diagnose and detect the cause of LBP by the physicians. In this paper, we present a method for automatic labeling of the lumbar spine disc pixels in axial view MRI using pixels locations and gray level as features. Clinical MRIs are used for the training and testing of the method. The pixel classification accuracy and the quality of the reconstructed disc images are used as the main performance indicators for our method. Our experiments show that high level of classification accuracy of 91.1% and 98.9% can be achieved using Weighted KNN and Fine Gaussian SVM classifiers respectively