Synthetic CO, H2 and H I surveys of the second galactic quadrant, and the properties of molecular gas

articleWe present CO, H2, H I and HISA (H I self-absorption) distributions from a set of simulations of grand design spirals including stellar feedback, self-gravity, heating and cooling. We replicate the emission of the second galactic quadrant by placing the observer inside the modelled galaxies and post-process the simulations using a radiative transfer code, so as to create synthetic observations. We compare the synthetic data cubes to observations of the second quadrant of the Milky Way to test the ability of the current models to reproduce the basic chemistry of the Galactic interstellar medium (ISM), as well as to test how sensitive such galaxy models are to different recipes of chemistry and/or feedback. We find that models which include feedback and self-gravity can reproduce the production of CO with respect to H2 as observed in our Galaxy, as well as the distribution of the material perpendicular to the Galactic plane. While changes in the chemistry/feedback recipes do not have a huge impact on the statistical properties of the chemistry in the simulated galaxies, we find that the inclusion of both feedback and self-gravity are crucial ingredients, as our test without feedback failed to reproduce all of the observables. Finally, even though the transition from H2 to CO seems to be robust, we find that all models seem to underproduce molecular gas, and have a lower molecular to atomic gas fraction than is observed. Nevertheless, our fiducial model with feedback and self-gravity has shown to be robust in reproducing the statistical properties of the basic molecular gas components of the ISM in our Galaxy.We thank the referee, Ralf Klessen, for his comments that helped strengthen the paper. ADC and CLD acknowledge funding from the European Research Council for the FP7 ERC starting grant project LOCALSTAR. The calculations for this paper were performed on the DiRAC Complexity machine, jointly funded by STFC and the Large Facilities Capital Fund of BIS, and the University of Exeter Supercomputer, a DiRAC Facility jointly funded by STFC, the Large Facilities Capital Fund of BIS and the University of Exeter. Fig. 1 was produced using SPLASH (Price 2007). We acknowledge the use of NASA’s SkyView facility (http://skyview.gsfc.nasa.gov) located at NASA Goddard Space Flight Center. We also thank A. Rodrigues for providing high-resolution dust column density maps for benchmarking

Automatic segmentation of prostate MRI using convolutional neural networks: Investigating the impact of network architecture on the accuracy of volume measurement and MRI-ultrasound registration

Convolutional neural networks (CNNs) have recently led to significant advances in automatic segmentations of anatomical structures in medical images, and a wide variety of network architectures are now available to the research community. For applications such as segmentation of the prostate in magnetic resonance images (MRI), the results of the PROMISE12 online algorithm evaluation platform have demonstrated differences between the best-performing segmentation algorithms in terms of numerical accuracy using standard metrics such as the Dice score and boundary distance. These small differences in the segmented regions/boundaries outputted by different algorithms may potentially have an unsubstantial impact on the results of downstream image analysis tasks, such as estimating organ volume and multimodal image registration, which inform clinical decisions. This impact has not been previously investigated. In this work, we quantified the accuracy of six different CNNs in segmenting the prostate in 3D patient T2-weighted MRI scans and compared the accuracy of organ volume estimation and MRI-ultrasound (US) registration errors using the prostate segmentations produced by different networks. Networks were trained and tested using a set of 232 patient MRIs with labels provided by experienced clinicians. A statistically significant difference was found among the Dice scores and boundary distances produced by these networks in a non-parametric analysis of variance (p < 0.001 and p < 0.001, respectively), where the following multiple comparison tests revealed that the statistically significant difference in segmentation errors were caused by at least one tested network. Gland volume errors (GVEs) and target registration errors (TREs) were then estimated using the CNN-generated segmentations. Interestingly, there was no statistical difference found in either GVEs or TREs among different networks, (p = 0.34 and p = 0.26, respectively). This result provides a real-world example that these networks with different segmentation performances may potentially provide indistinguishably adequate registration accuracies to assist prostate cancer imaging applications. We conclude by recommending that the differences in the accuracy of downstream image analysis tasks that make use of data output by automatic segmentation methods, such as CNNs, within a clinical pipeline should be taken into account when selecting between different network architectures, in addition to reporting the segmentation accuracy

Optically trapped bacteria pairs reveal discrete motile response to control aggregation upon cell–cell approach

Aggregation of bacteria plays a key role in the formation of many biofilms. The critical first step is cell–cell approach, and yet the ability of bacteria to control the likelihood of aggregation during this primary phase is unknown. Here, we use optical tweezers to measure the force between isolated Bacillus subtilis cells during approach. As we move the bacteria towards each other, cell motility (bacterial swimming) initiates the generation of repulsive forces at bacterial separations of ~3 μm. Moreover, the motile response displays spatial sensitivity with greater cell–cell repulsion evident as inter-bacterial distances decrease. To examine the environmental influence on the inter-bacterial forces, we perform the experiment with bacteria suspended in Tryptic Soy Broth, NaCl solution and deionised water. Our experiments demonstrate that repulsive forces are strongest in systems that inhibit biofilm formation (Tryptic Soy Broth), while attractive forces are weak and rare, even in systems where biofilms develop (NaCl solution). These results reveal that bacteria are able to control the likelihood of aggregation during the approach phase through a discretely modulated motile response. Clearly, the force-generating motility we observe during approach promotes biofilm prevention, rather than biofilm formation

Differences between <i>Trypanosoma brucei gambiense</i> groups 1 and 2 in their resistance to killing by Trypanolytic factor 1

&lt;p&gt;&lt;b&gt;Background:&lt;/b&gt; The three sub-species of &lt;i&gt;Trypanosoma brucei&lt;/i&gt; are important pathogens of sub-Saharan Africa. &lt;i&gt;T. b. brucei&lt;/i&gt; is unable to infect humans due to sensitivity to trypanosome lytic factors (TLF) 1 and 2 found in human serum. &lt;i&gt;T. b. rhodesiense&lt;/i&gt; and &lt;i&gt;T. b. gambiense&lt;/i&gt; are able to resist lysis by TLF. There are two distinct sub-groups of &lt;i&gt;T. b. gambiense&lt;/i&gt; that differ genetically and by human serum resistance phenotypes. Group 1 &lt;i&gt;T. b. gambiense&lt;/i&gt; have an invariant phenotype whereas group 2 show variable resistance. Previous data indicated that group 1 &lt;i&gt;T. b. gambiense&lt;/i&gt; are resistant to TLF-1 due in-part to reduced uptake of TLF-1 mediated by reduced expression of the TLF-1 receptor (the haptoglobin-hemoglobin receptor (&lt;i&gt;HpHbR&lt;/i&gt;)) gene. Here we investigate if this is also true in group 2 parasites.&lt;/p&gt; &lt;p&gt;&lt;b&gt;Methodology:&lt;/b&gt; Isogenic resistant and sensitive group 2 &lt;i&gt;T. b. gambiense&lt;/i&gt; were derived and compared to other T. brucei parasites. Both resistant and sensitive lines express the &lt;i&gt;HpHbR&lt;/i&gt; gene at similar levels and internalized fluorescently labeled TLF-1 similar fashion to &lt;i&gt;T. b. brucei&lt;/i&gt;. Both resistant and sensitive group 2, as well as group 1 &lt;i&gt;T. b. gambiense&lt;/i&gt;, internalize recombinant APOL1, but only sensitive group 2 parasites are lysed.&lt;/p&gt; &lt;p&gt;&lt;b&gt;Conclusions:&lt;/b&gt; Our data indicate that, despite group 1 &lt;i&gt;T. b. gambiense&lt;/i&gt; avoiding TLF-1, it is resistant to the main lytic component, APOL1. Similarly group 2 &lt;i&gt;T. b. gambiense&lt;/i&gt; is innately resistant to APOL1, which could be based on the same mechanism. However, group 2 &lt;i&gt;T. b. gambiense&lt;/i&gt; variably displays this phenotype and expression does not appear to correlate with a change in expression site or expression of &lt;i&gt;HpHbR&lt;/i&gt;. Thus there are differences in the mechanism of human serum resistance between &lt;i&gt;T. b. gambiense&lt;/i&gt; groups 1 and 2.&lt;/p&gt

An approximate model for cancellous bone screw fixation

This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ 2013 Taylor & Francis.This paper presents a finite element (FE) model to identify parameters that affect the performance of an improved cancellous bone screw fixation technique, and hence potentially improve fracture treatment. In cancellous bone of low apparent density, it can be difficult to achieve adequate screw fixation and hence provide stable fracture fixation that enables bone healing. Data from predictive FE models indicate that cements can have a significant potential to improve screw holding power in cancellous bone. These FE models are used to demonstrate the key parameters that determine pull-out strength in a variety of screw, bone and cement set-ups, and to compare the effectiveness of different configurations. The paper concludes that significant advantages, up to an order of magnitude, in screw pull-out strength in cancellous bone might be gained by the appropriate use of a currently approved calcium phosphate cement

A Relational Event Approach to Modeling Behavioral Dynamics

This chapter provides an introduction to the analysis of relational event data (i.e., actions, interactions, or other events involving multiple actors that occur over time) within the R/statnet platform. We begin by reviewing the basics of relational event modeling, with an emphasis on models with piecewise constant hazards. We then discuss estimation for dyadic and more general relational event models using the relevent package, with an emphasis on hands-on applications of the methods and interpretation of results. Statnet is a collection of packages for the R statistical computing system that supports the representation, manipulation, visualization, modeling, simulation, and analysis of relational data. Statnet packages are contributed by a team of volunteer developers, and are made freely available under the GNU Public License. These packages are written for the R statistical computing environment, and can be used with any computing platform that supports R (including Windows, Linux, and Mac).

Automatic slice segmentation of intraoperative transrectal ultrasound images using convolutional neural networks

Clinically important targets for ultrasound-guided prostate biopsy and prostate cancer focal therapy can be defined on MRI. However, localizing these targets on transrectal ultrasound (TRUS) remains challenging. Automatic segmentation of the prostate on intraoperative TRUS images is an important step towards automating most MRI-TRUS image registration workflows so that they become more acceptable in clinical practice. In this paper, we propose a deep learning method using convolutional neural networks (CNNs) for automatic prostate segmentation in 2D TRUS slices and 3D TRUS volumes. The method was evaluated on a clinical cohort of 110 patients who underwent TRUS-guided targeted biopsy. Segmentation accuracy was measured by comparison to manual prostate segmentation in 2D on 4055 TRUS images and in 3D on the corresponding 110 volumes, in a 10-fold patient-level cross validation. The proposed method achieved a mean 2D Dice score coefficient (DSC) of 0.91±0.12 and a mean absolute boundary segmentation error of 1.23±1.46mm. Dice scores (0.91±0.04) were also calculated for 3D volumes on the patient level. These suggest a promising approach to aid a wide range of TRUS-guided prostate cancer procedures needing multimodality data fusion