Resort workers: the role of social media in connecting youth travellers and mediating the neo-tribe

<div><p>The detection of the singleton attractors is of great significance for the systematic study of genetic regulatory network. In this paper, we design an algorithm to compute the singleton attractors and pre-images of the strong-inhibition Boolean networks which is a biophysically plausible gene model. Our algorithm can not only identify accurately the singleton attractors, but also find easily the pre-images of the network. Based on extensive computational experiments, we show that the computational time of the algorithm is proportional to the number of the singleton attractors, which indicates the algorithm has much advantage in finding the singleton attractors for the networks with high average degree and less inhibitory interactions. Our algorithm may shed light on understanding the function and structure of the strong-inhibition Boolean networks.</p></div

DNA-Interacting Characteristics of the Archaeal Rudiviral Protein SIRV2_Gp1

Whereas the infection cycles of many bacterial and eukaryotic viruses have been characterized in detail, those of archaeal viruses remain largely unexplored. Recently, studies on a few model archaeal viruses such as SIRV2 (Sulfolobus islandicus rod-shaped virus) have revealed an unusual lysis mechanism that involves the formation of pyramidal egress structures on the host cell surface. To expand understanding of the infection cycle of SIRV2, we aimed to functionally characterize gp1, which is a SIRV2 gene with unknown function. The SIRV2_Gp1 protein is highly expressed during early stages of infection and it is the only protein that is encoded twice on the viral genome. It harbours a helix-turn-helix motif and was therefore hypothesized to bind DNA. The DNA-binding behavior of SIRV2_Gp1 was characterized with electrophoretic mobility shift assays and atomic force microscopy. We provide evidence that the protein interacts with DNA and that it forms large aggregates, thereby causing extreme condensation of the DNA. Furthermore, the N-terminal domain of the protein mediates toxicity to the viral host Sulfolobus. Our findings may lead to biotechnological applications, such as the development of a toxic peptide for the containment of pathogenic bacteria, and add to our understanding of the Rudiviral infection cycle.status: publishe

Ionic Liquids Containing Block Copolymer Based Supramolecules

Block copolymer (BCP)-based supramolecules provide a versatile strategy to generate functional materials using noncovalent bond between small molecules and BCPs. Here, we report supramolecules composed of phenol-containing ionic liquids (ILs) hydrogen bonded to BCP, polystyrene-<i>block</i>-poly­(4-vinylpyridine) (PS-<i>b</i>-P4VP). IL-containing supramolecules exhibit ordered structures in a wide range of IL loading and chemistry. Rheological behaviors and nanostructures of IL-containing supramolecules can be tuned by controlling the IL loading without losing ordered structure. The hydrogen bonds and nanostructures can be retained in a wide range of temperatures with different IL chemistry. Supramolecules provide a diverse platform toward IL materials with ordered structure and tunable properties with high tolerance of thermal treatment and processing

A Robust Parallel Object Tracking Method for Illumination Variations

Illumination variation often occurs in visual tracking, which has a severe impact on the system performance. Many trackers based on Discriminative correlation filter (DCF) have recently obtained promising performance, showing robustness to illumination variation. However, when the target objects undergo significant appearance variation due to intense illumination variation, the features extracted from the object will not have the ability to be discriminated from the background, which causes the tracking algorithm to lose the target in the scene. In this paper, in order to improve the accuracy and robustness of the Discriminative correlation filter (DCF) trackers under intense illumination variation, we propose a very effective strategy by performing multiple region detection and using alternate templates (MRAT). Based on parallel computation, we are able to perform simultaneous detection of multiple regions, equivalently enlarging the search region. Meanwhile the alternate template is saved by a template update mechanism in order to improve the accuracy of the tracker under strong illumination variation. Experimental results on large-scale public benchmark datasets show the effectiveness of the proposed method compared to state-of-the-art methods

Histograms of for random configurations of a SW network.

<p>(a)–(d) <i>N</i> = 200 and with different rewiring probabilities <i>P</i>: <i>P</i> = 0.4, 0.3, 0.2, and 0.1, respectively. The open and solid squares represent the configurations of point-to-point-positive and point-to-point-negative correlations between the node masses and node degrees, respectively.</p

The results of for a random ER network.

<p>Similar to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0082161#pone-0082161-g001" target="_blank">Fig. 1</a> for the results of with the same ER network considered instead. Again the effect of one is enough appears, but this time becomes maximal for the single-point-negative correlation configuration in (d). For more details, see the text.</p

The results of for a random ER network.

<p><i>N</i> = 200, , , and , the plot includes completely random configurations (black points), the point-to-point-positive correlation configuration (open squares), and the point-to-point-negative correlation configuration (solid squares) in (a); the single-point-positive correlation configurations and in (c); and the single-point-negative correlation configurations in (d) and in (e). (b) The histogram for random configurations. (f) vs . A remarkable finding in (e) is 's are not only very small, but also their range is very narrow, indicating one is enough in determining the networked dynamics. For more details, see the text.</p

A Review of Deep-Learning-Based Medical Image Segmentation Methods

As an emerging biomedical image processing technology, medical image segmentation has made great contributions to sustainable medical care. Now it has become an important research direction in the field of computer vision. With the rapid development of deep learning, medical image processing based on deep convolutional neural networks has become a research hotspot. This paper focuses on the research of medical image segmentation based on deep learning. First, the basic ideas and characteristics of medical image segmentation based on deep learning are introduced. By explaining its research status and summarizing the three main methods of medical image segmentation and their own limitations, the future development direction is expanded. Based on the discussion of different pathological tissues and organs, the specificity between them and their classic segmentation algorithms are summarized. Despite the great achievements of medical image segmentation in recent years, medical image segmentation based on deep learning has still encountered difficulties in research. For example, the segmentation accuracy is not high, the number of medical images in the data set is small and the resolution is low. The inaccurate segmentation results are unable to meet the actual clinical requirements. Aiming at the above problems, a comprehensive review of current medical image segmentation methods based on deep learning is provided to help researchers solve existing problems

Use of a Green Familiar Faces Paradigm Improves P300-Speller Brain-Computer Interface Performance - Fig 5

<p>(a) Superimposed difference waveforms of ERPs elicited by the target and non-target trials (ERP_Target—ERP_Non-target) in the FF and GFF spelling paradigms. The gray square areas indicate the time periods during which the difference waveforms of ERPs elicited by the target and non-target trials (ERP_Target—ERP_Non-target) were significantly different (p < 0.01) between the FF and GFF spelling paradigms. (b) Scalp topographies for double-difference waveforms obtained by subtracting the (ERP_Target—ERP_Non-target) waveforms for the FF spelling paradigm from those for the GFF spelling paradigm for the time periods showing significant differences (160–220 ms, 160–260 ms, 300–400 ms, and 640–680 ms).</p

EEG setup consisting of 14 electrodes.

<p>Locations were Fz, F3, F4, FC1, FC2, Cz, C3, C4, Pz, P3, P4, Oz, O1, and O2.</p