A Comparison of Step-Detection Methods: How Well Can You Do?

Many biological machines function in discrete steps, and detection of such steps can provide insight into the machines’ dynamics. It is therefore crucial to develop an automated method to detect steps, and determine how its success is impaired by the significant noise usually present. A number of step detection methods have been used in previous studies, but their robustness and relative success rate have not been evaluated. Here, we compare the performance of four step detection methods on artificial benchmark data (simulating different data acquisition and stepping rates, as well as varying amounts of Gaussian noise). For each of the methods we investigate how to optimize performance both via parameter selection and via prefiltering of the data. While our analysis reveals that many of the tested methods have similar performance when optimized, we find that the method based on a chi-squared optimization procedure is simplest to optimize, and has excellent temporal resolution. Finally, we apply these step detection methods to the question of observed step sizes for cargoes moved by multiple kinesin motors in vitro. We conclude there is strong evidence for sub-8-nm steps of the cargo’s center of mass in our multiple motor records.     

Activation of Plant Nod-like Receptors: How Indirect Can It Be?

Pioneering plant research has shown that many Nod-like receptors (NLRs) detect pathogens indirectly via recognizing modifications of other host proteins. In this issue, two groups show that the RPM1 NLR is activated by phosphorylation of the host protein RIN4, probably resulting from activation of a host kinase by pathogen effectors.     

Any Way You Slice It—A Comparison of Confocal Microscopy Techniques

The confocal fluorescence microscope has become a popular tool for life sciences researchers, primarily because of its ability to remove blur from outside of the focal plane of the image. Several different kinds of confocal microscopes have been developed, each with advantages and disadvantages. This article will cover the grid confocal, classic confocal laser-scanning microscope (CLSM), the resonant scanning-CLSM, and the spinning-disk confocal microscope. The way each microscope technique works, the best applications the technique is suited for, the limitations of the technique, and new developments for each technology will be presented. Researchers who have access to a range of different confocal microscopes (e.g., through a local core facility) should find this paper helpful for choosing the best confocal technology for specific imaging applications. Others with funding to purchase an instrument should find the article helpful in deciding which technology is ideal for their area of research.     

Is an Idea Different from Cake: Can You Have It and Eat It,Too? A Violation of Permanence in Information Consumption

One of the basic features of information is permanence. This feature states that the consumption of information by one consumer does not affect the availability of that information to other consumers. We present examples in two studies indicating that both laymen and experts of information exchange may be motivated to make choices that violate the permanence feature in accepting (Study 1) and offering (Study 2) information. When they possess, but have not yet consumed information, people may suffer from the appearance of wastefulness. This apparent wastefulness may provide a potential explanation for the observed violation of the permanence feature. Our findings indicate that, as the material age evolves into the information age, information-exchange behavior has not evolved significantly away from material-exchange behavior.     

What Can Article-Level Metrics Do for You?

Article-level metrics (ALMs) provide a wide range of metrics about the uptake of an individual journal article by the scientific community after publication. They include citations, usage statistics, discussions in online comments and social media, social bookmarking, and recommendations. In this essay, we describe why article-level metrics are an important extension of traditional citation-based journal metrics and provide a number of example from ALM data collected for PLOS Biology.The scientific impact of a particular piece of research is reflected in how this work is taken up by the scientific community. The first systematic approach that was used to assess impact, based on the technology available at the time, was to track citations and aggregate them by journal. This strategy is not only no longer necessary—since now we can easily track citations for individual articles—but also, and more importantly, journal-based metrics are now considered a poor performance measure for individual articles [1],[2]. One major problem with journal-based metrics is the variation in citations per article, which means that a small percentage of articles can skew, and are responsible for, the majority of the journal-based citation impact factor, as shown by Campbell [1] for the 2004 Nature Journal Impact Factor. Figure 1 further illustrates this point, showing the wide distribution of citation counts between PLOS Biology research articles published in 2010. PLOS Biology research articles published in 2010 have been cited a median 19 times to date in Scopus, but 10% of them have been cited 50 or more times, and two articles [3],[4] more than 300 times. PLOS Biology metrics are used as examples throughout this essay, and the dataset is available in the supporting information (Data S1). Similar data are available for an increasing number of other publications and organizations.Open in a separate windowFigure 1Citation counts for PLOS Biology articles published in 2010.Scopus citation counts plotted as a probability distribution for all 197 PLOS Biology research articles published in 2010. Data collected May 20, 2013. Median 19 citations; 10% of papers have at least 50 citations.Scientific impact is a multi-dimensional construct that can not be adequately measured by any single indicator [2],[5],[6]. To this end, PLOS has collected and displayed a variety of metrics for all its articles since 2009. The array of different categorised article-level metrics (ALMs) used and provided by PLOS as of August 2013 are shown in Figure 2. In addition to citations and usage statistics, i.e., how often an article has been viewed and downloaded, PLOS also collects metrics about: how often an article has been saved in online reference managers, such as Mendeley; how often an article has been discussed in its comments section online, and also in science blogs or in social media; and how often an article has been recommended by other scientists. These additional metrics provide valuable information that we would miss if we only consider citations. Two important shortcomings of citation-based metrics are that (1) they take years to accumulate and (2) citation analysis is not always the best indicator of impact in more practical fields, such as clinical medicine [7]. Usage statistics often better reflect the impact of work in more practical fields, and they also sometimes better highlight articles of general interest (for example, the 2006 PLOS Biology article on the citation advantage of Open Access articles [8], one of the 10 most-viewed articles published in PLOS Biology).Open in a separate windowFigure 2Article-level metrics used by PLOS in August 2013 and their categories.Taken from [10] with permission by the authors.A bubble chart showing all 2010 PLOS Biology articles (Figure 3) gives a good overview of the year''s views and citations, plus it shows the influence that the article type (as indicated by dot color) has on an article''s performance as measured by these metrics. The weekly PLOS Biology publication schedule is reflected in this figure, with articles published on the same day present in a vertical line. Figure 3 also shows that the two most highly cited 2010 PLOS Biology research articles are also among the most viewed (indicated by the red arrows), but overall there isn''t a strong correlation between citations and views. The most-viewed article published in 2010 in PLOS Biology is an essay on Darwinian selection in robots [9]. Detailed usage statistics also allow speculatulation about the different ways that readers access and make use of published literature; some articles are browsed or read online due to general interest while others that are downloaded (and perhaps also printed) may reflect the reader''s intention to look at the data and results in detail and to return to the article more than once.Open in a separate windowFigure 3Views vs. citations for PLOS Biology articles published in 2010.All 304 PLOS Biology articles published in 2010. Bubble size correlates with number of Scopus citations. Research articles are labeled green; all other articles are grey. Red arrows indicate the two most highly cited papers. Data collected May 20, 2013.When readers first see an interesting article, their response is often to view or download it. By contrast, a citation may be one of the last outcomes of their interest, occuring only about 1 in 300 times a PLOS paper is viewed online. A lot of things happen in between these potential responses, ranging from discussions in comments, social media, and blogs, to bookmarking, to linking from websites. These activities are usually subsumed under the term “altmetrics,” and their variety can be overwhelming. Therefore, it helps to group them together into categories, and several organizations, including PLOS, are using the category labels of Viewed, Cited, Saved, Discussed, and Recommended (Figures 2 and ​and4,4, see also [10]).Open in a separate windowFigure 4Article-level metrics for PLOS Biology.Proportion of all 1,706 PLOS Biology research articles published up to May 20, 2013 mentioned by particular article-level metrics source. Colors indicate categories (Viewed, Cited, Saved, Discussed, Recommended), as used on the PLOS website.All PLOS Biology articles are viewed and downloaded, and almost all of them (all research articles and nearly all front matter) will be cited sooner or later. Almost all of them will also be bookmarked in online reference managers, such as Mendeley, but the percentage of articles that are discussed online is much smaller. Some of these percentages are time dependent; the use of social media discussion platforms, such as Twitter and Facebook for example, has increased in recent years (93% of PLOS Biology research articles published since June 2012 have been discussed on Twitter, and 63% mentioned on Facebook). These are the locations where most of the online discussion around published articles currently seems to take place; the percentage of papers with comments on the PLOS website or that have science blog posts written about them is much smaller. Not all of this online discussion is about research articles, and perhaps, not surprisingly, the most-tweeted PLOS article overall (with more than 1,100 tweets) is a PLOS Biology perspective on the use of social media for scientists [11].Some metrics are not so much indicators of a broad online discussion, but rather focus on highlighting articles of particular interest. For example, science blogs allow a more detailed discussion of an article as compared to comments or tweets, and journals themselves sometimes choose to highlight a paper on their own blogs, allowing for a more digestible explanation of the science for the non-expert reader [12]. Coverage by other bloggers also serves the same purpose; a good example of this is one recent post on the OpenHelix Blog [13] that contains video footage of the second author of a 2010 PLOS Biology article [14] discussing the turkey genome.F1000Prime, a commercial service of recommendations by expert scientists, was added to the PLOS Article-Level Metrics in August 2013. We now highlight on the PLOS website when any articles have received at least one recommendation within F1000Prime. We also monitor when an article has been cited within the widely used modern-day online encyclopedia, Wikipedia. A good example of the latter is the Tasmanian devil Wikipedia page [15] that links to a PLOS Biology research article published in 2010 [16]. While a F1000Prime recommendation is a strong endorsement from peer(s) in the scientific community, being included in a Wikipedia page is akin to making it into a textbook about the subject area and being read by a much wider audience that goes beyond the scientific community. PLOS Biology is the PLOS journal with the highest percentage of articles recommended in F1000Prime and mentioned in Wikipedia, but there is only partial overlap between the two groups of articles because they focus on different audiences (Figure 5). These recommendations and mentions in turn show correlations with other metrics, but not simple ones; you can''t assume, for example, that highly cited articles are more likely to be recommended by F1000Prime, so it will be interesting to monitor these trends now that we include this information.Open in a separate windowFigure 5 PLOS Biology articles: sites of recommendation and discussion.Number of PLOS Biology research articles published up to May 20, 2013 that have been recommended by F1000Prime (red) or mentioned in Wikipedia (blue).With the increasing availability of ALM data, there comes a growing need to provide tools that will allow the community to interrogate them. A good first step for researchers, research administrators, and others interested in looking at the metrics of a larger set of PLOS articles is the recently launched ALM Reports tool [17]. There are also a growing number of service providers, including Altmetric.com [18], ImpactStory [19], and Plum Analytics [20] that provide similar services for articles from other publishers.As article-level metrics become increasingly used by publishers, funders, universities, and researchers, one of the major challenges to overcome is ensuring that standards and best practices are widely adopted and understood. The National Information Standards Organization (NISO) was recently awarded a grant by the Alfred P. Sloan Foundation to work on this [21], and PLOS is actively involved in this project. We look forward to further developing our article-level metrics and to having them adopted by other publishers, which hopefully will pave the way to their wide incorporation into research and researcher assessments.     

Can You Catch a Liar? How Negative Emotions Affect Brain Responses when Lying or Telling the Truth

The capacity to deceive others is a complex mental skill that requires the ability to suppress truthful information. The polygraph is widely used in countries such as the USA to detect deception. However, little is known about the effects of emotional processes (such as the fear of being found guilty despite being innocent) on the physiological responses that are used to detect lies. The aim of this study was to investigate the time course and neural correlates of untruthful behavior by analyzing electrocortical indexes in response to visually presented neutral and affective questions. Affective questions included sexual, shameful or disgusting topics. A total of 296 questions that were inherently true or false were presented to 25 subjects while ERPs were recorded from 128 scalp sites. Subjects were asked to lie on half of the questions and to answer truthfully on the remaining half. Behavioral and ERP responses indicated an increased need for executive control functions, namely working memory, inhibition and task switching processes, during deceptive responses. Deceptive responses also elicited a more negative N400 over the prefrontal areas and a smaller late positivity (LP 550–750 ms) over the prefrontal and frontal areas. However, a reduction in LP amplitude was also elicited by truthful affective responses. The failure to observe a difference in LP responses across conditions likely results from emotional interference. A swLORETA inverse solution was computed on the N400 amplitude (300–400 ms) for the dishonest – honest contrast. These results showed the activation of the superior, medial, middle and inferior frontal gyri (BA9, 11, 47) and the anterior cingulate cortex during deceptive responses. Our results conclude that the N400 amplitude is a reliable neural marker of deception.     

Can You Sequence Ecology? Metagenomics of Adaptive Diversification

Few areas of science have benefited more from the expansion in sequencing capability than the study of microbial communities. Can sequence data, besides providing hypotheses of the functions the members possess, detect the evolutionary and ecological processes that are occurring? For example, can we determine if a species is adapting to one niche, or if it is diversifying into multiple specialists that inhabit distinct niches? Fortunately, adaptation of populations in the laboratory can serve as a model to test our ability to make such inferences about evolution and ecology from sequencing. Even adaptation to a single niche can give rise to complex temporal dynamics due to the transient presence of multiple competing lineages. If there are multiple niches, this complexity is augmented by segmentation of the population into multiple specialists that can each continue to evolve within their own niche. For a known example of parallel diversification that occurred in the laboratory, sequencing data gave surprisingly few obvious, unambiguous signs of the ecological complexity present. Whereas experimental systems are open to direct experimentation to test hypotheses of selection or ecological interaction, the difficulty in “seeing ecology” from sequencing for even such a simple system suggests translation to communities like the human microbiome will be quite challenging. This will require both improved empirical methods to enhance the depth and time resolution for the relevant polymorphisms and novel statistical approaches to rigorously examine time-series data for signs of various evolutionary and ecological phenomena within and between species.     

The Support for Smoke Free Policy and How It Is Influenced by Tolerance to Smoking – Experience of a Developing Country

This cross sectional survey was conducted to determine the support in making Penang UNESCO World Heritage Site (GTWHS) smoke free and to determine the influence of tolerance towards smoking on this support. This is the first phase in making Penang, Malaysia a smoke free state. A multistage sampling process was done to select a sample of respondents to represent the population of GTWHS. Attitude towards smoking was assessed using tolerance as a proxy. A total of 3,268 members of the community participated in the survey. A big majority (n = 2969; 90.9%) of the respondents supported the initiative. Support was lowest among the owners and residents/tenants, higher age groups, the Chinese, men, respondents who had poor knowledge of the places gazetted as smoke free, and respondents with poor knowledge of the health effects on smokers and on passive smokers. The odds (both adjusted and unadjusted) of not supporting the initiative was high among those tolerant to smoking in public areas. Tolerance towards smoking was associated with 80.3% risk of non-support in the respondents who were tolerant to smoking and a 57.2% risk in the population. Health promotion and education concerning the harm of tobacco smoke in Malaysia, which has mainly targeted smokers, must change. Health education concerning the risks of second hand smoke must also be given to non-smokers and efforts should be made to denormalize smoking.     

The Organically Handicapped Child—How Can the Family Physician Help?

The better his understanding of some of the ways in which an organic deficit might affect normal development of the handicapped child, the more able the family physician will be to offer guidance to the family aimed at preventing the development of secondary problems. He can thus be instrumental in helping a child achieve his maximal potential.First, it is important to take into account how the parents'' emotional and intellectual responses to having a defective child may interfere markedly in normal parent-child relationship. Second, ways in which each deficit will limit a child''s exposure to stimuli must not be over-looked. Third, one must consider how a deficit may indirectly distort the normal learning patterns when parents do not make age appropriate demands. Fourth, it is important to understand how specific interference in the area of language skills may cause further developmental retardation. Fifth, one must be aware of special problems that an organically handicapped child must face in the society outside of the family. Last of all, in an older child, one must consider the need for a full scale evaluation to sort out primary and secondary factors in the picture.