Inside the Mucosal Immune System

An intricate network of innate and immune cells and their derived mediators function in unison to protect us from toxic elements and infectious microbial diseases that are encountered in our environment. This vast network operates efficiently by use of a single cell epithelium in, for example, the gastrointestinal (GI) and upper respiratory (UR) tracts, fortified by adjoining cells and lymphoid tissues that protect its integrity. Perturbations certainly occur, sometimes resulting in inflammatory diseases or infections that can be debilitating and life threatening. For example, allergies in the eyes, skin, nose, and the UR or digestive tracts are common. Likewise, genetic background and environmental microbial encounters can lead to inflammatory bowel diseases (IBDs). This mucosal immune system (MIS) in both health and disease is currently under intense investigation worldwide by scientists with diverse expertise and interests. Despite this activity, there are numerous questions remaining that will require detailed answers in order to use the MIS to our advantage. In this issue of PLOS Biology, a research article describes a multi-scale in vivo systems approach to determine precisely how the gut epithelium responds to an inflammatory cytokine, tumor necrosis factor-alpha (TNF-α), given by the intravenous route. This article reveals a previously unknown pathway in which several cell types and their secreted mediators work in unison to prevent epithelial cell death in the mouse small intestine. The results of this interesting study illustrate how in vivo systems biology approaches can be used to unravel the complex mechanisms used to protect the host from its environment.Higher mammals have evolved a unique mucosal immune system (MIS) in order to protect the vast surfaces bathed by external secretions (which may exceed 300 m² in humans) that are exposed to a rather harsh environment. The first view of the MIS is a single-layer epithelium covered by mucus and antimicrobial products and fortified by both innate and adaptive components of host defense (Figure 1). To this, we can add a natural microbiota that lives in different niches, i.e., the distal small intestine and colon, the skin, the nasal and oral cavities, and the female reproductive tract. The largest microbial population can reach ∼10¹² bacteria/cm³ and occurs in the human large intestine [1]–[3]. This large intestinal microbiota includes over 1,000 bacterial species and the individual composition varies from person-to-person. Other epithelial sites harbor a separate type of microbiota, including the mouth, nose, skin, and other wet mucosal surfaces, that contributes to the host; in turn, the host benefits its microbial co-inhabitants. Gut bacteria grow by digesting complex carbohydrates, proteins, vitamins, and other components for absorption by the host, which in return rewards the microbiota by developing a natural immunity and tolerance (reviewed in [4]–[7]). Finally, the host microbiota influences the development and maturation of cells within lymphoid tissues of the MIS [8],[9].Open in a separate windowFigure 1The gut, nasal, upper respiratory and salivary, mammary, lacrimal, and other glands consist of a single layered epithelium.Projections of villi in the GI tract consist mainly of columnar epithelial cells (ECs), with other types including goblet and Paneth cells. Goblet cells exhibit several functions including secretion of mucins, which form a thick mucus covering. Paneth cells secrete chemokines, cytokines, and anti-microbial peptides (AMPs) termed α-defensins.Mucosal epithelial cells (ECs) are of central importance in host defense by providing both a physical barrier and innate immunity. For example, goblet cells secrete mucus, which forms a dense, protective covering for the entire epithelium (Figure 1). Peristalsis initiated by the brush border of gastrointestinal (GI) tract ECs allows food contents to be continuously digested and absorbed as it passes through the gut. In the upper respiratory (UR) tract, ciliated ECs capture inhaled, potentially toxic particles, and their beating moves them upward to expel them, thereby protecting the lungs. Damaged, infected, or apoptotic ECs in the GI tract move to the tips of villi and are excreted; newly formed ECs arise in the crypt region and continuously migrate upward. Paneth cells in crypt regions of the GI tract produce anti-microbial peptides (AMPs), or α-defensins, while ECs produce β-defensins [10],[11] for host protection (Figure 1). A major resident cell component of the mucosal epithelium are intraepithelial lymphocytes (IELs). The IELs consist of various T cell subsets that interact with ECs in order to maintain normal homeostasis [12]. Regulation is bi-directional, since ECs can also influence IEL T cell development and function [12]–[14].The MIS, simply speaking, can be separated into inductive and effector sites based upon their anatomical and functional properties. The migration of immune cells from mucosal inductive to effector tissues via the lymphatic system is the cellular basis for the immune response in the GI, the UR, and female reproductive tracts (Figure 2). Mucosal inductive sites include the gut-associated lymphoid tissues (GALT) and nasopharyngeal-associated lymphoid tissues (NALT), as well as less well characterized lymphoid sites (Box 1). Collectively, these comprise a mucosa-associated lymphoid tissue (MALT) network for the provision of a continuous source of memory B and T cells that then move to mucosal effector sites 13,14. The MALT contains T cell regions, B cell–enriched areas harboring a high frequency of surface IgA-positive (sIgA⁺) B cells, and a subepithelial area with antigen-presenting cells (APCs), including dendritic cells (DCs) for the initiation of specific immune responses (Figure 2). The MALT is covered by a subset of differentiated microfold (M) cells, ECs, but not goblet cells, and underlying lymphoid cells that play central roles in the initiation of mucosal immune responses. M cells take up antigens (Ags) from the lumen of the intestinal and nasal mucosa and transport them to the underlying DCs (Figure 2). The DCs carry Ags into the inductive sites of the Peyer''s patch or via draining lymphatics into the mesenteric lymph nodes (MLNs) for initiation of mucosal T and B cell responses (Figure 2). Retinoic acid (RA) producing DCs enhance the expression of mucosal homing receptors (α₄β₇ and CCR9) on activated T cells for subsequent migration through the lymphatics, the bloodstream, and into the GI tract lamina propria [15],[16]. Regulation within the MIS is critical; several T cell subsets including Th1, Th2, Th17, and Tregs serve this purpose [13],[14],[17] (Figure 2).Open in a separate windowFigure 2The mucosal immune system (MIS) is interconnected, enabling it to protect vast surface areas.This is accomplished by inductive sites of organized lymphoid tissues, e.g., in the gut the Peyer''s patches (PPs) and mesenteric lymph nodes (MLNs) comprise the GALT. Lumenal Ags can be easily sampled via M cells or by epithelial DCs since this surface is not covered by mucus due to an absence of goblet cells. Engested Ags in DCs trigger specific T and B cell responses in Peyer''s patches and MLNs. Homing of lymphocytes expressing specific receptors helps guide their eventual entry into major effector tissues, e.g., the lamina propria of the gut, the upper respiratory (UR) tract, the female reproductive tract, or acinar regions of exocrine glands. Terminal differentiation of plasma cells producing polymeric (mainly dimeric) IgA is then transported across ECs via the pIgR for subsequent release as S-IgA Abs.

Box 1. Major Inductive Sites for Mucosal Immune Responses

1. GALT (gut-associated lymphoid tissues)
   • Peyer''s patches (PPs)
   • Mesenteric lymph nodes (MLNs)
   • Isolated lymphoid follicles (ILFs)
2. NALT (nasopharyngeal-associated lymphoid tissues)
   • Tonsils/adenoids
   • Inducible bronchus-associated lymphoid tissue (iBALT)
   • Cervical lymph nodes (CLNs)
   • Hilar lymph nodes (HLNs)

Mucosal effector sites, including the lamina propria regions of the GI, the UR and female reproductive tracts as well as secretory glandular tissues (i.e., mammary, lacrimal, salivary, etc.) contain Ag-specific mucosal effector cells such as IgA-producing plasma cells, and memory B and T cells [18]. Adaptive mucosal immune responses result from CD4⁺ T cell help (provided by both CD4⁺ Th2 or CD4⁺ Th1 cells), which supports the development of IgA-producing plasma cells (Figure 2). Again, the ECs become a central player in the MIS by producing the polymeric Ig receptor (pIgR) (which binds both polymeric IgA and IgM) [19]. Lamina propria pIgA binds the pIgR on the basal surface of ECs, the bound pIgA is internalized, and then transported apically across the ECs (Figure 2). Release of pIgA bound to a portion of pIgR gives rise to secretory IgA (S-IgA) antibodies (Abs) with specificities for various Ags encountered in mucosal inductive sites [13],[14],[19]. In addition, commensal bacteria are ingested by epithelial DCs, which subsequently migrate to MLNs for induction of T cell–independent, IgA B cell responses [20]. In summary, two broad types of S-IgA Abs reach our external secretions by transport across ECs and protect the epithelial surfaces from environmental insults, including infectious diseases.It should be emphasized that several unique vaccine strategies are being developed to induce protective mucosal immunity. In this regard, delivery of mucosal vaccines by oral, nasal, or other mucosal routes requires specific adjuvants or delivery systems to initiate an immune response in MALT [21],[22]. However, a major benefit of mucosal vaccine delivery is the simultaneous induction of systemic immunity, including CD4⁺ Th1 and Th2, CD8⁺ cytotoxic T lymphocytes (CTLs), and Ab responses in the bloodstream, which are predominantly of the IgG isotype [21]. This, of course, provides a double layer of immunity in order to protect the host from microbial pathogens encountered by mucosal routes. This is especially promising for development of vaccines for developing countries, as well as those to protect our aging population [23],[24].In this issue of PLOS Biology, Lau et al. used a multi-scale in vivo systems approach to assess how cells of the intestinal MIS communicate with intestinal ECs in response to an inflammatory signal [25]. The present study centered on the use of the proinflammatory cytokine tumor necrosis factor-alpha (TNF-α) given intravenously (i.v.) to assess its effects on the gut epithelium in the presence (wild-type [WT] mice) or absence (Rag1 knockout mice) of adaptive T and B lymphocytes. It is well known that TNF-α regulates many EC effects, including programmed cell death (apoptosis), survival, proliferation, cell cycle arrest, and terminal differentiation [26]. The authors had previously shown that TNF-α given i.v. to WT mice resulted in two different response patterns in the small intestine [27]. In the duodenum, which adjoins the stomach, TNF-α enhanced EC apoptosis, while in the ileum, the part next to the colon, an enhancement of EC division was seen [27]. In the present study, i.v. injection of TNF-α induced apoptosis in the duodenum (but not ileum) of WT, with heightened cell death in Rag1 mice [25]. Loss of either T or B lymphocytes also led to increased EC apoptosis, suggesting that both cell types are required to protect the epithelium from cell death. Also intriguing was the finding that eliminating the gut microbiota by antibiotic treatment did not affect the degree of EC apoptosis seen. Mathematical modeling allowed the group to show that TNF-α-induced apoptosis involved several steps in mice lacking functional T and B cells. Analysis of potential cytokines involved revealed that only a single chemokine, monocyte chemotactic protein-1 (MCP-1, C-C motif ligand 2 [CCL2]), protected ECs from apoptosis [25]. This new finding complements recent studies showing that IL-22, which is produced by several immune cells in the gut, plays a major role in protecting ECs from inflammation, infection, and tissue damage (Figure 3) [28].Open in a separate windowFigure 3The gut epithelium exhibits several pathways that protect the integrity of this organ.Intestinal epithelial cells (ECs) produce stem cell factor (SCF), which induces proliferation and resistance to bacterial invasion. In addition, neighboring γδ intraepithelial lymphocytes (IELs) produce keratinocyte growth factor (KGF), which also stabilizes ECs. IL-22 produced by Th17, Th22, and γδ T cells as well as natural killer (NK) and lymphoid tissue inducer (LTi) cells plays a key role in both early and late phases of innate immunity in order to maintain the EC barrier. In addition, monocyte chemotactic protein (MCP-1) produced by Paneth cells and goblet cells down-regulates migration of plasmacytoid DCs (pDCs) into the intestinal lamina propria in order to decrease TNF-α-induced EC apoptosis.Several unexpected discoveries followed. First, both goblet and Paneth cells were the major sources of MCP-1, and not the lymphoid cell populations that normally produce this chemokine (Figure 3). Second, the MCP-1 produced did not directly protect ECs, but instead acted via downregulation of plasmacytoid DCs (pDCs), a lymphocyte-like DC that produces various cytokines [29]. Finally, the study established that loss of adaptive (immune) lymphocytes resulted in decreased MCP-1 production, leading to increased pDC numbers and enhanced EC apoptosis. In the final experiment, the authors again showed that pDCs in the duodenum of Rag-1 mice produced increased levels of interferon-gamma that directly induced EC apoptosis. The message given by this intricate study is that systems biology approaches are quite useful in unraveling the complexities posed by the MIS in both health and disease.The model developed by Lau et al. [25] could be useful to study several major problem areas. For example, a paucity of murine models exist to study food or milk allergies that usually affect the duodenum of the small intestine [30]. It is known that chemokine receptors control trafficking of Th2-type cells to the small intestine for IgE-dependent allergic diarrhea [31],[32]. The multi-scale systems approach could be used to assess much earlier responses to food or milk allergies in TNF-α-treated mice. A second avenue could well include the cell and molecular interactions that lead to intestinal EC damage resulting in IBD [33]. Clearly, progress is being made to study genetic aspects, regulatory T cells, and the contributions of the host microbiota to IBD development [17],[34]. Nevertheless, current mouse models have their “readout” as weight loss and chronic inflammation of the colon [17],[33],[34]. The Lau et al. approach could reveal cell-to-cell linkages that ultimately resulted in EC damage [25]. Further, this approach could reveal the earliest stages of pathogenesis of IBD before the influx of inflammatory cells causes the macroscopic changes characteristic of these diseases. Since the duodenum is normally sterile, one could have predicted their finding that antibiotic treatment to remove the gut microbiota would indeed be without effect. However, one wonders what effects would be seen in the stomach or in the colon, both of which can harbor a natural microbiota. Does TNF-α and antibiotic treatment alter the EC program in these mucosal tissue sites?Finally, the intriguing question arising from the Lau et al. study [25] involves the finding that a full-blown adaptive immune system was required to maintain homeostasis and thus reduce EC apoptosis in the GI tract. Note that the response to in vivo TNF-α was assessed after only 4 hours, well before T and B cell responses could be manifested. How are early T and B cell signals transmitted to ECs? What are the mediators involved between the innate and adaptive components in the MIS for communication with the epithelium? As always, insightful studies raise many more questions than are answered. Nevertheless, the multi-scale in vivo systems analysis identified effects on the epithelium in a manner not appreciated up to now. The advantages of using an in vivo perturbation system is far superior to cell culture studies where only a few cell types are present.     

How Insect Flight Steering Muscles Work

Insights into how exactly a fly powers and controls flight have been hindered by the need to unpick the dynamic complexity of the muscles involved. The wingbeats of insects are driven by two antagonistic groups of power muscles and the force is funneled to the wing via a very complex hinge mechanism. The hinge consists of several hardened and articulated cuticle elements called sclerites. This articulation is controlled by a great number of small steering muscles, whose function has been studied by means of kinematics and muscle activity. The details and partly novel function of some of these steering muscles and their tendons have now been revealed in research published in this issue of PLOS Biology. The new study from Graham Taylor and colleagues applies time-resolved X-ray microtomography to obtain a three-dimensional view of the blowfly wingbeat. Asymmetric power output is achieved by differential wingbeat amplitude on the left and right wing, which is mediated by muscular control of the hinge elements to mechanically block the wing stroke and by absorption of work by steering muscles on one of the sides. This new approach permits visualization of the motion of the thorax, wing muscles, and the hinge mechanism. This very promising line of work will help to reveal the complete picture of the flight motor of a fly. It also holds great potential for novel bio-inspired designs of fly-like micro air vehicles.The ability for powered flight has evolved four times in the animal kingdom and, thanks to their ability to fly, insects have diversified and moved into new regions and habitats with enormous success [1]. Powered flight requires an integrated system consisting of wings to generate aerodynamic force, muscles to move the wings, and a control system to modulate power output from the muscles. Insects are bewilderingly diverse with respect to flight morphology and behaviors, which in turn provides a real challenge to researchers wishing to understand how insects fly. In particular, the impressive flight maneuvers in flies, such as blowflies and fruit flies, have inspired scientists for many years [2]. The ability of a fly to accelerate, make tight turns, rolls, and loops that allow the creature to land upside down on a ceiling is unparalleled in any other organisms, as well as any manmade aircraft. Everybody knows how difficult it is to swat a fly with bare hands—the fly''s capacity for rapid take-off and accurate movement away from a perceived approaching threat is exquisite [3].The flight muscles of many insects, including flies, bees, and mosquitoes, are divided into a few large power muscles that simply contract cyclically to generate sheer power output and a greater number of smaller steering muscles that control the force transmission from the power muscles to the wing [4]–[6]. The power muscles of a fly consist of two sets of antagonistic muscles attached to the inside of the thorax (exoskeleton) (Figure 1). In many insects, including flies, these muscles are asynchronous, which means their contractions are uncoupled to the firing rate of the associated motor neuron [6],[7], i.e., the muscles continue to contract as long as the nerve tickles them. Another characteristic feature of the power muscles is that they are stretch-activated and contract as a response to being lengthened. Both sets of power muscles deform the thorax when contracted such that when the dorso-ventral muscles contract, the thorax is squeezed together dorso-ventrally while expanding longitudinally, and vice versa when the dorsal-longitudinal muscles contract as a response to prior lengthening. The result is an alternate contraction and lengthening of these perpendicular muscle groups and a resonance of the entire thorax that drives the wingbeat. Typical wingbeat frequencies are in the range from 100 Hz and even up to 1,000 Hz in the smallest species [5],[8].Open in a separate windowFigure 1The thorax with and dorsal longitudinal (upper left) and dorso-ventral (upper right) power flight muscles of a fly.The cartoon (bottom) shows a transverse section through the thorax with dorso-ventral muscles (DVM) and dorsal longitudinal muscles (DLM) indicated. The two upper illustrations are redrawn from [6].The forces from the flight muscles are transmitted to the wing through an intricate hinge mechanism (Figure 2). The hardened plates of cuticle between the thorax and wing (sclerites) are mobile and their positions relative to the thoracic outgrowths and wing determine the extent of the wing motion, i.e., the angular amplitude of the wingbeat [6].Open in a separate windowFigure 2Cartoon illustration of a transverse section of the thorax of a fly in rear view, showing some elements of the complex wing hinge of a fly, consisting of ridges and protrusions on the thorax and a number of hardened plates of cuticle (sclerites) between the body (thorax) and the wing root.The basalare sclerite (not shown) is positioned anterior of the first axillary sclerite (Ax1). The indicated structures are dorso-ventral power muscle (DVM), pleural wing process (PWP), post-medial notal process (PMNP), parascutal shelf (PSS), axial wing sclerites (Ax1, Ax2, Ax3), and radial stop (RS). Redrawn and modified from [18].Flight maneuvers arise owing to asymmetric force generation between the left and right wing. Aerodynamic force is proportional to the angle of attack (the angle between the wing surface and the airflow) and the speed squared relative to the air [9],[10]. Except from the turning points of each half-stroke, when the wings rotate about their span wise axes, the angle of attack is usually quite constant during the translational phases of the wingbeat [10], while asymmetric forces are mainly created by changing the wingbeat amplitude in flies [11]–[14]. With wingbeat frequency kept constant, changed amplitude changes the speed and hence force generated.The control of the elements forming the hinge mechanism of the wing is achieved by the steering muscles, which are tiny in terms of mass (<3% of the power muscle mass), but mean everything when it comes to making flight maneuvers. In contrast to the power muscles the steering muscles are synchronous, i.e., there is a 1∶1 correspondence between neural spikes and muscle contraction. No less than some 22 pairs of steering muscles are involved in the force transmission; a few of these indirectly modulate the output by affecting the resonating properties of the thorax, while others are directly attached to the sclerite elements of the hinge mechanism [6],[15]. Three small muscles (b1–b3) are attached to the basalare plate that is directly involved in wing articulation (Figure 3). The actual wing sclerites (Figure 2) are also controlled by specific steering muscles, also with the function of moving the sclerites in relation to required wing motion. The main control function of the hinge mechanism appears to be of the downward movement of the wing, i.e., the angle at the turning point at end of downstroke. For a detailed review about the steering muscles and their function see Dickinson and Tu [6].Open in a separate windowFigure 3The position of the three steering flight muscles b1–b3 inserted to the nail-shaped basalare sclerite.Contraction by the b1 and b2 muscles move the basalare forward and their antagonist b3 moves it backwards when contracted. Redrawn from [6].To date, the function of the steering muscles has been revealed mainly by electrophysiological studies on tethered subjects. Tethering means that the animal is glued to the end of a thin rod, often with force sensors attached to it, and then stimulated to “fly.” In many insects this can be achieved by simply blowing at them or placing them in a wind tunnel. On the tether the insect can either be presented with a visual stimulus or be rotated, which flies can sense via their halteres (hind wings modified to sensory gyroscopic sensory organs) [16]. By inserting electrode wires into the steering muscles, the neural impulses are measured at the same time as the wingbeat kinematics is recorded [13],[17]. What we know about the function of the steering muscles comes from the meticulous studies of correlations between muscle activity and the associated wing movement, including how the hinge mechanism works [6],[18]. Needless to say, such experiments are extremely difficult to achieve in small insects like blowflies and fruit flies that flap their wings at high frequencies. Recent studies of the wing and hinge kinematics provide some support for the hypothesis that the hinge may have a gear function that affects stroke amplitude, as well [18]. However, there are still many open questions regarding the exact function of the steering muscles and how they help in generating laterally asymmetric forces during a fly''s flight maneuver [6].In an article published in this issue of PLOS Biology, Walker and colleagues take a new approach for studying how steering muscles regulate the power output from power muscles [19], using time-resolved x-ray microtomography [20]. By rotating tethered blowflies (Calliphora vicina) in the X-ray beam, a 3D-movie was captured that shows how the steering muscles move. This by itself is a grand achievement at a wingbeat frequency of 145 Hz. As the flies could sense being rotated the steering muscles acted accordingly to achieve an asymmetric power output as a response to a perceived turn. The movies that accompany the article show how several of the key steering muscles and their sclerites operate in concert during the course of a wingbeat, and the visual results are supported by advanced statistical analyses of muscle strain rates and their phase offset. For example, the b1 and b3 muscles (Figure 3) work antagonistically, as was known before, but on the low-amplitude wing the oscillations are delayed by about a quarter of a wingbeat. The strain amplitudes of b1 and b3 were different between the two wings, which were found to be due to dorso-ventral movement of the basalare sclerite on the high-amplitude side and rotation on the low-amplitude side. This shows even higher complexity of the wing hinge than was previously envisaged.The measurements of strain rate in the muscle confirmed the results of a previous study, which showed that asymmetric power output is partially achieved by negative work [21], i.e., absorption of work, by the b1 muscle on the low-amplitude wing. As with other muscles, the steering muscles insert on the skeletal parts and sclerites by tendons. The tendon of the muscle (I1) associated with the first axillary sclerite was observed to buckle when the wing was elevated above the wing hinge, indicative of compressive force acting on it near the top of the wing stroke. This buckling of the tendon forces a reinterpretation of the function of this muscle: it is involved in reducing stroke amplitude at the bottom of the downstroke rather than exerting stress near the opposite end of the stroke. Tendon buckling was seen in some other muscles as well, and although this is its first observation, it may be a more general mechanism involved in control of insect wingbeat kinematics.What are the wider implications of this new study? First, it demonstrates the utility of a new approach to examine the in vivo operation of several insect flight muscles. This alone signals a methodological breakthrough that promises more. So far the flies were tethered and studied during one behavioral treatment (rotation about the yaw axis). Real flight maneuvers, however, also involve angular rotation about pitch and roll axes, acceleration, and braking. Thus, it remains to be seen how the steering muscles operate to control more subtle changes in wing kinematics during the turning saccades and advanced flight maneuvers that take place during free flight. The method involved exposure to lethal X-ray doses, which of course limits how long the experiments can be. Second, tethering is the prevailing paradigm for studying insect flight, but because it interrupts the sensory feedback loop [22], it would be useful for future studies to compare tethered and free flight in some commonly studied species. Furthermore, a more complete understanding of the flight muscle-hinge mechanism may help bio-inspired design of wing articulation systems for fly-like micro air vehicles. Until then, we can enjoy the stunning videos of the oscillating thorax and flight muscle system of the blowfly [19]. See the video from the related research article here (http://youtu.be/P6lBkK3J9wg) or [19].     

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.     

Bit by bit: the Darwinian basis of life

All known examples of life belong to the same biology, but there is increasing enthusiasm among astronomers, astrobiologists, and synthetic biologists that other forms of life may soon be discovered or synthesized. This enthusiasm should be tempered by the fact that the probability for life to originate is not known. As a guiding principle in parsing potential examples of alternative life, one should ask: How many heritable “bits” of information are involved, and where did they come from? A genetic system that contains more bits than the number that were required to initiate its operation might reasonably be considered a new form of life.Thanks to a combination of ground- and space-based astronomical observations, the number of confirmed extrasolar planets will soon exceed 1,000. An increasing number of these will be said to lie within the “habitable zone” and even be pronounced as “Earth-like.” Within a decade there will be observational data regarding the atmospheric composition of some of those planets, and just maybe those data will indicate something funny going on—something well outside the state of chemical equilibrium—on a potentially hospitable planet. Perhaps our astronomy colleagues should be forgiven for their enthusiasm in declaring that humanity is on the brink of discovering alien life.But haven''t we heard this before? Didn''t President Clinton announce in 1996 that a Martian meteorite recovered in Antarctica [1] “speaks of the possibility of life” on Mars? (No, it turned out to be mineralic artifacts.) Wasn''t some “alien” arsenic-based life discovered recently in Mono Lake, California [2]? (No, it''s a familiar proteobacterium struggling to survive in a toxic environment.) Didn''t Craig Venter and his colleagues recently create a synthetic bacterial cell [3], “the first self-replicating species we''ve had on the planet whose parent is a computer”? (No, its parent is Mycoplasma mycoides and its genome was dutifully reconstructed through DNA synthesis and PCR amplification.)Why are we so confused (or so lonely) that we have such trouble distinguishing life from non-life and distinguishing our biology from another? A key limitation is that we know of only one life form, causing us to regard life from that singular perspective (Figure 1). We see life as cellular, with a nucleic acid genome that is translated to a protein machinery. Life self-reproduces, transmits heritable information to its progeny, and undergoes Darwinian evolution based on natural selection. Life captures high-energy starting materials and converts them to lower-energy products to drive metabolic processes. Life exists on at least one temperate, rocky planet, where it has persisted for about four billion years. There are likely to be tens of thousands of “habitable” planets within a thousand light years of Earth, and more than a billion such planets in our galaxy, so surely (say the astronomers) we are not alone.Open in a separate windowFigure 1Phylogenetic tree of life based on small-subunit ribosomal RNA sequences, showing representative species from each of the three kingdoms (compiled by Pace [11]).The root of the tree is indicated by a horizontal line. The locations on the tree of Halomonas sp. (GFAJ-1) [2] and Mycoplasma mycoides (JCVI-syn1.0) [3] are indicated by black circles adjacent to Escherichia and Bacillus, respectively.     

Modulating Brain Oscillations to Drive Brain Function

Do neuronal oscillations play a causal role in brain function? In a study in this issue of PLOS Biology, Helfrich and colleagues address this long-standing question by attempting to drive brain oscillations using transcranial electrical current stimulation. Remarkably, they were able to manipulate visual perception by forcing brain oscillations of the left and right visual hemispheres into synchrony using oscillatory currents over both hemispheres. Under this condition, human observers more often perceived an inherently ambiguous visual stimulus in one of its perceptual instantiations. These findings shed light on the mechanisms underlying neuronal computation. They show that it is the neuronal oscillations that drive the visual experience, not the experience driving the oscillations. And they indicate that synchronized oscillatory activity groups brain areas into functional networks. This points to new ways for controlled experimental and possibly also clinical interventions for the study and modulation of brain oscillations and associated functions.How does the brain work? How does it code, transfer, and store information? How are conscious experiences generated? These, among others, are long-standing questions neuroscientists try to answer. One way to approach this is to study how the brain orchestrates behaviour, for instance, by measuring brain activity and relating it to behaviour. Yet, studying the brain–behaviour relationship raises another series of questions: What type of brain activity should one look at? Do we need to record directly from single neurons? Or can we make inferences also by recording from larger pools of neurons? And importantly, do these measures of brain activity provide mechanistic accounts of how the brain implements function, or are they just inevitable side-products, with limited explanatory power for the neural mechanisms underlying our experiences, thoughts, or actions?Certainly, one would have a good argument for brain activity causally underlying brain function if (i) this brain activity not only relates to sensory experiences or behavioural performance measures (revealing a correlative brain-behaviour relationship), but (ii) interventions into this brain activity would also modulate our experiences or performance (revealing a causal link). Recent developments allow addressing these central points for oscillatory brain activity, which is what Helfrich et al. [1] did in their study published in this issue of PLOS Biology.At the basis of Helfrich et al.''s study are two lines of research, one of which is concerned with the interpretation of a special type of brain activity, namely, brain oscillations. This type of brain activity represents voltage fluctuations of neuronal elements and was initially observed from one scalp electrode by Hans Berger [2]. Today, brain oscillations are typically recorded from multiple sensors distributed over the scalp or brain, for instance using electro- or magneto-encephalography (EEG/MEG), in order to make inferences about the orchestration of brain activity across distinct neuronal elements [3]. A prominent view is that these oscillations represent essential network activity. They become visible when neuronal elements of a network start to synchronize their oscillatory activity, i.e., temporarily couple together [4]. Notably, brain oscillations vary in frequencies depending on the task that is being executed and the region of the brain they are recorded from [3] (see Box 1 for example frequencies relevant for Helfrich et al.''s study). It is understood that this may reflect nested networks that oscillate at different frequencies and spatial scales [4] and that define functional architecture not only by synchronizing at the same frequency but also through complex cross-frequency interactions; this to allow for integration of processes at different temporal and spatial scales [5]–[7]. With respect to the above questions on how the brain operates, the most exciting aspect of oscillatory brain activity is probably that it offers mechanistic accounts. One example is the communication-through-coherence theory [8], which states that the relative timing of oscillatory activity of two neuronal elements enables the control of information transfer, with communication being maximal when phases of high excitability of these elements cycle in synchrony, and minimal when they cycle out of synchrony (see Fig. 1B Model).Open in a separate windowFigure 1Schematic representation of design, objectives, and insights from the study by Helfrich et al. A. Design and questions: Participants viewed an apparent motion stimulus, which elicits a bistable percept consisting of either horizontal (percept 1) or vertical motion (percept 2). A bi-hemispheric network of two posterior areas (blue and red squares) was interrogated as to the functionality of inter-area synchrony (see “?”) in generating these percepts, by recording of brain oscillations through electro-encephalography (EEG), and interventions into these oscillations through transcranial alternating current stimulation (tACS). B. Results and conclusion: EEG revealed that the horizontal motion percept was associated with enhanced synchrony (coherence) between oscillatory brain activity of the two posterior areas (as compared to vertical motion percept), in line with coupling of the two areas to a functional network by synchronization of their respective phases of high excitability (see Model). This provides information on a correlative relationship between network activation and function but cannot disentangle whether it is the percept that drives the network, or the network that drives the percept. Intervention with tACS supports the latter. Applying tACS in synchrony over the two areas enhances inter-area coherence of oscillatory activity as well as the horizontal motion percept (as opposed to applying tACS out of synchrony). Hence, synchrony of oscillatory brain activity underlies the formation of functional networks and mediates its associated functions.

Box 1. Glossary

Brain oscillations in the gamma frequency band (gamma-oscillations): This is a class of brain oscillations cycling at rapid frequencies (35–100 Hz). Gamma-oscillations are prominent in visual cortex (among other areas) and become evident also in scalp recordings when participants view specific types of visual stimuli. Alpha-band brain oscillations cycle at 8–12 Hz. Alpha-oscillations can co-occur with gamma-oscillations in visual areas, where these two classes of oscillations show an inverse relationship in terms of amplitude. Transcranial direct current stimulation (tDCS) and transcranial alternating current stimulation (tACS) use electrical currents applied through two or more scalp electrodes for transient, non-invasive brain stimulation, whereas transcranial magnetic stimulation (TMS) uses the principle of electromagnetic induction. In tACS, the currents are modulated in an oscillatory (sinusoidal) pattern, and can therefore be frequency-tuned to underlying brain oscillations. Likewise, TMS in its rhythmic form (rhythmic TMS) allows for periodic brain stimulation at frequencies of brain oscillations.The other line of research that is at the heart of Helfrich et al.''s study is concerned with interventions into brain activity by non-invasive brain stimulation techniques; this to probe the brain–behaviour relationship along a more causal dimension [9]. Such techniques are widely used in cognitive and clinical neuroscience, and employ either magnetic or electric fields to stimulate neurons directly (i.e., transcranially) to then test the behavioural consequences. Currently available techniques use transcranial magnetic stimulation (TMS), or a variety of electrical currents such as with transcranial direct current stimulation (tDCS) or transcranial alternating current stimulation (tACS) (see Box 1) [10]. While these techniques have been successfully employed in numerous studies, a recurrent question is how to improve specificity of effects in terms of enhancing focality [11] or targeting specific subpopulations within the stimulated neuronal pool [12]. In addition, simultaneous neuroimaging studies have revealed that the effect of the magnetic or electric field on the stimulated area (under the TMS coil or the stimulation electrode) is spreading to other areas, in many instances along anatomical connections [13],[14]. Hence, any behavioural outcome needs to be interpreted in the context of network effects. Intriguingly, and relevant for interactions with oscillatory brain activity, recent findings indicate that the specificity of these interventions into functionally relevant brain activity may be improved by taking into account not only the spatial dimension (i.e., what anatomical network to stimulate) but also the temporal dimension (what frequency to apply). This is suggested by recent studies using periodic transcranial stimulation protocols (such as tACS or rhythmic TMS) allowing a frequency tuning of stimulation (see Box 1). These studies demonstrate an immediate behavioural effect at specific stimulation frequencies, namely those that match the frequencies of intrinsic brain oscillations[15]–[21]; which may be caused by the periodic stimulation promoting the intrinsic oscillations [22]–[24].Capitalizing on the above, Helfrich et al. convincingly address in healthy human volunteers the long-standing issue of whether oscillatory brain activity indeed coordinates functional brain architecture, as opposed to representing a mere by-product, and thereby bridge a gap between recordings and interventional studies into brain oscillations (see Fig. 1 for a schematic representation of design, objectives, and insights of the study). They do so by examining the link between visual network activity and specific sensory experiences. To manipulate sensory experience (without changing sensory input), Helfrich et al. employed a visual motion paradigm (see Fig. 1A), in which pairs of diagonally opposed dots are presented on a screen in two alternating configurations (upper left/lower right dots followed by lower left/upper right dots, etc.). This leads to a bistable percept, consisting of time periods during which the two dots are perceived as moving horizontally (see Fig. 1A, apparent motion percept 1), alternating with time periods during which the same dots are perceived as moving vertically (Fig. 1A, apparent motion percept 2). Interestingly, recordings of brain oscillations from left and right occipito-parietal EEG sensors, i.e., from areas processing the right- versus left-sided dots respectively, revealed a temporally stable pattern of relative timing between these oscillations, depending on the percept (replicating [25]): during horizontal motion percepts when the demands for interhemispheric communication can be assumed to be high (as opposed to vertical percepts where motion integration can be resolved within each hemisphere) [26], these left and right oscillations show high coherence in the gamma frequency band (at approximately 35–100 Hz) (Fig. 1B EEG). In other words, oscillations in the left and right occipito-parietal areas are synchronized. This is suggestive of these areas forming a temporally stable network during horizontal as opposed to vertical motion integration, in line with models of network coordination by synchronization of brain oscillations (Fig. 1B Model) [8],[27]. Importantly, applying rhythmic brain stimulation in synchrony over the left and right occipito-parietal cortex using tACS at gamma frequency enhances both the gamma-band EEG coherence between the two hemispheres (without affecting gamma-power) and its associated percept (i.e., horizontal motion), as opposed to applying gamma-tACS out of synchrony (Fig. 1B tACS). See also Polania et al. [19] for a conceptually similar tACS result, without the direct evidence for concurrently enhanced EEG synchrony. This shows that in-synchrony tACS versus out-of-synchrony tACS over two elements of an oscillatory visual network can be used to stabilize/destabilize this network, and with meaningful perceptual consequences. This is in accord with brain oscillations not only indexing network coordination and associated functions, but causing them.The findings of Helfrich et al. make an important contribution. They more firmly link the dynamics of oscillatory brain activity to the formation of functional networks, as well as the orchestration of brain function (here phenomenological experience) and this along a causal dimension. This corroborates and extends a growing number of studies showing that brain oscillations can serve as targets for controlled interventions into brain activity and function, by non-invasive brain stimulation in periodic patterns [22]–[24]. The principle idea is to promote brain oscillations that have been associated with specific functions (as inferred from correlative brain-behavioural links) to cause performance changes, provided a causal relationship underlies the correlative data. For instance, it has been shown that promoting oscillations of the parietal cortex known to be related to attentional selection using frequency-tuned rhythmic TMS [22] biases perception towards the expected stimulus dimension [17],[20]. Likewise, tACS (or oscillatory tDCS) tuned to fronto-temporal oscillations, which have been associated with memory consolidation during slow-wave sleep or dream patterns during REM-sleep (e.g., lucid dreaming), have been shown to enhance memory or lucid dream content, respectively [15],[21]. And equivalent effects have been found for oscillatory motor system activity [16],[18]. This opens powerful opportunities for neuroscience and clinical interventions, not only allowing to test models of how brain activity implements function but also how it relates to dysfunction, to inform controlled intervention into the brain–behaviour relationship.These findings are exciting and indicate that it is promising to study brain oscillations, even at a macroscopic scale (such as measured with EEG/MEG), to answer some of the long-standing questions of how the brain works. They also take the emerging new approach of using periodic transcranial stimulation to interact with brain oscillations and function beyond the proof-of-principle stage. However, the usefulness of this approach will depend on the extent to which its specificity can be improved (e.g., up- versus down-regulating oscillations, tailoring to individual differences) and its mechanisms of actions understood. One unresolved point is the spatial extent of stimulation. With tACS, the conventional stimulation electrodes are large (several cm²) and require a “return” electrode which excites widespread areas. To render stimulation more focal, special electrode montages have been proposed [11], as also used by Helfrich et al., and which may explain some of the differences to a previous study of the same group using a less focal electrode montage [28]. Other developments are underway to funnel stimulation to specific target areas by the use of multichannel electrode configurations and computational (forward) models of electrical field distributions [29]. In this context, it will be of interest to compare the efficiency of frequency-tuned tACS with frequency-tuned rhythmic TMS, the latter thought to be more focal, but also more superficial. In addition, it is still largely unknown how these forms of rhythmic stimulation interact with intrinsic brain oscillations. There is growing evidence that the periodic electric or magnetic force may entrain the underlying oscillations during stimulation [22],[23], and that long-lasting effects may arise from this entrainment, possibly by inducing plasticity effects via spike-timing dependent plasticity in the circuits generating these oscillations [30]. It is the former, short-term effects that are of interest for experimental interventions in cognitive neuroscience for testing theory (because of their limited duration), but the latter, longer-lasting effects that are of relevance for clinical interventions. Finally, while Helfrich et al. report cross-frequency effects of gamma-tACS, in particular in the alpha frequency band (8–12 Hz), it remains to be studied in detail how the induced oscillations resonate in other, nested oscillatory networks. These and other points will need to be resolved in future work to be able to fully assess the extent of the impact of this emerging approach.     

Endemicity,Biogeography, Composition,and Community Structure On a Northeast Pacific Seamount

The deep ocean greater than 1 km covers the majority of the earth''s surface. Interspersed on the abyssal plains and continental slope are an estimated 14000 seamounts, topographic features extending 1000 m off the seafloor. A variety of hypotheses are posited that suggest the ecological, evolutionary, and oceanographic processes on seamounts differ from those governing the surrounding deep sea. The most prominent and oldest of these hypotheses, the seamount endemicity hypothesis (SMEH), states that seamounts possess a set of isolating mechanisms that produce highly endemic faunas. Here, we constructed a faunal inventory for Davidson Seamount, the first bathymetric feature to be characterized as a ‘seamount’, residing 120 km off the central California coast in approximately 3600 m of water (Fig 1). We find little support for the SMEH among megafauna of a Northeast Pacific seamount; instead, finding an assemblage of species that also occurs on adjacent continental margins. A large percentage of these species are also cosmopolitan with ranges extending over much of the Pacific Ocean Basin. Despite the similarity in composition between the seamount and non-seamount communities, we provide preliminary evidence that seamount communities may be structured differently and potentially serve as source of larvae for suboptimal, non-seamount habitats.Open in a separate windowFigure 1Bathymetric map of the Central California Coast with Monterey Canyon and Davidson Seamount.     

The Incubation Period of Buruli Ulcer (Mycobacterium ulcerans Infection)

Introduction

Buruli Ulcer (BU) is caused by the environmental microbe Mycobacterium ulcerans. Despite unclear transmission, contact with a BU endemic region is the key known risk factor. In Victoria, Australia, where endemic areas have been carefully mapped, we aimed to estimate the Incubation Period (IP) of BU by interviewing patients who reported defined periods of contact with an endemic area prior to BU diagnosis.

Method

A retrospective review was undertaken of 408 notifications of BU in Victoria from 2002 to 2012. Telephone interviews using a structured questionnaire and review of notification records were performed. Patients with a single visit exposure to a defined endemic area were included and the period from exposure to disease onset determined (IP).

Results

We identified 111 of 408 notified patients (27%) who had a residential address outside a known endemic area, of whom 23 (6%) reported a single visit exposure within the previous 24 months. The median age of included patients was 30 years (range: 6 to 73) and 65% were male. 61% had visited the Bellarine Peninsula, currently the most active endemic area. The median time from symptom onset to diagnosis was 71 days (range: 34–204 days). The midpoint of the reported IP range was utilized to calculate a point estimate of the IP for each case. Subsequently, the mean IP for the cohort was calculated at 135 days (IQR: 109–160; CI 95%: 113.9–156), corresponding to 4.5 months or 19.2 weeks. The shortest IP recorded was 32 days and longest 264 days (Figure 1 & 2). IP did not vary for variables investigated.Open in a separate windowFigure 1Geographic representation of Bellarine Peninsula, considered endemic for BU as of 2012.Bellarine Peninsula – east of line from Geelong to Torquay. Mornington and Westernport – southwest of line from Hampton to Tooradin (including Phillip Island).Open in a separate windowFigure 2Geographic representation of East Gippsland, considered endemic for BU as of 2012.East Gippsland: East of Sale and south of the great divide.

Conclusions

The estimated mean IP of BU in Victoria is 135 days (IQR: 109–160 days), 4.5 months. The shortest recorded was IP 34 days and longest 264 days. A greater understanding of BU IP will aid clinical risk assessment and future research.     

An inordinate fondness for eukaryotic diversity

Why do some groups of organisms, like beetles, have so many species, and others, like the tuataras, so few? This classic question in evolutionary biology has a deep history and has been studied using both fossils and phylogenetic trees. Phylogeny-based studies have focused on tree balance, which compares the number of species across clades of the same age in the tree. These studies have suggested that rates of speciation and extinction vary tremendously across the tree of life. In this issue, Rabosky et al. report the most ambitious study to date on the differences in species diversity across clades in the tree of life. The authors bring together a tremendously large dataset of multicellular eukaryotes, including all living species of plants, animals, and fungi; they divide these organisms into 1,397 clades, accounting for more than 1.2 million species in total. Rabosky et al. find tremendous variation in diversity across the tree of life. There are old clades with few species, young clades with many species, and everything in between. They also note a peculiar aspect of their data: it is difficult or impossible to predict how many species will be found in a particular clade knowing how long a clade has been diversifying from a common ancestor. This pattern suggests complex dynamics of speciation and extinction in the history of eukaryotes. Rabosky et al.''s paper represents the latest development in our efforts to understand the Earth''s biodiversity at the broadest scales.J.B.S. Haldane—one of the most quotable of all evolutionary biologists—had a favorite saying about what patterns of species richness tell us about the nature of the Universe: “God has an inordinate fondness for beetles” (see [1] for more details). With this quip, Haldane is referring to the overwhelming number of beetle species on Earth. We still don''t know exactly how many species of beetles there are on the Earth—perhaps around 400,000—but certainly, there are a lot.One of the primary mysteries in macroevolution is the tremendous difference in numbers of species among different taxonomic groups. Modern systematists classify species into clades (groups of species that represent all of the descendents of a common ancestor, like turtles or arthropods). Different clades in the tree of life have dramatically different diversities. This might not be surprising—after all, species in one clade can be distinct from other species in size, energy use, and a thousand other ways. Also, some clades are much older than others. However, even when we control for differences in age by comparing sister clades—that is, pairs of clades that are each others'' closest relative—we still see profound differences in number of species. For example, there are currently two living species of tuatara, a clade of lizard-like reptiles that currently inhabit small islands around New Zealand (Figure 1, left). These tuatara are the sister clade to the squamates, a clade of 7,000 species that includes all living snakes and lizards (Figure 1, right) [2]. Since these two groups are sister clades, they diverged from a common ancestor at exactly the same time (∼250 million years ago [3]). Tuataras used to be far more diverse in the past (though almost certainly not as diverse as squamates [4]), but their current diversity is dwarfed by the tremendous number and variety of snakes and lizards around the globe. Similar patterns occur across the whole tree of life. In fact, old, low diversity clades contain some of the most enigmatic species on Earth: ginkgo trees, coelacanths, tailed frogs, horseshoe crabs, and monotremes, among others. These species are sometimes called “living fossils,” although only some of them are actually thought to resemble their ancient ancestors [5].Open in a separate windowFigure 1The tuatara (Sphenodon punctatus, left) is one of only two surviving species in the clade Sphenodontia.The sister clade to the tuatara is Squamata, which includes the ∼7,000 living species of snakes and lizards, including the ornate day gecko (Phelsuma ornata, right). (Left) from Wikimedia commons, taken by user KeresH, http://commons.wikimedia.org/wiki/File:Henry_at_Invercargill.jpg; (Right) by the author.We can learn a lot about the differences in diversity across clades from phylogenetic trees. In particular, phylogenetic tree balance summarizes the pattern of differences in the number of species between sister clades across a whole phylogeny [6]. A phylogenetic tree can be completely balanced, such that each pair of sister taxa in the tree have exactly the same number of species (Figure 2A; this is only possible if the number of species in the tree is a power of 2: 2, 4, 8, 16, 32, etc.). A phylogenetic tree can also be completely imbalanced, so that every comparison of sister clades has a single species in one clade and the remainder in the other (such a tree is also called pectinate; Figure 2B). There are a few different ways to quantify tree balance, but they all work in basically the same way: compare the number of species between sister clades in the tree, and summarize those differences across a whole phylogeny [7].Open in a separate windowFigure 2Balanced (A), imbalanced (B), and random birth-death (C) phylogenetic trees of eight species (a–h).Imagine for a moment that you have the tree of life (a phylogenetic tree of all species on Earth). We don''t have such a tree yet, but scientists are moving in that direction and trees are getting bigger and bigger (e.g., [8]). The tree of life is a huge and complicated structure, and—as one might imagine at a scale that encompasses all living things, bacteria to beetles to beagles—resists generalizations. But even as the tree of life takes shape, we already know that it is highly imbalanced. This statement applies broadly across living things, and applies equally well to plants as it does to animals, and everything else (as far as we know). Dramatic differences in diversity among clades is a characteristic feature of life on Earth.To better understand patterns of balance in the tree of life, we can start with the birth-death model, a simple model of how phylogenetic trees might grow through time (reviewed in [9]). Under a birth-death model, phylogenetic trees “grow” through time following two processes: speciation, where one species splits into two, and extinction, when one species dies out. For simple birth-death models we assume that both of these processes happen at a constant rate through time for each species alive at that moment, and use the parameters λ (speciation rate) and μ (extinction rate). If we simulate this birth-death model using a computer, we will obtain a phylogenetic tree (Figure 2C). If the extinction rate is greater than zero, such a tree will include surviving species as well as species that have gone extinct. Since we will be comparing this tree to phylogenies of living species, we can assume that any species that went extinct before the present has been pruned. We can then measure the balance of the resulting birth-death tree (our simulated tree will also have branch lengths, but we will ignore those for now). If we repeat this process a large number of times, we obtain a statistical distribution of our tree balance measure, which represents the expectation of that distribution under the birth-death model. It turns out, perhaps surprisingly, that this distribution of balance depends only on the fact that the trees are simulated under a birth-death model; in terms of tree balance, the actual rates of speciation and extinction do not matter, as long as they are constant across clades [10].There is another counterintuitive feature of the balance of birth-death trees: these trees are surprisingly imbalanced compared to what, perhaps, your intuition might suggest. For example, imagine that a certain phylogenetic tree contains 100 species. If you look at the deepest split in that tree and compare the diversity of the two sister clades, what do you expect to find? Are you more likely to find an even number of species in each of these two clades—say, 50/50 or 49/51—or a very uneven number, like 2/98 or 1/99? The surprising answer is that all four of these listed possibilities are equally likely. In fact, all possible combinations of diversity for each of the two clades are equally probable [11].This property of birth-death models means that birth-death trees are quite imbalanced: it is not uncommon, for example, to find sister clades that differ in diversity by a factor of 20 just by random chance. However, the tree of life is imbalanced even compared to birth-death trees! This general observation was first discovered in an influential paper by Arne Mooers and colleagues [7]. In that paper, the authors measured the balance of phylogenetic trees that had been reconstructed using trait data, DNA sequences, or both. They then compared their balance to what we might expect under a birth-death model. They found that phylogenetic trees from a wide range of taxa are extraordinarily imbalanced. This paper showed that the general “shape” of the tree of life is highly imbalanced.The classical interpretation of the imbalanced tree of life is that clades vary in their rates of speciation and/or extinction. There are many reasons to suspect that species in some clades might speciate more frequently, or go extinct less frequently, than their relatives. For example, perhaps a species'' range affects its probability of speciating or going extinct—as suggested by a recent paper in PLOS Biology by Pigot et al. [12]—so that clades of species with different distributions of range sizes will experience different rates of diversification. Many studies have attempted to measure speciation and extinction rates in groups with a good fossil record, and compare these rates across different types of organisms and time periods (e.g., [13],[14]). These studies have generally found wide variation in both rates and in their difference (speciation−extinction = net diversification rate).Recent studies go beyond measures of tree balance by using the tree''s branch lengths to gain information about speciation rates. One simple way to do this is to compare the diversity of a clade to its age; one can then estimate the speciation rate as λ = ln(n)/t, where n is the number of living species in the clade and t is the clade''s stem age (the time since divergence from the clade''s sister group) [15]. There are also modifications of this equation that incorporate extinction and that can use the clade''s crown age (the time since all living species in the clade shared a common ancestor; see [15]).Perhaps surprisingly, one can even estimate extinction from a phylogenetic tree based only on living species [16]. This is because old and young lineages are hit by extinction with different probabilities: since young lineages have not been around very long, they are less likely to have gone extinct than older lineages. The phenomenon is called the “pull of the present” [16] and means that extinction leads to an overabundance of very young lineages in a tree. We can look for this pull in patterns of lineage accumulation through time, which can thus be used to estimate both speciation and extinction rates and to compare these rates across clades (e.g., [17]; but see [18], which points out that this method does not work well when its assumptions of rate constancy are strongly violated). More recently, new methods have been developed to search for variation in speciation and/or extinction rates across large phylogenetic trees, and to try to correlate these rates with the traits of lineages [19],[20].In this issue, Rabosky et al. [21] attempt the most ambitious study to date investigating the differences in species diversity across clades in the tree of life. The authors bring together a tremendously large dataset that spans the multicellular eukaryotes, including all living species of plants, animals, and fungi. For each of 1,397 eukaryotic clades, the authors gathered estimates of age and diversity from the literature – accounting for more than 1.2 million species in total. The authors also summarize the evolutionary relationships among these clades using a “backbone” phylogenetic tree with branch lengths in millions of years. This provides a remarkably complete view of what we currently know about the species diversity of clades across a huge section of the tree of life.The diversity data in Rabosky et al. [21] are broadly consistent with the historical background above: there are major differences in diversification rates across the tree of life. There are old clades with few species, young clades with many species, and everything in between. But Rabosky et al. also note a peculiar aspect of their data: there is typically either a very weak or no relationship between the number of species in a clade and its age. That is, in the data they analyze, it is difficult to guess how many species are in a clade on the basis of how long it has been diversifying from a common ancestor. The traditional explanation for this pattern would be differences in diversification rates across clades—although the authors use simulations to show that, at least under one scenario about how rates might vary across trees, one rarely finds such weak or absent relationships between age and diversity. The authors speculate about other possible explanations for this peculiar (lack of) pattern, from bias in the way clades are named to ecological processes that limit the number of coexisting, competing species.Rabosky et al.''s [21] analysis is not the final chapter; the tree of life is still under construction, and the total number of species in some clades is best viewed as an educated guess. Specifically, I suspect that we have very poor estimates of the extant diversity of many eukaryotic groups, particularly small, understudied organisms. Indeed, new techniques that use genomic sequencing to identify undiscovered species from DNA sequences from environmental samples often reveal that the species we know are only a small component of natural ecosystems [22]. One might also note that the total diversity of multicellular eukaryotes counted in this study might be a vast underestimate compared to recent estimates that use statistical analyses to correct for incomplete sampling [23]. Still, the results in Rabosky et al. [21] are intriguing and will certainly inspire further study, which I expect will be focused on testing more sophisticated mathematical models, beyond the constant-rate birth-death models prevalent today, that might be able to explain patterns in the data.I first learned of the Huxley quote that opens this article in a classic paper, “Homage to Santa Rosalia, or why are there so many kinds of animals?” [24]. This paper, written by the great ecologist G.E. Hutchinson, speculates about the mechanisms that allow so many different species to coexist in natural communities. Hutchinson describes collecting Italian water boatmen from a small pond in the shadow of Santa Rosalia''s shrine, and wondering why the pond contained two species of water beetle—no more, no less. Hutchinson says “Nothing in her history being known to the contrary, perhaps for the moment we may take Santa Rosalia as the patroness of evolutionary studies…” [24]. Rabosky et al.''s [21] paper represents the latest development in our efforts to understand why the Earth has the particular number of species that it has – no more, no less. Santa Rosalia would be proud.     

Splenogonadal Fusion: An Unusual Case of an Acute Scrotum

We highlight a case on a normal left testicle with a fibrovascular cord with three nodules consistent with splenic tissue. The torsed splenule demonstrated hemorrhage with neutrophilic infiltrate and thrombus consistent with chronic infarction and torsion. Splenogonadal fusion (SGF) is a rather rare entity, with approximately 184 cases reported in the literature. The most comprehensive review was that of 123 cases completed by Carragher in 1990. Since then, an additional 61 cases have been reported in the scientific literature. We have studied these 61 cases in detail and have included a summary of that information here.Key words: Splenogonadal fusion, Acute scrotumA 10-year-old boy presented with worsening left-sided scrotal pain of 12 hours’ duration. The patient reported similar previous episodes occurring intermittently over the past several months. His past medical history was significant for left hip dysplasia, requiring multiple hip surgeries. On examination, he was found to have an edematous left hemiscrotum with a left testicle that was rigid, tender, and noted to be in a transverse lie. The ultrasound revealed possible polyorchism, with two testicles on the left and one on the right (Figure 1), and left epididymitis. One of the left testicles demonstrated a loss of blood flow consistent with testicular torsion (Figure 2).Open in a separate windowFigure 1Ultrasound of the left hemiscrotum reveals two spherical structures; the one on the left is heterogeneous and hyperdense in comparison to the right.Open in a separate windowFigure 2Doppler ultrasound of left hemiscrotum. No evidence of blood flow to left spherical structure.The patient was taken to the operating room for immediate scrotal exploration. A normalappearing left testicle with a normal epididymis was noted. However, two accessory structures were noted, one of which was torsed 720°; (Figure 3). An inguinal incision was then made and a third accessory structure was noted. All three structures were connected with fibrous tissue, giving a “rosary bead” appearance. The left accessory structures were removed, a left testicular biopsy was taken, and bilateral scrotal orchipexies were performed.Open in a separate windowFigure 3Torsed accessory spleen with splenogonadal fusion.Pathology revealed a normal left testicle with a fibrovascular cord with three nodules consistent with splenic tissue. The torsed splenule demonstrated hemorrhage with neutrophillic infiltrate and thrombus consistent with chronic infarction and torsion (Figure 4).Open in a separate windowFigure 4Splenogonadal fusion, continuous type with three accessory structures.     

Promoting Effects of the Adipokine,Apelin, on Diabetic Nephropathy

Angiogenesis, increased glomerular permeability, and albuminuria are thought to contribute to the progression of diabetic nephropathy (DN). Apelin receptor (APLNR) and the endogenous ligand of APLNR, apelin, induce the sprouting of endothelial cells in an autocrine or paracrine manner, which may be one of the mechanisms of DN. The aim of this study was to investigate the role of apelin in the pathogenesis of DN. Therefore, we observed apelin/APLNR expression in kidneys from patients with type 2 diabetes as well as the correlation between albuminuria and serum apelin in patients with type 2 diabetes. We also measured the proliferating, migrating, and chemotactic effects of apelin on glomerular endothelial cells. To measure the permeability of apelin in glomerular endothelial cells, we used transwells to detect FITC-BSA penetration through monolayered glomerular endothelial cells. The results showed that serum apelin was significantly higher in the patients with type 2 diabetes compared to healthy people (p<0.05, Fig. 1B) and that urinary albumin was positively correlated with serum apelin (R = 0.78, p<0.05). Apelin enhanced the migration, proliferation, and chemotaxis of glomerular endothelial cells in a dose-dependent manner (p<0.05). Apelin also promoted the permeability of glomerular endothelial cells (p<0.05) and upregulated the expression of VEGFR2 and Tie2 in glomerular endothelial cells (p<0.05). These results indicated that upregulated apelin in type 2 diabetes, which may be attributed to increased fat mass, promotes angiogenesis in glomeruli to form abnormal vessels and that enhanced apelin increases permeability via upregulating the expression of VEGFR2 and Tie2 in glomerular endothelial cells.Open in a separate windowFigure 1Correlation between apelin and albuminuria.A: The apelin concentration in serum was positively correlated with albuminuria (R = 0.78, *p<0.05). B: The apelin concentration in serum was significantly increased in patients with type 2 diabetes (2DM, n = 60) compared to healthy people (control, n = 32). The data are expressed as the means±SD (*p<0.05 vs. control grou ). C: The graphs show the promoting effect of apelin on FITC-BSA passing through the glomerular endothelial cell monolayers at the indicated time point. The data are expressed as the means±SD (n = 6, *p<0.01 vs. control group).     

Assessing Social – Ecological Trade-Offs to Advance Ecosystem-Based Fisheries Management

Modern resource management faces trade-offs in the provision of various ecosystem goods and services to humanity. For fisheries management to develop into an ecosystem-based approach, the goal is not only to maximize economic profits, but to consider equally important conservation and social equity goals. We introduce such a triple-bottom line approach to the management of multi-species fisheries using the Baltic Sea as a case study. We apply a coupled ecological-economic optimization model to address the actual fisheries management challenge of trading-off the recovery of collapsed cod stocks versus the health of ecologically important forage fish populations. Management strategies based on profit maximization would rebuild the cod stock to high levels but may cause the risk of stock collapse for forage species with low market value, such as Baltic sprat (Fig. 1A). Economically efficient conservation efforts to protect sprat would be borne almost exclusively by the forage fishery as sprat fishing effort and profits would strongly be reduced. Unless compensation is paid, this would challenge equity between fishing sectors (Fig. 1B). Optimizing equity while respecting sprat biomass precautionary levels would reduce potential profits of the overall Baltic fishery, but may offer an acceptable balance between overall profits, species conservation and social equity (Fig. 1C). Our case study shows a practical example of how an ecosystem-based fisheries management will be able to offer society options to solve common conflicts between different resource uses. Adding equity considerations to the traditional trade-off between economy and ecology will greatly enhance credibility and hence compliance to management decisions, a further footstep towards healthy fish stocks and sustainable fisheries in the world ocean.Open in a separate windowFigure 1Summary of multispecies management options in the Baltic.(A) Profit maximum. (B) Economic optimum while respecting sprat B_PA. (C) Equitable optimum while respecting sprat B_PA. Central numbers indicate total profits (million €/year) as well as an equity measure (in brackets). Area of each pie slice is relative to status quo values 2008-2010 (black circle), with error bars from sensitivity analysis.     

Caring for Offspring in a World of Cheats

Parents providing care to offspring face the same problem that exists in every biological system in which some individuals offer resources to others: cheaters, who exploit these benefits. In almost all species in which males contribute to parental care, females mate with multiple males. As a result, males frequently provide efforts for unrelated offspring at a cost to their own reproductive fitness. In a new study, Griffin et al. find that across a wide range of animal species, males flexibly adjust their contribution to parental care in relation to extra-pair paternity. However, adjustment is not perfect, because males are limited by the potential costs of withholding help to their own offspring, which is only outweighed if cheating occurs frequently and if providing care reduces a male''s future reproductive success. These findings illustrate how in biological systems cheater and cheated can adapt to changes in each other, preventing either one from gaining control.Whatever your personal feelings, evolutionary biologists will tell you that caring for offspring is not an easy affair. Pick up any current textbook on behavioural ecology, and you will find that the word “family” is invariably followed by the word “conflict” (e.g., [1]). Conflicts between family members arise because selection favours individuals aiming to maximize reproductive fitness, and these aims frequently collide because selection pressures differ even among related individuals [2]–[4]. Offspring can improve their reproductive fitness by obtaining the maximal amount of investment from both of their parents. However, parents frequently provide less than the maximum because any increased investment into current offspring impacts their ability to produce additional offspring in the future. Caring for offspring in all its forms is energetically expensive and may impair a parent''s ability to have additional offspring in a variety of ways. For example, when a female of the golden egg bug (Phyllomorpha laciniata) lays her eggs on a male rather than on a plant, her offspring will have increased survival, but the father carrying the eggs has a higher risk of being eaten by a bird [5]. In bighorn sheep (Ovis canadiensis) [6], mothers are less likely to have a surviving offspring in the year after rearing a son, as males are generally heavier at birth and suckle more frequently because being larger provides an advantage when competing against other males. In European starlings (Sturnus vulgaris), males who participate less in the incubation of the offspring have a higher chance of gaining a secondary female [7].Given the costs of providing parental care, we would expect that individuals should not expend energy if they do not gain any fitness at all, as is the case when they care for offspring that are not their own [2],[8]–[10]. Individuals that are potential victims of cheating are predicted to have evolved a range of counteradaptations to reduce the risks and costs of raising unrelated offspring [11],[12] ([13].

Table 1

Strategies to minimize the risks and costs of being exploited by cheaters.

Centre of the Cell: Science Comes to Life

Centre of the Cell is a unique biomedical science education centre, a widening participation and outreach project in London’s East End. This article describes Centre of the Cell’s first five years of operation, the evolution of the project in response to audience demand, and the impact of siting a major public engagement project within a research laboratory.Centre of the Cell is a unique cell-shaped science centre suspended above a real biomedical research laboratory in the heart of London’s East End. It is one of the few, perhaps the only, science education centres in the world to be situated inside a research lab—in the Blizard Institute at the Whitechapel medical and dental campus of Queen Mary University of London (QMUL). Since its opening in September 2009, over 100,000 people have participated in Centre of the Cell activities (Fig 1) with approximately one million visits to the interactive website www.centreofthecell.org. With visitor numbers and activities increasing year on year, we believe this is an example of an innovative and successful public engagement project that may serve as an example for similar initiatives in biomedical research institutes.Open in a separate windowFig 1Visitor statistics for Centre of the Cell years 3–6 (2011–2015).These monthly statistics include all visitors on site as well as visitors to our workshops and shows in schools and other locations. Year 1 and 2 figures (not shown here) were 15,387 and 19,585, respectively. These data show a consistent pattern from month to month. 2013–2014 numbers were lower, due to planned maintenance shutdown of the Pod and introduction of charging for Pod shows that led to a dip in visitor numbers that largely recovered during 2014.     

A new species of Stenoloba Staudinger, 1892 from China (Lepidoptera,Noctuidae, Bryophilinae)

A new species of Stenoloba from the olivacea species group, Stenoloba solaris, sp. n. (Lepidoptera, Noctuidae), is described from Yunnan, China. Illustrations of the male holotype and its genitalia are provided. A diagnostic comparison is made with Stenoloba albistriata Kononenko & Ronkay, 2000, Stenoloba olivacea (Wileman, 1914), and Stenoloba benedeki Ronkay, 2001 (Fig. 4).Open in a separate windowFigures 1–5.Stenoloba spp. adults and biotope. 1 Stenoloba solaris, sp. n., male, holotypus, Yunnan, China (GBG/ZSM) 2 Stenoloba albistriata, male, paratypus, N. Vietnam (ZFMK) 3 Stenoloba olivacea, male, Taiwan (HNHM) 4 Stenoloba benedeki, male, paratypus, N. Vietnam (HNHM) 5 Type locality of Stenoloba solaris, sp. n. China, NW Yunnan, Lijiang/Zhongdian near Tuguancum, 27°29''700"N, 99°53''700"E.