Coherent neural spiking and local field potentials are believed to be signatures of the binding and transfer of information in the brain. Coherent activity has now been measured experimentally in many regions of mammalian cortex. Recently experimental evidence has been presented suggesting that neural information is encoded and transferred in packets, i.e., in stereotypical, correlated spiking patterns of neural activity. Due to their relevance to coherent spiking, synfire chains are one of the main theoretical constructs that have been appealed to in order to describe coherent spiking and information transfer phenomena. However, for some time, it has been known that synchronous activity in feedforward networks asymptotically either approaches an attractor with fixed waveform and amplitude, or fails to propagate. This has limited the classical synfire chain’s ability to explain graded neuronal responses. Recently, we have shown that pulse-gated synfire chains are capable of propagating graded information coded in mean population current or firing rate amplitudes. In particular, we showed that it is possible to use one synfire chain to provide gating pulses and a second, pulse-gated synfire chain to propagate graded information. We called these circuits synfire-gated synfire chains (SGSCs). Here, we present SGSCs in which graded information can rapidly cascade through a neural circuit, and show a correspondence between this type of transfer and a mean-field model in which gating pulses overlap in time. We show that SGSCs are robust in the presence of variability in population size, pulse timing and synaptic strength. Finally, we demonstrate the computational capabilities of SGSC-based information coding by implementing a self-contained, spike-based, modular neural circuit that is triggered by streaming input, processes the input, then makes a decision based on the processed information and shuts itself down. Cognitive tasks are associated with the dynamic excitation of neural assemblies. When we consider how quickly and flexibly such collectives may be formed and incorporated in a task, a persistent question has been: how can the brain rapidly evoke and involve different neural assemblies in a computation, when synaptic coupling changes only slowly? Here, we demonstrate mechanisms whereby information may be rapidly and selectively routed through a neural circuit, and sub-circuits may be turned on and off. The resulting information processing framework achieves the goal that has been pursued, but until now largely not attained, of achieving faithful, flexible information transfer across many synapses and dynamic excitation of neural assemblies with fixed connectivities. Functioning neuronal networks need to store, transmit, integrate and transform their inputs to achieve the neural computation performed by the brain. How this happens in vivo has not been understood. Many proposed mechanisms rely on rate model formulations with proposed mechanisms ranging from how oscillations are generated , to how With the advent of modern supercomputing, there have been efforts to simulate the entire (or a large portion of the) brain with realistic spiking neurons –6. Theree.g. hubs or oscillations) can stabilize the propagation of synchronous activity and select specific pathways for signal transmission [Numerical studies investigating fundamental computational mechanisms such as information propagation –15 have smission –20. Despcomputational level that ask what the brain does and why, 2) studies at the algorithmic level that attack how the brain performs specific computations, the information representations used and the processes that manipulate this information, and 3) studies at the implementational level that look for biophysical mechanisms that make up the building blocks to be used at higher levels.From the above studies and the large literature on this subject, it is seen that there is a range of levels of approximation at which to understand and model neural function. Marr categorized this range  as: 1) simplementation level that may be used as a fundamental building block to construct higher level information processing algorithms, and, we hope, may be used to build yet more abstract structures such as the cognitive operating system used by the brain with its many regions and sub-structures [We have demonstrated an information propagation mechanism at the ructures . Our mecructures . They shInformation packets by themselves are insufficient to form the basis for an information processing system. The system must also be powerful enough to perform general computation. That is, it must be as powerful as a universal Turing machine and be capable of storing, transmitting, integrating and transforming information. In our previous work, we demonstrated that graded information could be faithfully propagated through many layers of a neural circuit and that arbitrary linear maps could be performed using appropriate synaptic connectivities . HoweverT0 to the graded chain rapidly enough that there is an overlap in the integration of graded information and its transmission from one layer to the next. Graded information, in the form of synaptic currents and firing rates, is faithfully propagated across all 12 layers in the simulation.In T are offset by time T0. The solution is divided into segments which are the result of the integration of spikes in the corresponding segment (shifted by T0) from the previous layer during the gating pulse. In the I&F model, both T and T0 result from intrinsic neuronal dynamics and synaptic delays. See The observation that spike volleys in successive layers of the SGSC overlap in time led us to consider an extension of our previous mean-field model  to allowT and T0, we find time translationally-invariant solutions for synaptic input currents for special values of the feedforward coupling strength, S = Sexact, in the mean-field model  where we can find time translationally-invariant solutions for which graded propagation is possible.For fixed ηfit, αfit) that gave the best-fitting mean-field solution to the simulation data, given known T0. Here, we define α ≡ S(T0/τ)eT0/τ−, so that α = 1 at η = 1. In this expression, τ represents a synaptic time scale , can be used to model the I&F simulation. We show details of this fitting procedure for two different T0’s  for large η. Here, η = 2.5. Similarly, jittering T0 has little effect on the variability of current transfer  circuit would implement the recognition of a person’s face, transformed from visual information as it streams into the visual cortex, or the recognition of a word or phrase in the auditory system. Upon recognition, the circuit might send on the information and transfer control to a downstream circuit responsible for the reaction to the recognition.The neural circuit that we demonstrate is triggered by a streaming input. The input is a function that oscillates in time. The circuit encodes the input in graded pulses, then transforms it in order to determine frequency and phase information. It then makes a decision based on the transformed input that affects subsequent processing .The neural circuit consists of see  1) a triT from the streaming input.This probabilistic, spike-based algorithm uses a self-exciting population coupled to a streaming input to trigger the computation see , then coTo implement a conditional copy of the transformed data, we combine the output of the (arbitrarily chosen) eighth Hadamard coefficient in the present Hadamard output (representing that a particular frequency and phase of the input was detected) and the first population in the “Vigilance” gating chain. This operation causes the graded pulse to activate the Output Copy chain when its amplitude is sufficiently high, conditionally causing a pulse to cascade through 4 gating populations with the last population gating the transfer from the subsequent Hadamard output to the 8 output neurons. Once the Hadamard output is copied, it activates the Shutdown population, which inhibits all populations in the gating chains, terminating the computation.In this Section, we first discuss some technical issues related to the models and circuits that we presented above. Second, we present some ideas for understanding some experimental aspects of spike packets, à la LMH, using our SGSC-based information processing framework. Third, we discuss aspects of oscillatory coherent activity and its possible relationship to pulse-gating. Finally, we discuss some distinctive aspects of information processing using SGSCs and their use.Technical Issues: In parallel circuits such as the decision making circuit depicted in K22) uses a single chain of populations to gate the working memories and the Hadamard transform. Therefore, as long as gating is consistently timed, there can be no race condition in this circuit, since the pulses that control the parallel transfer of information arise from the same source, and thus gate the entire working memory or Hadamard populations at the same time. Furthermore, due to our use of an attractor synfire chain for gating, the timing is stereotypical and consistent.N ≈ 100–1000 is sufficient. Analysis of our Fokker-Planck simulations suggests that the dynamics of graded transfer may be captured by the mean firing rate and a few additional variables encapsulating the equilibrium voltage probability distributions [Sexact, and the state of each neuronal population  in the transmission of firing rates and rate correlations.The fact that our idealized mean-field model predicts the value and the range of synaptic strengths for exact transfer so well relies on an underlying time-translational invariance of the spiking and membrane potential probability distribution functions. This correspondence can be made more explicit by using a Fokker-Planck approach and suggests that a mean-firing rate description may work well even when the number of neurons in each population is not so large. Our I&F simulations indicate that ibutions . OngoingPackets: In our propagation mechanism, an upstream neuronal population sends spikes downstream, the spikes are integrated in the synaptic current of the downstream population, but unless a gating pulse is provided by a pulse-gating population to the downstream population, the information does not propagate. Gating pulses are provided by a feedforward chain with strong connectivity. Graded information propagates through neural assemblies with sparse, weaker connectivity. LMH conjecture that the cortex rests on a skeleton of stronger connections immersed in a sea of weaker ones. Thus, a correspondence may exist between the skeleton of strong connections and a gating chain and the sea of weakly connected neurons and a graded chain.Other evidence LMH discuss is: 1) spike-timing reliability progressively decays during a packet giving way to stimulus related firing rates. In our framework, if one considers the combined gating and graded pulses to be a spike packet received by a neuron in a graded population, then the gating pulse occurs at the beginning of a packet, where gating and graded spikes are combined early on. Later spikes in the packet, after the gating pulse ends, consist of spikes from the graded waveform, which decays as spikes are integrated downstream. The early gating pulse would be expected to be the most stereotypical aspect of the mechanism, and gating is purely timing related so many early spikes should be strongly correlated with timing. Also, later spikes (after the gating pulse ends) would consist purely of graded information, and so the transformation to largely firing-rate type statistics would also be expected. 2) Later spiking in a group of packets may represent feedback containing information concerning behavioral choices. We have constructed and demonstrated re-entrant circuits in previous work, that are nonetheless pulse-gated. In these circuits information is retained in packet form, but can be used to modify subsequent circuit properties. So, pulse-gating and feedback are not inconsistent and may be incorporated in the same packet-based information processing system. 3) LMH mention a wide range of functions that packets can serve, including triggering of firing patterns, recall and imagination of sensory stimuli, and attention. The neural circuit of Coherent Activity: The emerging picture from accumulating experimental evidence is that coherent activity is a fundamental contributor to cognitive function. Accumulating experimental evidence implicates coherent activity as an important element of cognition. Since its discovery [iscovery , activitiscovery –28, numeiscovery , 34–37, iscovery . Gamma biscovery  and contiscovery  tuning iiscovery . Attentiiscovery , LIP andiscovery , and MT iscovery . In geneiscovery .Theta-band oscillations are associated with spatial memory , 45, wheAs defined mathematically We suggested in our previous work that a natural manifestation of neural circuits that repeatedly analyze passive streaming input would be the existence of oscillatory, sub-threshold pulses generated by pulse-gated control signals from gating chains. However, coherent, oscillatory activity in the gamma-band, either spiking activity or sub-threshold voltage oscillations, is typically a transient phenomenon, at least in visual cortex . This maRapid visual categorization (RVC) experiments have demonstrated that objects can be recognized as early as 250–300 ms after presentation. It has been conjectured that massively parallel, feedforward networks are used during RVC computations for maximum speed , 51–53. In , using cSGSC: To our minds, the success of the SGSC graded information propagation mechanism rests on the structural robustness of the pulse gating mechanism. One contribution to robustness is that the synfire chain that is used for gating pulse generation approaches a fixed amplitude attractor with fixed temporal offset. A second contribution is that by providing overlapping temporal windows for information integration, the constraints on parametric precision to achieve graded information transfer are relaxed . The current σ population produced by spikes of the σ′ population. The parameter Sσσ′ denotes the synaptic coupling strength and the neurons from layer σ to layer σ′ are connected in an IID fashion, with coupling probability given by pσσ′. fσ and rate νσ.Individual current-based, I&F point neurons in the SGSC have membrane potentials described byGeneral SGSC circuits can incorporate a number of subcircuits, such as short-term memory and processing due to non-trivial synaptic connectivities  such as To analyze graded propagation for the case in which the integration of graded information in successive populations overlaps in time, we assume that the gating pulse is square with amplitude sufficient to bring neuronal populations up to the firing threshold. We also assume that above threshold the activity function is linear .S is a synaptic coupling strength, Id(t) is the downstream synaptic current, and τ is a synaptic timescale. In a thresholded-linear model, the upstream firing rate isp(t) = θ(t) − θ(t − T) and θ is the Heaviside step function, causing the downstream population to integrate Iu(t), giving the currentFiring in the upstream population is integrated by the downstream population. Thus, the downstream synaptic current obeysT. The offset between successive gating pulses is given by T0  denotes a vector gating pulse on the upstream population. This results in the solution Id(t − T) = PKIu(t), where P is a diagonal matrix with the gating pulse vector, p, of 0s and 1s on the diagonal indicating which neurons were pulsed during the transfer.As we demonstrate in Results, current amplitude transfer for the SGSC may be modeled with a piecewise linear activity function, therefore synaptic connectivities between two layers each containing a vector of populations, perform a linear operation on the currents in the upstream layer . For insThis discussion has identified three components of an information processing framework that naturally arises from mechanisms such as the SGSC:Iinformation content—graded current, Kinformation processing—synaptic weights, pinformation control—pulses, p, serves as a gating mechanism for routing neural information into (or out of) a processing circuit. We, therefore, refer to amplitude packets, I, that are guided through a neural circuit by a set of stereotyped pulses as “bound” information. In the SGSC, information content is carried by the graded chain Click here for additional data file.


Despite the great importance of lepidopteran wing patterns in various biological disciplines, homologies between wing pattern elements in different moth and butterfly lineages are still not understood. Among other reasons, this may be due to an incomplete understanding of the relationship between color pattern and wing venation; many individual wing pattern elements have a known relationship with venation, but a framework to unite all wing pattern elements with venation is lacking. Though plesiomorphic wing veins are known to influence color patterning even when not expressed in the adult wing, most studies of wing pattern evolution have focused on derived taxa with a reduced suite of wing veins.Sabatinca just as it does for Micropterix. However, the pattern elements that are lightly colored in Micropterix are dark in Sabatinca, and vice-versa. When plotted onto a hypothetical nymphalid wing in accordance with the relationship between pattern and venation discussed here, the wing pattern of Sabatinca doroxena very closely resembles the nymphalid groundplan.The present study aims to address this gap through an examination of Micropterigidae, a very early-diverged moth family in which all known plesiomorphic lepidopteran veins are expressed in the adult wing. The relationship between wing pattern and venation was examined in 66 species belonging to 9 genera. The relationship between venation and pattern element location, predicted based on moths in the family Tortricidae, holds for Micropterix and Sabatinca indicates that homologies exist among the contrast boundaries that divide wing pattern elements, and that color itself is not a reliable indicator of homology. The similarity between the wing pattern of Sabatinca doroxena and the nymphalid groundplan suggests that the nymphalid groundplan may have originated from a Sabatinca-like wing pattern subjected to changes in wing shape and reduced expression of venation.The color difference in pattern elements between The online version of this article (doi:10.1186/s12862-016-0687-z) contains supplementary material, which is available to authorized users. Color pattern in the animal kingdom has been an area of intense study for well over a century . InsectsSabatinca and other genera in the family Micropterigidae. Recent studies have confirmed that Micropterigidae, along with Agathiphagidae, are the most basal living moths imitive (plesiomorphic) condition for the Lepidoptera” . HoweverThe two mutually exclusive hypotheses for the origin of symmetry systems rely on conflicting assumptions; a test of these assumptions would be a first step toward rejecting one, or perhaps ultimately both, of these hypotheses. In addition to the primitive state for lepidopteran wing pattern elements, another question must be resolved in order to reconcile microlepidopteran wing pattern with the origin of symmetry systems: the influence of wing venation on wing pattern development. In Lepidoptera with symmetry systems, such as butterflies, certain types of wing pattern elements – such as venous stripes – are unquestionably dependent on venation . Other pArchaeolepis mane, only one branch of the Sc vein is visible [A. mane is highly unlikely to represent the ancestral state for lepidopteran wing venation.Though we do know with absolute certainty that plesiomorphic wing veins can influence color pattern elements (such as eyespots) even when not expressed, the relevant groundplan of primitive lepidopteran wing venation remains less certain. Because lepidopteran color patterns are unique in that they arise from scales, a reasonable working hypothesis is that the veins with potential to influence the development of color pattern are those that were present in the ancestral lineage in which scales first originated, regardless of whether these primitive scales expressed color. However, identification of this lineage, and the wing veins that it possessed, is hindered by the nature of the lepidopteran fossil record. The timing of the split between Trichoptera and Lepidoptera is unknown; the earliest definitive Trichoptera and Lepidoptera both date to the Mesozoic, but the recent finding of putative caddisfly cases from the early Permian would move this split much farther into the past . Moths h visible , 35. But visible –18, A. mSabatinca. There is reason to doubt that this hypothesis is complete: it contains a three-branched M vein, as is found in Sabatinca and other Micropterigidae, but the presence of a four-branched M vein in Permotrichoptera [Other Jurassic fossil moths offer limited additional information about ancestral wing venation. New discoveries are very rare , and foschoptera , 18, Meschoptera , extant choptera , and thechoptera  – recentchoptera , 11 – suSabatinca, 12 other micropterigid genera, and trichopteran outgroups [Micropterix, whose wing pattern has already been examined [Epimartyria, a genus with three species, two of which have wing patterns comprised of large, light brown spots against a dark brown ground color [Sabatinca plus the “southern sabatincoid” lineage, which is dispersed throughout the Southern Hemisphere and includes Austromartyria, Hypomartyria, and Agrionympha; and the second, far less diverse “Australian group” which is restricted to Australia, New Zealand, and New Caledonia and is dominated by Tasmantrix and also includes Aureopterix, Zealandopterix, and Nannopterix. The terms “southern sabatincoid” and “Australian group” are used here in accordance with previous contributions [Micropterigidae are small moths with varied wing patterns Fig. . Modern utgroups . Resultsutgroups . The Laund color ; and varnd color . The Gonibutions .Fig. 3ReStudies of micropterigid wing scales have found that they are always internally “fused” and therefore lack a cavity to hold pigment sacs . Photos Sabatinca. Schachat and Brown hypothesized that a third branch of Sc, known to be plesiomorphic for Amphiesmenoptera, plays a key role in micropterigid wing pattern; this hypothesized vein is referred to here as “pSc,” for plesiomorphic Sc ; these are the same numbers that were used to refer to undescribed species included in the recent Sabatinca phylogeny [Terminology is used as follows. A “wing pattern element” formed by two or more adjacent wing scales of the same color, and can take the form of a spot, band, patch, etc., and the term “band” is used as shorthand for “transverse band,” which is a band that runs more or less between the costal and dorsal margins of the wing. The term “fascia” is rarely used here; this term has recently been subject to varying interpretations regarding wing pattern in Micropterigidae, having been applied to both light bands , 44 and  Sc Fig. . Many sphylogeny .Sabatinca clade shown to be the most basal in the genus, called the “calliarcha group” [Sabatinca lucilia has the simplest wing pattern in this group, with a small, dark pattern element straddling the humeral vein; a very light-colored, uninterrupted band straddling Sc1 at the costa, surrounded by a dark band on each side; another very light band straddling R1b at the costa, also surrounded by a dark band on each side but interrupted by a dark patch connecting the two dark bands; and one last very light band, straddling Rs4 and M1 along the dorsum and sometimes Rs3 along the costa, also bordered by a dark band along its basal edge  through Rs2. At the wing apex, Rs3 violates this pattern of alternation because it is surrounded by the same light pattern element as Rs2.In  12 Fig. , all vei. 6 Fig. , various 18 Fig. , Sc1 usu 10 Fig.  has a wi 37 Fig.  have win 15 Fig. , dark sc 37 Fig. , Sc1 is Sabatinca spp. 22, 21, 5, and 7; and the viettei clade, consisting of Sabatinca spp. 44, 39, 32, 45, and viettei [Sabatinca spp. 43, 20, 47, 46, and 11  and the distal portion of the distal band of the central symmetry system (“dBC”), a feature that is illustrated as a discrete entity by some authors [Sabatinca, originated from a band that hypertrophied not once but twice. A discrete, visible “dBC” pattern element, marked with an asterisk , also cWntA is involved with the development of various symmetry systems on butterfly wings, and is sufficiently well understood that its expression can be used to identify pattern homologies in situations where morphology is confusing or unclear [wingless and WntA. Certain existing studies that use the nymphalid groundplan as a starting point have included various lineages of moths [The gene product  unclear . In theoof moths ; the resMicropterix and Sabatinca shows that the color of each pattern element can confound identification of homologies. The contrast boundaries that divide pattern elements, as opposed to the colors of pattern elements themselves, are the best indicators of homology.A comparison of wing pattern in Sabatinca species with the most obviously fasciate wing patterns – Sabatinca doroxena and S. aurella , and is arguably also the more plausible of the two explanations given that multiple bands preceding Sc1 are not known from any micropterigid genus besides Sabatinca.Though the “wing-margin” and “vein-fork” models share the assumption that transverse bands are the primitive wing pattern element for Lepidoptera, the two models differ in that the “vein-fork” model proposed a primitive groundplan with seven dark bands whereas the “wing-margin” proposes a primitive groundplan with either five or six dark bands, depending on whether Rsies Fig. . The banein Fig. . There a1 are a reality, a groundplan with these additional contrast boundaries could only include the seven dark bands proposed by Lemche on wings where Rs4 terminates along the costa, and this rarely occurs. One possible groundplan that could underlie wing patterns with seven dark transverse bands would include individual dark bands straddling every vein at the costa, instead of straddling alternating veins. Dark spots such as those that have accumulated around each vein at the costa in various Sabatinca species  – many of which are distantly related  Because the various colors on the wing of Sabatinca sp. 37 are limited to constrained to small portions of the wing, potential relevance to the “wing-margin” model may be easier to deduce. 37 Fig.  has a wiSabatinca species from New Caledonia have wing patterns with spots and transverse bands in light brown, dark brown, and iridescent blue; these wing patterns have been hypothesized to mimic the jumping spiders that appear on the island [Sabatinca spp. 48 and 18 , and, in combination with the “split-band” hypothesis, can predict the nymphalid groundplan based on wing pattern in Sabatinca, the “wing-margin” model and the “split-band” hypothesis appear to have great potential to explain wing pattern diversity in the order Lepidoptera.From an examination of micropterigid wing patterns that are comprised of two colors, it appears that the ancestral state for this family – and therefore quite possibly for the order Lepidoptera – is a wing pattern of alternating light and dark bands, with each band straddling one vein along the costa. This ancestral state conforms to the predictions of the “wing-margin” model, originally based on Tortricidae , 14. Howy Lemche , thus stMicropterix was not included here because this genus has already been examined [Sabatinca spp. 5b, 49, and 50 were not included here because these species are only known from specimens preserved in ethanol; and Epimartyria auricrinella and the genera Paramartyria, Palaeomicroides, Issikiomartyria, Kurokopteryx, and Neomicropteryx were not included here because these species’ wings are of only a single color, a dark brown similar to the color of dark bands in Micropterix [Epimartyria (E. bimaculella) and Tasmantrix , and species representing the additional genera Agrionympha  and Nannopterix (N. choreutes) were included here, despite being absent from the preliminary phylogeny, because specimens were available and because the affinities of these additional genera have already been discussed in the literature [The specimens examined for this study are held in the Australian National Insect Collection in Canberra, Australia; Victoria University in Wellington, New Zealand; and the Smithsonian Institution in Washington DC, USA. Only forewings were examined, because hindwings have very light scales of only one color. A total of 918 wings were examined, which may have included one or both forewings from a given specimen – the only wings that were excluded are those in which the relationship between venation and patterning cannot be deduced because the scales are worn off or the wing is broken. These 918 wings represent 66 species and 9 genera of Micropterigidae. Taxa were selected to match those sampled in the existing preliminary micropterigid phylogeny . Samplinexamined ; Sabatinropterix , 40. Addterature , 61.Epimartyria bimaculella, and by examination of wing slides prepared by George Gibbs and held at Victoria University for all other species; for the 7 species for which wing slides are not available , the wing slide of a sister species was examined for Sabatinca and published illustrations were consulted for Agrionympha [The methods used here to examine wing pattern morphology parallel those developed by Schachat and Brown  and are ionympha .4 vein terminates because, although this vein does not terminate along the costa in any of the species examined for the present study, it does terminate along the costa in Micropterix [To verify that the illustrations fully represent the species to which they correspond, a total of up to 20 forewings were examined under a light stereomicroscope.  Variations were noted at all locations along the costa where veins terminate, with the frequent exception of the humeral vein, which often cannot be detected on scaled specimens. Variations were also noted in between the two visible branches of the Sc vein, because an ancestral vein in this location has been hypothesized to constrain wing pattern , and varropterix  and occaropterix . To creaThe location of the wing vein 1A + 2A could not be observed in all pinned specimens because of the overlap between the forewing and hindwing, and therefore had to be inferred based on wing slides and previously described venation , 61, 62;


A few times a year, volunteers fan out along the causeway that links the South Carolina mainland with the seashore community of Folly Beach to clean up plastic bottles, straws, bags, and other debris from along the road and the salt marsh. Some of this debris has come from cities miles away. On windy days, litter is often blown off city streets into waterways. During rainstorms, debris floats into drains that empty into rivers. Other trash probably came from places closer to home. “I see bags and other plastic flying off the beds of pickup trucks going down the causeway,” says Marty Morganello, who organizes the cleanups for the Charleston-area chapter of the nonprofit Surfrider Foundation. “I see them coming out the open windows of cars and out the backs of garbage trucks and even recycling trucks. This material is lightweight, and if you don’t secure it, it will fly away.”Beach cleanups yield enormous amounts of trash, with plastic items a major constituent.Lost and discarded nets and lines from fishing vessels are important contributors to marine debris, especially in heavily fished areas. These vessels also lose plastic floats, traps, pots, and other gear. Other sea-based sources of plastic pollution include oil and gas platforms, aquaculture facilities, and cargo ships that lose containers to the sea.Plastic debris from land comes primarily from two sources: first, ordinary litter; and, second, material disposed in open dumps or landfills that blows or washes away, entering the ocean from inland waterways, wastewater outflows, and the wind.Lightweight plastic items tend to float in water and can be carried by currents great distances. By one report, plastic cargo lost from ships has been found more than 10,000 kilometers from where it was lost.A working group of researchers recently estimated that just 20 countries, out of a total of 192 with coastlines, are responsible for 83% of the plastic debris put into the world’s oceans. Lead author Jenna R. Jambeck, an environmental engineer at the University of Georgia, and her colleagues estimated that, all together, these 192 countries produce some 275 million metric tons of plastic waste each year. Of that volume, about 4.8–12.7 million metric tons of mismanaged plastic waste is thought to have entered the ocean in 2010.“That is the same as five five-gallon bags filled with mixed plastic on every foot of coastline around the world,” says Jambeck. Without improvements to waste management infrastructure, and assuming a business-as-usual projection of increasing coastal populations, economic growth, and use of plastics, the authors predict this volume of plastic debris could more than double by 2025.The United States makes a significant contribution to marine plastic pollution, but it’s only twentieth on the list of coastal nations that produce the most plastic waste from land. The top spots are filled by a number of rapidly developing countries with expanding populations near coastlines and poor systems of waste management, including China, Indonesia, and the Philippines.One of the major drivers of this trend in developing countries is the very rapid growth of “megacities,” defined as urban areas with populations exceeding 10 million. More than 70% of megacity growth is said to occur outside the formal planning process, and nearly a third of the urban population in developing countries lives in slums or informal settlements that lack city services, including solid-waste disposal.According to Jambeck and colleagues, a nation’s population density within 50 kilometers of the coast is the primary determinant of its land-based contribution to marine pollution.The third determinant is how much of a country’s waste, including plastic material, is mismanaged. The United States does well on that score. “U.S. mismanaged waste is only due to litter,” says Jambeck. “We have a waste-management infrastructure that allows everyone an opportunity to throw something away properly.” China’s coastal population is about 2.5 times larger than that of the United States but is estimated to produce more than 30 times more mismanaged plastic waste.The geographies of countries play an important part in their contribution to marine debris. Among the top 20 ocean polluters are Sri Lanka, an island nation; archipelago countries, such as the Philippines and Indonesia; and countries with long coastlines, such as China and Vietnam.“This study [by Jambeck et al.] provides a first cut at how you could focus efforts in places around the world and then build some strategies to stem that flow of plastics,” says George H. Leonard, chief scientist of Ocean Conservancy, an advocacy organization based in Washington, DC. “Marine debris is a global problem, but this study shows that you can work on a smaller suite of geographies [and] that you could solve a big part of the problem at the global level.” The key, he says, is to improve waste management in a relatively small number of countries.Some European nations have developed a model that other countries and regions could emulate to better manage their plastic waste and reduce marine pollution. It is based on the principle of extended producer responsibility (EPR), which was first formally outlined in an internal Swedish government report in 1990.In an EPR scheme, brand owners must pay the costs of tracking, managing, and recycling or disposing of packaging after their products have been used.Many European nations have not only passed EPR laws to increase reuse and recycling of plastics but also are diverting plastics to power plants for use as fuel for heat and electricity .,Nine European nations have banned landfills in part because available land is scarce in their densely populated areas. One result of this legislative decision is that 90–100% of plastics are recycled or used for energy production in these countries. But several other countries still landfill more than 60% of their waste, and some of these, especially in Eastern Europe, still depend totally on landfills. PlasticsEurope is calling for zero plastic waste going to European landfills by 2020.“Europeans have developed pretty robust recycling and energy recovery systems to manage their plastic waste,” says Steve Russell, vice president of the plastics division of the American Chemistry Council. “A primary driver for those systems has been the desire to capture energy and to remain as energy-independent as they can [while also dealing] with the landfill bans.” The United States, by contrast, has more acreage to build landfills and much lower prices for conventional energy sources, Russell says. “Whatever systems we design in the U.S.,” he says, “need to reflect the local conditions we have here.”Plastics in the ocean degrade into smaller pieces from the effects of sunlight, oxidation, and the abrasion of waves and currents, becoming smaller and smaller often to the point they are no longer visible to the naked eye.The consumption of plastic by marine organisms adds persistent, bioaccumulative, and toxic substances to the aquatic food chain.For years, China was the primary buyer for low-quality “mixed bales” of plastic scrap—often contaminated with food, dirt, and nonrecyclable materials—that do not have a commercial market in the United States. China converts much of this scrap into feedstock, or resin, for its expanding manufacturing sector. But some portion of the scrap can’t be recycled and ends up in China’s landfills.In 2013 China enacted its so-called Green Fence operation to improve quality controls and stem the import of low-quality plastic scrap that would have to be landfilled. While exports of U.S. plastic scrap to China fell 18% from 2012 to 2013,Despite China’s restrictions, U.S. plastics recycling continued to grow in 2013, with plastic bottle recycling up by 4.3% over 2012,Plastic polymers are almost completely derived from petrochemicals, depending on which feedstock is most cost effective at the point where the feedstock is produced.  Plastic recycling increases when it’s cheaper to create resin from recycled waste that it is to create it directly from fossil fuels, says Jim Glauser, a specialty chemicals expert and associate director at IHS, a U.S.-based information business. To improve recycling rates, “you need to improve the collecting, sorting, and processing of plastic waste to lower the cost and improve the quality of resin from recyclables,” Glauser says.Over the coming decades, the volume of plastic waste moving from the land into the sea is expected to increase if the many coastal economies and populations around the world continue to expand without taking steps to manage their municipal solid waste.Marine plastic pollution will remain a difficult problem to solve because it represents a “fundamental market failure” on a worldwide scale, says Leonard of Ocean Conservancy. He explains, “The production of plastic is ramping up,NOAA’s Wallace believes gear markings or a global system for reporting lost gear would be helpful in managing sea-based plastic pollution. “Today, we can’t trace any of that gear back [to its source],” she says. “It would be good to know which countries many of these nets are coming from, so we could find practices that would stop it from happening in the first place.”In 2012 Ocean Conservancy mobilized a new effort called the Trash Free Seas Alliance®, which includes chemical and plastics companies, producers of plastic consumer items, economists, environmental scientists, and conservation groups. The alliance is using the cross-sector expertise of its members to develop innovative, sustainable strategies to eliminate ocean waste.“We need a better understanding of the economic restraints to solve the plastic debris problem,” Leonard says. “We are looking at business practices that could allow greater recapture, recovery, reuse. Then we can identify and craft a suite of locally relevant solutions that make sense for business and for the ocean.”


There is ample evidence on the effects of prices and taxes on heavy drinking, including binge drinking . ExperimNevertheless, in an issue of Health Economics Review, Dr. Jon Nelson published a “systematic” review article with the following conclusion: “Increased alcohol taxes or prices are unlikely to be effective as a means to reduce binge drinking, regardless of gender or age group.” . We haveThere is a general consensus that the scientifically recommended approach to estimate an overall effect across multiple studies is meta-analysis. Meta-analysis is the final step in the standardized protocol detailed in Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), which was cited in the Nelson paper. However, instead of conducting a rigorous meta-analysis among the 56 studies that he manually selected and screened, Dr. Nelson deviated from the PRISMA approach by simply counting the number of studies with p ≤ 0.05. Dr. Nelson’s approach failed to account for the heterogeneity of the effect sizes and sample variation due to different studies, and potential publication bias. Dr. Nelson stated that due to “diversity of models and results,” quantitative coefficient estimates for a meta-analysis were not collected. Dr. Nelson’s reference to “diversity of models and results” is the exact reason why quantitative meta-analysis is necessary to obtain a common effect estimate in the first place.Furthermore, among the exclusion criteria, Dr. Nelson stated that studies were excluded if they are “based on a laboratory experiment” or use an interrupted time-series analysis. He did not explain why these two research designs were excluded. Not only do they have better internal validity than other designs, interrupted time series analysis is an ideal method for evaluating natural experiments, a type of study included in Dr. Nelson’s paper and stated in the article title.Based on his characterization of our own work, there is considerable evidence of misconstrued presentation of the literature in Dr. Nelson’s article. For example, Dr. Nelson cited a study by Xuan et al.  and statDr. Nelson further asserted that evidence in several widely-cited studies is drawn from aggregate econometric studies, and here he cited another one of our studies . His asDr. Nelson subsequently stated that “price and tax elasticity estimates for general populations may not apply equally to binge drinkers and other excessive drinkers.” To support this statement, Dr. Nelson cited Ayyagari and colleagues , which wDr. Nelson disclosed that “Research leading to this paper was supported in part by the International Center for Alcohol Policies, Washington, DC.” However, the author failed to disclose his role as a consultant (2012–2015) and member of the Research Advisory Board of ICAP, an alcohol industry-funded “social aspect organization”. ICAP-sponsored research has been criticized for faulty methodology and erroneous interpretations . Nor doeIn summary, Dr. Nelson’s conclusion that taxes were unlikely to be effective at reducing binge drinking is based on faulty methodology and an incorrect interpretation of the findings from studies, and his conclusion contradicts the extensive scientific literature. Binge drinking causes more than half of nearly 80,000 alcohol-attributable deaths in the U.S. each year and accounts for three-quarters of the 224 billion dollars in annual economic costs . This is


Benford’s Law states that, in naturally occurring systems, the frequency of numbers’ first digits is not evenly distributed. Numbers beginning with a 1 occur roughly 30% of the time, and are six times more common than numbers beginning with a 9. We show that Benford’s Law applies to social and behavioral features of users in online social networks. Using social data from five major social networks , we show that the distribution of first significant digits of friend and follower counts for users in these systems follow Benford’s Law. The same is true for the number of posts users make. We extend this to egocentric networks, showing that friend counts among the people in an individual’s social network also follows the expected distribution. We discuss how this can be used to detect suspicious or fraudulent activity online and to validate datasets. P predicted for a digit d is given by this formula:Benford’s Law states that, in naturally occurring systems, the frequency of numbers’ first digits is not evenly distributed. Numbers beginning with a “1” are far more common than numbers beginning with “9”—more than six times as frequent. The exact frequency Benford’s Law is frequently used in forensic accounting, where a distribution of first digits that is outside the expected distribution may indicate fraud . ResearcWe conducted an analysis over five of the most popular social networking websites and found that Benford’s Law applies to the social network structure in all of them. Specifically, the first significant digit (FSD) of users’ friend and follower counts on Facebook, Twitter, Google Plus, Pinterest, and LiveJournal all follow Benford’s Law. Users’ numbers of posts also conform to Benford. To our knowledge, this is the first time Benford’s Law has been applied to social networks. We show that exceptions to this rule can uncover configurations within social media systems that lead to unexpected results.We also show that, for any individual, the distribution of friend counts within his or her egocentric network also follows Benford’s Law. When the expected distribution is violated, it indicates unusual behavior. A preliminary analysis of over 20,000 Twitter accounts showed that the 100 users whose egocentric networks deviated most strongly from the Benford’s Law distribution were all engaged in suspicious activity.We discuss how these results lead to the possibility of Benford’s Law being used to detect malicious or irregular behavior on social media. We also show that it could be used to validate the sampling in social media datasets.It was astronomer Simon Newcomb who first formulated what came to be known as Benford’s Law in the 1880s. He noticed that books with logarithm tables showed a lot more wear toward the front, where the numbers beginning with 1 were, than in the back toward the 9s. Concluding that numbers beginning with 1 must be more common, he calculated the probability formula mentioned above.Physicist Frank Benford noticed the same phenomenon. He validated the observation by collecting naturally occurring numbers from many sources: the surface area of rivers, atomic weights, and numbers appearing in Reader’s Digest . All valThe formula for the law, Nevertheless, the law holds across many variations in measurement . TemperaBenford’s Law describes all these naturally occurring sets of numbers, and more. Specifically, some applications of Benford’s Law are more relevant to our work. Benford often applies to systems that follow a power law distribution . Power lWe analyzed data from five major social networking websites: Facebook, Twitter, Google Plus, Pinterest, and LiveJournal.We collected the number of friends in each network and followers when appropriate. On Google Plus, Twitter, and LiveJournal, we also had access to egocentric network data. For each user, we obtained a list of friends and the count of outgoing edges for each of those friends. With this data, we could analyze the distribution of FSDs for an individual person’s social networkOn Twitter and Pinterest, we also had access to the number of posts each person made. This provides another interesting insight into the general patterns of behavior on social media and whether Benford’s Law applies. We collected some of these datasets ourselves and used other datasets that had been created by others. The following sections detail our process with each network.We accessed user profiles using the Facebook Graph API with requests for friend counts of a numeric Facebook user ID. Once we accessed a user’s data, we incremented the user ID by 10,000 and make the next request. If there was no data available for a given user, we incremented the user ID by 1 and tried the next person until we found a match. We collected friend counts for 18,298 users.We collected the numbers of followers and “friends” (i.e. people the user is following) the user had, and the number of people each of those friends were following. This allowed us to analyze the distribution of FSDs within egocentric social networks. In addition to this network data, we collected the number of status updates for each user.Although there are existing Twitter social network datasets online, we collected our own data in this project in order to work with non-anonymized users so we could later analyze their account activity.We accessed data via the Twitter API, using users’ numeric Twitter user ID. Our process was to access a user’s data and then increase the user ID by 50,000. This gave us a fairly uniform distribution of users, and we were able to collect the data in a reasonable amount of time (over a few weeks) given Twitter’s API limits.If an a user ID was protected or not linked to an account, we incremented user the ID by 1 and tried the next person until we found a match.Because we considered counts for friends, followers, and status updates, we only included users in our sample who had at least one of each. This allowed us to consider that same set of users for all three attributes.For egocentric network analysis, we only included users who had at least 100 friends so the distribution of FSDs would be measured over a reasonably large sample.Our final dataset had 78,225 users. For 21,135 of these, we also had egocentric networks with friend, follower, and status counts for all the people they were following.The Google Plus network  is part The Pinterest data was provided as part of the Social Curation Dataset . We usedLiveJournal Dataset , 19 is oWe found that the distribution of FSD among friends in all five datasets closely followed the values expected from Benford’s Law, with one interesting exception: the Pinterest following relationship. We discuss this later, but we will set this aside for now. For Facebook, Google Plus, and LiveJournal friends, Twitter friends and followers, and Pinterest followers, all the distributions of FSDs followed Benford’s Law. Note that with datasets of this size, it is not appropriate to conduct a statistical hypothesis test for goodness of fit; over tens of thousands or millions of people, even a very tiny deviation would cause us to reject the null hypothesis. Furthermore, conformance with Benford’s Law has never been about a perfect statistical match to the predicted values—not even in Benford’s original work on the subject , 9. Rathr values are > 0.990. We also ran Kolmogorov–Smirnov tests to check the fit of the data with the Benford’s Law distribution. The p-values, which indicate the probability that the social network’s FSD distribution is the same as Benford’s, are also shown in Pearson correlations are a common way to measure how closely a distribution adheres to Benford’s Law –22. The Other user behavior also fit Benford’s Law as well. We had data for the number of posts users made on Pinterest (number of pins) and Twitter (number of tweets). In both cases, correlation with Benford’s predictions was extremely high: 0.9998 and 0.9960, respectively.following count did not adhere to Benford’s Law , the FSDs adhered to Benford’s Law, as shown above.We investigated this issue on Pinterest more deeply and found the explanation for the frequent 5s. When new users sign up for Pinterest, they are prompted to choose “interests” to follow. Users This exposes an important point about applying Benford’s Law: it can be violated when there is external influence over people’s natural behavior. In the Pinterest case, we discovered the influence was an artifact of the system configuration.Benford’s Law extends to second digits, as well. The distribution is much flatter, ranging from 0.1197 for the frequency of 1s to 0.0850 for the frequency of 9s. Our networks agreed with the second digit distribution quite well. The Pearson correlations for all five networks, including Twitter friend counts and follower counts, are all > 0.97. The Kolmogorov–Smirnov p-values are > 0.98 for Twitter friends, Facebook, Pinterest, and Google Plus.This second digit analysis eliminated the frequent 5s artifact seen in Pinterest. The Kolmogorov–Smirnov p-values for LiveJournal and Twitter follower counts are a bit less impressive , though some existing literature suggests this might be the expected with the flatter distribution of the second digit , 23.The adherence to Benford’s Law carries through into FSD distributions within individual egocentric networks. Using data from Twitter, Google Plus, and LiveJournal, we selected individuals with at least 100 friends, and then obtained the number of social connections that each of those friends had. We then determined FSD distributions in the friend-of-friend counts of each egocentric network. Overall, the vast majority of egocentric networks conformed to Benford’s Law.On Google Plus, 91.5% of users’ egocentric networks’ FSD distributions had a correlation of over 0.9 with Benford’s Law predictions. This was true for 85.1% of LiveJournal egocentric networks.In the Twitter data, 89.7% of users had a correlation of over 0.9. Of our 21,135 users, only 170 (< 1%) had a correlation under 0.5.Since we had non-anonymized data for Twitter, we were able to investigate these accounts with low correlations. Nearly every last one of the 170 accounts mentioned above appeared to be engaged in suspicious activity. Some accounts were spam, but most were part of a network of Russian bots that posted random snippets of literary works or quotations, often pulled arbitrarily from the middle of a sentence. All the Russian accounts behaved the same way: following other accounts of their type, posting exactly one stock photo image, and using a different stock photo image as the profile picture. While we are currently investigating the purpose of these bot accounts’ existence, their deviation from Benford’s Law made it quite easy to identify their highly unusual behavior. Of the 170 accounts, only 2 seemed to belong to legitimate users.Figs We have shown that Benford’s Law applies to relationships in online social networks. This is true for social networking sites as a whole, and for individual users’ egocentric networks. Data from Twitter and Pinterest also suggest that it applies to the number of posts users make on social media sites, as well. In the one network where Benford’s Law did not hold, closer inspection revealed that it due to a feature of the system that altered users’ behavior.Next are some applications for these results, followed by some closing thoughts on the work.no accounts that naturally deviate from expected patterns; rather, deviation from a Benford distribution can flag accounts for additional review.First, Benford’s Law can be used to detect users who are behaving in unexpected ways. As we found in our Twitter dataset, the vast majority of accounts that strongly deviated from the expected FSD distributions were engaged in unusual behavior. As is the case with forensic accounting investigations using Benford’s Law, a deviation does not necessarily mean there is fraud happening. Given the large number of users on social media, it would be statistically unusual to have These insights can also be used to validate experimental datasets. It is often the case that data can be hard to collect from social media sites, especially when researchers are looking for detailed personal information. Truly random or representative sampling is difficult to do—and essentially impossible when connected components of a social network are important to the analysis. This raises the question as to whether the sample of accounts collected by a research team seriously deviates from normal patterns. While Benford’s Law only addresses one aspect of expected behavior, major differences between a sample’s FSD distribution and Benford’s Law could indicate serious sampling problems.We tested this by analyzing the FSD distributions on a number of datasets collected for various projects and experiments.http://nodexlgraphgallery.org). These were all generated by collecting the networks of users who had tweeted a given search term. For each graph, we analyzed friend, follower, and tweet counts for the users in each dataset. On all graphs and each of the three measures, the Pearson correlation with Benford’s expected values was > 0.990. This shows that, structurally, the networks look like we would expect.We randomly selected 50 Twitter-based networks from the NodeXL Graph Gallery . In this example, friend counts were self-reported, and 94.5% of those began with a 1—more than triple the expected 30.1%.Such deviations do not necessarily imply a problem with the data; indeed, this distribution may be irrelevant to the analysis being performed. However, it hints that the subjects are not reflecting an expected distribution, and thus may vary from the larger population in other ways. Further research is needed to understand the implications of deviation in experimental samples.There is a growing understanding of the subtle patterns of natural behavior which humans have difficulty replicating in unnatural circumstances. The applicability of Benford’s Law to social media is a new tool for analyzing user behavior, understanding when and why natural deviations may occur, and ultimately, detecting when abnormal forces are at work.


But as current research indicates, that’s only the beginning of what we can do with the technology, broadly referred to as “remote sensing.” In the coming years, new satellites will offer higher-resolution imagery in conjunction with more robust and precise algorithms to process the data they deliver. As a result, researchers expect to dramatically expand their ability to view and understand Earth’s land, water, and air, from its remotest ocean waters to its largest cities.The National Aeronautics and Space Administration (NASA) launched its first satellite in 1958,Indeed, by March 2015 NASA will have launched 6 Earth-observing missions in 12 months,Along with technological and scientific advances, a third development is leading to new and improved applications of satellite data: NASA and the National Oceanic and Atmospheric Administration (NOAA) have made their satellite data available free of charge, Haynes says, while the European Space Agency (ESA) has reduced prices and promised to provide free access to data from its next generation of instruments.“More people use the data, and you get more out of it than when you try to restrict it,” says Raphael Kudela, an oceanographer at the University of California, Santa Cruz, who uses satellite imagery to study HABs. This free sharing of data has been instrumental in his field, allowing researchers at institutions around the world to study HABs from above and to improve systems to track and predict them.,HABs are a growing global concern due to increases in aquaculture activity (which both contributes to and is impacted by blooms), widespread runoff of nutrient-rich fertilizer and sewage into coastal waters, transport of HAB species via ship ballast water, and climate change, which may expand the ranges of some marine species and increase the size and frequency of freshwater blooms.HAB research was used as a major justification for some instrument launches, says Don Anderson, a senior scientist and the HAB program lead at the Woods Hole Oceanographic Institution. Research assistant professor Tim Moore of the University of New Hampshire says that through remote sensing, not only can we collect observations of hard-to-reach places, but we’re also able to do it on a continual basis: a double benefit. “That’s why remote sensing is such an appealing platform,” he says.in situ sensors and traditional water sampling. “Now we’re in a much more realistic phase where people understand the limitations and understand the strengths, and they’re much more careful about what is promised or projected,” Anderson says. “It’s a much healthier environment in that way.”However, researchers have also come to understand that remote sensing can’t operate alone in HAB research and is best used in conjunction with Alexandrium fundyense, a highly toxic dinoflagellate common along the New England coast.Satellite imagery cannot detect low-biomass blooms such as Remote sensing also falls short when it comes to scanning small freshwater bodies for toxic cyanobacteria because existing satellites do not offer sufficiently high resolution. At least 3 contiguous pixels of imagery are necessary to convey an accurate sense of surface conditions, says Stumpf.A third shortcoming is that satellites do not always provide the “revisiting frequency” necessary for optimal bloom detection and tracking. In other words, because their sensors are not constantly trained on any one patch of water, and because the pictures they do take may be obscured , researchers may in some cases be limited to just one good image per week, says Moore.Karenia brevis blooms dominate other algal species and concentrate on the ocean’s surface during the daytime; and for Lake Erie, where Microcystis aeruginosa blooms float to the surface, forming a bright, readily visible scum, Stumpf says. Microcystis threatens the drinking water of millions of people around Lake Erie each summerStill, in many cases across the United States and around the world, remote sensing is a critical tool for monitoring HABs, offering a holistic, big-picture view and more spatial context than is available using discrete surface sensors, Kudela says. NOAA relies on satellite imagery to develop its free weekly forecasts for the Gulf of Mexico, where toxic ESA’s Sentinel-3 mission, set to launch in 2015,Microcystis in Lake Erie and other freshwater bodies by capitalizing on the organism’s ability to absorb and scatter light.In anticipation of higher-resolution sensors on the horizon, current research is focused on improving and specializing quantitative algorithms to make better use of the imagery collected by satellites. Moore is developing new algorithms that can better assess the presence of Meanwhile on the West Coast, assistant professor Angelicque White at Oregon State University is using remote sensing to identify predictors of HABs off the Oregon coast, assess risk periods for these events, and develop satellite-detectable proxies of bloom biomass such as sea surface temperature. Yet no matter how refined these tools become, she says, the measurements will never be able to stand alone in a functioning early-warning system. “Remote sensing is a relatively powerful tool for HAB monitoring,” White says, “but it has to be married to some sort of monitoring program.”,Satellite data also exhibit both shortcomings and great promise in projecting outbreaks of infectious diseases. Researchers have found that environmental factors bear strong relationships to the spread of diseases such as malariaYet, much as for HABs, satellite imagery does not provide a complete or perfectly reliable picture of what’s happening on the surface and should be verified by and blended with ground-sensed data, says Madeline Thomson, a senior research scientist with the International Research Institute for Climate and Society (IRI) at Columbia University. For example, the bright surface reflectance in semi-arid areas makes it difficult for satellites to “see” dust there, says Carlos Pérez García-Pando, an associate research scientist at NASA’s Goddard Institute for Space Studies and Columbia University.Scientists are just beginning to develop hands-on tools rooted in remote sensing such as online maps that help government agencies and health organizations get in front of disease outbreaks. “Abilities of remote sensing to identify particular land-cover types, to monitor changes in vegetation, to provide estimates of rainfall, etc., really go back to the 1980s,” Thomson says. “But for a researcher, our ability to transform that information into something really practical has taken a long time in getting there. I think we’re just getting there now.”The science of using satellite data to predict outbreaks is most developed around malaria, which has been prioritized because of its global burden, according to Thomson, who has worked in the field for 20 years. Rain is the disease’s primary environmental trigger, as it activates the life cycle of the mosquito by creating breeding sites.,The IRI provides free satellite images and maps displaying malaria risk factors via an extensive online data library.“I’ve been working with the ministries of health and the ministries of agriculture [of various countries], and they do use the tool,” Ceccato says. “It’s easy to map those conditions, and they use that information to make decisions.”The IRI has developed similar maps and predictive tools for meningococcal meningitis outbreaks in Africa’s “meningitis belt,” which extends from Senegal to Ethiopia across the continent’s midsection. More than 800,000 cases of the disease were reported in this region between 1996 and 2010, with 10% being fatal.The polysaccharide vaccines for meningitis used in the region typically provide immunity for only 2 to 3 years. Therefore, for economic and logistic reasons, the strategy has been reactive vaccination, says Pérez García-Pando; when the number of cases in a certain district reaches a threshold, vaccinations are ordered. But sometimes the response comes too late, so scientists have sought to use what they know about environmental factors in the disease’s transmission to develop forecasts, which they hope will dramatically improve the effectiveness of vaccine distribution.Meningitis outbreaks in this region of the world have been tied to dry weather and elevated dust levels.No matter the mechanism, researchers have observed that just as dust storms in the dry season facilitate the transmission of the disease, high-humidity events tamp down and often put an end to epidemics, says Thomson. Monitoring humidity levels could thus prove a valuable tool to health ministries who need to know when and where to provide vaccines.A long-term study of the link between weather and meningitis by the Colorado-based University Corporation for Atmospheric Research (UCAR) has determined that the best predictor of meningitis activity is the relative humidity 2 weeks prior.2) and fine particles (PM2.5) suspended near the Earth’s surface. Recent advances have shed new light on our understanding of global air pollutant concentrations, with significant implications for public health. Even short-term exposures to elevated NO2 levels may trigger a range of adverse respiratory effects, in both sick and healthy people,Satellites can even be used to detect more ethereal threats, such as nitrogen dioxide  aboard NASA’s Aura satelliteTo detect NO2.5, available instruments include the Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA’s Aqua and Terra satellitesAnd for PMEHP was the first to show a long-term global map of PM2.5 distribution, in this case representing a 6-year average.2.5 concentrations. A broad swath extending from Africa’s Sahara Desert to eastern Asia appears orange, red, and deep red, representing very high levels, while much of central Europe is colored yellow, representing medium levels, and the vast majority of the rest of the planet, including most of the United States, is colored light and dark blue, representing low levels.  part of ESA’s Sentinel-5P mission, set to launch in 2016.2 concentrations in higher resolution on an hourly basis, offering greater levels of spatial and temporal resolution.OMI’s resolution of about 10 to 20 km“There will be much improved data quality that we can look at in the next couple of years,” Lamsal says. “There are certainly huge improvements in the last ten years regarding information on nitrogen dioxide, but that will improve [even more] in the next couple years.”2, Martin believes ground sensors will remain essential counterparts, offering both high spatial resolution and vertical profile information.In the meantime, he and other NASA researchers continue to refine the models and algorithms that convert available satellite data into useful products, as well as assist air-quality researchers and managers in applying them through the agency’s Air Quality Applied Sciences Team.Most of the instruments that make environmental monitoring possible live aboard satellites in low-Earth orbit, meaning they’re at an altitude between 160 km  and 2,000 km . NASA currently operates 17 such satellites, Haynes says, which serve human health research and management in many ways, including through natural-disaster forecasting, mitigation, and response; ecological forecasting, including for HABs; supporting air-quality management and policy; and assessing water availability and quality.2, sulfur dioxide, carbon monoxide, methane, formaldehyde, and aerosols. It can detect everything that its predecessor OMI can, plus more that it can’t, all at a higher resolution.These will soon be joined, or replaced, by next-generation environmental sensors that can support all the existing avenues of research as well as open up new ones, initiating a new era in satellite-based environmental health research. For instance, TROPOMI, developed by ESA in partnership with the Netherlands, will make daily observations of a broad range of atmospheric compounds of health concern: ozone, NOSentinel-3’s Ocean Land Colour Instrument, also from ESA, will prove a boon to HAB researchers with its higher resolution. , formed in 2003 to improve access to Earth-science data,While not infallible, the new satellites are brimming with promise. They will help scientists identify microscopic algal cells from hundreds of kilometers away, provide health-care workers with life-saving information about disease epidemics, and remotely monitor the air being breathed by residents of some of the planet’s most far-flung locales—all this within six decades of man’s first foray into space.


A common approach to obtain health state valuations is the time-tradeoff (TTO) method. Much remains unknown regarding the influence of responder characteristics on TTO answers. The objective of this study is to increase understanding of the influence that beliefs regarding future health and death, as well as desires to witness certain life events, have on respondents’ health state valuations.An online survey was designed, including three TTO questions using a 10 year timeframe. Moreover, respondents completed demographic questions, the Health-Risk Attitude Scale (HRAS), the Expectations Regarding Aging (ERA) questionnaire, questions about beliefs regarding future health (i.e. life expectancy) and death , and about important life events taking place within the TTO timeframe. Regression analyses were performed in order to assess the influence of these different variables.One thousand sixty-seven respondents were included in the analyses. The following variables were significantly associated with years traded off: ERA mental health (decrease), ERA physical health (increase), HRAS (increase), support for euthanasia (increase), fear of death (decrease) and consideration of an important life event (decrease). The explained variance of the final model was low (0.08).TTO responses may be influenced by considerations of future health, including life events and attitudes regarding health risks and death. Further investigation of TTO responses remains warranted.The online version of this article (doi:10.1186/s12955-015-0402-6) contains supplementary material, which is available to authorized users. Several countries use cost-effectiveness analyses in the context of deciding on the reimbursement and funding of new medical technologies . For the effectiveness component, some authorities prefer the use of quality adjusted life years (QALYs), as these are believed to allow for a universal comparison across diseases areas . QALYs cDifferent valuation techniques are used to obtain health state valuations from the general public. The most widely used approach is the time-trade off (TTO) method. In a TTO exercise, a respondent is presented with two health streams. One of the health streams is a fixed lifespan (e.g. 10 years) lived in some imperfect health state ‘A’. The other stream entails a shorter lifespan but lived in perfect health. Respondents are subsequently asked to indicate the minimum number of years lived in perfect health required to become indifferent between the two streams. If a respondent is not willing to live shorter in perfect health relative to the imperfect health state A, its value is assumed to be equal to that of perfect health . When a respondent is willing to give up all remaining years, the value of health state A is considered to be equal to being dead  . If a reDespite the widespread use of TTO as valuation method for health state utilities in economic evaluations, relatively little is known about which characteristics of respondents are associated with responses to time trade-off exercises. Most of the previous research was focused on the typical demographics like age, gender, marital status and education –7, but fLoss of future life years is a critical element of a TTO exercise, as length of life is traded off against quality of life. Therefore, attitudes towards future health and death for instance could play an important role. Some research has been performed in this direction by investigating the influence of subjective remaining life expectancy . SLE turned out to have a negative relationship with the numbers of years traded-off in a TTO exercise. This means that a higher SLE is associated with a lower the number of years traded-off –22. NextGiven the widespread use of TTO and its potential impact on reimbursement decisions, better understanding of such associations is important, not only to understand what drives TTO answers, but also for purposes of representative sampling. If these associations exist, comparing TTO scores from one study to another might not be valid if the sampled populations differ with respect to these variables.The aim of this study is to obtain more insight in this underexplored relation of responses to TTO exercises with beliefs regarding future health and death.A questionnaire was administered online by a survey company to a representative sample of the Dutch general public in the range 18 to 65 years, in terms of age, gender and level of education. Respondents who completed the survey in less than 15 min were considered to have devoted too little attention to the questions and, consequently, were excluded from the analyses. (This threshold for speeding through the questionnaire was based on the distribution of completion times in the pilot test.) The methods are described in more detail in Van Nooten et al. , who useThe questionnaire first covered common demographic characteristics such as age, gender, marital status, nationality, education, having children, followed by questions regarding current health status, using EQ-5D and EQ-VAS . Next, rAfter the TTO exercises, respondents were asked whether they had thought that the specified period of 10 years would start immediately. If answered affirmatively, respondents were asked if they had thought of a minimum period they wanted to stay alive, for example to witness a certain event or to reach a certain age, regardless of health). In case this question was answered affirmatively, respondents were asked to describe the event.Respondents also answered a number of questions regarding beliefs about future health. First, respondents reported their subjective life expectancy (SLE) by providing a point estimate of expected lifetime.Secondly, respondents completed the Health-Risk Attitude Scale (HRAS), which was developed to understand health related risk attitude . The HRAThirdly, respondents completed the Expectations Regarding Aging (ERA) survey . The ERAFinally, respondents were asked several questions regarding beliefs about death, because the TTO involves shortening life duration in order to improve quality of life. Respondents were asked: “Do you believe in life after death?”, with the following answering possibilities (1) “no, I don’t believe in life after death”, (2) “yes, I believe heaven exists”, (3) “yes, I believe in reincarnation”, and (4) “yes, other (please explain)”. Then respondents were asked: “Are you afraid of death?” using a 0–100 visual analog scale, with 0 representing no fear and 100 extreme fear. Finally, respondents were asked: “What is your attitude regarding euthanasia?” The following four answering possibilities were provided: (1) “I think that euthanasia should not be allowed under any circumstances”, (2) “I think that euthanasia should only be allowed under very strict circumstances (for example in case of unbearable suffering without any hope for the future)”, (3) “I think that euthanasia should be allowed after careful consideration and with professional support”, and (4) “I think that people should be free to opt for euthanasia”.Correlations coefficients were computed and were used to understand the relationship between the different variables included in this study, both the demographic variables  and the beliefs regarding future health and death .Next, regression analyses were conducted. The dependent variable in the regression analyses was the number of years a respondent was willing to give up from the 10 year timeframe in order to regain full health, calculated by subtracting the TTO answer from 10. Remaining SLE was calculated by subtracting the actual age of the respondent from expected age of death. The variable for life after death was a dichotomous variable, in which the response options “yes, I believe heaven exists”, “yes, I believe in reincarnation” and “yes, other (please explain)” were classified as 1 and “no, I don’t believe in life after death” as 0. The variable called “event” was created in the following way: if respondents had answered affirmatively to both the question regarding whether they had thought of a minimum period they wanted to stay alive to witness a life event as well as the question whether they thought the 10 year period would start immediately, the variable event was defined as 1, otherwise as 0. Moreover, a dichotomous variable for euthanasia was created in which “I think that euthanasia is not allowed under any circumstances” was coded 1, whereas “I think that euthanasia is only allowed under very strict circumstances (for example in case of unbearable suffering without any hope for the future)”, “I think that euthanasia is allowed after careful consideration and with professional support”, and “I think that people should be free to opt for euthanasia” were coded 0.First, we estimated a base model, including the commonly investigated variables in the context of explaining TTO answers: age, gender, partnership status, having children, educational status, own health using the EQ-5D or VAS and SLE .p < 0.05) in explaining years traded off in the TTO exercises were included in the final model.Next, we expanded the base model with the variables related to beliefs regarding future health and death, and any important life events respondents wanted to witness within the 10 year timeframe of the TTO exercise. First, a principal-components factor analysis was conducted to explore the structure in the relationships between these additional variables . The factor analysis allowed for combining correlated variables into independent groups of variables, which were then added separately to the base regression model to determine which individual variables could further explain the TTO scores. Those variables that reached statistical significance .From the original 1223 respondents who completed the survey, 156 were excluded for speeding through the questionnaire, which left a total of 1067 respondents for the analyses. Table In general, the correlations between the demographic variables , beliefs regarding future health and death  and staying alive to witness a life event were weak, ranged between −0.2 and 0.2.Table The factor analysis resulted in three groups of variables to be added to the base model: 1) expectations regarding aging , 2) beliefs regarding death , and 3) health related risk attitude (HRAS), and consideration of a future life event. Support for euthanasia did not load into any of the factors and showed limited variation (only 5 % of the population was against euthanasia). It was added separately to the base case model.R2 remained modest.These three groups of variables were first added to the base case model independently in order to assess their separate association with years traded off. For the variables in group 1 (expectations regarding aging), only the ERA mental and physical scales proved to be significantly associated with years traded off. For the variables in the other two groups  all variables were statistically significantly associated with years traded off when added separately to the base model. When added, support for euthanasia also proved to be significantly associated with years traded off. Therefore, the following variables were included in the final model: ERA mental and physical, fear of death, belief in life after death, HRAS and consideration of a future life event. When these variables were jointly added to the base case model, the following statistically significant results were observed: ERA mental health (increase), ERA physical health (increase), fear of death (decrease), HRAS (increase), consideration of a future life event (increase) and support for euthanasia (increase) (Table While different responder characteristics can influence TTO responses –7, previBefore discussing the implications of this study, several limitations need to be noted. First, we used a web-based design in our study, which may have had consequences on, for instance, the involvement of respondents in the questionnaire and did not allow face-to-face explanations of questions. Based on a pilot test of the questionnaire we determined a minimum acceptable completion time of 15 min, in order to limit the effect of speeding through the questionnaire and low involvement. Second, there was no separate valuation exercise for health states ranked as being worse than dead, which may have influenced our results. Valuing worse than dead states using a distinct valuation exercise was considered cognitively demanding and alternative methods, which allow better than dead and worse than dead states to be valued in one exercise, such as the lead time TTO  may haveNotwithstanding these limitations, our results showed an influence of beliefs about future health and death on TTO answers. Given the limited knowledge so far in this area, these findings add to the existing literature. First of all, respondents who have higher expectations about future mental health, who aim to stay alive in order to witness a particular life event and those who are afraid of death, all traded off fewer years in the TTO exercises. Respondents who were not opposed to euthanasia were willing to give up more years, as well as those who were more risk seeking in the health domain. Although the explained variance of the final model remained low, the influence of support for euthanasia and the desire to witness a particular life event was remarkably high compared to other statistically significant variables in the model.Research investigating the influence of beliefs about death on TTO scores is scarce. Rutten-van Molken et al.  found thmore years sacrificed. This rather counterintuitive result may be related to the skewed distribution of ERA physical health (see Table Another interesting finding was that the ERA variable mental health was negatively associated with years traded off, suggesting that when responders have higher expectations regarding mental aging they are willing to give up fewer years. Although not in line with the instruction and intention of the TTO exercise, which specified a stable health state for the 10 year timeframe and should therefore render own expectations irrelevant, this implies that future years are more easily traded when one expects these will be spent in relatively poor mental health. However, higher expectations regarding physical health were associated with It should be noted that a 10-year timehorizon was applied for the health state valuation in this study. However it could be expected that in a TTO exercise with a longer time horizon, e.g. life time, the associations highlighted in this study may play an even greater role and this should be investigated.This study showed that TTO scores are associated with considerations of future life events and beliefs about future health and death, next to the more common demographic variables observed in the literature like age and SLE. However, the overall impact on providing a better understanding of what drives responders to make choices in TTO scoring remained limited. Therefore, much remains unknown and more research in this area is warranted. Nonetheless, the results of this study suggest that beliefs about the future can be influential in TTO exercises and should therefore be considered in future research on predictors of TTO responses. These findings may also be relevant in sampling for representative societal valuations of health states.