ChIPOTle is a new software tool designed specifically for the analysis of ChIP-chip data. bona fide sites of protein-DNA interaction.ChIPOTle (Chromatin ImmunoPrecipitation On Tiled arrays) takes advantage of two unique properties of ChIP-chip data: the single-tailed nature of the data, caused by specific enrichment but not specific depletion of genomic fragments; and the predictable enrichment of DNA fragments adjacent to sites of direct protein-DNA interaction. Implemented as a Microsoft Excel macro written in Visual Basic, ChIPOTle uses a sliding window approach that yields improvements in the identification of  Interactions between proteins and DNA facilitate and regulate many basic cellular functions, including transcription, DNA replication, recombination, and DNA repair. For example, the process of transcription is regulated by a class of proteins referred to as transcription factors, which often bind to specific DNA sequences upstream of gene coding regions. This control mechanism allows cells to respond to developmental or environmental signals by using the same transcription factor to coordinate expression of many genes. Therefore, it is of interest to determine where regulatory proteins of this and other types are bound to the genome.The genomic-binding location of transcription factors can be determined using chromatin immunoprecipitation (ChIP) followed by detection of the enriched fragments by DNA microarray hybridization. This procedure, also known as ChIP-chip, has been reviewed extensively -5. To apAfter growing the cells of interest under the desired conditions, chromatin is usually cross-linked with formaldehyde to preserve sites of interaction between proteins and DNA. The cross-linked chromatin is then sheared by sonication or enzymatic digestion. Shearing creates a population of chromatin fragments of varying size, generally ranging from 200 to 1,000 base-pairs. The protein of interest, along with the DNA associated with it, is then isolated by using an antibody specific to that protein or by affinity purification utilizing an epitope or affinity tag fused to the protein. The ChIPed DNA is then purified. Because yields from most samples are low, amplification is often required. DNA fragments enriched in the procedure are then detected by comparative hybridization to a DNA microarray. Standard technical recommendations common to all microarray experiments  apply equally to ChIP-chip experiments. The result of the hybridization allows one to identify which segments of the genome were bound by the protein of interest during immunoprecipitation.The interpretation of data generated by a ChIP-chip experiment is in many respects similar to interpretation of traditional gene expression microarrays, but it differs in two important ways. First, in traditional expression experiments, each element on the microarray measures the abundance of RNA molecules of a fixed length.  In contrast, with ChIP-chip experiments each element measures the abundance of a population of fragments of various lengths due to the effects of chromatin shearing. As a consequence, arrayed elements representing genomic regions both at the binding site and near the binding site will detect enrichment Figure .Depending on the method and degree of chromatin shearing, and the resolution of the arrayed elements, this effect produces a 'peak' of signal centered over the binding site, which may span several arrayed elements representing genomically adjacent DNA. This 'neighbor effect' is not an expected property of noise or other spuriously high ratio measurements, and thus is a source of information that can be used for analysis.2 ratios is therefore asymmetric about zero, with a distinct, positively oriented skew  [In this report we describe ChIPOTle (Chromatin ImmunoPrecipitation On Tiled arrays), software created expressly for the analysis of ChIP-chip data obtained using tiled arrays, which allow us to exploit both the 'single-tail' and 'neighbor effect'. ChIPOTle uses a sliding window approach to identify potential sites of enrichment, and then estimates the significance of enrichment for a genomic region using a standard Gaussian error function. ChIPOTle is delivered as a Microsoft Excel macro written in Visual Basic, which should facilitate widespread adoption and provide a platform for custom applications. Before ChIPOTle, to our knowledge the only publicly available program designed expressly for ChIP-chip data analysis was PeakFinder . ChIPOTl file 1) .2 ratio for the window is calculated by taking the simple average of all ratios reported by arrayed elements that overlap with the window to any degree. The average is unweighted, and therefore it is not dependent on the proportion of the element within the window; it depends only on whether it is present or absent. The window is then moved unidirectionally along the chromosome by the step size and the same calculation is repeated for each distinct window, until the end of the chromosome is reached. The arrayed elements need not be evenly spaced or of equal lengths. ChIPOTle can be used with any genome.ChIPOTle first sorts the arrayed elements by genomic location. To find potential areas of ChIP enrichment, a window of user-defined size (default 1 kilobase) is then moved stepwise  along the tiled region. At each step the average log2 ratio on the vertical axis. In this way, genomic binding locations are represented as a series of peaks  as follows:ChIPOTle assigns a P values reported by ChIPOTle are corrected for multiple comparisons using the conservative Bonferroni correction. As an alternative to using a Gaussian distribution for the background, ChIPOTle can estimate the P value for a region using a permutation-based approach .where σ is the standard deviation for the background distribution and n is the number of microarray elements used in the window. The 2 ratio from the ChIP-chip experiment(s). The ratio values supplied to ChIPOTle can be a single measurement from a single experiment or an average, weighted average, or median of ratio values calculated from multiple replicates. When using data from multiple replicates, before combining the data each array must be appropriately normalized to remove systematic nonbiological effects that might otherwise influence the results [Detailed instructions for the installation and use of ChIPOTle are available in the read-me file that accompanies the program . Once ChIPOTle has been correctly added to the Excel Add-Ins menu or opened manually, a new menu option will appear in the Excel Tools menu. ChIPOTle must be run from an active Excel spreadsheet containing five columns: the name of each arrayed element, chromosome name, start coordinate in base-pairs, end coordinate in base-pairs, and the log results . For sinP values; or (3) estimate the background distribution for calculation of window average P values via a permutation-based simulation. If option 1 is selected then the user is prompted to enter the peak height; for option 2 the user is prompted to provide the significance P value cutoff; and for option 3 the user is prompted to provide the number of simulations and the significance P value cutoff to be used in the permutation analysis. Any region with a P value lower then the selected cutoff will be recorded and summarized in the "Significant Regions" and "Peaks" worksheets.Through a dialog window, ChIPOTle will ask for the location of each data column. The user will also be prompted to provide the window size, step size, and the desired technique for determining peak significance. For the latter parameter, the user can choose (1) a simple peak height cutoff; (2) assume a Gaussian background distribution for calculation of window average P value cutoff, window size, and step size. These parameters will affect the output, and can be adjusted according to the experiment and the array design. The P value cutoff should be set at a level that produces a false discovery rate with which the user is comfortable. The "Significant Negative Regions" sheet provides an empirical estimate of the number of false-positive findings for the selected P value cutoff, and so the user can use this information to estimate the false-positive rate and adjust the P value cutoff (see below). The numbers of acceptable false-positive and false-negative findings will vary depending on the goals of the study.As described above, ChIPOTle has three important user-defined parameters: The next parameter to set is window size. Ideally, for a given protein-DNA interaction, one would like to capture the maximal amount of ChIP signal associated with a single binding event, and none of the noise, in a single window. Therefore, in most ChIP-chip experiments the window size should be adjusted to approximately the average shear size of the chromatin. The average shear size is suggested because the size of the window must be balanced against making it so large that noise from adjacent genomic regions is included in the measurement, and against making the window so small that data from adjacent spots is excluded, diminishing the power of windowing to utilize the neighbor effect. Although this parameter is largely independent of array platform or array resolution, slightly smaller windows may be more effective on higher resolution arrays.Optimization of step size depends on both the array resolution and the window size. The step size should be adjusted such that it is less than half of the array resolution, with array resolution defined as the distance between the start of one arrayed element and the start of the next. Thereby, the measurement recorded at each arrayed element will be used in the calculation of at least three windows, ensuring that every arrayed element has the opportunity to be centered under a peak. Window size is also an important factor because some overlap of windows is desirable in order to detect peaks at unknown locations. Taken together, we suggest setting the step size to the maximum value that is both less than half of the array resolution and less than or equal to one-quarter of the window size. For very high-resolution arrays (less than about 50 base-pairs), step sizes smaller than the array resolution may not improve results.ChIPOTle creates several output sheets with the following names: SummarySheet, Significant Regions, Significant Negative Regions, Chromosomes aveP, Peaks, and Description. The SummarySheet contains all the input data used to run ChIPOTle, now sorted by chromosome and start coordinate. For each window that meets the significance criteria specified by the user, the Significant Regions sheet contains the following: chromosome assignment, center coordinate, number of independent arrayed elements within each window, and names of the arrayed elements that comprise the window. Significant Negative Regions is similar to Significant Regions, but instead it contains all of the windows that meet the significance criteria but are sign-flipped. The number of windows reported in this sheet can be used as an estimate of the number of false-positive findings expected for the selected or estimated cutoff. Chromosome aveP contains the names of the arrayed elements that comprise each window, and the chromosome, center coordinate, and value of all windows, regardless of whether they meet the significance criterion. The values from this sheet, for example, were used to make Figure P value that meets the significance criterion defined by the user and all neighboring windows that also meet the significance criteria. In this sheet, each peak is listed in order of its occurrence along the chromosome, along with the highest window for each peak, highest raw log2 ratio for any element within the peak, start coordinate of the peak, the width of portion of the peak above the significance cutoff, 'array density' of the peak, and the P value for that peak. The array density value is defined as the average number of arrayed elements used to calculate the window values for all windows that comprise the peak. Therefore, the array density value provides an estimate of the number of actual raw data measurements that underlie each peak.The data written to the "Peaks" sheet are similar to those reported in "Significant Regions", except that all neighboring windows meeting the significance criteria are collapsed into a single peak. Therefore, a peak is defined as any window with a The last sheet, Description, contains a summary of the ChIPOTle execution parameters, which include the date and time, the selected window size, the step size, the significance method chosen and corresponding parameters, the number of significantly enriched peaks, and the total number of windows.A plot of the sliding window values generated by ChIPOTle for a Rap1p ChIP-chip reveals two important characteristics of this type of data Figure . The firP value (ChIPOTle and SAEM), median percentile rank (percentile rank), or ySmooth value (PeakFinder). We then used receiver operator characteristic (ROC) plots to show how true positives (sensitivity) were captured in relation to false positives (specificity) for all values output by each method  ,7,12, ped Figure . The powP value, percentile rank, or ySmooth) [In using the Rap1p ChIP-chip data to identify the promoters of RPGs, all of the techniques worked well, but ChIPOTle Figure  was estiySmooth) .We next compared the ability of ChIPOTle, SAEM, and PeakFinder to identify accurately the RPG promoters from a ChIP-chip hybridization to a single microarray. This analysis cannot be performed with the percentile rank analysis because this technique requires experimental replicates. We analyzed each individual experiment independently and determined the average true-positive rate versus the false-positive rate Figure . All thrChIPOTle is a Microsoft Excel macro that is designed for use in the analysis of data from ChIP-chip experiments. ChIPOTle exploits the unique characteristics of ChIP-chip data, including enrichment of DNA genomically adjacent to sites of protein-DNA interaction, and the single-tailed nature of the data, to define peaks of enrichment and their significance. ChIPOTle is very quick and easy to use. The user is prompted to select the five columns containing their data and the significance technique to be used. The program then returns the genomic regions that were enriched by the ChIP according to the data and the specified statistical parameters. In its current implementation, ChIPOTle is restricted in functionality by the limitations of Excel worksheets to 65,536 rows by 256 columns. Therefore, if the dataset of interest is derived from an array containing more then 65,536 unique elements or if the total number of windows generated exceeds 5.5 million, then the data will have to be separated into subsets  if they are to be analyzed using ChIPOTle.2 ratios of the arrayed elements are independent and Gaussian distributed, with mean zero and common variance. Under this assumption, a nominal P value may be assigned to each window using the standard Gaussian cumulative distribution function, or an appropriate bound having closed form. Multiple comparisons can then be addressed via a Bonferroni correction or through an estimated false-discovery rate. In either case, the tail behavior of the Gaussian distribution will have a strong effect on the corrected P values.As currently implemented, the significance analysis in ChIPOTle is carried out under the assumption that the logP values from each window using a null distribution with heavier tails than the Gaussian. A natural choice, consistent with the observed histogram of log2 ratios, is a t-type distribution. Formally, one may adopt the null hypothesis that the observed log2 ratios are independent and distributed as cT, where c is a positive scaling factor and T has a standard t distribution with v degrees of freedom. In order to obtain nominal P values, one then needs estimates of c and v, and bounds on the probability that a sum of independent t-distributed random variables exceeds a threshold. Estimates of c and v can be obtained through moment-based methods. Suitable probability bounds with good small-sample properties are currently under investigation.As a more conservative alternative to the Gaussian approach, one could derive nominal ChIPOTle, while using novel approaches, identifies a set of sites similar to that defined by other techniques  used for analysis of data from ChIP-chip experiments. However, the use of a sliding window allows ChIPOTle to identify enriched regions more accurately, especially after only one experiment. This is useful because when one is performing a ChIP-chip experiment for the first time with a new protein or antibody, it is often difficult to determine whether the ChIP was successful, especially for a protein with an undefined binding pattern. The ability to determine binding sites correctly using fewer replicates will be very important for larger, more complex genomes. Complete high-density tiled arrays for mammalian genomes require many arrays for each experiment, meaning that performing the ideal number of replicates can be prohibitively expensive. In mammalian systems, instead of performing all of the replicates of a ChIP-chip experiment on whole-genome arrays, preliminary experiments using whole-genome arrays can be used to find likely targets. Once these likely targets are identified, the array could be redesigned to include all prospective targets and appropriate controls on a single array. In addition to its utility as a general ChIP-chip analysis tool, ChIPOTle will make prescreening more accurate and will enhance the power and accuracy of this approach.The following additional files are included with the online version of this paper: The Excel Add-In ChIPOTle v 1.0 (Additional data file An Excel Add-In ChIPOTle v 1.0.Click here for fileA PDF file containing detailed instructions for the installation and use of ChIPOTle.Click here for fileAn Excel file containing the Rap1p binding data used to make the comparisons between the different techniques.Click here for file


This study assessed the feasibility, self-efficacy and cost of providing a high fidelity medical simulation experience in the difficult environment of an air ambulance helicopter.Seven of 12 EM residents in their first postgraduate year participated in an EMS flight simulation as the flight physician. The simulation used the Laerdal SimMan™ to present a cardiac and a trauma case in an EMS helicopter while running at flight idle. Before and after the simulation, subjects completed visual analog scales and a semi-structured interview to measure their self-efficacy, i.e. comfort with their ability to treat patients in the helicopter, and recognition of obstacles to care in the helicopter environment. After all 12 residents had completed their first non-simulated flight as the flight physician; they were surveyed about self-assessed comfort and perceived value of the simulation. Continuous data were compared between pre- and post-simulation using a paired samples t-test, and between residents participating in the simulation and those who did not using an independent samples t-test. Categorical data were compared using Fisher's exact test. Cost data for the simulation experience were estimated by the investigators.The simulations functioned correctly 5 out of 7 times; suggesting some refinement is necessary. Cost data indicated a monetary cost of $440 and a time cost of 22 hours of skilled instructor time. The simulation and non-simulation groups were similar in their demographics and pre-hospital experiences. The simulation did not improve residents' self-assessed comfort prior to their first flight (p > 0.234), but did improve understanding of the obstacles to patient care in the helicopter (p = 0.029). Every resident undertaking the simulation agreed it was educational and it should be included in their training. Qualitative data suggested residents would benefit from high fidelity simulation in other environments, including ground transport and for running codes in hospital.It is feasible to provide a high fidelity medical simulation experience in the difficult environment of the air ambulance helicopter, although further experience is necessary to eliminate practical problems. Simulation improves recognition of the challenges present and provides an important opportunity for training in challenging environments. However, use of simulation technology is expensive both in terms of monetary outlay and of personnel involvement. The benefits of this technology must be weighed against the cost for each institution. Emergency medicine training is a complicated endeavor. Emergency medicine residents strive to master a broad knowledge base while attempting to provide quality, efficient care in the world's teaching hospitals. Training programs must provide high quality care while simultaneously allowing novices the opportunity for experience. This apprentice-modeled, experiential learning design has been traditionally summarized by the dictum of "see one, do one, teach one". This model is no longer considered optimal,2. The 1Medical simulation is becoming a mainstream method of medical education -8 and evMedical simulation has mainly used static displays of mannequins with multiple operators and teams of learners in simulated patient rooms. Little high fidelity medical simulation has been attempted in the prehospital setting where providers must act alone under far from ideal conditions, where environmental noise, vibration and motion all act to reduce performance. The air medical setting is particularly challenging. High levels of noise and vibration in helicopters are associated with the revolution rates of the rotors, gearbox, engine and other rotating parts. The high noise and vibration result in additional stresses and difficulties for the patient, medical equipment, and crew. Cabin noise levels of around 97 dB(A) are typical in air medical helicopters during cruise, and may The emergency medicine residency at our institution is a four-year training program that includes extensive experience as a member of a helicopter emergency medical services flight crew consisting of a flight nurse and a flight physician. At the beginning of the second post-graduate year, physicians fly on a BK-117 EMS helicopter as the primary flight physician. Current air medical training consists of a rigorous two-day didactic course, and a system of "buddy flights". These flights allow residents at the end of their first year to experience the flight medicine environment under the supervision of a more experienced resident as the primary flight physician.We proposed that the current training can be significantly augmented by a novel simulation experience utilizing the Laerdal SimMan™. We hypothesized that a high fidelity simulated flight medicine experience can be given to trainees in the difficult environment of a modern air ambulance helicopter. Secondarily, we used qualitative and quantitative means to attempt to measure self-efficacy, i.e. what the residents felt they gained, through this simulation. Similarly we estimate the manpower and monetary cost of the projectThis was a pre- post-educational intervention study approved by the Institutional Review Board. Both quantitative and qualitative data were gathered to evaluate feasibility and perceived value of the simulation.Twelve emergency medicine residents completing their first year of training were invited to participate in the training. Invitation was by electronic mail and verbal communication by the primary investigator during weekly educational conferences. All twelve residents were invited to participate in the educational experience of the simulation irrespective of their consent to be enrolled in the study group. Seven residents were available for the educational experience and all seven elected to participate in the trial; this group was designated the simulation group. The five residents who were unavailable did not participate because of clinical and scheduling conflicts; this group was designated the non-simulation group and received no experience with the medical simulator. All twelve residents participated in the standard "buddy flight" system of training.A Laerdal SimMan™ was loaded onto a BK117 EMS equipped helicopter and situated in the same way as a live patient Figures , 3, 4. T-2 r.m.s. are expected, which are likely to be "fairly uncomfortable" for seated personnel. Measurements in transport helicopters suggest that vibration magnitudes experienced while standing on the ground are typical of those that occur in cruising flight, provided that the engines are running at flight idle; in aircraft where it is normal practice to reduce the engine revolutions while standing on the ground, the rotation frequencies will be lowered, altering the spectral shape of the vibration[The aircraft was run at flight idle setting to provide noise, vibration, lighting, olfactory input and heat similar to the conditions encountered during flight. The largest vibration acceleration magnitudes on the airframe tend to occur at the main rotor passage frequency. Typical 4-blade helicopters have rotor passage frequencies between 17 and 20 Hz. The vibration is multi-directional, with similar magnitudes in all three translational axes. The highest vibration magnitudes occur during landing, take-off or maneuvering, all of which involve hovering and low speed flight. During vibration.An experienced operator managed the simulator while two of the investigators functioned as flight nurse and observer. Each study subject was given a one minute briefing concerning the simulated patient's medical condition, prior to boarding the helicopter. One of two pre-programmed simulations was conducted for each resident. The first simulation mimicked a young woman who had been involved in a motor vehicle accident. The simulation began after she had been loaded into the helicopter on a backboard with cervical spine protection in place and with one intravenous line established. Shortly after the simulation starting point, the patient exhibited signs of hypovolemia and tension pnuemothorax and requires fluid resuscitation, needle thoracostomy, and endotracheal intubation. The second simulation represented a middle-aged man sustaining an anterior myocardial infarction. He had received thrombolytic therapy and was being transferred to a tertiary care hospital for acute intervention. At the beginning of the simulation he underwent ventricular fibrillation and required advanced cardiac life support and endotracheal intubation.Prior to participating in the simulation, the resident physicians completed a brief questionnaire Figure . The queOver the next three months, all twelve residents, both the simulation and the non-simulation groups, completed a questionnaire after their first non-simulated flight as a flight physician. This questionnaire surveyed prior pre-hospital experience and quantified self-perceived comfort, self-perceived ability to care for the patient in the helicopter, and pre-flight awareness obstacles in the helicopter setting. The simulation group was also questioned about the perceived value of the simulation. The questions are shown as the headings for Table Continuous data were compared between pre- and post-simulation using a paired samples t-test. Categorical data were compared using Fisher's Exact test. The audiotapes from the semi-structured interviews were transcribed verbatim into electronic format. The transcriptions were evaluated by two investigators for common themes and areas for expanding the simulation for training in prehospital care. Data were analyzed using SPSS v 13.0 The simulation and non-simulation groups were similar in their prehospital experiences; one resident in each group had experience in the air medical transport of critically ill patients prior to residency. Demographics were similar; mean age in the simulation group was 29 years, and 6 were male while mean age in the non-simulation group was 30 years, all 5 were male.The simulation event was carried out successfully. All participants completed one simulation each. Two of the simulations were interrupted by software difficulties that arose secondary to the use of a unfamiliar computer to operate the SimMan™ operating system. Despite this difficulty, all simulations were run to completion after a software restart. The costs of the simulation were $440 and 22 total hours of highly skilled instructor and technician time. Table Compared to before the training, participating residents tended to be more comfortable and more aware of obstacles to treating patients in the air medical environment after their training was completed Figure , althougCommon themes in the structured interview that were agreed upon by the two transcript reviewers included recognition of the value of the training, particularly of the realistic environment. Most felt more prepared to fly in the future and some noted the need to prepare more fully prior to their first flight. Most felt that simulation, though not perfect, was a useful addition to their training and should be incorporated as a regular part of residency training.High fidelity medical simulation provides a patient-safe means of educating medical trainees, and is becoming common at multiple levels of medical education. We have demonstrated that it is feasible to carry the simulation experience into the challenging environment of an air ambulance helicopter and provide a high quality experience. While we did experience several problems with simulation implementation, these would have been avoided through increased experience with the Laerdal SimMan and further knowledge of the computer used to control the simulator. Even with these problems, this experience was deemed as valuable by the learners and actively demonstrated to them the limitations and challenges of the flight medicine environment. Simulation provides the opportunity to improve self-confidence in a patient safe context. This information may be generalized to other challenging arenas ranging from the battlefield to the trauma room. Medical simulation no longer needs to be relegated only to a discrete education laboratory environment. It can be brought to the practice arena and allow learners to acquire the skills, knowledge and attitudes required to effectively practice in a specific environment. Simulation may provide an important opportunity for training in critical care and lifesaving procedures in all manors of challenging settings. Further study of the benefits of high fidelity simulation in austere environments and in the teaching of rarely performed procedures is warranted.While our experience was feasible and self-assessed as efficacious by the participants, it was expensive and labor intensive for our relatively small educational endeavor. Table The limitations of this study include small sample size and selection bias through self-selection of groups. However the primary purpose of this study was to prove feasibility of simulation in the challenging environment. Secondarily, we attempted to measure self-efficacy and cost. All conclusions concerning material learned were gathered for hypothesis generation only.A second area of limitation arose from the helicopter running at flight idle. While the conditions in the helicopter running at flight idle on the ground are typical of those encountered during transport, the conditions encountered during take off, landing, and maneuvering are not simulated. Although the SimMan is capable of running from compressed air and therefore being useable in the air, the cost of seven flights would have been several thousand dollars and would be prohibitive for a pilot feasibility program. If further work demonstrates that the high fidelity simulation improves performance, the cost of training may be easier to justify.in situ prior to commencing training. We found high fidelity simulation to improve self-assessed recognition of the challenges present in the prehospital, flight environment, and that Emergency Medicine residents value high fidelity simulation as an aspect of their training in this resource-limited, physically challenging arena.We concluded that high fidelity simulation is feasible, but expensive to implement in the challenging environment of the air medical setting. We recommend that those wishing to implement this technology become familiar with operating the simulator The author(s) declare that they have no competing interests.Each author made the following contributions to the manuscript:SW contributed to the study concept and design, acquisition of the data, analysis and interpretation of the data, drafting of the manuscript, critical revision of the manuscript for important intellectual content, statistical expertise, obtained funding, administrative, technical, or material support, study supervisionCL contributed to the study concept and design, analysis and interpretation of the data, critical revision of the manuscript for important intellectual content, and statistical expertiseWH contributed to the acquisition of the data and critical revision of the manuscript for important intellectual contentAW contributed to the acquisition of the data and critical revision of the manuscript for important intellectual contentCH contributed to the study concept and design, acquisition of the data and critical revision of the manuscript for important intellectual contentCHL provided critical revision of the manuscript for important intellectual contentGH contributed to the critical revision of the manuscript for important intellectual content and provided administrative, technical, or material supportAll authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:


Anatomy of the Brain, the Oxford physician and neuroanatomist reported that given the “influx of blood being denied by this route, it seemed remarkable that this person had not previously died of an apoplexy.” A stroke was averted, Willis explained, because the circular arrangement of the arteries on the base of the brain—later named the circle of Willis—created redundant connections in the circulatory network.While examining the brain of a man who had died of a mesenteric tumor, Sir Thomas Willis was shocked to find a nearly totally blocked right carotid artery, the brain's main blood source. In his 1664 book,Scientists are still elucidating the details of vascular resilience—and how stroke can result when it is compromised. Much as the circle of Willis protects large-scale blood flow, it's been a matter of conjecture if a similar mechanism ensures microvessel flow to local brain regions. Until recently, scientists have lacked the appropriate methods to test this hypothesis. In a new study, Chris Schaffer, David Kleinfeld, and their physics and medical school colleagues take advantage of cutting-edge techniques in microscopy and laser-induced clotting (photothrombosis) to directly measure the resilience and dynamics of cortical blood flow. By manipulating and monitoring blood flow in the surface vasculature of rat parietal cortex, they show that the arteriole network rapidly reestablishes blood flow following a targeted occlusion to a surface vessel.To analyze flow dynamics among the interconnected arterioles on the cortical surface, Schaffer et al. labeled blood plasma with a dye that allowed them to map the vessel architecture and also measure the flow of individual blood cells, using two-photon microscopy. Candidate vessels were selected for photothrombotic clotting, a technique that uses photosensitive molecules and focal laser beams to produce free radicals and damage the vessel wall, ultimately triggering an occluding blood clot. This enabled the authors to pinpoint a single vessel for occlusion without harming any neighboring vessels. They further used two-photon microscopy to determine the direction and speed of red blood cells by repetitively scanning the axis of each vessel, which allowed them to monitor the pattern of blood flow throughout the surface vascular network in real time.Schaffer et al. first tested the brain's vascular resilience by inducing targeted photothrombotic clots in an individual surface arteriole upstream of a branch point. Despite the blockage, blood flow was maintained in both downstream branches because of a reversal in the direction of flow through one of the two branches—just one second after occlusion. Of a total 47 clots in 34 rats, all showed the same result. The redistribution in flow was sufficiently robust so that little change in flow occurred in vessels farther downstream from the occlusion.A second test of the brain's vascular resilience involved a more traditional method to block flow that uses a fine filament threaded through the carotid to partially obstruct the middle cerebral artery, the major source of blood to the parietal cortex. While the flow is reduced throughout the entire surface network of communicating arterioles, a pattern of reversal in flows was also seen. Thus, reversals are a common feature in the redistribution of blood across the cortex.Schaffer et al. show that the architecture of the cortical surface arterioles, with redundant connections between branches of the middle cerebral artery, ensures persistent blood flow and protects against localized occlusions. This extends the concept of redundant connections from a single loop in the circle of Willis, at the base of the brain, to the network of communicating arterioles on the cortical surface. Since humans and rats share a similar surface vasculature, these results could help identify potential links between vascular topology and stroke vulnerability in different regions of the brain.


New quantitative methods to collect and analyze data have produced novel findings in ethnobiology. A common application of quantitative methods in ethnobiology is to assess the traditional ecological knowledge of individuals. Few studies have addressed reliability of indices of traditional ecological knowledge constructed with different quantitative methods.We assessed the associations among eight indices of traditional ecological knowledge from data collected from 650 native Amazonians. We computed Spearman correlations, Chronbach's alpha, and principal components factor analysis for the eight indices.We found that indices derived from different raw data were weakly correlated (rho<0.5), whereas indices derived from the same raw data were highly correlated . We also found a relatively high internal consistency across data from the eight indices . Last, results from a principal components factor analysis of the eight indices suggest that the eight indices were positively related, although the association was low when considering only the first factor.A possible explanation for the relatively low correlation between indices derived from different raw data, but relatively high internal consistency of the eight indices is that the methods capture different aspects of an individual's traditional ecological knowledge. To develop a reliable measure of traditional ecological knowledge, researchers should collect raw data using a variety of methods and then generate an aggregated measure that contains data from the various components of traditional ecological knowledge. Failure to do this will hinder cross-cultural comparisons. Methodological contributions are essential in any branch of science and many researchers have shown concern with a perceived lack of methodological advances in contemporary ethnobiology . EthnobiAlthough the use of quantitative methods is becoming common in ethnobiology, we still lack studies assessing the reliability of data collected with different methods. For example, researchers have applied quantitative methods to assess the traditional ecological knowledge of individuals and groups, but the methods used to collect and to transform the data vary across studies. Thus, to collect raw data on the traditional ecological knowledge of individuals, researchers have used open-ended interviews , structuAssessing the correspondence of data collected and transformed with different methods should contribute to ethnobiological studies, and particularly to studies of traditional ecological knowledge, in two ways. First, assessing the correspondence of data collected and transformed with different methods will enhance cross-cultural comparisons. For example, quantitative studies about the determinants of the loss of traditional ecological knowledge show conflicting results across sites. Some studies suggest that socio-economic changes do not decrease traditional ecological knowledge , others The second reason why assessing the correspondence of data collected and transformed with different methods might enhance ethnobiological research is that such studies might help identify methods apt to measure various aspects of traditional ecological knowledge. Researchers have presented many definitions of traditional ecological knowledge. A universal, measurable definition is not likely because traditional ecological knowledge comprises many fields  and many dimensions . Specific methods appropriate to capture information in one field or dimension might not be appropriate for another field or dimension. For example, observing people's use of plants might shows us their ability to apply traditional knowledge, but it says little about the individual's theoretical knowledge of the same plants. By studying the correspondence of data collected and analyzed with different methods, we might be able to identify the kind of knowledge captured by each method.The goal of this paper is to assess the correspondence between eight indices of traditional ecological knowledge. We used four different methods to collect raw data from 650 Tsimane' Amazonians in Bolivia, and from that data we constructed eight indices. The study forms part of a long-term research to measure the effect of markets on the quality of life of indigenous peoples .The information used in this paper is unique in at least three ways. First, the data were collected by a multidisciplinary team of anthropologists, biologists, and agronomists who lived in the study area during 18 consecutive months (May 2002-November 2003). This contrasts with studies of traditional ecological knowledge that have been done by authors from a single discipline or over short periods of time. Long periods of research and multidisciplinary teams, should enhance the quality of the data collected. Second, the sample size (n = 650) was more than five times larger than the average sample size of the typical study measuring individual traditional ecological knowledge . A largeThird, we distinguished between the theoretical and the practical dimensions of traditional ecological knowledge and collected information on both dimensions. Knowledge refers to the theoretical dimension, or intellectual ability, such as the ability to name plants. Skill refers to the ability to put knowledge into practice. For example, some people may know the potential uses of a plant, but they may not know how to use the plant. By including more than one dimension of traditional ecological knowledge, we can test whether the two dimensions reflect an underlying construct. This contrasts with other studies that focus on either theoretical knowledge or practical skills.We conducted research among the Tsimane', a native Amazonian population of about 8,000 people living in about 100 villages in the department of Beni, Bolivia. Recent publications ,17-19 prWe collected information from all the adults (>16 years of age) from 13 villages along the Maniqui River. To select the 13 villages for the study, we used village distance from the town of San Borja, the regional commercial center . We interviewed all people over the age of 16 because, at this age Tsimane' adolescents start forming their own households and enter adulthood. A total of 650 adults from 13 Tsimane' villages participated in the study, but only 375 (or 48% of the 650) provided information for all the methods of data collection. Although we obtained complete data only for about half of the sample, the results are representative of the villages surveyed. We refer to the sample with raw data for all the methods as "permanent sample", to differentiate it form the "non-permanent sample", or people who did not provided raw information for one or more methods. Because the goal of the article is to compare results across methods, we mostly use data from the permanent sample for the analysis. We use the additional information provided by the non-permanent sample to test whether the results hold across samples. The permanent sample was almost evenly split between women  and men (n = 174). The average age of the person in the permanent sample was 35.4 years of age (sd = 15.1).For each participant, we collected four sets of raw data. For three of the four sets of raw data, we collected information over three months, and for the last method we conducted weekly interviews on the uses of plants over a year (October 2002-October 2003). We have given detailed explanations of most of the methods presented here in previous publications ,23,26,27Multiple-choice. We constructed two different multiple-choice tasks to measure knowledge of local plants. We selected multiple-choice tasks to measure theoretical knowledge because multiple-choice tasks are the most common method used to measure traditional ecological knowledge . AdditioTo construct the first task, we randomly selected 21 plants from a list of Tsimane' useful plants . The tasThe second multiple-choice task had to do with the ecology of plants. We asked about biological characteristics of ten plants randomly chosen from the list of the wild plants mentioned before, and presented the participants with three possible answers from which they could chose only one. For example, we asked: "Which is the color of the mahogany flower? a) red, b) green, or c) white". If participants were not sure about the answer we asked them to provide their best guess. Questions related to the habitat where the plant is found, phenotypic traits , and the ecology of the plants .Interviews about the uses of plants. To capture the practical dimension of traditional ecological knowledge, we used interviews to measure daily uses of plants. Every week, on a day chosen at random, we visited all households during a three-hour block falling from 7am until 7pm; we also chose the blocks of time at random. During those visits, we asked each adult present in the household to name all of the wild plants the person had brought to the household during the previous 24 hours. We only included adults present in the household at the time of the interview. Absent adults were coded as missing.Questionnaire on skills. To collect information on participant's practical abilities we used a questionnaire on self-reported skills about crafting objects from wild plants. We asked participants whether they had ever made on their own 18 objects from a list of 15 different plants . Three kWe transformed data collected with the above methods to obtain eight indices of individual traditional ecological knowledge.cultural knowledge of plant uses, for each individual we computed the proportion of individual questions coinciding with the most frequent response in the group.We analyzed data from the multiple-choice task on uses of plants with the cultural consensus method . To calcWith data from the multiple-choice task on uses of plants we also generated an index measuring the degree of individual agreement with the experts of the group. To do so, we first generated an "answer key" to the multiple-choice task using answers from individuals over 55 years of age. We considered elders as experts because studies suggest that traditional ecological knowledge bears a positive association with age ,29,30. Wecological cultural knowledge.We also used the cultural consensus method to analyze data on plant ecology from the multiple-choice task. We called the proportion of individual questions coinciding with the most frequent response of the group ecological knowledge. For each participant, we added the number of times a response matched ecological data from the area. For example, Killeen [We used data from the multiple-choice task on plant ecology to generate an index of  Killeen  reports  Killeen ,33 and K Killeen .We used data from repeated interviews on uses of plants to generate an index measuring the average number of wild plants used by a person on a given day. We generated the index by adding the number of different plants each person brought during the entire period of research and dividing it by the number of observations for each person .We also used data from interviews on uses of plants to generate an index measuring the total number of plants used by a person over the research period . To consThis index captures the richness or diversity of species used by a person over the research period . To consWe used responses to the questionnaire on skills to construct another index of the practical dimension of traditional ecological knowledge. To calculate the index, we summed the number of goods each person reported knowing how to make from the skill questionnaire.We test for the normality of the sample using the skewness and kurtosis tests of normality. Since our data were not normally distributed, we used non-parametric statistics. We first compare results across the permanent and non-permanent samples using Mann-Whitney two-sample tests. We then computed the Spearman rank correlation coefficients (rho) between the eight different indices. To test the internal consistency of the eight indices, we computed the Chronbach's alpha and the principal components factors for the eight indices. The Chronbach's alpha computes the average inter-correlation between all the items in a scale. Since our indices had different units, before computing the Chronbach's alpha we standardized all the indices (mean = 0 and standard deviation = 1).Table cultural knowledge of uses and agreement with experts was 0.85 (p < 0.001) and the correlation coefficient between ecological cultural knowledge and ecological knowledge was 0.55 (p < 0.001).Table ecological knowledge correlated in a significant and positive way with the indices of cultural knowledge of uses and agreement with experts, but the correlation coefficients were low (rho<0.35). The correlation of ecological cultural knowledge with the indices from raw data from the first multiple-choice task was even lower (rho<0.15 for agreement with expert) or non existent . We found similar results when comparing measures of practical skill. The three indices derived from interviews on daily uses of plants correlated in a statistically significant and positive way with one another . The correlation coefficients of those indices with the index of skills using plants were below 0.25 (p < 0.001).We found low correlation coefficients between indices derived from raw data collected with different methods. For example, the index of ecological cultural knowledge (rho<0.15). The index of practical skills that showed the lowest correlation with the indices of theoretical knowledge was skills using plants (rho<0.22).Last, we found that the indices of theoretical knowledge also correlated with the indices of practical skills, but all the correlation coefficients were low (rho<0.50). The index of theoretical knowledge that showed the lowest correlation coefficient with the indices of practical skills was To test the robustness of the associations, we ran the same correlations but using the total sample, i.e. including the permanent and the non-permanent samples (not shown). We found similar coefficients and levels of statistical significance for all the indices.To test the internal consistency of the eight indices, we calculated the Chronbach's alpha of the different indices. We found that the Chronbach's alpha for the four indices of theoretical knowledge and practical skills were 0.69 and 0.72. We then computed the Chronback's alpha for the eight indices together and found that the eight items were positively related. The Chronbach's alpha of the eight indices was 0.78, higher than the Chronbach's alpha coefficients of indices of theoretical knowledge and indices of practical skills.As a last test of reliability, we ran a principal components factor analysis of the eight indices Table . As in pskills using plants loaded weakly on three factors. In fact, the index of skills using plants had the lowest factor loadings (0.27), meaning that 83% of the variance of the index remained unexplained by the first three components.We found that the two indices derived from the multiple-choice task in uses of plants and the three indices derived from interviews about the daily use of plants loaded strongly and positively on the first factor and negatively on the second factor. We also found that the two indices derived from the multiple-choice questions about ecology loaded weakly on the first factor, but positively and strongly on the second factor. Last, the index of In this article we have tested the degree of correspondence among eight indices of traditional ecological knowledge from different methods. We found that indices derived from different raw data were weakly correlated, but we also found that all the indices reflected a single underlying construct, as shown by the results of Chronbach's alpha and a principal components factor analyses. Why did the indices show low partial correlation coefficients but a high degree of overall agreement?We can think of two possible explanations for the low correlations found when comparing raw data collected with different methods. First, it is possible that some methods are more prone to bias and random measurement error than others. For example, some of the tasks may have been harder to understand than other tasks. Data collected with tasks harder to understand may contain more random measurement error than data collected with tasks that are easier to understand. Furthermore, each task might be subject to its own type of random measurement error. For example, data collected through weekly interviews may be subject to informant's recall error; participants might neglect to mention less significant plants brought to the household, thereby underestimating the number of plants used.cultural knowledge of uses and ecological cultural knowledge. The low correlation between both variables cannot be attributed to random measurement error because data were collected and transformed using the same methods. However, the lack of correlation between the two variables could reflect differences in the domain of traditional ecological knowledge that they capture. The first variable, cultural knowledge of uses, measures knowledge that is culturally specific, whereas the second variable, ecological cultural knowledge, measures the correspondence of local knowledge with scientifically validated ecological knowledge.A second possible explanation for the low correlations found when comparing raw data collected with different methods is that the methods measured different aspects of traditional ecological knowledge. For example, previous research comparing theoretical knowledge and practical uses of plants suggests that the two types of knowledge do not correlate well ,14,22,35If different methods measure different aspects of traditional ecological knowledge, why then do results of Chronbach's alpha suggest the existence of one underlying construct? Recall that when we computed Chronbach's alpha for the eight indices we found that it was higher than the Chronbach's alpha for the indices of theoretical knowledge and of practical skills separately. Because we found a relatively high association between the eight indices, we can assume that they reflect the same underlying phenomenon , but because we are trying to measure a complex phenomenon, composed of many dimensions and domains, it is reasonable to think that comparisons of the different components do not show high correlations.While a high value for Cronbach's alpha indicates good internal consistency of the indices used, it does not mean that the construct analyzed is uni-dimensional. Results from the principal component factor analysis help us to identify some of the different components or dimensions of traditional ecological knowledge. The principal component factor analysis suggests the presence of at least three components of traditional ecological knowledge. The first component refers to knowledge that is culturally constructed. This component was captured by the five indices measuring uses of plants, or the indices that loaded strongly and positively on the first factor and loaded negatively on the second factor. The second component referred to ecological knowledge that could have been scientifically validated. This component was captured by the two indices measuring ecological knowledge, which loaded weakly on the first factor, and positively and strongly on the second factor. The third component reflected the participant's practical skills to use plants. The third component was not well explained by our data, possibly because we only constructed one index for practical skills.As noted in the introduction, researchers have suggested that traditional ecological knowledge comprises many fields and many dimensions of knowledge. The results of our empirical work confirm the idea that specific methods appropriate to capture information in one field or dimension of traditional ecological knowledge might not be appropriate to capture information in another field or dimension. For example, none of the indices of theoretical traditional ecological knowledge is highly associated with any of the indices of practical skills. Therefore, methods designed to capture theoretical knowledge will not capture accurately the ability to use that knowledge.agreement with experts, was higher than the index from the non-permanent sample. The finding suggests that living outside a community does not affect the individual theoretical knowledge, but it does affect the individual's practical abilities to use the knowledge. The finding has implications for sample selection in future studies attempting to measure individual practical knowledge. The selection of a sample that lives permanently in the village might raise the estimates of individual's practical knowledge.A last point requires discussion. When comparing the permanent and non-permanent samples, we found that all the indices of practical knowledge from the permanent sample were significantly higher than the indices of practical knowledge from the non-permanent sample, but that only one of the indices of theoretical knowledge, We conclude with two suggestions to improve future research. First, we need further studies assessing the reliability of different methods. Researchers have used many quantitative methods to collect data and construct indices of traditional ecological knowledge, but they have not paid enough attention to the reliability of the various methods used. To develop a metric of individual traditional ecological knowledge that can be used in cross-cultural research, we need to assess the reliability of methods of data collection. Second, we recommend the development of a comprehensive measure of traditional ecological knowledge. Traditional ecological knowledge is a complex construct, so developing a comprehensive measure of traditional ecological knowledge will require the use of a variety of methods to collect data on knowledge, skills, and beliefs of different fields of ecological knowledge.The author(s) declare that they have no competing interests.


Alternatively spliced exons play an important role in the diversification of gene function in most metazoans and are highly regulated by conserved motifs in exons and introns. Two contradicting properties have been associated to evolutionary conserved alternative exons: higher sequence conservation and higher rate of non-synonymous substitutions, relative to constitutive exons. In order to clarify this issue, we have performed an analysis of the evolution of alternative and constitutive exons, using a large set of protein coding exons conserved between human and mouse and taking into account the conservation of the transcript exonic structure. Further, we have also defined a measure of the variation of the arrangement of exonic splicing enhancers (ESE-conservation score) to study the evolution of splicing regulatory sequences. We have used this measure to correlate the changes in the arrangement of ESEs with the divergence of exon and intron sequences.We find evidence for a relation between the lack of conservation of the exonic structure and the weakening of the sequence evolutionary constraints in alternative and constitutive exons. Exons in transcripts with non-conserved exonic structures have higher synonymous (dS) and non-synonymous (dN) substitution rates than exons in conserved structures. Moreover, alternative exons in transcripts with non-conserved exonic structure are the least constrained in sequence evolution, and at high EST-inclusion levels they are found to be very similar to constitutive exons, whereas alternative exons in transcripts with conserved exonic structure have a dS significantly lower than average at all EST-inclusion levels. We also find higher conservation in the arrangement of ESEs in constitutive exons compared to alternative ones. Additionally, the sequence conservation at flanking introns remains constant for constitutive exons at all ESE-conservation values, but increases for alternative exons at high ESE-conservation values.We conclude that most of the differences in dN observed between alternative and constitutive exons can be explained by the conservation of the transcript exonic structure. Low dS values are more characteristic of alternative exons with conserved exonic structure, but not of those with non-conserved exonic structure. Additionally, constitutive exons are characterized by a higher conservation in the arrangement of ESEs, and alternative exons with an ESE-conservation similar to that of constitutive exons are characterized by a conservation of the flanking intron sequences higher than average, indicating the presence of more intronic regulatory signals. Alternative splicing (AS) can have a biologically relevant effect on protein structure, as it allows the shuffling of protein domains rather than disrupting them . ConsequOrthologous exons with similar splicing regulation show sequence conservation of the cis-acting motifs . On the We considered a set 1211 human and mouse orthologous genes containing conserved constitutively and alternatively spliced exons. From these we selected 2133 exons that were alternative in human and mouse and 8788 exons that were constitutive in human and mouse (see Methods for details). For this set we found lower synonymous substitution rates (dS) (Kolmogorov-Smirnov (KS) test p-value < 2.2e-16) and higher non-synonymous substitution rates (dN)  for alternative exons compared to constitutive ones. We also found that alternative exons have higher values for omega (= dN/dS) than constitutive ones . We tested these results by performing the comparisons in a different approach. We concatenated, for every gene, all constitutive exons into what we called a constitutive region, and all alternative exons into an alternative region, and compared the dN, dS and omega values in both regions within each gene. We found significant differences in dS  and dN  between alternative and constitutive regions. Figures S1 and S2, provided as supplementary material see , show loWe separated the constitutive and alternative exons into four groups according to whether they were part of a transcript with an exonic structure that is conserved in mouse or not (see Methods for the details of this classification). Exons in transcripts with conserved exonic structure (CES) are called CES exons, whereas exons in transcripts with non-conserved exonic structure are called non-CES exons. A non-CES exon is such that there is a pattern of splicing of the pre-mRNA, which includes this exon, and which is never the same in the orthologous mRNA when the orthologous exon is included.Analysis of the sequence identity conservation of these four groups revealed statistically significant differences between the four distributions  approximates the average dN of the corresponding type in constitutive exons at high EST-inclusion levels Figure . HoweverWe tested for possible biases in our classification and whether these could influence our results. We found that genes that contain non-CES exons are more frequently long and with many exons, compared to genes that contain CES exons. Using equal-sized random samples of CES and non-CES exons corresponding to the same distribution of gene lengths and exons per gene, we found the same results as reported above. Thus the gene length and the number of exons per gene do not influence our results. We also verified that the difference in the number of transcripts and exons between orthologous genes does not influence our findings. Details of this analysis are given as supplementary information see .regulatory regions in human exons. A regulatory region is defined as a range in an exon that is covered by one or more ESEs (see Methods for details).In order to study the evolution of the regulatory signals and its relation to the evolution of exons and exonic structures, we analyzed two sets of predicted regulatory motifs. We used 238 human  and 380 motif conservation score as the fraction of the total region covered by motifs that is part of a motif in both human and mouse. More specifically, this is computed as the number of nucleotides (not necessarily the same) that are in a motif in human and mouse over the total length of the alignment covered by a motif in human or mouse, or both (see Methods for a detailed explanation). This score measures the equivalence between the regulatory regions in human and mouse, regardless of the sequence conservation. A conservation score of 1 means that all regions associated with splicing regulators in human are also splicing regulators in mouse. On the other hand, a conservation score of 0 means that no nucleotide associated with a regulator in human is part of a regulator in mouse. We expect orthologous exons with similar regulation to have some overlap between the regulatory regions in either exon. This overlap is not necessarily 100%, as regulators can maintain their functionality even after shifting their position. However, if this shift is too large, the functionality may be lost [Putative ESEs are found all along exon sequences -29. Howe be lost . Thus we be lost . For comFinally, we analyzed the conservation of introns flanking constitutive and alternative exons, performing the alignment of 100 bp at the donor and acceptor sites Figures  and 7b. Using a large set of alternative and constitutive orthologous exons in human and mouse, we have revisited the analysis of the evolutionary rates in alternatively spliced regions. The results we found are consistent with the recent literature: a purifying selective pressure against silent mutations and a diversifying selective pressure for amino acid change in the alternatively spliced protein-coding regions of genes. After separating the exons according to the conservation of the exonic structure, we found that most of the differences in evolutionary rates can be explained by the pattern of conservation of the exonic structure. In particular, we have found that constitutive exons with non-conserved exonic structure (non-CES) have greater non-synonymous (dN) and synonymous (dS) sequence divergence than those with conserved exonic structure (CES). Likewise, alternative non-CES exons have greater sequence divergence than alternative CES exons. These results generalize recent findings  and indiWe have also observed that substitution rates in alternative (CES and non-CES) exons approximate those of constitutive exons for increasing EST-inclusion levels. The non-synonymous rate of each alternative exon type (CES or non-CES) approximates the corresponding constitutive type, such that, at high inclusion levels, alternative and constitutive exons of the same type have indistinguishable dN distributions. However, at high inclusion levels, CES and non-CES exons can still be separated by their dN. On the other hand, the synonymous rate of alternative non-CES exons increases such that at high EST-inclusion levels they cannot be separated from constitutive ones, whereas alternative CES exons keep a synonymous rate low enough to be separable from constitutive exons at high EST-inclusion levels. These findings give further indication that the differences in non-synonymous rates can be mostly explained by the differences in the pattern of conservation of the exonic structure. Furthermore, we can also conclude that a low synonymous substitution rate characterizes alternative CES exons at all inclusion levels.The relations we have found between the evolution of sequence and exonic structure provide an explanation for the apparent contradictions between reports of a high sequence conservation of exon-skipping events ,18,24,30motif conservation score. We have found that the motif conservation score for ESEs can separate constitutive exons from alternative ones: constitutive exons have on average higher motif conservation score. This indicates that the conservation of ESEs is important for maintaining the "constitutiveness" of an exon. For constitutive exons, CES exons have on average higher ESE motif conservation score, which indicates a relation between the conservation in the arrangement of splicing regulators and the conservation of the exonic structure. We also observed that alternative exons with high ESE motif conservation scores have higher sequence conservation in the flanking intron sequence. Strikingly, constitutive exons maintain the same average conservation at the flanking introns, independently of the level of conservation of the ESE-arrangement. This indicates that for constitutive exons, the regulation takes place mainly at the exon sequence. On the other hand, alternative exons with high sequence conservation have a pattern of ESE-arrangement conservation very similar to constitutive exons and have a conservation at the flanking introns greater than average, possibly due to a higher density of conserved regulatory motifs [Finally, we have studied the evolutionary properties of the exonic signals regulating the splicing. We localized two independent sets of human and mouse predicted exon splicing enhancers (ESEs) in our set of alternative and constitutive exons, and defined a measure of the conservation of the arrangement of regulatory signals, the y motifs . This iny motifs ,31. Finay motifs  is intimy motifs ,34.We extracted the Ensembl annotations for human  and mouse , and the EST alignments from the UCSC browser (Dec 2004). EST alignments were processed to obtain an exonic structure for each EST and only spliced alignments were used. Comparing the Ensembl annotations to the ESTs we extracted a set of constitutive and cassette exons in human and mouse. The EST data was only used to deduce the constitutive or alternative nature of the exon, and all exons used were from annotated Ensembl CDSs, which are based on protein and mRNA data , we compWe performed a global alignment using ClustalW  of the cIn order to compare the dN, dS and omega values between alternative and constitutive exons we performed a Kolmogorov-Smirnov test. For the analysis of regions, we performed Wilcoxon signed-ranks test to check if the distributions in the per-gene comparisons were equal or not. The tests were performed with the R statistical package .We calculated dN, dS and omega for all the genes in two different ways. First, for each gene we concatenated together the sequence of all the alternative exons on one hand and of all constitutive exons on the other hand, and calculated the difference in dN, dS and omega between constitutive and alternative sequences for each gene. In the second approach, we calculated dN, dS and omega for every exon. Moreover, we considered only those exons that had dS ≤ 2 and dN ≤ 0.5. These constraints were enforced to ensure that the exons were real orthologs. For the per-gene comparisons, only those genes for which all the exons had dS ≤ 2 and dN ≤ 0.5 were analyzed. For the analysis of dN and dS for the exons separately, 2133 alternative exons and 8788 constitutive exons were included. For the omega analysis 90 alternative exons and 41 constitutive exons were removed because they had omega undefined. For the analysis of dN and dS using the concatenated sequences 1072 genes were considered, and for the analysis of omega 35 genes were removed because they had omega not defined.We have classified the alternative and constitutive exons into four groups according to whether they are part of a transcript with conserved exonic structure (CES) or not (non-CES): constitutive CES and non-CES, and alternative CES and non-CES. A non-CES exon is such that there is a pattern of splicing of the pre-mRNA, which includes this exon, and which is never the same in the orthologous pre-mRNA when the orthologous exon is included. An exon is considered to be of CES type if there is a transcript to which belongs such that there is a transcript in the orthologous gene that contains the exon and has very similar exonic structure. As the exonic structure is conserved, the splicing regulation must be conserved, and in particular, the orthologous exons must have similar splicing regulation. The criterion chosen to determine the conservation of exonic structures is as follows: both transcripts must have at least three exons and the number of exons can differ at most by one. Performing a global alignment where the aligned symbols are the exons, there cannot be internal gaps (missing exons) and aligned exons must have the same phase. Using this criterion we obtained 4688 constitutive CES exons, 4100 constitutive non-CES exons, 977 alternative CES exons and 1156 alternative non-CES exons.N the total length of the alignment covered by human and mouse ESEs, i.e., the number of aligned nucleotides labelled as H and M in Figure M the total length of the alignment covered by a motif in human or mouse, or both, i.e., the number of aligned nucleotides labelled as H or M, or both, in Figure motif conservation score S is therefore defined as S = N/M. For the example in Figure N = 22 and M = 54, hence the motif conservation score for this exon is 0.4.In order to illustrate the calculation of the conservation score, we consider the example shown in Figure Our aim was to compare the arrangement of the ESE motifs with the arrangements of hexamers that have similar frequency of conservation (see Figure S6 of We extracted 100 bp from the intronic sequences flanking our sets of exons. Orthologous sequences were aligned with ClustalW, and the percentage identity conservation was calculated from the alignment. Alignments with a misaligned GT or AG di-nucleotide at the donor or acceptor sites, respectively, were rejected.MP carried out the analysis of the evolutionary measures for the different exon sets and contributed to the writing of the manuscript. EE did the analysis of the regulatory sequences, coordinated the research and led the writing of the manuscript. Both authors read and approved the final manuscript.File with supplementary tests and information.Click here for fileFile with the data used to perform the analysis.Click here for file