The shifting baseline syndrome is a concept from ecology that can be analyzed in the context of ethnobotanical research. Evidence of shifting baseline syndrome can be found in studies dealing with intracultural variation of knowledge, when knowledge from different generations is compared and combined with information about changes in the environment and/or natural resources.We reviewed 84 studies published between 1993 and 2012 that made comparisons of ethnobotanical knowledge according to different age classes. After analyzing these studies for evidence of the shifting baseline syndrome , we searched within these studies for the use of the expressions “cultural erosion”, “loss of knowledge”, or “acculturation”.The studies focused on different groups of plants . More than half of all 84 studies (57%) mentioned a concern towards cultural erosion or knowledge loss; 54% of the studies showed evidence of the shifting baseline syndrome; and 37% of the studies did not provide any evidence of shifting baselines .The general perception of knowledge loss among young people when comparing ethnobotanical repertoires among different age groups should be analyzed with caution. Changes in the landscape or in the abundance of plant resources may be associated with changes in ethnobotanical repertoires held by people of different age groups. Also, the relationship between the availability of resources and current plant use practices rely on a complexity of factors. Fluctuations in these variables can cause changes in the reference (baseline) of different generations and consequently be responsible for differences in intergenerational knowledge. Unraveling the complexity of changes in local knowledge systems in relation to environmental changes will allow the identification of more meaningful information for resource conservation. Traditional ecological knowledge (TEK) is an important component in the improvement of natural resource management-4 and inshifting baselines syndrome’, proposed by Pauly[These different references in relation to a baseline can be understood under the  by Pauly in a semby Pauly noticed by Pauly-12, in sby Pauly,14, birdby Pauly.This syndrome allows a historical approach in assessing an environment, which can be combined with aspects of the current local situation. For a sOne of the problems is that, for several areas and species, there are no well-known starting points, or baselines. This inEthnobotanical research addressing the shifting baseline on vegetation or plant resources is still very scarce and recent,14. SimiThe main objective of this paper is to analyze the ideas behind the shifting baseline in the context of ethnobotanical research. Evidence of the shifting baseline syndrome can be found in studies dealing with intracultural variation of knowledge, when knowledge from different informant generations is compared and changes in the environment and/or resources are also mentioned. In other words, we are assuming that there is evidence of shifting baseline in a study when: (1) research data in the paper point to differences in intergenerational knowledge, with knowledge being lower in younger generations; (2) local community members or the researchers themselves mention that one or more biological resources are disappearing. The latter information was most often encountered in the papers as anecdotal evidence. Our main argument is to reinforce the role of this evidence of environmental change when analyzing age differences found in studies dealing with the distribution of ethnobotanical knowledge in a given group. A review of ethnobotanical studies was conducted, which investigated age differences and changes in local knowledge. The intent is to add more elements to the analysis of traditional knowledge in ethnobotany, where traditions and transformations are intrinsically mixed.We used the bibliographic databases Scopus, Biological Abstracts and Medline for this review, covering studies published between 1993 and 2012. The goal was to select studies with comparisons of ethnobotanical knowledge according to different classes of age or age groups, in order to make inferences regarding changes in baselines interfering in people’s perceptions. Thus, the variable “age” was a priority for the selection of studies, recognizing that there are other important variables influencing the ethnobotanical repertoire. In this search the keyword “ethnobotany” was used added to a combination of expressions: “shifting baseline”, “age”, “age comparisons”, “older” and “younger”, “age class”, “age group”, “knowledge”, “knowledge loss”. A total of 168 studies were found, of which 84 were selected for analysis, according to two inclusion criteria: the studies had to involve age group comparisons of ethnobotanical knowledge and to show results on, or discuss changes in, the abundance of local plant resources. Excluded were studies, for example, that focused on the ethnopharmacology of a given plant without information about age comparisons.Analysis consisted of ranking the papers from the literature search into four categories: (1) evidence of shifting baseline; (2) no evidence of shifting baseline; (3) no changes in knowledge occur; and (4) ambiguous. We considered “evidence of shifting baseline” when a paper showed differences in intergenerational knowledge (lower knowledge levels in younger generations) and mentioned declining abundance of natural resources (through own research or from the literature). “No evidence of shifting baseline” occured when there were intergenerational differences in knowledge but there was no information in the paper about the environment or abundance of natural resources. “No changes” was when there were no intergenerational differences, usually when knowledge was widely shared, independent of perceived changes in the environment. Some studies were not clear about evidence of declining of resources or changes in the environment and were considered “ambiguous”.After analyzing the studies for evidence of the shifting baseline syndrome, we also searched within these studies for the use of the expressions “cultural erosion”, “loss of knowledge”, or “acculturation”. These concepts are often used to explain differences in knowledge and perceptions occurring in different age classes. We believe that these concepts should be treated with a lot of caution, since one cannot conclude straightforward that there is a process of cultural erosion, acculturation, or loss of knowledge when simply making comparisons between observed knowledge in different age groups. First of all, learning and experiences require time. Therefore, an alternative explanation is that older people tend to accumulate knowledge over time compared to younger people. Second, older people have different perceptions than younger people because their reference points are different.e.g.[The 84 selected studies Table  comprisee.g.-22), ande.g. medicinal plants, food plants, Table e.g.[e.g.[e.g.[e.g.[e.g.[e.g.[e.g.[e.g.[The keywords used in the search resulted in a compilation of studies with different goals. Nonetheless, all studies analyzed ethnobotanical knowledge according to the age of informants. Articles were grouped by their similarity of plant uses (le e.g.). The cag.[e.g.), herbacg.[e.g.), and ing.[e.g.). Some sg.[e.g.) or a feg.[e.g.) and werg.[e.g.) and foog.[e.g.).et al.[Sampling methods and data collection varied according to the objectives of each study. Data collection through interviews included both intentional sampling, and systematic sampling, the latter being a sampling procedure with a higher degree of randomness. Other data collection tools included focus group discussions and participative workshops. Sample sizes were highly variable Figure , ranginget al. who studet al.[It is important to consider that the comparison between age classes is relative, not absolute. Therefore, age classes vary among the studies. In some studies they were set at intervals of approximately 5 years,57 or 10et al. split inet al.,100,104.et al.,41,47,93Over half of the 84 studies discussed a concern towards cultural erosion or knowledge loss (57%), using these arguments to explain the results found. These arguments were absent in 43% of the studies. Sometimes these expressions were used with a more detailed discussion of occurring changes, such as Quinlan and Quinlan who conset al.[More than half of the 84 articles 54%) showed some evidence of the shifting baseline syndrome, through the existence of intergenerational differences in knowledge and information about the declining of biological resources reported by local community members or by the researchers. Usually the researchers made that observation anecdotally, either from reports by participants, or from their own observation of the local situation Figure . Some of% showed eg.[This article illustrates the complexity of perspectives on plant knowledge at different ages. Declining knowledge due to disruptions in the social transmission of knowledge between generations has been widely reported in ethnobotanical studies  variable to evaluate people’s knowledge because knowledge may be transformed,108, alteg. medicinal plants), the knowledge can remain stable or increase compared to other categories of use[It may be necessary to associate the number of plants recognized to the type of use, because while there is a decrease of knowledge for a category of plant . Inet al.[Our main point of contention is that the general perception of knowledge loss among young people when comparing ethnobotanical repertoires among different age groups should be analyzed with caution. Almeida et al. argued tet al.. This inet al..Sometimes medicinal plant knowledge does not depend only on the level of plant diversity, degree of modernization or absence of Western health care infrastructure; other social factors such as the healing tradition of the extensive family, can be also fundamental to the survival of medicinal plant knowledge. Thus, aet al.[Baseline changes can be related to different issues that are sometimes linked. First, changes in the landscape or in the abundance of plant resources may be associated with changes in ethnobotanical repertoires held by people of different age groups. According to Sáenz-Arroyo et al., there aet al.. These let al.[Second, the relationship between the availability of resources and the current practices of using plants rely on a complexity of factors. Changes in plant species composition over time may result from socio-cultural and economic changes affecting a given human group. Such changes can cause changes in the reference (baseline) of different generations and consequently resulting in a framework of different intergenerational knowledge. According to Baum and Meyers, informaet al. consideret al. argued tet al.[Culture and knowledge are dynamic components in people’s lives, as well as the environment in which they live. According to Brosi et al. people oet al.. Thus, cet al..Despite the need to collect data that indicate changes in knowledge over time, as well as its causes, we have to assume that there exists a limitation of the human perception about changes in vegetation, because the knowledge about the environment’s past may not show an original condition. In other words, some changes that occurred over time may not be recorded or remembered by individuals and not be known by science. Withoutet al.[In the case of plant resources, historical data, old photos, aerial photos, satellite images, and other records of different times may detect changes in vegetation and support data on people’s perceptions of a particular site or resource. Methodologically, the most interesting would be the integration of different methods of collecting and analyzing data, in order to better understand the changes occurring over time and the origin of these changes. According to Godoy et al. and Quinet al., we stilet al.. FurtherThe authors declare that they have no competing interests.NH, DFH and MSM, conceptual idea, literature review, data analysis, discussion and writing; IV, literature review, discussion and writing. All authors read and approved the final manuscript.


But (ii), by the Humean divide between is and ought, arbitration is logically independent from the description of reasoning. And clearly (iii) the main goal of psychological inquiry is just such a description. It follows that normative concerns about diagnoses of (ir)rationality cannot serve the proper scientific purposes of the psychology of reasoning, and would better be left aside altogether in this area. A recent cornerstone for this debate is Elqayam and Evans (Can the issue of human (ir)rationality contribute to the scientific study of reasoning? A tempting line of argument seems to indicate that it can't. Here it is. (i) To discuss diagnoses of (ir)rationality arising from research in the psychology of reasoning one has to deal with nd Evans . Part ofD. These include elements explicitly provided , but possibly also further background assumptions. Note that, as a rule, D is not already framed in a technical language such as that of, say, probability theory: cover stories and experimental scenarios are predominantly verbal in nature, although they may embed more formal fragments . On the basis of D, participants then have to produce one among a set of possible responses R, for instance an item chosen in a set of options or an estimate in a range of values allowed . Here again, the possible responses do not belong to a particular formal jargon .In reasoning experiments, participants are assumed to rely on some amount of information, or data, r in R turns out to be widespread among human reasoners and is said to be irrational. Such a diagnosis, we submit, has to rely on four premises. (i) First, one has to identify a formal theory of reasoning T as having normative forceD onto a formalized counterpart D* belonging to the technical language employed in T. (iii) Third, one has to map R, too, onto a formalized counterpart R* belonging to the technical language of T. This step implies, in particular, that the target response r within R be translated into its appropriate counterpart r*. (iv) And finally, one has to show that, given D*, r* does contradict T. If either of (i)–(iv) is rejected, the charge of irrationality fails. We thus have a classification of the ways in which one can question diagnoses of irrationality that may be attached to the results of a reasoning experiment. Depending on whether (i), (ii), (iii), or (iv) is the main focus of controversy, we will talk about arbitration, data mismatch, response mismatch, and norm misapplication, respectively. Relying on this partition, let us now consider three prominent cases in which normative concerns have entered psychological research on reasoning.Suppose that some particular response The debate on Wason's selection task is said to have sparked the rise of a new paradigm in the psychology of reasoning . For instance, most physicians judge that a patient who had pulmonary embolism is more likely to experience “dyspnea and hemiparesis” than “hemiparesis.” Tversky and Kahneman (Pr(x ∧ y) = Pr(x) for any x, y, regardless of what information may be available. Note that the latter clause prevents rescue of the rationality of human judgment by an appeal to data mismatch. In fact, in debates about the conjunction fallacy, it is response mismatch that has been relentlessly discussed. Given how fundamental and startling this judgment bias seemed, almost all conceivable worries have been aired over the years. Maybe, in the presence of a conjunctive statement “x and y,” pragmatic considerations led participants to treat the isolated conjunct “x” as “x ∧ not-y.” Or maybe the ordinary language conjunction “x and y” was mapped onto a logical disjunction (“x ∨ y”), or a conditional expression . Or the quantities to be ranked were not meant to be Pr(x ∧ y) and Pr(x) because the reference of the ordinary language term “probable” eluded the basic properties of mathematical probability. In each of these cases, the suggested rendition r* of the modal response r (here: that statement “x and y” was more probable than “x”) would have not contradicted probability theory, thus deflating the charge of irrationality.Upon experimental investigation, individuals often rank a conjunctive statement “Kahneman  famouslyHere again, there is no logical reason to saddle this debate with any subtlety concerning the normative appeal of the target formal theory  for human reasoning. And while all of the above worries of response mismatch had been already addressed by Tversky and Kahneman  see, e., their rh and ¬h , two pieces of evidence e and f  and one likelihood value, such as Pr(e|h) = 65%. Participants have to select the most useful among three further likelihood values, Pr(e|¬h), Pr(f|h), and Pr(f|¬h). In the classical interpretation of this phenomenon, participants were said to have “actively chose[n] irrelevant information  and ignored relevant information  which was equally easily available”  of the experimental scenario (D) and the response set (R) that were consistent with the classical reading of the task . Thus no data or response mismatch was invoked, in our current terms. Crupi et al., submitted, instead, that the relevant norms of reasoning had been misapplied in the standard interpretation: far from contradicting the benchmark theory, the appropriate formal counterpart r* of the participants' modal response r in pseudodiagnosticity experiments turns out to be actually optimal for a Bayesian agent (given D*). Tweeney et al. rationality of participants' responses are better left out of the psychologist's outlook for they would invariably led her to plod on the shaky ground of arbitration. We have challenged this assumption by means of three key examples. The selection task, the conjunction fallacy and pseudodiagnosticity have been extensively investigated in the psychology of reasoning, and all raised lively controversies about human rationality. Yet, issues of arbitration hardly played any substantive role. Once properly reconstructed, the relevant problem was not whether it is rational to depart from the implications of allegedly compelling normative theories such as logic or probability theory. Instead, much of the research done with these classical paradigms was focussed on whether and how those implications could connect with observed behavior given that data mismatch, response mismatch or norm misapplication may have occurred.is/ought divide: arbitration does require specific forms of argumentation that lie outside the usual scope of empirical research (see, e.g., Schurz, Arbitration between competing norms of reasoning is central to certain areas of philosophy but remains marginal in psychological research, and for good reasons, loosely related to the so-called


Dear Editor,We read with interest the recent publication by Prada et al.  about thAnterior transdural spinal cord herniation (ATSCH) has been recognised as a rare, probably under-diagnosed, but treatable cause of (progressive) thoracic myelopathy. It is typically a diagnosis of the MRI era, which has resulted in an increase in the number of publications over the past decade, comprising a total of approximately 170 cases to date. Clinical experience with this entity is limited. As a result, treatment strategies of ATSCH are based on individual cases and on the small series reported in the literature.The authors’ surgical remedy consists of a posterior approach (laminectomy) with reduction of the herniated portion of the thoracic cord, and filling of the dura defect with a muscular plug, which is covered with a patch, and either sutured or “glued”, or both (suture and glue).an extensive review of the literature”. Their manuscript was submitted in August 2011, and accepted 3 months later. It is surprising that a significant number of very relevant recent publications which appeared in renowned neurosurgical journals before that date  have noOn page 724 the authors write that ”all patients except two had a combination of sensory, motor and sphincter dysfunction”, but from Table 1 it can be deduced that only four patients had sphincter dysfunction.With respect to surgical outcome, the text and Table 1 likewise are conflicting: on page 726 it is stated that “worsening of preoperative symptoms occurred in two patients”, “one patient became plegic immediately after surgery…”, and “a mild worsening of motor and sensory functions occurred in two other patients…” . Thus, fLong-term outcome, as reported in Table 1, is reported unchanged in six cases 50%), while six patients improved (50%). It remains unclear into which category the patient that “dramatically worsened” … “requiring implantation of a Soletra spinal cord stimulator 16 months after surgery”  outcome as separate items?Postoperative imaging  is not adequately reported and documented.The present report by Prada et al. adds another 12 patients to the international series, and should serve the readership to define the preferred treatment when facing a patient with ATSCH in their own neurosurgical practice. However, while scrutinising the “Results” section, a number of inconsistencies appear, which definitely confuse the reader:In the “Discussion” section (page 728), the authors stated that “the enlargement of the dural defect should not be considered a treatment for SCH alone” . HoweverThe authors promote the use of a muscle plug and glue as an essential part of the closing procedure. However, based on the results reported, we are less optimistic about the benefit of muscle and glue. It is known from previous papers that such material might cause adhesion and tethering of the spinal cord in the postoperative stage. This can be detected on postoperative MRI. In our opinion, especially in cases that did not improve, MR confirmation of adequate reduction of cord herniation, of realignment of the thoracic spinal cord into the anatomical position, and verification of the re-establishment of the normal CSF signal around the spinal cord, is essential. The need to check on these items is illustrated in Fig. 3 (page 726): the spinal cord seems in close contact with the ventral dura mater, while the posterior dura is very close to the dorsal aspect of the spinal cord. It would be interesting to see a gadolinium-enhanced T1-weighted MRI of this area, because the images depicted suggest tethering of the spinal cord. Unfortunately, a number that would refer to the corresponding patient is missing. Otherwise the reader could have checked in the Table whether this case had improved or not.In rare disorders like ATSCH, large prospective series from single departments or from multicentre studies are not available, and evidence-based operative strategies are lacking because the limited number of cases will never meet the level of sufficient statistical power. Standardisation of case reports, however, would allow for better comparison of individual cases, and enable IPD Meta-analysis, and improve the rationale for treatment. For this purpose, a consistent and sound report, with a clear presentation of patient characteristics and (post)operative data, are essential. Dr. Prada and co-authors are to be commended for their effort to report their series of ATSCH, but it is unfortunate that certain essential items were left out and others are confusing. Clinicians should be encouraged to report new cases and to describe the details that are discussed, in order to improve the knowledge and the rationale of treatment in this rare and intriguing disorder.


In their 2005 study, Adamic and Glance coined the memorable phrase ‘divided they blog’, referring to a trend of cyberbalkanization in the political blogosphere, with liberal and conservative blogs tending to link to other blogs with a similar political slant, and not to one another. As political discussion and activity increasingly moves online, the power of framing political discourses is shifting from mass media to social media.Continued examination of political interactions online is critical, and we extend this line of research by examining the activities of political users within the Wikipedia community. First, we examined how users in Wikipedia choose to display their political affiliation. Next, we analyzed the patterns of cross-party interaction and community participation among those users proclaiming a political affiliation. In contrast to previous analyses of other social media, we did not find strong trends indicating a preference to interact with members of the same political party within the Wikipedia community.Our results indicate that users who proclaim their political affiliation within the community tend to proclaim their identity as a ‘Wikipedian’ even more loudly. It seems that the shared identity of ‘being Wikipedian’ may be strong enough to triumph over other potentially divisive facets of personal identity, such as political affiliation. Online media have become an increasingly important source of political information in recent years. This trend emerged most notably in the 2004 U.S. presidential campaign. For the first time, political blogs served as a prominent information source regarding the campaign and candidates Much of the research examining political interaction online has provided support for a trend of polarization. One of the seminal studies in this area was Adamic and Glance's In a similar study Hargittai, Gallo, and Kane Political polarization has also been observed in other online contexts. For example, Feller, Kuhnert, Sprenger, and Welpe In contrast to the findings from the blogosphere and Twitter, research on interactions in political newsgroups does not paint a clear picture of polarized interactions. Kelly, Fisher, and Smith The aforementioned studies have provided an unclear and somewhat conflictive picture of what cross-party political interaction looks like online. In some contexts  interactions that cross ideological divides are rare. However, in other settings (e.g. online discussion boards), there is evidence for higher rates of interactions across party lines. Taken together these finding indicate that the degree of interaction and engagement with politically dissimilar others varies across contexts.Understanding when and why people engage in political debate and discussion online is important. The degree of interaction or insularity of political groups producing political information online can have important consequences for information consumers, because it may influence the extent to which issues are presented in a biased or neutral way. The present research seeks to address this issue, and to shed light on patterns of political interaction within the Wikipedia community.Wikipedia, currently the 6th most trafficked site in the world Wikipedia is unique when compared to other online references. In the world of online information there is professional content, some of which aims for a neutral stance and some of which has a self-proclaimed bias, and there is user generated content (UGC), which, at its core, reflects the beliefs and ideologies of those who create it. Wikipedia is built entirely on UGC; however at the same time explicitly espouses neutrality. One of the fundamental rules of the community is that all articles must be edited from a neutral point of view (NPOV). In Wikipedia, neutrality “means carefully and critically analyzing a variety of reliable sources and then attempting to convey to the reader the information contained in them clearly and accurately. Wikipedia aims to “describe disputes, but not engage in them” While neutrality is a fundamental principle of Wikipedia, members have a diverse array of beliefs and values. Therefore, it is particularly interesting to examine how diverse, and at times contentious, groups interact on the site. How is it that these people come together to create neutral content? Is there fragmentation, as we see in the blogosphere and on Twitter, or is there interaction and debate like we see in communities such as Usenet?Extant research has generally failed to consider interactions among members of different political parties on Wikipedia. However, there are a couple of exceptions. One is a recent study that examined edits made to the 2012 Republican presidential candidate Mitt Romney's Wikipedia page Social identity theory “…self categories can be ordered in terms of a hierarchy of abstraction and include personal identities  and social identities (which define them as similar to other in-group members and different from out-groups on relevant dimensions). In sum, the salient self category is highly flexible and context dependent.” Social identity can be derived from membership in a formal group (e.g. a soccer team), but can also be derived from more abstract groups or categorizations . Tajfel and Turner Social identity theory has been applied to the domain of politics, and research has demonstrated that people can develop social identities stemming from political party affiliation A later study extended these findings Membership in an online community may also be a source of social identity. Recent theoretical work by Ren, Kraut, and Kiesler One source of common identity in online communities is task interdependence. When community members are working together to accomplish a joint task, this can foster a sense of shared identity members of the tribe and gain awareness of social roles in the community. This transformation is accompanied by a shift in activity, as members move “from a local focus on individual articles to a concern for the quality of the Wikipedia content as a whole and the health of the community” A study conducted by Bryant, Forte and Bruckman It is important to note that the presence of a Wikipedian identity does not preclude the existence of other identities. Depending on the activities that a community member is performing the Wikipedian identity may or may not be activated. For example, when making edits to a particular page, the social identity a user has that is associated with that particular topic  may become activated.The preceding discussion has reviewed the formation of social identity, and the influence that identity can have on intergroup dynamics. Individuals may have multiple social identities that become more or less salient depending on the social context. Members of the Wikipedia community who publicly declare their political party affiliation represent a minority of users. However, the fact that these users choose to call attention to this aspect of their identity is noteworthy. Therefore, it seems possible that either the social identity of party affiliation or of being Wikipedian could be activated in the context of this community. In the present research we examine user practices of representation and identity and examine patterns of cross-party interaction. In light of the preceding review, we pose the following research questions:RQ1: What are the identity and representation practices of users who claim their affiliation to a party within the Wikipedia community?RQ2: Do we see a division in patterns of participation along party lines?RQ3: Do users exhibit a preference for interacting with members of their same political party?RQ4: Does political affiliation of users affect the amount of conflict in discussions?We conducted a mixed-methods analysis of patterns of activity, interaction, and identity representation practices among 1,390 members of the Wikipedia community who explicitly proclaimed their political affiliation as either a Republican or Democrat. In order to determine user political affiliation, user pages were examined. Content analysis was used to evaluate user representation practices in user profiles, and to categorize and thematically group the most edited articles by members of each party. Social network analysis was used to explore the research questions regarding patterns of interactions. Finally, content analysis was used to examine differences to conflict across and within parties, and to evaluate user representation practices in user profiles.On personal user pages users have the ability to display userboxes. A userbox is “a small colored box designed to appear only on a Wikipedian's user page as a communicative notice about the user, in order to directly (or indirectly) help Wikipedians collaborate more effectively on articles” Members of the Wikipedia community have the option of creating a customized user page. Pages can be personalized to reflect the preferences and interests of the individual users, and one of the primary ways that users personalize their pages is through the use of userboxes, which were described above. A qualitative analysis of the userboxes of a randomly selected sample of fifty Democratic and fifty Republican users was conducted.not political party boxes was also tallied. Politically oriented userboxes were coded as “conservative”, “liberal”, or “other”. Boxes coded as conservative were those that expressed what is generally considered a conservative ideology. Examples include, “This user is pro-life”, “This user supports LEGAL immigration”, and “this user thinks the global warming issue has been immensely exaggerated”. Boxes coded as liberal were those that expressed what is generally considered a liberal ideology. Examples include, “This user supports the legalization of same-sex marriage”, “This user is pro-choice”, and “This user supports immigration and the right to travel freely upon the planet we share”. Issues coded as “other” were those that dealt with some political issue, but are not generally assigned to a particular political ideology. Examples include, “This user wants ZERO net carbon emission from human activity”, “This user is against monarchy”, and “This user condemns and opposes Srebrenica Genocide denial”.First, the overall number of userboxes for each user was tallied. Next, the number of political party boxes that a user listed on his or her page was tallied. A box was counted as a party box if it explicitly expressed support for, or membership in, the Republican or Democratic Party. The number of politically oriented userboxes that were Content analysis of user walls was performed on a randomly selected subsample of 100 user pages . Intercoder reliability was assessed using Holsti's reliability score To verify that these subsamples represent the original samples, t-tests have been performed comparing the registration time and the logarithm of the number of comments and edits plus one The logarithm was used to account for the heavy tailed nature of these distributions. Details on the corresponding distributions are given in the following section. In all cases and for both groups of partisan users, the null hypothesis that sample and subsample are extracted from the same distributions cannot be rejected.th 2010. First, we considered edit activity. We counted all edits made by users in our sample to each Wikipedia article. Activity and interaction data came from a complete dump of the English Wikipedia, dated March 12article talk pages, where users can discuss issues concerning the corresponding articles, and user talk pages , which are used by editors to exchange personal messages. Data were obtained by parsing the source text of talk pages and identifying user signatures and comment indentation to reconstruct the thread structure, as described by Laniado, Tasso, Volkovich and Kaltenbrunner We also extracted comments written by users in talk pages, i.e. special wiki pages devoted to communication among editors . We considered both The distribution of the number of comments per user in article talk pages  shows thA further comparison between the partisan users and the overall Wikipedia users is made in reply network, by establishing a connection from user A to user B if user A has replied to a comment written by user B. From user walls we extracted a wall network by connecting user A to user B if user A has left a message on user B's personal talk page. Basic statistics of the two resulting directed unweighted networks are given in To analyze patterns of communication among our set of users, we identified two networks of interactions based on messages written by the users in talk pages. From article talk pages we extracted a Lists of the most frequently edited articles among Democrats, Republicans, and all Wikipedia editors were generated. The number of articles shared by various groups was calculated. The articles were also coded according to topics. Articles were coded as “Political”  or “Not Political” . Political pages were further coded as “Conservative” , “Liberal” , or “Neutral” .To assess whether there is a preference for interaction among editors belonging to the same party, or to different parties, we studied the mixing coefficient of the networks, and we performed a shuffle test in order to assess statistical significance.rrand of the mixing coefficient in these networks, and the standard deviation σrand; finally we computed the Z-score as the difference between the value observed in the real network and the average over the randomized networks, in units of the standard deviation (Z-score = (r−rrand)/σrand). High positive values of Z indicate a preference for inter-party interactions, while high negative values indicate a preference for intra-party interactions. Results low in absolute values (|Z|<2) correspond to neutral mixing, i.e. no statistically significant preference for either inter- or intra-party interaction We first extracted from each network a matrix representing how many connections (based on comments) are directed from a Democrat to a Democrat, from a Democrat to a Republican, and so forth. We then normalized these matrices and computed the mixing coefficient as the preference for inter-party or for intra-party interaction, according to Newman We examined discussion thread conflict in order to assess whether or not users exhibit different interaction styles with same party members versus different party members. All discussion threads on article talk pages that included at least two Democrats and no Republicans (572), at least two Republicans and no Democrats (147), or at least one Democrat and one Republican (583) from our sample were extracted from the data set. From this sample, we then selected all threads related to articles that dealt with political or other potentially controversial topics. Examples include “War on Terrorism”, “Mike Huckabee”, “Eliot Spitzer”, and “Mahmoud Ahmadinejad”. These threads were then coded for whether or not they were conflictual, and for whether or not the conflict was political in nature. This resulted in 144 threads with at least one Democrat and one Republican, 130 threads with two or more Democrats, and 71 threads with two or more Republicans. Holsti's reliability score t = 4, p<0.001), while Democrats  had a significantly higher number of liberally valenced user boxes than Republicans  . Next we looked to see if there were any differences in number of Wikipedia related user boxes listed on the user pages of Democrats and Republicans, but we did not find any (p = 0.07). Finally, we examined differences between the number of “Wikipedia” boxes and “Party” boxes for members of each party. Democrats had significantly more “Wikipedia” boxes  than “Party” boxes , t = 3.1, p<0.01. Republicans also had significantly more “Wikipedia” boxes  than “Party” boxes , t = 3.5, p<0.001. First, we tested to see if there were differences in the average number of userboxes listed on the profiles of Republicans and Democrats. There was no significant difference p = 0.3). Unsurprisingly, Republicans  had a significantly higher number of conservatively valenced user boxes than Democrats  , the identity of being a Wikipedian is activated and, as a result, the political identity is not salient. In the context of interactions on user walls, where personal activities take greater precedence, the importance of political ideology may shine through more strongly.Finally, we found that levels of conflict were high both within and across parties when the discussion threads dealt with political or other potentially controversial topics. Interestingly, there were a significantly greater number of conflictive cross-party and Republican threads, indicating that Democrats have lower rates of within party conflict in the context of these controversial threads.Although Democrats and Republicans seem to maintain their political identity within the Wikipedia community, our findings show that users displayed more “Wikipedia” boxes than political boxes on their user pages, indicating that the identity of being a Wikipedian may be more salient in the context of this community. Further, the lack of preference to interact with same-party members in the context of article discussions does not indicate the same polarization that has been observed in other contexts


Risk prediction models estimate the risk of developing future outcomes for individuals based on one or more underlying characteristics (predictors). We review how researchers develop and validate risk prediction models within an individual participant data (IPD) meta-analysis, in order to assess the feasibility and conduct of the approach.A qualitative review of the aims, methodology, and reporting in 15 articles that developed a risk prediction model using IPD from multiple studies.all available studies and perform only an internal validation (on the same set of data). Ten of the 15 articles did not allow for any study differences in baseline risk (intercepts), potentially limiting their model’s applicability and performance in some populations. Only two articles used external validation (on different data), including a novel method which develops the model on all but one of the IPD studies, tests performance in the excluded study, and repeats by rotating the omitted study.The IPD approach offers many opportunities but methodological challenges exist, including: unavailability of requested IPD, missing patient data and predictors, and between-study heterogeneity in methods of measurement, outcome definitions and predictor effects. Most articles develop their model using IPD from An IPD meta-analysis offers unique opportunities for risk prediction research. Researchers can make more of this by allowing separate model intercept terms for each study (population) to improve generalisability, and by using ‘internal-external cross-validation’ to simultaneously develop and validate their model. Methodological challenges can be reduced by prospectively planned collaborations that share IPD for risk prediction. One of the cornerstones of health and clinical research is to identify individuals who have a high risk of developing an adverse outcome over a specific time period, so that they can be targeted for early preventative strategies and possibly treatment. For example individuals who are seemingly healthy but are found to have a high risk of developing cardiovascular disease could be recommended to modify their lifestyle and behaviour  to reduce their future risk. They may also be prioritised for clinical investigation, which could lead to early diagnosis of an underlying condition  and preventative treatment (e.g. statins or aspirin) to manage it.risk prediction modelling, [prognostic model  or more generally a clinical prediction model (used for both diseased or non-diseased settings) Similarly the word ‘model’ is often replaced with ‘score’, ‘tool’, ‘index’, or ‘rule’. However, the same principle remains: to accurately predict the risk of future occurrence of an outcome in an individual by utilising the values or levels of multiple individual characteristics. We refer here to such characteristics simply as predictors, but they are also termed prognostic factors, risk factors, prognostic variables, and prognostic markers [For this purpose of prognostic risk assessments there is a growing interest in delling, -3 where  markers . They ofDiagnostic risk prediction models also exist, where the risk of already having a disease is calculated; however, the focus in this article is on predicting the risk of future outcomes. Unless the outcome prediction relates to the very near future , singleBefore evaluation of its impact in daily practice ,9,10, riUnfortunately most publications on risk prediction models describe model development, and only a small number report external validation studies . This miIPD meta-analysis in this context can also go beyond using IPD from multiple studies, and more broadly consider synthesising IPD from any relevant clusters in the wider population of interest. For example, large electronic databases and registries are increasingly available that contain routinely collected patient records and risk factor measurements, which can be linked to health outcomes using, for example, Health Episode Statistics (HES) linkage. An example is the THIN database , which cThe aim of this article is to perform a qualitative review to examine how researchers are developing and validating risk prediction models when IPD from multiple studies are sought and then combined for this purpose. The aim is to identify the current research standards and techniques; the role of IPD meta-analysis methods toward development and validation; and the common challenges and methodological problems researchers face. This allows us to generate a set of recommendations for how research in this area can be improved, and to flag those methodological techniques and issues researchers should recognise when modelling risk prediction using multiple sources of IPD.Our review aimed to identify and then evaluate published articles that developed and/or validated a risk prediction model using IPD from multiple studies. We now describe our review methods in detail.To identify potentially relevant articles, we used an existing database of 385 IPD meta-analyses articles that was formed using a systematic review to identify all IPD meta-analyses (on any topic) published up to March 2009 . The revall risk prediction research using IPD from multiple studies, but rather to identify the main methodological methods, limitations and challenges therein. Qualitatively we felt we would achieve saturation with the existing database, and therefore we did not consider it necessary to update our review with newer articles since 2009.Note that our aim was not to review an exhaustive set of A relevant article was defined as one which sought and then used IPD from multiple studies to develop and/or validate a risk prediction model based on one or more predictors. There were no restrictions on the type of outcome being predicted or baseline disease/health of the patients under investigation, or the types of study  being utilised. We use ‘studies’ here loosely to refer to different research sources, and so it could therefore relate to different research centres or collaborating groups. However, there needed to have been a clear step for obtaining IPD from the multiple sources. We did not include articles that used an existing database already containing the multiple sources (e.g. practices). Though the analysis within such articles has similar issues, we wanted to focus on the broader picture of firstly obtaining and then analysing IPD from the multiple sources .Similarly ‘model’ is loosely used for any developed equation, tool, or classification approach that allowed an individual’s risk to be predicted. Articles that evaluated one or more factors for their association with outcome but not their ability to predict individual outcome risk were excluded; for example prognostic factor, risk factor and causal factor studies were excluded if they only considered factors in relation to relative risk  and not also absolute risk (e.g. probability of death by one year).IA screened the abstracts and titles of each of the 385 articles and classified them in regards their risk prediction model status as either ‘yes’, ‘unsure’, or ‘no’. TD then also independently classified each article as ‘yes’, ‘unsure’ and ‘no’. Finally RR checked all the ‘yes’ and ‘unsure’ articles and a random 10% of the ‘no’ articles, along with any ‘no’ article containing the words ‘prognostic model’ or ‘prognostic index’ or ‘prediction rule’ or ‘prediction model’ or ‘risk model’. Any discrepancies between the three reviewers were resolved through discussion and by obtaining the full papers. Any article deemed ‘yes’ or ‘unsure’ after this screening was then obtained and read in full by IA and TD, and a final set of relevant articles decided upon. Any discrepancies were checked by RR and a final decision was then made.Each article finally classed as a ‘yes’ was used for in-depth evaluation. A data extraction form was developed that included over 70 questions  validating the prediction model, how multiple studies were handled in this process, etc.Potential for bias: e.g. potential impact of studies not willing/able to provide IPD on the estimates of model performance  for the intended model’s use and target population.Conclusions: e.g. the key conclusions and recommendations, and the limitations and problems discussed.IA read each article in full and extracted information that answered these 77 questions, and then TD also independently answered these questions for each article. Any discrepancies in responses were resolved with RR.The classification process identified 15 relevant articles each of which used IPD from multiple studies to develop and/or validate a prediction model (containing one or more predictors) for outcome risk ,23-35. TThe central location of the 15 IPD projects (where the first author was located) included the USA and Australasia, but most were from Europe (11), especially The Netherlands (4) and the UK (3). Just three of the 15 articles referred to a protocol for their IPD project, and only six mentioned obtaining ethics approval.Thirteen articles considered patients who were diseased at baseline; for example Sylvester et al.  estimateThirteen of the 15 articles primarily described a multivariable risk prediction model; that is, a model which included multiple predictors. The other two articles examined the predictive accuracy of a single predictor. Noordzij et al.  considerAcross the 15 articles, there were two competing approaches to initiating risk prediction model research using IPD from multiple studies: either perform a (systematic) literature review and seek IPD from relevant studies identified (seven articles) -28,31,34Of the seven articles using a literature review, only three described how they contacted study authors to obtain their IPD and this included e-mail, postal mail, and telephone. Six of the seven articles did not obtain IPD for all studies desired, and only three of these six articles explained why they could not obtain all IPD desired. For example, Heffner et al.  stated tThe types of studies providing IPD were stated by 12 of the 15 articles. Seven articles used IPD from only randomised controlled trials (RCTs), four used only observational studies, and the remainder  used frA summary of the sample population (i.e. mean and summary patients characteristics) were given for each IPD study separately in eight of the 15 articles , whilstFourteen of the 15 articles gave the number of patients within each of the IPD studies, and the remaining article  gave jupupillary reactivity was not recorded in two trials”.Eight of the 15 articles mentioned having missing patient data for some variables. For example Noordzij et al.  that sayone study was excluded because we could not distinguish between patients with tumours of the pancreatic head and tumours of the pancreatic body or tail, and two others were excluded because information on metastases was lacking or incomplete”.If an article reported that missing patient data occurred for a variable, then generally the article either stated that patients with missing data were excluded from the analysis , or theWe now summarise the statistical analysis methods used for model development. Our main focus here is on how IPD from multiple studies were handled.separately towards the prediction model development was given for 10 of the 15 articles, , with tThe two articles that considered a single predictor examined predictive performance by calculating ROC curves from the data. In the 13 articles developing a multivariate model, six used Cox regression , five u“The homogeneity assumption was checked by including treatment as a dummy-coded variable into both models (stratification per study). Initially we found a significant survival benefit for patients treated by surgical bypass procedures compared with endoscopic stents. However, this effect disappeared after adjustment for Karnofsky’s index  in the study in which this variable was available, which legitimises pooling”.All 15 articles used the IPD from multiple studies to develop their models (i.e. none used just one study for development). Ten of the 15 articles pooled all the IPD available into one big dataset and analysed it ignoring clustering of patients within studies or collaborative groups , mostlyThree of the remaining five articles developed their model via a ‘one-step meta-analysis analysis accounting for clustering’, where the IPD from all studies/collaborative groups are analysed together but with clustering by study/group accounted for . In anoSimilarly, study-specific effects were assessed with interaction terms between study and each predictor. Interaction terms between predictors were examined with likelihood ratio tests, but none was of sufficient relevance to extend the models beyond the main effects for each predictor”. Rovers et al. [2 statistic and state: “To determine whether pooling was justified, heterogeneity between studies was assessed with the I2statistic. Because the I2value was 25%, pooling was performed”. Fowkes et al. [2), for one of their predictors; they then applied random effects in their analysis which, although not explicitly stated, is likely due to heterogeneity being detected.Twelve articles did not consider between-study heterogeneity in the predictive effect of their included predictors. Of the three articles that did consider such heterogeneity, Steyerberg et al.  state: “s et al.  assessedFor the continuous predictor’s age, glucose, and Hb, a linear relationship with outcome was found to be a good approximation after assessment of non-linearity using restricted cubic splines”. The remaining 11 articles either categorized or dichotomized the continuous predictors of interest. For example, Heffner et al. [continuous variables were entered as dichotomous indicator variables with test thresholds determined by ROC analysis”, and Chau et al. [Laboratory variables were initially coded as continuous variables and subsequently dichotomised with the cut-off points chosen at the median value of each variable”.Continuous predictors were analysed on a continuous scale in four of the 15 articles. For example, Steyerberg et al.  state: “r et al.  state thu et al.  state: “To standardise definitions among the different studies, the presence of metastases and of pain and weight loss at diagnosis were classified as ‘present’ or ‘absent’”, whilst Noordzij et al. [Three of the 15 articles mentioned the need to standardise the coding of predictors and outcome definitions across studies. For example, Terwee et al.  state “Tj et al.  standardVariables that were found by univariate analysis with p < 0.10 were entered in the model. Variables were removed from the model if their p values were > 0.05. This process identified 4 clinical variables. The four clinical variables were entered into the logistic regression, and only pH was identified as an independent predictor”. Four of the articles used a selection procedure such as forwards, backwards or stepwise selection.Of those 13 articles developing a multivariable model with multiple candidate predictors for inclusion, the most common approach was to use p-values to decide which factors were included. For example Heffner et al.  state: “Of those 13 articles developing a multivariable model, the developed model was poorly reported on its original scale. For example, of the six articles that used a Cox model, only Terwee et al.  gave theWe now consider if and how articles validated their developed model. We refer to ‘internal validation’ when the same data are used to validate the model as to develop it, and ‘external validation’ when the data are used to validate the model are different than the data used for development. We also refer to ‘internal-external cross-validation’ ,17, whicFour articles did not use any validation approach. Nine of the other 11 articles performed internal validation, using the same IPD as that used to develop the model. For example, Noordzij et al.  examinedWe aimed to validate all models externally using data from selected patients in the CRASH trial. However, lab values were not recorded in this trial, nor were hypoxia, hypotension, and EDH. We therefore validated the core model, and a variant of the extended model, in which only the Marshall CT classification and presence of tSAH were added to the core model ”. Interestingly, their developed model was stratified by study and so had multiple intercepts to choose from. However, in their validation they appear to choose one intercept related to a single trial as “it represented typical proportions of mortality  and unfavourable outcome ”.Steyerberg et al.  used botAUC was calculated in a cross-validation procedure, where each study was omitted in turn. Results were pooled over the ten imputed datasets for eight studies with sufficient numbers for reliable validation (n > 500)”. Yap et al. [The use of several different studies provided an opportunity to validate the general method using data drawn from a different patient population using an internal–external cross-validation system proposed by Royston et al. that leave-one-out cross-validation on a cohort basis. We therefore, sequentially designated one of the trials as a test study. The remaining studies acted as a training data set, which was used to generate the risk scores”.When performing internal-external validation, Steyerberg et al.  state: “p et al.  also useValidation criteria focused on discrimination, calibration, and goodness of fit. Of the 11 articles that examined validation, 10 gave discrimination statistics such as sensitivity, specificity, AUC and C-statistic. Three articles reported calibration statistics such as the Hosmer-Lemeshow goodness-of-fit test. Eight provided figures to show model discrimination, accuracy, or calibration. The most common figure was an ROC curve, but Steyerberg et al.  present We may not have identified all centres, which monitor MES after carotid surgery: it is likely that small series of patients remain unpublished and have therefore escaped our attention”. None of the six articles gave the number of patients and events in the missing IPD studies, and only one article discussed the qualitative or quantitative differences between those studies providing IPD and those studies not able to.In six of the seven literature review articles Figure , IPD hadPTH assay, when checked 1 to 6 hours after thyroidectomy, has excellent accuracy in determining which patients will become symptomatically hypocalcemic”, whilst Yap et al. [our study suggests that in post-MI patients, pre-selected using LVEF or frequent ventricular premature beats, the additional use of a simple prognostic indicator based on demographic and baseline information was able to segregate patients that were at high risk of dying, for 3 different modes of mortality”.The developed risk prediction models had large potential for use in clinical practice, according to the discussion of the 15 articles, highlights the value of collecting IPD from multiple studies. For example, Noordzij et al. [p et al.  state “oHowever, numerous limitations and problems of the IPD projects were also noted in the articles’ discussion section, especially dealing with missing data and between-study heterogeneity in definitions, time-periods, treatments used, study quality, and different methods of measurement. The key methodological problems are summarised in Table Risk prediction models have the potential to inform strategies for disease prevention, early diagnosis, patient counselling and therapeutic care . As for In this article we have reviewed 15 articles that each used IPD from multiple studies to develop and potentially validate a risk prediction model for the development of future outcomes. This has allowed us to identify good practice, useful statistical methods, methodological challenges Table , and somIn our review, 10 of the 15 articles did not account for clustering of patients within different IPD studies and therefore their developed prediction model did not allow for any study differences in baseline risk. Although such models can still perform adequately on average , when applied in practice to particular populations the model performance may deteriorate considerably if the population’s baseline risk is very different from the average estimated during model development. In other words, the developed model may require re-calibration in particular populations. Statistically it is also know than omission of an important predictor (i.e. study) can lead to biased effect estimates and reduced power . To addrWhere the intercept can be well-matched to the excluded study, Debray et al.  show thaA similar recommendation is for researchers to examine between-study heterogeneity in predictor effects. This is currently rarely done. Debray et al.  show thaA major finding from our review is that, despite the availability of multiple studies, most researchers develop their model by using the IPD from all available studies, and so then perform an internal validation (on the same set of data) rather than an external validation (on different data). Only two of the 15 articles used a form of external validation, and so most models require further validation in order to investigate their true performance. One plausible reason why researchers choose not to use IPD for external validation is that they want to maximise the data available for model development; this is understandable, especially when faced with a large set of candidate predictors and possible non-linear relationships. Furthermore, even if researchers do decide to hold-back some IPD for external validation, it is not easy to decide how much IPD (and how many studies) should be removed.For these reasons, the internal-external cross-validation approach is highly appealing ,17, yet The internal-external cross-validation approach involves removing just one study from the development phase of the model, fitting the model on the remaining IPD, and then testing performance in the excluded study. This framework is then repeated by rotating the omitted study and assessing the validation in all the possible scenarios. Model estimates are therefore always based on the majority of IPD, and its model fit and predictive ability can be appraised across all the studies simultaneously. Where performance is consistently adequate across all combinations of the omitted study, a final step can be to utilise the IPD from all studies to produce the final specification of the model. In situations where model fit appears inadequate in some excluded studies, then this identifies a lack of generalisability and highlights populations (studies) for which the model is not currently suitable for. It also signals the need to improve the current model specification, for example by including additional predictors with homogenous effects across studies..[Royston et al. original.. This fr. recentlyWe recognise that implementing these two recommendations may be difficult given only a few IPD studies and/or when the number of patients per study is small. In particular, there is a need for methodological research on the necessary sample size to implement the internal-external approach, in terms of both the total number of IPD studies needed . We hope that most ethics committees will support this view, but it should at least be checked that the studies providing IPD actually had ethics approval themselves.It is paramount to consider statistical and methodological issues when planning to develop and/or validate a risk prediction model from multiple datasets, in order to avoid poorly generalisable and poorly performing models. Our review highlights that the IPD meta-analysis approach is highly appealing, as it allows the use of internal-external cross validation to develop a model and simultaneously evaluate its performance across multiple populations. However, researchers are faced with numerous challenges when the IPD is collated, in particular missing data and heterogeneity in study quality and methods of measurement. Perhaps an ideal way forward is a prospective IPD meta-analysis, where researchers agree at their study onset to use set quality standards and record particular variables in a common way so that, upon their own study completion, they can supply their IPD to those developing/validating a risk prediction model. Heterogeneity can then be limited by researchers agreeing, before data collection, to standardize predictor definitions, measurement methods, and outcome recoding.Our review has only considered articles that developed or validated a prediction model. For risk prediction models to become more common in practice, research also needs to show they have a positive impact on health outcomes. Such impact studies are currently rare , but theThe authors declare that they have no competing interests.RR and KM conceived the study. IA developed the questions for the data extraction form, which were then refined and extended by all authors. IA and TD independently extracted information from the articles to answer each question. IA collated responses, and resolved any disagreements with TD and RR. IA wrote the results up in full for his thesis, including tables and figures. RR converted this chapter into the form presented in this article, and this was edited and added to by KM, TD and IA. All authors contributed to revisions of the article in response to reviewers’ comments. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2288/14/3/prepubFull list of questions used to evaluate the 15 articles.Click here for file


In this Formal Comment, Blomqvist et al. note that the main points of their Perspective, “Does the Shoe Fit? Real versus Imagined Ecological Footprints,” are robust to Rees and Wackernagel's response, “The Shoe Fits, but the Footprint is Larger than Earth.” The appropriate scale for these indicators will, in many cases, be local and regional. At this level, the EF is a measure of net exports or imports of biomass and carbon-absorptive capacity The Formal Comment by Rees and Wackernagel


Background. Oligonucleotide microarrays allow for high-throughput gene expression profiling assays. The technology relies on the fundamental assumption that observed hybridization signal intensities (HSIs) for each intended target, on average, correlate with their target's true concentration in the sample. However, systematic, nonbiological variation from several sources undermines this hypothesis. Background hybridization signal has been previously identified as one such important source, one manifestation of which appears in the form of spatial autocorrelation. Results. We propose an algorithm, pyn, for the elimination of spatial autocorrelation in HSIs, exploiting the duality of desirable mutual information shared by probes in a common probe set and undesirable mutual information shared by spatially proximate probes. We show that this correction procedure reduces spatial autocorrelation in HSIs; increases HSI reproducibility across replicate arrays; increases differentially expressed gene detection power; and performs better than previously published methods. Conclusions. The proposed algorithm increases both precision and accuracy, while requiring virtually no changes to users' current analysis pipelines: the correction consists merely of a transformation of raw HSIs . A free, open-source implementation is provided as an R package, compatible with standard Bioconductor tools. The approach may also be tailored to other platform types and other sources of bias. In situ oligonucleotide arrays are by far the most popular type, representing at the time of writing 70% of all arrays deposited in the Gene Expression Omnibus (GEO), a public microarray database, in the last year; of these, 58% are Affymetrix GeneChips [probe set); copies of each of these probes are covalently linked to a solid surface at a predetermined location on a grid; a labelled RNA sample is allowed to hybridize to each of these probes; and finally a hybridization signal intensity (HSI) is obtained for each probe [Microarray technology, a fairly recent yet already well-established and extensively dissected method, allows for the simultaneous quantification of expression levels of entire genomes or subsets thereof . In situeneChips . These ach probe . The tecch probe .biologically interesting variation from all other sources of obscuring variation is of utmost importance [Because the objective of such experiments is generally to assess gene expression differences between one or more biological samples, separating portance ; consequportance . As has portance ; probe-tportance , specifiportance , and spaportance –14; secoportance  and tranportance , and othportance  such as portance ; scannerportance ; and algportance —and undeportance .An initial attempt to address nonuniform “background intensity” was incorporated directly in the design of the GeneChip platform: for each “perfect match” (PM) probe, there is a corresponding “mismatch” (MM) probe which features a single different base . The intIn order to make data from multiple arrays directly comparable, normalization methods such as locally weighted scatterplot smoothing (LOWESS or loess) –28 and Bin situ oligonucleotide arrays as well, although none is commonly used. Reimers and Weinstein proposed to visualize spatial biases by computing the deviation of each probe intensity from a robust average of that probe's intensity across all arrays in the experiment and plotting these values on the array surface [It has been noted that the choice of background correction methodology has a significant impact on downstream analysis accuracy , which i surface ; Suárez- surface . Upton a surface . Arteaga surface . Various surface , some of surface  and SNP  surface .in situ oligonucleotide array data, when one is used at all, consists of performing quality control steps to identify arrays deemed to be beyond arbitrary acceptability thresholds, and discarding these while leaving others intact [The currently advocated standard operating procedure with respect to the well-known issue of spatial bias in s intact . While ts intact . Discards intact , 11, 42 s intact . We belia priori information about sources of systematic variation, in the form of known relationships between probes, can be exploited to identify, quantify, visualize, and effectively correct probe-level systematic errors. Here, we present an algorithm, pyn, to correct for sources of obscuring variation dependent on spatial location of probes on the array. The algorithm works by leveraging the power of expected mutual information found in probe sets with that of unexpected mutual information found in spatially proximate probes.We posit that Yijn be the log2-transformed HSI of probe j = 1,…, Jn belonging to probe set n = 1,…, N, on array i = 1,…, I. Error estimates for observed intensities can then be expressed as deviation from some given intensity estimatorRijn, is thus simply the deviation of each HSI from the mean observed in its probe set on the same array.Using notation inspired by , let Yia priori information about relationships between probes provides a form of relative truth; and second, this bias estimate is better used as a correction term than as an accuracy assessment.In justifying the use of spike-in and dilution datasets for assessing accuracy, Cope et al. assert that “to estimate bias in measurements, we need truth, in an absolute or relative form, or at least a different, more accurate measurement of the same samples” . We propkNN(jn) returns the k nearest neighbours of probe jn, physically on the array, in terms of Euclidian distance.Although a given probe residual merely quantifies one HSI's deviation from an estimate and thus contains contributions from many probe-specific biases  and random noise, a sufficiently large pool of probe residuals with similar locations on the array provides a summary of the average bias induced by such a location. Noting that residuals  share units and scale with HSIs, we thus simply propose to subtract this estimate of location-induced bias from each HSI to obtain corrected signals:k increases, the computed estimates of local bias tend to approach a constant as they become less local and approach a global measure on the entire array; in the limiting case, where k → ∑nJn, the correction factor is almost exactly zero. Of practical interest is that this is also the expected behaviour in the case of an ideal array  with a reasonable k.It is of theoretical interest to note that, as k, the only free parameter of our algorithm, is explored in the Results section. When a value is not given in the text, k = 20 is to be assumed.The optimal choice of In this paper, three typical Affymetrix Human Genome U133A datasets are used to evaluate the proposed algorithm: two datasets obtained from the public microarray repository GEO  and a wepyn”), with critical parts written in C++ for efficiency. An “AffyBatch” object (“batch”) can be replaced with a corrected version by running the following command within an R session:An implementation of the proposed spatial correction procedure is provided as part of an R package AffyBatch object, independent of array platform. The user can then proceed with his usual analysis pipeline, for example, “rma” and “limma.” The package also contains some utility functions for generating assessment figures similar to those found in this paper; users are referred to the package's internal documentation for instructions. The package is released under a BSD license and is available at http://lemieux.iric.ca/pyn/.The procedure runs for approximately one second per array (Intel Core 2 Duo 3.33 GHz) and accepts any valid upton.lloyd.2005.” We now briefly describe these three algorithms.In this paper, “CPP” and “LPE” refer to the two algorithms proposed by Arteaga-Salas et al. in ; implemeFor each probe on each array in a batch, a value similar to our residual is computed, relating the deviation of the probe's intensity from its median intensity across all arrays. A “code” is then computed for each probe, which identifies the array where this value is the largest and whether it is positive or negative. Then, to determine whether a given location in a batch of arrays is affected by spatial bias, PM and MM probes in a 5 × 5 window are inspected; if an “unusual” number of these exhibit the same code, the location on the array identified by this code is flagged for correction. Finally, for each flagged location, a standardized average of the previously defined deviations within its window is subtracted.Residuals are computed for each probe on each array as in LPE, but the authors require in this case for all arrays to be replicates of one another. Each PM intensity is corrected by subtracting its MM counterpart's residual and vice versa. Residuals are scaled before subtraction to address differences in PM and MM distributions.m + 1) × (2m + 1) window is centred on it is subtracted; if the result is negative, it is replaced with zero. The authors find that m = 1 works best.From each intensity at a given location on an array, the smallest intensity found in a (2μ | <10−17). The parameters of this distribution vary from one array to another, based on their intensity distributions, which notably vary in offset (“background intensity”) and scale (“dynamic range”); however, arrays in a common “batch” usually share these parameters. Thus, identifying outliers in residual distributions within a batch may be useful in identifying arrays significantly affected by spatial bias.Under the assumption that probes are randomly located on the array, these residuals are expected to be randomly spatially distributed across the array; thus, any spatial patterns must be ascribable to some form of technical error. In practice, we frequently observe various manifestations of such patterns. The spatial patterns identified in pyn. Qualitatively, all three types of artefacts previously identified appear to have been eliminated or at least severely reduced, thereby marginalizing spatial dependence between residuals.spatial autocorrelation, the bidimensional extension to autocorrelation, which itself is a special case of correlation in which the two vectors under analysis u and v are such that ui = vi+1 for i = 1, …, | u|. This additional dimensionality and directionality leads to multiple alternative formulations of a quantitative metric: Geary's C [pyn results in the second lowest.This “spatial dependence” can be defined quantitatively as eary's C  and Moraeary's C  are wideeary's C  and Wilseary's C  have propyn performs best, Upton-Lloyd critically inflates standard deviation, and CPP and LPE appear to have no effect whatsoever; at higher expression levels, all methods appear to have virtually no effect.As effective correction of array-specific spatial biases should result in greater reproducibility, we evaluated the impact of each spatial correction method on variance across replicate arrays. affycomp package [x-axis conveys 1 − specificity, while the y-axis conveys sensitivity  is computed for each ROC curve. k, separately for low-, medium-, and high-concentration spike-in genes. Small values of k produce less robust (noisy) estimates, as they are more susceptible to contain contributions from other sources of probe-specific, but not location-dependent, variations; thus, as expected, small values of k(<6) perform badly. Conversely, as k increases, estimates of local bias, that is, the k = 20 then levelling off . Above the k = 6 threshold, high-concentration DEG detection power is unaffected. As explained in the Algorithm subsection, as k continues to increase, the correction gradually becomes a simple scaling of all intensities by a constant; thus, the curves in k continues to increase (data not shown).Finally, we assessed the effect of parameter ooled in . ROC curSystematic, nonbiological variations have been long known to obscure microarray data. In the case of spotted arrays, array- and print-tip-dependent biases were the first to be considered. Array bias could trivially be visualized by inspecting box plots of a batch of arrays or using more sophisticated approaches such as RLE and NUSE plots ; correctin situ oligonucleotide arrays, perhaps based on the assumption that streamlined commercial manufacturing of such high-density arrays is much less prone to significant systematic errors. For example, background correction and normalization steps in two of the most popular analysis packages, RMA [This strategy of addressing known sources of bias individually has been somewhat abandoned in the case of ges, RMA  and GCRMges, RMA , do not ges, RMA  and quanges, RMA , respecta priori information is preferable to global, blind, generic normalization, and/or reliance on unverified modelling assumptions [We posit, as initially asserted by Dudoit et al. in 2002, that correcting for known biases using umptions . We havepyn's working with residuals, which may avoid subtracting the “baseline” spatial autocorrelation inherent to each microarray platform.We have shown that this method reduces spatial autocorrelation in HSIs, reduces variance in gene expression measures across replicate arrays, improves DEG detection power, and performs better than previously published methods in terms of replicate variance reduction and DEG detection power increase. As for spatial autocorrelation reduction, we conclude that Upton-Lloyd removes “too much” due to its working directly on HSIs as opposed to k, the number of neighbouring residuals pooled in  = 3 × 91 = 273 curves are averaged to generate plots such as An assessment based on a spike-in benchmark dataset indicated that DEG detection power is increased for low- and medium-concentration genes and is insignificantly affected for high-concentration genes. However, it should be noted that, by its very design, this central within each probe set. Thus, although recent studies have uncovered a significant dependence between probe locations and their sequences by observing spatial patterns when spatially mapping probe sequence GC content on the array [per se, but is likely worthy of further investigation in order to fully understand its implications. Although the “random” spatial distribution of probes on the array surface was presented in this paper as a necessary assumption, this is an oversimplification and, thus, not strictly correct. In reality, the underlying assumption is that probe locations are independent of their targets, or—in more practical terms—of the locations of probes in the same probe set, such that any correlation between locations and residuals is always considered undesirable noise or bias; this can also be expressed as the assumption that probes are randomly spatially distributed on the array surface he array , 14, ourpyn: probe your neighbours) should be applicable to other biases such as probe composition as well as to other microarray platforms such as tiling arrays, and preliminary results indicate that this is indeed the case. Finally, the framework established herein provides opportunity for implementing further types of microarray data pretreatments: correction of a specific source of bias which can be expressed as the presence of undesirable mutual information shared by “neighbouring” probes in some given coordinate space  or based on some given distance metric, as opposed to expected, desirable mutual information shared by some other sets of probes . This approach (which we dub http://lemieux.iric.ca/pyn/.Oligonucleotide array data is invariably biased by a number of confounding factors, some of which can be effectively quantified and eliminated. We have proposed a method for correcting bias arising from a known source and show the efficacy of one case, namely, spatial bias in Affymetrix GeneChip data. An implementation is provided as a convenient R package, released under the BSD license, available at