The prevalence and value of patient-generated health text are increasing, but processing such text remains problematic. Although existing biomedical natural language processing (NLP) tools are appealing, most were developed to process clinician- or researcher-generated text, such as clinical notes or journal articles. In addition to being constructed for different types of text, other challenges of using existing NLP include constantly changing technologies, source vocabularies, and characteristics of text. These continuously evolving challenges warrant the need for applying low-cost systematic assessment. However, the primarily accepted evaluation method in NLP, manual annotation, requires tremendous effort and time.The primary objective of this study is to explore an alternative approach—using low-cost, automated methods to detect failures  when processing patient-generated text with existing biomedical NLP tools. We first characterize common failures that NLP tools can make in processing online community text. We then demonstrate the feasibility of our automated approach in detecting these common failures using one of the most popular biomedical NLP tools, MetaMap.Using 9657 posts from an online cancer community, we explored our automated failure detection approach in two steps: (1) to characterize the failure types, we first manually reviewed MetaMap’s commonly occurring failures, grouped the inaccurate mappings into failure types, and then identified causes of the failures through iterative rounds of manual review using open coding, and (2) to automatically detect these failure types, we then explored combinations of existing NLP techniques and dictionary-based matching for each failure cause. Finally, we manually evaluated the automatically detected failures.From our manual review, we characterized three types of failure: (1) boundary failures, (2) missed term failures, and (3) word ambiguity failures. Within these three failure types, we discovered 12 causes of inaccurate mappings of concepts. We used automated methods to detect almost half of 383,572 MetaMap’s mappings as problematic. Word sense ambiguity failure was the most widely occurring, comprising 82.22% of failures. Boundary failure was the second most frequent, amounting to 15.90% of failures, while missed term failures were the least common, making up 1.88% of failures. The automated failure detection achieved precision, recall, accuracy, and F1 score of 83.00%, 92.57%, 88.17%, and 87.52%, respectively.We illustrate the challenges of processing patient-generated online health community text and characterize failures of NLP tools on this patient-generated health text, demonstrating the feasibility of our low-cost approach to automatically detect those failures. Our approach shows the potential for scalable and effective solutions to automatically assess the constantly evolving NLP tools and source vocabularies to process patient-generated text. The Internet pervades our everyday life, including health care . For insOne scalable approach to process text-based patient-generated data is natural language processing (NLP). An increasing number of researchers studying patient-generated text, such as in online health communities, have used statistical methods based on manually annotated datasets -15. UtilExisting biomedical NLP tools have the potential to be used immediately and promise to provide greater generalizability than statistical approaches while providing semantic connections. Researchers have developed various NLP techniques and applications in the biomedical domain. For example, the Clinical Text Analysis and Knowledge Extraction System (cTakes)  was deveOne of the most widely regarded NLP applications in biomedicine is MetaMap , which wHowever, MetaMap and many other traditional biomedical NLP tools were developed to process biomedical literature and clinical notes, rather than patient-generated text in online communities. One of the biggest challenges in applying these biomedical NLP tools to a different type of text is the difference in vocabulary. For example, Zeng et al recognize differences in the vocabulary used by patients and clinicians . Smith aRecognizing the differences in vocabulary, a number of efforts to expand the UMLS to include patient-generated text have been reported ,27-31. OThe effort to process patient-generated text, such as email ,30 and sThese prior studies show that many have worked to improve biomedical NLP tools to process patient-generated text. As NLP technologies and source vocabularies continue to evolve, we need easy, low-cost methods to systematically assess the performance of those tools. Traditionally in NLP, evaluations involve a great deal of manual effort, such as creating a manually annotated dataset. Moreover, a new evaluation for different types of text requires additional annotated datasets, thus maintenance can often be difficult. Recognizing the potential benefits of performing a low-cost assessment of NLP tools, we explore automated methods to detect failures without producing annotated datasets. Given MetaMap’s long history of use in biomedical contexts, its configurability, and its scalability, we apply our failure detection tool to MetaMap in processing patient-generated text from an online cancer community to demonstrate the feasibility of automatically detecting occurrence of failures. We first present the dataset and MetaMap configuration, followed by the specific methods and results for (1) characterizing failure types, (2) automated failure detection, and (3) manual performance evaluation of our automated failure detection approach.Our dataset consists of community posts from the CancerConnect website, an online cancer community for cancer patients, their families, friends, and caregivers to exchange support and advice. The dataset consists of a total of 2010 unique user members and 9657 user member–generated posts from March 2010 to January 2013.We processed the online community posts with MetaMap version 2011AA and configured the word sense disambiguation feature and included only the top-ranked concept from the output. In the default setting, MetaMap suggests a number of candidate concepts with candidate scores indicating relationships among concepts found in the text. However, in real-world usage on large amounts of text, considering multiple suggestions for each processed term could be overwhelming to assess manually. Thus, we assessed only the top-ranked scored concept to simulate how MetaMap would be used in real-world settings. However, we used default settings for all other options for generalizability. A single mapped term/concept served as the unit of analysis.To characterize the types of failures, we assessed MetaMap’s output collaboratively through iterative rounds of manual review among the five authors. We reviewed the output following an open coding process  to identFrom our manual review, we characterized three types of failure: (1) boundary failures, (2) missed term failures, and (3) word ambiguity failures. A boundary failure occurred when a single coherent term was incorrectly parsed into multiple incomplete terms. A missed term failure occurred when a relevant term had not been identified. A word sense ambiguity failure occurred when a relevant term was mapped to a wrong concept. Within these three failure types, we discovered 12 causes of failures. In the sections below, we describe each type of failure and then identify potential causes within each failure type.Boundary failures, in which a single coherent term is incorrectly parsed into multiple terms, are well documented in biomedical NLP literature ,29,34-36Our patient-generated text contained extensive descriptive phrases  and colloquial language , contrasting with typical biomedical text that usually contained concepts from standard terminologies. Theoretically, boundary failures can result from standard medical terminologies. However, descriptive phrases and colloquial language highlight the parsing problem of biomedical NLP because colloquial language and descriptive phrases that patients use in online health communities cannot all be included in the UMLS. For instance, UMLS included “feeling sick” as a synonym of a concept, although a similar descriptive phrase “feeling great” was not included in the UMLS. Consequently in our analysis, “feeling sick” was recognized as one concept, while “feeling great” was parsed into two separate terms “Emotions” and “Large” delivering different interpretations than intended.Boundary failures also occurred even when proper concepts were available in the UMLS. For instance, a colloquial term “chemo brain” was commonly used to describe the single concept of cognitive deterioration of cancer patients after chemotherapy. In our analysis, the term was recognized as two UMLS concepts—“chemotherapy” and “brain-body part”—even though UMLS contained a concept for “chemo brain”. From our experience, we inferred that the lack of colloquial language and descriptive phrases concepts in the UMLS as well as standard medical terminologies parser were causing boundary failure when processing patient-generated text.Missed term failures occurred when a relevant term was not identified ,34. We eCommunity-specific nomenclature refers to members of a community using terms that either are commonly used in a different way elsewhere or not commonly used at all. In online communities, members frequently create their own nomenclature that, over time, can become vernacular that is well understood in the community . CommuniIn particular, community nomenclature regularly referred to relevant health-related content but resulted in three major challenges. First, many of the community-specific terms were not found in the UMLS. For instance, “PC” referred to “Prostate Cancer”; however, this acronym was not contained in UMLS. Second, community nomenclature was typically context and community-specific. For instance, the acronym “BC” was used for “before cancer”, “blood count”, or “breast cancer” depending on the context. This type of ambiguous usage was also seen with commonly accepted abbreviations. For instance, “rad” was a common abbreviation for “radiation therapy” in the cancer community, but “rad” could also be used for “radiation absorbed dose”, “reactive airway disease”, “reactive attachment disorder”, or “RRAD gene” depending on the community. Third, novel abbreviations and acronyms constantly showed up in our data, similar to what researchers of online communities found . For insPrevious research showed that patients made more medically related misspellings at a significantly higher rate compared to clinicians . MisspelThe most prevalent failure was word sense ambiguity, which occurred when a term was mapped to the wrong concept because the two concepts are spelled the same way, share the same acronym -1,3,5-triazine”), or were spelled the same as one of their acronyms . This failure had been identified in previous research ,29,34-36Frequent use of standard abbreviations and contractions was common in our online health communities. Online community members frequently used contractions such as “I’d” or abbreviations such as “i.e.” in their text. Although the use of these shortened forms was common in informal text, it could be a source of errors for many NLP tools. For example, MetaMap maps “I’d” to “Incision and drainage” and mapped “i.e.” to “Internal-External Locus of Control Scale” due to partial matches with synonyms. Also, MetaMap was inconsistent with some of its correct mappings for abbreviations. For instance, abbreviations for some US states were mapped correctly , whereas others were often missed even though they were in the UMLS  or were mismapped .Colloquial language, such as “hi” was prevalent in our dataset and caused many failures. Although these terms are obvious to human readers, we found they were often mapped to incorrect terms in the UMLS. For instance, our previous example “hi”, rather than being left unmapped, was mapped to “Hawaii”, “ABCC8 gene”, or “AKAP4 gene” because “hi” was a synonym for all three concepts. In our analysis, this failure was found with many semantic types; however, terms mapped to the semantic type of “Gene or Genome” were particularly troublesome because of their unusual naming conventions.Our online community posts often contained numbers that convey important information, such as a patient’s disease status . Other times, numbers conveyed more logistical information, such as time of day and dates, which were misinterpreted. For instance, in the phrase, “I got there at 4:12pm”, “12pm” was mapped to “Maxillary left first premolar mesial prosthesis” because it was a complete match for one of its synonyms in the UMLS. Numbers that were used to convey diagnostic information were crucial for the identity of many community members, and such information was often included in an automated signature line  at the end of posts. Numbers indicating dates and times often resulted in false positives, whereas health status numbers often resulted in a different failure type . We saw this type of failure across many different semantic types, including “Amino Acid, Peptide, or Protein”, “Finding”, “Gene or Genome”, “Intellectual Product”, “Medical Device”, “Quantitative Concept”, and “Research Activity”.Online community members frequently mentioned URLs and email addresses in our dataset. They often pointed to websites that they found useful and gave out email addresses to start private conversations. Parts of email addresses and URLs were incorrectly mapped in our analysis. For instance, “net” at the end of an email address was often mapped to the “SPINK5 gene” because one of its synonyms was “nets”. Also, “en”, a language code that referred the English language in URLs, incorrectly mapped to “NT5E gene” because one of its synonyms was “eN”.Internet slang and SMS language, such as “LOL”  or “XOXO”  are highly prevalent in online community text but not in typical biomedical texts. Although these terms should be obvious to human readers, our analysis showed that Internet slang and SMS language were often mapped to incorrect biomedical terms in the UMLS. In particular, Internet slang and SMS language were often mistaken for gene names, such as the mapping of “LOL” to the LOX1 gene and “XO” to the XDH gene. To manage the different variations of concepts, the UMLS included many synonyms of terms. Varieties of these synonyms overlapped with commonly used Internet slang and SMS language resulting in word sense ambiguity failure.The use of names is also prevalent in online community posts, particularly when posts address specific individuals. Community members also often include their first names in a signature line and call out other members by first names or community handles. In our analysis, common first names were often mistaken for UMLS concepts, such as “Meg” being mistaken for “megestrol”, “Rebecca” for “becatecarin”, “Don” for “Diazooxonorleucine”, and “Candy” for “candy dosage form”. Each individual name was a complete match for one of the UMLS concepts. We identified these mismatches across multiple semantic types, including “Antibiotic”, “Biomedical or Dental Material”, “Clinical Attribute”, “Diagnostic Procedure”, “Disease or Syndrome”, “Finding”, “Hormone”, “Injury or Poisoning”, “Laboratory Procedure”, “Mental Process”, “Pathologic Function”, “Pharmacologic Substance”, and “Sign of Symptom”.Patients share a wide variety of personal experiences in narrative form in online health communities. Thus, the use of the pronoun “I” is prevalent in community posts but is a source of misinterpretation. For example, over the course of the study we discovered that “I” is typically mapped to either “Blood group antibody I” or “Iodides”, which belong to “Amino Acid, Peptide, or Protein”, “Immunologic Factor”, or “Inorganic Chemical” semantic types.One of the most fundamental components of NLP tools is a part-of-speech (POS) tagger, which marks up words with their corresponding POS  in a phrase, sentence, or paragraph. POS taggers are commonly used in NLP and have many different applications, such as phrase parsers. In our analysis, we discovered that MetaMap uses a POS tagger called MedPost SKR (Semantic Knowledge Representation)  to split“stage 4 ER+ breast cancer”). Despite the seemingly similar sentence structures and usage of the term in the sentence, our findings showed that MetaMap inconsistently mapped “stage” to different UMLS concepts. Six different semantic types were identified for the UMLS concepts mapping to “stage”  C0442757. We used specific regular expressions that focused on numbers with “am” or “pm”, as well as a string of numbers with or without non-alphanumeric characters in between numbers to identify dates, times, and other numbers that do not indicate disease status.Our detection process for email addresses and URLs was completed using regular expressions to identify all the email addresses and URLs, and then we counted the number of terms that were mapped from email addresses or URLs. In our approach, we used specific regular expressions matching “@” and a typical structure of domain name  for identifying email addresses and “http” or a typical structure of domain name for identifying URLs.We detected improperly mapped Internet slang and SMS language using a 3-step process. First, we identified an Internet dictionary with a list of chat acronyms and text shorthand . Second,To identify improperly mapped names, we first combined a number of name dictionaries that consist of first names  with a lWe identified a number of cases where the pronoun “I” was improperly assumed to be an abbreviation, such as for Iodine, because the NLP tool did not consider the contextual knowledge from the term’s POS. One of the most fundamental components of NLP tools is a POS tagger, which marks up words with their corresponding POS  in a phrase, sentence, or paragraph. “I” as an abbreviation for Iodine should be recognized as a noun, whereas “I” meaning the individual should be recognized as a pronoun by a POS tagger. Our tool used data derived from the Stanford POS Tagger  to countTo identify the improperly mapped terms without discriminating between verbs and nouns, we used POS information from the Stanford POS Tagger  to countDetecting word sense ambiguity failures leading up to this section consisted of cases where terms were consistently mapped improperly. However, for other word sense ambiguity failures, MetaMap inconsistently mapped terms, both correctly and incorrectly. The inconsistency was the result of poor performance by MetaMap’s word sense disambiguation feature that was designed to select the best matching concepts out of many candidate concepts available in the UMLS. We detected inconsistent mappings by (1) assuming that patients used terms consistently, and (2) MetaMap accurately selecting the best matching concepts the majority of the time. For instance, in our online cancer community dataset, we assumed that patients always used the term “blood test” to convey the “Hematologic Tests” concept (CUI: C0018941), which was how MetaMap interpreted this term two thirds of the time, rather than the less frequent mapping to the “Blood test device” concept (CUI: C0994779). Based on these assumptions, we detected inconsistent mappings in two steps. First, we created a term frequency table based on a term’s spelling and its CUI. Second, assuming the most frequently mapped CUI was the correct concept, we counted the number of cases where the term was mapped to less frequent CUIs.The automated methods detected that at least 49.12%  of MetaMap’s mappings for our dataset were problematic. Word sense ambiguity failures were the most widely occurring, comprising 82.22% among the total detected failures. Boundary failures were the second most frequent, amounting to 15.90% among the total detected failures, while missed term failures were the least common, making up 1.88% of the detected failures. We found that word sense ambiguity failures were not mutually exclusive, and several cases had multiple causes. Thus, in We manually evaluated the performance of our failure detection tool in two parts: overall performance evaluation and individual component level performance evaluation.We randomly selected 50 cases  that our tool identified as incorrect mappings from each of the 12 causes of failures, totaling 600 cases that served as positive cases. We then randomly selected another 600 cases from the rest of the mappings not detected as incorrect mappings according to our tool to serve as the negative cases. We then mixed up the selected 1200 cases and manually assessed the accuracy of mappings through a blind procedure.We also measured individual performance on each of the 12 detection techniques. We used the previously selected 600 negative cases and individual technique’s 50 positive cases to assess the performance. For boundary failure, we examined whether the mapped terms could deliver precise conceptual meaning independent of additional phrases. For missed term failure, we investigated whether the tool had accurately corrected the spellings and verified the results of the new mappings. For word sense ambiguity failures, we examined whether MetaMap appropriately mapped terms based on the rest of the context. The unit of analysis was a single mapping, and we evaluated our results using precision, recall, accuracy, and F1 score. Precision measures the proportion of predicted positive instances that are correct. Recall measures the proportion of positive instances that were predicted. Accuracy measures the percentages of correctly predicted instances among the total number of instances examined. F1 score is the weighted harmonic mean—reflecting both performance and balance—of precision and recall. In all measures, higher scores reflect better performance.Our automatic failure detection tool identified 15.90% of the total failures as due to splitting a phrase. The performance evaluation of this task achieved precision, recall, accuracy, and F1 score of 82.00%, 78.85%, 96.78%, and 80.39%, respectively. It is important to note that a single concept can produce multiple split phrase failures. For instance, the phrase “stage 4 Melanoma” was mapped to three concepts: “stage”, “4”, and “Melanoma”. Two boundary failures occurred in this phrase. The first failure occurred between “stage” and “4”; the second failure occurred between “4” and “Melanoma”. By focusing on a pair of mapped terms at a time, we correctly identified two failures that occurred in the phrase “stage 4 Melanoma”. We considered only adjacent paired mappings because splitting a single coherent phrase into two or more UMLS concepts was clearly a more significant problem. However, split phrase failures could occur in non-paired mappings as well, and we are underestimating the prevalence of split phrases.Less than 1% of failures were due to community-specific nomenclature, and the automatic detection system achieved precision, recall, accuracy, and F1 score of 88.00%, 100.00%, 99.02%, and 93.62%, respectively. It should be noted that we underestimated the number of missed terms because the algorithm  can idenWe automatically assessed that misspellings were responsible for 1.26% of failures. However, we observed few cases of incorrect assessment due to failures of Google’s query suggestion service. For instance, some medications were incorrectly recommended. “Donesaub”, a misspelling of “Denosumab” was mapped to “dinosaur”. Furthermore, even with correct recommendation, MetaMap did not always map to the right concept. For instance, “Wsihng” was correctly recommended to be “Wishing”, but MetaMap mapped it to “NCKIPSD gene”. Despite a few cases of incorrect assessment, the misspelling component performed relatively well, achieving precision, recall, accuracy, and F1 score of 80.00%, 93.02%, 97.88%, and 86.02%, respectively.Improperly mapped abbreviations comprised less than 1% of failures. Although this was seldom, the automatic detection system performed relatively well, achieving precision, recall, accuracy, and F1 score of 82.00%, 95.35%, 98.20%, and 88.17%, respectively.Incorrectly mapped “gene or genome” semantic types comprised 2.21% of failures, and the automatic detection system achieved precision, recall, accuracy, and F1 score of 100.00%, 100.00%, 100.00%, and 100.00%, respectively. With this process, we also detected terms like “lord” and “wish” that may not be perceived as colloquial language. Nevertheless, they were improperly mapped as “gene or genome” semantic type. It is also important to note that different disease-specific communities should utilize different gene dictionaries.Our automatic failure detection tool identified less than 1% of failures as improperly mapped numbers. The performance evaluation of this task achieved precision, recall, accuracy, and F1 score of 100.00%, 100.00%, 100.00%, and 100.00%, respectively. However, we are underestimating this failure prevalence because MetaMap improperly mapped more than half of the “Quantitative Concept” semantic type concepts in our dataset. We did not include this semantic type and underestimated this particular failure because few cases were correctly mapped.Improperly mapped email addresses or URLs comprised less than 1% of failures, and the automatic detection system achieved precision, recall, accuracy, and F1 score of 100.00%, 100.00%, 100.00%, and 100.00%, respectively. It is important to note that the basis for our manual assessments was how patients had intended to use the term. For instance, MetaMap mapped “org” at the end of a URL to “Professional Organization or Group” concept. Although assessment of such cases can be subjective, we followed the basic rule of reflecting patients’ intentions.A total of 1.83% of failures resulted from Internet slang and SMS language terms. Like other dictionary-based matching techniques, our automatic detection system performed relatively well, accomplishing precision, recall, accuracy, and F1 score of 100.00%, 100.00%, 100.00%, and 100.00%, respectively.We automatically assessed that names accounted for 5.34% of failures. However, the name dictionary matching did not perform as well as other dictionary-based matching components. We discovered that unique but popular names, such as “Sunday”, “Faith”, and “Hope” were incorrectly mapped when used as nouns in a sentence. The name dictionary component achieved precision, recall, accuracy, and F1 score of 66.00%, 100.00%, 97.21%, and 79.52%, respectively., we noted it as a different cause of failure due to its high frequency. The automatic detection system accomplished precision, recall, accuracy, and F1 score of 100.00%, 100.00%, 100.00%, and 100.00%, respectively.We found that 32.44% of failures resulted from pronoun “I”. Although the use of the pronoun “I” could be considered a part of colloquial languageWe automatically assessed that mismapped verbs accounted for 27.17% of failures; however, the detecting mismapped verbs component performed poorly, achieving precision, recall, accuracy, and F1 score of 32.00%, 100.00%, 94.43%, and 48.48%, respectively. We discovered that although Stanford POS Tagger has identified verbs correctly, we made the false assumption that verbs did not belong to the entity part of the UMLS ontology. However, verbs like “lost” and “wait” belong to the “Functional Concept” semantic type, which is under the entity part of the UMLS tree. Thus, the detecting mismapped verbs component of our automatic failure detection tool incorrectly identified such verbs as failures.Our automatic failure detection tool identified 15.56% of the total failures due to inconsistent mappings. The performance evaluation of this task achieved precision, recall, accuracy, and F1 score of 66.00%, 53.23%, 92.80%, and 58.93%, respectively. We found two reasons for the relatively low precision. First, we did not account for cases where the most commonly mapped concept is not the correct mapping. For instance, in our dataset “radiation” was mapped to “radiotherapy research” (CUI: C1524021) two-thirds of the time when community members actually meant “therapeutic radiology procedure” (CUI: C1522449). We incorrectly assessed if less frequent mappings were accurate. Second, we missed cases when correct mappings do not exist. For instance, the verb “go” was incorrectly but consistently mapped as “GORAB gene”. In our automated failure detection analysis, our tool overlooked terms like “go” that were consistently mismapped.We characterized (1) boundary failures, (2) missed term failures, and (3) word ambiguity failures and discovered 12 causes for these failures in our manual review. We then used automated methods and detected that almost half of 383,572 MetaMap’s mappings were failures. 82.22% of failures were word sense ambiguity. 15.90% of failures were boundary failure. 1.88% of failures were missed term failures. The automated failure detection achieved precision, recall, accuracy, and F1 score of 83.00%, 92.57%, 88.17%, and 87.52%, respectively.We first discuss challenges of using out-of-the-box biomedical NLP tools, such as MetaMap, to process patient-generated text. We then discuss the contributions and wider implications of our study for research activities that need to manage the constantly changing and overwhelming amount of patient-generated data. We end with summarizing our contributions to the medical Internet research community.Some of these failures are already known problematic failures of MetaMap ,29,34-36We focused our research on MetaMap; however, findings from our study can apply to other NLP tools in a similar manner. Few failure causes, such as inconsistency of word sense disambiguation feature, pertain more to MetaMap than other tools. However, any NLP tools that provide semantic connections require a similar word sense ambiguity feature. Moreover, different NLP tools could excel in different areas, and our automated failure detection can cost-effectively highlight problematic areas. Similarly, our techniques for detecting failures could strengthen the performance of other NLP tools to process patient-generated text and more traditional types of text. For instance, the word sense ambiguity failure caused by neglecting POS information can also be problematic in different types of text, including biomedical literature. That failure might surface less frequently due to differences in sentence structure between the biomedical literature and patient-generated text. Nevertheless, it is a significant problem that applies to both types of text. Applying such POS information when mapping a term could increase the accuracy of the mappings from a variety of texts. Another example is the missed term failure caused by community nomenclature. MetaMap or other NLP tools will miss terms if particular synonyms are missing from the vocabulary source. Researchers could use the algorithm by Schwartz and Hearst  to colleThe dictionary-based matching and NLP techniques used in our detection process were evaluated in previous studies -46. HoweMoreover, a number of updates were made for both the UMLS and MetaMap  since thAlthough our study focused on online health community text, the insights inform efforts to apply NLP tools to process various types of patient-generated text, including blogs or online journals, which share similar narrative writing styles and colloquial language. Moreover, Facebook and email provide conversational interactions similar to the interaction in online health communities. Tweets about emergency responses , public Processing patient-generated text provides unique opportunities. However, this process is fraught with challenges. We identified three types of failures that biomedical NLP tools could produce when processing patient-generated text from an online health community. We further identified causes for each failure type, which became the basis for applying automated failure detection methods using pre-validated NLP and dictionary-based techniques. Using these techniques, we showed the feasibility of identifying common failures in processing patient-generated health text, at a low cost. The value of our approach lies in helping researchers and developers quickly assess the capability of NLP tools for processing patient-generated text.


Declared relationship status on Facebook can serve as a public commitment and as an extra layer of a couple’s security. However, the question arises: do those who report the relationship status feel stronger romantic love and jealousy toward their partners than those who do not share such information publicly? To test this assumption, profile information and questionnaire data of romantic love and jealousy were gathered from 292, 230 females) respondents that were in a relationship. Our results suggest that announcing the relationship status is associated with elevated romantic love and jealousy. Therefore, being “Facebook official” can be interpreted as a tie-sign indicating that the couple is “out of the market,” and can promote their unity as a “digital wedding ring.” For heterosexual American couples who met in 2009, the Internet was one of three most common scenes of meeting. Whereas the proportion of couples whose first meeting was mediated by friends has decreased since the ’90s, the proportion of couples who met online has continuously increased during the last 20 years . Accordi“the highest form of trust”  will report higher scores on (a) romantic love, and on (b) jealousy scales. Furthermore, we aim at measuring how well posting relationship status as “in a relationship” predicts self-reported love and jealousy independently from variables such as gender, length of relationship, and Facebook use Intensity.Hypothesized control variables for love included: jealousy, length of relationship, intensity of Facebook use, gender, and age for the following reasons. Jealousy as a control variable can be taken into account on the basis of previous studies , as it iIn the case of jealousy we chose intensity of Facebook use, gender, and love as control variables. On the basis of M = 24.88, SD = 6.94). Respondents were recruited by posting an online survey link on Facebook. Among them, we chose a subsample  ranging between 16 and 69 years-old  who, based on the online survey, were currently in a relationship. In order to assess relationship status, respondents of the online survey answered the following question: “Are you currently dating with a romantic partner and/or living in a relationship?” by choosing the following possibilities: “No” , “Rather no” , “Rather yes” , “Yes” . We only analyzed the data of the individuals who answered “Yes” to this question. In this subsample 66 (22.6%) respondents displayed nothing in their relationship status, 8 (2.7%) displayed “Single,” 179 (61.3%) displayed “In a relationship,” 12 (4.1%) displayed “Engaged,” and 27 respondents (9.2%) displayed “Married.” In the regression analysis, respondents belonging to the group are those who indicated a relationship status on Facebook, by announcing either “In a relationship” or “Engaged” or “Married.” There were no gender differences regarding proportions of shared relationship statuses. The average length of the respondents’ relationship was 37.83 months (SD = 48.19). Nine respondents (3.6%) had an elementary school degree, 13 (17.4%) had a vocational school degree, 150 (59.3%) had a high-school degree, and 81 (32%) had a higher education degree. Among them only seven participants’ partner has not got a Facebook profile.The total sample consisted of 532 Hungarian participants , aged between 16 and 69 years . The Hungarian version has one factor and it contains eight items 3. While this scale was constructed for being factorically unitary, it covers three aspects of romantic love: (a) affiliative and dependent need, (b) predisposition to help, (c) exclusiveness and absorption. The second scale is the Hungarian version of Interpersonal Jealousy Scale includes 12 items. It contains three factors: jealousy toward ex-partner (I become sad if I see a picture about X and his/her ex-partner), exclusivity , and anticipated infidelity [I don’t believe that if X would flirt with someone from opposite sex (R)]. In the present study, the subscales were not separated. Instead, we used the aggregated scores of the three factors 4. The last scale is Facebook Intensity Scale  with three different Likert-type scales  following ale FBI; , which at-tests were performed to examine differences between participants who are in relationship but who do not declare their relationship status on Facebook and those respondents who declared their relationship on Facebook. In line with our assumptions, results indicate significant differences between the two groups on Romantic Love Scale and Interpersonal Jealousy Scale (see Table 1).First, independent Later, a hierarchical multiple regression analysis was conducted to evaluate how well posting relationship status as “in a relationship” predicts the results on R2 = 0.11, adjusted R2 = 0.09, F = 5.63, p < 0.001. Only Jealousy t(288) = 4.03, p < 0.001 scores of the control predictors accounted for a significant amount of love scores variation, R2change = 0.08, Fchange = 4.99, p < 0.001, whereas sharing one’s relationship status t(288) = 2.86, p = 0.005 accounted for a significant amount of variance of love scores R2change = 0.03, Fchange = 8.16, p = 0.005 (Table 2). After controlling for the effects of Facebook Intensity scores, jealousy made a small, but significant independent, contribution to the variance in love scores. The multiple regression results suggest that beyond the effect of jealousy, those individuals who declare their relationship on Facebook report a more elevated love than those who are in a relationship but do not display their togetherness on Facebook.The control predictors and the relationship status together relate significantly to love scores 5, while Block 2 contained relationship status as a dummy variable . Only those predictors were included in the analysis which significantly correlated with jealousy scores.Another Hierarchical multiple regression analysis was conducted from relevant control variables and relationship status in order to predict scores on Interpersonal Jealousy Scale. In this analysis, Block 1 included gender as a dummy variable , Facebook intensity scores and scores on the These control variables were chosen because, on the basis of R2 = 0.11, adjusted R2 = 0.09, F = 8.55, p < 0.001. Gender t(288) = 2.64, p = 0.009 and Love t(288) = 3.49, p = 0.001 as control predictors accounted for a significant amount of jealousy scores, whereas tendency was measured in the case of Facebook Intensity t(288) = 1.71, p = 0.09; R2change = 0.09, Fchange = 9.81, p < 0.001. However, declaring a relationship status t(288) = 2.10, p = 0.037 is also accounted for a significant amount variability of jealousy scores R2change = 0.02 Fchange = 4.41, p = 0.037 (Table 3) .The regression equation was significant regarding jealousy, 6.These results suggest that declaring the relationship status publicly offers a small additional, independent predictive power of Interpersonal Jealousy scores beyond that contributed by control predictors. Therefore, the multiple regression results suggest that women, intensive Facebook users, and individuals who experience elevated love are more jealous. In addition to these effects, those who report their relationship status experience more jealousy compared to those who are in a relationship but do not indicate it on Facebookp > 0.23; jealousy model p > 0.89).We also examined further interactive effects of Facebook intensity scores, gender, length of relationship, age, love, and jealousy (respectively) on love and jealousy scores but none obtained statistical significance . Other limitations are lack of information about their partners and small effect sizes. Furthermore, it would be interesting to see quantitatively in what form expectations from the partner and norms of peer groups have an impact on declaring relationship status on Facebook. Considering the shortcomings of the present study, future research should examine these questions with alternative measures, such as multidimensional love scales  and In summary, those who declare their relationship on Facebook—with or without the name of the partner—report stronger romantic love toward their partner than those who are not “Facebook official.” In addition to the stronger love, they also report higher levels of jealousy, which can indicate their intentions to protect the relationship. We conclude that disclosing relationship status is a modern, online tie-sign. It can be interpreted as an expression of commitment which reflects on a new, more stable phase of the relationship with stronger romantic love and jealousy. Therefore, being a Facebook official can be interpreted as a “digital wedding ring” or one of the virtual maturity indicators of the advancement in a relationship.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Here, we report the results of a study on the effects of ethanol exposure on human red blood cells (RBCs) using quantitative phase imaging techniques at the level of individual cells. Three-dimensional refractive index tomograms and dynamic membrane fluctuations of RBCs were measured using common-path diffraction optical tomography, from which morphological ; biochemical (hemoglobin (Hb) concentration and Hb content); and biomechanical (membrane fluctuation) parameters were retrieved at various concentrations of ethanol. RBCs exposed to the ethanol concentration of 0.1 and 0.3% v/v exhibited cell sphericities higher than those of normal cells. However, mean surface area and sphericity of RBCs in a lethal alcoholic condition (0.5% v/v) are not statistically different with those of healthy RBCs. Meanwhile, significant decreases of Hb content and concentration in RBC cytoplasm at the lethal condition were observed. Furthermore, dynamic fluctuation of RBC membranes increased significantly upon ethanol treatments, indicating ethanol-induced membrane fluidization. Ethanol  effects on human physiology is an important research topic, given the pervasiveness of alcohol abuse and the myriad of health and social problems associated with heavy drinking. Ethanol and similar anesthetic drugs cause significant alterations of cells, tissues, and organs. In particular, ethanol exposure induces cell membrane remodeling in various cell types and lipid vesicles, including membrane fluidization. Ethanol-induced membrane fluidization, in particular, has been extensively explored using electron paramagnetic resonance (EPR) spectroscopy –3 and flin-vitro ethanol exposures ·Δφ, where 〈Δn〉 is the mean RI difference between the sample n and the surrounding medium nmedium: 〈Δn〉 = 〈n〉 − nmedium, which was obtained from a 3-D RI tomographic measurement. Details about the field retrieval algorithms, the MatLab code for 3-D reconstruction, and the regularization algorithms can be found elsewhere . The SI has a value of ‘1’ for a perfect sphere and ‘0’ for a flat disk.Cell volume n〉 is linearly proportional to [Hb] via the following equation: [Hb] = 〈Δn〉/α where α is a refraction increment and has a value of 0.2 mL/g for Hb , was calculated from a measured RI tomogram assuming that cytoplasm mainly consists of Hb solution. The mean RI difference between the cell and its surrounding medium 〈Δg for Hb , 57. Theh − 〈h〉temporal)2〉temporal]1/2, from which RBC membrane fluctuation can then be obtained by averaging over the cell area. The details of the analysis procedure can also be found elsewhere [Dynamic fluctuations in RBC membranes show strong correlation with cell deformability –60 and ilsewhere .p-values in texts were adjusted by Bonferroni correction and the numbers follow the ± sign are standard deviations. The threshold of family-wise error rate was set to be 0.05 in deciding statistical difference.One-way ANOVA was performed for comparing RBC parameters between various ethanol concentrations based on Bonferroni adjustments. All the presented


A. fistulosum L.Cytoplasmic male sterility (CMS) is important for exploiting heterosis in crop plants and also serves as a model for investigating nuclear–cytoplasmic interaction. The molecular mechanism of cytoplasmic male sterility and fertility restoration was investigated in several important economic crops but remains poorly understood in the Welsh onion. Therefore, we compared the differences between the CMS line 64-2 and its maintainer line 64-1 using transcriptome sequencing with the aim of determining critical genes and pathways associated with male sterility. This study combined two years of RNA-seq data; there were 1504 unigenes (in May 2013) and 2928 unigenes (in May 2014) that were differentially expressed between the CMS and cytoplasmic male maintainer Welsh onion varieties. Known CMS-related genes were found in the set of differentially expressed genes and checked by qPCR. These genes included F-type ATPase, NADH dehydrogenase, cytochrome c oxidase, etc. Overall, this study demonstrated that the CMS regulatory genes and pathways may be associated with the mitochondria and nucleus in the Welsh onion. We believe that this transcriptome dataset will accelerate the research on CMS gene clones and other functional genomics research on  Allium fistulosum L.) is cultivated worldwide from tropical Asia to Northeast Asia. Because it is rich in propylene sulfide having bactericidal and anti-inflammatory effects, the Welsh onion has also been used as an herbal medicine for diverse diseases, such as febrile disease, headache, diarrhea, abdominal pain, eye-related disorders, and habitual abortion [As an important vegetable crop, The Welsh onion  [CMS is a phenotypic trait that is widespread among plants and results in the inability of a plant to produce viable pollen , using Culations ,5, crossulations ,7, physiulations , and plaulations ,10. As eulations . Thereafosum L.) . It has osum L.) ,14,15.A. fistulosum has been reported [Rf) locus were successfully developed [Recently, the chromosomal location of the pollen fertility-restoring gene in reported . Furtherreported ,17,18. Seveloped . HoweverA. fistulosum.Some studies have shown that male-sterility-inducing factors are present in mitochondrial genomes , while oTM 2000. After stringent quality assessment and data filtering, we generated 57.19 million read pairs that corresponded to 11.55 Gb of sequence data in May 2013, and 56.82 million read pairs that corresponded to 11.48 Gb of sequence data in May 2014  and 8201 of 70,360  unigenes were annotated with COG in each assembly . The uniWe applied GO terms to classify the functions of the assembled transcripts and describe gene products. A total of 20,599 unigenes (819 different unigenes in May 2013) and 17,541 unigenes (1201 different unigenes in May 2014) were assigned to 56 functional groups using GO assignment. The major different sub-categories within each of the three main categories  of the GO classification are shown in +-translocating)”, “ND5” (NADH dehydrogenase 5), “COX2” (cytochrome-c oxidase 2), and “F-type ATPase α” (Eukaryotes) were related to down-regulated genes, whereas “inorganic diphosphatase”, “ATP phosphohydrolase” (H+-exporting), and “F-type ATPase E” (Eukaryotes) were related to up-regulated genes. “ATP phosphohydrolase” (H+-transporting) was related to both up- and down-regulated genes.To categorize the biological functions of the DEGs, we performed a KEGG pathway enrichment analysis. The DEGs that significantly enriched pathways in May 2013 and May 2014 were the “plant-pathogen interaction” pathway, followed by the “oxidative phosphorylation” and “plant hormone signal transduction” pathways. In addition, pathways such as “starch and sucrose metabolism”, “glycerophospholipid metabolism”, and “protein processing in endoplasmic reticulum” composed a large component of the DEGs with pathway annotation . In the Cucumissativus plasma membrane ATPase 4-like” (LOC101221564) (c116086), “Hibiscus cannabinus cultivar P3B cytochrome c oxidase subunit II (cox2) gene” (c175619), “Allium cepa cultivar saski cytochrome oxidase subunit 3 (cox3) gene, complete cds; mitochondrial” (c159049), and “Allium cepa cultivar saski NADH dehydrogenase subunit 1 (nad1) gene, partial cds; and ATPase α subunit (atpA) gene, complete cds; mitochondrial” (c160965).This study utilized RNA-seq technology to explore CMS related genes between a CMS line and its maintainer line in the Welsh onion. All of the DEGs obtained from two years of RNA-seq data were investigated by BLAST in the NCBI database, and 559 unigenes were predicted to associate with CMS. Due to the DEGs that significantly enriched oxidative phosphorylation pathways in May 2013 and May 2014, the mitochondrial respiratory chain enzymes and enzyme complexes in oxidative phosphorylation pathways were important to CMS lines in other plants ,23,24. TIn this study, we profiled the Welsh onion transcriptome using the Illumina HiSeq 2000 platform, and classified the functions of the unigenes using the COG and GO classification and pathway enrichment analysis. A total of 1504 unigenes (in May 2013) and 2928 unigenes (in May 2014) were differentially expressed between the CMS line and its maintainer line in the Welsh onion.In this study, we found many DEGs enriched in oxidative phosphorylation of the mitochondrial respiratory chain. The GO terms that identified the DEGs were related to CMS, i.e., “mitochondrion”, “mitochondrial respiratory chain”, “phosphorylation”, and “oxidation-reduction process”, and the cluster frequency of these DEGs was >5% . The DEGs from the pathway analysis also showed many enzymes associated with the respiratory chain, and the regulation of numerous enzymes enriched in the “Oxidative phosphorylation” pathway, which is consistent in the RNA-seq data from both years.+-ATPase [orf 456, was found to be inserted into the 3′-end of the coxII gene, and it may change the sterile characterization in the CMS line [WA352 encodes a protein that interacts with the nuclear-encoded mitochondrial protein COXII, thus causing premature tapetal programmed cell death and consequent male sterility [+-translocating)”, “ND5” (NADH dehydrogenase 5), “COXII” (cytochrome-c oxidase II), and “F-type ATPase α” (Eukaryotes). Two unigenes, c175619 and c159049, were annotated in “Energy production and conversion (COG)”; the unigene c116086 was identified as a plasma membrane ATPase (Swiss-Prot annotation), and the unigene c160965 was an ATPase α subunit (atpA) gene (nr annotation). In our previous study, the analysis of Two-Dimensional Electrophoresis results showed the ATPase α subunit was specifically expressed in the CMS line compared with the cytoplasmic male maintainer line (unpublished data). Based on the above research, we conjectured that CMS was closely related to the respiratory chain enzymes and enzyme complexes.In electron transfer system of mitochondrial respiratory chain, the respiratory chain enzymes and enzyme complexes  play important roles in plants, their major function is to link electron transfer with proton translocation out of the mitochondrion, generating a transmembrane proton motive force that subsequently drives ATP synthesis by H+-ATPase . In addi+-ATPase ,23. In tCMS line . Luo et terility . The F-tterility . In thisIn addition, more DEGs genes were obtained in the nucleus than in the mitochondrion: their cellular component functions (GO) were clustered in “nucleus” (Cluster frequency 25.42% in May 2013 and 13.43% in May 2014), “cytoplasmic membrane-bounded vesicle” (Cluster frequency 13.84% in May 2013 and 16.24% in May 2014), and “membrane” (Cluster frequency 15.68% in May 2013 and 12.70% in May 2014). In the COG functional classification, the “replication, recombination and repair” (Cluster frequency 10.22% in May 2013 and 13.27% in May 2014) and “transcription” (Cluster frequency 10.02% in May 2013 and 11.14% in May 2014) were the largest groups, and the processes of gene transcription mainly occur in the nucleus. Some research has reported that the sterility is caused by chimeric mitochondrial genes regulated by nuclear genes ,27,28,29Two Welsh onion varieties (termed 64) were used in this study: 64-1, a CMS mutant in natural populations, and 64-2, a cytoplasmic male maintainer line, which when bred with 64-1 can produce fertile offspring . Both liAll of the datasets from the Illumina sequencing platform can be found in the Short Read Archive (SRA) database of the National Center for Biotechnology Information (NCBI) under accession number SRP071555.Q-value ≤5 [The Bcl2fastq software was used for pre-processing. The raw reads were cleaned by removing the adaptor sequences, duplication sequences, low-quality reads (reads with ambiguous bases “N”), and reads with more than 50% of the bases with a value ≤5  to obtaivalue ≤5  was usedvalue ≤5  was usedvalue ≤5  was usede-value of 10−5. The unigenes were aligned to the COG and KEGG databases to predict and classify the possible functions and metabolic pathways of the unigenes. For differential gene expression analysis, Differentially expressed genes were identified using DESeq software [The unigenes were aligned with the NCBI’s nucleotide (nt) databases and non-redundant (nr) protein, the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database, the Swiss-Prot protein database, and the Cluster of Orthologous Groups (COG) database. The Blast2GO program was used to assign GO terms . The BLAsoftware ,39 in eahttp://frodo.wi.mit.edu/primer3/), are shown in The cDNA was synthesized from the flowering stage of the Welsh onion with total RNA in a 10-µL reaction system using PrimeScript II RTase . The reverse transcription reaction mixture contained 5 µL of total RNA (0.8 µg), 3 µL of diethylpyrocarbonate (DEPC) water, 1 µL of oligo dT (50 µM) and 1 µL of dNTP (10 mM), and the reaction was performed following the kit protocol. Three biological replicates and three technical replicates for each experiment were performed. The qPCR reactions were performed in 96-well plates on the LightCycler 480 instrument  using SYBR Green I . The qPCR primers, which were designed using Primer3 (c oxidase were observed by RNA-seq and validated by RT-qPCR. These genes were not only closely related to mitochondria but also to the nuclear–mitochondria interaction. Further functional characterization is in progress to determine the specific functions of these genes in relation to CMS in Allium fistulosum L. This study will accelerate the research on CMS gene clones and other functional genomics research on A. fistulosum.This study investigated the important CMS-related genes and pathways in the Welsh onion by transcriptome analysis. Several key genes such as F-type ATPase, NADH dehydrogenase, and cytochrome


Metapopulation dynamics are jointly regulated by local and spatial factors. These factors may affect the dynamics of local populations and of the entire metapopulation differently. Previous studies have shown that dispersal can stabilize local populations; however, as dispersal also tends to increase spatial synchrony, its net effect on metapopulation stability has been controversial. Here we present a simple metapopulation model to study how dispersal, in interaction with other spatial and local processes, affects the temporal variability of metapopulations in a stochastic environment. Our results show that in homogeneous metapopulations, the local stabilizing and spatial synchronizing effects of dispersal cancel each other out, such that dispersal has no effect on metapopulation variability. This result is robust to moderate heterogeneities in local and spatial parameters. When local and spatial dynamics exhibit high heterogeneities, however, dispersal can either stabilize or destabilize metapopulation dynamics through various mechanisms. Our findings have important theoretical and practical implications. We show that dispersal functions as a form of spatial intraspecific mutualism in metapopulation dynamics and that its effect on metapopulation stability is opposite to that of interspecific competition on local community stability. Our results also suggest that conservation corridors should be designed with appreciation of spatial heterogeneities in population dynamics in order to maximize metapopulation stability. One important question in spatial ecology is how spatial coupling of local populations affects the dynamics and stability of metapopulations. Natural populations undergo various sources of stochasticity and fluctuate permanently over time . A commoAt the local scale, theoretical and empirical studies have widely documented that dispersal can decrease population variability and hence local extinction rate through spatial averaging or rescue effects . HoweverRecently, we have developed a general framework that links population or ecosystem stability across multiple scales . When apIn this study, we use simple metapopulation models to investigate analytically how dispersal, in interaction with other factors, regulates temporal variability at different scales. In all our models, local populations have feasible and stable equilibria in the absence of environmental stochasticity; however, due to environmental stochasticity, populations fluctuate permanently around these equilibria. This assumption allows us to analyze our models with the linearization approximation (see Methods). We first study a homogeneous metapopulation in which local  and spatial  parameters are all identical among patches. In this case, we derive analytic formulae that quantify the local stabilizing and spatial synchronizing effects of dispersal. Interestingly, we show that these two effects cancel each other out, such that dispersal has no net effect on metapopulation variability. We then consider more general cases with spatially heterogeneous population dynamics and/or asymmetric dispersal rates. Spatial heterogeneities in the environment  can result in variation in population dynamics among patches . In addim local patches. The dynamics of local populations are governed by density-dependent growth and density-independent dispersal between patches. We use a continuous-time model as follows: iN(t) represents the population size (or biomass) in patch i at time t, ir and ik represent the intrinsic growth rate and carrying capacity in patch i, respectively, and id represents the rate for each individual in patch i to immigrate into other patches. Here, we consider an implicit spatial structure so that individuals from patch i have equal probabilities to reach any other patch (id/(m − 1)). The random variables εi(t) represent environmental stochasticity in the growth rate of population i at time t. For simplicity, we assume that the environmental stochasticity is independent through time . The spatial correlation of the white-noise variables εi(t) are characterized by the covariance matrix Vε, for which we assume the following symmetric structure: Vε = σ2 for any i and Vε = ρσ2 for any i ≠ j  = N* and εi(t) = 0, J is the Jacobian matrix, and the equilibrium is locally stable when r > 0 \docFor general cases with heterogeneous local and spatial parameters, we analyze two-patch metapopulation models using similar procedures as above see . First, trix see .The covariance matrices are then used to calculate temporal variability at the population and metapopulation scales . To evaluate the accuracy of our linearized solutions, we also perform stochastic simulations based on the nonlinear CV), i.e., the ratio of the standard deviation to the mean, to measure variability. For instance, the temporal CV of population i is: CV: cvγ) is defined as the square of the temporal CV of total metapopulation size: β = 1/pφ, where the spatial synchrony is defined as: Within a metapopulation, variability can be measured at alpha, beta, and gamma scales, which correspond to local population variability, spatial asynchrony among local populations, and metapopulation variability, respectively . All thecvγ = cvα ⋅ pφ = cvα/β  synchrony as follows:  = αcv/β . For theDα) can be defined as the ratio of alpha variability without dispersal to that with dispersal (Dφ) can be defined as the ratio of spatial synchrony with dispersal to that without dispersal: Dα and Dφ quantify the local stabilizing and spatial synchronizing effects of dispersal, respectively. The effect of dispersal on metapopulation stability (Dγ) is determined by the relative magnitudes of these two effects, i.e., Dγ is larger than 1, the local stabilizing effect is larger than the spatial synchronizing effect, and thus dispersal decreases metapopulation variability. Otherwise, dispersal increases metapopulation variability.As demonstrated by previous studies, dispersal can simultaneously provide stabilizing and synchronizing effects on local population dynamics . The disispersal : \documeDα) is always identical to the dispersal-induced synchrony (Dφ); both equal the ratio of spatial population synchrony (pφ) to the synchrony of environmental responses  see : (7)\docρ) and the number of patches (m) affect the multi-scale variability mainly through their effects on the spatial synchrony of population environmental responses , alpha and gamma variability both increase, and the beta variability decreases (r) increases, the temporal variability at alpha and gamma scales all decrease  to affect local alpha variability and for the local parameter (r) to affect spatial asynchrony. When there is no dispersal (d = 0), alpha variability is independent of ρ and m, and spatial asynchrony is independent of r  generally increase gamma variability (k) has faster local dynamics (larger r), such heterogeneity can contribute to reducing gamma variability if environmental responses are highly synchronous (k), dispersal can be destabilizing, particularly when environmental responses are highly synchronous  homogeneous, asymmetry in dispersal rates generally increases gamma variability (r), its dominance may contribute to reducing gamma variability, especially when environmental responses are highly synchronous , dispersal can provide stabilizing effects through interacting with nonlinearity and spatial heterogeneity . Our mod10.7717/peerj.1295/supp-1Supplemental Information 1Appendix 1, Continuous-time models and their analytic solutions in homogeneous metapopulations. Appendix 2, Continuous-time models with spatial heterogeneity. Appendix 3, Discrete-time models and their analytic solutions in homogeneous metapopulations. Appendix 4, Environmental stochasticity beyond white noise.Click here for additional data file.10.7717/peerj.1295/supp-2Figure SA1m = 2, r = 0.5, σ2 in , d in , and ρ = − 0.9, 0 or 0.9.The effect of dispersal and the variance of environmental stochasticity on the gamma variability of homogeneous metacommunities, based on stochastic simulations  and analytic solutions from linear approximations . Note that (A) and (B) are of same scale, (C) and (D) are of same scale, and (E) and (F) are of same scale. Parameters: Click here for additional data file.10.7717/peerj.1295/supp-3Figure SA2eφ = 0). The two patches differ in their intrinsic population growth rate (r) and/or carrying capacity (k), where a larger s indicates a higher heterogeneity. Note that the patterns of gamma variability (cvγ) have been shown in Effect of spatial heterogeneities in local dynamical parameters and of (symmetric) dispersal rate on the multi-scale variability of two-patch metapopulations when environmental responses are perfectly asynchronous . The two patches differ in their intrinsic population growth rate (r) and/or carrying capacity (k), where a larger s indicates a higher heterogeneity. Note that the patterns of gamma variability (cvγ) have been shown in Effect of spatial heterogeneities in local dynamical parameters and of (symmetric) dispersal rate on the multi-scale variability of two-patch metapopulations when environmental responses are perfectly synchronous . Note that the patterns of gamma variability (cvγ) have been shown in  when environmental responses are perfectly asynchronous . Note that the patterns of gamma variability (cvγ) have been shown in  when environmental responses are perfectly synchronous (shown in –5F.Click here for additional data file.


Two thousand five hundred meter elevation in the Swiss Alps. Foliar δ13C and δ15N varied among species by about 3–4‰ and 7–8‰ respectively. However, there was no overall difference in means of δ13C and δ15N for species sampled in different plant communities or when bulk plant dry matter harvests of different plant communities were compared. δ13C was found to be highly species specific, so that the ranking among species was mostly maintained across 11 habitats. However, δ15N varied significantly from place to place in all species (a range of 2.7‰) except in Fabaceae  and Juncaceae (Luzula lutea). There was also a substantial variation among individuals of the same species collected next to each other. No difference was found in foliar δ15N of non-legumes, which were either collected next to or away from the most common legume, T. alpinum. δ15N data place Cyperaceae and Juncaceae, just like Fabaceae, in a low discrimination category, well separated from other families. Soil δ15N was higher than in plants and increased with soil depth. The results indicate a high functional diversity in alpine plants that is similar to that reported for low elevation plants. We conclude that the surprisingly high variation in δ13C and δ15N signals in the studied high elevation plants is largely species specific (genetic) and insensitive to obvious environmental cues.Stable carbon and nitrogen isotope signals in plant tissues integrate plant-environment interactions over long periods. In this study, we hypothesized that humid alpine life conditions are narrowing the scope for significant deviations from common carbon, water and nitrogen relations as captured by stable isotope signals. We explored the variation in δ Kenya, and the northwestern Argentinean Andes, showed very similar δ13C values despite a decline in precipitation from >3000 mm a-1 (δ13C: -25.9‰) to 300 mm a-1 (δ13C: -26.3‰), thus revealing no differentiation by water relations  within such high elevation habitats  obtained a high proportion of their N from N2 fixation and played an important role in N cycling in the alpine site of Niwot Ridge in the Rocky Mountains. There are also other symbiotic N2 fixing associations in alpine vegetation, for example, actinomycorrhizal ones by Dryas species and subalpine Alnus , we (2) hypothesized that the balance of carbon and water relations as expressed in δ13C will result in a narrow signal range, at high absolute signals compared to low elevation, due to the 25% reduced atmospheric pressure. Based on previous studies, we (3) expected to find substantial differences in δ15N between legumes and non-legumes, and between early and late successional plant communities, but little additional variation within those categories, given the otherwise common life conditions. In addition, we (4) expected a strong influence of legumes on the δ15N signals of their non-legume neighbors. Finally, we had a methodological aim: we wanted to identify variation (and its causes) that may also influence the outcome of random sampling of isotope signals such as for instance from herbaria, where no information on site conditions, neighborhood, replication and often no choice in tissue is given. We, thus, systematically explored the influence of tissue type, variation among individuals sampled from the very same location, variation across species in interaction with different sites, community means for different community types, bulk biomass, and soil organic matter (SOM).The primary objective of this study was to separate potential phenotypic (environment induced) from genotypic (phylogenetic) causes of carbon and nitrogen isotopic composition of plant species at high elevations. We (1) hypothesized that the variation in isotope discrimination is largely genotypic, given the supposed uniformity of the physical environmental conditions. Given the overall humid conditions in the test region  at Furka pass  in locations between 2430 and 2500 m a.s.l. in the upper Reuss catchment  in July 2012. In 2012, snowmelt started in early to mid June and the season ended with plant senescence between early and mid September.Samples were collected in the vicinity of the ALPFOR Research Station . Annual precipitation varies around 1900 mm, with ca. 500 mm falling during the growing season (mid June to mid September), always exceeding evapotranspiration . Soils uin situ interaction with other plants, soil, and microbes (mycorrhizae), while belonging to the same metapopulation.In this project we adopted a ‘quasi-common-garden’ approach, by sampling the same species or individuals of the same species from homogenous sampling plots at different positions  in an alpine landscape. This way, we can exclude significant environmental variation within a given sampling plot, cover a spectrum of different plant communities (including the same target species) at otherwise minor climatic and moisture contrasts among plots, and account for potential genotypic variation and legume-neigbourhood effects. In contrast to classical common gardens, the plants sampled were not transplanted but had a long history of Figure 1), three types of which were chosen that encompass typical plant communities such as Nardus-rich heathland (P1.1 and P1.2), Festuca violacea meadows (P4.1 and P4.2) and snowbed communities , one rush (Luzula lutea), one legume , two forbs , and one grass  were collected from 6 to 8 additional sites along a ca. 2 km transect across varied alpine terrain in the same region , including three of the permanent plot locations . In order to investigate the impact of N2-fixing species on foliar 15N of adjacent non-N2-fixing species, leaves of three individuals of each of 6–8 non-N2-fixing species were collected in plots P1.1 and P1.2, co-occurring near (<10 cm) a patch of one common legume . Except when we studied individuals, we pooled leaves for each species from each sampling location .For foliar sampling, 3–5 healthy and green leaves from each of 3–5 (mostly 5) individuals per plant species were collected from a sampling location. In general, the species sampled were representative and important for each studied plant community  were used at the edge of each of the permanent plots. On each subplot, plants were clipped to 1 cm above ground, leaving the bottom moss layer untouched. Above-ground dry matter was then sorted into live (biomass), standing dead (necromass) and litter fractions. From the same subplot, a central soil core was extracted down to 30 cm depth using a 2.5 cm diameter metal corer. For each soil profile, segments at depths down to 2.5, 5, 10, 15, 20, 25, and 30 cm were bagged separately. The soil samples were sieved (<1 mm) after roots had been removed.C. foetida, C. sempervirens, Nardus stricta, and T. alpinum) and collected three individuals from each species close to plot P8.2. For each plant individual, different organs including terminal (older) and lower (younger) parts of green leaves , dead leaves, flowering stalks, fruit heads, seeds and roots were collected.To assess the C and N isotopic compositions of different organs within a species, we have chosen four common species in the study region  soil samples were ground manually with a mortar, and 4–5 mg was weighed into tin capsule.13C and15N) of plant and soil materials were determined using an isotope ratio mass spectrometer . Isotope ratios were calculated as:The isotopic signature  and the free atmosphere (15N), serving as references.where R13C and δ15N among different organs within one plant species were examined using one-way ANOVA. Differences in δ13C and δ15N values between any two organs were examined using post hoc multiple comparison (LSD). Differences in δ13C and δ15N among species co-occurring in P1.1 and P1.2 in the Nardus heathland were examined using one-way ANOVA. Differences among mean values of foliar δ13C and δ15N of species in Cyperaceae , Juncaceae (L. lutea) and Fabaceae  and species of other families co-occurring in P1.1 and P1.2 were examined using the Independent Samples Test. Difference in mean amplitude of δ13C and δ15N values for all examined species between P1.1 and P1.2 was examined using the Independent Samples Test. Difference in mean values of foliar δ13C and δ15N for the sampling sites at the six permanent plots, and among the 8 or 11 sites sampled along the transect, were examined using one-way ANOVA. The difference in foliar δ13C and δ15N between any two sites of the transect was examined using post hoc multiple comparison (LSD). For comparing differences in values of foliar δ13C and δ15N of non-N2-fixing species near and away from legumes , data of foliar δ15N and δ13C in three individuals of each non-N2-fixing species was averaged for both positions and tested using the Independent Samples Test. Data for bulk community dry matter and each depth of the soil profile from different permanent plots but belonging to the same type of plant community were combined because signals between the two samples within a plot varied similarly than among plots yielding four replicates per habitat type. Then differences in bulk community dry matter were examined across the three types of communities using one-way ANOVA. Differences in δ13C and δ15N values of bulk green biomass and means for all species studied individually either with or without legumes in each plant community were examined using one-way ANOVA and post hoc multiple comparison (LSD). Differences in soil δ13C and δ15N were examined across the three plant community types and the different soil depths using the general linear model (GLM) procedure. These analyses were done using the SPSS package .Differences in δ13C was found in C. foetida (p = 0.26) and C. sempervirens (p = 0.48), although, some post hoc pairwise tests for individual organ types showed a difference in C. foetida . However, the organ effect on δ13C was significant in N. stricta (p < 0.001), and marginally significant in T. alpinum (p = 0.07), with fruits, seeds, roots, and flowering stalks generally less negative than green foliage . No statistically significant organ effect in δ15N was found in the four studied plant species, but again, pairwise tests revealed some significant contrasts . For δ15N, there was a trend for roots being different from the rest, but with no clear direction (and no such trend in Trifolium). The range among different means for different organs in the same species varied from 0.5 to 2.5‰ in δ13C, and from 2 to 4.5‰ in δ15N, hence can be substantial, and for each species, the difference in δ15N between green leaves and other organs was wider than in δ13C . In the following we will report green leaf data only.Overall, no statistically significant organ effect on δ13C of ca. 1.0‰ among three individuals grown next to each other . That variation was consistent across two different test sites (P1.1 and P1.2) in the Nardus heathland. The single highest deviation in δ13C signals among the three tested individuals per location was 1.6 and 1.5‰, with a mean range size (amplitude) for all examined species of 1.1 and 0.9‰ for P1.1 and P1.2, respectively. In δ15N signals, consistent across sites, one individual may differ from another one sampled at the same location (separated by a few centimeters) by up to 1.5 and 2.1‰, with a mean amplitude among three individuals of 1.1 and 1.3‰ for P1.1 and P1.2, respectively . However, no significant difference was found in mean amplitude of δ13C and δ15N values for all examined species between P1.1 and P1.2.In green leaves of 7–9 species, there was a consistent intra-specific variation in δ13C and δ15N was found across all 11 sites where the four species P. alpina, Geum montanum, Homogyne alpina, and L. lutea jointly occurred . When site effects were tested site by site, foliar δ13C at site S11 (a steep meadow close to a glacier fore-field) showed significantly  less negative signals than site S1, S2, and S3 . Additionally, foliar δ13C at site S5 were marginally less negative than foliar δ13C at site S3. Also foliar δ15N made site S11 special, as leaves had significantly  more negative δ15N than these collected in sites S2, S3, S4, and S6 . Similarly, no overall differences in species-specific foliar δ13C and δ15N were found across those eight sites where the same six species co-occurred . Tested site-by-site, similar differences emerged as for the four-species-comparison reported above.No significant difference in foliar δ13C, Trifolium and Carex were consistently less negative than Geum, Poa, and Luzula, with Homogyne, in contrast, changing from -29‰ at S1 to -25‰ at S11 . In δ15N, Trifolium, Carex, and Luzula were consistently less negative than Geum, Poa, and Homogyne. However, the difference from one location to the other may reach 2.7‰ (3.8‰ in Homogyne) in these species, except for Trifolium and Luzula, which hardly varied . Disregarding Homogyne, the mean site to site variation in the remaining four species was >0.5 and <1‰, with most of the variance produced by site 11 . Similarly, four out of the seven species co-occurring in both sampling plots in the Nardus heathland showed significant  differences in δ15N between P1.1 and P1.2 . However, no difference was found in foliar δ13C between P1.1 and P1.2. Therefore, the intra-specific amplitude of changes between locations (P1.1 and P1.2) was wider for foliar δ15N than for foliar δ13C, in particular for species classified as ‘other families’ .In δ13C values of co-occurring species in the three alpine plant communities tested varied from -25.5‰ to -29.5‰, with mean signal ranges per community of around 3.2, 3.8, and 4.4‰ for the Nardus-rich heathland (plots P1.1 and P1.2), the F. violacea meadow (plots P4.1 and P4.2) and the snow-bed community (plot P8.1 and P8.2), respectively . Variation in δ15N within each community was even wider . In alpine Nardus-rich heathland, the δ15N values ranged from 1.4‰  to -6.9‰ , a range of 8.3‰. After excluding data of Trifolium, sedges and rushes, the δ15N value of co-occurring species in this plant community still ranged from about -6.9 to -0.6‰. For the F. violacea rich meadows, the amplitude covered a range in δ15N of 7.3‰ irrespective of whether data of Trifolium, sedges and rushes were excluded or not. For the snow-bed community, the amplitude was similar at 7‰ . The δ15N values of most forbs and grasses were lower (more negative) and varied more widely than those in sedges , rushes , and legumes . Similarly, significant differences in δ13C and δ15N were found among species occurring in both Nardus heathland plots (p < 0.001 for both isotopes), with higher mean values of δ13C and δ15N in Cyperaceae , Juncaceae (L. lutea), and Fabaceae  than species in other families .Overall, the δG. montanum, P. alpina, Potentilla aurea) were studied in all six permanent plots and did not reveal any spatial pattern of δ13C and δ15N, but as a group, the three species were significantly different in their δ15N signature from either Trifolium or sedges/rushes and the combined legume/sedge/rush signal obtained from the same sampling locations. No significant differences in δ13C and δ15N were observed across all six plots and communities co-composed by these three species .Of the forbs, three species  when compared across all sites and species. However, these two parameters were marginally correlated when data of Trifolium, sedges and rushes were removed from the analysis .The foliar δ15N for 6–8 species either growing next to or away from T. alpinum showed no significant difference  and the rush (L. lutea) were removed from the data set (Supplementary Table δ13C and δ15N between live (green) plant material (biomass), necromass and litter across the three studied plant communities . When samples in bulk green biomass were compared with the unweighted (disregarding species abundance) mean of the species sampled in each community for δ13C and δ15N , either with or without legumes in each studied community, no significant difference in δ13C among communities was found . However, δ15N in bulk biomass was 2–3‰ more negative than the corresponding means calculated from the species data , indicating that species with more negative δ15N contributed over-proportionally to total biomass .No significant difference was found in δ13C signal in soil organic carbon showed significantly higher (less negative) values than the bulk litter fraction in the heathland (p < 0.001), the meadow (p = 0.001) and the snowbed (p = 0.017) communities, thus did not mirror the mean litter signal. There was no significant effect of soil depth on δ13C . Also soil δ15N was much higher than the values for most plant species’ tissue and for bulk dry matter of each plot studied. In contrast to δ13C, δ15N increased significantly with soil depth at all sites . Overall, no difference in soil δ15N was found across the three types of plant communities. However, the soil δ15N in the heath community (plots P1.1 and P1.2) was marginally higher than that in the meadow community .The δ13C and δ15N in plants in three different plant communities and in a number of different habitats related to topography in the central Alps. By examining δ13C and δ15N values in different tissue types as well as bulk community biomass, we were able to illustrate the full spectrum of sources of isotopic variation. Since we conducted this survey in an alpine landscape on well developed soils with no biologically relevant variation in moisture availability, the substantial signal variation at all organizational levels, except the ecosystem/community level, came at a surprise.To our knowledge, no systematic study has been conducted in alpine terrain to separate environmental from phylogenetic C and N stable isotope signals of plant species. In order to explain phenotypic signals, we examined δ13C of ca. 1.1‰ (extreme 1.6‰) and the mean variation among tissue types of ca. 1.2‰ (extreme 3.2‰), together exert a largely unexplained ‘noise’ that calls for broad and highly replicated sampling for obtaining a species-specific and/or habitat-specific, representative signal. The intraspecific variation in δ15N was almost twice as large as that in δ13C. The δ13C and δ15N signals in the species examined, varied strongly among all plant families, and inconsistently with sampling location, but the rank order among species was retained across sites with only few exceptions . In the following we will discuss the intra- and inter-specific variation in isotope signals in this alpine environment.Overall, the mean intra-species variation in δNardus stricta and Trifolium alpinum) out of the four species explored for all major tissue (organ) types showed significant variation in δ13C values across different organs (mostly around 1‰). Substantial variation in δ13C in different parts of plants was reported in previous studies  of five individuals sampled along a several km transect with locations separated by at least 50 m distance. For differences in δ13C between individuals growing next to each other, we have no other explanation than individualistic (genotypic) differences in gas exchange traits, given the consistently high soil moisture and the same climatic conditions. However, for δ15N, the remarkable intra-specific  variability may reflect the heterogeneity of substrate and/or rooting patterns, or differences in mycorrhization (13C by as much as different tissues on the same plant do (ca. 1‰) with the variation nearly twice as large in δ15N. Hence, the local plant to plant variation was nearly as high as that among the means for micro-populations of five individuals per site, across the 8–11, widely spaced sites (disregarding Homogyne that varies more with site). Possible causes for the twice as large variation in δ15N among these sites could be different N-availability, uptake of different N forms  or mycorrhization  may be related to the frequent presence of sheep , with significantly less negative δot ratio . In addi13C (on top of intra-species and tissue-specific variation as discussed above) is very large, causing the actual signals obtained for a given plant community and elevation to strongly depend on the species included, sample replication per species and tissue type. For archive-based assessments of environmental signals for a given type of ecosystem, this requires a multi-species approach (presumably > 30 species) to overcome the random nature of such vouchers with regard to individuals, tissue quality and potential micro-habitat effects.Inter-specific variation in δ13C among co-occurring plant species within each studied community falls in the 2–5‰ range in foliar δ13C for terrestrial plant communities composed of C3 species reported by 2 uptake efficiency have been related to foliar δ13C through their influence on factors such as leaf boundary layer resistance, diffusive conductance  and mesophyll resistance to CO2 uptake  values than the other four species tested across eight sites. Legumes can perform dark fixation of CO2 in root nodules by PEP carboxylase . Again, this could be related to a greater contribution of 13C enriched carbohydrate reserves to the rapid formation of new leaves after snow melt. Forthcoming study may now be needed to examine the seasonal course of carbon isotope discrimination in alpine plant species to evaluate changes in gas exchange process in response to biotic and abiotic factors.It is interesting that foliar δboxylase , which hase  within each studied community. Similar ranges in foliar δ15N were reported for arctic tundra communities in Alaska and Swedish Lapland  when foliar δ15N data for some coniferous species were included. In the Rocky Mountains of Colorado, 15N by the species-specific ability to absorb NH4+, NO3- or organic N . Low (very negative) foliar δ15N occurred in shallow rooted ericaceous species , while high (less negative or even positive) values occurred in deeply rooted sedges (Eriophorum and Carex) in arctic tundra, where δ15N in soil was found to increase with soil depth  than non-N2-fixing plants, because of the small isotopic fractionation during fixation of atmospheric N2 by rhizobia . Yet, we see no significant differences in δ15N between non-N2-fixing plants close to and away from N2-fixing T. alpinum, which is puzzling. We attribute this to two facts: (1) the unknown past location of legume individuals in these communities , and (2) the legume signal matches with that of the most abundant sedges, the neighborhood of which no species can avoid in this environment. Similarly, N2-fixing Acacia tortilis in savanna regions was found to exert no influence on adjacent non-N2-fixing plants  soils in all studied communities, matching observations from deciduous forest, coniferous temperate forest, alpine dry meadow and arctic tundra community  environmental conditions the target plants species experience. The observed signal variation cannot or not obviously be attributed to the variation in growth conditions. The negligible influence of both, topography and the proximity of N2-fixing legumes, further suggest that δ13C and δ15N signals in the studied alpine plant communities varied for largely genotypic reasons and/or – in the case of 15N – for reasons related to species-specific plant-microbe/fungus interactions. We joining 15N signals. While the significant inter-specific differences of foliar δ15N within a community, most likely reflect access to different soil N sources, the variation in δ13C must reflect inherently different discrimination regimes  in leaves during photosynthesis. These large and consistent inter-specific differences in stable isotope signatures among such herbaceous alpine plant species underline that elevational trends in stable isotope signals either need to be studied within a taxon [e.g., congeneric comparisons such as those by We found a much wider spectrum of δ13C, more in δ15N), similar to the range of variation among individuals of the same species growing next to each other. Combined, our findings evidence that alpine plant species have evolved rather diverse means for acquiring and using CO2 and nitrogen. Moreover, the fairly consistent rank order of species in both, foliar 13C and 15N across different habitats, suggests that these traits are fixed and likely contribute to the functionality of alpine plant species diversity, very similar to what had been reported recently for sand dunes , does impose a significant isotopic fingerprint on the ecosystem N cycle, thus, masking isotopic legume neighborhood effects on non-N2-fixing taxa.Sampling different tissue types will, on average, add at least another 1–2‰ of ‘noise’ (less in δnd dunes . At ecosCK conceived and designed the work. YY performed work and analyzed the data. YY and CK wrote the manuscript. RS provided editorial advice on work and writing of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The introduction of combined antiretroviral treatment (cART) has reduced HIV-associated morbidity and mortality, and changed the patients’ perspective of life. As a result, Health Related Quality of Life (HRQOL) has become a crucial clinical issue.Assessment of HRQOL in a sample of Italian patients from IANUA study. Investigate correlation between CD4 cell counts, viral load and changes in HRQOL.EQ-5D-3L self-reported questionnaire has been used in the evaluation of HRQOL. It assesses five dimensions: “mobility,” “self care,” “usual activities,” “pain/discomfort” and “anxiety/depression.” Each dimension has three levels: no problems, some problems and extreme problems. In addition, it includes a Visual Analogue Scale (VAS) where one's own health “today” is rated from 0 “worst imaginable health” to 100 “best imaginable health.” The respondents provide information on marital status, education, employment/unemployment, other treatments used in addition to HAART  and number of hospitalizations due to HIV/AIDS.684 patients completed the questionnaire: 231 females and 453 males. The mean age of the sample was 51 years (range 21–78). The mean VAS score was 69.9. 558 patients (81.5%) reported no problems in mobility. 642 patients (93.5%) had no problems in self care. 423 patients (61.8%) had no pain/discomfort while 219 had some problems. 326 patients (46.1%) had some problems in anxiety/depression.The analysis of self-reported questionnaires indicates that HRQOL in our sample group is not deeply affected by HIV/AIDS. The dimensions that are affected in the least are “mobility” and “self care” while the major problem is “anxiety/depression” with half of the sample reporting moderate or high level.


In recent years, genome-wide association studies (GWAS) have identified many loci that are shared among common disorders and this has raised interest in pleiotropy. For performing appropriate analysis, several methods have been proposed, e.g. conducting a look-up in external sources or exploiting GWAS results by meta-analysis based methods. We recently proposed the Compare & Contrast Meta-Analysis (CCMA) approach where significance thresholds were obtained by simulation. Here we present analytical formulae for the density and cumulative distribution function of the CCMA test statistic under the null hypothesis of no pleiotropy and no association, which, conveniently for practical reasons, turns out to be exponentially distributed. This allows researchers to apply the CCMA method without having to rely on simulations. Finally, we show that CCMA demonstrates power to detect disease-specific, agonistic and antagonistic loci comparable to the frequently used Subset-Based Meta-Analysis approach, while better controlling the type I error rate. Genome-wide association studies (GWAS) have identified many loci that are shared among common disorders.  The inteP < 10−5) and genome wide significant (P < 10−8) association by simulation. In this work we present an analytical cumulative distribution function for the CCMA test statistic, which is in good accordance with the levels derived by simulation studies.For performing an appropriate analysis, several methods , 2 have T1 and T2, which are asymptotically normally distributed and signed according to the direction of effect of a certain reference allele. Furthermore, two z-scores for meta analysis are defined, assuming an agonistic or an antagonistic action of the variant on the two traits [As we previously described , the CCMo traits . Then thtmax, the null distribution was empirically determined by simulating R = 1,000,000,000 replicates of two normally distributed random variables Z1 and Z2. Then In order to derive a P-value for an observed realization 0) of no pleiotropy and no association between the SNV and any trait. By design, each of the four transformed variables follows a 0  against Plotting −logght line .X1, X2, …, Xk be continuous iid variables and Xmax = max their maximum, then the cumulative distribution function of Xmax can be written as follows:A general formula for the distribution and density function of the maximum of independent identically-distributed (iid) variables has been described in Chapter 2.11 of Ewens & Grant . Let X1,Furthermore it is known that the sum of two iid FZA(z) ≥ FZB(z) for two test statistics ZA and ZB, we have to show that ZA ≤ ZB for every scenario, i.e., for every set of To prove that +Z22 see .This concludes the proof of Fλ(z) with scale parameter Fλ(z) is connected to z by a log-linear relationIt is important that 10(P) and 0 is a straight line ⋅, we can write Using the relationship 1010-transformed P-value and the test statistic it follows that In conclusion, from the empirically derived linear relation between the logb. To this end, we conducted two simulations of 100 empirical b ≈ 0.228 11) we caZmax statistic and we compare it with its simulated values from the previous study. We find theoretical thresholds for suggestive (10−5) and genomewide (10−8) significance of Zmax = 4.68 and Zmax = 5.92, respectively  disease specific, (b) agonistic and (c) antagonistic effects.We compared the power and type 1 error see  of the Cα = 0.01), then ASSET and CCMA exhibit almost identical power . We obtained comparable results by setting equal baseline risks for both diseases (data not shown).In the simulation-based power analysis we found that the CCMA method is only marginally less powerful for detecting disease specific, agonistic and antagonistic effects than the ASSET method . Howeverw1 and w2  as well as to other research questions such as those encountered in environmental epidemiology. Here, the influence of environmental exposures or lifestyle factors on two different traits of interest can be analyzed with regard to their concordant or contrasting effects.We have previously shown that the CCMA method is an appealing approach to screen for shared and disease-specific loci as well as to leverage additional cross-phenotype association information using available GWAS data . We haveIn subgroup meta-analysis similar questions are addressed by e.g. comparing group A vs. group B using a Z-test ar(effB) . This Z-ar(effB) . AlthougIn conclusion, the proposed CCMA method has some attractive properties for investigating the effect of exposure variables on two different traits. The simply constructed test statistic follows an exponential distribution under the null hypothesis, which allows a fast and easy implementation as well as a direct deduction of the mode of pleiotropy. The method can be conveniently applied to similar questions in other domains and can also exploit summary statistics from two single studies.S1 FigZmax = 4.68) and genomewide significance (Zmax = 5.92).Dotted and solid grey lines indicate the thresholds of suggestive ((TIF)Click here for additional data file.S2 Figα = 0.001 and α = 10−5 was applied.For each power estimate, we ran R = 1,000 simulations with n = 8,000 individuals for various MAF and OR values and assigned the disease status by a multinomial model. A significance threshold of (PDF)Click here for additional data file.S3 Figα = 0.001 and α = 10−5 was applied.For each power estimate, we ran R = 1,000 simulations with n = 8,000 individuals for various MAF and OR values and assigned the disease status by a multinomial model. A significance threshold of (PDF)Click here for additional data file.S4 Figα = 0.001 and α = 10−5 was applied.For each power estimate, we ran R = 1,000 simulations with n = 8,000 individuals for various MAF and OR values and assigned the disease status by a multinomial model. A significance threshold of (PDF)Click here for additional data file.S5 Fig0. Several significance thresholds were considered for comparison α = .We ran R = 100,000 simulations with n = 8,000 individuals for various MAF values under H(PDF)Click here for additional data file.S1 Table0. Several significance thresholds were considered for comparison α = .We ran R = 100,000 simulations with n = 8,000 individuals for various MAF under H(PDF)Click here for additional data file.S2 TableFor each power estimate, we ran R = 1,000 simulations with n = 8,000 individuals for various MAF and OR values and assigned the disease status by a multinomial model and distributed controls equally to both case sets.(PDF)Click here for additional data file.S3 TableFor each power estimate, we ran R = 1,000 simulations with n = 8,000 individuals for various MAF and OR values and assigned the disease status by a multinomial model and distributed controls proportionally to the case sets.(PDF)Click here for additional data file.S1 Appendix(PDF)Click here for additional data file.S2 Appendix(PDF)Click here for additional data file.S3 Appendix(PDF)Click here for additional data file.S4 Appendix(PDF)Click here for additional data file.