Delay by men in seeking healthcare results in their higher mortality while on HIV or tuberculosis (TB) treatment and contributes to ongoing community-level disease transmission before going on treatment.To understand masculinity's role in delay in healthcare seeking for men, with a focus on TB-suggestive symptoms.n=8) and health workers (n=2), in-depth interviews with 20 TB patients  and 20 uninvestigated chronic coughers , and a 3-day participatory workshop with 27 health stakeholder representatives. The research process drew to a large extent on grounded theory principles in the manner of Strauss and Corbin (1998) and also Charmaz (1995).Data were collected between March 2011 and March 2012 in low-income suburbs in urban Blantyre using focus group discussions with community members (Role descriptions by both men and women in the study universally assigned men as primary material providers for their immediate family, that is, the ones earning and bringing livelihood and additional material needs. In a context where collectivism was valued, men were also expected to lead the provision of support to wider kin. Successful role enactment was considered key to achieving recognition as an adequate man; at the same time, job scarcity and insecurity, and low earnings gravely impeded men. Pressures to generate continuing income then meant constantly looking for jobs, or working continuously to retain insecure jobs or to raise money through self-employment. All this led men to relegate their health considerations.Early engagement with formal healthcare is critical to dealing with TB and HIV. However, role constructions as portrayed for men in this study, along with the opportunity costs of acknowledging illness seem, in conditions of vulnerability, important barriers to care-seeking. There is a need to address hidden care-seeking costs and to consider more complex interventions, including reducing precarity, in efforts to improve men's engagement with their health. Tuberculosis (TB) is a leading cause of adult morbidity and mortality globally. In 2012, it was responsible for 8.6 million cases and 1.3 million deaths of which 0.32 million deaths were in people living with HIV Given limited understandings and the complexity around the research topic, we triangulated methods and data sources, and purposefully chose the sample and varied it by sex to explore different dimensions while including appropriate participants , 38. ChrThe study questions and insights about gender roles and relations that we brought to the study informed our decisions regarding the initial samples as well as the content of the tools. More specific questions emerged during fieldwork and tools were modified to follow emerging crucial leads. The research process was in this sense reflexive and nonlinear , 41. Coma priori, taking into consideration the numbers deemed adequate to explore phenomena in qualitative research , both fluent in Chichewa, collected the FGD and IDI data. We set minimum sample sizes for the different categories of participants research . Informaresearch .]Malawi College of Medicine and Human Sciences Research Council research ethics committees approved the study. We sought and received clearance to enter the communities from executive leaders of Blantyre and local leaders. The district medical officer and facility managers also granted permission to access patients’ records. Written informed consent and permission to record data were sought from participants, and anonymity and confidentiality were maintained at all stages of the research. Participants were provided refreshments and reimbursed their transport costs.Focus group and IDI data were recorded, transcribed, and translated by trained personnel; checked for accuracy by (MK); and for intelligibility by (JC), who is fluent in a related regional language, Shona. The transcripts were entered into NVivo data analysis software  and open coded partly using predetermined questions but largely inductively through identifying emerging concepts. In the process of coding and abstracting from the data, concepts were classified according to their properties, and the emerging categories reviewed and refined through the coding process and as they were related to each other and to emerging theory , 41.Emerging themes presented separately included the manifestation of masculinity as control and the healthcare seeking implications, masculinity-mediated links made between TB and HIV, and health system barriers affecting women and men generally. This paper focuses on another theme elucidated during the study: men's material provider representation. Role descriptions by both men and women in the study universally assigned men as primary material providers for their immediate family, that is, the ones earning and bringing livelihood and additional material needs. In a context where collectivism was valued, men were also expected to lead the provision of support to wider kin. Successful role enactment was considered key to achieving recognition as an adequate man; at the same time, job scarcity and insecurity, and low earnings gravely impeded men. Pressures to generate continuing income then meant constantly looking for jobs, or working continuously to retain insecure jobs or to raise money through self-employment. All this led men to relegate their health considerations.Women and men affirmed the gender role distinction that assigns men the task of earning cash income and women responsibility for managing the domestic domain. Men were held responsible for their immediate families’ entire breadth of material requirements. Participants in a community women's FGD described the responsibility as involving ‘ensuring that a woman gets all her needs’, ‘finding food for us’, ‘buying us clothes’, ‘building a house for us’, ‘upon children entering high school … sends fees fast … and uniform too’.… all relatives expect a man to see that his family has a good house, good toilet, storeroom full of food; the children are in school, the wife is well taken care of, and relatives needing assistance are getting it. (Community men's FGD)The role's emergence and basis within the political and economic context of colonial Southern Africa is well documented [see also ]. The roRelatives, you don't do what they expect, they call you bad … both wife and husband you are ‘bad’…. You also may not receive help when a problem comes your way. (Community men's FGD)My relatives expect a lot…. You give that little when people bring up a small problem. But with this cough, I don't get money every day … when you give, people assume you have more … not knowing you're just trying … It depresses me a lot. The accounts then illuminated stress and tensions around acquiring, managing, and sharing resources where they were simply scarce. The collectivism that characterises African social systems  meant, fbusiness but not enough (to) help at the right time. … What I get … is little, but my responsibility huge. … things aren't going as I wish. I get money from my … My wife doesn't work … my elderly mother stays with us … I have a young brother in high school … all five of us together expecting food … clothes … Problems at the village too are my responsibility. … Any minute something pitches … maybe at my in-laws’ … and my wife just glares at me. In particular, being responsible for large numbers of dependants while living under harsh economic conditions and beset with poor health  made men's expectation to provide materially burdensome.screen (television), she has everything, but I don't.’ (Woman in mixed-sex community FGD)We get to be envious: ‘Oh my, this one, her house! Mine is nowhere near that … And that one, God blessed her with a weak , just from looking at and admiring that friend of yours. (Community women's FGD)We look at how our friends in other families live, maybe they dress and eat well, and we ask, ‘Why does this husband of mine behave like this? Nothing at all like dressing well in our family’ … so you become The urban milieu and its attendant flux and ‘competitive materialism’ , p. 41),We make this man carry too much responsibility … saying, ‘Everything will be done by him’ … But it's often difficult for men. In the end, it appears there's no love in the family…. (Community women's FGD)tight (difficult) for him. … He then looks for a chance to run … from his responsibility, leaving the woman and children … why? Because we're making him carry all the responsibility. (Community women's FGD)… in terms of money … it gets The accounts portrayed general conflict, struggle, and contradictions, as people seemed torn among multiple worlds. The value put on assisting kin materially in a treacherous economy, for instance, coexisted with growing pressures emanating within the same economy to adopt more individualistic lifestyles. Similar ambivalence was expressed when women described their sympathy for men's situation arising from being saddled with impossible demands while already overwhelmed.mowa (liquor) and then sleep, come morning, wake up and just sit. Meanwhile their peers are doing business, supporting their households and children…. (30-year-old married businesswoman in a mixed-sex FGD)It's not all about being employed … Most get money, spend on Rider. Ten Rider, they're sorted. To then think ‘How do I support my family?’ they can't. … just always drunk. (24-year-old unmarried businesswoman in women only FGD)… once they lay hands on a MK100 (usd0.30) note, they're immediately on The women then turned around and castigated men for lacking initiative, being irresponsible with money, and being addicted to liquor. In women's view, notwithstanding the adverse circumstances, men abetted and were largely responsible for their own failure.He just must have money … work here and there, doing something, any piecework … sweeping … digging latrines … whatever is available …. Someone asks ‘Please help thatch my house’, and that way he gets money …. Going up the mountain to fetch firewood to sell; because he must always think: What will my children eat? Buying and selling bananas ‘Just so my children get something to eat.’ … Even touting at taxi ranks. (Community women's FGDs)Women exhorted men to look harder for jobs, take up any that arose, and work without respite. Their tone and emphasis seemed to give men little room to consider health ahead of earning.They're in big trouble, don't receive respect and seen as useless … you lead an isolated life without friends, your marriage breaks … and your own children won't respect you. (Man in mixed-sex community FGD)Those men are humiliated, starting in their families all the way to outsiders and relatives … to the point of being denied food in their homes and teased when walking the street. (Woman in mixed-sex community FGD)The accounts also described the treatment and experiences of men who failed. Failure seemed especially grave when visible to the public world, for example, when one's children cried persistently and pestered neighbours, ‘showing that as a man you are failing your responsibility’ (Community men's FGD). The men were said to be humiliated, devalued socially and shunned by peers, and abused psychologically in and out of home.chimfine (‘flu’ said to be treatable by drinking a lot of water), ‘stomach ache’, ‘mere cough’ , kungomva kuzizira (‘feeling a bit cold’ ‘fever’), and kathupi kakutentha pang'ono (‘mild aches’). Overlooking what clearly seemed to be vague and largely speculative descriptions of what constitutes minor illness, men were expected by both men and women to either ignore such illness or self-medicate, and continue functioning and fending for their families.… the whole family looks up to him … It can't be whenever he has a headache, he jumps in bed. … Things in his house would stall. (Community women's FGD)Aspirin; for this mere cough they simply chew Conjex, gain strength and continue with the work they must do. (Woman in community mixed-sex FGD)“… We look up to the man to bring. If he's weak against minor diseases, everything else stops. If it's a headache, they must take More direct connections were drawn between men's role construction as material provider and their health response. It was stressed, for instance, that as heads of and also sources of inspiration and income to their families, men must not promptly acknowledge illnesses particularly ‘minor’ ones. Minor illness was illustrated with ‘headache’, He'll say he's sick yet still go around, looking for things. He even tells his wife his body is not well, but still starts off for work, because of thinking about his responsibility in the home. The woman remains behind, anxious … only to see him return in the evening; still saying he's not well. Interestingly, contrary to views that men lacked the motivation to work, it seemed many already pursued earning even to the extent of overlooking their health. In addition, the images of power and agency often associated with men were absent, and the men in this study appeared to be shunted by an unmanageable drive to earn money and retain insecure jobs, with no control over their lives.past five, and return only around seven … including Sunday and Saturday …. So really, I'm just thinking they should … do the process and find out if it is TB. I just haven't had time … been very busy working, so I just push myself on – coughing. I leave in the morning around A chronic cougher described how he was dragging himself every day and working long hours, sparing no time to be investigated although he seemed ready.finished (wasting). He (my boss) was wondering where I still was … then the other drivers around told him I'd drunk so much the previous day I wouldn't make it to work. Furious, he told me never to touch his car ever again … But two weeks later, everyone could see I was Men's precarious employment situation and difficulties balancing failing health with keeping insecure jobs were further demonstrated when a former metered-taxi driver recounted the events leading to his dismissal, which were triggered by missing a day of work due to illness. His description that his health plummeted shortly afterwards suggests the possible effects of unemployment-related stress but also that he had perhaps nursed the illness for some time.Panado to relieve the body pains and continue with my work. If I focus on this sickness's pain, then I won't work and achieve what I want. I'd rather get … Men don't like going to the hospital; they see it like wasting time instead of working. Under immense pressure to earn while unwell, men resorted to painkillers just to manage but also avoid taking ‘expensive’ breaks from work. The distantly located primary care facilities made it even less convenient to initiate formal care-seeking.The connection between men's material provider concept and their healthcare seeking behaviours is rarely addressed in the literature from the African continent, a scenario attributable in part to the prior focus within the gender and health field on women. In this study of men's care-seeking delay, we had therefore not expected the role to emerge in the way it did; rather, we had expected that masculinity would manifest through commonly documented representations such as power, agency, strength, and invulnerability. Although the signifiers emerged in the present study, they assumed more complex forms than had been foreseen.In some settings in Africa, the provider role for men has been linked to colonial intervention and especially the measures instituted to extract cheap labour for mines and farms , 50, 51 Threats to achieving desired masculinity representations are reported to fuel in men intensified efforts to prove that they (can) meet the grade; alternatively, men redirect their focus to more accessible versions which they still proceed to enact to extremes , 28, 54.In this study, the urban setting and associated consumerism are portrayed exerting pressure on women, who in turn relay it to men helping shape the criteria by which the latter's adequacy is assessed. Furthermore, when struggling to meet basic survival requirements, families and men are forced to make pragmatic choices, in this case dissuading able-bodied men in the households from seeking healthcare or even acknowledging illness until it is unequivocally determined to be serious. There seems to be a paradox thus, whereby the same power and control men are documented to wield over resources, and which they reportedly use to dictate women's and children's access to care, also affects men's own utilisation of care. Ultimately, though, the construction of men's health behaviours within gender relational dynamics  means thLiterature frequently mentions men's provider role in the context of immediate families. African social systems, however, stress communality and extended family form , althougMen, families, and communities seem to exist in conditions of ‘hybridity’, under a milieu characterised by tension, struggle, and ambivalence, amid widespread economic hardship all largely linked to globalisation and related socio-economic changes. It is possible this scenario partly explains the alcohol abuse described for men in this study, which may be important given studies report men using alcohol to suppress illness pain and symptoms .The present findings also need to be located in the context where international efforts to deal with HIV and AIDS – and by extension TB – are placing greater emphasis on early engagement with formal care, and on groups that are over-represented among those left out of care . Frequen


To the editor: Zika virus infection, which has been associated with microcephaly and other neurologic disorders, has reached the level of public health emergency of international concern  is transmitted by mosquitos of the genus Aedes ; fever, generally low-grade, was reported or observed in 67%. Because our study design was retrospective in nature, wherein we reviewed records for selected patients in whom Zika was subsequently found to be laboratory-confirmed by using rRT-PCR, we may have introduced selection bias to our sample, limiting the generalizability and comparability of our results. For example, clinic staff may have seen patients with mild symptoms but decided not to test for the virus, leading to a bias toward testing patients with more severe rash. It is also possible, considering the retrospective nature of our data collection, that some data points were not accurately recorded and could not be validated. Despite these limitations, our data suggest the term “Zika fever” is not a helpful substitute term for Zika virus disease. Furthermore, referring to the illness caused by this virus as “Zika fever” , myalgia (77%), arthralgia (73%), and fever (73%) (


In recent years, research on Brain-Computer Interface (BCI) technology for healthy users has attracted considerable interest, and BCI games are especially popular. This study reviews the current status of, and describes future directions, in the field of BCI games. To this end, we conducted a literature search and found that BCI control paradigms using electroencephalographic signals  have been the primary focus of research. We also conducted a survey of nearly three hundred participants that included researchers, game developers and users around the world. From this survey, we found that all three groups  agreed on the significant influence and applicability of BCI and BCI games, and they all selected prostheses, rehabilitation and games as the most promising BCI applications. User and developer groups tended to give low priority to passive BCI and the whole head sensor array. Developers gave higher priorities to “the easiness of playing” and the “development platform” as important elements for BCI games and the market. Based on our assessment, we discuss the critical point at which BCI games will be able to progress from their current stage to widespread marketing to consumers. In conclusion, we propose three critical elements important for expansion of the BCI game market: standards, gameplay and appropriate integration. Although the original aim of BCI technology was to provide a new pathway of communication for patients suffering from complete paralysis, some researchers have focused recently on the application of BCI to games for use by healthy people. Studies have demonstrated examples of BCI applications in such well-known games as “Pacman” , “Pinbalet al.  a2.3.www.scopus.com), the abstract and citation database of peer-reviewed literature. In order to survey articles related to BCI games, we searched publications from 2007 to 2013 for the following keywords and operators: ((“brain computer interface” OR “BCI”) AND (“game” OR “entertainment”)) in “Article Title, Abstract, Keywords.” Initially, 272 articles were found; this list included conference proceedings, as well as research, review and survey papers. Among these, we excluded articles that were not related directly to BCI games, and brief conference versions of extended journal papers. From this search, we found that approximately half of the application and research papers were published in conference proceedings, but not in journals. Finally, a total of 180 articles was retrieved and used to review the current progress of BCI games.To understand the current status of BCI games, we conducted a literature search using SCOPUS (2.3.1.www.neurosky.com) and “Emotiv EPOC” (www.emotiv.com) were released in 2007 and 2009, each at a price more modest than that of other research purpose devices.2.3.2.Although we found 180 published articles, not all of them were directly related to the implementation of BCI games. Therefore, we separated studies on the development of BCI games from others, such as review papers, in order to help us understand how many games were actually developed during this trend of increased publications on BCI games. We found that approximately 47% of publications did not develop games, while the remaining studies did: newly-developed games (42%), or BCI implementation with existing games (11%), such as Tetris, Hangman, and World of Warcraft, are shown in 2.3.3.The chart in www.kickstarter.com). Looking at the success of this fundraising effort, “Emotiv Insight”, for example, achieved over 1600% of its goal; this fact demonstrates the strong interest of the public in BCI and BCI hardware.The dramatic increase in publications after 2007  seems to3.3.1.Our goal was to understand the discrepancies and similarities in opinions, and to bridge the gap among various stakeholders in the area of BCI games—producers (researchers and developers) and consumers (users). In this survey, we confined our inquiry to researchers working in the BCI or bio-signal processing fields, and regarded respondents who are neither researchers nor game developers as those who may use BCI games for entertainment. For simplicity, we referred to respondents in these three groups as researchers, developers and users.The usability of BCI: Awareness of the applicability and influence of BCI and BCI games is an interesting issue. To obtain opinions with respect to those issues, we first asked respondents to express their evaluations of the future applicability and influence of BCI and BCI games. Second, several examples were presented and respondents were asked to state their personal opinions of the applicability for each purpose. In these questions, respondents' opinions were recorded on a five-point Likert scale as “very high”, “high”, “not strong”, “low” and “very low”. For example, we asked the following question: “Please state your evaluation of the applicability and influence of BCI in the future”. One of five opinions was received from each participant.The preference rating for BCI paradigms: The overall system environments, such as the task that a user has to perform, the sensor location and the stimulation may all depend on the control paradigm. Therefore, evaluating the control paradigm is an important issue, as we do not know whether users will readily accept the system environments of BCIs that are now considered promising. As we stated in Section 2, we described three types of control paradigms that are used most widely, especially in non-invasive BCI. With active, reactive and passive BCI control paradigms, we specified their functionality in the context of interaction and asked users and developers to give their personal evaluations of the paradigms on a five-point Likert scale ranging from “not interesting”, “somewhat interesting”, “fairly interesting” and “very interesting” to “one of the most interesting”. For this question, we introduced motor imagery as active BCI, P300 and SSVEP as reactive BCI, and mental state as passive BCI. This question was presented more directly to the researchers, who were asked about their interest in active, reactive and passive BCI.Sensor position: With respect to the control paradigms, we may distinguish three important brain areas; frontal, centro-parietal and occipital. In addition to these, we can also use the overall area covering from frontal to occipital. In this question, the respondents were given four sensor locations, and instructed to order them according to their preference. To help the respondents understand the question, it was presented with illustrations of positions on the head.Reasonable costs: For guidelines pertaining to cost, we provided the reference cost of two recognizable devices, Microsoft “Xbox Kinect”  and Nintendo “Wii” . We thought that it would be helpful for the respondents to understand the trends in price of the new interface devices, although this is not absolutely comparable information. Thus, we set up the items as, “less than $100,” “between $100 and $200,” “between $200 and $300,” “between $300 and $400,” and “over $400.”Expected years until availability: We also asked the respondents how many years they thought it would be before such games would be available to the public. This question included 6 items specifying a range of years.The survey was designed to obtain personal opinions about the applicability of BCI or BCI games, how usable or acceptable respondents considered the most often used control paradigms in the BCI field, and expectations/considerations that BCI games will become common entertainment in our daily lives. The questionnaire procedure was as follows. At the beginning of the survey, a consent form was given to and signed by the participants. In step 2, personal information was gathered and then brief explanations about BCI were presented with pictorial examples. After this initial process, the main survey began, but the form of the questionnaire was designed slightly differently depending on the group to which the respondent belonged. For researchers and developers, we asked the number of years they had spent in their career and what they thought were the important elements for BCI games and for stimulating the field of BCI game development. Users were not asked these questions; instead, users/developers were asked whether/how they knew about BCI. Apart from these, most of the questions were the same. There were 10 to 12 items depending upon the group. Questions common to all groups are as follows.3.2.A pilot study was conducted with 20 respondents who are BCI researchers currently working at our institute, as well as game developers and members of the public in Korea. The form was distributed to them and they were asked to complete the questionnaires and give feedback with respect to the completeness or ambiguity of sentences in the questions, any possible difficulties in conducting the survey, and further comments. Through this procedure, we found that completion of the survey took approximately 5 min; we were also able to correct and revise the survey items to ensure that their meanings were conveyed in a clear and unambiguous manner. In addition, we added one more example in the question for “BCI paradigm”: “speeding up the velocity of a car through the user's concentration level” , because3.3.www.neurogadget.com), and other community bulletin boards. In addition, we notified societies and community groups about the survey . The survey was conducted between August and October 2013.The final form of the survey is presented in the 3.4.In order to determine which opinions among the groups were statistically significant, we applied the Wilcoxon rank-sum test with the hypothesis that the medians of two variables (each variable represents one group opinion for the given question) are equal. For this statistical test, answers to the given question were properly digitized (transformed into integers)–for example, “one of the most interesting” was assigned a 5, “very interesting” a 4, “fairly interesting” a 3, “somewhat interesting” a 2, and “not interesting” a 1.4.4.1.A total of 294 people participated in the survey. The details of respondents' profiles are presented in In 4.2.All three groups were asked about their awareness of the applicability and influence of BCI and BCI games. As shown in For the second question, respondents indicated on the same scale their perceptions of the influence of each application example presented. Overall, the respondents agreed that BCI applications had high potential, but the degree of their opinions varied. These results are presented in 4.3.The survey also attempted to evaluate the BCI control paradigms. From the results in The device design is far more important, provided that BCI performance is not heavily depending on sensor location”.Next, we asked about preferences for sensor positions that may be related to hardware design and thereby may affect the users' convenience. From the results shown in 4.4.Among the elements that influence a consumer's motivation to purchase a product, one of the most important is cost. For this reason, all three groups were asked what they believed was an acceptable price for a device. In 4.5.In a question similar to that of the Asilomar survey , we aske4.6.We thought that groups that are heavily involved in BCI game implementation might hold different or similar opinions on the subject of the elements important to the public success of BCI games. Thus, every developer and researcher was asked two additional questions. One addressed the importance of the given elements of BCI games, and the second asked how those elements would be important in stimulating the public success of BCI games. They were asked to indicate their agreement with these questions. The results from the two groups are presented in As seen in Game developers do not need to study BCI research.” Similarly, a developer stated, “Software development kit (SDK) should be provided, so that one can implement games without detailed BCI knowledge”. From these comments, it is clear that the platform is much more important to game developers. Comparing other elements, researchers tended to pay more attention to two elements: the public's positive awareness (“one of the most important” or “very important”: 70%) and the quality of the device (“one of the most important” or “very important”: 79%), although developers also agreed that these were important.With respect to the second question addressed only to developers and researchers, we asked about the importance of the various elements in stimulating the market for BCI games. As we observed in the previous question about the elements required to develop BCI games , develop4.7.p < 0.0001, developers and researchers: p < 0.001), as was mentioned in Section 4.3. For questions related to price and the expected number of years for BCI to reach the market, researchers' opinions of an affordable cost were significantly higher than those of developers (p < 0.05) and users (p < 0.05). On the other hand, developers expected a significantly longer time would be required for BCI to reach the market than did researchers (p < 0.01) and users (p < 0.05). With respect to questions about the important elements for a BCI game and for stimulating the market, several elements differed significantly between developers and researchers. For BCI games, “exciting application” (p < 0.05); “easiness of playing” (p < 0.0001); “development platform” (p < 0.0001); “short training time” (p < 0.001), and “BCI knowledge” (p < 0.05) were significant. For the market, “stimulate the market for BCI entertainment” (p < 0.001); “public's positive awareness about BCI” (p < 0.01), and “appearance of BCI game platform” (p < 0.001) were significant. Overall, these statistical results lend support to the fact that there are different viewpoints among developers, researchers, and users. In-depth discussion on different viewpoints among three groups will be addressed in Sections 5.1 and 5.4.We conducted a quantitative analysis of our survey and similarities and differences among the opinions of the groups were observed. In addition, a statistical test was conducted as described in Section 3.4. We found that researchers' preference for passive BCI was significantly greater than in the other groups  agreed that both BCI and BCI games would have a large influence as an applied technology in the future.Prostheses and rehabilitation were distinguished as the most promising applications, followed by games.With respect to the control paradigm, users and developers indicated a preference for active and reactive BCIs, while researchers preferred reactive BCI.Users and developers gave the lowest priority to the whole channel covering the head; interestingly, however, the researchers were divided. One group (39%) most preferred the whole head sensor position, while the other (40%) liked it least.The majority of each group thought that an affordable price for a BCI device is over $100. Considering that the existing EEG devices or those that are being prepared for the market are priced at approximately $200, the cost to develop BCI generally meets the price acceptable to users.The researchers expected that BCI games would be available sooner than did the other two groups.As important elements in BCI games, researchers were most concerned with the practical obstacles that they face or will face in their studies, such as signal processing, sensors and ways to make games interesting. On the other hand, developers seemed to take into account the user's view, as well as their own position, more than did the researchers. Compared to researchers, they gave a higher rank to “the easiness of playing” and the “development platform”.When asked about elements important in stimulating the BCI game market, developers and researchers agreed that creating communities for interaction was a low priority. However, the developers reiterated the importance of the platform, while the researchers responded that “emergence of simple and precise device[s]” and the “public's positive awareness about BCI” are high priorities.In this study, we reviewed the current research trends in BCI games and attempted to collect the opinions of all stakeholders: users, developers and researchers. Through our literature search, we observed some notable points. We found that in recent years, a marginal number of articles have shown that there has been continuous research on BCI games, and that motor imagery, P300 and SSVEP-based control paradigms have been introduced widely for control purposes. Moreover, the passive paradigm that reads mental or emotional states accounted for a relatively large percentage of the control paradigms used (35%). We also confirmed that EEG is the most preferred measurement method in the development of BCI games, and we expect that this preference for EEG will continue with the advent of wireless EEG devices. A questionnaire was conducted with nearly 300 participants in order to investigate their awareness of and opinions about BCI games. Here are the points that we learned from the survey:There is considerable work that remains to be performed before BCI enters smoothly into the field of entertainment. BCI games need to be evaluated continuously by the public by giving them particular experiences with the devices, and the current market status should be included in any considerations. From the following, we discuss certain questions raised by and limitations of our study.5.1.Generating signals should be intuitive, like motor imagery, or made intuitive by context, like selecting items in a menu with (fast) P300 or SSVEP.” Thus, the interaction between a user and a game should be as intuitive as possible. On the other hand, one developer made the interesting point that: “It should guarantee no medical side effect”. There is a report that SSVEP may entail a higher risk of photic or pattern-induced epileptic seizures  interesting story or unique game system even though the technology in [the] game device looks fancy. For example, ‘Doom3’ was the greatest in the notable graphic aspects, but finally ‘Half-life’ succeeded  game. “Half-life” proved to be revolutionary in a number of ways in the history of FPS games. It featured stunning graphics, a highly immersive game story, and gameplay. For a brief history of FPS, please refer to “History of First Person Shooter (FPS) Games.”  indicated the significant influence of gameplay with an example: “o et al.  reviewed [et al.  tried to [et al. . In this [et al. . These s [et al.  and heurwww.playstation.com). Perhaps this direction is more promising than developing BCI as its own market. Interestingly, one company continues development efforts to integrate BCI into entertainment content. They have produced BCI entertainment ideas and products: “Necomimi” (moving cat's ear), “Shippo” (moving tail), and “Neurocam” (recording videos of interesting scenarios: www.neurowear.com).Third, the integration may constitute a breakthrough in overcoming the limitations of BCI. Given the low reliability and performance of BCI compared to conventional interfaces, the combination with other signals or systems is the inevitable option required to allow BCI to be used in game playing ,62. In f5.5.In this section, we discuss certain implicit and explicit limitations of our survey. First, the participant sample is somewhat biased. Among the 294 participants in this online survey, most were from Asia, North America and Europe. Therefore, it cannot reasonably reflect the opinions of people from other continents. Recruitment of participants was limited because it was conducted only online, its promotion was campaigned primarily online, and the questionnaire was accessible only through the internet. Thus, this survey did not cover groups that are not active in social network services or involved in games. Particularly, it would be very interesting to investigate thoroughly the opinions of patients  who have tried BCI technology. This will be addressed in future work.Second, the questionnaire did not address certain sophisticated issues. First, we did not consider the possibility of multiple roles of participants. For example, one can be both a researcher and a gamer. A researcher who also enjoys games may have different ideas or opinions compared to one who does not play games. Such a point may influence some findings from this survey. Considering the increase in BCI research –67 on usThird, this survey was conducted only once. As a result, it may be difficult to draw sound and statistically significant conclusions. To achieve such confident global assessments of BCI games, repeated surveys or administration of surveys both on- and offline would be greatly beneficial. However, conducting such comprehensive surveys of large samples would be too costly and would require a large research collaboration network. Due to such difficulties, this survey was conducted once and only online. With this limitation, the Wilcoxon rank-sum test was conducted after digitization of opinions. Nevertheless, we believe that this study is a meaningful step in assessing the interactions among stakeholders in BCI games.6.In the past few years, the potential of BCI for healthy users has attracted considerable attention. Among various applications, entertainment and games are expected to constitute a large market of potential users. In the role of producers, researchers and game developers play different roles, while users are the undeniable consumers. The feedback we obtained from these stakeholders with regard to BCI games is important for the development of high quality games. For this purpose, we presented and summarized their opinions from a survey. Our results confirmed that both the same and different opinions existed among the three groups. This means that further opinion exchange and continuing feedback among these groups are essential. Researchers must think about BCI from the viewpoint of implementers and consumers, while developers and users must remain attentive to BCI development and continue to offer their feedback. We also summarized the results of game demonstration to compare users' evaluations of passive and active paradigms, and presented briefly the state of the market. Three critical elements—standards, gameplay and appropriate integration—were discussed. Investigation of these is the direction that the BCI community must take now in order to advance BCI game market expansion. We hope that this effort will be helpful to researchers, as well as developers, and will stimulate the collaboration or exchange of opinion between those two or even all three stakeholders.


Recently, ensemble learning methods have been widely used to improve classification performance in machine learning. In this paper, we present a novel ensemble learning method: argumentation based multi-agent joint learning (AMAJL), which integrates ideas from multi-agent argumentation, ensemble learning, and association rule mining. In AMAJL, argumentation technology is introduced as an ensemble strategy to integrate multiple base classifiers and generate a high performance ensemble classifier. We design an argumentation framework named Arena as a communication platform for knowledge integration. Through argumentation based joint learning, high quality individual knowledge can be extracted, and thus a refined global knowledge base can be generated and used independently for classification. We perform numerous experiments on multiple public datasets using AMAJL and other benchmark methods. The results demonstrate that our method can effectively extract high quality knowledge for ensemble classifier and improve the performance of classification. Nowadays ensemble methods represent one of the main current research lines in machine learning , 2 and tHowever, traditional ensemble learning methods have some drawbacks. 1) From the perspective of integration manner, traditional ensemble methods mostly use weighted voting (such as Bagging) to integrate multiple base classifiers. While this “majority-voting” approach is relatively too simple and lack of the interpretability for users. 2) From the perspective of integration object, ensemble methods only integrate classification results of base classifiers rather than internal classification knowledge in base classifiers. 3) From the perspective of integration result, traditional ensemble classifier consists of all base classifiers which are essential in the classification process. Therefore, ensemble classifier cannot be independent of base classifiers, which leads to high classification cost. In order to overcome the weaknesses of traditional ensemble learning methods, we introduce a new ensemble strategy based on multi-agent argumentation.With the development of artificial intelligence, argumentation technology as a new way of multi-agent interaction can imitate human decision-making process to realize the conflict resolution and knowledge integration, which takes advantage of group intelligence for problem solving. In recent years, argumentation with data mining technology has been extensively studied, such as argumentation in association rule mining –17, machDue to the above advantages of argumentation, this paper proposes a novel ensemble learning approach: argumentation based joint learning, which introduces multi-agent argumentation technology into the field of ensemble learning. In our method, agents perform data mining on respective datasets to construct base classifiers independently at first. Then, base classifiers evaluate their individual knowledge by using argumentation. Finally, high quality global knowledge can be extracted to generate ensemble classifier.Argumentation based joint learning method has the following advantages. 1) The ensemble strategy of argumentation is more logical and explicable than voting. This is because argumentation can make use of classification knowledge as arguments to realize the strict dialectic analysis process. 2) Argumentation technology can evaluate the knowledge of base classifiers, and automatically extract high-quality knowledge to form the global knowledge base for classification, which achieves the integration of classification knowledge, rather than a simple combination of classification results. 3) The global knowledge base of ensemble classifier can be directly applied to solve the classification problem, and base classifiers are unnecessary in the final classification process, which can greatly improve the efficiency of classification.The article is organized as follows. Section 2 introduces the related research works about ensemble learning and argumentation; Section 3 presents the principles of argumentation based joint learning model; then realization process of the model and algorithm will be given in Section 4; Section 5 introduces the argumentation platform Arena used in joint learning model. Experimental results on public datasets are presented and discussed in section 6 and 7. Finally section 8 summaries the contributions and future works of this paper.Recent years, ensemble learning and argumentation technology both attract much attention in the field of data mining. In this section, we will focus on the related works about ensemble learning and argumentation in data mining.Research in many areas has shown the advantages of ensemble methods both theoretically and empirically , 26. In When forming ensembles, creating diverse classifiers (but maintaining their consistency with the training set) is a key factor to make them accurate. The research in  makes a The vast majority of ensemble methods are implicit, in that they provide different random subsets of the training data to each learner. The random differences between the datasets might be in the selection of examples , the selection of features  in training dataset, agents use individual knowledge to perform argumentation on Arena for obtaining an optimal knowledge of this topic, which will be stored in the global knowledge base of ensemble classifier. Meanwhile, global knowledge in ensemble classifier can give a feedback to guide the subsequent joint learning process. With the increase of input topics, global knowledge base will keep growing until it converges. In classification process, ensemble classifier can be used directly to classify the test data.From In the above process, argumentation technology plays a crucial role in joint learning method. By using argumentation technology, joint learning model can evaluate the individual knowledge, eliminate low-quality knowledge and extract the optimal classification knowledge to construct high performance ensemble classifier.With the idea of swarm intelligence (intelligent behavior manifested by group cooperation), our method focuses on knowledge integration and extraction by multi-agent argumentation which is not only more similar to human decision-making process, but also easier for users to understand than traditional ensemble methods.Argumentation based joint learning can be divided into two processes: base classifier generation and argumentation based integration. In base classifier generation process, agents use sampling with replacement to get different training sets, and then data mining technology is used to generate base classifiers. In integration process, multiple base classifiers use argumentation technology to generate ensemble classifier.Si for individual agenti from the original training data set D. The size of training sample Si of each agent is same with the original training set D. However, due to the sampling with replacement, training samples for each classifier are different, and there may be duplicate cases in individual sample Si.In this paper, we focus on data variation-based ensembles , which cIn traditional ensemble learning, base classifier must be able to classify. However, in our method, base classifier is used to generate classification knowledge rather than classification result. Therefore, lots of classification algorithms (such as Support Vector Machine ), which Therefore, we choose association rule mining algorithm to generate base classifiers. This is because association rule can effectively represent not only the individual knowledge of agents but also the argument for agents to communicate in argumentation process. In addition, association rule can be directly used in classification as well.Si to get association rules exceeding certain support and confidence threshold. In addition, we not only record association rules, but also record application condition as well as exception cases of each association rules. Application condition means the necessary condition that all the cases must satisfy when the conclusion of association rule is true. In other word, if an attribute is always accompanied with the appearance of investigated conclusion of the association rule in every case, we can infer that the attribute is application condition of the conclusion. Exception case means the cases in sample that contains the premises of the rule but not contains the conclusion.In this process, each agent performs association rule mining using Apriori-TFP algorithm  on localAR), which can be treated as individual knowledge of base classifier. An element of AR is defined as follows: Conclusion: w; Premises: l1, l2, …, ln; Confidence: c; Conditions:u1, u2, …, us;¬v1, ¬v2, …, ¬vt; Exceptions:e1, …, ek.Through the above process, each agent can get a set of extended association rules ; and the second is classification rules of the global knowledge base K can better match the current topic t than the winning rule kt (∃k ∈ K∧premises(k) ⊂ t∧length(k) > length(kt), where premises(k) is the set of premises of rule k, and length(k) is the length of premises of rule k).In addition, the global knowledge base also gives a feedback on the argumentation process. Under the two conditions, winning rules K in ensemble classifier is composed of extended association rules, which can be used for prediction independently. When a new (unknown) case x comes, ensemble classifier selects the rule k which matches the case x (k ∈ K∧premises(k) ⊂ x) and has the highest confidence (conf(k) = max{conf(k′)∣k′ ∈ K}) to predict the class label of the case x.The global knowledge base The main control algorithm of argumentation based joint learning model is in In argumentation based joint learning method, multi-agent argumentation is implemented on the Arena model. This section will describe the basic structure of Arena, arguments, speech acts and terminate conditions of argumentation.Arena is a dialectic analysis model for multiparty argument games. In Arena, we designed four roles: Referee, Master, Challenger and Spectator. The whole process of argumentation is stored in the dialectic analysis trees. The basic structure of Arena model is shown in In Arena, Referee doesn’t participate in argumentation but manages the argumentation process according to dialogue rules of Arena. All the participating agents will play a role of Master, Challenger and Spectator. During an argumentation, participating agents need to compete for Master or Challenger continually. And there can be only one Master and one Challenger to take part in the argumentation, while other participants are not allowed to speak when they are just Spectators.At the beginning of an argumentation, Referee broadcasts the discussion topic, and the first agent who proposes its opinion about the current topic will be selected as the Master of Arena. All the other participants whose options are different from Master can challenge the Master and form the queue of challengers, and the first participant in the queue is selected to be the Challenger of Arena. All the other participant agents except Master and Challenger are Spectators of Arena.Once Master and Challenger are identified, agents can use the speech acts for constructing Master-Challenger dialogues in Arena.AR.In argumentation, for each topic under discussion, agents can produce arguments from their individual knowledge base x represents a topic under discussion. Argument schema is defined as follows: {Premises: l1(x), l2(x), …, ln(x) → Conclusion: w(x); Confidence: c; Exceptions:e1, …, ek; Conditions: u1(x), u2(x), …, us(x); ¬v1(x), ¬v2(x), …, ¬vt(x)}.Suppose that x doesn’t belong to {e1, …, ek}, with features u1, u2, …, us and not with features v1, v2, …, vt, then x with features l1, l2, …, ln, are W with probability c.Such argument schema for experience can be read as follows: In my experience, if anything ProposeOpinion, DistinguishRule, CounterRule, BeInapplicable, BeAnException, and Defeated.Speech act is the language of participant to communicate with others in argumentation. In Arena, there are six speech acts: ProposeOpinion: agent Ai uses this speech act to propose the opinion about the topic t by selecting the rule k which matches the topic t (k ∈ ARi∧premises(k) ⊂ t) and has the highest confidence from his local knowledge ARi (conf(k) = max{conf(k′)∣k′ ∈ ARi}).CounterRule: agent Ai uses this speech act to attack the adversary’s argument k′ by selecting the rule k with higher confidence (k ∈ ARi∧premises(k) ⊂ t∧conf(k) > conf(k′)).DistinguishRule: agent Ai uses this speech act to attack the adversary’s argument k′, and allows the addition of some new premise(s) to a previously proposed rule k′, so that the confidence of the new rule k is lower than k′ (k ∈ ARi∧premises(k) ⊂ t∧premises(k) ⊃ premises(k′)∧conf(k) < conf(k′)). It means the adversary’s argument k′ is unreasonable because of not considering enough information about the topic t.BeInapplicable: agent Ai uses this speech act to state that the adversary’s argument k′ is inapplicable to this topic t, since the Conditions {u1, u2, …, us;¬v1, ¬v2, …, ¬vt} of k′ in his own knowledge base ARi doesn’t satisfy topic t ∨), which means agents have different experience for k′.BeAnException: agent Ai uses this speech act to state that the topic t is in exception {e1, …, ek} of adversary’s argument k′ with the same Premises and Conclusion in his own knowledge base ARi .Defeated: agent Ai uses this speech act to state that he is defeated.In Arena, each agent only uses these speech acts from his knowledge base to attack the adversary according to If Master is defeated by Challenger, this Challenger will become the new Master, and he can propose his opinion about the current topic from his own knowledge base. All the other participants decide whether or not to challenge this new opinion. Noted that the defeated argument of the old Master can’t be used again, the old Master can only produce a new argument to apply for Master once more.Otherwise, if Challenger is defeated, the next participant in the challenger queue will be selected as the new Challenger, and the argumentation continues.Termination conditions of Arena are as follows: if Master can defeat all the challengers, Master wins the argumentation and Master’s winning rule will be considered as the optimal knowledge for the current topic; if there is no agent applying for Master, the argumentation is tie. Since the number of arguments produced by participants is finite and the defeated arguments can’t be allowed to use repeatedly, the termination of argumentation can be guaranteed.In this section we will first introduce data sets collected for experiment and representative baseline methods for comparison. Then we will describe the details of experimental methodology.In order to empirically evaluate AMAJL we use seven public machine learning datasets in breast-cancer and voting. In this experiment, we use the confidence and support threshold in In Item is a common term in association rule mining to represent the attribute value of instances. The association rule model represents rules where some set of items is associated to another set of items. Therefore, the number of items has direct impact on the complexity of association rule mining.NC, the average number of rules in knowledge bases is NR, and the number of attributes is NA, then computation complexity of argumentation is O(NC × NR × NA2). As can be seen, the dimension of datasets directly affects the computation complexity of argumentation. With the dimension increases, the efficiency of argumentation will be reduced.In our model, the computation complexity of base classifier generation is largely dependent on the complexity of the underlying association rule mining algorithm. The computation complexity of argumentation is dependent on the complexity of finding rules matching the instances in knowledge base. We assume that the number of base classifiers is In order to validate the performance of our method AMAJL, we will use three ensemble learning methods Bagging , 39, AdaAll these baseline methods except TFPC are implemented on the data mining software WEKA , and basIn the experiment, we use four base classifiers for AMAJL, Bagging and AdaBoost, and choose 100 trees for Random Forest. In addition, baseline methods based on association rule  use the same confidence and support threshold in In order to get more accurate results in the experiment, we apply “Ten-fold Cross Validation” (TCV) tests to evaluate the performance of different methods. In the experiment, we use Accuracy and Rule Number as the evaluation measures to compare the classification performance of different methods.Accuracy is used to measure the classification ability of classifier, which is calculated as the number of cases classifier correctly predicted.Rule Number represents the scale of classification knowledge in classifier which is used to evaluate the ability for knowledge extraction. In our experiment, rule number of AMAJL is the number of association rules in the global knowledge base of ensemble classifier. While rule number of TFPC method is the number of classification rules contained in the single classifier. As to Bagging and AdaBoost, multiple base classifiers need to be involved in classification, so the rule number results of Bagging and AdaBoost correspond to the average of the rule number obtained by each base classifier.In this section we first give a specific case to illustrate the argumentation process on Arena, and then describe the knowledge accumulation and convergence process in AMAJL model. Next we evaluate the classification performance of AMAJL method compared with other baseline methods, and finally give analysis of experimental parameters.Nursery dataset to show the multi-agent argumentation process. Nursery dataset was derived from a hierarchical decision model originally developed to rank applications for nursery schools. It was used during several years in 1980’s when there was excessive enrollment to these schools in Ljubljana, Slovenia, and the rejected applications frequently needed an objective explanation. In this experiment, we used 4 agent involved in the argumentation.In order to better illustrate the argumentation process, we use a specific case in Given a case as the topic, it should be classified as “special priory”. The case has attributes: the parents have a usual occupation, has critical nursery, complete family, 2 children, critical housing, inconvenient finance, problematic social conditions and priority health conditions.After the Referee broadcasts this discussion topic, each agent produces his opinion for the case and applies for Master.Agent2 becomes the first Master, and proposes his opinion as follows:R1 = {critical_nursery, critical_housing, priory_health → spec_prior; 97.67%; E1; ∅; not_recom_health.}”“According to my experience, this case should be ‘special priory’ based on the rule: Agent4 challenges Agent2 by using DistinguishRule as follows:R1 is unreasonable. Because R2 = { critical_nursery, critical_housing, inconvenient_finance,priory_health → spec_prior; 94.28%; E2; ∅;not_recom_health }, R2 add the premise ‘inconvenient_finance’ to R1, while the confidence drops 3.39%.”“According to my experience, Agent2 cannot propose any rule to attack Agent4 and concedes that he is defeated.Agent4 becomes the new master, and proposes his own opinion. Then argumentation process continues to move forward.Then The dialectic analysis tree of the whole argumentation process is depicted on Figs Agent1 wins the game, and this case should be classified as “special priority”;Finding the winning rule which can be seen as the high-quality knowledge: {critical_nursery, critical_housing, inconvenient_finance, priory_health → spec_prior; 100%; ∅; ∅;not_recom_health}From the above process, we can draw the conclusion as follows:Through the above argumentation process, our model can evaluate and screen individual classification knowledge. At the end of argumentation, we can obtain a valuable winning rule which will be stored in the global knowledge base.Though the above argumentation process, we have described how to extract high quality knowledge for classification. In this section, we design an experiment to demonstrate the knowledge accumulation process in the global knowledge base of ensemble classifier.In this experiment, topics in training dataset are input into Arena continually until the global knowledge base converges. Here we define one group as 10 training cases. The convergence condition is that the rule number of global knowledge base does not increase with continuous five groups of training data.Then rules keep accumulating and eventually reach the convergent state. For the three datasets, rule numbers of the global knowledge base converge to 84, 66 and 60 respectively.Experimental results show that, AMAJL method can effectively accumulate the high quality knowledge and the global knowledge base of ensemble classifier can converge in the three datasets.Through the above process of knowledge accumulation, we have obtained the ensemble classifier which can be directly applied in classification. Here we present an experimental analysis of our method. We address the task of classifying the unknown cases from the test set on several public datasets, and compare the results with the baseline methods.First, we use following baseline methods to perform classification on the datasets, the results are reported in It should be noted that TFPC, the association rule based classifier, has a relatively low classification performance. This is because TFPC just screen the association rules by pruning, rather than evaluating the quality of rules. Therefore, it cannot find high quality association rules for classification. To solve this problem, by using argumentation to integrate base classifiers, AMAJL can effectively extract high quality rules, which has significant advantages with respect to traditional ensemble methods.The TCV test results of AMAJL on the seven datasets are shown in In order to illustrate that AMAJL can effectively integrate base classifiers, we compare the results with Bagging and AdaBoost whose base classifiers choose CBA algorithm (Classification Based on Associations) in Tables In contrast with centralized classification algorithm TFPC, our method can outperform TFPC on all the seven datasets. The accuracy of AMAJL is higher than TFPC from a low of 2.71%, to a high of 23.78%. Bagging and AdaBoost methods are both superior to TFPC. Classification accuracy of TFPC is relatively low since TFPC method only uses single classifier for classification. AMAJL can combine multiple classifiers to get the high classification accuracy.Tic-Tac-Toe, AMAJL can obtain accuracy of up to 91.05% while accuracy of Bagging and AdaBoost are lower than 80%. Accuracy of AMAJL is 13.9% and 12.7% higher than Bagging and AdaBoost respectively. Similarly, in Breast-Cancer, Lymph and Voting, AMAJL can also outperform Bagging and AdaBoost. And in Spect Heart, Nursery and Scale, classification accuracy of AMAJL are comparable with Bagging and AdaBoost.In contrast with traditional ensemble methods, we can find that, AMAJL as a novel ensemble learning algorithm has more advantages than Bagging and AdaBoost methods. For example, in Nursery and Tic-Tac-Toe, where AMAJL can only use 100 and 64.2 rules which are approximately half of rules in Bagging and AdaBoost. While in Breast-Cancer, Lymph, Voting and Spect Heart, rule numbers of AMAJL are relatively higher than Bagging and AdaBoost.Furthermore, it should be noticed that we should also focus on knowledge scale of classifier in addition to classification accuracy. From Nursery, scale and Tic-Tac-Toe, we set low support thresholds, thereby generating a large number of rules. Baseline methods only use pruning to control the number of rules, which is difficult to compress knowledge scale. While AMAJL uses argumentation to eliminate weaker rules and extract high quality rules, so rules in AMAJL can be better compressed. In resting datasets, we set high support thresholds, thereby generating fewer rules. Baseline methods remove a large number of valuable rules by pruning. Although rule numbers of baseline methods are lower, it is difficult to ensure classification performance.This is because that in As a conclusion, from the above experimental results, we can find that AMAJL can effectively extract high quality knowledge for ensemble classifier and ensure high accuracy in classification as well.In ensemble learning methods, classification performance of ensemble classifier depends on not only integration strategy but also the ability of base classifiers. As a result, base classifier generation in AMAJL has a crucial effect on the quality of ensemble classifier. In this section, we will put particular emphasis on the parameter setting of AMAJL in the process of base classifier generation.In base classifier generation, AMAJL uses association rule mining algorithm to achieve the base classifier construction. The pre-set of support and confidence threshold parameter is essential for association rule mining. Different support and confidence thresholds will lead to different mining results. Therefore, support and confidence threshold will directly affect the quality of association rule mining, then further affecting the classification performance of ensemble classifier. However, the support and confidence threshold is often difficult to set accurately and in most cases users need to judge by experience.In order to analyze the effect of different parameters, we set different support and confidence thresholds in scale dataset to analyze the influence of parameter on the classification results. First, we fix the confidence threshold as 50% and 70%, and vary the support threshold from 2% to 15% in this experiment. The results are shown in Figs As can be seen from Figs Seen from the above experimental results, we can conclude that accuracy and rule number are sensitive to change of support and confidence threshold. This is because that support and confidence thresholds are used to filter rules in association rule mining process. Higher thresholds mean more strict measures to mine association rules, so rules in base classifiers will be much fewer for integration. As a result, knowledge in the global knowledge base will become scarce and limit the ability of ensemble classifier. In contrast, base classifiers can obtain more rules at lower thresholds resulting in better classification performance.In order to generate high performance ensemble classifier, we propose a novel joint learning method based on argumentation, AMAJL. In this method, argumentation is used as a novel ensemble strategy for knowledge integration, which is more logical and explicable than traditional ensemble method (such as voting). Meanwhile, by using argumentation, high-quality individual knowledge can be extracted to generate the independent ensemble classifier, which can be directly applied for classification without base classifiers.Experiments show that, as a novel ensemble method, AMAJL outperforms the centralized classification method (TFPC) significantly. Meanwhile, compared to traditional ensemble methods, AMAJL can effectively extract high quality knowledge for ensemble classifier and ensure high classification accuracy as well, which indicates that argumentation as a novel ensemble strategy can improve the capability of knowledge integration effectively.In our future research, we will further deepen the argumentation mechanism on joint learning method with bipolar argumentation model which will involve support behavior on the basis of existing attack behavior. Furthermore we will also try to apply other data mining methods such as decision tree to generate base classifiers in order to evaluate the universality of AMAJL.


Extant genomes share regions where genes have the same order and orientation, which are thought to arise from the conservation of an ancestral order of genes during evolution. Such regions of so-called conserved synteny, or synteny blocks, must be precisely identified and quantified, as a prerequisite to better understand the evolutionary history of genomes.gapmax. This parameter is theoretically estimated, and together with a utility to graphically display results, contributes to making PhylDiag a user friendly method. In addition, putative synteny blocks are subject to a statistical validation to verify that they are unlikely to be due to a random combination of genes.Here we describe PhylDiag, a software that identifies statistically significant synteny blocks in pairwise comparisons of eukaryote genomes. Compared to previous methods, PhylDiag uses gene trees to define gene homologies, thus allowing gene deletions to be considered as events that may break the synteny. PhylDiag also accounts for gene orientations, blocks of tandem duplicates and lineage specific de novo gene births. Starting from two genomes and the corresponding gene trees, PhylDiag returns synteny blocks with gaps less than or equal to the maximum gap parameter We benchmark several known metrics to measure 2D-distances in a matrix of homologies and we compare PhylDiag to i-ADHoRe 3.0 on real and simulated data. We show that PhylDiag correctly identifies small synteny blocks even with insertions, deletions, incorrect annotations or micro-inversions. Finally, PhylDiag allowed us to identify the most relevant distance metric for 2D-distance calculation between homologies.The online version of this article (doi:10.1186/1471-2105-15-268) contains supplementary material, which is available to authorized users. Changes in the order of genes in a genome are caused by two categories of mutational events: genic events, which include de novo gene births, deletions, duplications, and genomic rearrangements, which include chromosome fusions and fissions, segmental translocations or segmental inversions. Synteny blocks are composed of those genes that retain an ancestral organisation despite these events, and one way to understand how genic events and genomic rearrangements affect genome evolution is to identify such synteny blocks. The extremities of synteny blocks also define the positions of breakpoints where rearrangements took place. Precisely defining synteny blocks thus allows, in turn, an accurate definition of breakpoints , which hSeveral methods have been developed to identify synteny blocks from extant chromosomes comparisons. In the field of bacterial genome evolution, algorithms tend to focus on the notion of “gene team” , which dArabidopsis thaliana and rice, among plants, and mammals such as human, mouse, dog and rat, among metazoans. These algorithms can be broadly classified according to their heuristic and features.However, because gene order conservation in eukaryotes is stronger  comparedFour distinct heuristics are used to infer synteny blocks. The first builds two-dimensional matrices filled with homologies , 22, 24.A second heuristic uses optimisation techniques and dynamic programming , 21, 28.Some algorithms compare genomes by performing pairwise comparisons of genomes whereas others perform multi-genomes comparisons. Combining pairwise comparisons does not capture the additional significance of genes that are conserved in more than two regions, resulting in under-estimation of cluster significance . Multi-gThe transcriptional orientations of genes on the chromosome are used by some algorithms and provide informations about micro-rearrangements and may contribute to making the correct choice when there are several possibilities to extend a synteny block. In addition, accounting for gene orientations increases the statistical relevance of small synteny blocks, see  where is (respectively ie) is the index of the starting (respectively ending) gene in the sub-list.Sa and Sb as a set of neighbouring genes with gene content, gene order and gene orientations conserved during the evolution from LCA to Sa and Sb. Two genes are neighbours if they are separated by less than a user-defined parameter gapmax. During evolution we consider that a set of neighbouring genes remains a synteny block until: − a chromosomal rearrangement creates a breakpoint within the sb and changes the order or the orientations of genesgapmax genes − the gap between any two neighbouring genes, caused by gene insertions and/or gene deletions, exceeds Intuitively  we define a Synteny Block  between two species An ancestral sequence of genes remains a sb even if tandem duplications occur within the synteny block.The evolution of a gene can be represented by a rooted binary tree called a gene tree. The root of a gene tree is the first ancestral gene, the nodes correspond to events of speciations or duplications that occurred during the evolutionary history of the descending genes, and the leaves of the gene tree correspond to extant genes originating from the first gene.a and gb is denoted Sa,Sb) to Sa and Sb, we pruned all gene trees to define a gene family as a set of genes that come from a unique gene of LCA. Families are defined with these genes, so that two genes are in the same family if and only if they come from the same ancestral gene of LCA. We note that, depending on the purpose of the analysis, PhylDiag offers the possibilty to prune gene trees at an ancestor that precedes LCA, so that more paralogy relationships are included in the gene family, see =. The size of a tb is equal to the number of tandem gene copies that it contains, for instance the last tb has a size 3. A gene which has no tandem duplicate is in a tb of size 1. By convention tbs are always maximum, i.e. a given tb cannot be contained within another tb. Like genes, tbs also have an orientation. However, in a tb, tandem duplicates may or may not all have the same orientation. When they all share the same orientation, the tb itself is oriented with the same orientation as the orientation of the genes thus, either o(tb)=+1 or o(tb)=−1. When tandem duplicates have different orientations, the orientation of the tb is considered to be unknown, and In a chromosome, under a parsimonious reasoning, homologous and adjacent genes are tandem duplicates. Here, we refer to such blocks as “tandem block”. Formally, a tandem block  of a chromosome na is the number of tbs in ca. na≤Na and na=Na if and only if there is no tandem duplicate in ca.It is possible to rewrite chromosomes as a unique ordered list of oriented tbs. For instance a of Sa is said to be in a homology relation with a tandem block tbb of Sb if the genes of the two tbs are in the same family. We will also say that in this case tba and tbb are homologs or even that tba and tbb are homologous tandem blocks. Using the same notation as for genes, a and tbb are homologs. If tba and tbb are homologs, they share a Last Common Ancestral gene in LCA and we note LCAg the Last Common Ancestral gene of tba and tbb. LCAg is defined as soon as it is observed that a and tbb are not necessarily of the same size if deletions or tandem duplications took place specifically in the branches of Sa or Sb after LCA.A tandem block tbThe classic Matrix of Homologies ga• gb is the “sign” of the homology of ga and gbWhere A MH can be represented as an array of values equal to +1,−1 or 0. Non-0 values correspond to homologies.a (in ca) and tbb (in cb). A hp is always maximum, i.e. a hp cannot be contained within another hp. Graphically, a hp appears as a rectangle of non-0 values in a MH. Each hp has a last common ancestral gene in LCA denoted LCAg(hp) and equal to LCAg. Tandem duplications generate vertical, horizontal, or rectangular hps in a MH, making it difficult to identify sbs as diagonals. However, the rewriting of a chromosome in a way that collapses these hps to unique values in the MH, as described above, greatly simplifies this problem. Indeed, once ca and cb are rewritten as ordered lists of tbs, it becomes possible to define a matrix whose non-0 values correspond to hps of the two chromosomes ca and cb.A Homology Pack  is the set of homology relationships between the tandem duplicates of two homologous tandem blocks tbca is rewritten in cb is rewritten in Given that a∙tbb the “sign” of the hp of tba and tbbWhith tbca and cb, with tbs instead of genes and hps instead of gene homologies. The only difference is that while genes always have a known orientation, tbs can have unknown orientations that generate hps with signs equal to ca ordered from tba,1 to cb ordered from tbb,1 to na,0] corresponds to the bottom-right corner, MHP corresponds to the top-left corner and MHP corresponds to the top right corner of the array.In other words, the matrix construction is the same as for the MH of ]k∈. Similarly, m hps form a strict backslash diagonal if the list of m hps can be written ]k∈. In both cases,  is the position of the first hp of the diagonal.We define a “strict” diagonal as a diagonal that has no gap between its hps. Thus, − either a slash diagonal only composed of hps with signs equal to either +1 or− or a backslash diagonal only composed of hps with signs equal to either −1 orWe also define a “consistent” diagonal as a diagonal composed of hps with signs consistent with hps order: In addition, we consider that the distance between two diagonals corresponds to the distance between their closest extremities.m tbs with gaps ≤ gapmax of a comparison of two genomes Ga and Gb, as a chain of m tbs with gaps ≤ gapmax that, during the evolution from LCA to Sa and Sb, remains a chain of m tbs with gaps ≤ gapmax. Within a synteny block, tbs order is conserved and tbs orientations either remain conserved or change from a known to an unknown orientation. Synteny blocks are chosen maximal, i.e. not included in another synteny block.We formally define a “synteny block” of gapmax=0). In [Additional file gapmax generates a consistent diagonal with gaps ≤ gapmax. However, using the ED, MD or the DPD distance metrics, a synteny block with gaps ≤ gapmax may generate a consistent diagonal with gaps >gapmax, although every consistent diagonal with gaps ≤ gapmax always represents a putative synteny block with gaps ≤ gapmax. It should be noted that, given the CD distance metric and a gapmax, our definition of a diagonal is similar to the definition 4.1 of a “max-gap cluster” in [In addition we define a “strict synteny block” as a synteny block with no gaps between tbs  is chosen equal to the orientation of tba. However, if the orientation of tba is unknown, the orientation of LCAg may still be inferred using the diagonal type of the current diagonal and a known orientation of tbb, see Equation Strict and consistent diagonals are recorded as chains of ordered and oriented (whenever it is possible) ancestral genes. By convention the orientation of an ancestral gene LCAg. Figure Wab with a maximum gap g=2 tbs reached on ca. The window Wab has a size 6 × 4. The chromosomal windows Wa and Wb are the projections of Wab on each chromosome. Wa has a length of la= 6 tbs and Wb has a length of lb= 4 tbs. As in previous works [We compare two chromosomes, us works –35, hereα. This is equivalent to selecting consistent diagonals that are unlikely to be the result of chance, which we wish to quantify here by a probability, a p-value.A given consistent diagonal is a statistically significant signature of a sb if it cannot be obtained from a random distribution of tbs (null-hypothesis) up to a fixed probability threshold m hps contained in a window Wab of size la× lb with a maximum gap between hps equal to g, the probability that such a consistent diagonal  arises by chance is denoted pVal. To compute this value we first compute pd the probability of obtaining exactly k hps in the window Wab, knowing the MHP density in terms of non-0 values. We next compute pg,2D, the probability that k hps in Wab are spaced with gaps ≤ g, knowing that there is at least k hps in Wab. We also calculate po,o(k) the probability that k hps have consistent order and signs. By summing and multiplying these probabilities in an appropriate manner we calculate pw, the probability corresponding to a window sampling search. Finally, we use the former probability to compute the p-value pVal corresponding to a whole genome comparison. The formulas of the two first probabilities are based on [pw to the pVal is based on [po,o(k), to account for tbs order and orientations.We calculate the p-value of each consistent diagonal in five steps. Considering a consistent diagonal of based on , 35 respbased on . Here wena× nb without dispersed paralogy (see Discussion), involving nab hps, the probability of obtaining exactly k hps in a window Wab of size la× lb is:Using the reasoning of , in a MHd stands for density because this probability takes into account the density of the MHP. The demonstration of this formula is in [Additional file The subscript k marked tbs (in any order) form a chain with gaps ≤ ganywhere within a window composed of l tbs is:Using the reasoning of , the prowkg= k+(k−1)g is the maximum length of a chain containing k tbs with a maximum gap g, andWhere k tbs so that they form a chain with gaps shorter or equal to g anywhere within a window of l tbs even if wkg>l+1, to address edge effects.the number of ways of arranging Wab contains at least k hps, the probability that Wab contains k marked hps spaced with gaps ≤ g is:Thus, knowing that k hps are close enough, the probability that they form a consistent slash diagonal with gaps ≤ g is:Then, if P(sign = s) is the probability that one hp sign equals s, this probability calculation is explained in [Additional file k homologous tbs of chromosome cb have the same order as the corresponding k homologous tbs of chromosome ca and k signs of the hps are consistent with a slash diagonal. pbackslash(k) is defined similarly.Where k hps are close enough, the probability that they form a consistent diagonal with gaps ≤ g is:Thus, if o,o stands for consistent tbs Order and tbs Orientations. The demonstration of the po,o formula can be found in [Additional file The subscript na× nb without dispersed paralogy (see Discussion), involving nab hps, the probability that in a window Wab of size la× lb there is at least one consistent diagonal containing at leastm hps spaced with gaps ≤ g is:Now, in a MHP of size w stands for Window because this probability corresponds to a window sampling [The subscript sampling  scenarioO(nanb). Thus, relying on the reasoning of section 4.2 of [Finally, since PhylDiag performs a whole chromosome comparison, it is not possible to use the probability of a window sampling method that would underestimate the probability to find a consistent diagonal by a factor of n 4.2 of  we adjusna× nb containing nab hps without dispersed paralogy (see Discussion), the probability of finding at least one window Wab of size la× lb containing at least a consistent diagonal of at leastm hps spaced by gaps ≤ g can be approximated by:In a MHP of size la and height lb in the MHP such that no window overlap with any other window. The underlying assumption of this formula is justified in [Additional file where statisticalValidation that takes a list of consistent diagonals as input and returns statistically validated sbs.In Algorithm 1, the statistical validation is encapsulated in the function gapmax) to allow gaps in sbs. However, the user may find it difficult to estimate the optimal value for this parameter. In order to avoid guessing or multiple trials before finding the optimal gapmax value, PhylDiag uses the dependency between the probability of finding a consistent diagonal of m hps spaced by gaps ≤ gapmax and the gapmax value. The complete reasoning used to calculate the recommended maximum gap parameter can be found in [Additional file All algorithms designed to identify synteny blocks use a maximum gap parameter  that a cluster should display.When ADHoRe compares two chromosomes, it first generates “baseclusters” which correspond to PhylDiag’s sbs. ADHoRe uses the DPD metric to build baseclusters containing gaps ≤“gap_size” in the matrix of homologies. ADHoRe also uses the “prob_cutoff” parameter for the statistical filtering and a last parameter is the “q_value”, a real value between 0 and 1, indicating the minimum Gh) and the mouse genome (Gm) of Ensembl v72. As explained in section ‘Gene family and homology’, families correspond to genes that are descended from a unique gene of LCA= Euarchontoglire.In a first comparison, we provided the same input based on real genomic data to PhylDiag and ADHoRe. We used the human genome . Figure PhylDiag computes a recommended Ganc with a user defined number of genes and chromosomes. The lengths of chromosomes in Ganc are expressed in number of genes, and are determined randomly. Simulated evolution gives rise to the two extant genomes Ga and Gb of two extant species. The simulator performs genic events, which include de novo gene births, deletions, duplications (tandem and dispersed), and genomic rearrangements, which include chromosome fusions and fissions, segmental translocations or segmental inversions. The evolutionary scenario is calibrated so as to fit the known evolution of the human and the mouse genome from the Euarchontoglire genome using phylogenetic gene tree reconstructions from Ensembl Compara version 72. See [Additional file Our simulator first designs an ancestral genome gapmax found by PhylDiag is still 5. Results of PhylDiag with a gapmax=5 and ADHoRe with gap_size= 5 are shown in Table We performed 100 simulations of the evolution of the human and the mouse genome, and analysed them with PhylDiag and ADHoRe to identify sbs. The PhylDiag merging process was performed with the 4 different distance metrics . For ADHoRe the DPD is the only distance metric available. As in the comparison with real data, since the simulation is calibrated to fit real evolutionnary rates, the recommended Coverage is the fraction of the number of gene families  contained in sbs over the total number of ancestral genes conserved in both the simulated human genome and the simulated mouse genome. N50 is the length of the sb such that all sbs of greater lengths represent 50% of the ancestral genes contained in sbs. Sensitivity is the fraction of the number of correctly inferred ancestral adjacencies over the total number of ancestral genes conserved in both the simulated human genome and the simulated mouse genome. Specificity is the fraction of the number of correctly inferred ancestral adjacencies over the total number of inferred ancestral adjacencies, false inferences included.Ganc even if their relative orientation is different compared to the ancestral relative orientation), and second by taking gene orientations into account (to be correct an inferred adjacency must contain genes with a relative orientation that is the same as in the Ganc).Specificity and sensitivity are calculated twice: first by ignoring gene orientations . In addition, as soon as gene orientations are considered in the analysis, PhylDiag improves substantially, in part because of Equation We have compared PhylDiag to i-ADHoRe 3.0, a state-of-the-art algorithm including advanced features which are not present in PhylDiag, including the possibility to identify sbs in the “twilight zone”, i.e. sbs highly diverged or separated by a WGD, where many gene deletions may have occurred. ADHoRe uses “profiles” across more than two genomes to identify poorly conserved sbs, for example due to long divergence times. These features were not exploited here because unlike ADHoRe, PhylDiag only performs pairwise comparisons of genomes since our primary interest is to identify sbs in closely related species.gapmax independently of the metric chosen. Thus comparing metrics is fair. Finally, contrary to ADHoRe, PhylDiag can return sbs containing 2 hps if their p-value is under the p-value threshold of the user.We explored different distance metrics to measure distances in matrices of homology, and found that the DPD used in ADHoRe, which favours fusions of diagonals along ±45° axes in the MHP Figures E and 2F,% of tbs are dispersed duplicates in the human genome (3.2% in mouse) using our family definition (section ‘Gene family and homology’).PhylDiag includes a new statistical validation to estimate the probability that a putative sb may be due to chance. Unlike other tests, it accounts for gene orientations, thus providing increased sensitivity. It also accounts for tandem duplications but ignores the possibility that duplicate gene copies may be dispersed. Neglecting dispersed duplicates underestimates the p-values of sbs and the significance of sbs are thus overestimated. However models considering gene families exist , 23 and α is chosen to distinguish significant sbs from non-significant sbs. This inconsistency represents an area of further development, in order to better account for heterogeneous densities of hps depending on which chromosomes are being compared.The p-value used by PhylDiag is relative to a comparison of two chromosomes, and therefore assumes that random consistent diagonals might arise based on the number of tbs and hps relevant to the two chromosomes only. In contrast, a global (i.e. genome wide) threshold gapmax, distance metric, p-value threshold, filtering of extant genomes and ancestor for the gene family definition. Their values can be set by PhylDiag  or set by the user. These features, together with post-processing graphical analysing tools and printed statistics  contribute to making PhylDiag a user-friendly method to find synteny blocks.PhylDiag is designed around a heuristic-independent formal definition of synteny blocks. Its implementation and benchmarking using real and simulated data allowed us to rank 2D-distance metrics in terms of sensitivity and specificity, and to evaluate its performance in comparison with ADHoRe. Results show that the DPD distance metric yields the poorest performances when identifying synteny blocks, both with ADHoRe and PhylDiag. In contrast, PhylDiag highlights the interesting sensitivity-specificity trade-off achieved by the MD distance metric, closely followed by the CD and the ED distance metrics. Compared to ADHoRe and other algorithms that infer synteny blocks, the definition of gene families in PhylDiag is based on gene trees. Most notably, this feature offers the opportunity to precisely group extant genes into families that descend from a unique gene in the last common ancestor of the two species being compared. Furthermore, a meticulous attention to tandem duplicates and gene orientations allow PhylDiag to reach a high resolution in the analysis of rearrangements, down to single gene inversion. Finally, the statistical validation of putative synteny blocks filters out putative false positives due to randomly convergent gene order. PhylDiag is a software for synteny block inference that benefits from extensive parameters, including Project name: PhylDiag Project home page: https://github.com/DyogenIBENS/PhylDiagOperating system(s): Platform independent Programming language: Python Other requirements: Python 2.7 or higher License: GNU GPL v3 or later, and the CeCiLL v2 license in France Any restrictions to use by non-academics: NoSupplementary Data.(PDF 793 KB)Additional file 1: