Screening of various gene markers such as single nucleotide polymorphism (SNP) and correlation between these markers and development of multifactorial disease have previously been studied. Here, we propose a susceptible marker-selectable artificial neural network (ANN) for predicting development of allergic disease.To predict development of childhood allergic asthma (CAA) and select susceptible SNPs, we used an ANN with a parameter decreasing method (PDM) to analyze 25 SNPs of 17 genes in 344 Japanese people, and select 10 susceptible SNPs of CAA. The accuracy of the ANN model with 10 SNPs was 97.7% for learning data and 74.4% for evaluation data. Important combinations were determined by effective combination value (ECV) defined in the present paper. Effective 2-SNP or 3-SNP combinations were found to be concentrated among the 10 selected SNPs.ANN can reliably select SNP combinations that are associated with CAA. Thus, the ANN can be used to characterize development of complex diseases caused by multiple factors. This is the first report of automatic selection of SNPs related to development of multifactorial disease from SNP data of more than 300 patients. In recent years, the number of patients suffering from allergic asthma has increased , and allIn the present study, we analyzed associations between SNPs and childhood allergic asthma (CAA), which is more strongly influenced by genetic factors than other types of allergic asthma. We performed this analysis using an artificial neural network (ANN), which is a computer-based algorithm that can be trained to recognize and categorize complex patterns -8. ANNs . Associations between combinations of important SNPs and CAA pathogenesis were investigated. A χ2 test was performed for all 2-SNP and 3-SNP combinations.Here, we propose an ANN model , IL-4Rα (148 G/A), ADRB2 (265 A/G) and C5 (4266 G/A) were 0.0036, 0.0155, 0.0541 and 0.0581, respectively. When CysLT2 (108 C/A), which had the lowest P-value of 25 SNPs, was used for discrimination between case and control as a sole factor, prediction accuracy was 54.4%, and the sensitivity and specificity was 12.8% and 95.9%, respectively, compared with the number of case and control subjects to assess discrimination performance . Thus, we constructed a susceptible marker-selectable ANN model, which can discriminate between cases and controls using the selected susceptible SNPs, and which can include the association between combinations of SNPs and development of CAA.Several reports have suggested linkage between asthma and chromosomes. For example, genes in the 5q31-5q33 region code for Th2-type cytokines (IL-4, IL-13, which regulate B cell heavy-chain class-switching to IgE production)  and ADRBWe used a three-layered ANN with input, hidden and output layers Figure . An ANN et al. [N'case/Ncase. In this case, N'case is the number of cases whose genotype pattern is match to control's genotype pattern at least one control (Ncase = 172 subjects). As shown in Figure Ritchie et al.  reportedet al. . If impoIL-4Rα (148 G/A), CysLT2 (2534 A/G) and IL-10 (-571 C/A)) to 17 SNPs  according to the order of Table To investigate the important SNPs, we counted the number of SNPs that remained within the last 10 input variables in 5 trials. The significance order of remaining SNPs was listed, and a score of order ranging from 1 to 10 points was determined, based on the significance order. The remaining SNPs were reordered according to sums of scores, as shown in Table CysLT2 254 A/G andCysLT2 254 A/G andThe results of diagnostic prediction using the 10 SNPs selected by PDM are shown in Figure P-value using the χ2 test. In models using 10 SNPs selected by PDM or the other 15 SNPs, the total number of 2-SNP combinations and 3-SNP combinations (Ncomb) is 90 (10P2) or 210 (15P2), and 360 (10P3/2) or 1365 (15P3/2), respectively. With respect to 2-SNP combination between the SNP of interest and SNP A, P-value was calculated as follows. When patients were limited with certain pattern of another SNP, such as AA major homozygote of SNP A, patient distribution of the SNP of interest was investigated. With respect to 3-SNP combination, between the SNP of interest, and SNP A and B, P-value was calculated as follows. When patients were limited with certain pattern of two other SNPs, such as AA major homozygote of SNP A and BB major homozygote of SNP B, patient distribution of the SNP of interest was investigated.To understand the importance of the 10 SNPs selected, we analyzed combinations of these 10 SNPs. We paid particular attention to SNP combinations associated with CAA, and assessed whether any combinations consisting of SNPs selected by ANN were associated with CAA. The relationships between 2-SNP or 3-SNP combinations and CAA development were examined by calculating P-value of the combination, the usual Bonferroni correction of P-values was first investigated. To select the 2-SNP combination accompanied with minimum false positive, the criterion was P < 0.05/300. Here 300 cases correspond to 25C2. Under this severe condition, there were no significant SNPs. As the same as 2-SNP combination, any significant combination was not obtained on 3-SNP combination under the threshold of P < 0.05/2300. Next, to determine important combination, P-value without Bonferroni correction was used, that is P < 0.05. Results are shown in Table P < 0.05 among total 90 combinations. In the case of 3-SNP, 72 combinations with P < 0.05 were existed in 360 exhaustive combinations. However, combinations possibly include several false positive significant combinations. Therefore, we paid attention to the SNP, of which P-value effectively decreases by combining with genotype or allele of other SNPs. We defined effective combination value (ECV). ECV2 or ECV3 is the ratio of 2 or 3-SNPs P-value to the product of each P-value. ECV is not indicator for avoiding false positives but for evaluation of interaction. For example, in 2 SNP combinations, when patients were limited with certain pattern of another SNP, such as AA major homozygote of SNP A, patient distribution of the SNP X of interest is investigated (P = Pax). If the 2-SNP combination is independent (no interaction) each other, Pax equals multiplication of Pa and Px. ECV<1 means that the 2-SNP combination is not independent and two SNPs have any interaction each other.To evaluate NECV2<1 = 47). Among 13 combinations with P < 0.05 mentioned above, 11 combination also satisfied the same condition (NECV2<1,P = 11). When ECV2<0.5, NECV2<0.5 decreased the number to 27 and 10 of NECV2<1,P = 11 still remained. When ECV2<0.1, NECV2<0.1 became small and it was thought that positive combination may be lost. In the case of 3-SNP combination, only 20% of the total combination satisfied the condition P < 0.05. The combinations of 12% among the total combinations (43 combinations) satisfied ECV3<1. All of these 43 combinations also satisfied the condition P < 0.05. From these results, it was concluded that P < 0.05 is not strict criterion for 3-SNP combination analysis. In the case of 2-SNP combination, ECV2<0.5 was adequate as a selection of effective combination, because 77% of the combination with P < 0.05 still remained. From these consideration, we selected these two evaluation bases  in order to determine effective combinations and the combination with NECV<0.5,P (P < 0.05 and ECV<0.5) was picked up. The combinations were used for the following investigation. The number of combination which satisfies the condition, P < 0.05 and ECV2<0.5 in 2-SNP combination and P < 0.05 and ECV3<0.5 in 3-SNP combination was designated as Nef, the number of effective combination, respectively  for the 10 SNPs selected by PDM, 0.64 (23/36) for combinations between the 10 selected SNPs and the remaining 15 SNPs, and 0.08 (3/36) for combinations of the remaining 15 SNPs (Table Ncom/15P2 shown in Table 15P2) for the 10 SNPs selected by PDM, 0.5 (300/15P2) for combinations between the 10 selected SNPs and the remaining 15 SNPs, and 0.35 (210/15P2) for combinations of the remaining 15 SNPs . This is about 3% of the value multiplied each P-value of 2-SNP combination (0.5060). For patients with genotype GA of IL-4Rα  and genotype CT of C3 (4896 C/T), patient frequency against genotype of C3 (1692 G/A) had a P-value of 0.01784. For C3 (1692 G/A) alone, a P-value of 0.6993 was obtained, which was 40 times greater than the P-value of the 3-SNP combination. Thus the rate of correct identification of effective combinations evaluated by adjusted P-value and ECV selected based on PDM trials was higher than the corresponding randomized rate, implying that the ANN can reliably select SNP combinations that are associated with CAA.In the next step, 3-SNP combinations were analyzed. The effective rate, the random selection rate, and the concentration ratio were calculated as well as the case of 2-SNP combination Table . It was CysLT2 (2534 A/G) and IL-4Rα , among subjects with a CysLT2 (2534 A/G) genotype of AG or GG , there was an important correlation with IL-4Rα  genotype of GG, GA, AA (P = 0.00030). We examined the distributions of important combinations among subjects. A total of 52 CAA subjects and 24 healthy controls had genotype AG or GG at CysLT2 and genotype GG at IL-4Rα (148 G/A)  Figure .IL-10 (-571 C/A), IL-4 (-590 C/T) and C3 (1692 G/A) is a susceptible factor of CAA (P = 0.00426). No association with CAA was found for any of these 3 SNPs alone . Subjects with genotype CA at IL-10 (-571 C/A), genotype CT at IL-4 (-590 C/T)  and genotype GG at C3 (1692 G/A)  were estimated to be at high risk for pathogenesis of CAA. Furthermore, among the subjects with the same genotype pattern, the number of subjects with genotype AA at C3 (1692 G/A) were CAA, 3 and healthy controls, 13, respectively  and TT genotype at C3 (4896C/T) was 4 times the number of controls with that genotype combination  (P = 0.00271). There are no previous reports of association between these genotype combinations and CAA. The combination of IL-4Rα  and IL-4 (-590 C/T) was also associated with CAA (P = 0.00689); association between allergic asthma and this combination has previously been reported [Other remarkable combinations shown in Table reported ,16.IL-4Rα polymorphisms may be associated with enhancement of IL-4Rα function [IL-4, it was reported that the -590T allele increases the strength of the IL-4 promoter compared with the -590C allele [C3-deficient mice challenged with allergen show diminished airway hyperresponsiveness and lung eosinophilia, with dramatic reduction of the number of IL-4-producing cells and attenuation of IgE responses [C3 (4896 C/T) and genotype GG at IL-4Rα (148 G/A) may be associated with CAA, but details of interaction between these polymorphisms combinations and development mechanisms have not been clarified. The present findings indicate that, among subjects with an IL-10 (-571 C/A) genotype of CA and an IL-4 (-590 C/T) genotype of CT, there is important correlation with a C3 (1692 G/A) genotype of GG or AA /R5511827G) combination of function . As concC allele . C3 is aC allele . C3-defi27G combiA Figure .IL-4 (-590 C/T), AG or GG genotype at CysLT2 (2534 A/G) and GG genotype at IL-4Rα (148 G/A) were estimated to be at high risk for pathogenesis of CAA (P = 0.00022). However, 2-SNP interaction between CysLT2 (2534 A/G) and IL-4Rα (148 G/A) (P = 0.00030) markedly affected the 3-SNP interaction.CysLTs, which are produced by inflammatory cells including eosinophils, are mediators of leukotrienes, and have been implicated in the pathogenesis of allergic diseases. Recently, it has been reported that CysLTs can act as autocrine or paracrine mediators to stimulate rapid, nonexocytotic release of IL-4 . These fIn the present study, we examined correlation between CAA and 25 SNPs in 17 genes using an ANN model. We think that there are not a few main effects and interactions which can explain development of multifactorial disease like CAA, because it is thought that interactions of genetic risk factors might be different individually among CAA patients in spite of same disease. So it is very important to select multiple genetic factor models associated with multifactorial disease like CAA with high concentration ratio. We found that 10 of these SNPs are important factors in development of CAA. Important combinations among these 10 SNPs were also extracted. As described above, several of these combinations  for homozygote of the major allele, (0.1, 0.9) for heterozygote, and (0.9, 0.9) for homozygote of the minor allele. Positive and negative diagnoses were also converted into numerical data: 0.9 and 0.1, respectively.Nerror (number of missed points) and Er (sum of squared error) were defined and calculated for learning data and evaluation data as follows:For SNP analysis, we used a three-layered ANN with input, hidden and output layers Figure . For modNerror = Nerror,l + Nerror,e     (3)Y and T represent the predicted value and the teacher value, respectively. Nl and Ne represent the number of subjects as learning and evaluation data, respectively. Nerror is the number of output data with an error of >0.4 between the predicted value and teacher value as shown above. Er is calculated with the square of error as shown above. For ANN learning, the connection weights were initially randomly set from 0 to 1, and were altered using the back propagation methods [Erl. Learning rates of 0.1, 0.2, 0.3, 0.4 and 0.5 were examined. The maximum learning time was 2000 iterations. The best ANN model  was that in which Nerror reached minimum within the maximum learning time. When minimum Nerror was equal to that of other models within the maximum learning time, the model with minimum Er value was selected.where  methods  with leaPrediction accuracy of the constructed model was defined as follows. Threshold was set at 0.5. If the teacher value was 0.9, and the predicted value was greater than 0.5, the prediction was true ; for predicted values lower than 0.5, the prediction was false . If the teacher value was 0.1, and the predicted value was lower than 0.5, the prediction was true ; for predicted values greater than 0.5, the prediction was false .Ac) as follows:We calculated the prediction accuracy (Se) and specificity (Sp) of predicted values were defined as follows:The sensitivity  after the ANN model with 25 SNPs was constructed. In PDM, 1 SNP was excluded from input variables in turn, and ANN models were constructed with the remaining 24 SNPs by performing the cross-validation described below. From among the 25 models thus constructed, the model with minimum Cross-validation allows estimation of the prediction error of a model by leaving out a portion of the data as an evaluation data . In the An LR model was constructed using SPSS 11.5J statistic software for Windows (SPSS Japan Inc., Tokyo), for comparison with the ANN model. All 25 SNPs were used as input variables of LR. For LR analysis, we used 50 main effects plus an intercept but not any interaction terms. As with the ANN model, the data set was divided into 5 groups and the cross-validation was performed. Prediction accuracy, sensitivity and specificity were calculated.P-value using a χ2 test. The χ2 test was used to evaluate the differences in frequencies of alleles or genotypes between cases and controls. The P-values shown in Table P-value with χ2 test. In order to determine important combinations, we use two evaluation bases ) mentioned in Results section.We also examined association between CAA and combinations of SNPs by calculating P-value using a χ2 test. ST and YH carried out the basic analysis using ANN and data preprocessing. YS and TS participated in providing of SNP data and the design of LR analysis. TK participated in the design of the study. HH conceived of the study, and participated in its design and coordination. All authors read and approved the final manuscript.YT carried out ANN modeling of SNP data including PDM and calculating


The fourth-year Obstetrics and Gynaecology course at our institution had previously been taught using theory classes alone. A new teaching model was introduced to provide a better link with professional practice. We wished to evaluate the impact of the introduction of case discussions and other practical activities upon students' perceptions of the learning process.Small-group discussions of cases and practical activities were introduced for the teaching of a fourth-year class in 2003 . Comparisons were made with the fourth-year class of 2002 , from before the new programme was introduced. Students were asked to rate their satisfaction with various elements of the teaching programme. Statistical differences in their ratings were analysed using the chi-square and Bonferroni tests.Group II gave higher ratings to the clarity of theory classes and lecturers' teaching abilities (p < 0.05) and lecturers' punctuality (p < 0.001) than did Group I. Group II had greater belief that the knowledge assessment tests were useful (p < 0.001) and that their understanding of the subject was good (p < 0.001) than did Group I. Group II gave a higher overall rating to the course (p < 0.05) than did Group I. However, there was no difference in the groups' assessments of the use made of the timetabled hours available for the subject or lecturers' concern for students' learning.Students were very receptive to the new teaching model. In Brazil, medical school courses last for six years and demand full-time study. In our school, the curriculum follows the traditional model, and it is divided into the basic cycle (first and second years), clinical cycle (third and fourth years) and pre-intern cycle .very weak, weak, regular, good or very good. The annual report consists of the evaluations on each subject, for all the above-mentioned variables, and is handed in to each lecturer in charge and the department representative [The practice of institutional self-evaluation, especially for educational institutions, has become part of the country's recent culture . It has entative .The teaching of medicine centred on diseases and hospital care and, consequently, centred on the less prevalent disorders, has arisen as a consequence of the current curricular model. This has proven to be inadequate, inefficient and onerous for Brazil's health care sector. Students' participation in the health system is practically non-existent in the basic cycle of the current medical curriculum. In this light, over the last 15 years or so, there has been a series of movements among institutions, aiming towards changing the Brazilian medical school system .In addition to the implementation of curricular directives of greater efficacy, other measures may stimulate medical students and better prepare them for professional practice. Such invigorating measures may include the linking of basic sciences with all the phases of the professional cycle, scientific initiation programmes that are accessible to all students, support programmes and academic guidance .To adapt our medical sciences course to these new concepts within the Brazilian setting of health care and medical teaching, a committee for discussing and drawing up a new teaching system was set up. The process began in the second semester of 2000 and has gradually involved more and more of the lecturers, students, members of the board of directors and the school's sponsoring foundation.Thus, in the new model that was proposed, problem-solving techniques would be the main teaching tool . The modConsequently, the content of the fourth-year programme was restructured for the 2003 academic year, with the aim of providing the new teaching tool of clinical case discussions alongside the learning of theory. Students' responses to these changes were assessed in comparison with the 2002 course. Our working hypothesis was that we would be promoting four positive actions: a) integration of theoretical and practical learning from the beginning of the students' contact with the speciality; b) greater consolidation of knowledge of the speciality; c) optimisation of the time that is made available for the speciality in the fourth year of the course; and d) preparation of the staff for the curricular model that would be implemented in the coming years.Each year-group of the medical course at Centro Universitário Lusíada (UNILUS) consists of 120 students. The Obstetrics and Gynaecology course is given in the fourth year  and the fifth and sixth years . Up to and including 2002, the fourth-year course was taught by means of theory classes only, which aimed to cover all normal aspects of Obstetrics and Gynaecology and principal diseases encountered. Subsequently, during the pre-intern cycle, other relevant topics concerning disorders within the speciality were dealt with through visits to patients and seminars prepared by the students under staff supervision. In 2003, small-group discussions of cases and practical activities based on the normal aspects and diagnosis methods of the speciality were introduced for the fourth year. These activities replaced 50% of the theory content . The theory content taken from the fourth-year curriculum would be taught during the pre-intern cycle, together with the practical learning.A large proportion of the staff was mobilised. Each week, during the four hours of teaching, students were divided into two groups. Sixty students attended two theory classes (one topic within Obstetrics and the other within Gynaecology), while the other sixty students were divided into six groups of ten students for discussions of clinical cases or practical activities. The two groups of sixty students alternated throughout the year. It should be stressed that the theory classes were taught by the same lecturers, using the same teaching material, during the two years of the present study.new methodology – activities in small groups was introduced. Each of these items was rated by the students as very weak , weak, regular, good or very good. Group I consisted of 108 fourth-year students on the medical sciences course who answered the questionnaire in 2002 and group II consisted of 113 fourth-year students on the course in 2003.The study began with a standardised SEP questionnaire that was administered to all fourth-year students in the second semester of the teaching year. This was done on the day of their assessment test, to ensure full attendance. The questionnaire bore the institution's official stamp and consisted of several questions, as described in the introduction, above. The questions used in the present study sought ratings for the use made of the timetabled hours available for the subject, lecturers' concern for students' learning, clarity of theory classes and lecturers' teaching abilities, lecturers' punctuality, quality of the assessment tests, students' learning of the subject and general evaluation of the subject. In the 2003 questionnaire, the variable ® Excel 2002 for later evaluation. For analysis purposes, positive evaluations were considered to be the sum of the good and very good ratings, and negative evaluations the sum of the weak and very weak ratings. For statistical analysis, the variables were represented by absolute (n) and relative (%) frequency, and the difference between them was analysed using the chi-square test (χ2). The significance level adopted was 0.05 (α = 5%), and descriptive levels (p) that were less than this value were considered significant and marked by an asterisk (*). Significant values were also submitted to the Bonferroni test to ratify their statistical value.The findings were tabulated using Microsoft Group I consisted of 66 female (61%) and 42 male students (39%), whose average age was 23.1 years, while group II consisted of 67 female (59%) and 46 male students (41%), whose average age was 23.6 years. There was no significant difference between their ages.In group I, 95 students (88%) gave a positive rating for the use made of the timetabled hours available for the subject and 13 (12%) gave a regular rating for it, whereas 101 students in group II (89%) gave a positive rating and 12 (11%) a regular rating. There was no significant difference in this evaluation between the two groups  and from 98 students (87%) in group II. This was a statistically significant difference Table  – item 3Lecturers' punctuality received a positive evaluation from all 108 students in 2002, but only from 72% (81 students) in 2003, and this was statistically significant (Table In 2002, 60 students (56%) rated their learning of the subject as good or very good, while in 2003, 97 students (86%) rated it as positive, which was a significant difference Table  – item 6The new methodology adopted in 2003 for the Obstetrics and Gynaecology course was considered to be good or very good by 89% of the students, regular by 8% and weak or very weak by 3%.The traditional curriculum model was developed with reference to the Flexner report of 1910 . In thisOn the other hand, the teaching concept of meaningful learning calls for linkage between the roles of universities, health care administrators and social services. It suggests that there should be co-operation in the selection of content, production of knowledge and development of professional competence. In meaningful learning, the teacher is no longer the main source of information, but the facilitator of the teaching-learning process. The teacher's aim is to stimulate the learner to take on an active, critical and reflective attitude in the knowledge building process. The content dealt with must have the potential to be meaningful , giving value to matters that are pertinent and correlatable with students' cognitive structure. However, the absorption by students of knowledge of the so-called basic subjects in this context presents a great challenge .The curricular directives for medical courses (Report 583/01, of August 7, 2001) from the Brazilian National Education Council (part of the Ministry of Education) give guidance on the changes to be made to the teaching model for courses. They indicate that courses must involve students in practical activities from the outset and promote active integration between health care service users and professionals from the beginning of their instruction, using methodology which reinforces students' active participation in knowledge-building, thereby bridging the gap between academic medical learning and the social needs of Brazilian health care. It is evident that the new curricular directives have used the concepts and logic of problem-based learning as their reference point. They have been based on various American and European curricula that, over the past decade, have been giving emphasis to free time for self-study instead of traditional lectures -14. ThusIn the "problematization" methodology based on Maguerez's Arch, as presented by Bordenave , five phIn problem-based learning, the cognitive objectives are all previously established, while in "problematization", total control over the resultant knowledge does not exist. The essence of problem-based learning is that the problems define objective concepts to be learned and non-objective concepts that can be excluded from the learning because they are not relevant to the study in question .Although it may be difficult and scientifically dangerous to compare results from conventional curricula (lecture-based learning) and models like problem-based learning or "problematization" -23, thisThe present study does not prove that the "modernised" curriculum is better than the previous one, but it emphasises that the strengths of the "new" curriculum are worthy of more exploration. In our opinion, the perception that a qualitative improvement in students' learning has taken place during the course is the first step towards a more substantial and effective change in the teaching-learning process. In the present study, the intention was to transform a totally theoretical course into a more stimulating and efficient course. In this, concepts acquired during classes would be applied clinically to real cases obtained by the students themselves in the wards. A recent study at Manchester University  has showThere was no significant difference in students' evaluations of the use made of the time available for the subject between the two groups, because there was already a positive assessment among the 2002 group Table  – item 1An improvement in the assessment of the course can be seen from item 3 of Table The decrease in the rating of lecturers' punctuality can be easily explained by the fact that the theory classes were always predictably held in the same place in 2002 (group I), while group II used various locations that were specially booked for them. On some occasions in 2003, unexpected events occurred at the beginning of the activities Table  – item 4Assessment tests for Obstetrics and Gynaecology are traditionally considered to be difficult. There was a perception in our school that they did not reflect the overall knowledge of the subject that is required. The tests consist of forty to fifty multiple-choice questions  and five essay-type questions. The former perception can be seen among the 2002 year-group in item 5 of Table One of the most important objectives in a change in the teaching system is to obtain greater course efficiency and increased student learning. Items 6 and 7 of Table Students responded well to the new method, as shown by the positive rating of 89% given by the 2003 year-group. This provides us with the basis for further advances in this subject in the years to come. It gives the staff the confidence to institute significant changes in the curricular reform that has been under discussion for three years.Although the staff's level of satisfaction was not an objective of our study, initial observation of this indicates great commitment to the course and, probably, better performance. However, it will only be through future longitudinal studies that we will know whether there has really been greater consolidation of knowledge and course efficiency.Students were very receptive to the new teaching model in this study. An active role in their learning process seems to be more pleasant and productive than usual method. Thus, active learning methodology should be stimulated on the medical courses throughout the world.The author(s) declare that they have no competing interests.All authors participated in the study design and application. JCM was the study coordinator. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:


A Mental Health First Aid course has been developed which trains members of the public in how to give initial help in mental health crisis situations and to support people developing mental health problems. This course has previously been evaluated in a randomized controlled trial in a workplace setting and found to produce a number of positive effects. However, this was an efficacy trial under relatively ideal conditions. Here we report the results of an effectiveness trial in which the course is given under more typical conditions.The course was taught to members of the public in a large rural area in Australia by staff of an area health service. The 16 Local Government Areas that made up the area were grouped into pairs matched for size, geography and socio-economic level. One of each Local Government Area pair was randomised to receive immediate training while one served as a wait-list control. There were 753 participants in the trial: 416 in the 8 trained areas and 337 in the 8 control areas. Outcomes measured before the course started and 4 months after it ended were knowledge of mental disorders, confidence in providing help, actual help provided, and social distance towards people with mental disorders. The data were analysed taking account of the clustered design and using an intention-to-treat approach.Training was found to produce significantly greater recognition of the disorders, increased agreement with health professionals about which interventions are likely to be helpful, decreased social distance, increased confidence in providing help to others, and an increase in help actually provided. There was no change in the number of people with mental health problems that trainees had contact with nor in the percentage advising someone to seek professional help.Mental Health First Aid training produces positive changes in knowledge, attitudes and behaviour when the course is given to members of the public by instructors from the local health service. Community surveys have shown that the public in many countries have poor mental health literacy . Many peTo help improve mental health literacy, a Mental Health First Aid training course has been developed. This course uses the first aid model that has been successfully applied to training members of the public to help in accidents and emergencies . The MenAn initial uncontrolled evaluation of the course involved comparing the first 210 participants at the beginning and end of the course, and at 6 months follow-up . The couThe next stage in the evaluation of Mental Health First Aid involved a randomized controlled trial with 301 employees of two large government departments . ParticiThis study involved an "efficacy" trial in that it was carried out under fairly ideal conditions which permitted rigorous experimental control. There was only one instructor who was the originator of the Mental Health First Aid course and very experienced, the trial was carried out in a workplace setting where employees were allowed time off to participate, the participants were a relatively well educated group of civil servants, and it was possible to randomly allocate participants relatively easily. In order to evaluate the course under more typical circumstances, we have now carried out a second trial. This "effectiveness" trial involved members of the public in a large rural area of Australia, who were taught by trained Mental Health First Aid instructors from the local health service. As in the previous trial, participants who received training were compared to a wait-list control group. Participants were randomized by Local Government Area clusters rather than individually because (1) there might have been contamination of information provided across allocated groups (2) the wait list group might have been difficult to maintain if others in the same locality were seen to be receiving training, and (3) individual randomization in some small communities may not have produced sufficient numbers to run a course.The reason for basing the trial in a rural area is that people living in rural Australia are less likely to receive general practitioner services for common mental disorders and also have more limited access to specialist mental health services ,6. ThereThe details of this trial have been reported according to the CONSORT statement for cluster randomized trials .Eligible participants were residents of the catchment area of the New South Wales  Southern Area Health Service who were over 17 years of age, who volunteered for training in response to publicity, who were available over the period of the trial, and who were willing to receive interviews assessing trial outcomes. Participants had to volunteer as individuals rather than as a group (e.g. a whole workplace). Publicity took the form of talks to community groups, newspaper ads, a press release and radio interviews.Eligible clusters were the 16 Local Government Areas (cities or shires) in the catchment area of the Southern Area Health Service in 2003. This catchment is located in south-east New South Wales, runs approximately 370 km from north to south and approximately 160 km from east to west, and had a population of 194,435 in 2001. The Local Government Areas varied from popular coastal areas to farming communities to rural towns and ranged in population size from less than 5000 to over 50,000.Participants received a nine-hour Mental Health First Aid course, in three weekly sessions of three hours each. Training was administered in the local area in groups of up to 25 participants, with a minimum of 10 participants per course. As documentation of the intervention, there is a lesson plan for each session and a participants' manual containing material that was given to take away [Training was administered by educators who were recruited from the staff of the Southern Area Health Service. Expressions of interest to become Mental Health First Aid instructors were sought from staff of the Area Health Service and associated community organisations. Five Mental Health first Aid instructors were recruited from a pool of 10 applicants for these positions. All the instructors had experience in mental health work and also a background in training, working with communities or health promotion work. A project coordinator with experience in mental health and health promotion (Ms Karen Peterson), who was employed to work on the project half time, also trained as an instructor. The same instructors taught courses in each paired Local Government Area, so that this factor did not differ between the immediate and wait-list Local Government Areas. The coordinator monitored a sample of courses taught during the trial to assess fidelity to the lesson plans. A fidelity checklist of topics that had to be covered was developed for each session. Four of the instructors had all three course sessions checked, while one of the instructors only had two sessions checked. The percentage of topics covered correctly was 100% for four of the instructors and 81% for one of the instructors.The hypotheses were that individuals trained in Mental Health First Aid, when compared to wait-list controls, would have increased knowledge of mental disorders and their treatments, decreased social distance, increased confidence in providing help, and that they would provide greater help to people experiencing mental health problems.Outcomes were measured in January–February of 2003 (the pre-test assessment), the courses were run for the intervention group in March–April of 2003, and outcomes were measured again in July–August 2003 (the follow-up assessment). The wait-list control group received courses in September–October 2003, after the follow-up assessment was completed.1. not at all to 5. extremely), contact in the last six months with anyone with a mental health problem (yes/no), how many people, whether any help offered (yes/no), what type of help (open-ended question), recognition of the problem in a case vignette (randomly assigned to be a case of depression or one of schizophrenia), what participant would do to help if they knew the person in the vignette , ratings of the likely helpfulness of a range of interventions for the person in the vignette , a sociAll outcomes were measured by a scripted telephone interview administered by professional interviewers. In order to reduce the length of the interview, participants were individually randomly assigned to receive either a depression vignette or a schizophrenia vignette, with the same questions asked in respect to each vignette. The interviewers were provided with an ID, name and phone number of each participant and knew whether they were giving the first or second interview to the participant. While they were not told whether the participant was in the experimental or control group, information about which group they were assigned to was given at the end of the interview script. As far as was practical given the very different sizes of the Local Government Area pairs, the same interviewers interviewed participants in each pair.For power calculations and sample size determination, a conservative assumption was made that the waitlist control group would show improvements, possibly due to increased awareness of mental health issues, of about 50% of that of the experimental group. This corresponds to effect sizes in the range 0.28–0.31 for changes on scales and in the range 0.02–0.04 for changes in identifying the correct diagnosis. Sample size estimates using nQuery Advisor software  indicateRandomization to immediate participation or wait-list was at the level of Local Government Area. The Local Government Areas were matched in pairs to have similar population and social characteristics. The variables used for matching were population size, interior vs coastal location, and an index of population education/occupation. The first listed LGA of each pair was assigned to the immediate or wait-list group at random, using the Random Integers option of Random.org  to generEach individual participant was randomly assigned a variable  to determine which case vignette they received during their interviews. This was done using the Random Integers option of Random.org . Those aAllocation was on the basis of cluster. In other words, the participant's Local Government Area determined whether they received immediate or wait-list training. Participants were not informed about their allocation to immediate or wait-list training until the end of their baseline interview.Local Government Areas were matched in pairs and Anthony Jorm assigned these randomly to immediate training or wait-list. Participants were not able to attend a class from outside their own Local Government Area. There was a recruitment period for all Local Government Areas which was organized by the coordinator Karen Peterson. The coordinator and the participants who were recruited were blind to the allocation of the Local Government Area during the recruitment period. Anthony Jorm revealed the allocation to Karen Peterson after the recruitment period ended. Karen Peterson then organized class times either immediately or after a waiting period, depending on the allocation of each Local Government Area in the pair.At the time of the baseline interview, the participants did not know whether they were in an immediate or wait-list Local Government Area. However, interviewers had information at the end of the interview script telling whether the participant was assigned an immediate class or had to wait. Blinding of participants was not possible at subsequent interviews. Participants knew whether or not they had received training. While interviewers were not told the allocation of the participants in subsequent interviews, this might have become obvious during the interview if participants mentioned whether or not they had done the course. Interviewers were given a scripted interview to minimize any bias in the assessment due to knowledge of allocation.Ethical approval for the study was given by the Australian National University Human Research Ethics Committee and by the ethics committee of the South Western Sydney Area Health Service.For outcomes measured on a numeric scale, the change from pre-test to follow-up was analysed using linear regression. For binary outcomes, individuals scoring the same at pre-test and at follow-up were not used, and for those who changed, the direction of change was analysed as a binary outcome using logistic regression. Standard errors and p-values were adjusted for the cluster design using the Huber-White "sandwich" variance estimator, treating the 16 LGAs as the clusters. Analyses were corrected for differences between the LGA pairs by including this as an 8-level fixed-effect factor in the regression models. Missing data were imputed using best-subsets regression. All analysis was done using Stata version 8.2 .Recruitment of participants took place in October and November of 2002. Figure Table The data were analyzed by an intention-to-treat approach, with single imputation used for missing data. As shown in Figure Tables The intraclass correlations for the continuous outcomes were: for agreement with health professionals about treatment, 0.15 ; for number of people in contact with that had a mental health problem, 0.02 (0, 0.06); for confidence in providing help, 0.03 (0, 0.07); for mental health first aid intention, 0.002 (0, 0.02); and for social distance, 0.04 (0, 0.08). Thus for all but one outcome, the correlation was small, justifying our assumption of a modest design effect.Given that an educational intervention was evaluated with a non-clinical sample, there was no justification for a systematic inquiry into adverse events. Informally, no adverse events were reported.This study has found that the Mental Health First Aid training produced a number of significant changes in participants compared to a wait-list control group. A number of changes related to how people responded to a vignette of a person with either depression or schizophrenia. We found that there was greater recognition of the disorders in a vignettes, increased agreement with health professionals about which interventions are likely to be helpful, decreased social distance towards the people portrayed in the vignettes. These changes were seen equally with both vignettes. There was also a non-significant trend for those in the trained group to have more ideas for how to help the person in the vignette if it had been someone they knew.Other outcomes with significant changes related more directly to the provision of mental health first aid. There was increased confidence in providing help to others and an increase in help actually provided. There was no change in the number of people with mental health problems that trainees had contact with or in the percentage advising someone to seek professional help.One potential concern of Mental Health First Aid training is that it will lead to over-diagnosis of life problems as mental disorders. In previous trials we have found no evidence that the training affects the perception that the participant or their family have mental health problems ,4. By coThese findings are similar to those of the earlier efficacy trial. However, the courses were taught by instructors who were not the originators of the Mental Health First Aid program under conditions which more closely approximate those that are typical in practice. The findings are therefore more generalizable than those reported previously.While the more typical conditions of this trial are an advantage for generalizability, they produced greater practical difficulties in running the trial. An important weakness was that attendance data on participants were not collected by some of the instructors. We are therefore uncertain what proportion of the participants received the complete training course. A similar problem was determining the adherence of the instructors to the curriculum. We were able to carry out some formal observation of the instructors'adherence to a list of topics covered by the curriculum and found 100% adherence for most of the instructors, but one had only 81% adherence.Another limitation of this study is that we did not directly measure the mental health of participants. In the earlier trial, we unexpectedly found a mental health benefit and this requires replication. The reason that a mental health measure was not included was that we did not have the results of the earlier trial at the time we designed this one. Another factor was the limited time available in the telephone interviews used to assess outcomes.We used an intention-to-treat approach to the data. Whereas many trials use a last observation carried forward approach to handle missing post-test data, we used data imputation by best-subsets regression. This approach is likely to give better estimates than conventional approaches to missing data even when the missing-at-random assumption is not met .Since this and the earlier trials were started, the Mental Health First Aid course has been extended from 9 to 12 hours on the basis of consistent requests from trainees for a longer course. The longer course does not add new content, but rather extends the time available to deal with each topic. We have yet to evaluate whether this extension adds to the effectiveness of the training.A nine-hour Mental Health First Aid training produces positive changes in knowledge, attitudes and behavior when the course is given to members of the public by instructors from the local health service. This finding shows that the effects of the course are generalizable beyond its originators and when run under typical conditions.BAK and AFJ were the developers of the Mental Health First Aid course.AFJ was involved in securing funding for the study, had a major role in the design of the study, co-developed the evaluation questionnaire, contributed to the data analysis and had a major role in writing the manuscript.BAK was involved in securing funding for the study, developed and taught the Mental Health First Aid Instructor course, had a role in the design of the study, co-developed the evaluation questionnaire, organized the outcome assessment and had a minor role in writing the manuscript.ROK was involved in securing funding for the study, had a role in the design of the study, had a major role in planning and managing the trial's implementation in its initial stages, recruited and supervised the study staff, established and maintained organisational support in the Southern Area, and had a role in the writing of the manuscript.KBGD had a major role in the data analysis and a minor role in writing the manuscript.All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:


A key step in the analysis of microarray expression profiling data is the identification of genes that display statistically significant changes in expression signals between two biological conditions.We describe a new method, Rank Difference Analysis of Microarrays (RDAM), which estimates the total number of truly varying genes and assigns a p-value to each signal variation. Information on a group of differentially expressed genes includes the sensitivity and the false discovery rate. We demonstrate the feasibility and efficiency of our approach by applying it to a large synthetic expression data set and to a biological data set obtained by comparing vegetatively-growing wild type and tor2-mutant yeast strains. In both cases we observed a significant improvement of the power of analysis when our method is compared to another popular nonparametric method.This study provided a valuable new statistical method to analyze microarray data. We conclude that the good quality of the results obtained by RDAM is mainly due to the quasi-perfect equalization of variation distribution, which is related to the standardization procedure used and to the measurement of variation by rank difference. In a typical microarray experiment, thousands of genes have their relative expression levels measured in parallel under different biological states ,2. To idWe describe here, in detail, a new analysis method that has been used to analyze the transcriptome in yeast . This meAnalysis of synthetic data sets allowed us to specify the error distribution of all the estimators used (FDR, TV and S), and to demonstrate the strong predominance that the number of varying genes and the distribution of their variation have on the quality of the results.S. cerevisiae, TOR2 has two essential signaling functions. One, shared with TOR1, is required for translation initiation, transcription, and cell growth in response to the presence of nutrients [We also analysed the transcriptional effects of the TOR2-controlled signaling function using a genome-wide microarray approach in yeast. In utrients -10. The utrients ,11. A prutrients . In thisutrients .The simplest system to which our method can be applied comprises three experimental points, of which two are replicates, as described by the expression {Exp1A, Exp1B, Exp2A}, where the number refers to the biological condition and the final letter refers to the replicates. To identify significant variations in the comparison Exp2A vs. Exp1A, we have to first calculate the variation of gene i, VARi. From among several possibilities, we tested three different variation units: the fold change (FC), corresponding to the ratio of signals, the signal difference (SD), and the rank difference (RD). The RD uses a standardized signal measure that is independent of the scanner settings, because the signal is replaced by its rank, expressed on a 0–100 scale. This normalizing procedure consists of first calculating the absolute rank (AR) of each gene by ordering their signals from 0 to N  and then transforming the absolute rank value into a relative one (R = AR*100/N). In this way, all the signals are expressed on the same scale and are directly comparable.We studied the variation distribution between the two replicates, i.e. Exp1A and Exp1B, reasoning that the observed empirical variation distribution would be an excellent approximation of the null distribution corresponding to the null hypothesis. The null hypothesis we have in mind states that all observed mRNA changes occurring under replicated conditions are due to a combination of biological and technological noise, and are not the result of any biologically significant process.i(Exp1A) - Ri(Exp1B)| vs min{Ri(Exp1A), Ri(Exp1B)} for gene i). This mode of presentation can be interpreted in either of two ways: as a plot of the positive variations of both comparisons, or as the plot of the positive and the negative variations of a single comparison, with both of variation being represented by a positive number. Whichever interpretation is prefered, it should be underlined that this presentation allows all the gene variations to be taken in account, and ensures the uniqueness of the resulting variation distribution.We first restricted our study to positive variations. As the distribution of positive variations should be the same in both comparisons – Exp1B vs Exp1A and Exp1A vs Exp1B – we plotted the positive variations against rank for both comparisons on a single graph. This revealed that the most salient property of variation distribution, common to all tested measures of variation, is its dependence on the signal rank. This is exemplified for the RD in figure We tried to eliminate dependency of positive variation distribution on the signal rank by standardizing variations according to the general formula:i, the rank of gene i. This notation reflects the fact that the VAR distribution is not gene specific, but rank dependent.where VAR is to be replaced by any one of the variation units tested (SD, FC or RD). Using this expression, the sample mean and standard deviation (μVAR and stdVAR) were calculated for all genes having a rank within a given neighbourhood of Ri was standardized according to (1). Figure Figure Comparable results are obtained if the standardization is applied to the FC or the SD, but the zRD gives the best results in terms of distribution equalization: figure i follows the same zRD distribution. Therefore, we reasoned that the empirical cumulative frequency distribution, ecfd(zRD), approximates the distribution of zRD for any gene i under the null hypothesis, and we used Fo = 1 - ecfd(zRD), based on a comparison between two replicated experiments, to assign a p-value to any zRD calculated in a comparison between two different biological conditions . Because of the very large number of genes present on a chip, the null distribution is sampled a great number of times, generating a quasi continuous set of points that spans a wide range of values. This improves the precision of the Fo curve and allows accurate p-values to be assigned for even large variations.The fact that the zRD distribution is independent of rank can be explained by the fact that each gene's zRDThe entire procedure can then be applied to the comparison Exp2A vs. Exp1A. Because standardization curves constructed on the basis of the two replicates are used in the standardization process, the calculated zRD can be justifiably compared to the null distribution and interpolated on the Fo curve in order to assign a p-value to each gene variation. At this step positive and negative variations can be processed together, although it is necessary to keep track of the actual type of variation, i.e. positive or negative, in order to conduct subsequent analysis.i(Exp1A) - Ri(Exp1B)| vs max{Ri(Exp1A), Ri(Exp1B)} for gene i). It is clear that the weak signals are characterized by a truncation of their variation distribution, as evidenced by the clear alignment of points between ranks 0 and 20. This explains why the standardization procedure applied in figure We also tested to see if it is possible to apply the same standardization techniques to negative variations. In figure INC for the positive variations and FDEC for the negative variations (in both cases F = 1 - ecfd (zRD)). Then we plot FINC (or FDEC) and F0 on the same graph, corresponding to the observed positive (or negative) variation distributions and to the expected variation distribution according to the null hypothesis, respectively (figure INC or FDEC (same remark for TV, FDR, S, K and N). In most encountered situations, F is on top of F0. F(x) gives the probability, for any variant or invariant gene i, of observing a zRDi that is at least as high as x, and NF(x) gives the corresponding number of genes. If 0 <= K <= 1 is the fraction of invariant genes, then KNF0(x) is the number of invariant genes that have a zRDi at least as high as x. As a consequence, NF(x) - KNF0(x) is an estimate of the number of variant genes with zRD equal to or greater than x. We can call k the value of x that gives the maximum number of variant genes, such as NF(k) - KNF0(k) = max(NF(x) - KNF0(x)), and use this value as an estimate of the total variation (TV), that is, the number of truly varying genes. As these quantities must verify the equation N = KN + TV, i.e., N = KN + NF(k) - KNF0(k), we can deduce that .Because the test is repeated N times, issues related to multitesting must be considered: the more tests that are performed, the more an outlier outcome becomes probable. In view of this, we first compute the observed distribution of zRD in the comparison Exp2A vs. Exp1A for increased and decreased genes, giving two curves: Fy figure . In the For each value of x, we estimate the False Discovery Rate,and the sensitivity,In the context of a transcriptome analysis, p-values reflect how probable it is that a variation reaches or exceeds an observed value. P-values can always be used to rank genes, but the selection of significant variations in the context of multiple testing requires defining significance levels that are far more stringent than 0.01 or 0.05, as used in single testing. FDR and S parameters allow this difficulty to be overcome: for each c used as a potential critical value, S(c) reflects the fraction of truly-varying genes that are selected by zRD>c, and FDR(c) estimates the fraction of selected genes that are likely to be invariant genes. It is therefore possible to plot FDR and S against c, and to construct, for positive and negative variations, what we call a selection abacus figure .The simplest example of a replicated experimental scheme is the system {Exp1A, Exp1B, Exp2A, Exp2B}. While it would be tempting to average signals or ranks for each experimental condition and apply the method described above, this is not possible because averaging changes statistics and we have no practical way of obtaining the corresponding empirical null distribution.i and p2i. It could occur that gene i is detected as an increasing variation in the first comparison and as a decreasing variation in the second comparison. In this case, we apply a direction rule to decide on the final direction of variation. We consider simply that the lowest p-value is in favor of its corresponding variation direction, and we set the p-value of the discordant comparison to one. Once we have calculated and possibly corrected the p-values, we construct a new random variable, the product of p-values, ppvi = p1i × p2i. To obtain an unbiased value for ppv, we apply the same procedure to a second round of comparison by exchanging Exp2A and Exp2B between the two comparisons, giving a second ppv value. The direction rule is applied to the two ppv before obtaining the final, averaged ppv.In a first round of comparison, we conducted two analyses in parallel by applying RDAM to the first comparison Exp2A vs. Exp1A and to the second comparison Exp2B vs. Exp1B. Based on this first round of comparison, we obtained two p-values for gene i: p1i|H0) is a uniform distribution over the interval [0,1]. Therefore, for cases in which two independent comparisons between two sets of duplicates were to be considered, we constructed two sets, U1 and U2, of 100000 points uniformly distributed over the interval [-1,1], to take into account the possibility of increased and decreased variation for each point. These sets were randomized to make them independent in order to model the independence of measurement according to the null hypothesis. This hypothesis states that all variations are due to noise, and that for a particular gene all corresponding p values must be independent. Then, we apply the direction rule to the pair U1, U2 and calculate ppv for genes that are detected as increased. Thus, the F0 = 1 - cfd(log10(1/ppv)) curve allows the significance of any value for ppv to be tested. The significance of ppv combines the significance of variation within each individual comparison and the significance of the correlation between these variations. F curve is, as usual, the observed 1 - ecfd(log10(1/ppv)), and we get exactly the same kind of selector abacus, as shown in figure The advantage of the random variable ppv is the ease of constructing its null distribution. In fact, the cfd, we simply apply a circular permutation – the circular permutation of ABC consists in the subset of permutations ABC, BCA and CAB – to the replicates inside one of the biological condition which allows the number of rounds of comparison to be restricted to the number of replicates (n).The generalization of the entire procedure to more than two replicates is straightforward. For example, with three replicates {Exp1A, Exp1B, Exp1C, Exp2A, Exp2B, Exp2C}, there are 3 × 2 ways of arranging the experiments in order to obtain different sets of comparisons. Each round of comparison gives three p-values for the gene i - p1i to each gene i. New values Ri are obtained by adding or subtracting to the rank R'i of Exp1A' the rank difference RDi calculated by applying to zRDi the inverse of the normalization function (1). Rank values are finally back-converted into signal values by interpolation of the rank on the graph of signal vs. rank constructed with one of the original data sets. This procedure allows two data sets to be obtained, Exp1C and Exp1D, which are statistically indistinguishable from the original data. To obtain a synthetic data set in which a pre-determined subset of genes receives a significant variation value we can possibly add a second step. We selected in Exp1C and Exp1D a random subset of genes, for example 500 increasing genes and 500 decreasing genes. To these genes, a second random variation value is applied, but instead of drawing random numbers on the interval [0,1], we limit the selection to the interval [0,p]. If we set the limiting p to 0.10, then the variation applied to the subset will have a p-value <= 0.1. For the genes receiving an additional variation contribution, the mean magnitude of zRD that is calculated between the synthetic and the original data is proportional to the magnitude of the applied p-value, as shown in figure In order to test our method, we devised a way of generating synthetic data having similar statistical properties as real biological data. We selected two replicated experiments, Exp1A and Exp1B, as seeds for generating synthetic data, traced standardization curves, and calculated ecfd(zRD) for (Exp1A, Exp1B). We randomly selected half of the genes and exchanged the signals for Exp1A and Exp1B, giving two new data sets Exp1A' and Exp1B'. Random numbers uniformly distributed over interval [0,1] are generated for each gene. Each random number is interpolated on the inverse of the cfd of zRD to assign a random standardized variation zRDThis entire procedure, composed of two successive steps, results in synthetic data sets of high quality, because the generation of data mimics the observed variation of genes. We compared the synthetic data sets Exp1C and Exp1D to the same natural data set Exp1A, and plotted the corresponding zRD of one comparison against the other, as shown in figure We generated several synthetic data sets from the two experiments (Wt_t0a and Wt_t0b) by letting the number of increased and decreased genes equal 0, 100 or 500, and letting the maximum p-value of extra variation equal 0.3, 0.1, 0.05 or 0.01.50, defined as the FDR observed when S = 50%, can be used to demonstrate these effects. Increasing the number of replicates improves the scoring performance, but this improvement is strongly modulated by the magnitude of the applied variation. For example, when the number of replicates equals, successively, 2, 3 and 5, the FDR50 equals, respectively, 85%, 74% and 41% for small variations (p <= 0.3) and 3%, 0% and 0% for large variations (p <= 0.01). For variations that we consider from our experience to be realistic, i.e., p <= 0.10, the FDR50 is equal to 58%, 30% and 5%, respectively. The number of varying genes also has an important impact, since under the same variation conditions, but with 500 increased genes instead of 100, we measured FDR50 values of 24%, 8% and 1%, respectively, which represents a mean decrease in the FDR50 of 20 percentage points.The first question we addressed relates to the effectiveness of our scoring method in discriminating among real variations: does the overall process rank the genes correctly? To address this point, genes were ranked according to their ppv, and the number of hits was computed in a series of sublists of increasing length, selected from the top. From this number of hits, and the number of genes in the sublist, we calculated the real S and FDR. Plotting FDR against S allowed us to visualize the respective effects of the number of replicates, the magnitude of the variations, the number of varying genes, and the direction of variation on the performance of our rank difference method. Figure The second question we addressed is the quality of the FDR and S estimators. The genes were ranked according to their estimated FDR (or S), and the number of hits computed in a series of sublists of increasing length, selected from the top. From this number of hits, and from the number of genes in the sublist, we calculated the mean real FDR and S figure . Both FDFinally, we tested to see whether the independent analysis of positive and negative variations subsequent to standardization was dispensable, or if a one-step procedure could be used instead. In order to illustrate this point, we have constructed, from the experimental replicates Exp1A and Exp1B (sh100 at t = 0 h), a first group of two synthetic replicates having 500 increased and no decreased genes and a second group of two synthetic replicates without changed genes in order to reveal any clear differences that may exist between the one-step and the two-step procedures. Table The generation of synthetic data is also a powerful tool for comparing different methods of analysis. As an example, we conducted a systematic comparison between RDAM and SAM. We selected this method because it is popular, easy to use (there exists an Excel add-in), and can be considered as representative of numerous other nonparametric methods, which apply Monte Carlo procedures to estimate the distribution of the statistics used to quantify the relative difference of gene expression. Figure When strains SH121 and SH100 were shifted to 37°C, RDAM detected roughly 2300–2500 genes as being either increased or decreased in each strain .Our method has been developed within the framework of hypothesis testing and requires knowledge of the variation distribution for each gene when the null hypothesis is verified. The rationale of our approach considers replicated experiments as precisely representing a system in which all genes follow this hypothesis. However, it has long been recognized that the variation distribution expressed as a ratio or fold change is dependent upon the level of gene expression , and we In the algorithmic implementation of our method, we chose to proceed in two steps and deal with increased and decreased variations independently. First, this ensures that symmetrical comparisons (e.g. Exp1 vs. Exp2 and Exp2 vs. Exp1) give perfectly symmetrical results. Second, even if it were possible to devise another method that would allow one to proceed in one step, it seems more logical to consider increased and decreased variation separately. To clarify this point, a clear distinction must be made between up- or down-regulated mRNAs and increased and decreased variation. Regardless of the experimental points that are being compared, one always observes increased and decreased variations, but these variations have no absolute meaning because one only has to reverse the comparison to change the direction of variation. On the contrary, we can speak of up- or down- regulated mRNAs only when a causal effect exists, such as in a differentiation process or a kinetics or drug assay. In other words, a positive variation observed, for example, between two successive time points in a kinetic can be considered as an up-regulation whatever its mechanism – gene or post-transcriptional regulation – but it is meaningless to invoke any particular form of regulation when comparing, for example, two unrelated cancer tissues. In the case of down-regulated genes, the variation distribution of all weakly-expressed genes is truncated, due to the impossibility of a decreasing signal crossing the zero line. In the case of up-regulated genes, we do not observe the same effect for increasing variation of highly-expressed genes, first because the signal distribution is heavily skewed towards low values, and second because the variance of highly expressed genes is very small figure . The obsWe did not try to reduce the amount of raw data when using replicates, and devised a two-step method. A first statistics, the standardized rank difference zRD is constructed on each independent comparison, and p-values are assigned by considering an empirical distribution that matches the null hypothesis. Then a second statistics, the product of p-values ppv, is calculated and p-values are assigned from the null distribution obtained by simulation. Simulation of the null distribution used exactly the same steps that the analysis process follows, i.e. application of the direction rule and construction of the product of p-values, and resulted in a null distribution model we found appropriate seeing, both with experimental and synthetic data sets, that the observed distribution of ppv matches the null distribution when no variation occurs (data not shown). It turned out that this scheme is very flexible and of general applicability: because the second step is rooted in a rigorous statistical method that uses only p-values as input data, it is possible to adapt or to improve the entire process simply by focusing on the first step of p-value estimation. For example, to apply our method to cDNA glass arrays, the only step to be modified would be the variation standardization. Alternatively we could use the segmental approach proposed by Yang and colleagues , which i, see equation 3.8 and [The way in which we estimated FDR is exactly the same as that suggested by B. Efron et al. in their demonstration of the equivalence of empirical Bayes and frequentist approaches (Efron B, Storey J. D. and Tibshirani R.  3.8 and ). We did 3.8 and  could beUsing the empirical noise distribution observed between two replicates, we devised a method for constructing synthetic data sets. Most published methods add noise to a signal that is supposed to represent the true signal of the gene. We showed here that raw signals without denoising could be used and gave excellent result as judged both by the final distribution of signals and by indirect controls such as the preservation of variation distribution and the possibility of successfully analyzing synthetic data substituted for the original data.50, the FDR at S = 50%, as a comparative index between different methods and showed that RDAM has an FDR50 that is 30 percentage points smaller than SAM in the case of three replicates and applied changes with p <= 0.10 , which 0 figure .RDAM is a new statistical method whose performances have been precisely evaluated through extensive analysis of synthetic data sets. When applied to TOR experiment, our method succeeded in finding the few genes of biological interest which were concealed in the mass of varying genes induced by the temperature shift. Comparison with SAM showed that our method obtained the same (if not better) results but with a smaller consumption of chips We conclude that the good quality of the results obtained by RDAM is mostly due to the use of replicates to calibrate the noise and to the quasi-perfect equalization of variation distribution, which is related to the standardization procedure used and to the measurement of variation by rank difference.Saccharomyces cerevisiae strains SH100 and SH121 [Upon harvesting by centrifugation (2 min, 3000 × g) at 4°C, cells were washed once in ice-cold water, centrifuged again, and the cell pellet was flash frozen in liquid nitrogen. Total RNA was extracted using a hot phenol method essentially as described by Schmitt, M.E. et al. .Affymetrix™ S98 Yeast Genome GeneChips, containing 6,400 S. cerevisiae (S288C strain) genes and 600 additional probe sets representing putative open reading frames , were usThe scanned microarray images were analysed using the algorithm implemented in MAS 5.0 (Affymetrix, Santa Clara, CA) and the generated raw data were further processed by scripts written in Matlab language (MathWorks, Natick, MA.). SAM analysis  of synth.Raw data files were uploaded to NCBI's GEO repository under the series number GSE1814 RD, rank difference; zRD, standardized rank difference; FDR, false discovery rate, S, sensitivity; TV; total variationDM and MH initiated and conducted the TOR experiment. DM and PD processed probe preparation and chip hybridization. MB developped and tested RDAM method. All authors read and approved the final manuscript.Table 5 - Genes found decreased in the comparison sh121 vs sh 100 at 0 h and selected at FDR = 10%Click here for fileTable 6 - Genes found increased in the comparison sh121 vs sh 100 at 2 h and selected at FDR = 10%Click here for fileTable 7 - Genes found increased in the comparison sh121 vs sh 100 at 6 h and selected at FDR = 10%Click here for file


Four human coronaviruses are currently known to infect the respiratory tract: human coronaviruses OC43 (HCoV-OC43) and 229E (HCoV-229E), SARS associated coronavirus (SARS-CoV) and the recently identified human coronavirus NL63 (HCoV-NL63). In this study we explored the incidence of HCoV-NL63 infection in children diagnosed with respiratory tract infections in Belgium.Samples from children hospitalized with respiratory diseases during the winter seasons of 2003 and 2004 were evaluated for the presence of HCoV-NL63 using a optimized pancoronavirus RT-PCR assay.Seven HCoV-NL63 positive samples were identified, six were collected during January/February 2003 and one at the end of February 2004.Our results support the notation that HCoV-NL63 can cause serious respiratory symptoms in children. Sequence analysis of the S gene showed that our isolates could be classified into two subtypes corresponding to the two prototype HCoV-NL63 sequences isolated in The Netherlands in 1988 and 2003, indicating that these two subtypes may currently be cocirculating. Coronaviruses are large, enveloped, positive stranded RNA-viruses . The virUntil recently only three human coronaviruses were thoroughly studied. Human coronavirus OC43  and human coronavirus 229E  were identified in the 1960s. They are responsible for 10–30% of all common colds, and infections occur mainly during winter and early spring . A thirdA new human coronavirus associated with respiratory illness, HCoV-NL63, was recently identified by a research team in The Netherlands . The virHCoV-NL63 can be considered as a new important cause of respiratory illnesses and two different subtypes might be currently cocirculating in the human population . In thisWe studied 309 isolates from 279 patients with severe respiratory symptoms collected from January 2003 until March 2004 at the University Hospital in Leuven, Belgium. These isolates originated from bronchoalveolar lavages, pharyngeal swabs, nasopharyngeal aspirates, and sputum samples. Routine diagnostic testing was performed for respiratory syncytial virus (RSV), influenza virus, parainfluenza virus and adenovirus. No prior amplification by cell culture was performed. The results of diagnostic tests for RSV were negative for 244 isolates, while 65 isolates were positive for RSV. Patients ranged in age from 1 month to 16 years, with a mean age of 2 years. The temporal distribution of the isolates corresponded to the yearly RSV epidemic period: 236 samples were collected from January to June 2003 and 73 samples were recovered during the first trimester of 2004 Figure .RNA was extracted from the collected specimens by using the QIAamp Viral RNA Mini kit (QIAGEN, Westburg, The Netherlands) according to instructions of the manufacturer. Screening of the samples was performed by amplifying a 251 bp fragment of the polymerase gene using the following primer set: Cor-FW (5'-ACWCARHTVAAYYTNAARTAYGC-3') and Cor-RV (5'-TCRCAYTTDGGRTARTCCCA-3')  and N3-PCR1 (5'-CTCACTATCAAAGAATAACGCAGCCTG-3', nt 26982-nt 27008). Secondly a 237-bp fragment in ORF1b was amplified using the primers repSZ-1 and repSZ-3 described by van der Hoek and colleagues . A thirdChromatogram sequencing files were inspected with Chromas 2.2 , and contigs were prepared using SeqMan II (DNASTAR, Madison, WI, USA). The obtained consensus sequences were compared with the prototype HCoV-NL63 sequences available in GenBank database release 142.0 using BLAST analysis (NCBI BLAST server). Multiple sequence alignments were prepared using CLUSTAL X version 1.82 , and manThe sequences determined in this study have been deposited in the GenBank sequence database under accession numbers AY758276 to AY758301.Our collection of samples was also screened using the pancoronavirus RT-PCR assay for the presence of HCoV-OC43 and HCoV-229E. Positive results were confirmed by one-step RT-PCR using HCoV-OC43 and HCoV-229E specific primer pairs located in the membrane glycoprotein region  . One-ste3 RNA copies per μl nasopharyngeal aspirate.A pancoronavirus RT-PCR assay is a usefull tool to test for all coronaviruses in a clinical sample. Besides quick screening for several pathogens in one assay, it supplies the possibility to identify previously unknown coronaviruses. The consensus RT-PCR assay as described by Stephensen et al., designed to amplify all known coronaviruses, is not able to detect HCoV-NL63 because of several mismatches with the primer sequences ,17. We mThe pancoronavirus RT-PCR assay was used for screening of specimens from hospitalized patients with respiratory symptoms collected between January 2003 and March 2004. Samples, from which a 251 bp fragment could be amplified, were further identified by sequencing using the pancoronavirus primers. We studied 309 specimens with a temporal distribution that corresponded with the yearly RSV epidemic period Figure . A totalThe seven HCoV-NL63 positive respiratory samples were confirmed by alternative RT-PCR assays. Amplification of a fragment of the nucleocapside gene and ORF1b was carried out. Sequence analysis of the N gene fragments and the ORF1b fragments showed 98–100% similarity to the prototype HCoV-NL63 sequences available in the GenBank database (AY567487, AY518894). A third one-step RT-PCR was carried out for each positive sample to amplify part of the ORF1a gene. Sequence analysis of the ORF1a PCR-products revealed 99% sequence identity with both HCoV-NL63 prototype sequences available in GenBank. A neighbor-joining phylogenetic tree was constructed based on an alignment of the ORF1a nucleotide sequences from the HCoV-NL63 positive samples and the available HCoV-NL63 sequences in GenBank. HCoV-229E was used as an outgroup. The dendrogram shows that all HCoV-NL63 sequences cluster together, but two subclusters can be observed , and the prototype HCoV-NL63 sequences, was used to constitute a neighbor-joining phylogenetic tree. The neighbor-joining tree was evaluated by 500 bootstrap pseudoreplicates. Two clusters can again be observed  and HCoV-229E in one sample (0.3%). The seven HCoV-OC43 positive samples were collected during the winter and early spring of 2003 and 2004. The sample in which we detected HCoV-229E was collected in April 2003. The positive samples were confirmed by RT-PCR using specific HCoV-OC43 and HCoV-229E primer pairs that amplify part of the M gene . The HCoRSV, influenza viruses, adenoviruses, and parainfluenzaviruses are probably the most important viral agents of severe respiratory diseases. However, a substantial part of respiratory tract infections can not be attributed to any known pathogen. Underlying conditions and immunosuppression largely determine the impact of respiratory viruses on individuals . The comResults from epidemiological surveys conducted in the 1970's have led to the conclusion that human coronaviruses are distributed worldwide and circulate during seasonal outbreaks . Our resSequence analysis of the highly conserved nucleocapsid region showed that the Belgian isolates are similar to the two prototype HCoV-NL63 complete genome sequences in GenBank isolated in the Netherlands in 1988 and 2003. Furthermore, phylogenetic analysis of part of the ORF 1a region of our patients showed the same subclusters of HCoV-NL63 that were described previously  Figure . This fiScreening of our sample collection for the presence of HCoV-OC43 and HCoV-229E revealed seven HCoV-OC43 positive samples and only one HCoV-229E positive sample. All positive samples were isolated during winter and early spring, which is concordant with the results of previous epidemiological studies. HCoV-OC43 infected samples were mainly identified during February 2003 and February 2004 Figure . These dThe development of a pancoronavirus RT-PCR assay using a primer set that matches all known coronaviruses might be useful for the identification of new coronaviruses. This pancoronavirus RT-PCR-assay can also be used as a diagnostic tool to detect any of the four currently known human coronaviruses in clinical samples.Human coronavirus NL63 is a new important respiratory pathogen that can cause severe respiratory infections in children. Sequence analysis of the HCoV-NL63 isolates detected in our study demonstrates that our Belgian isolates can be classified into two subtypes corresponding to the two prototype HCoV-NL63 sequences isolated in The Netherlands in 1988 and 2003. Our findings indicate that these two subtypes may currently be cocirculating.The author(s) declare that they have no competing interests.EM conceived of the study and designed it together with LV, EK, and MVR. EM developed the pancoronavirus RT-PCR and performed the RT-PCR and sequencing reactions. EM and LV drafted the manuscript. KZ assembled the respiratory samples. SL performed the RT-PCR sensitivity assays. PM was responsible for the graphical support of the manuscript. LVDH, KP and BB developed the HCoV-NL63 RT-PCRs and helped with the design of the study and the writing of the manuscript. All authors contributed to the final version of the manuscript, read and approved it.The pre-publication history for this paper can be accessed here: