Identification of language-related cortical functions can be carried out noninvasively by transcranial magnetic stimulation (TMS) and magnetoencephalography (MEG), which allow for lesion-based interrogation and global temporospatial investigation of cortices, respectively. Combining these two modalities can improve the accuracy of the identification, but the relationships between them remain unclear. We compared TMS and MEG responses during the same language task to elucidate their temporospatial relationships and used the results to develop a novel method to identify language-related cortical functions.Twelve healthy right-handed volunteers performed a picture-naming task during TMS and MEG. TMS was applied on the right or left inferior frontal gyrus (IFG) at five time points, and the reaction times (RTs) for naming the pictures were measured. The temporospatial oscillatory changes measured by MEG during the same task were then compared with the TMS results.Transcranial magnetic stimulation of the left IFG significantly lengthened RTs at 300 and 375 msec after picture presentation, whereas TMS of the right IFG did not change RTs significantly. Interestingly, the stimulus time point at which RTs increased significantly for each individual was correlated with when the low gamma event-related desynchronizations (ERDs) peaked in the left IFG. Moreover, combining the results of TMS and MEG improved the detection rate for identifying the laterality of language function.These results suggest that the low gamma ERDs measured by MEG strongly relate to the language function of picture naming in the left IFG. Finally, we propose a novel method to identify language-related cortical functions by combining TMS and MEG. The temporospatial identification of language-related cortical functions is of great relevance across a wide spectrum of basic and clinical neuroscience   participated in this study. All participants were strongly right-handed  was used to present pictures to subjects and to trigger TMS to start. Notably, there was no adverse effect of TMS such as the significant pain sensations elicited by the TMS. The TMS was conducted following the guideline for the use of TMS in clinical practice and research  and a figure-eight coil (D70 coil). The maximum outputs of this coil and stimulator combination were approximately 1.9 t-test).Event-related triple-pulse TMS was applied with an interpulse interval of 25 msec (40 Hz) at 150, 225, 300, 375, or 450 msec after the onset of picture presentation. The presentation of the pictures and the timing of TMS were fully randomized across trials within each session. RTs of left and right side were compared between the real and sham stimulations at each time point  on the basis of a coregistered individual T1-weighted MR image. Stimulation sites for each subject were transformed into Talairach coordinates .t-test, Bonferroni corrected, P < 0.05). At these time points, either side of the IFG was assumed to have essential language functions. Then, to estimate the laterality of language function for each individual, changes in RTs between the real and sham stimulations were compared among both sides of IFG at each time point. A delay rate was calculated for each trial as 100 ×  − (mean RTs of sham TMS))/(mean RTs of sham TMS). The delay rates were compared among TMSs on the right and left IFGs to determine the side on which the delay rates were significantly larger . For example, if delay rates of the left side significantly exceeded the delay rates of the right side , the laterality was inferred as being left. When the delay rates of both sides were not significantly different, the laterality was not estimated.First, mean RTs of 12 participants for real stimulation were compared among both sides of IFG at the five time points to select the ones at which the mean RTs were significantly different between both sides  with a word familiarity of 5–7 . The participant lay supine on a bed with his or her head centered. Before and after recording, the head position was measured with five coils placed on the face . Pictures were displayed on a projection screen 325 mm from the participant's eyes by means of a visual presentation system  and a liquid-crystal projector . Data were sampled at a rate of 1000 Hz and filtered with an online low-pass filter at 200 Hz. After data acquisition, a notch filter at 60 Hz was applied to eliminate AC line noise. To reduce contamination from muscle activities and eye movements, we instructed the participants to avoid shoulder movements and to watch the center of the display without moving their eyes or blinking. Some apparent artifacts were removed before the offline analysis.T values  and superimposed on the MNI template brain by means of MRIcron software .Individual statistical maps were generated by normalizing the beam-forming functional volumes to standard space for each time window and frequency band. First, a participant's MR image was coregistered with the MEG data by identifying the anatomical landmarks in the MR image that were used for placing the MEG head-localization coils . The coregistered MR image was then spatially nonlinearly registered  to a template MR image using the SEG-toolbox in SPM8 . Moreover, the mean pseudo-T values were compared between the right and left IFGs at each time window . Finally, for each subject, we compared the peak time point of the low gamma ERDs and the time point at which the RTs of TMS on the left IFG significantly increased by real TMS compared to sham TMS using Spearman rank correlation coefficient (rs).Among temporospatial changes in each frequency bands, the low gamma ERDs for the left IFG were selected to characterize the cortical language function, based on the results of this study and our previous study , which was calculated from the peak pseudo-t-test, P < 0.05).First, for each individual, a time window showing a peak of the low gamma ERDs in the IFG was selected. Then, at that time window, the delay rates were compared among TMSs on the right and left IFGs to determine the side on which the delay rates were significantly larger . Individual MRI data consisted of T1-weighted sequences in 130 sagittal slices (1.4-mm thickness) with fiducial skin markers at the nasion and bilateral preauricular points.Anatomic MRI data were obtained with a 3.0-t-test, Bonferroni corrected, P < 0.05). In contrast, there was no significant difference in RTs of TMS on the right IFG at any stimulus time point . Notably, the point at which the RTs of TMS on the left IFG significantly increased with real TMS compared to sham TMS varied among participants; 300 or 375 msec for nine of 12 participants, 150 or 225 msec for 2, and none for 1.The RTs of real TMS and sham TMS on the right and left IFGs were tested at the five stimulus time points. For TMS on the left IFG, the mean RTs of real TMS was significantly longer than those of the sham TMS at 300 and 375 msec after the onset of picture presentation Fig.A . And the pseudo-T value of the left IFG was significantly higher than that of the right IFG at 250–350 and 300–400 msec . The low gamma ERDs were found to be characteristically strengthened on the left IFG around 300 msec after picture presentation.The time courses of the low gamma ERDs were compared between both sides of the IFG Fig.B to clarT value on the left IFG was concordant with the effective timing of the TMS on the left IFG. The stimulus time point of TMS on the left IFG at which RTs increased significantly between real TMS and sham TMS was significantly correlated with the time window during which the low gamma ERDs on the left IFG peaked . In contrast, no such significant correlation was observed for the other frequency bands such as theta, alpha, beta, and high gamma band .For each subject, the peak timing of the low gamma ERDs was examined in relation to the time point at which the RTs of TMS on the left IFG significantly increased by real TMS compared to sham TMS. Interestingly, the peak timing of the pseudo-t-test, P < 0.05) and the delay rates of left side exceeded those of right side  to be evaluated as being left sided. For the remaining three participants, there were no significant differences in RTs between the real and sham TMSs. Therefore, the delay rates of each side were not significantly different for them (Table First, the dominant hemisphere of language function was evaluated using either MEG or TMS. As previously mentioned, the results of the TMS experiment showed that the RTs were significantly different between the real TMS and the sham TMS at 300 and 375 msec. Then, for both stimulus time points, the RTs of the real TMS and the sham TMS were compared for each individual. For nine of 12 participants, the RTs of real TMS exceeded the RTs of the sham TMS only for left side (Student's The LI, which was estimated by the low gamma ERDs in the IFG within the time window of 300–400 msec, showed the laterality of language function as left sided in 10 of 12 participants, bilateral in one, and right sided in one (Table T value of the low gamma ERDs reached a peak for each individual. That is, the RTs were evaluated at different time points for each individual depending on their low gamma ERDs. Using this method, 11 of 12 participants were determined to have left-sided laterality (Fig.Next, a novel method combining TMS and MEG was applied to estimate the laterality. The delay rates of RTs on both sides of the IFG were compared at the time points at which the pseudo-lity Fig.. NotablyIn this study, we used TMS to evaluate the functional significance of temporospatial characteristics of language-related oscillatory changes in MEG during a picture-naming task. For each participant, RTs were significantly delayed when TMS was applied at the time point at which the low gamma ERDs on the left IFG were largest. This result suggests that low gamma ERDs occurred in a cortical area that was functionally essential for language processing. Moreover, by combining MEG and TMS, we precisely identified the language-related cortical regions with high temporospatial resolution, and we could therefore identify the laterality of language function. Our results demonstrate that combining MEG and TMS offers a novel method to evaluate the significant characteristics of language-related cortical functions.Using TMS to modulate neural activity in the IFG, we identified the exact time point at which Broca's area was critically engaged in the process of picture naming. Specifically, real TMS on the left IFG significantly increased RTs compared to the sham TMS when the stimulus was applied at 300 or 375 msec after picture presentation. Therefore, the effective time window to modulate this picture-naming task was defined as 300–375 msec, which is compatible with previously reported estimates of the time course of language functions (Salmelin et al. Some previous studies have estimated the temporal changes in speech production based on chronometrical behavioral and electrophysiological data (Indefrey and Levelt On the basis of that assumption, triple-pulse TMS at 300 or 375 msec after picture presentation onset is likely to disturb Broca's area in the process of syllabification. The timing differed between individuals, and TMS revealed the individual characteristics of language processing on a time scale of milliseconds.The frequency bands of MEG responses, such as alpha, beta, low gamma, and high gamma, have characteristic temporospatial modulations during language tasks (Salmelin et al. Notably, in our study, high gamma activity was not shown to be related to the effects of TMS on language functions. However, because detecting activities in the high gamma band by MEG is difficult, the relationship between TMS and high gamma activities might be underestimated. Alternately, the high gamma band might be more associated with language function or processing than the low gamma band. Further investigation by means of an invasive method, such as electrocorticography, is necessary to elucidate the most important frequency bands for language function and to verify the relationship between high gamma and low gamma activities.In this study, we found that the effective timing and location for real TMS corresponded to the peak of the low gamma ERDs measured by MEG. By combining TMS and MEG, we could detect language lateralization with greater sensitivity than we could with a single technique. Therefore, we propose the following novel method for evaluating language function based on combining MEG and TMS. First, MEG should be conducted to determine the time windows and brain areas at which the low gamma ERDs peaks, with the goal of determining the optimal conditions for TMS. TMS should then be conducted on the delineated time windows and areas at which ERDs peak. In this study, we were able to determine the laterality of the language-related area in 11 of 12 participants by combining TMS and MEG. We were also able to identify the laterality of language areas for one participant for whom laterality could not be determined by MEG alone. Notably, this result depends on the assumption that the laterality was left for all right-handed participants. To conclusively confirm this result, further studies are necessary to compare the proposed method with invasive techniques such as the Wada test (Pelletier et al. In this study, we used MEG in combination with TMS to evaluate the temporospatial characteristics of IFG activity related to language functions. Our results suggest that TMS on the left IFG affected cortical language functions accompanied by low gamma ERDs. By combining MEG and TMS, the individual differences in language function among the study participants were successfully evaluated.


Health-Related Quality of Life (HRQoL) measures are becoming increasingly used in clinical trials as primary outcome measures. Investigators are now asking statisticians for advice on how to analyse studies that have used HRQoL outcomes.HRQoL outcomes, like the SF-36, are usually measured on an ordinal scale. However, most investigators assume that there exists an underlying continuous latent variable that measures HRQoL, and that the actual measured outcomes (the ordered categories), reflect contiguous intervals along this continuum.t-test and linear regression that assume Normality and constant variance may not be appropriate. For this reason, conventional statistical advice would suggest that non-parametric methods be used to analyse HRQoL data. The bootstrap is one such computer intensive non-parametric method for analysing data.The ordinal scaling of HRQoL measures means they tend to generate data that have discrete, bounded and skewed distributions. Thus, standard methods of analysis such as the t-tests, linear regression, summary measures and General Linear Models.We used the bootstrap for hypothesis testing and the estimation of standard errors and confidence intervals for parameters, in four datasets (which illustrate the different aspects of study design). We then compared and contrasted the bootstrap with standard methods of analysing HRQoL outcomes. The standard methods included t-test and linear regression are robust to the violations of assumptions that HRQoL data are likely to cause . While particular to our datasets, these findings are likely to generalise to other HRQoL outcomes, which have discrete, bounded and skewed distributions. Future research with other HRQoL outcome measures, interventions and populations, is required to confirm this conclusion.Overall, in the datasets we studied, using the SF-36 outcome, bootstrap methods produce results similar to conventional statistical methods. This is likely because the  Health Related Quality of Life (HRQoL) measures are now frequently used in clinical trials and health services research, both as primary and secondary endpoints . InvestiHRQoL measures such as the Short Form (SF)-36, Nottingham Health Profile (NHP) and European Organisation for Research and Treatment of Cancer (EORTC) QLQ-C30 are described in Fayers and Machin  and are This ordinal scaling of HRQoL measures means they generate data with discrete, bounded and non-standard distributions, which may lead to several problems in determining sample size and analysing the data ,3. SinceThe bootstrap ,5 is a dt-tests; multiple regression/analysis of covariance (ANCOVA) models fitted via ordinary least squares (OLS); response feature analysis or summary measures such as the Area Under the Curve (AUC) [Conventional methods of analysis of HRQoL outcomes are extensively described in Fayers and Machin  and Fairve (AUC)  and Geneve (AUC) ] fitted ve (AUC) .The remainder of this paper is structured into the following sections. The SF-36 HRQoL outcome is briefly described in Section 2. Section 2 also describes how the bootstrap can be used for hypothesis testing and confidence interval estimation. Section 2 ends with a description of the four example datasets. The results of conventional methods of analysis and bootstrap methods are compared in Section 3. Other issues, such as withdrawals and study sizes are discussed in Section 4. The final section (5) ends with a summary and conclusions.to pull oneself up by one's bootstraps". The phrase is thought to be based on one of the eighteenth century Adventures of Baron Munchausen by Rudolph Erich Raspe. The Baron had fallen to the bottom of a deep lake. Just when it looked like all was lost, he thought to pick himself up by his own bootstraps !bootstrap sample, and each provides an estimate of the parameter of interest, e.g. mean. The "with replacement" means that any observation can be sampled more than once in each bootstrap sample. It is important because sampling without replacement would simply give a random permutation of the original data, with many statistics such as the mean being exactly the same  each in. The standard Normal based intervals are symmetrical about , and hence have shape = 1.00. Shape is a measure of skewness of the CI about the point estimate. A shape > 1.00, implies the CI is positively skewed, with a long tail to the right, whereas shape < 1.00 implies the CI is negatively skewed.'Shape' measures the symmetry of the interval about the point estimate test statistic and a null distribution for the data under the null hypothesis. Given these, we generate bootstrap values of the test statistic under the null distribution for the data and estimate the achieved significance level (ASL) by calculating the proportion of the bootstrap values of the test statistics, which are greater than or equal to the observed value of the test statistic from the original data.Bootstrap methods can also be used for hypothesis testing. The two quantities that we must choose when carrying out a bootstrap hypothesis test are a Several bootstrap test statistics are available for comparing the distribution of sample data in two independent groups. In considering a bootstrap hypothesis for comparing the two means, there is no compelling reason to assume equal variances and so we do not make this assumption. We used a bootstrap test statistic for comparing two means that use only the assumption of a common mean, under the null hypothesis .Table We were interested in seeing whether or not there was a difference in HRQoL in OA patients after TKR surgery compared with pharmacologically treated patients. From previous studies using the SF-36 we know that HRQoL varies with age and gender ,20. SincThe analysis involved using OLS to fit the multiple regression model with six month follow-up HRQoL as the outcome variable and age in years at baseline; gender of the patient ; baseline HRQoL and treatment group variable  as explanatory covariates. The group regression coefficient estimate represents the difference in six-month follow-up HRQoL between the Rheumatology Clinic and TKR Surgery groups after adjustment for the patient's age, gender and baseline HRQoL. A positive value for the regression coefficient indicates the Surgery group has a better mean HRQoL at six months follow-up than the Clinic group after adjustment for the other covariates.Table The regression analysis suggests that at six month follow-up TKR surgical patients have significantly better HRQoL than Rheumatology treated clinic patients on five dimensions of the SF-36  after adjustment for age, gender and baseline HRQoL. As can be seen from Table Qualitatively all of the intervals from the three methods either include or exclude zero so the interpretation of the group regression coefficient is the same. Therefore, again in this example dataset, there appears to be little advantage in using bootstrap case or model based re-sampling to estimate standard errors and confidence intervals compared to conventional methods of confidence interval estimation from the OLS multiple regression model.We are interested in comparing the HRQoL over the one-year follow-up between the Home and Clinic treated groups. The two groups were well matched at baseline for age, gender and HRQoL, except for the RE dimension of the SF-36, where there was some reliable statistical evidence of a difference (p = 0.052).The overall HRQoL of the leg ulcer patients over the 12-month study period (and three HRQoL assessments) can be summarised by the AUC. If we set the time units for the AUC calculation as a fraction of a year, then an AUC value of 100 implies the leg ulcer patient has been in "good health" for the entire 12-month follow-up period. Conversely an AUC value of 0 implies the leg ulcer patient has been in "poor health" for the entire 12-month follow-up period.t-test, the MW test and the bootstrap hypothesis test.Table 7 See  gives thp-values from the t-test and the ASL from the bootstrap hypothesis tests are very similar. None of the p-values for the eight SF-36 dimensions are less than 0.05. Therefore there is no reliable statistical evidence to suggest a difference in mean AUC between the Clinic and Home treated leg-ulcer patients. Only the results of the MW test on the RE dimension of the SF-36 provide (p = 0.071) any evidence of a difference in AUC distributions between the groups, although even this p-value is not statistically significant using the conventional cut-off of 0.05.The t-test with the bootstrap BCa limits. The lengths of the intervals are very similar, although the bootstrap BCa intervals tend to have a non-symmetric shape. All the estimated CIs include zero, again suggesting no evidence of a difference in mean AUC (HRQoL) between the Clinic and Home group patients in the Leg Ulcer study.The table also contrasts the Normal theory based CI estimates from the In the Early RA study, HRQoL assessment was carried out at 0, 8, 16, 24, 32, 40 and 48 weeks. With seven repeated HRQoL measurements, such as this, the best approach is to model the longitudinal data using GLMs.The modelling of longitudinal data takes into account the fact that successive HRQoL assessments by a particular subject are likely to be correlated. We used a marginal model with the Early RA data and used GEEs to estimate the regression coefficients. Marginal models are appropriate when inferences about the population average are the focus. For example, in a clinical trial the average difference between control and treatment is most important, not the difference for any one individual. In a marginal model, the regression of the response on explanatory variables is modelled separately from the within-person correlation.auto- or serial correlation, which is the strength of the association between successive longitudinal measurements of a single HRQoL variable on the same patient.The marginal model is an extension of the linear regression model used with the OA Knee data. Longitudinal models require the specification of the independent (sometimes termed random) if the off diagonal terms of the auto-correlation matrix are zero. The repeated HRQoL observations on the same subject are then independent of each other, and can be regarded as though they were observations from different individuals. On the other hand, if all the correlations are approximately equal or uniform then the matrix of correlation coefficients is termed exchangeable, or compound symmetric. This means that we can re-order (exchange) the successive observations in any way we choose in our data file without affecting the pattern in the correlation matrix. As the time or lag between successive observations increases, the auto-correlation between the observations decreases. A correlation matrix of this form is said to have an autoregressive structure .Several underlying patterns of the auto-correlation matrix are used in the modelling of HRQoL data. The error structure is Table The process of fitting marginal models using GEE begins by assuming the simple independence form for the autocorrelation matrix, and fitting the model as if each assessment were from a different patient. Once this model is obtained the corresponding residuals are calculated and these are then used to estimate the autocorrelation matrix assuming it is of the exchangeable (or autoregressive) type. This matrix is then used to fit the model again, the residuals are once more calculated, and the autocorrelation matrix obtained. The iteration process is repeated until the corresponding regression coefficients that are obtained in the successive models converge or differ little on successive occasions .Fayers and Machin  states,The results with the OA Knee data show that there is little to choose from between the case and model based resampling for the multiple linear regression model for estimating SEs and CIs. Since there was very little difference in the SE and CI estimates from the datasets used, for simplicity one would tend to favour a case based resampling approach. Indeed this was the resampling method for the longitudinal marginal model for the Early RA data.In the longitudinal Early RA for simplicity we used only a simple case based resampling for the marginal model and effectively carried out a stratified random resampling with replacement. That is we sampled with replacement blocks or clusters of each patients' repeated HRQoL responses. In theory, one should be able to use model or residual based resampling for the marginal model. The resampling procedure would be rather complex particularly for autoregressive autocorrelation structures and for unbalanced datasets, with HRQoL assessments at unequally spaced time points. One would have to take into account that the residuals were not independent and uncorrelated, and for the autoregressive correlation structure, that the correlation between residuals within a patient declined over time. This is a very interesting avenue and requires further exploration with other longitudinal datasets.Finally, are the results all that surprising or unexpected? We have shown that the use of bootstrap methods for analysis  appears to offer little advantage compared to standard methods in the four datasets studied.If we assume that there exists an underlying continuous latent variable that quantifies the HRQoL response of interest and that the goal of the analysis is to assess the magnitude of a treatment effect on the HRQoL outcome, by comparing means between groups. Then statistical theory says that if the distribution of the HRQoL data is Normal, so will be the distribution of the sample mean. Much more importantly, even if the distribution of HRQoL data is not Normal, as is frequently the case, that of the sample mean will become closer to a Normal distribution as the sample size gets larger. This is a consequence of the Central Limit Theorem (CLT) . The NoThus, if the investigator is planning a large study and the sample mean is an appropriate summary measure of the HRQoL outcome, then pragmatically there is no need to worry about the distribution of the HRQoL outcome and we can use standard methods to estimate sample sizes and analyse the data. Since dramatic effects are unlikely in HRQoL studies using the SF-36 as an outcome, large samples sizes are likely to be required ,3,6. So So our research using the SF-36 HRQoL outcome and the four datasets has shown that bootstrap methods appear to produce p-values, SEs and CIs similar to conventional methods. When the standard and the bootstrap methods agree, we can be more confident about the inference we are making and this is an important use of the bootstrap [Page 118, [t-test and OLS multiple regression are robust to the violations of assumptions that HRQoL data are likely to cause . While particular to our datasets, these findings are likely to generalise to other HRQoL outcomes, which have discrete, bounded and skewed distributions. They may not generalise to HRQoL studies with smaller sample sizes of less than 100 subjects. Future research with other HRQoL outcome measures, interventions and populations, is required to confirm this conclusion.In the datasets we studied, using the SF-36 as an outcome measure, bootstrap methods produce results similar to conventional statistical methods. This is likely because the Table 7 – Leg Ulcer study simple cross-sectional comparison of AUC for Home vs. Clinic GroupsClick here for file


Bone adapts to mechanical strain by rearranging the trabecular geometry and bone density. The common finite element methods used to simulate this adaptation have inconsistencies regarding material properties at each node and are computationally demanding. Here, a volume-based, non-continuum formulation is proposed as an alternative. Adaptive processes corresponding to various external mechanical loading conditions are simulated for the femur.Bone adaptations were modeled for one-legged stance, abduction and adduction. One-legged stance generally results in higher bone densities than the other two loading cases. The femoral head and neck are the regions where densities change most drastically under different loading conditions while the distal area always contains the lowest densities regardless of the loading conditions. In the proposed formulation, the inconsistency of material densities or strain energy densities, which is a common problem to finite element based approaches, is eliminated. The computational task is alleviated through introduction of the quasi-binary connectivity matrix and linearization operations in the Jacobian matrix and is therefore computationally less demanding.The results demonstrated the viability of the proposed formulation to study bone functional adaptation under mechanical loading. Much research effort has been devoted to understanding the functional adaptation of bone under physiological loading ever since the idea of bone functional adaptation was proposed by Wolff more than one hundred years ago -14. VariThe common finite element approach is to take the element densities as the state variables and define elements with either constant or varying densities, then update the material densities for the next step of computation according to the computed strain energy density ,23,26,27One common problem is the inconsistency of material densities on element boundaries . During Some effort has been made trying to solve this problem. For example, a node-based variant of the finite element method was tried with focus on the densities of the nodes rather than densities of the whole elements ,30. The Other previous work has used Voronoi structures  to studyThe long existing problems and the limitations of assuming a continuum drive this new effort to explore the possibility of a non-continuum formulation of bone functional adaptations through nodal analysis in the hope of eliminating the errors present in the previous approaches. In the proposed non-continuum formulation, neighboring nodes are connected by struts that are defined with invariant material densities with respect to time but strut volumes are defined as state variables indicating different configurations of bone structure. The updating of strut volume will depend on the strain energy density in the strut in the previous step. As a result, there is no conflict either in density or in strain energy density. The shift of state variables from bone densities to bone volumes not only eliminates the errors inherent to the density-based finite element approaches but also transforms the continuum formulation to a non-continuum formulation . The advThe proposed volume-based non-continuum formulation shows computational advantages in modeling bone functional adaptations and has much potential for clinical applications in this field.Fig. 3 is used as the maximum value in the present formulation.The nodal density is a percentage relative to its maximum value (0~100%). It is 'converted' based on volume information (BV/TV) for the purpose of easy visual inspection. As suggested by Zhu , the valIn the combined loading case and one-legged stance, the nodal densities are generally high in the proximal area where a considerable number of nodes reach the highest density due to the relatively high load, while in the large distal area, the densities become lower. In the case of abduction, the external load is the smallest out of the three loading cases and the nodal density in this case seldom reaches the maximum. Although the high densities also appear in the proximal area of femoral head, the densities are lower than those of other loading cases. In the large distal area, the node densities generally range at very low levels. In the case of adduction, the highest densities still appear in the proximal area of femoral head, but in the large diaphyseal area, the densities range at very low levels.In summary, one-legged stance and the combined loading case generally result in higher bone densities than the other two loading cases due to higher mechanical loading. The femoral head and neck are the regions where densities change most drastically under different loading conditions while the distal area always contains the lowest densities regardless of the loading conditions.Starting from a uniform material distribution, the program computes the strain energy density, adjusts the strut configurations, and continues on to the next iteration. Fig. A prediction of bone fracture risk has been proposed using stress range levels  from whiA volume-based non-continuum formulation has been developed that describes the adaptation of bone to various mechanical loading situations. In the finite element approach to bone adaptation simulation, the integration of the entries in the coefficient matrix can be a heavy computing task ,23,26,27As mentioned in the introduction, one common problem is the inconsistency of material densities or strain energy densities on element boundaries . Since aAs described above, previous work -33 addreHip fracture is one typical manifestation of osteoporosis, and the results obtained by the simulation indicate that considerable changes of bone structure take place in the regions of femoral head and neck, where the stress level is normally higher than that of distal regions. The variations in stress level as shown in Fig. In the current literature, the time scale for adaptive processes is not very well defined. This general lack of knowledge poses a problem for any experimental proof of concept – while the numbers of strain repetition can be predefined, they must be done within biologically suitable time frames. If a given strain comes too sudden, the bone may break instead of remodel; if the strain is applied over too long a period, it may not be a sufficient stimulus to activate adaptive processes. The lack of well-defined temporal constraints, however, is common. Kim's approach  is very The simulation model presented here may, beyond theoretical calculations, be applied to look at two clinical questions. Firstly, the simulation can be adjusted so that a realistic density distribution is the starting point, and outcomes following certain loading conditions, such as a predefined number of load cycles can then be predicted. Secondly, the program can integrate the measured bone density of a given patient to estimate the fracture risk based on stress level calculations.By eliminating the common inconsistencies at each node, the formulation presented here shows good numerical performance and successfully predicts reasonable bone structure changes under different loading conditions. It is viable to serve as an alternative method apart from the traditional finite element based approached to study bone adaptations. In conclusion, the volume based non-continuum formulation is a new approach to bone adaptation study and has its own advantages. can be obtained though the resultant of the vectorial material components of the struts, as described by equation (1) and (2).In the volume-based non-continuum formulation used here, the trabecular structure is represented by a connected strut system and each strut can take different sizes according to the mechanical loading requirements, that is, strain energy density. The strut representation is shown in Fig. vi is the volume of the i-th strut in the j-th basic unit, Vj is the volume of the j-th basic unit, Rj is the material orientation of the j-th basic unit,  is the orientation of the i-th strut in the j-th basic unit and N is the number of struts in the j-th basic unit.where For the formulation proposed here, the bone structure is decomposed into and represented by a connected network of struts. These struts are a mathematical abstraction of the physical bone structure, from which the BV/TV ratio can be derived. The thickness of a strut is adapted during the bone adaptation process in mimicry of the physiological processes.The bone mass will vary under mechanical loading. In engineering, the general relationship between varying mass, density and volume is described as:, simply vanishes and the mass variation is realized through volume variation under mechanical loading. Based on the density-based adaptation proposed by Zhu X. et al (39), which is stated as:Since the density here is taken as constant regarding time, the second term on the right hand side, the volume-based adaptation can thus be stated as:βi = Ui / ρik, which is a comparative coefficient describing the comparison of a given mechanical stimulus in each sensor cell with the reference value k, and Ui represents the strain energy density for the I-th sensor unit; N is the number of sensor cells and fi(x) is the spatial influence function; B(t) is a remodeling coefficient; α indicates the remodeling power of strain energy density [where  density .With the whole bone represented by a volume-based strut system, the non-continuum formulation can be noted as follows:A is a connectivity matrix describing the connecting relationship between the struts, α is the linearized Jacobian matrix,  is the nodal displacement vector to be solved for and  is the loading vector derived from the external mechanical load. The generalized conjugate residue method is used to solve this formulation [where mulation ,40.Connectivity matrix A is the matrix to show the relationship between connected struts with the entries of 1, -1 or 0. A strut starts from the node with the index corresponding to the entry 1 and ends at the node with the index corresponding to -1. It is further illustrated in Fig. Finally, the different loading conditions to be applied are shown in Fig. The author(s) declare that they have no competing interests.Zhengyuan Wang developed the formulation and partly prepared the manuscript, Adrian Mondry participated in the adaptation controls and partly prepared the manuscript.


Signal transduction is one of the most important biological processes by which cells convert an external signal into a response. Novel computational approaches to mapping proteins onto signaling pathways are needed to fully take advantage of the rapid accumulation of genomic and proteomics information. However, despite their importance, research on signaling pathways reconstruction utilizing large-scale genomics and proteomics information has been limited.We have developed an approach for predicting the order of signaling pathway components, assuming all the components on the pathways are known. Our method is built on a score function that integrates protein-protein interaction data and microarray gene expression data. Compared to the individual datasets, either protein interactions or gene transcript abundance measurements, the integrated approach leads to better identification of the order of the pathway components.As demonstrated in our study on the yeast MAPK signaling pathways, the integration analysis of high-throughput genomics and proteomics data can be a powerful means to infer the order of pathway components, enabling the transformation from molecular data into knowledge of cellular mechanisms. Signal transduction is the primary means by which eukaryotic cells respond to external signals from their environment and coordinate complex cellular changes. It plays an important role in the control of most fundamental cellular processes including cell proliferation, metabolism, differentiation, and survival . ExtraceRecently, with the accumulation of genome sequence information, large-scale genomic and proteomic techniques have offered insights into the components of signal transduction pathways and the molecular and cellular responses to cell signaling. For example, large-scale yeast two-hybrid screening methods and Co-IP technique have been used to identify physical interactions between proteins -5. Synthab to that of a single mutant a, or a single mutant b. However, this analysis is time-consuming, expensive and sometimes the results can be misinterpreted  byThe two data sources are considered with equal importance, so we rescale the score Smin and Smax are the minimum and the maximum scores of all the possible pathways respectively for either protein-protein interaction data or gene expression data. The rescaling procedure is performed on both data sets. The integrated score is the sum of the rescaled scores for each individual data set.where SYL designed the study, performed the pathway analysis, and drafted the manuscript. HZ conceived and guided the study. Both authors read and approved the final manuscript.


The imputation of missing values is necessary for the efficient use of DNA microarray data, because many clustering algorithms and some statistical analysis require a complete data set. A few imputation methods for DNA microarray data have been introduced, but the efficiency of the methods was low and the validity of imputed values in these methods had not been fully checked.We developed a new cluster-based imputation method called sequential K-nearest neighbor (SKNN) method. This imputes the missing values sequentially from the gene having least missing values, and uses the imputed values for the later imputation. Although it uses the imputed values, the efficiency of this new method is greatly improved in its accuracy and computational complexity over the conventional KNN-based method and other methods based on maximum likelihood estimation. The performance of SKNN was in particular higher than other imputation methods for the data with high missing rates and large number of experiments.Application of Expectation Maximization (EM) to the SKNN method improved the accuracy, but increased computational time proportional to the number of iterations. The Multiple Imputation (MI) method, which is well known but not applied previously to microarray data, showed a similarly high accuracy as the SKNN method, with slightly higher dependency on the types of data sets.Sequential reuse of imputed data in KNN-based imputation greatly increases the efficiency of imputation. The SKNN method should be practically useful to save the data of some microarray experiments which have high amounts of missing entries. The SKNN method generates reliable imputed values which can be used for further cluster-based analysis of microarray data. DNA microarray is a popular high-throughput technology for the monitoring of thousands of gene expression levels simultaneously under different conditions . The typThe analysis performed to meet the purposes of microarray studies mentioned above usually involves clustering genes according to their pattern of expression levels in various experimental conditions. In fact, cluster analysis means grouping samples (or genes) by similarity in expression patterns. To measure the similarity in cluster analysis, correlation distance and Euclidean distance are widely used. PrincipMissing values of microarray data commonly occur during data preparation mainly due to imperfections in the various steps in DNA microarray experiments. One of the yeast microarray data sets shows that the number of genes having at least one missing value was 2419 of 6198 rows (genes) ; and 174Recently, several methods have been applied to the imputation of microarray data, including row average , singulak was 10 for time-series data and mixed type data regardless of the missing rate, and the RMS errors of the non-time series data showed similarly low values when k values were between 10 and 20. For comparison of different imputation methods, we used 10 for k which showed a minimal RMS error in every data type with different missing rates.We evaluated the efficiency of our new SKNN method and the EM-SKNN method with three other imputation methods: KNN-based imputation, the MLE method, the MI method, by applying them to three different types of microarray data sets with different missing rates. The appropriate number (k) of nearest neighbors was dependent on the data types and missing rates. The RMS errors were minimal when k-nearest neighbors to be used for imputation. The overall similarity of the entire data set can affect, on average, the similarity of all possible k-nearest neighbors. The time-series data set, which has the narrowest distribution of Euclidean distances among genes, shows the least RMS error after imputation as we can see in Figure k-neighbors than the conventional KNN method as the missing rate grows. In the conventional KNN method, the selection pool and the dimension  of the distance measurement of neighbor genes are reduced according to the increase of missing rate. In this situation, the method inevitably selects less related (or less similar) neighbors for imputation. In addition, the size of data set can limit the maximum missing rate for stable imputation. In our data sets, the size of a mixed data set is about 20% larger than other data sets, which may affect the mixed data set in terms of having stable RMS error in a relatively larger (10%) range of missing rate.The performance of the KNN-based imputation method depends on the similarity of We tested the performance of other well known non-KNN-based methods such as maximum likelihood estimation (MLE) and multiple imputations (MI) methods. These methods are well known imputation methods but there has been no report on their application in microarray data analysis. The efficiency of the MLE method was much worse than the SKNN method for all tested data sets. The RMS errors in the MLE method were 0.11 to 0.33 in time-series data, 0.30 to 0.38 in mixed data, and 0.58 to 0.69 in non-time series data. The efficiency of the MI method was generally similar to SKNN but the former is more dependent on data types. The efficiency of the MI method was better at a lower missing rate, but slightly worse at a higher missing rate for the time-series data set. The MI method was worse than SKNN in terms of overall range of missing rate of non-time series data. However, the best imputation method for mixed data set proved to be the MI method. We can conclude that the MI method is as efficient as the SKNN method for the imputation of microarray data, even though the efficiency of the MI method experienced more fluctuations than the SKNN method depending on the data type.The result is similar in a comparison of overall RMS error after imputation of a data set having unequally distributed missing entries over the columns. We show a comparison of one of the data sets (time series data set) in Table For more careful estimation of imputation efficiency, we examined the structure of data after imputation. We calculated the Pearson correlation coefficients for each column (experiment) between original data and imputed data. The larger the correlation coefficient is, the better the relationship between original complete data and imputed data is preserved in a column. Figure O(m2n2) in the conventional KNN method and O(mn log m) in the SKNN method for a matrix with m rows (genes) and n columns (experiments). This is because the sequential KNN algorithm imputes all missing values in a gene simultaneously with given nearest neighbors, while the conventional KNN method must calculate neighbors for each missing entry. The application of Expectation Maximization (EM) to the sequential KNN method marginally improved the accuracy in compensation for the increase of computational time proportional to the number of iterations. For MI methods, the execution time increased as M times of single imputation method when MI used M multiple imputation. Using the SKNN imputation method, it took 28.3 seconds on a Pentium IV 2.4 GHz computer to estimate missing values for a data set with 4489 genes, 18 experiments and a 40% missing rate. The processing time using the EM-SKNN method was proportional to the number of iterations.The SKNN algorithm improves execution time for imputation. The computational complexities are approximately The SKNN method offers better performance than the previously developed KNN method for both time series and non-time series microarray data sets and for data sets having different missing patterns. As the missing rate increased, sequential reuse of imputed data did not propagate errors of imputation as in the conventional KNN method. It showed the best improvement of accuracy for the data set with a high missing rate.Notably, the SKNN method is also robust on the imputation of a data set with unequally distributed missing entries. A real microarray data set usually has non-random distribution of missing data. Furthermore, some systematic errors during the experiment can generate an abnormal increase in distribution of missing entries for the corresponding column of microarray data set. In this type of data, the SKNN method, which is especially efficient on the data set having heavy missing entries, can exert relatively more accurate imputation than other imputation methods as shown in our model data set. The MI method has not been well introduced in the field of microarray analysis, although it is a well known imputation method in other fields . In compThe computational complexity is reduced in the SKNN method for the dimension of both the number of genes and the experiments compared with the simple KNN method. Particularly, computation time can be saved substantially for microarray data with a large number of experiments. The SKNN method works efficiently in a wider range of missing rate with high speed. We want to emphasize that our results showed that the method using estimated values achieved even better accuracy than the method using only observed values in the case of the KNN-based imputation method. We suppose that this result could be applicable to other cluster-based analysis. It would be hardly acceptable for the experimentalist to use imputed data for further analysis. However, analysis could become more errorneous without imputation due to loss of information caused by missing values. The use of imputed data should definitely depend on the type of later process. If the next process is a cluster-based analysis, the genes with imputed values could be efficiently used, as we had good results for KNN-based imputation with the reuse of imputed values. For future works, it may be possible to integrate the imputation and gene clustering of microarray data for classification of genes with proper evaluation steps. This may offer more and better information sources of microarray data for the final decision of gene classification. All the procedures used in this paper are done by R-code and C++ and the programs are available upon request.The SKNN method is an especially efficient imputation method on data having high missing entries. It can be practically useful in saving data of some accidental microarray experiments having high missing entries. Our results also suggest that the imputed values generated by the SKNN method can be used reliably for further cluster-based analysis of microarray data.We developed and implemented SKNN and EM-SKNN methods for the imputation of microarray data, and we compared their accuracies with the previously developed KNN imputation method. Data sets used in this work were selected from publically available microarray data. The data sets were from a study of gene expression in yeast Saccharomyces cerevisiae cell-cycle regulation , calcineTo assess the relative efficiency of the imputation methods, we implemented known KNN imputation method developed by Troyanskaya et al. 2001)[01[20]. TThe matrix form of microarray data is composed of rows and columns that represent genes and experimental conditions respectively. Before any further process, the rows of original data sets containing missing values are removed to make complete matrices and test data sets for imputation methods were generated by random deletion of values in the complete matrices. The sizes of test data sets were 4489×18 for time series data set, 4380×24 for mixed data set, and 3779×22 for non-time series data set. The missing rates generated randomly in the test data sets were between 1% and 70% . The occurrence of missing values can depend on the specific experiment in real miroarray data. Considering this case, we also generated test data set having missing values non-randomly along the columns which represent each experiment. The overall missing rate of data set was fixed to one of the value ranging from 10% to 60%. In a data set, the missing rates for two experiments (columns) were set to 80% and remaining columns have randomly generated missing entries.k-nearest neighbor genes are taken from the whole matrix of the test data set except any genes that has missing value at the same position with the gene to be imputed. Euclidean distance is used as the metric to estimate the similarity of neighboring genes. To compare the similarity by this metric, each gene should have the same dimension and missing positions of values inside. Missing value is imputed with weighted average of the corresponding column of the k-nearest genes.In KNN method, ith gene is calculated as equation (1), where k is the number of selected genes and Di is the distance between ith gene and a gene to be imputed.The weight of k-values for each data set and each method with different missing rates. Different k-values ranging from 1 to 500 were tested and we selected the k-values with the least error between imputed values and real values.For the performance comparison of the imputation methods, we selected appropriate SKNN method that we suggest in this report is distinguished in two main points from previously implemented KNN method described above. In SKNN method, genes are ordered by its missing rate and the imputation was executed sequentially from the gene that had least missing rate. In addition, these sequentially imputed genes are used for the later imputation of the other genes. The test data set was separated into incomplete and complete set that has or has not missing values respectively. The genes in incomplete set were imputed by the order of missing rate. Missing value was filled by the weighted mean value of corresponding column of the nearest neighbor genes in complete set. Once all missing values in a gene are imputed, the imputed gene was moved into the complete set and used for the imputation of the rest of genes in incomplete set. In this process, all missing values in one gene can be imputed simultaneously from the selected neighbor genes in complete set. This reduced execution time from previously developed KNN method that should select nearest neighbors for each imputation.k-nearest neighbors for the estimation of missing values. EM-style method executes this process repeatedly until the differences between newly updated values and previous values converge. Because all the imputed values were converged within less than 10 iterations, we did 10 iterations for the comparison of accuracy with the other methods.EM-style imputation algorithm was originally suggested by Rich Caruana (2001). EM-stylIn MLE method, data set with missing values are centered, scaled, and sorted by the patterns of missing through the preliminary manipulations. Missing entries in a data matrix are estimated under the multivariate normal model with user-supplied parameters and observed data (non-missing entries in the data set). The parameters are estimated using imputed and observed data. A vector of parameters representing the MLE, means and variance-covariance matrix are returned by using EM algorithm. Missing values are estimated through this iterative process until estimated parameters converge. We executed MLE method to estimate missing values by using 'norm' library of R  based onThe whole MI procedure is made of three steps. They are imputation, analysis, and pooling processes. We applied only the first step, imputation process, of the three steps because our interest is to fill in missing values with estimated values. We used Predictive Mean Matching (PMM) as a method for missing values estimation. It uses a linear regression on observed variables to impute missing values. The estimated coefficients provide the mean vector and the variance matrix to generate multiple sets of coefficients that leads M imputed sets. M plausible values for missing observations were created by above MI algorithm and then the mean of M imputed values was filled in the missing value. We implemented Multiple imputation method using 'mice' library of R  based onThe accuracy of imputation method was evaluated by calculating error between actual values and imputed values after missing values were estimated. The metric used to assess the accuracy of estimation was RMS error. RMS error was calculated as follows,Ri is the real value, Ii is the imputed value, and N is the number of missing values.where Besides RMS error, Pearson correlation coefficients were used to evaluate the sequential KNN method. Correlation coefficients were calculated between imputed data and complete data for each column. From this evaluation, we could find how the data structure of each column was preserved after imputation with different imputation methods.KNN: K Nearest Neighbor; SKNN: Sequential KNN; EM: Expectation Maximization; RMS: Root Mean Squared; PCA: Principal Component Analysis; MLE: Maximum Likelihood Estimation; MI: Multiple ImputationKK participated in the design of algorithms, performed statistical analysis and drafted the manuscript. BK participated in the design of algorithms and carried out C++ programming. GY conceived of the study, participated in its design and coordination, and finalized manuscript. All authors read and approved the final manuscript.


Drosophila melanogaster for two consecutive years; susceptibility to three organophosphates tended to increase in the fall. To examine the hypothesis that variation in fitness among resistant and susceptible genotypes could trigger the change of genetic constitution within the fall population, we investigated density-independent population projection trajectories starting from single adult females with characteristics of chromosome-substituted lines resistant and susceptible to the three organophosphates.Seasonal fluctuations in susceptibility to organophosphate insecticides were observed in the Katsunuma population of Density-independent population projection trajectories, expressed as the ratios of the number of each chromosome-substituted line to that of line SSS, for which all chromosomes were derived from the susceptible line, showed significant declines in numbers with time for all the resistant chromosome-substituted lines.The declining tendency in the density-independent population projection trajectories of the resistant chromosome-substituted lines could explain the simultaneous decline in the levels of resistance to the three organophosphates, observed in the Katsunuma population in the fall. The development of insecticide resistance in insect pest populations is a population genetic process, in which insecticides select for initially rare resistant mutants within a population . AlthougR factors that, in every respect except insecticide resistance, are deleterious from a survival standpoint.""Since the genes causing insecticide resistance were at low frequency in the population before the insecticide began to be applied, it must ordinarily be true that they are to some extent disadvantageous; otherwise they would have been common. Therefore the selection for resistance should ordinarily involve the replacement of the original genes with Under this theory, it could be expected that there is variation in fitness among resistant and susceptible genotypes, and that resistant genotypes have lower fitness than susceptible genotypes, which could result in the change of frequencies of resistance factors within a population.Drosophila melanogaster is one of the well-established natural populations in Japan )In the previous study, age-specific fecundity and survival probabilities over 19 days, based on each individual, were used for calculating the intrinsic rate of increase for each replication. In this study, the age-specific fecundity and survival probabilities over 19 days were used to construct a 19 × 19 Leslie matrix Fig. , after aDensity-independent population projection trajectories were estimated as the ratios of the number of each chromosome-substituted line, totaled over all age-classes, to that of line SSS for 100 days. 95% upper confidence bounds  were seTM carried out data collection and analyses, and drafted the manuscript. BC contributed to the computer programming and provided the theoretical background. All authors read and approved the final manuscript.


Public repositories of microarray data contain an incredible amount of information that is potentially relevant to explore functional relationships among genes by meta-analysis of expression profiles. However, the widespread use of this resource by the scientific community is at the moment limited by the limited availability of effective tools of analysis. We here describe CLOE, a simple cDNA microarray data mining strategy based on meta-analysis of datasets from pairs of species. The method consists in ranking EST probes in the datasets of the two species according to the similarity of their expression profiles with that of two EST probes from orthologous genes, and extracting orthologous EST pairs from a given top interval of the ranked lists. The Gene Ontology annotation of the obtained candidate partners is then analyzed for keywords overrepresentation.We demonstrate the capabilities of the approach by testing its predictive power on three proteomically-defined mammalian protein complexes, in comparison with single and multiple species meta-analysis approaches. Our results show that CLOE can find candidate partners for a greater number of genes, if compared to multiple species co-expression analysis, but retains a comparable specificity even when applied to species as close as mouse and human. On the other hand, it is much more specific than single organisms co-expression analysis, strongly reducing the number of potential candidate partners for a given gene of interest.CLOE represents a simple and effective data mining approach that can be easily used for meta-analysis of cDNA microarray experiments characterized by very heterogeneous coverage. Importantly, it produces for genes of interest an average number of high confidence putative partners that is in the range of standard experimental validation techniques. The availability of genome sequences from several model organisms, including humans, and of high-throughput technologies to study gene function is dramatically changing the approach to biological problems. In particular, the consolidated reductionist gene-by-gene strategy is being replaced by a 'modular approach', in which several genes are studied simultaneously to gather a more comprehensive picture of the many different cellular processes : in liviIt is well recognized that one of the most important and widespread mechanisms used by cells to regulate functional modules is the coordinate transcriptional and/or post-transcriptional modulation of mRNA levels of the interacting genes. Therefore, DNA microarrays represent a fundamental tool to unravel biological complexity on a genome-wide scale. Information concerning the expression of thousands of genes, and also of different transcripts from the same gene, can be obtained in a single experiment, and the relationships among gene expression patterns can be studied systematically . The extBesides being useful to other researchers to confirm the published results, the deposited datasets can be used as a substrate for new analysis, aimed at discovering functional modules by searching for related expression profiles. Recent studies have shown that, if the expression of two or more genes is consistently related throughout many independent microarray datasets, the genes display a significant degree of functional similarity ,8. HowevHomo sapiens, Saccharomyces cerevisiae, Drosophila melanogaster and Caenorhabditis elegans was very effective in identifying new genes involved in core biological functions [Phylogenetic conservation has been recently proposed as a very strong criterion to identify functionally relevant co-expression links among genes . Signifiunctions . AlthougSince regulatory regions diverge much more rapidly than coding sequences ,13, a siAdditionally, when using microarray data to establish significant correlations among gene expression profiles, almost invariably the information obtained with probes covering different gene portions is averaged . Though Coexpression-based Linking of Orthologous ESTs) a new data mining method for the identification of transcripts showing evolutionary conserved co-expression in cDNA microarray datasets. This approach is based on the pairwise comparison of data from two species. The predictive capability of the method was proved by comparing human with mouse data. Our results show that CLOE is a valuable tool for biologists that can be used to identify putative partners for genes of interest and/or to predict some of their functional properties.In this study we describe CLOE   . We then generated the respective ranked lists, subdivided them in 1% rank intervals and analyzed the number of orthologous pairs in corresponding rank intervals. As a control, we performed the same analysis on an equal number of randomly chosen (and hence non-orthologous) human-mouse EST pairs. The analysis was repeated three times with essentially identical results.-94, chi square test). The difference was still very significant in the 2% rank interval, even though with a much higher p value (p = 1.3·10-10), but was not detectable below that threshold. Interestingly, a slight enrichment was also observed in the last rank interval . The latter observation is consistent with the previously noted fact that negative correlations tend to be less common and significant than positive correlations [As shown in Figure Saccharomyces cerevisiae [Drosophila melanogaster [Caenorhabditis elegans [We next investigated the effectiveness of our approach, by comparing it to single and multiple organisms co-expression analysis. To address this point, we analyzed the ability of the three methods to predict known physical and functional interactions among mammalian proteins. Protein-protein complexes have begun to be determined on a genome-wide scale only for revisiae , Drosophnogaster  and Caen elegans , but no  elegans  . On the other hand, the average percentage of CLOE links that correspond to a documented protein-protein interaction was 6.6 %, i.e. approximately 5 times higher than that obtained with the single organism method  ratios.Within these datasets, the number of ESTs representing a given transcription unit, as well as the number of valid experiments for each EST, are highly variable. While this feature would pose serious problems, if one should attempt to average the data of all the probes belonging to the same gene, it may offer extremely valuable information when every EST is considered independently, since each clone explores the expression properties of a particular group of exons, in a particular combination of experimental situations.Non-correlated or anti-correlated expression between two well-represented EST probes belonging to the same gene would strongly suggest that they correspond to alternatively expressed transcripts. For this reason, we decided not to merge the data of probes belonging to the same gene, but to treat separately every EST probe in the datasets.The choice of the most representative EST for the genes of interest represents a particularly critical aspect of our procedure. If interested to a single gene, the more exhaustive solution to this problem would be to generate and evaluate a list of candidate partners for every possible pairwise combination of ESTs probes. However, this would greatly complicate the analysis should one be interested in analyzing the potential partners of many genes. An alternative possibility is to focus, for every Unigene cluster, on the most representative ESTs, i.e. the probes with the highest number of experiments. For these reasons, the decision about what ESTs to analyze is left to the end user.Our implementation of the method can accept as input both a UniGene cluster ID or the results of a BLAST search performed with the sequence of interest against the EST database. In both cases, it retrieves a list of all probes found in the two datasets for the orthologous UniGene clusters.To help the user decide which ESTs to analyze, the program provides basic information about all the EST probes representing the gene of interest in the two datasets. Moreover, to help the user identify the most representative EST probe in each dataset, i.e. the probe with the highest number of experiments, it also provides the number of valid data points for every probe. Finally, to assess the redundancy of the information provided by the different probes, i.e. whether they represent different experiments and display similar/different expression profiles, the program calculates, for every pair of probes belonging to the same UniGene cluster, the number of common experiments and the Pearson correlation coefficient between their expression profiles.r) with every other EST in the respective dataset. The raw r is the normalized for the number of common data points (n) obtained for the analyzed ESTs. This is done by multiplying r for , since the statistical significance of r is a function of the product ·r. Such normalization is particularly important when, as in our case, n has a very wide range of variation. The ESTs are then ranked by decreasing normalized score. Finally, a user-defined top percentage of the two ranked lists is compared to identify those ESTs that are associated to the same InParanoid cluster ID. A non-redundant list of the positive InParanoid clusters, sorted by the average highest rank obtained in the two organisms, represents the main output of the program ;readme.txt (detailed instructions for using our program).MP: development of most of the software, execution of the described analysis.PP: development of the routines used to calculate the normalized Pearson, supervision of the statistical analysis.LS: assessment of the biological significance of results.FD: supervision of the project, manuscript writing.


Political instability and the civil war in Southern Sudan have resulted in numerous atrocities, mass violence, and forced migration for vast parts of the civilian population in the West Nile region. High exposure to traumatic experiences has been particularly prominent in the Ugandan and Sudanese of the West Nile Region, representing an indication of the psychological strain posed by years of armed conflict.In this study the impact of traumatic events on the prevalence and severity of posttraumatic stress disorder (PTSD) in a random sample of 3.339 Ugandan nationals, Sudanese nationals, and Sudanese refugees (1.831 households) of the West Nile region is assessed.Results show a positive correlation between the number of traumatic events and the number of endorsed PTSD symptoms. Of the 58 respondents who experienced the greatest number of traumatizing experiences, all reported symptoms which met the DSM-IV criteria for PTSD.There is a clear dose-effect relationship between traumatic exposure and PTSD in the studied populations with high levels of traumatic events. In this context, it is probable that any individual could develop PTSD regardless of other risk-factors once the trauma load reaches a certain threshold. The debate about the impact of traumatic life events on psychiatric disorders has a long tradition in psychiatry. The introduction of posttraumatic stress disorder (PTSD) into the Diagnostic and Statistical Manual of Mental Disorders (DSM-III ) manifesContrary to this assumption, the following research showed that the development of chronic PTSD is rather the exception than the rule after the experience of a traumatic event. Community studies in the US showed that whereas more than 50% of the population reported the experience of a traumatic event, the prevalence of PTSD was not higher than 7.8% . Among tA popular and intuitively plausible assumption in this context is the dose-response model of PTSD. This hypothesis predicts that the probability for the development of PTSD after the experience of a traumatic event mainly depends on the severity of trauma exposure. Some studies tried to test this hypothesis by relating the objective severity of the traumatic event to symptoms of PTSD. However, the empirical evidence for this model is scarce, with some findings supporting this hypothesis but many failing to confirm a relationship with meaningful effect sizes ,9.The probability of detecting a relationship between trauma exposure and PTSD depends on the range and variance of traumatic exposure that is present in the population studied. Studies investigating the relationship between the objective severity of single events and PTSD are restricted to a narrow variance of traumatic exposure. Community studies that assess trauma exposure across different types of traumatic events should be more adequate to examine a dose-effect hypothesis. From a worldwide perspective, even community studies in industrialized countries are restricted to a relatively narrow range of trauma exposure. In contrast, community studies in civil populations affected by war enable the examination of a much wider range of traumatic exposure. These populations present a continuum of subjects ranging from individuals without any history of traumatic events to victims with a history of high numbers of severe events that are rarely to be found in communities without a history of war. Studying a community sample of Cambodian refugees who had fled the Pol Pot regime, Mollica  actuallytraumatic event types experienced or witnessed so far. We considered this measurement to be more reliable than assessing the frequency of traumatic events as many survivors of civil wars reported countless exposures to specific traumatic events. To examine the impact of recent traumatic exposure, we also assessed the traumatic event types experienced or witnessed in the last year.Consequently, it can be hypothesized that each individual who has experienced or is experiencing traumatic events will develop PTSD after reaching a certain threshold of traumatic exposure. As this threshold is probably very high, a large number of subjects exposed to a large variance of traumatic events is necessary to test this hypothesis. We examined the dose-response relationship in the context of a large survey in the West-Nile regions of Sudan and Uganda. The study included Ugandan nationals with a quite peaceful development in the last decade, as well as Sudanese nationals living in the Southern Sudan war region and Sudanese refugees who had fled to Uganda. Among these groups we expected a sufficient variance of traumatic exposure to test for the specified dose-response hypothesis, including an adequate number of subjects who had to experience a series of extremely severe traumatic events. Cumulative trauma exposure was estimated by assessing the number of different As part of a study designed to better understand the impact of forced migration on fertility, mortality, violence and traumatic stress among Sudanese nationals living in southern Sudan and Ugandan nationals and Sudanese refugees living in northern Uganda, we interviewed 3371 individuals from 1842 households in the Ugandan and Sudanese populations in the West Nile. Interviews were structured and were administered in the native languages of Lugbara or Juba Arabic. The study's design involved a multi-stage sampling design.The full training of the interviewers took two months. The project objectives and the rationale behind the structure of the survey instrument as well as that of each question in the questionnaire were discussed in detail. Great attention was also paid to issues such as initial contacts, maintaining a professional attitude while in the field, avoiding influencing the respondent, and reducing interviewer and courtesy biases. The importance of collecting information by means of standardized questions so that the same question was asked to all respondents is stressed and questioning and probing skills were developed. Supervisors were instructed separately on data collection guidelines, their roles and their responsibility to ensure data quality. Keeping in mind the sensitive nature of some of the questions regarding violence and trauma and the fact that the team members were from the study population and probably had experiences similar to the respondents, a workshop on sexual and gender-based-violence was conducted by a consultant to the UNICEF office in Kampala, before the survey. The aim of this workshop was to increase awareness and sensitivity of the team towards respondents and their experiences. Another consultant to the project reviewed the team's interviewing skills and the project's data quality control measures just before the start of the survey. Problem areas were identified and remedied.Data were complete and analyzed for N = 3179 respondents: 2,540 (75 %) of the respondents were women (15–50 years of age) and 831 (25%) were men (20–55 years of age). Details of the sampling, translation and assessment procedures, as well as the socio-demographic characteristics of the populations, have been described elsewhere [ever  and if it happened in the past year. PTSD in respondents was assessed using the Posttraumatic Stress Diagnostic Scale (PDS), modified for assessment by trained lay interviewers [Traumatic events were assessed using a checklist consisting of possible war and non-war related traumatic event types . The checklist was compiled after interviews with key informants  and 30 respondents from all three populations about their personal history of stressful events. Following these interviews, the single events obtained in these studies were rated as being potentially traumatic by experts. The following pilot checklist was pre-tested among further 44 Ugandans and Sudanese in areas not selected for the survey and modified according to the suggestions of the respondents. A primary item analysis based on inter-item correlations led to the exclusion of some events that were obviously not directly related to traumatic histories, e.g. the experiencing of witchcraft. Events included 19 experienced events and 12 witnessed events. Respondents were asked for each event type if they had experienced or witnessed such an event rviewers . The PDSConfidentiality was assured and it was explained that researchers were not working for any UN or Ugandan government organization. Informed consent was obtained using a standardized form explaining the potential risks of participation and explaining that no compensation would be provided. Informed consent forms were signed by the respondent and a witness; fingerprints were taken from illiterate respondents. No financial incentives were provided and respondents were informed that no improvements in living conditions were to be expected as a result of participating in the survey. Respondents were provided with referrals to counseling services provided by NGOs where available.As no major clustering effects were expected in this large sample, statistical analyses were carried out on unweighted data. To examine the relationship between continuous PTSD symptoms and the number of event types reported, we correlated the PDS score and its subscales, intrusion, avoidance and arousal with the number of event types. The number of event types in life correlated with the frequency of intrusions (r = .49), hyperarousal r = .41) and avoidance (r = .47), all P < 0.001. The PDS sumscore correlated significantly (P < 0.001) with the number of traumatic events in the past year (r = .45) and for the whole life , these groups were merged to two groups of 12–19 and 20–27 event types. Figure Overall, 31.6% of the male and 40.1% of the female respondents (N = 3179) fulfilled the DSM criteria for a PTSD-diagnosis. We divided the whole population studied in the survey into different groups based on the number of traumatic event types reported, separately for the events reported for High prevalence rates of PTSD have been reported for three different population groups in the West Nile: Sudanese nationals (44.6%), Sudanese refugees (50.5%) and Ugandan residents (23.2%) . Here weAs demonstrated, the number of traumatic events correlated equally strong with avoidance and with re-experiencing symptoms but coefficients were weaker, although still significant, for the hyperarousal cluster. These results are in agreement with , who alsTypically, even severe single traumatic event produce PTSD in not more than half of those affected. Therefore, PTSD is not an inevitable consequence of potentially traumatizing events. Results from this study, however, suggest that there may be no ultimate resilience to ward off PTSD or that a psychobiological breaking point exists for even the most resistant individual. In the three population groups that were surveyed, each respondent experiencing 28 or more different traumatic event types developed the full set of symptoms of PTSD. This cumulative trauma threshold identified in this study is very high and affected only a small minority of persons even in a war-torn population. Nevertheless, if the cumulative exposure to traumatic events is high enough, these results indicate that anybody will develop chronic PTSD. We conclude that there is no ultimate resilience to traumatic stress and that the repeated occurrence of traumatic stress has a cumulative damaging effect on the mental health of the victim. In these conditions, the effect of pre-trauma factors is reduced to the modulation of the probability of exposure to traumatic events itself. The factors that determine who is exposed to many traumatic events and who manages to flee to secure places may depend on pre-trauma psychological factors. Further studies with war-populations should examine whether the exposure to traumatic events only depends on uncontrollable external factors or whether individual factors contribute to a person's ability to seek safe places.High levels of trauma exposure is found in populations affected by civil war. We show that PTSD, the major psychological consequence of war events, is linearly correlated with traumatic exposure, thus explaining the high prevalence rates of PTSD generally found in war-torn societies. These findings highlight the need for reducing the frequent exposure to traumatic events by preventing wars, controlling the violence in wars, and providing safe and stable living environments for refugees. At the same time, the presence of high numbers of PTSD cases requires the implementation of individual and community based treatment programs. Given very limited resources in refugee communities, these centers must be created to provide short-term care and must be manageable by local personnel ,19. The The author(s) declare that they have no competing interests.FN, MS, UK & TE designed the study. FN, MS and UK composed the set of instruments. UK was responsible for original instrument translation and data collection, FN, MS, CK and TE for the validation part. FN and CK performed the data analysis. CR, TE and FN drafted the original manuscript and all authors revised and approved the final manuscript.The pre-publication history for this paper can be accessed here:


Non-invasive monitoring of respiratory muscle function is an area of increasing research interest, resulting in the appearance of new monitoring devices, one of these being piezoelectric contact sensors. The present study was designed to test whether the use of piezoelectric contact (non-invasive) sensors could be useful in respiratory monitoring, in particular in measuring the timing of diaphragmatic contraction.Experiments were performed in an animal model: three pentobarbital anesthetized mongrel dogs. The motion of the thoracic cage was acquired by means of a piezoelectric contact sensor placed on the costal wall. This signal is compared with direct measurements of the diaphragmatic muscle length, made by sonomicrometry. Furthermore, to assess the diaphragmatic function other respiratory signals were acquired: respiratory airflow and transdiaphragmatic pressure. Diaphragm contraction time was estimated with these four signals. Using diaphragm length signal as reference, contraction times estimated with the other three signals were compared with the contraction time estimated with diaphragm length signal.The contraction time estimated with the TM signal tends to give a reading 0.06 seconds lower than the measure made with the DL signal , with a standard deviation of 0.05 seconds . Correlation coefficients indicated a close link between time contraction estimated with TM signal and contraction time estimated with DL signal . In general, correlation coefficients and mean and standard deviation of the difference were better in the inspiratory load respiratory test than in spontaneous ventilation tests.The technique presented in this work provides a non-invasive method to assess the timing of diaphragmatic contraction in canines, using a piezoelectric contact sensor placed on the costal wall. Non-invasive monitoring of respiratory function is an area of increasing research interest, resulting in the appearance of new monitoring devices . At presThe purpose of this study is to evaluate a non-invasive method to study the timing of the diaphragmatic function, using an animal model (dogs). Accordingly, the present study was designed to test whether the use of contact (non-invasive) piezoelectric sensors, placed on the dogs' costal wall, could be useful in monitoring the diaphragm contraction period in different respiratory conditions, comparing it with other physiological signals such as transdiaphragmatic pressure, diaphragm length measured by sonomicrometry, and respìratory airflow. Diaphragm contraction time is expected to be very close to inspiratory time, which is one of the most utilized parameters in the studies of breathing pattern under experimental or clinical conditions.Three mongrel dogs (15–20 kg) were surgically instrumented under general anesthesia given via a femoral vein catheter . Respiratory flow was recorded with a Fleisch pneumotachograph. Diaphragm shortening was measured via two piezoelectric crystals , as described in . The diaAll analogical signals were amplified (HP 8802A), filtered and digitised with a 12 bit A/D system at a sampling rate of 4 kHz. Inspiratory airflow (FL), diaphragm length (DL), thoracic cage motion (TM), transdiaphragmatic pressure (DP) and diaphragmatic electromyography (EMGdi) signals were decimated at a new sampling rate  and digitally filtered . The sampling frequencies and filter bands were selected to be adapted to the frequency content of the signals. Figure ti, tf), the integral of TM signal and the first derivative of the DL, FL and DP signals were computed. Initial contraction time is detected when these signals reach 10 % of the maximum. In a similar way, final contraction time is detected when the signals reach 10 % of their minimum. We also computed contraction period (TC = tf - ti). The EMGdi signal was included initially in the study but later was rejected because the postinspiratory activity present in this signal hindered the detection of the end of the diaphragm shortening contraction time , standard deviation (STD). DL signal was used as a goldstandard  for muscle shortening, since it is a direct measure of the reduction of the diaphragm muscle length, contrary to flow and trandiaphragmatic pressure which are more remote markers of muscle contraction.Relationships between contraction periods obtained by means DL and TM signals, DL and FL, and DL and DP were analyzed by diverse statistical methods. Different correlation coefficient were calculated: the Pearson correlation coefficient (r), the intra-class correlation coefficient (or reliability coefficient: R) , the sloThe obtained results of the comparison of contraction period estimated with the DL signal (a direct measure of diaphragm shortening) with the contraction periods estimated with the TM, FL and DP signals, are shown in Tables In the Fig. In Table Table In the SVN and SVW respiratory tests (except for the SVN test of the first dog), results showed a moderate relationship, but, in general, correlation coefficients estimated in TM signal were better than estimations in FL and DP signal. Relationship between the contraction period estimated with TM signal and contraction period estimated with DL signal showed Pearson correlation coefficients between 0.60 and 0.91, reliability coefficients between 0.23 and 0.77, slope of the linear regression lines between 0.55 and 1, and Spearman's rank-order correlation coefficient between 0.58 and 0.84.In Fig. Finally, Figure In the present work we have compared the rib cage motion recorded by surface sensors with the changes in diaphragm activity registered by sonomicrometry, transdiaphragmatic pressure and airflow recorded in dogs during spontaneous and inspiratory load breathing. Diaphragmatic time contraction measured with a surface sensor has a good correlation with the rest of signals, especially during the inspiratory load test.In a recent study we observed that the beginning and the end of diaphragmatic contraction were indicated with inflexion points in the thoracic cage motion signal, acquired with a piezoelectric contact sensor placed on the costal wall of the thorax ,7. An alSpinal anaesthesia was used in the present study as a means to isolate diaphragmatic activity by eliminating the activity of the intercostals muscles. In this way, TM, FL and DP signals are directly related with the contraction of diaphragm muscle in the SVW and ILW respiratory tests . In the SVN test the morphologies of TM, FL and DP signals are influenced by the activity of intercostal muscles. However, time differences between contraction periods measured in SVN and SVW respiratory tests were very similar (except for the SVN test of the first dog). This could be explained taking into account that during spontaneous breathing the activity of intercostals and respiratory accessory muscles is minimal ,7.A close relationship between the different methods of diaphragm contraction time estimation has been found during the inspiratory load test. However, during spontaneous ventilation, the correlation was lower than that obtained in the resistive load test. This difference could be explained by the fact that during spontaneous ventilation, the motion of the respiratory system is very low and in consequence, the signal recorded by the contact sensor  has not sufficient intensity to detect with the same precision the beginning and ending of diaphragmatic activity. Besides, during inspiratory resistive loading, there is a marked distortion of the rib cage  and this produces a more marked thoracic-cage motion signal.Another important finding of this work was that time contraction differences between signals were less than 0.1 s for spontaneous ventilation and inspiratory load (except in the irregular case of the SVN respiratory test of the first dog), which indicates that the application of contact sensors constitutes an indirect way to detect the diaphragmatic activity. This could be seen in the graphs of Fig. The contraction of the costal diaphragm acts to displace the rib cage through its insertions at the costal margins and by changing the pressure on the inner surface of the rib cage in the area of apposition. The crural diaphragm is not inserted on the rib cage, but it is considered to have an action through the central tendon. Therefore, in spontaneous respiration the crural part has an important inflationary action on the lower rib cage , and altFinally, to acquire the diaphragm shortening signal, it has been necessary to surgically isolate their diaphragms. The effect of diaphragm isolation could be to favour the operation of the piezoelectric contact sensor to measure diaphragmatic contraction timing. This should be kept in mind when extrapolating these results to human.The technique presented in this work represents a non-invasive method to assess the timing of diaphragm contraction in dogs. We believe that in the future this technique could provide a new potentially useful method for non-invasive respiratory timing monitoring in humans.None declaredJAF, JM, RJ and AG conceived the study, and participated in its design and coordination. JAF and AG designed and conducted the experiments. AT participated in the design of the study and performed the signal processing and statistical analysis. BG and JG provided advice on analysis of the data and manuscript writing. JAF, AT and RJ wrote the first draft of this manuscript. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here: