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Test Number : 8002
Test Name : II- Mathematical Foundations of Risk(R) Measurement
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: 132 Dumps Questions

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II- Mathematical Foundations of Risk(R) Measurement exam

ache-free resting-state functional mind connectivity predicts particular person pain sensitivity | 8002 Dumps and Real test Questions with VCE Practice Test

widespread considerations

The examine design become based with careful consideration of fresh strategies, necessities and necessities for neuroimaging biomarkers50 (neuromarkers) and influenced with the aid of right here suggestions.

Maximise predictive efficiency

We employed a standardized preprocessing pipeline to ensure premier sensitivity of the neuromarker, as adequate impact dimension is a primary requirement of any medical utility50. They used high-precision image alignment, incorporating individual anatomy when extracting fMRI timeseries information. furthermore, they adopted fresh recommendations and protocols51 regarding artefact reduction and optimised their workflow to satisfy the particular needs of connectome-based mostly analysis. They used their in-condominium developed, open-source python software library Pipelines Utilising a Modular inventory (PUMI, https://github.com/spisakt/PUMI), which is in keeping with nipype52, a community-based mostly Python assignment proposing a uniform interface to current neuroimaging utility and, partly, re-used code from the C-PAC53 and the niworkflows54 open-source initiatives. A predictive modelling (computer studying) strategy changed into utilised to make the most the rich facts offered by way of resting-state useful mind networks and, probably, take capabilities of fMRI hyperacuity55.

Assessing predictive vigour beneath simple situations

We used a pre-registered, external validation approach, that strictly separated mannequin training and performance assessment. For mannequin training, they solely used statistics from examine 1. They performed two unbiased sub-stories (stories 2 and three) in distinctive research centres, with diverse gadget and diverse research workforce for validation. They used a liberal alignment of analysis settings, enabling for an inexpensive heterogeneity in techniques, gadget, imaging sequences, language of participant-researcher communique across study-centres, introducing a reasonable heterogeneity within the validation method to ensure generalizability.

make certain that prediction is driven through neural signal and is specific to ache sensitivity

To ensure that the proposed marker of ache sensitivity is certainly driven via neural alerts associated with pain sensitivity, they evaluated the correlation of the envisioned score with a variety of pre-described (and pre-registered) confounder and validator variables.

be sure accessibility of consequences

We utilized a finished pre-registration and made the source code of the formulation open-supply and freely accessible for the community. furthermore, they deliver a platform-impartial, convenient to use docker container, which provides the chance to make use of their predictive mannequin as a analysis product50, to reap out-of-the container ache sensitivity predictions kind any appropriate imaging datasets.


a total of N = 116 fit, younger volunteers were concerned in three sub-reviews. Age and sex of the contributors is suggested in Supplementary desk 1. examine 1 concerned N1 = 39 contributors (the equal trial as in ref. 8). It changed into carried out on the Ruhr college Bochum (Germany) through MZ and TSW and used as the working towards pattern for the desktop-discovering-primarily based prediction of pain sensitivity and additionally, served as a groundwork for the internal validation of the prediction. experiences 2 and three (N2 = forty eight, N3 = 29) were carried out on the university health facility Essen (Germany) via FS and TS and at the tuition of Szeged (Hungary) through BK and TK, respectively, and served as samples for exterior validation. Inclusion standards and exclusion criteria have been mostly identical in all three centres and are listed in table three. Recruitment and reimbursement guidelines dissimilar throughout centres; participants bought 20 €/h in experiences 1 and 2 and no repayment in examine 3.

desk three Inclusion and exclusion criteria all through the recruitment manner.

metallic implant, unremovable piercing, peacemaker, tattoo in head/neck place, pregnancy or universal claustrophobia had been considered as contraindication for MR dimension. individuals have been required to abstain from consuming caffeine two hours before experiments (apart from in study 3) and from drinking alcohol on the day of trying out and the outdated day.

The examine was carried out in line with the declaration of Helsinki, complies with all principal moral rules for work with human individuals and become authorised with the aid of the local or country wide ethics committees (Register Numbers: 4974-14, 18-8020-BO and 057617/2015/OTIG on the Ruhr school Bochum, university hospital Essen and ETT TUKEB Hungary, respectively.) All members gave written counseled consent earlier than trying out.

Imaging and quantitative sensory testing (QST) have been carried out on the same day in look at 1 and in average 2–3 days apart in studies 2 and three (see Supplementary table 1 for details). MRI measurement all the time preceded the QST session.

Measures—useful MRI

excessive-resolution anatomical and open-eyed resting-state fMRI measurements have been received from all contributors. Scanning parameters (including device) distinct across centres and are listed in desk 4. all through measurements, members were suggested to lie still and cozy, with out falling asleep, and stay away from any flow. Foam padding, and in reviews 1 and 2, pneumatic pillows have been used to hinder head movements. All anatomical MRI measurements had been screened for incidental findings.

table four MRI scanner and sequence parameters for each and every centre. Measures—QST

warmth (HPT), bloodless (CPT) and mechanical (MPT) pain thresholds had been obtained according to the QST protocol28. heat (WDT), cold (CDT) and in study 2 and analyze 3, mechanical (MDT) detection thresholds were got as further control measures. All sensory measurements had been acquired from the palmar left forearm, proximal to the wrist crest. within the QST framework, thermal thresholds are determined the usage of a method of limits. To this conclusion, increasing and lowering temperatures were utilized to the skin with an MSA thermal stimulator (Somedic, Hörby, Sweden) in examine 1 and Pathway thermal stimulators (Medoc Ltd., Ramat Yishai, Israel) in reports 2 and 3. In all reviews, ATS thermodes had been used on a skin surface of 30 × 30mm, with a baseline temperature of 32 °C. contributors had been recommended to indicate the onset of pain via button press. For all thermal thresholds 6, as opposed to 3 (as within the common protocol)28, stimulus repetitions had been carried out to in the reduction of between-area variance. additionally, the first dimension become discarded from evaluation as a check stimulus. HPT and CPT have been calculated because the arithmetic means of the 5 final threshold temperatures. MPTs and MDTs had been decided the usage of a staircase method. five increasing and 5 decreasing trains of pinprick (MRC systems, Heidelberg, Germany) stimuli have been utilized to the palmar left forearm in an alternating vogue, whereas the participant turned into steered to categorize the stimuli as noxious, or non-noxious. Mechanical detection threshold was assessed analogously with von Frey filament stimulations. MPT and MDT were computed as the log-changed geometric mean force decided in five ascending and descending staircase-thresholding-runs.

additional measures

Age, intercourse, self-mentioned height, weight and, for feminine contributors, the date of the first day of the last menses and the use of contraceptives, become recorded ahead of all measurements. additionally, self-mentioned weekly alcohol consumption and degree of training (simple college, secondary college, institution) was recorder for experiences 1 and a couple of. before the QST, members filled out the pain Sensitivity Questionnaire (PSQ)fifty six, the pain Catastrophizing Scale (PCS)57, the State-Trait anxiousness inventory (STAI)fifty eight, the short German edition of the depression Scale (advertisements-okay, core for Epidemiologic studies)fifty nine and, moreover in experiences 2 and 3, the Pittsburgh Sleep pleasant Index (PSQI)60 and the perceived stress questionnaire (PSQ20)sixty one. In stories 2 and three, blood pressure became measured each before the MRI and the QST measurements. moreover, for trial 1, T50 values were obtainable from a parallel test carried out on the day earlier than fMRI checking out. T50 represents the temperature (in °C) necessary to result in a warmth-pain ranking of 50 (on a scale starting from 0, no ache to a hundred unbearable ache). T50 values were bought from a non-linear (second-order polynomial) interpolation of ratings bought in line with 15 tonic warmth-pain stimuli (period: sixteen s) between 42.5 °C and forty eight °C, introduced in pseudo-randomized grid-search vogue.

Calculation of ache sensitivity

The target variable for the prediction was a single composite measure of individual ache sensitivity summarizing HPT, CPT and MPT as described in ref. eight.

In look at 1, HPT, CPT and MPT have been Z-changed (mean centred and standardized) and HPT, as well as MPT had been inverted (multiplied with the aid of −1), so that larger Z-values denoted larger pain sensitivity. Then, the arithmetic imply of the Z-converted variables became computed for each participant and defined as ache-sensitivity score. In experiences 2 and 3, the identical process became applied, except that Z-transformation was in keeping with the inhabitants-suggest and general deviation of study 1, to be sure that the equal scale become used throughout reports. severe QST values had been defined the use of the normative 95% percentiles stated in ref. 28; participants displaying excessive HPT, CPT or MPT values in at least two of the three modalities have been excluded. This screening resulted in with the exception of 0, 3 and a couple of participants in Samples 1, 2 and three, respectively (Supplementary desk 2).

fMRI preprocessing

As fMRI-based practical connectivity is susceptible to in-scanner movement artefacts62,63, appropriate preprocessing and signal cleaning is key to a hit connectivity-based mostly prediction. Resting-state functional MRI facts had been preprocessed identically in all three experiences. The applied, nipype-primarily based workflow is depicted on Supplementary Fig. 1. It utilised third-party neuroimaging application, code adapted from the utility equipment C-PAC53 and niworkflows54, and in-condo python routines.

brain extraction from both the anatomical and the structural images, as smartly, as tissue-segmentation from the anatomical photographs become carried out with FSL wager and fast64. Anatomical images were linearly and non-linearly co-registered to the 1mm-decision MNI152 commonplace brain template mind with ANTs65 (see https://gist.github.com/spisakt/0caa7ec4bc18d3ed736d3a4e49da7415 for supply code).

practical photos have been co-registered to the anatomical pictures with the boundary-based mostly registration technique of FSL flirt. All resulting transformations were saved for additional use. The preprocessing of useful photos took place in the native picture space, devoid of resampling. Realignment-primarily based movement-correction changed into carried out with FSL mcflirt. The ensuing six head motion estimates (3 rotations, three translations), their squared types, their derivates and the squared derivates (prevalent because the Friston-24-expansion66) was calculated and saved for nuisance correction. moreover, head action was summarised as framewise displacement (FD) timeseries, according to vigor’s method63, for use in statistics censoring and exclusion. After motion-correction, outliers (e.g. action spikes) in timeseries statistics had been attenuated the usage of AFNI despike67. The union of the eroded white-rely maps and ventricle masks were modified to the native practical space and used for extracting noise-signal for anatomical CompCor correction68.

In a nuisance regression step, 6 CompCor parameters (the 6 first important components of the noise-region timeseries), the Friston-24 motion parameters and the linear fashion were faraway from the timeseries information with a time-honored linear mannequin. On the residual facts, temporal bandpass filtering turned into carried out with AFNI’s 3DBandpass to maintain the 0.008–0.08 Hz frequency band. The prior use of AFNI’s despike is expected to reduce aliasing of residual action artefacts into the neighbouring time-frames all the way through bandpass filtering69. To further attenuate the have an effect on of motion artefacts, doubtlessly motion-contaminated time-frames, described with the aid of a conservative FD > 0.15 mm threshold, have been dropped from the facts (called scrubbing the facts)70. contributors have been excluded from additional evaluation if the suggest FD passed 0.15 mm, or when greater then 30% of frames have been scrubbed. This resulted in exclusion of four, 8 and seven members in Samples 1, 2 and 3, respectively (Supplementary desk 2). great-control (registration-determine, carpet-plots, see e.g. Supplementary Figs. 2–4) was carried out right through the workflow.

practical connectivity evaluation

The 122-parcel edition of the MIST71 multi-decision purposeful mind atlas and gray-count masks received from the anatomical picture have been modified to the native useful house. This atlas (built with with the aid of the BASC method, i.e. bootstrap analysis of good clusters) was lately proven to operate neatly in connectivity-based mostly predictive modelling72. Native-space atlas areas were masked with the gray-rely masks that were got from the anatomical photograph and modified to useful house up to now. With this atlas-individualization method, the remaining regional sign will originate—with a excessive chance—from grey-depend voxels for every subject (which they carefully checked manually for all topics), while with the conventional system, a variable ratio of gray- and white-rely voxels are blanketed for every area. therefore, inputting information from the tissue-segmentation technique is anticipated to lessen field-to-area variability (see Supplementary Fig. 5 for examples). Voxel-timeseries have been averaged over these individualised MIST regions and, at the side of the mean gray-rely sign, retained for graph-based connectivity analysis.

Regional timeseries had been ordered into huge-scale purposeful modules (defined by means of the 7-parcel MIST atlas) for visualization purposes (Fig. 1). Partial correlation was computed throughout all pairs of regions (and international grey matter), as implemented within the nilearn73 python module. Partial, as opposed to basic correlations have been used to rule out indirect connectivity74. Their graph-modelling method ensured that the international gray-rely sign is handled as a confound all over computing the partial correlation coefficients but, on the same time, additionally regarded it as a sign of pastime, as it may also signify vigilance connected processes75. Partial correlation coefficients have been organised to 123 via 123 (122 regions + global gray-matter signal) symmetric connectivity matrices. The upper triangle of these matrices turned into used because the feature house for computing device-researching-based predictive modelling.

Predictive model practicing and validation

total-brain resting-state purposeful connectivity statistics of look at 1 (N1 = 35, in any case exclusions, as in ref. eight, Supplementary table 2) became used because the enter feature-house (P = 7503 features per participant) to foretell particular person ache sensitivity scores, resulting in a huge P—small N environment.

We constructed a computing device-discovering pipeline (https://github.com/spisakt/RPN-signature/blob/master/PAINTeR/mannequin.py) in scikit-learn76, such as powerful feature scaling (removes the median and scales with facts quantiles), pre-choice of features77, identifying the ok surest features with strongest relationships to the goal variable and an Elastic internet regression model78 (a linear mannequin with combined L1 and L2-norms as regulariser). using elastic internet was a choice drawn ahead of the evaluation. Their leading motivation to select elastic web changed into that it makes it possible for to optimize sparsity (L1 vs. L2 regularization) as a hyperparameter, in order that they didn't need to make any a-priory assumptions about the sparsity of the discriminative ground truth (see ref. seventy nine for purpose). To summarise, free hyperparameters of the computing device-studying pipeline were the variety of pre-chosen aspects (ok), the ratio of the L1/L2-regularization and the burden (alpha) of regularisation. Hyperparameters were optimised with a grid-search method and bad imply squared error as can charge function. Values for okay ranged from 10 to 200 with increments of 5, and included [0.1, 0.5, 0.7, 0.9, 0.95, 0.99, 0.999] for the L1/L2 ratio [0.001, 0.005, 0.01, 0.05, 0.1, 0.5] for alpha. Hyperparameter optimisation became carried out in a leave-one-participant-out move-validation (inside validation part). move-validation included the complete laptop-discovering pipeline to stay away from introducing dependencies between the practicing and look at various samples. word that fMRI preprocessing was unbiased between topics, as a result not blanketed within the go-validation. premier hyperparameters have been discovered to be okay = 25, L1/L2-ratio = 0.999 and alpha = 0.005.

exterior validation become performed with the aid of making use of the RPN-signature on the fMRI facts of stories 2 and 3 (N2 = 37, N3 = 19, after exclusions, Supplementary desk 2), simply by means of making use of the feature transformation (scaling) obtained on trial 1 and then calculating the dot product between individual connectivity matrices and the non-zero feature weights obtained in pattern 1. The resulting predictions have been compared with the accompanied QST-based mostly pain sensitivity rankings by calculating suggest absolute error (MAE), suggest squared error (MSE) and explained variance. Permutation-based p-values had been received for all three measures, the usage of the mlxtend python kit. furthermore, bootstrapping with conditional coverage80 turned into used to supply p-values for predictive connectivity weights to help interpretation. They developed ten thousand bootstrap samples (with substitute), with a measurement equal to the customary pattern, consisting of paired mind and influence facts. The predictive model with the best hyperparameters changed into outfitted to every sample. Uncorrected P-values had been calculated for each and every selected connection in keeping with the proportion of weights beneath or above zero, as in e.g. ref. 30. observe that the interpretation of those p-values and self belief intervals (Supplementary desk four) is still limited as they are conditioned to the feature-selection manner.

Confounder evaluation

To discover advantage confounding variables, the envisioned pain sensitivity-ratings (or pass-validated predictions in case of trial 1) have been contrasted to suggest and median FD, the percent of scrubbed volumes, systolic and diastolic blood drive before each the MRI and QST size (as blood drive became past reported81 to be associated with sensitivity to mechanical pain), the time extend between MRI and QST testing (to look at various for temporal steadiness of the prediction), age, sex, BMI, variety of days seeing that the primary day of the last menses, alcohol consumption (instruments/week), level of training, state and trait nervousness (STAI), ranking of depressive symptoms (adverts-ok), self-pronounced pain sensitivity (PSQ) and ache catastrophising (PCS), perceived stress (PSQ20), quality of sleep (PSQI), and non-noxious QST detection thresholds (CDT, WDT and MDT, the place purchasable). additionally, in examine 1 predictions were in comparison to T50-values and MR spectroscopy-based GABA and Glutamate/Glutamine ranges in pain-processing brain areas (see ref. 8 for particulars). Associations were Tested with permutation-based mostly linear models.

Visualization of the predictive network

The predictive interregional connections highlighted via the non-zero regression coefficients of the RPN-signature were displayed as a ribbon plot the usage of the R-kit circlize (Fig. three). Corresponding individualised brain place masks had been modified lower back to common house to create a look at-selected regional likelihood map (reflecting co-registration accuracy and individual variability in morphology). likelihood maps were improved by means of the sum of corresponding regression coefficients to create a regional predictive energy map, which turned into then visualised with FSLeyes and MRIcroGL.

(https://www.mccauslandcenter.sc.edu/mricrogl) (Fig. 3). The evaluation of enormous-scale resting-state network-involvement (as defined by way of the MIST71 mind atlas) was performed with the aid of summarising and Z-reworking the voxel values across the seven regions-of-pastime. Polar plot turned into made with the R-kit ggplot2.

application availability

The RPN-signature rankings will also be computed in keeping with structural and resting-state useful datasets through the utility device with the equal identify. The RPN-signature software device consists of the described MRI processing pipeline and the purposeful connectome-based predictive model. it is available as supply code at https://github.com/spisakt/RPN-signature. as the utility follows the brain Imaging statistics structure (BIDS)eighty two and the BIDS-App specification, it provides a common command line interface and depends on Docker-know-how. The docker photo is deposited on Docker Hub: (https://cloud.docker.com/repository/docker/tspisak/rpn-signature) and doesn't depend upon any application backyard the container photograph. This, together with the entirely clear continuous integration-primarily based construction and automated tagging and versioning, enhances application availability and helps reproducibility of RPN-signature consequences.

Reporting summary

further suggestions on analysis design is attainable within the Nature research Reporting abstract linked to this article.

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Dassault [2 Certification Exam(s) ]
DELL [13 Certification Exam(s) ]
DMI [1 Certification Exam(s) ]
DRI [1 Certification Exam(s) ]
ECCouncil [24 Certification Exam(s) ]
ECDL [1 Certification Exam(s) ]
EMC [134 Certification Exam(s) ]
Enterasys [13 Certification Exam(s) ]
Ericsson [5 Certification Exam(s) ]
ESPA [1 Certification Exam(s) ]
Esri [2 Certification Exam(s) ]
ExamExpress [15 Certification Exam(s) ]
Exin [42 Certification Exam(s) ]
ExtremeNetworks [3 Certification Exam(s) ]
F5-Networks [20 Certification Exam(s) ]
FCTC [2 Certification Exam(s) ]
Filemaker [9 Certification Exam(s) ]
Financial [36 Certification Exam(s) ]
Food [4 Certification Exam(s) ]
Fortinet [16 Certification Exam(s) ]
Foundry [6 Certification Exam(s) ]
FSMTB [1 Certification Exam(s) ]
Fujitsu [2 Certification Exam(s) ]
GAQM [11 Certification Exam(s) ]
Genesys [4 Certification Exam(s) ]
GIAC [15 Certification Exam(s) ]
Google [6 Certification Exam(s) ]
GuidanceSoftware [2 Certification Exam(s) ]
H3C [1 Certification Exam(s) ]
HDI [9 Certification Exam(s) ]
Healthcare [3 Certification Exam(s) ]
HIPAA [2 Certification Exam(s) ]
Hitachi [30 Certification Exam(s) ]
Hortonworks [5 Certification Exam(s) ]
Hospitality [2 Certification Exam(s) ]
HP [764 Certification Exam(s) ]
HR [4 Certification Exam(s) ]
HRCI [1 Certification Exam(s) ]
Huawei [33 Certification Exam(s) ]
Hyperion [10 Certification Exam(s) ]
IAAP [1 Certification Exam(s) ]
IAHCSMM [1 Certification Exam(s) ]
IBM [1547 Certification Exam(s) ]
IBQH [1 Certification Exam(s) ]
ICAI [1 Certification Exam(s) ]
ICDL [6 Certification Exam(s) ]
IEEE [1 Certification Exam(s) ]
IELTS [1 Certification Exam(s) ]
IFPUG [1 Certification Exam(s) ]
IIA [3 Certification Exam(s) ]
IIBA [2 Certification Exam(s) ]
IISFA [1 Certification Exam(s) ]
Intel [2 Certification Exam(s) ]
IQN [1 Certification Exam(s) ]
IRS [1 Certification Exam(s) ]
ISA [1 Certification Exam(s) ]
ISACA [4 Certification Exam(s) ]
ISC2 [6 Certification Exam(s) ]
ISEB [24 Certification Exam(s) ]
Isilon [4 Certification Exam(s) ]
ISM [6 Certification Exam(s) ]
iSQI [9 Certification Exam(s) ]
ITEC [1 Certification Exam(s) ]
ITIL [1 Certification Exam(s) ]
Juniper [68 Certification Exam(s) ]
LEED [1 Certification Exam(s) ]
Legato [5 Certification Exam(s) ]
Liferay [1 Certification Exam(s) ]
Logical-Operations [1 Certification Exam(s) ]
Lotus [66 Certification Exam(s) ]
LPI [25 Certification Exam(s) ]
LSI [3 Certification Exam(s) ]
Magento [3 Certification Exam(s) ]
Maintenance [2 Certification Exam(s) ]
McAfee [9 Certification Exam(s) ]
McData [3 Certification Exam(s) ]
Medical [68 Certification Exam(s) ]
Microsoft [403 Certification Exam(s) ]
Mile2 [3 Certification Exam(s) ]
Military [1 Certification Exam(s) ]
Misc [3 Certification Exam(s) ]
Motorola [7 Certification Exam(s) ]
mySQL [4 Certification Exam(s) ]
NBSTSA [1 Certification Exam(s) ]
NCEES [2 Certification Exam(s) ]
NCIDQ [1 Certification Exam(s) ]
NCLEX [3 Certification Exam(s) ]
Network-General [12 Certification Exam(s) ]
NetworkAppliance [42 Certification Exam(s) ]
NetworkAppliances [1 Certification Exam(s) ]
NI [1 Certification Exam(s) ]
NIELIT [1 Certification Exam(s) ]
Nokia [8 Certification Exam(s) ]
Nortel [130 Certification Exam(s) ]
Novell [38 Certification Exam(s) ]
OMG [10 Certification Exam(s) ]
Oracle [315 Certification Exam(s) ]
P&C [2 Certification Exam(s) ]
Palo-Alto [4 Certification Exam(s) ]
PARCC [1 Certification Exam(s) ]
PayPal [1 Certification Exam(s) ]
PCI-Security [1 Certification Exam(s) ]
Pegasystems [18 Certification Exam(s) ]
PEOPLECERT [4 Certification Exam(s) ]
PMI [16 Certification Exam(s) ]
Polycom [2 Certification Exam(s) ]
PostgreSQL-CE [1 Certification Exam(s) ]
Prince2 [7 Certification Exam(s) ]
PRMIA [1 Certification Exam(s) ]
PsychCorp [1 Certification Exam(s) ]
PTCB [2 Certification Exam(s) ]
QAI [1 Certification Exam(s) ]
QlikView [2 Certification Exam(s) ]
Quality-Assurance [7 Certification Exam(s) ]
RACC [1 Certification Exam(s) ]
Real Estate [1 Certification Exam(s) ]
Real-Estate [1 Certification Exam(s) ]
RedHat [8 Certification Exam(s) ]
RES [5 Certification Exam(s) ]
Riverbed [9 Certification Exam(s) ]
RSA [16 Certification Exam(s) ]
Sair [8 Certification Exam(s) ]
Salesforce [7 Certification Exam(s) ]
SANS [1 Certification Exam(s) ]
SAP [98 Certification Exam(s) ]
SASInstitute [15 Certification Exam(s) ]
SAT [2 Certification Exam(s) ]
SCO [10 Certification Exam(s) ]
SCP [6 Certification Exam(s) ]
SDI [3 Certification Exam(s) ]
See-Beyond [1 Certification Exam(s) ]
Siemens [1 Certification Exam(s) ]
Snia [7 Certification Exam(s) ]
SOA [15 Certification Exam(s) ]
Social-Work-Board [4 Certification Exam(s) ]
SpringSource [1 Certification Exam(s) ]
SUN [63 Certification Exam(s) ]
SUSE [1 Certification Exam(s) ]
Sybase [17 Certification Exam(s) ]
Symantec [137 Certification Exam(s) ]
Teacher-Certification [4 Certification Exam(s) ]
The-Open-Group [8 Certification Exam(s) ]
TIA [3 Certification Exam(s) ]
Tibco [18 Certification Exam(s) ]
Trainers [3 Certification Exam(s) ]
Trend [1 Certification Exam(s) ]
TruSecure [1 Certification Exam(s) ]
USMLE [1 Certification Exam(s) ]
VCE [7 Certification Exam(s) ]
Veeam [2 Certification Exam(s) ]
Veritas [33 Certification Exam(s) ]
Vmware [72 Certification Exam(s) ]
Wonderlic [2 Certification Exam(s) ]
Worldatwork [2 Certification Exam(s) ]
XML-Master [3 Certification Exam(s) ]
Zend [6 Certification Exam(s) ]

References :

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