In social settings, speech waveforms from nearby speakers mix together in our ear canals. Normally, the brain unmixes the attended speech stream from the chorus of background speakers using a combination of fast temporal processing and cognitive active listening mechanisms. Of >100,000 patient records,~10% of adults visited our clinic because of reduced hearing, only to learn that their hearing was clinically normal and should not cause communication difficulties. We found that multi-talker speech intelligibility thresholds varied widely in normal hearing adults, but could be predicted from neural phase-locking to frequency modulation (FM) cues measured with ear canal EEG recordings. Combining neural temporal fine structure processing, pupil-indexed listening effort, and behavioral FM thresholds accounted for 78% of the variability in multi-talker speech intelligibility. The disordered bottom-up and top-down markers of poor multi-talker speech perception identified here could inform the design of next-generation clinical tests for hidden hearing disorders. Our ears were not designed for the society our brains created. The World Health Organization estimates that a billion young adults are at risk for hearing problems due to prolonged exposure to high levels of noise. For many people, the first symptoms of hearing loss consist in an inability to follow a single speaker in crowded places such as restaurants.However, when Parthasarathy et al. examined over 100,000 records from the Massachusetts Eye and Ear audiology database, they found that around 10% of patients who complained about hearing difficulties were sent home with a clean bill of hearing health. This is because existing tests do not detect common problems related to understanding speech in complex, real-world environments: new tests are needed to spot these hidden hearing disorders. Parthasarathy et al. therefore focused on identifying biological measures that would reflect these issues.Normally, the brain can ‘unmix’ different speakers and focus on one person, but even in the context of normal hearing, some people are better at this than others. Parthasarathy et al pinpointed several behavioral and biological markers which, when combined, could predict most of this variability. This involved, for example, measuring the diameter of the pupil while people are listening to speech in the presence of several distracting voices (which mirrors how intensively they have to focus on the task) or measuring the participants’ ability to detect subtle changes in frequency (which reflects how fast-changing sound elements are encoded early on in the hearing system). The findings show that an over-reliance on high-level cognitive processes, such as increased listening effort, coupled with problems in the early processing of certain sound traits, was associated with problems in following a speaker in a busy environment.The biological and behavioral markers highlighted by Parthasarathy et al do not require specialized equipment or marathon sessions to be recorded. In theory, these tests could be implemented into most hospital hearing clinics to give patients and health providers objective data to understand, treat and monitor these hearing difficulties. Slow fluctuations in the sound pressure envelope are sufficient for accurate speech perception in quiet backgrounds . EnvelopAccurate processing of a target speaker in a multi-talker background reflects a harmony between high-fidelity encoding of bottom-up acoustic features such as sTFS alongside cognitive signatures of active listening including attention, listening effort, memory, multisensory integration and prediction . These tWe identified the first visit records of English-speaking adult patients from the Massachusetts Eye and Ear audiology database over a 16 year period, with complete bilateral audiometric records at six octave frequencies from 250 Hz to 8000 Hz according to the inclusion criteria in Our database analysis suggested that approximately one in ten adults arrived to our clinic seeking care for reduced hearing, only to be told that their hearing was fine. This was not entirely surprising, as most clinical tests are not designed to capture difficulties with ‘real world’ speech communication problems that likely prompted their visit to the clinic. To better understand the nature of their suprathreshold hearing problems, we recruited 23 young or middle-aged listeners (mean age: 28.3 ± 0.9 years) that matched the clinically normal hearing from the database profile . Our sub0 = 115 Hz) producing a stream of four digits in the presence of one male and one female distracting speakers . The distracting speakers produced digits simultaneously at variable signal-to-noise ratios (SNRs)  . Performs (SNRs) . Speech s (SNRs) . We founs (SNRs) .We first determined whether simple adaptations of existing clinical tests could identify deficits in multi-talker speech intelligibility. We measured hearing thresholds at extended high frequencies, a marker for early noise damage . SubjectPoor processing of sTFS cues has long been associated with elevated speech recognition thresholds, especially in patients with hearing loss . In a clTo test the hypothesis that early neural processing of sTFS cues in the FM tone is associated with superior speech-in-noise processing, we developed a non-invasive physiological measure of neural sTFS phase-locking at early stages of auditory processing. We first determined that an ear canal to Fz electrode montage was sensitive to evoked potentials generated by the auditory nerve , but we We found that robust low-level encoding of FM cues was highly predictive of an individual’s performance on the FM psychophysical detection task , creating the percept of a tone that moved from the center to the sides of the head. To eliminate phase transition artifacts, an amplitude modulation of ~41 Hz was imposed on the tone, such that the instantaneous phase shift always coincided with the null of the amplitude envelope  Haywood. To testSpeech recognition is a whole brain phenomenon that is intimately linked to cortical processing as well as cognitive resource allocation such as listening effort, spatial attention, working memory, and prediction . In thisWe confirmed here that the fractional change in pupil diameter was linearly related to the SNR of the target speaker  in 16 suOur overall motivation was to develop objective physiological markers that might explain complaints of poor speech communication in individuals with clinically normal hearing. Here, we examined whether poor speech-in-noise intelligibility was associated with poor auditory nerve integrity (indexed here by ABR wave one amplitude), poor encoding of monaural sTFS cues (as indexed by the FMFR), generally poor fast temporal processing (indexed here by IPDFR) and increased utilization of cognitive resources related to effortful listening (indexed here by pupil change). Importantly, none of these indices were correlated with each other, suggesting that – in principle – each of these markers could account for statistically independent components of the total variance in speech performance , right. 2 values, after adding each successive variable. Variables were added in decreasing order of individual R2 values. The adjusted R2 penalizes for model complexity incurred due to the addition of more variables . With this model, listening effort indexed by pupil diameter explained 24% of the variance in the digit comprehension task. Adding in monaural sTFS processing measured using the FMFR increased the adjusted R2, explaining 49% of the overall variance. Adding in the ABR wave one provided only a minimal additional increase in predictive power, raising the total explained variance to 52%  and Partners Healthcare (Protocol #2019P002423). Twenty seven subjects  were recruited and provided informed consent to be tested as part of the study. Of these, 4 subjects were excluded for either failing to meet the inclusion criteria  or not completing more than 60% of the test battery . One subject  did not participate in the electrophysiology tests, but data from the other two sessions were included for relevant analyses. Subjects were compensated per hour for their participation in the study.< 20 dB HL for frequencies up to 8 kHz. Participants then performed high frequency audiometry (Headphones: Sennheiser HDA200) and the digits comprehension task paired with pupillometry (described in detail below). Subjects were then sent home with tablet computers (Microsoft Surface Pro 2) and calibrated headphones (Bose AE2). Subjects were asked to complete additional suprathreshold testing  and questionnaires - Noise exposure questionnaire (NEQ) , depression , tinnitus , use of assistive listening devices  and familiarity with English . Eligible participants were then tested in a double walled acoustically isolated chamber with an audiometer  to confirm normal audiograms with thresholds re (NEQ)  Speech, re (NEQ) ,Tinnitusre (NEQ)  in a qui0 = 115 Hz) as he produced a string of four randomly selected digits  with 0.68 s between the onset of each digit. Once familiarized, the task required subjects to attend to the target speech steam in the presence of two additional speakers  that produced randomly selected digits with matched target-onset times. The two competing speakers could not produce the same digit as the target speaker or each other, but otherwise digits were selected at random. The target speaker was presented at 65 dB SPL. The signal-to-noise ratio of the distractors ranged from 0 to 20 dB SNR. Subjects reported the target 4-digit sequence using a virtual keypad on the tablet screen 1 s following the presentation of the 4th digit. Subjects were initially provided with visual feedback on the accuracy of their report in four practice blocks comprised of 5 trials each and 4 SNRs . Testing consisted of 40 blocks of 8 trials each, with SNRs of 9, 6, 3 and 0 dB presented in a randomized order for each cycle of four blocks. The first three trials of each block served as refreshers to familiarize the subject with the target speaker at 20 dB SNR before progressively decreasing to the test SNR presented in the last five trials of each block. Trials were scored as correct if all four digits entered into the keypad matched the target speaker sequence. The response curve was constructed using percent correct as a function of SNR, and the 70.7% correct point on the response curve was defined as the speech reception threshold. Subjects with thresholds better than 0 dB SNR (n = 4) were marked as 0.Subjects were introduced to the target male speaker (FSubjects were introduced to the percept corresponding to frequency modulation (FM) through a virtual slider on the tablet computer that they manipulated to increase and decrease the FM excursion depth of a 500 Hz tone. High excursions were labeled ‘squiggly’ to allow the subjects to associate the sound with a label that could be used when completing the 2-interval 2-alternative forced choice detection task. After initial familiarization, two tones  were binaurally presented with the same starting phase to subjects, with an interstimulus interval of 0.5 s. Frequency modulation was applied at a rate of 2 Hz to one of the two tones (order selected at random) and the other tone had no FM. A quasi-sinusoidal amplitude modulation  was applied to both tones to reduce cochlear excitation pattern cues . The subSensitivity to interaural phase difference was tested using a 2-interval 2-alternative forced choice task. Sound tokens consisted of tones presented simultaneously to both ears at the same carrier frequency (520 Hz), amplitude modulation rate (100% depth at 40.8 Hz), duration (1 s) and level . Each token was separated by a 0.5 s silent interval. Both tokens started in phase. But for one of the two tokens, a phase shift was applied to the tone in each ear in opposing polarity, 0.5 s after tone onset. This produced a perceptual switch, where the sound ‘moved’ from a diotic to a dichotic percept. The subjects were asked which of two sound tokens ‘moved’ in the middle. Subjects were familiarized with the task in two practice blocks of ten trials each and provided visual feedback about their accuracy in identifying the tone that ‘moved’. A two-down-one-up procedure was used to converge on the phase shift that could be identified with 70.7% correct accuracy. The phase shift was initially set to 81 degrees and changed by a factor of 1.5 for the first four reversals, decreasing to a factor of 1.2 for the last six reversals. The geometric mean of the last six reversals was used to compute the run value. The criteria for determining the number of runs and the threshold matched the FM detection task above.EEG recordings were performed in an electrically shielded sound attenuating chamber. Subjects reclined in a chair and were instructed to minimize movements. Arousal state was monitored but not regulated. Most subjects reported sleeping through the recordings. The recording session lasted ~3 hr and subjects were given breaks as necessary. Recordings were done on a 16-channel EEG system  with two foil electrodes positioned in the ear canals (Etymotic) and six cup electrodes (Grass) placed at Fz, Pz, Oz, C7, and both ear lobes, all referenced to ground at the nape. Impedances were kept below 1 kΩ by prepping the skin  and applying a layer of conductive gel between the electrode and the skin . Stimuli were delivered using calibrated ER3A (Etymotic) insert earphones. Stimulus delivery (sampling rate: 100 kHz) and signal acquisition (sampling rate: 25 kHz) were coordinated using the TDT system and a presentation and acquisition software (LabView).https://github.com/bburan/abr; Auditory brainstem responses were measured in response to 3 kHz tone pips of 5 ms duration. Stimuli had 2.5 ms raised cosine ramps, and were presented at 11.1 repetitions per second. Presentation level was fixed at 105 dB SPL. Stimulus polarity was alternated across trials and 1000 repetitions per polarity were collected. ABRs from the Fz-tiptrode montage were filtered offline between 300 Hz to 3 kHz. Peaks and following troughs of ABR waves were visually marked by an experienced observer, and wave amplitudes were measured using a peak analysis software , 2, 5, 8, and 10 Hz. The stimuli were 1 s in duration with 5 ms raised cosine ramps and presented once every 1.19 s. Stimulus polarity was alternated across trials and 200 samples were acquired for each polarity. The level was fixed at 90 dB SPL. FMFRs from the Fz-tiptrode montage were used for subsequent analyses. Cochlear neural responses to low frequency tones, including the carrier of our FM stimuli, are phase-sensitive in such a way that the summed response to alternating polarities is effectively rectified, and thus periodic at twice the stimulus frequency . Therefoo (no shift), 22.5o, 45o and 90o. Presentation level was fixed at 85 dB SPL. Each stimulus condition was presented continuously for 1.47 min, epoched at 294.3 ms to contain 300 epochs with two phase shifts each  and averaged. IPDFR amplitudes and envelope following response amplitudes were calculated from an FFT performed on the averaged waveforms of the Fz-C7 electrode montage at a resolution equal to 1/epoch length (~3.1 Hz), at 6.8 Hz for the IPD response, and at 40.8 Hz for the AM response. Control recordings consisted of phase shifts of 90o in both ears, but in the same polarity to eliminate the binaural component, which showed no responses at the frequency of the interaural phase shift (6.8 Hz).Interaural phase difference following responses were collected to a 520 Hz tone carrier whose amplitude was modulated at 40.8 Hz. The phase of the carrier was modulated to shift in and out of phase at a rate of 6.8 Hz. The amplitude modulation rate and the rate of inter-aural phase shifts were chosen such that the minima of the amplitude modulation coincided with the point of phase shift. The degree of shift per ear was 0Task-related changes in pupil diameter were collected with a head mounted pupillometry system at a 30 Hz sampling rate (Argus Science ET-Mobile), while the subjects used a tablet computer to complete the digits comprehension task (Microsoft Surface Pro 2). The dynamic range in pupil diameter was initially characterized in each subject by presenting alternating bright and dark screens via the tablet computer. Ambient light level was then adjusted to obtain a baseline pupil diameter in the middle of the dynamic range. Pupil measurements were made while subjects were instructed to look at a fixation point on the screen during the presentation of the digits, as confirmed by the experimenter with real time gaze tracking. Pupil data were preprocessed to interpolate for blinks and missing periods using a cubic spline fit, outlier values were removed with a Hampel filter, and the signal was smoothed using a 5-point moving average window. Subjects were excluded if they had extended periods of missing data or if the ambient light could not be adjusted to account for excessive dilation. Reliable data was obtained from 16 subjects who were included for subsequent analyses. A single trial included a 2 s pre-trial baseline period, 2 s for presentation of the digit sequences, a 2 s wait period and the presentation of the virtual keypad to indicate their response. The pupil analysis described here comes from the 2 s digit presentation period. Pupil measurements were normalized to the average baseline pupil diameter for each block, collected in the last 500 ms of the baseline period. A single measure of pupil-indexed listening effort was operationally defined as the peak value of the average fractional change in pupil diameter function, calculated independently for each SNR block as ((post-stimulus time point – baseline)/baseline). The amplitude value for each SNR was then expressed as a ratio of the complete dynamic range for each subject to reduce variability due to recording conditions, arousal states and eye anatomy.2 calculated . Data from the 15 subjects who had reliable pupil and FMFR measures were used in the model building, due to the requirement for a balanced dataset across all metrics. Each predictor was then added to the model from highest to lowest R2, and the adjusted R2 calculated using the formula2=sample R-square, p=number of predictors, N = total sample size. The adjusted R2 penalizes for increasing complexity by adding more predictors.The distributions of all of the variables were summarized and examined for outliers. Pairwise linear correlations were computed using Pearson’s correlations (r). To assess which sets of predictors best account for variability in our outcome measure (Digits comprehension threshold), all predictors were considered in univariable models and the R In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.Acceptance summary:This work tackles an important and interesting issue, that is, why subjects diagnosed with normal hearing show difficulty in speech recognition, by systematically assessing the behavioral and physiological measures of both low- (FM tracking) and high-level (pupil response) auditory processing. The study identifies an efficient and robust protocol that could account for large variability (to around 85%) in speech-in-noise performance and thus provides a promising mechanistic interpretation for the "hidden hearing loss" group. Importantly, based on pupil dynamics and ear canal EEG to non-speech FM sounds, they could largely predict the somewhat complex multi-talker speech tracking ability in human subjects. The findings will be of major interest to broad readership, including auditory neuroscientists, clinicians, hearing-aid and cochlear-implant manufactures, and computer scientists who might use AI to diagnose hidden hearing loss.Decision letter after peer review:eLife. Your article has been reviewed by three peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by Barbara Shinn-Cunningham as the Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: Fan-Gang Zeng (Reviewer #2); Christian Lorenzi (Reviewer #3).Thank you for submitting your article "Neural signatures of disordered multi-talker speech perception in adults with normal hearing" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.Summary:This work addresses an important and timely issue, that is, why subjects diagnosed with normal hearing show difficulty in speech recognition("hidden hearing loss"). The study systematically examined peripheral and central performance and identified a combination of behavioral and physiological measures of low- and high-level auditory processing that could largely account for speech-in-noise performance in the "hidden hearing loss" group. All the reviewers agree that it is a timely and carefully performed study and would also have great impacts in a wide range of audience.Essential revisions:1) An important implication of the study is to possibly separate the bottom-up and top-down factors that contribute to speech recognition difficulty on a subject-by-subject basis. Could the authors examine the relative ratio of the two factors, e.g., some subjects show more bottom-up contribution whereas other show more top-down limitations?2) Missing link between the 19% of the 106,787 patients  and the 23 subjects  who participated in the present study. At first, the reviewers thought these 23 subjects were part of those who complained about hearing difficulty but had normal audiograms, but nowhere can the reviewers find any info to confirm this connection and in fact, the 23 subjects seemed to be independently recruited to meet the audiogram, age, and other criteria. If it was true that those 23 subjects didn't seek any medical intervention because of hearing difficulty, then this fact needs to be explicitly spelled out.3) The reviewers would strongly encourage the authors to discuss their FMFR data and methods in relationship with a previous study by Gockel et al., 2015, suggesting that FFR for low-frequency pure tones at medium to high levels mainly originates from neurons tuned to higher frequencies. Essential revisions:1) An important implication of the study is to possibly separate the bottom-up and top-down factors that contribute to speech recognition difficulty on a subject-by-subject basis. Could the authors examine the relative ratio of the two factors, e.g., some subjects show more bottom-up contribution whereas other show more top-down limitations?We completely agree; it would be important to understand how bottom-up and top-down resources are leveraged to process low SNR speech, whether their benefits are interchangeable, or whether instead their benefits are complementary and are fully realized when they are both applied together. While this is a question of great importance, it wasn’t the question we set out to answer so one major caveat is that our study design was not powered to fully explore individual strategies. As such, the conclusions we offer below might be better considered as suggestions that could be addressed in a separate study designed with this purpose in mind.To the reviewers’ question, no, it does not seem that our proxies for bottom-up (FMFR) and top-down (pupil) are interchangeable, such that a subject with strong bottom-up would have weak top-down, or vice versa. This conclusion is based on the following observations:– No significant correlations are observed between the FMFR and the pupil measures . They both make contributions towards explained variance in the multivariate model, but the relationship is more complex than a simple tradeoff, where one is strong while the other is weak.– To address the relationship of each measure with multi-talker speech recognition, we plotted the FMFR and the pupil diameter for each subject and color coded the symbol to represent their speech intelligibility, with darker colors indicated better performance on the digits task. No systematic differences were noted, though the better performers tended to cluster to the bottom left, indicating lesser effort and better FMFRs , left.pupil – OPFMFR)/(OPpupil + OPFMFR). An OP index >0 would identifies subjects that rank higher in their utilization of top-down than bottom-up measures, an OP index <0 would indicate a relatively greater emphasis on bottom-up resources, and an index of 0 would indicate a balanced relative utilization of each. We then plotted this OP index as a function of the performance on the digits task but did not find any significant linear correlations. We did find that the subjects with the best multi-talker speech recognition thresholds tended to have stronger bias – in either direction – for utilization of either top-down or bottom-up processing. Subjects with poor speech recognition thresholds all had OP indices closer to 0. .”2) Missing link between the 19% of the 106,787 patients  and the 23 subjects  who participated in the present study. At first, the reviewers thought these 23 subjects were part of those who complained about hearing difficulty but had normal audiograms, but nowhere can the reviewers find any info to confirm this connection and in fact, the 23 subjects seemed to be independently recruited to meet the audiogram, age, and other criteria. If it was true that those 23 subjects didn't seek any medical intervention because of hearing difficulty, then this fact needs to be explicitly spelled out.We thank the reviewers for identifying something in our original manuscript that may have caused confusion among our readers. We recruited subjects that matched the hearing profiles of subjects in the database, but we did not contact the former patients to be in our study. The clinical audiological assessment of these 23 subjects matched the distribution of our clinical sample and the SSQ questionnaire also identified communication difficulties, particularly in related to multi-talker speech intelligibility showed the greatest variability. For these reasons, the 23 subjects we tested were representative of the sample described from the audiology database. To make this explicit, the revised manuscript has been modified as follows:“To better understand the nature of their suprathreshold hearing problems, we recruited 23 young or middle-aged listeners (mean age: 28.3+0.9 years) that matched the clinically normal hearing from the database profile . […] Like the patients from the clinical database, the audiograms from these subjects were clinically normal, yet many reported difficulties with speech intelligibility, particularly in listening conditions with multiple overlapping speakers .”3) The reviewers would strongly encourage the authors to discuss their FMFR data and methods in relationship with a previous study by Gockel et al., 2015, suggesting that FFR for low-frequency pure tones at medium to high levels mainly originates from neurons tuned to higher frequencies.We consider it unlikely that the FMFR signal arises mainly from neurons tuned to higher frequencies based on data shown in The Discussion has been modified to include these points:“The spatial distribution of neural generators for the FMFR also deserves additional study, as some off-channel higher frequency neurons may be combined with low-frequency tonotopically aligned neurons .”


As access to mobile technology improves in low- and middle-income countries, it becomes easier to provide information about sensitive issues, such as contraception and abortion. In Bangladesh, 97% of the population has access to a mobile signal, and the equity gap is closing in mobile phone ownership. Bangladesh has a high pregnancy termination rate and improving effective use of contraception after abortion is essential to reducing subsequent unwanted pregnancies.This study examines the feasibility and acceptability of implementing a short message service (SMS) text message-based mHealth intervention to support postabortion contraceptive use among abortion clients in Bangladesh, including women’s interest in the intervention, intervention preferences, and privacy concerns.This feasibility study was conducted in four urban, high abortion caseload facilities. Women enrolled in the study were randomized into an intervention (n=60) or control group (n=60) using block randomization. Women completed a baseline interview on the day of their abortion procedure and a follow-up interview 4 months later . Women in the intervention group received text message reminders to use their selected postabortion contraceptive methods and reminders to contact the facility if they had problems or concerns with their method. Women who did not select a method received weekly messages that they could visit the clinic if they would like to start a method. Women in the control group did not receive any messages.P=.61). The intervention was not effective in increasing modern contraceptive use at follow-up, but 93% (51/55) of women reported at follow-up that the text reminders helped them use their method correctly and 76% (42/55) said they would sign up for this service again. Approximately half of the participants  said that someone they did not want to know about the text message reminders found out, mostly their husbands or children.Almost all women in the feasibility study reported using their mobile phones at least once per day  and 77.5% (93/120) used their phones for text messaging. In the intervention group, 87% (48/55) of women were using modern contraception at the 4-month follow-up, whereas 90% (47/52) were using contraception in the control group (In this small-scale feasibility study, text reminders did not increase postabortion contraceptive use. Despite the ineffectiveness of the text reminder intervention, implementation of a mHealth intervention among abortion clients in urban Bangladesh was feasible in that women were interested in receiving follow-up messages after their abortion and mobile phone use was common. Text messages may not be the best modality for a mHealth intervention due to relatively low baseline SMS text message use and privacy concerns. Mobile technology has potential applications in many aspects of health, and studies have begun to explore how mobile health  interventions can be used to support women to use their contraceptive method correctly and consistently to prevent unwanted pregnancy . A recenThe majority of wireless subscribers live in low- and middle-income countries . BangladBangladesh has a high pregnancy termination rate at 37 per 1000 women of reproductive age, compared to the average for South Asia at 26 per 1000 women ,13. ImprThis study examines the feasibility of implementing a SMS text message-based mHealth intervention among abortion clients in urban Bangladesh to support contraceptive continuation among abortion clients who accept short-acting postabortion contraceptive methods and to promote contraceptive uptake among those who did not select a method. The project used method-specific text message reminders based on the contraceptive method the woman selected on the day of her abortion procedure. We report on mobile phone usage, satisfaction, and privacy concerns associated with the intervention.This prospective study recruited 120 women from four urban sexual and reproductive health clinics run by the Reproductive Health Services Training and Education Program (RHSTEP) in the divisional capitals of Dhaka, Chittagong, Rajshahi, and Sylhet. Women were eligible for study participation if they received abortion services, selected a short-acting postabortion contraceptive method or no method on the day of their abortion procedure, did not intend to become pregnant in the next four months, did not intend to use their selected method as a temporary method , and had a personal mobile telephone that used Global System for Mobiles (GSM) technology. Participants were randomized into the intervention or control group using computer-generated block randomization. The intervention group (n=60) received the full schedule of method-specific text reminders described subsequently and the control group (n=60) received no reminder messages.Women in the intervention group received method-specific text message reminders to use their selected method . The mobBaseline data collection took place from March to June 2013. After obtaining written informed consent, participants completed a 30-minute interviewer-administered survey conducted in Bangla on the day of their abortion procedure. The survey included women’s sociodemographic characteristics, fertility intentions, and their frequency of mobile phone usage. All respondents were asked to complete a follow-up survey 4 months after their procedure. Up to three attempts were made to contact each participant to schedule their follow-up interview, and the retention rate was 89.1% (107/120). In the follow-up interview, women in the intervention group were asked about their satisfaction with the intervention and any privacy concerns related to receiving SMS text message reminders. Follow-up data collection occurred from July to October 2013. All data were collected by trained female interviewers from a locally contracted nongovernmental organization, the Bangladesh Association for Prevention of Septic Abortion.Completed questionnaires were checked for quality and consistency by research officers and entered into EpiData 3.1 software. The primary outcome of interest was modern contraceptive use at 4-month follow-up, which was assessed for the intervention and control group and compared using a chi-square test. Sociodemographic characteristics of women participating in the study, intervention preferences, and experiences with the SMS text message reminder intervention are presented. Percentages are computed among nonmissing responses. Data were analyzed using Stata/SE 14.0.Institutional Review Board approval was obtained from the Bangladesh Medical Research Council and Allendale Institutional Review Board in the United States.A SMS text message system was designed and developed with technical assistance from a local information technology company, Iris Technology Bangladesh. The system used a Web-based platform with secured log-in and delivery to groups of clients depending on their selected postabortion contraceptive method and intervention preferences. Women were able to select their preferred time of day  to receive the messages to improve privacy. For example, women could select to have the messages sent during the afternoon if they would be at home alone during that time. Women were also able to select their preferred language for the messages, including Bangla (Unicode), English, or phonetic Bangla in English fonts. The platform supported all GSM phones (used by more than 90% of Bangladesh Telecom subscribers) ,17, but P=.005). Almost 78.3% (94/120) lived in an urban setting, as expected given the recruitment sites. A total of 44.1% (53/120) of participants selected pills as their postabortion contraceptive method, although 33.3% (40/120) selected injectables, 20.8% (25/120) selected condoms, and 1.7% (2/120) did not select a postabortion contraceptive method.All 120 women in the sample were married and two-thirds (79/120) had one to two children . In all,Baseline mobile phone use was assessed among all 120 women enrolled in the study. Overall, 98.3% (118/120) of the women enrolled in the study reported using their mobile phone at least once per day. Although 77.5% (93/120) of women used their mobile phones for text messaging, the frequency of use was low. Approximately one-quarter  of women reported using text messaging every day, slightly more than one-third  used text messaging once a week and 20.0% (24/120 women) used text messaging less than once per week.The majority of women in the intervention group  preferred phonetic Bangla in English fonts, primarily because their mobile phones did not support Bangla fonts. In all, 30% (18/60) selected Bangla (Unicode), and 7% (4/60) selected English as the language for their SMS text message reminders. Approximately three-quarters of participants  preferred to receive the messages in the evening, 23% (14/60) during the afternoon, and 3% (2/60) in the morning. At the time of study enrollment, a test message was sent to check readability and 93% (56/60) of women were able to read the full message in their selected language. All women were able to read at least part of the message.P=.61).Intervention effectiveness was assessed at 4 months postabortion among the 89% (107/120) of women who completed the follow-up interview. Loss to follow-up was differential by education and financial status with poorer and less-educated women more likely to be lost to follow-up. A statistically significant difference in modern contraceptive use 4 months postabortion was not observed between intervention and control groups. In the intervention group, 87% (48/55) of women were using modern contraception at the time of the 4-month follow-up, whereas 90% (47/52) were using a method in the control group  of women reported at follow-up that the text reminders helped them use their method correctly. Approximately 87% (48/55) of women said they were satisfied with the timing of the text message reminders and 84% (46/55) said they read the messages regularly. Approximately three-quarters  of women said they would sign up for the service again. Women who used their phones for SMS text messages regularly  and those who did not use their phones for SMS text messaging at baseline  were most likely to say that they would sign up again, whereas women who used their phones for SMS text messaging infrequently were less likely to be interested in signing up again . Among t.Women were ineligible for study participation if they reported during the consent process that they shared their phone with someone else. Despite this precaution and asking women to select the time of day they wished to receive messages, some women reported challenges with privacy of text messages at follow-up. The majority of participants  reported that they were satisfied with the privacy of receiving text message reminders, but approximately half of participants  responded affirmatively to the statement “someone I did not want to know about the text reminders found out.” For most women , it was her husband who found out about the messages, for five women it was her children who found out, for three it was her sister, and for one woman, it was her in-laws who found out. When women were asked further questions about how another person found out, almost half of women who reported that their husbands found out  said they showed the message themselves, more than one-third  said their husband saw it when it appeared on their phone, and 12% (3/25) reported their husband found it when he checked her phone . For chiIn the follow-up interview, participants were asked to provide suggestions for improving the SMS text message service. Of 27 participants who offered their suggestions, approximately half (14/27) indicated that automated voice messages or phone call reminders would be better for privacy. Two respondents suggested the traditional mode of interpersonal communication through courtyard meetings. One respondent suggested having a number to call to get information. When asked specifically about interest in a hotline for information on abortion and contraception, 96% (53/55) of intervention participants responded that they would be interested in using a hotline. Being able to talk to a counselor was important to some women in the study. Participants were given a mobile phone number enabling them to call the researchers to withdraw from the study if they wanted, but according to call log records, 13% of participants (8/60) called the number with questions on contraceptive methods, although the number was not meant for this purpose. Women were also asked if they would answer questions using mobile text messaging if that incurred a cost. In response, 82% (45/55) of women reported that they would answer text messages if the cost was within the limit of 0.5 to 1.5 Taka (<US $0.01) per message.The simple text reminder intervention did not increase postabortion contraceptive use 4 months postabortion. Despite the ineffectiveness of the text reminder intervention, this study has important findings for those seeking to implement mHealth interventions in the potentially vulnerable population of women receiving abortion services. This study finds that it is feasible to conduct a mHealth intervention with abortion clients in urban Bangladesh because mobile phone use was common and women were interested in receiving follow-up messages after their abortion. However, women also raised privacy concerns associated with text messages appearing on their phones unexpectedly. Reaching out to urban abortion clients through mobile phones could be a feasible strategy, but the best modality should be explored through additional formative work and privacy concerns addressed.Approximately 93% (51/55) of women reported that the mobile text message helped them use their contraceptive method correctly, but the intervention had no effect on modern contraceptive use at 4 months postabortion. Previous studies have shown that mobile messaging can be a simple and effective means of supporting women’s access to sexual and reproductive health information and services ,19. HoweThere was interest from participants in this study in having a hotline number that they could call with questions . Even though a majority of participants reported using text messaging, regular use was low with only 23% 28/120) using their phone for SMS text messaging at least once per day, and verbal communication may be preferred. Interest in signing up for the intervention again varied by SMS text message use at baseline, suggesting that women who already use SMS text messages regularly and those who have little experience with SMS text messages (and may find it novel), might be more interested in receiving SMS text messages than others. A hotline option might be acceptable to a broader group of women and would possibly increase privacy because women could call the hotline at their convenience. Some studies have suggested that interventions using verbal communication increase contraceptive uptake and use [/120 usinThere were some concerns about privacy related to this intervention, and more work is needed to fully understand women’s concerns and preferences. Although 93% (51/55) of women said they were satisfied with the privacy of the SMS text message reminders, approximately half (29/55) reported that someone they did not want to find out about the SMS text message reminders found out. These findings point to the importance of asking multiple questions related to privacy and not relying only on reports of satisfaction with the privacy that a mHealth intervention affords. When women were further asked how the person found out, most women reported showing the message themselves. The nuanced findings related to self-initiated sharing of text messages may suggest that mHealth interventions could contribute to increased communication about contraception between women and their husbands or partners. Studies have shown that mHealth interventions can lead to improved communication about sexual and reproductive health among partners and to positively influence gender dynamics through increased male cooperation and involvement in health areas that are normally the domain of the woman . HoweverThe feasibility of using the mHealth intervention with abortion clients should be judged within the context of this specific study sample. The study was conducted only in urban areas and findings may not generalize to rural areas of Bangladesh. Further investigation of mobile phone use in rural areas can provide insight into the feasibility of this type of intervention throughout the country. In addition, the small sample size limits power to detect changes in postabortion contraceptive use between baseline and follow-up. Women in the intervention group were wealthier than those in the control group and this baseline imbalance between the two groups could contribute to differences in contraceptive outcomes at follow-up. In addition, poorer and less-educated women were more likely to be lost to follow-up, which could result in an overestimate of postabortion contraceptive use at follow-up. Finally, we lack data on technologic aspects of the intervention, such as the message success rate, and on provider opinions about the intervention, which would have provided more information on the feasibility of conducting a mHealth intervention in this population.Women’s interest and satisfaction with the intervention suggests that a mHealth intervention to support postabortion contraceptive use is feasible in this context, but a more interactive intervention may be required to influence women’s postabortion contraceptive outcomes. In addition, SMS may not be the best intervention modality. Abortion clients in urban Bangladesh regularly use their mobile phones, but mobile use for SMS text messages is less common. In addition, lack of privacy of text messages raises concerns, and it may be more acceptable and effective to share information through an intervention that uses a voice messaging system or connects women with a call center counselor to allow for two-way conversation. Additional formative research is needed to customize the modality and content of a mHealth intervention that will be effective in supporting women’s postabortion contraceptive use.


MC1R locus associated with chestnut coat color may have been targeted by selection for a different mutation much earlier on than the recent selection for chestnut color. We also identified regions that show signatures of selection specific to Thoroughbreds. In addition, we found that intense inbreeding early in the history of the Thoroughbred breed and also before the formation of the breed has a significant impact on the genomic architecture of modern Thoroughbreds. Our study demonstrates that the horse 670k array can be utilized to gain important insight into the domestication process of horses and to understand the genetic basis of the phenotypic diversity in horses.The domestication process of plants and animals typically involves intense inbreeding and directional selection for various traits. Here, we genotyped 370 Japanese Thoroughbred horses using the recently developed 670k SNP array and performed various genome-wide analysis also using genotype data of other horse breeds. We identified a number of regions showing interesting patterns of polymorphisms. For instance, the region containing the  Humans have been selectively breeding several plants and animals for a wide range of desirable traits throughout history. This domestication process offers an excellent opportunity to understand the genetic basis of phenotypic variation, and also to understand how forces such as directional selection, population bottleneck, and inbreeding shape the genome , 2. A nuDespite the economical value of Thoroughbreds, genetic information has traditionally not been considered in the breeding of Thoroughbreds. Even with the progress in DNA sequencing in the recent years and the availability of a reference genome , genomicHere, we investigated the genetic variation of Japanese Thoroughbred racehorses by genotyping 370 Japanese Thoroughbreds using the 670k SNP array. Our study provides insight into the impact of inbreeding and artificial selection on the genetic makeup of Thoroughbred horses. Our study also provides a platform to identify genomic loci associated with various traits which should help improve the breeding of Thoroughbreds.hsphase [r2 in our study is slightly lower and drops below 0.2 at ∼70kb ..K = 14 i [et al.  are highhsphase for the half-sib families, we were able to infer the haplotype blocks in the half-sibs inherited from the sires and the recombination events that occurred during meiosis. Examples of three chromosomes of the largest half-sib family are shown in The presence of several half-sibs sharing the same sire makes it possible to accurately phase the chromosomes of each half-sib, and also infer the phased chromosomes of the sire even though the sires were not genotyped. This allows us to examine how the homologous chromosomes of the sire are recombined and passed on to each offspring. Based on the results of Thoroughbreds are highly inbred with a closed breeding system where foals with a non-Thoroughbred sire or dam cannot be registered as Thoroughbreds. Very strong inbreeding (e.g. between first cousins) is generally avoided at least in recent years. All modern Thoroughbreds can be traced back to a small number of founder stallions and mares, indicating severe bottleneck during the establishment of the breed –10, 18. F for each individual based on the pedigree of the past 5 (FPED5) and 10 (FPED10) generations (FPED5) was low for most individuals, with most horses having FPED5 <0.03. The inbreeding coefficients were much larger when calculated based on pedigrees of the past 10 generations (FPED10), even though these estimates should be underestimates due to the incompleteness of the pedigree record. We then calculated the inbreeding coefficient F from the genome-wide SNP data based on runs of homozygosity (ROH) of >0.5 Mb (FROH>0.5Mb) and >5 Mb (FROH>5Mb)  . ROH is pedigree . Each hopedigree . The avepedigree . The amoGray locus (STX17) on chromosome 25 have a gray coat color [Extension locus (MC1R) on chromosome 3 have a chestnut coat color [Gray locus on chromosome 25 showed an extremely strong association with gray coat color, whereas a large region on chromosome 3 including the Extension locus showed an extremely strong association with the chestnut phenotype  is often used to identify candidate regions associated with traits of interest. To examine the utility of our dataset for GWAS, we first performed GWAS for 2 known color coat loci, gray and chestnut. It is known that all Thoroughbred horses (excluding very rare white horses) with a mutation in the at color , and of at color . Our samat color , which fhenotype . Althoughenotype . This ren humans . One pren humans .MC1R gene  responsible for the chestnut coat color showed reduced levels of diversity in Thoroughbreds and in the other breeds   was calcbreds of , and allocessing , 48. Polocessing . Other gocessing .MC1R gene may have been the target of a selective sweep much earlier on [Population genomic studies have proven extremely useful in identifying genomic loci associated with agronomically important traits and also to understand the domestication process of these species , 2. Hererlier on , 14, 42,FROH and FPED was also observed for the other horse breeds  [Our results offer interesting insights regarding the effect of historical inbreeding in Thoroughbreds. We found that the degree of inbreeding estimated based on pedigree information considerably underestimates the degree of inbreeding observed in the genome as measured by segments of ROH. This is likely due to the excess of historical inbreeding that is not captured by the recent pedigree. One recent study examined the pedigree-based inbreeding coefficients in 135,572 Australian Thoroughbreds by compiling an extensive pedigree dating back to the founders of the population . They foctively) , suggestHere, by performing population genetic analyses, we identified a number of regions that are likely to be associated with various important traits in Thoroughbreds, and showed that historical inbreeding has played an important role in shaping the genome of modern Thoroughbreds. Our results demonstrate the utility of horses as an interesting system to study genetics thanks to the presence of a high-density SNP array, detailed pedigree record, a large number of half-sibs, and several breeds with high phenotypic diversity. Future studies with a larger number of samples and detailed records of various phenotypic traits should enable us to understand the genetic basis of various phenotypic traits.https://www.ebi.ac.uk, Project: PRJEB32686, Analyses: ERZ936110 at European Variation Archive). The genomic coordinates based on the equCab2 assembly is used throughout this article. 54 samples with an average genotype call rate of <98% were removed, resulting in 370 samples that were used in this study for genome-wide analyses. SNPs that could not be genotyped in >1% of the samples were removed, resulting in 536,448 SNPs that were used in this study. For phasing, hsphase [hsphase and the unphased genotypes of the remaining samples were combined into a single VCF file, and BEAGLE v4.1 [This research was approved by the Animal Care and Use Committee at Hidaka Training and Research Center. Blood samples were collected from 424 Thoroughbred racehorses owned by the Hidaka Training and Research Center . All horses were born between 2009 and 2015. DNA was extracted by Maxwell 16 (Promega) with the Maxwell 16 LEV Blood DNA Kit. SNP genotyping was performed using the Axiom Equine Genotyping Array (Axiom MNEC670) which includes a total of 670,796 SNP markers. The data file containing all 670,796 SNP calls of the 370 samples are available as a VCF format file from EMBL-EBI  was estimated by calculating r2  between r2 . Out of r2  were retr2  of the cth MEGA7  and was th MEGA7 . Bootstrth MEGA7  with a mMC1R on chromosome 3.The heterozygosity was calculated for each site separately for the 370 Thoroughbreds of this study, 24 Thoroughbreds of , and theFST values [et al. [FST values were then transformed to obtain estimates of the population divergence time T in units scaled by the population size following [T = −log(1 − FST). For each genomic window this value was calculated between the Japanese Thoroughbreds and the Thoroughbreds of Schaefer et al. (TJTST), the Japanese Thoroughbreds and the non-Thoroughbred horses (TJTnT), and the Thoroughbreds of Schaefer et al. and the non-Thoroughbred horses (TSTnT). The length of the branch leading to the Japanese Thoroughbreds was then calculated asThe population branch statistic (PBS) was calculated over an overlapping sliding window of 1 Mb with an increment of 200 kb  following  in orderT values  were cal [et al. , the Jap [et al. , and the [et al.  and all ollowing  as T = −FPED [www.netkeiba.com. Segments of runs of homozygosity (ROH) of >0.5 Mb and >5 Mb were detected using PLINK v1.07 [FROH were calculated as the proportion of the genome covered by ROH segments assuming that the length of the genome is 2243 Gb.The pedigree-based inbreeding coefficients FPED  for eachNK v1.07  with thewww.netkeiba.com. Association with the gray coat color was tested using the 18 gray horses as cases and the remaining 352 non-gray horses as controls. Association with the chestnut coat color was tested using the 94 chestnut horses as cases and the 258 non-gray, non-chestnut horses as controls. Association with racing performances was done by classifying high-performance horses and low-performance horses based on their lifetime earnings at JRA races. The racing records until the end of 2018 for all horses born before 2015 were obtained from www.netkeiba.com. Horses that had earned ≥30 million JPY were classified as high-performance horses and horses that had earned <5 million JPY despite having raced at least 3 times were classified as low-performance horses. In the JRA racing system, a win at a maiden race (a race between horses that have not won a race yet) earns ∼5 million yen. Horses that cannot win until the end of the age of 3 lose their eligibility to enter maiden races and often end up retiring. Thus, most if not all horses classified as low-performances will not be able to earn any more at JRA races. Genome-wide association studies were performed using GEMMA [The coat color of all 370 horses were obtained from ng GEMMA  by firstS1 Figr2 is plotted as a function of distance in kilobase up to 1 Mb. The average r2 values over each non-overlapping 1 kb bin are shown.(EPS)Click here for additional data file.S2 FigSNPs from a randomly selected subset of 10 Thoroughbreds genotyped in this study and the 311 samples from 20 breeds of  were use(EPS)Click here for additional data file.S3 FigSNPs from the 370 Thoroughbreds genotyped in this study and the 311 samples from 20 breeds of  were use(TIF)Click here for additional data file.S4 FigSNPs from a randomly selected subset of 10 Thoroughbreds genotyped in this study and the 311 samples from 20 breeds of  were use(TIF)Click here for additional data file.S5 FigThe two homologous chromosomes of the sire are shown at the top in red and blue, and the chromosomes inherited from the sire in each genotyped half-sib are shown below. Results of all chromosomes for each half-sib family with 10 or more half-sibs are shown.(EPS)Click here for additional data file.S1 TableProtein-coding genes in the top 10 regions with the highest PBS values are listed.(XLSX)Click here for additional data file.


Whole-genome sequencing is a promising approach for human autosomal dominant disease studies. However, the vast number of genetic variants observed by this method constitutes a challenge when trying to identify the causal variants. This is often handled by restricting disease studies to the most damaging variants, e.g. those found in coding regions, and overlooking the remaining genetic variation. Such a biased approach explains in part why the genetic causes of many families with dominantly inherited diseases, in spite of being included in whole-genome sequencing studies, are left unsolved today. Here we explore the use of a geographically matched control population to minimize the number of candidate disease-causing variants without excluding variants based on assumptions on genomic position or functional predictions. To exemplify the benefit of the geographically matched control population we apply a typical disease variant filtering strategy in a family with an autosomal dominant form of colorectal cancer. With the use of the geographically matched control population we end up with 26 candidate variants genome wide. This is in contrast to the tens of thousands of candidates left when only making use of available public variant datasets. The effect of the local control population is dual, it (1) reduces the total number of candidate variants shared between affected individuals, and more importantly (2) increases the rate by which the number of candidate variants are reduced as additional affected family members are included in the filtering strategy. We demonstrate that the application of a geographically matched control population effectively limits the number of candidate disease-causing variants and may provide the means by which variants suitable for functional studies are identified genome wide. With the introduction of next-generation sequencing technologies, expectations were high that disease-causing genetic variants in familial diseases could be identified. However, the discoveries in recent years have in many cases been limited to the “low hanging fruit”, resolving familial diseases with a strong phenotype and an early age of onset and predominantly finding variants in coding regions and in known disease genes , 2. A maLarge-scale projects that attempt to map the functional elements of the genome, such as ENCODE, have suggested that as much as 80% of the genome might be involved in biological processes or interact with proteins . Others Disease-variant filtering strategies today include initial steps that remove variants that are highly unlikely to be disease causing e.g. based on their frequency in the general population or because they do not segregate according to the disease model . This isIt is well known that whole-genome sequencing (WGS) the human genome results in a daunting number of genetic variants, forcing many disease studies to restrict their analysis to the coding genome (the exomes) or even to nonsense mutations . In an aThe large number of candidate variants remaining after the filtering analysis illustrates that we have a substantial pool of variants in northern Sweden that are rare or absent in other populations, i.e. they were not excluded when applying MAF filters to remove variants that are common in public variant datasets . AdditioFamily CRC1 originates from Västerbotten, a county situated in northern Sweden with a population of approximately 265,000 people . Our conA total of 17,070,830 variants–including 14,414,452 single nucleotide polymorphisms (SNPs) and 2,656,378 insertions/deletions (indels)–were found in ACpop. Among these, 2,828,043 variants were unique to ACpop compared to the six publically available variant datasets 1000g, ESP, ExAC, UK10K, GoNL, and deCODE. Out of these 2.8 million variants, 706,315 (25%) had an allele frequency of over 1% . In otheThe impact of the matched control population became apparent when removing all ACpop variants from the variants in family CRC1. The 11 affected individuals together carried 7.8 million variants , and thiNone of the 36,522 variants remaining in the above analysis were shared by all 11 affected individuals in family CRC1, thus 0 candidate disease-causing variants were left in the filtering analysis . HoweverThe 26 variants from the above analysis were considered for initial downstream evaluation . Twenty-To determine the impact of the control population when investigating smaller families and to see what effect a small shift in the family’s geographical origin might have on the number of candidate variants, we whole genome sequenced two unrelated trios, one from Västerbotten (TRIO1) and one from a neighboring county (TRIO2). In both cases, one parent and one child were affected, while the second parent was unaffected. The inclusion of ACpop in the filtering analysis resulted in 2,666 and 5,556 candidate variants in the two trios, respectively. This was a 28-fold decrease of candidate variants in the trio from Västerbotten as compared to only relying on the six public variant datasets and a 16-fold reduction in the trio from outside Västerbotten . Out of As mentioned above, there is a relationship between the relatedness between sequenced individuals and the final number of candidate disease-causing variants. To further explore this relationship with access to ACpop, we carried out a series of filtrations on a decreasing level of relatedness in three larger families, one from Västerbotten (family CRC1) and two from a neighboring county (families FAM1 and FAM2). In this case, we did not include information about affection state, and we only considered the relatedness to exemplify how many candidate variants were shared. When ACpop was not included and only variants that are common (MAF >1%) in the six variant datasets were removed during the filtering analysis, we saw again on average a 20% decrease in the number of shared candidate variants when the fraction of shared genome was halved. When also including ACpop, we found on average a 50% decrease in the number of shared candidate variants when the fraction of shared genome was halved . This ilIn this study we have shown that it is possible to limit the final number of candidate disease-causing variants to a level where functional assays can be applied to identify the causal variant of a monogenic disease, even when using a whole-genome sequencing approach and without the use of common practice functional prediction filters that may produce false negatives. In other words, it is possible to successfully employ an unbiased filtering strategy where variants found across the entire genome, regardless of predicted functional importance, are included in the analysis. There is, however, an absolute requirement to remove variants that are common and therefore not disease-causing, in the geographic area from where the studied family originates.We used a geographically matched control population to remove common variants in the selected geographic region. This could in theory also be achieved by sequencing a number of healthy individuals within the family. Acquiring these samples is, however, not trivial for many diseases. For example, in this study of hereditary colon cancer, the carrier status of unaffected family members is in many cases uncertain because they are younger than the average age of disease onset. Other challenges include reduced penetrance and preventive treatment that reduce the risk for disease. Parents who are married into the family should not be carriers but are often already deceased because of the late onset of the disease. In our case, we sequenced 27 family members in family CRC1 with uncertain phenotype see . IncludiThe explosive population growth over the last 400 generations has resulted in a large number of population specific rare variants . WithoutDopazo et al. recently illustrated the importance of a local Spanish control population in the filtering analysis of whole exome studies . Here weIn this study, we have whole-genome sequenced blood samples from five families originating from northern Sweden . Our primary focus was on family CRC1, a large family with high incidence of colorectal cancer. There are more than 100 known family members in CRC1, and there are medical records available for 60 of them (27 women and 33 men). Many of the individuals in the older generation were deceased, but blood samples could be obtained from 38 of the 60 family members with medical records (19 women and 19 men). These samples were all sent for whole genome sequencing. To illustrate the impact of the control population (ACpop) in our filtration strategy, we also collected blood and sequenced individuals from families called TRIO1-2 and FAM1-2. Blood samples have additionally been collected from at least one individual in another 95 smaller pedigrees. While these samples have not been sent for whole-genome sequencing, they were available for screening of candidate disease-causing variants.The lineage of family CRC1 can be traced back to the second half of the 19th century, and the inheritance pattern in the pedigree suggests that the family suffers from an autosomal dominant form of hereditary colorectal cancer. The family comprises 12 cases of colon cancer and 2 cases of stomach cancer out of the 60 followed family members. The family has been offered surveillance with colonoscopies since the mid-1990s, and all suspected pre-stages of cancer (polyps and adenomas) are removed from the colon. This preventive treatment has decreased the number of tumors in younger generations –23. A coIn total, 300 people were sequenced for the control population, which was given the name ACpop . The samples were selected from the Västerbotten Intervention Programme (VIP), 25, a cDNA was either extracted from blood samples provided by family members  or, in cases where there was a sample available, collected from the clinic. DNA samples from the 300 individuals in the control population were obtained from the NSHDS. All DNA samples underwent a quality check by the sequencing facility. DNA samples from families were collected and sequenced over a period of several years. Initially the samples were sequenced at Illumina (San Diego) and later at the Genomics Production site in Stockholm (NGI-S) and the SNP&SEQ facility in Uppsala (NGI-U) (Sweden) using the HiSeq2000 and HiSeqX systems. The 300 samples of the control population were sequenced at NGI-U using Illumina HiSeqX. PCR-free library preparation kits were used on all occasions, and samples were paired-end sequenced with a read length increasing from 100 bp to 150 bp. All samples were sequenced to at least 30× depth.The raw data from all samples were jointly processed to ensure that the same steps and software versions were used. All analyses were performed according to the GATK best practice, 27. Bri10.17044/NBIS/G000005 (https://doi.org/10.17044/nbis/g000005), and hosted at the SweFreq searchable web-portal for specific allele-frequencies and read depth (https://swefreq.nbis.se).SNPs and small indels were called using HaplotypeCaller according to GATK’s best practice with version 3.3.0 of the GATK software suite , 27. Brigenome command in PLINK (v1.90b3) was used to calculate identity by descent (IBD) sharing between all samples. Variants that were included in the analysis were common (MAF >10%) biallelic SNPs with a successful genotyping rate of at least 99%. The variant set was LD-pruned using the—indep-pairwise command in PLINK with a window of 10,000 SNPs, a window step of 10 SNPs, and the r2 threshold set to 0.3. In total, 178,498 variants were used for the IBD calculation. Allele frequencies were calculated from ACpop. All samples from family CRC1 were predicted to have relationships in accordance with the pedigree. We found two pairs of siblings in ACpop. Additionally, a third-degree relationship was predicted between one individual in ACpop and one individual in family CRC1. The individual in ACpop was, however, not related to any of the other cousins of the CRC1 individual and was therefore assumed to be related on the mother’s side, who married into the family.The software PLINK  was usedThe VCF files from SNP and indel calling were decomposed and normalized using vt (v.0.5), annotatduohmm option active. The number of conditioning states was set to 1000, the window size was set to 300, and the MCMC iterations were set to have 15 burn-in and pruning iterations and 50 main iterations.Phasing of variants to obtain haplotypes within family CRC1 was done using SHAPEIT2 . SamplesSNP regions were PCR amplified, and the DNA was sent to Eurofins Genomics for purification and sequencing .S1 Table(XLSX)Click here for additional data file.S1 FigThe figure illustrates a schematic score plot of the first and second component from a PCA model. Pink dots represent the data points (samples) and diamonds indicate selected samples. The baseline selection for all municipalities was made according to a full factorial design (A), whereas for the more populous municipalities an extended design was used (B).(TIF)Click here for additional data file.


One of the most intriguing puzzles in biology is the degree to which evolution is repeatable. The repeatability of evolution, or parallel evolution, has been studied in a variety of model systems, but has rarely been investigated with clinically relevant viruses. To investigate parallel evolution of HIV-1, we passaged two replicate HIV-1 populations for almost 1 year in each of two human T-cell lines. For each of the four evolution lines, we determined the genetic composition of the viral population at nine time points by deep sequencing the entire genome. Mutations that were carried by the majority of the viral population accumulated continuously over 1 year in each evolution line. Many majority mutations appeared in more than one evolution line, that is, our experiments showed an extreme degree of parallel evolution. In one of the evolution lines, 62% of the majority mutations also occur in another line. The parallelism impairs our ability to reconstruct the evolutionary history by phylogenetic methods. We show that one can infer the correct phylogenetic topology by including minority mutations in our analysis. We also find that mutation diversity at the beginning of the experiment is predictive of the frequency of majority mutations at the end of the experiment. In long-term evolution experiments, organisms are passaged for many generations in controlled environments in order to understand basic evolutionary principles . One of Gould’s hypothesis has been experimentally tested numerous times by letting populations of organisms evolve repeatedly in the same environment. In such experiments, the parallel evolution of similar traits and, in rare cases, also identical mutations, have been observed in bacteria and viruses  is one of the most relevant and deadly infections in the human population. In 2012, about 36.9 million people were infected with HIV . Due to Part of the reason for the evolutionary success of HIV-1 in infecting the human population is its high mutation rate, which contributes to its ability to evade the human immune response and persist for years within the host . Over thTo study the extent of parallel evolution in a constant environment, we track the evolution of four independent HIV-1 populations for 315 days, which corresponds to about 180 generations . We idenWe investigated the long-term evolution of HIV-1 in the laboratory by passaging HIV-1 NL4-3 for 315 days in the absence of antiviral drugs in two different human T-cell cultures (MT-2 and MT-4), with two replicates on each T-cell culture . The virR0). R0 of ∼10  of 0.0001–0.004  online, 0 of ∼10  and the 0 of ∼10  that areWe observed a total of 92 mutations across the four evolution lines (69 unique mutations) that became more frequent in the population than the wild-type nucleotide A. We calTo understand why mutations continue to accumulate at a high rate even toward the end of the experiment, we determined the time point of the maximum fold increase of the frequencies for each majority mutation B. These E. coli, which the authors explained through an accumulation of a large number of small effect mutations . Our results suggest that neutral majority mutations appear at the end of the experiment because it takes time for neutral mutations to accumulate in a population before they can become majority mutations  at transfer 90, 12 were shared with the other evolution lines. Of the remaining five private majority mutations from MT-4_2, two were also present at frequencies >10% in other evolution lines  that occurred across all four replicates and six mutations that occurred in three replicates in parallel C.P value = 0.42). The degree of parallelism is higher for majority mutations in untranslated regions of the genome: Of 26 mutations, 7 were shared .Interestingly, there was one synonymous mutation that also evolved in parallel (g4808a) of a total of 16 synonymous mutations. Out of 41 nonsynonymous mutations, we observed seven majority mutations in more than one line. Hence, the ratio of parallel mutations to private mutations is about three times as high in nonsynonymous mutations (17%) compared with synonymous mutations (6.2%). This difference, however, is not significant .The correct evolutionary history B can be After failing to reconstruct the correct phylogeny for our evolution lines from majority mutations, we decided to include minority mutations in our analysis. Unfortunately, commonly used phylogenetic methods cannot take the frequency of minority mutations into account when reconstructing phylogenies. Hence, we developed our own method. This method calculates the genetic distance between two populations by summing up the absolute differences in frequency of all wild-type nucleotides and all three possible point mutations. We calculated the distances between all sample pairs and with this distance matrix inferred a phylogeny with Neighbor Joining . The infN/dS methods. These methods assume synonymous substitutions as selectively neutral (N/dS)  . An excel (N/dS)  also takWe used CodeML to infer positively selected codons. In our case, we can provide CodeML with the consensus sequences of the four evolution lines at all time points and the three different trees from In our experiment, we do not have to solely rely on majority mutations to gauge selection strength, we can also include all mutation frequencies in the population by using common diversity indices. We find that over time the nucleotide diversity  in all four HIV-1 populations increases A. This mnef and the 5′ and 3′ noncoding regions , MT-4_1 , and MT-4_2 , respectively. Hence, nucleotide diversity at earlier time points is predictive of the number of parallel majority mutations observed at the end of our experiment, at a gene by gene basis.We hypothesize that genes with high nucleotide diversity should accumulate large numbers of parallel majority mutations and genes with low nucleotide diversity should accumulate low numbers of parallel majority mutations. When we correlate the nucleotide diversity at transfer 90 with the number of parallel majority mutations at transfer 90, we find relatively weak correlations ranging from  of 0.04 A. Howeve of 0.04 A. For MTIn this study, we investigate the long-term evolutionary dynamics of HIV-1 NL4-3 in four independent evolutionary lines. We find that the four viral populations rapidly and continuously diverge from the ancestor over the course of almost an entire year. The mutation dynamics suggest that at least the first mutations that accumulate in our experiment confer large fitness benefits to the virus. Hence, our four evolution lines respond to large selective pressures despite the absence of antiviral drugs or the immune system. The selective pressures the viral populations respond to could be due to fluctuating selection , adaptation to different T-cells, or adaptation to growing in flask cell cultures.Ne) that MT-4 viral populations undergo . This mutation is a reversion to the database consensus of glycine at position 547 in the ENV protein. Aspartate at position 36 has been shown to confer resistance to enfuvirtide, a fusion inhibitor . There iMost of our parallel mutations (7 out of 15) occurred in the untranslated part of the HIV-1 genome, which only makes up 5.5% of the HIV-1 genome. Unfortunately, there is little information on the function of these mutations. However, there is evidence that the host cell environment significantly affects replication rates for different LTR  sequences . InteresThe large extent of parallel evolution has also a strong effect on our ability to infer the evolutionary history from the sequence data at the end of the experiment. If parallel evolution also plays a significant role in vivo, then we expect HIV-1 phylogenies to also reflect host similarities, particularly when the viruses have evolved for long periods of time within these hosts. Indeed, parallel evolutionary changes have been observed in vivo in genetically identical twins infected with the same viral strain as well as in HIV-1 populations during early infection and to a smaller extent also in other studies .Nevertheless, reconstructed transmission trees have been found to be consistent with a small number of epidemiologically confirmed transmission chains . AlthougThe levels of parallel evolution we found may be particularly relevant for phyloanatomical approaches  that havWe tried to alleviate this issue by building a tree from a distance matrix calculated by subtracting mutation frequencies between different sequencing samples. The resulting phylogeny separates the two MT-4 lines better than the tree that was only built from majority mutations. Nevertheless, there are still numerous inaccuracies of the tree topology, which will lead to issues in downstream analyses as shown with our CodeML analysis. However, since most studies nowadays take advantage of deep sequencing this method could supplement classical phylogenetic analyses when the evolutionary history of sampled populations is not known.The extent of parallel evolution in our experiment is likely to be determined by the number of high-fitness effect mutations available during adaptation . InteresApart from low levels of sign epistasis and large fitness effects, parallel evolution could also be explained by differences in mutation rates across the genome, which could for example happen when APOBEC3G affects viral evolution . High muWe also show that the level of nucleotide diversity in each gene early in the experiment is predictive of the number of majority mutations that accumulate in these genes by the end of the experiment . InteresE. coli . Smaller population sizes lead to significantly shorter fixation times. Neutral mutations sweep through the population in about 2 Ne generations  of 0.01 resulting in four independent T-cell cultures  were transferred based on the extent of cytopathic effects microscopically observed in the cell cultures.The human T-cell leukemia cell lines MT-2 and MT-4  were obtcultures . Virus pThe HIV-1 NL4-3 ancestor has been sequenced and assembled previously . DiffereNear full-length genomes of the virus stock HIV-1 NL4-3 (ancestor) and transfers 10, 20, 30, 40, 50, 60, 70, 80, and 90 were sequenced using the Illumina MiSeq next-generation sequencing platform as previously described . BrieflyTo determine the number of infectious viruses, we plated increasing dilutions of the cell free supernatant on 1,000 cells each. For each of the nine time points and four evolution lines, we performed four replicates. After 7 days, we determined the number of cell cultures that were successfully infected by checking for cytopathic effects under the microscope. This allows us to estimate the 50% tissue culture infectious dose (TCID50) . Using tIllumina sequences were aligned to the ancestral sequence via Bowtie 2 . All aliConsensus sequences were determined by selecting the most common base at each nucleotide site.For each transfer, we simulated sequence evolution under a neutral evolutionary model. We randomly distributed the same number of majority mutations that occurred from one transfer to the next for each evolution line across the HIV-1 genome. When introducing mutations we maintained the GC content of the ancestral HIV genome. Hence, at the end of the simulation, the sequences contained the exact same number of majority mutations as the consensus sequence sampled in our experiment. From these sequences, we built the phylogenetic tree shown in 69 times greater than the correct trees. The most likely tree does not correspond to the real evolutionary history of the sequences because it clusters MT-4 strains together, although in the experiment they kept completely separate from each other. In To determine the robustness of the neutral phylogenetic tree, we repeated the sequence simulation 100 times. The log-likelihood of the correct trees (inferred from randomly distributed mutations) for the observed consensus sequence alignment is −13,375.7 with a SD of 1.1. We call trees correct that resemble the true setup of the experiment, that is, trees expected for neutrally and independently evolving sequences with even mutation rates across the genome. In contrast, the most likely tree for our consensus sequences has a log-likelihood of −13,218.1, which is ∼3.5×10We inferred all phylogenetic trees with PhyML  under a ij The absolute distance between population i and population j. L Length of the HIV genome. P Nucleotide at position o. F Frequency of the nucleotide p at position o in population i or j.DTo calculate a high-quality phylogeny, we first eliminated all mutations that occurred at frequencies of <1% in the population and included only sites with >1,000-fold sequence coverage. For the remaining mutations, we calculated the absolute distances From the distance matrix  package .To assess the likelihood that the sequences of our experiment evolved under a neutral model of evolution, we determined the likelihood of the simulated correct tree for the observed consensus sequence and compared it to the likelihood of the tree inferred from the consensus sequence alignment.10(fitness) declines by 0.006779 per transfer) was insignificantly different from zero . For MT-2, the decline slope was even smaller (log10(fitness) declines by −0.001345 per transfer) and also not significantly different from zero . For MT-4, however, the decline slope was larger (log10(fitness) declines by 0.02762 per transfer) and significantly different from zero .For each mutation that became a majority mutation at transfer 90, we determined the maximum fitness by calculating the maximum increase in mutation frequency between two consecutive 10th transfers. We also distinguished between viruses growing on MT-2 and MT-4 T-cells. We then fitted a linear model to the three data sets and got the following results. For the entire data set, the decline slope  is the number of majority mutations found in gene x at transfer tWe defined the per site majority mutation numbers as:For For R0=44). The viral population is allowed to replicate exponentially for two generations. Consistent with the experimental setup, we simulate a bottleneck every second viral generation, by randomly selecting a small number of individuals from the previous population. The size of the bottleneck, that is, the number of viruses transferred at every passage, was empirically determined. To this end, we determined bottleneck sizes every 20 generations in −5 (To predict how many neutral mutations would accumulate in our experiments, we simulated the evolution of individual HIV-1 genomes of length 9,130 for 180 generations A. We stans in −5 .env sequence data during early infection (We determined the level of parallel evolution that we would observe for neutrally evolving sequences. For this purpose, we randomly distribute the same number of mutations that we observed in the four evolution lines across the HIV-1 genome. When distributing substitutions we first randomly chose a position in the genome. The mutation introduced at this position was then determined from transition probabilities inferred from the mutations observed in full length nfection . Hence, To identify positively selected sites in the translated part of the HIV-1 genome, we supplied CodeML with an alignment of the translated regions of all evolution lines and sequenced time points in the experiment where majority mutations are substituted for the wild-type allele. We ran CodeML three times each time supplying a different phylogeny, that is, the inferred phylogeny A, the trMolecular Biology and Evolution online.msz155_Supplementary_DataClick here for additional data file.


The increasing use of common data elements (CDEs) in numerous research projects and clinical applications has made it imperative to create an effective classification scheme for the efficient management of these data elements. We applied high-level integrative modeling of entire clinical documents from real-world practice to create the Clinical MetaData Ontology (CMDO) for the appropriate classification and integration of CDEs that are in practical use in current clinical documents.CMDO was developed using the General Formal Ontology method with a manual iterative process comprising five steps: (1) defining the scope of CMDO by conceptualizing its first-level terms based on an analysis of clinical-practice procedures, (2) identifying CMDO concepts for representing clinical data of general CDEs by examining how and what clinical data are generated with flows of clinical care practices, (3) assigning hierarchical relationships for CMDO concepts, (4) developing CMDO properties  for each CMDO concept, and (5) evaluating the utility of CMDO.Description, Event, Finding, and Procedure. CMDO has 256 definitions that cover the 189 CMDO concepts, with 459 synonyms for 139 (74.0%) of the concepts. All of the CDEs extracted from 6 HL7 templates, 25 clinical documents of 5 teaching hospitals, and 1 personal health record specification were successfully annotated by 41 (21.9%), 89 (47.6%), and 13 (7.0%) of the CMDO concepts, respectively. We created a CMDO Browser to facilitate navigation of the CMDO concept hierarchy and a CMDO-enabled CDE Browser for displaying the relationships between CMDO concepts and the CDEs extracted from the clinical documents that are used in current practice.We created CMDO comprising 189 concepts under the 4 first-level classes of CMDO is an ontology and classification scheme for CDEs used in clinical documents. Given the increasing use of CDEs in many studies and real-world clinical documentation, CMDO will be a useful tool for integrating numerous CDEs from different research projects and clinical documents. The CMDO Browser and CMDO-enabled CDE Browser make it easy to search, share, and reuse CDEs, and also effectively integrate and manage CDEs from different studies and clinical documents.The online version of this article (10.1186/s12911-019-0877-x) contains supplementary material, which is available to authorized users. Clinical data should be collected in a consistent manner by applying a standardized format so as to facilitate unified data collection, sharing, and integration. Clinical data from multiple sites need to be effectively integrated and compared in order to improve patient care and clinical research. There have been numerous efforts to standardize clinical data. One approach is to construct a common data model, which we call a top-down approach since a top-level knowledge model agreement is applied to the underlying data models of the interoperating parties to ensure successful data exchange . The HL7During the accident I sustained an injury to the back of my head and neck.’ No concept in UMLS corresponds to the problem ‘injury to the back of the head and neck.’ Instead, the problem concept Injury (C3263722), the direction Back (C0205095), and anatomical locations Head (C0018670) and Neck (C0027530) must be postcoordinated into an on-the-fly concept  and (2) 25 clinical documents, of which 5 were Admission Note, Outpatient Note, Discharge Note, Emergency Note, and Operation Note documents from 5 teaching hospitals in South Korea . These 5 documents from SNUH and 663 clinical documents mentioned in the Methods section were mutually exclusive. Additional file  6 documeDescription|Past history|Developmental history and the other choosing Description|Past history|Developmental history and Description|Past history|Social history. However, there was also a case of disagreement, in that for the data element ‘Estimated Blood Loss’ in an Operation Note from the HL7 template, one nurse chose Procedure|Surgery and the other chose Finding|Surgery|Problem.To evaluate the suitability of CMDO for facilitating the classification and integration of CDEs, we first applied CMDO annotations to the 96 and 559 CDEs extracted from the 2 clinical document sets. The CMDO annotation process was performed by two independent nurses while considering the most-granular terms in CMDO (where this was possible). Each CDE could be annotated with multiple CMDO concepts. The two nurses who performed the evaluation were certified medical record administrators who had an average of 5 years of work experience. We allowed all cases of agreement or disagreement among these two annotators as following examples. For example, the two annotators chose similar results for the data element ‘Secondary Sexual Character of Adolescent Type Category’ in an Admission Note at Ajou University Hospital, with one nurse choosing Two administrators of medical records separately validated the two CMDO annotation sets. To complete the CMDO annotation process, at least two medical informatics researchers confirmed the above-four CMDO annotation sets and rated their coverage of CMDO into the following categories: adequate, too broad , or too specific . We also examined whether one kind of clinical document (the PHR) could be classified by CMDO.Clinical metadata, which has four first-level classes. The total number of CMDO concepts is 189. Table Finding is the first-level class with the largest number of child terms (n = 82). Additional file The root term of CMDO is n = 164, 87.7%) of the CMDO concepts were matched to UMLS preferred terms. Most of the UMLS-unmatched CMDO concepts were postcoordinated CMDO concepts or concepts that were too specific, such as Medication for Skin and Gallium Scan.CMDO provides 459 synonyms for 139 (74.0%) CMDO concepts, and 256 definitions for 188 (100%) CMDO concepts. Most  , since tProcedure estimated blood loss specify’ (and hence were rated as being too broad), which comprised 14.6, 2.7, and 0% of concepts for the 6 HL7 templates, 25 clinical documents, and the CCR+ specification, respectively. It also found that some CMDO concepts were too specific for utilization as a general classification and used for only specific CDEs such as ‘Consciousness State of Patient Specify’ (and hence were rated as being too specific), which comprised 4.1, 1.3, and 0% of concepts for the 6 HL7 templates, 25 clinical documents, and the CCR+ specification, respectively. Additional file All of the CDEs extracted from two clinical document sets  were manually annotated with CMDO concepts. Table Table Description (n = 34) and Finding (n = 316) classes, respectively. Most of the CDEs in both clinical document sets were annotated second-level CMDO concepts of each class, except for the Description and Finding classes in the 25 clinical documents. Since multiple annotations were allowed, 23 (= 119–96) and 68 (= 620–552) CDEs were annotated by multiple CMDO concepts in both clinical document sets. However, we found no multiple annotations in the CCR+ specification, and the CMDO concepts in the Description class  were frequently used for annotating CDEs from there. Additional file The most frequently used CMDO concepts for CMDO-matched CDEs in the 6 HL7 templates and 25 clinical documents were from the ing n = 36 classesWe have created CMDO as a CS for CDEs created by increasingly popular CDE-related projects with an emphasis on their application to clinical documentation. The most-popular headings extracted from the clinical documents were assigned to CMDO concepts. A relationship between subordinates and superiors among CMDO concepts was defined manually by clinical informatics experts, while the remaining ontology development process was conducted according to the GFO method.Despite the ongoing and rapid advances in informatics technology, it is still impossible to fully automate the management of the full semantics of clinical documents and their data elements, which is due to the data elements and their values being semantically too diverse and unscalable. In other words, human resources are still needed to manage the full semantics of clinical documents. CMDO may serve as a suitable CS for facilitating interactions between human resources and machines. We can expect CMDO to be useful as (1) a CS for CDEs for clinical documents and CRFs, and (2) a tool for integrating CDEs from diverse clinical documents and CRFs.The HL7/LOINC Document Ontology (DO) was developed to provide a standard representation of the attributes of clinical documents using a multiaxis structure and to standardize the names of clinical documents as an essential first step toward the optimal use, management, and exchange of documents both within and between institutions , 34. A CCMDO provides precise and comprehensive semantic annotations in terms of UMLS, in that each CMDO concept has properties including synonyms and definitions that are mapped to the UMLS CUI, adopting UMLS preferred terms considering CMDO as a part of UMLS in the clinical domain to classify CDEs. Each CMDO concept has properties to show how it can cover the same or similar meanings for discovering semantic correspondences.Finding class exhibited the highest CMDO mapping rate for CDEs in clinical documents. It seems that the CDEs in the 25 real-world clinical documents contained more observational concepts than the CDEs for the exemplar items in the 6 HL7 templates. We also found that the 25 clinical documents contained more-specific CDEs than the 6 HL7 templates, since 3 times as many CMDO terminal concepts were used for mapping the CDEs in the clinical documents.We found that CMDO concepts in the It should be noted that while CMDO appears to be a feasible ontology for annotating CDEs in clinical documents, but it has the limitation of an insufficient concept coverage due to its contents and granularity; our evaluation revealed that 5% of the CMDO items are either too broad or too specific. Our construction, implementation and use of the ontology also revealed two further limitations. First, when constructing CMDO we did not consider other methods and compare them with the GFO method, instead only considering the GFO since we considered it an appropriate method for conceptualizing ontologies. Second, when using CMDO we adopted an approach that we called ‘middle-out,’ meaning that we systematically linked CDEs up to the ontology and down to the vocabularies. It was implicitly assumed that there were associated appropriate CDEs with the correct CMDO concepts; if this assumption was not valid, it would take more time to match CMDO concepts to CDEs.In future work we plan to extend the current version of CMDO to also cover the parts that were identified in the evaluation as being either too broad or too specific. We will also apply other ontology methods to CMDO to check it for semantic consistency, and consider adding more concept relationships, such as part-of relationships. We also plan to determine how to simplify the process of using CMDO in order to save time and ensure high usability.The sharing, understanding, and integration of data from multiple different domains can be facilitated by standardization. An MDR-based CDE is considered a type of standardized data with specified concept and VDs. This study has demonstrated that a clinical-content-based ontology can be used to identify standardized CDEs. The rapid expansion of CDEs from many types of clinical documents in numerous studies makes CMDO a useful CS and integration tool.Additional file 1:Table S1. List of clinical documents used in building and evaluating CMDO. Table S2. List of CMDO concepts and its hierarchical structure. Table S3. Full names of definition sources. In form type, HL7 and CDA are represented 6 HL7 templates and 25 clinical documents from 5 teaching hospitals, respectively. Table S4. List of CDEs matched to CMDO concepts from two source data. In form type, HL7 and CDA are represented 6 HL7 templates and 25 clinical documents from 5 teaching hospitals, respectively. (DOCX 114 kb)