Research investigating prognosis in musculoskeletal pain conditions has only been moderately successful in predicting which patients are unlikely to recover. Clinical decision making could potentially be improved by combining information taken at baseline and re-consultation.Data from four prospective clinical cohorts of adults presenting to UK and Dutch primary care with low-back or shoulder pain was analysed, assessing long-term disability at 6 or 12 months and including baseline and 4–6 week assessments of pain. Baseline versus short-term assessments of pain, and previously validated multivariable prediction models versus repeat assessment, were compared to assess predictive performance of long-term disability outcome. A hypothetical clinical scenario was explored which made efficient use of both baseline and repeated assessment to identify patients likely to have a poor prognosis and decide on further treatment.Short-term repeat assessment of pain was better than short-term change or baseline score at predicting long-term disability improvement across all cohorts. Short-term repeat assessment of pain was only slightly more predictive of long-term recovery  than a multivariable baseline prognostic model in the two cohorts presenting such a model . Combining optimal prediction at baseline using a multivariable prognostic model with short-term repeat assessment of pain in those with uncertain prognosis in a hypothetical clinical scenario resulted in reduction in the number of patients with an uncertain probability of recovery, thereby reducing the instances where patients may be inappropriately referred or reassured.Incorporating short-term repeat assessment of pain into prognostic models could potentially optimise the clinical usefulness of prognostic information.The online version of this article (doi:10.1186/s12891-017-1502-8) contains supplementary material, which is available to authorized users. Musculoskeletal conditions account for 14% of all general practice (GP) consultations, with a quarter of the registered population consulting each year with a musculoskeletal problem –5 and reAlthough the methods for prognostic model research have advanced over the past 10 years, little research has addressed issues related to the clinical usefulness and impact of prognostic information. A feature of most published studies in this field has been the reliance on information on prognostic factors assessed at one point in time (baseline), with prediction of future outcome based on this one assessment only. However, prognostic information will often include time-varying factors, and using information from a single assessment only is likely to result in suboptimal prognostic performance as patients tend to consult when their symptoms are at their worst, and short-term improvements following the consultation are likely . These sWe hypothesised that incorporating information from repeated assessments of pain in prognostic models will significantly improve prognostic performance and reduce uncertainty in clinical decision making when estimating the likelihood of long-term outcomes in patients consulting with musculoskeletal pain.To explore the prognostic performance of repeated (4–6 week) assessment or change in pain compared with baseline assessment only in the prediction of longer-term (6–12 month) outcome in painful musculoskeletal conditions, and evaluate the consistency of the findings across different samples;To assess the prognostic performance of the incorporation of short-term repeated assessment of pain with data collected on multiple prognostic factors at baseline, and assess the unique contribution of short-term repeat assessment of pain, in the prediction of long-term outcome;To test a strategy for accurately and efficiently classifying patients as being at low or high probability of recovery, based on prognostic information measured at a single point in time in combination with a short-term repeated assessment in those at intermediate risk of a poor prognosis at baseline.Potentially eligible studies were identified from two bibliographic databases held at Keele University that include the results of sensitive searches for prognosis studies in primary care. A systematic search of eligible studies was not conducted; the aim was instead to identify studies of different musculoskeletal conditions that met specific criteria, allowing us to test the study hypothesis. First authors or principal investigators of eligible studies were contacted to ask for their collaboration and request necessary data.Design: prospective cohort study;Population: patients consulting with spinal pain (back or neck), shoulder pain, or knee pain in primary care;Outcome measures: pain-specific disability (e.g. Roland-Morris Disability Questionnaire (RMDQ)  or ShoulPrognostic factor measurement: at least two assessments of pain: at baseline and short-term (within 4–6 weeks of baseline assessment);Outcome ascertainment: Longer-term follow-up (at least 6 months after baseline assessment).Descriptive statistics were used to characterise each of the study samples, present long-term outcome scores for disability, and present short-term changes and repeat assessment scores in pain. Baseline and short-term pain scores were analysed on their original scale .The long-term recovery outcome for objective 1 was defined based on a validated cut-off point for minimal important change in disability for the RMDQ and SDQ  allp value of <0.05 indicates a statistically significant difference in the c-statistics.For each study, pain measured at baseline was entered into separate logistic regression models with the dichotomised variable for disability improvement as the outcome measure, to investigate whether this baseline variable predicted improvement in disability at long-term follow-up. The same analysis was then repeated using short-term change (difference between baseline and shortest follow-up point from each study) and absolute short-term follow-up score (repeat assessment) of pain as predictors of disability improvement at long-term follow-up, in separate analyses. The strength of association (odds ratios (ORs) with 95% CI), model goodness-of-fit (Hosmer & Lemeshow and Nagelkerke pseudo r-square), and prognostic performance (AUC (c-statistic)) were compared for each model . A c-staOnly datasets that had already obtained a prediction model  from the optimal baseline prognostic model. Cut-offs for what constituted low, intermediate and high probability of recovery were chosen based on consensus among the clinicians and methodologists within the study team, given the absence of empirically derived cut-offs in this area.n = 229) by reclassification as low, intermediate, and high probability of recovery at six months based on the results of the full baseline model plus a repeat assessment of pain six weeks after their baseline assessment. Cut-off points for predicted probabilities were the same as those used in step 1.Discrimination at short-term follow-up in those with intermediate probability of long-term recovery (Patient perceived recovery at six months was used as the outcome measure (as in objective 2). Two stages of analyses were performed to assess prognostic performance of the combined strategy:The results of these two stages were then compared to assess how many patients originally allocated to the intermediate probability (uncertain) group (stage 1) had moved to either the high or low probability group by short-term follow-up (stage 2), and how many of these were correctly or incorrectly classified in terms of their long-term outcomes.Four datasets were identified, requested and obtained , 22, 23.2 values were higher for the repeat assessment of pain models. The c-statistics (AUC) also suggest that a repeat assessment of pain at short-term follow-up best predicts the outcome of disability improvement, followed by the short-term change models. For three of the datasets  were found between the c-statistics for the models using baseline pain and repeat assessment score. A statistically significant difference in c-statistics was found between repeat assessment score and change in pain in two of the datasets (Swinkels-Meewisse [This objective compared the performance of the different models in predicting long-term disability outcome: (1) baseline pain scores only, 2) short-term pain score (repeat assessment at short-term follow-up), and (3) short-term change in pain (change since baseline). The Hosmer & Lemeshow tests indicated good fit for nearly all analyses , statisMeewisse  and Van Meewisse ). Overaln = 232) in the Kuijpers [n = 218) in the Van der Windt [p < 0.05) for the Kuijpers dataset , but not for the Van der Windt dataset  = 109.23, p < 0.05; van der Windt X2(4) = 35.00, p < 0.05) to the full baseline prognostic model significantly improve the fit of the model.In the two datasets used for objective 2  in stage 2. These results suggest that the final combined model is slightly better at identifying participants at high and low probability of recovery (79% and 75% of patients in these two groups correctly classified respectively) than the stage 1 model. The combined model misclassifies 18% of those who did not recover (38/209 would have been incorrectly reassured) and 15% of those who did recover (35/232 would have been unnecessarily referred).In stage 1, the full model at baseline presentation leaves 229 (52%) participants in the uncertain (intermediate) category. Repeat assessment of pain at six weeks in this intermediate group reduces this number to 121 (27.4%), reclassifying an equal number to low or high probability of recovery  datasets covering two common musculoskeletal complaints. The studies included different measures of disability and used slightly different measurement time points, but the results overall were very similar, which gives strength to the study findings [This study included four relatively large (findings . The numfindings ), althouThe inclusion of different musculoskeletal pain regions could be considered problematic as shoulder pain problems may have a different rate of recovery, and different prognostic factors compared to back pain problems. However, recent cohort studies and systematic reviews have highlighted similarities in symptom trajectories and identified generic prognostic factors across different regional musculoskeletal pain problems e.g. , 29, 30), 3029, 3The included studies were selected according to specific criteria to test the present study hypothesis, but were not systematically searched for. This means that we may have missed additional studies that could have been included in our analysis. However, a search of the wider literature found that very few prognosis studies set in primary care include a short-term assessment point, which was a requirement for our analysis. We would however welcome further analysis of the prognostic value of repeat pain assessment in other settings or populations.Not every dataset could be used for analysis of each objective. While both the Van der Windt and Kuijpers datasets included a prognostic model, differences between these models in terms of the proportion of participants recovered (outcome event rate) would require different classifications for predicted probabilities in order to address objective 3, making them difficult to compare. Only the Kuijpers model was therefore used for this analysis to provide an example for how such a strategy may be used in clinical practice and how researchers can investigate prognosis in the future. To strengthen these findings, replication in other datasets and other musculoskeletal conditions is needed, perhaps especially because prognostic performance of the models presented here could be considered as moderate at best.The datasets examined here only contain patients who consulted for musculoskeletal pain, and provided data at both baseline and follow-up, and therefore may not be representative of people who have musculoskeletal pain but choose not to consult, or would not re-consult when invited for a repeat assessment. The datasets included in this study did not all take baseline assessments at the point of consultation. As most patients are likely to consult when their pain is at its worst, and may therefore experience a natural reduction in pain shortly after consulting, the scores obtained in these studies may give a different interpretation and prognostic value than if scores were obtained during the consultation. Studies investigating prognostic factors at the point of care found that pain reduced shortly following consultation,  suggestiThe clinical scenario presented here does not offer optimum prediction. However, this version of the scenario , while realistic for this example, was meant as an illustration of this approach; it could be that different cut-off points for low, intermediate and high probability would result in fewer people being asked to re-consult. The choice of cut-off point should therefore depend on clinically relevant thresholds for treatment and referral. The number of patients in the intermediate group at baseline who needed to return for repeat assessment was still large in our scenario, highlighting the large amount of uncertainty in the prognosis of musculoskeletal pain conditions. Similar proportions for intermediate groups were used in the STarT Back study,  which inThe hypothetical scenario itself, while aiming to reduce the number of inappropriate clinical decisions, may also lead to an increase in consultations with an impact on GP time and costs. It could be that the single question about pain could be asked via a phone call, SMS text message/smart phone application 4–6 weeks after the initial consultation, with only those who still report pain at that point being invited back to see their GP.This study investigated a clinical scenario Fig.  that couPrognostic models would potentially benefit from the inclusion of short-term assessments of key prognostic factors, in addition to baseline assessments. Further research should investigate to what extent the proposed strategy improves management compared to more extensive data collection at a single point in time (baseline), across different health conditions, in order to further test the added value of monitoring for estimating prognosis and optimising clinical decision making.


Current non-invasive diagnostics for acute myocardial infarction (MI) identify myocardial necrosis rather than the primary cause and therapeutic target—plaque disruption and resultant thrombosis. The aim of this study was to identify changes specific to plaque disruption and pathological thrombosis that are distinct from acute myocardial necrosis.We quantified 1,032 plasma metabolites by mass spectrometry in 11 thrombotic MI, 12 non-thrombotic MI, and 15 stable coronary artery disease (CAD) subjects at two acute phase  and one quiescent (>3 months follow-up) time points. A statistical classifier was constructed utilizing baseline (T0) abundances of a parsimonious set of 17 qualifying metabolites. Qualifying metabolites were those that demonstrated a significant change between the quiescent phase and the acute phase and that were distinct from any change seen in non-thrombotic MI or stable CAD subjects. Classifier performance as estimated by 10-fold cross-validation was suggestive of high sensitivity and specificity for differentiating thrombotic from non-thrombotic MI and stable CAD subjects at presentation.Nineteen metabolites demonstrated an intra-subject change from time of acute thrombotic MI presentation to the quiescent state that was distinct from any change measured in both the non-thrombotic MI and stable CAD subjects undergoing cardiac catheterization over the same time course .We have identified a candidate metabolic signature that differentiates acute thrombotic MI from quiescent state after MI, from acute non-thrombotic MI, and from stable CAD. Further validation of these metabolites is warranted given their potential as diagnostic biomarkers and novel therapeutic targets for the prevention or treatment of acute MI. Acute myocardial infarction (MI) remains a leading cause of death worldwide. Although plaque rupture and the ensuing coronary thrombosis are hallmarks of myocardial infarction (MI), many non-thrombotic causes of myocardial infarction / necrosis are known . AlthoughThe limitations to existing diagnostic strategies are highlighted by the fact that 70% of the ~8 million US patients presenting to the hospital with chest pain are diagnosed with benign chest pain, at an annual cost of $10 billion; however, 2–5% of these discharges are subsequently found to have an acute MI with a worse prognosis than those correctly diagnosed on the initial encounter.–8 FurtheDue to its unpredictable nature, complex etiology, and inability to produce or detect spontaneous coronary thrombosis in an animal model, the pathophysiology of thrombotic MI has not been studied directly. The mechanisms leading to plaque disruption and those that determine the nature and the extent of the ensuing thrombotic responses remain unclear. Nearly 80% of plaque ruptures are “healed” via a controlled hemostatic response; these do not result in occlusive coronary thrombosis. ConverseIn patients presenting with symptoms concerning for acute MI, the ability to identify a thrombotic MI prior to substantial myocardial necrosis (troponin release) and / or to quickly distinguish thrombotic from non-thrombotic MI and stable coronary artery disease (CAD) would improve triaging of patients for expedited administration of appropriate therapies resulting in improved outcomes at lower cost. Such a distinction would allow for more judicious use of anticoagulant therapy, thereby improving patient safety without sacrificing efficacy. Furthermore, the identification of metabolites that change at the time of and are specific to acute thrombotic MI could lead to a new understanding of the mechanisms underlying plaque disruption that lead to coronary thrombosis and ischemia rather than a “homeostatic” response without immediate clinical consequence. Such understanding could lead to the development of novel therapies for one of the world’s most common cause of death—acute MI. Therefore, in this study we tested the hypothesis that changes in circulating metabolites at the time of acute thrombotic MI are distinct from changes at the time of acute non-thrombotic MI and diagnosis of stable CAD.Following institutional review board approval (University of Lousiville), subjects with suspected acute MI and stable CAD, scheduled for cardiac catheterization, were recruited from two hospitals in Louisville, Kentucky between March 2012 and August 2013 and followed prospectively. All subjects provided written informed consent. Additional details of the enrollment criterion are noted in the supplement .Given the lack of accepted guidelines for differentiating thrombotic and non-thrombotic MI, we developed novel and stringent criteria  to elimiWe performed unbiased discovery metabolomics on plasma samples from each subject at three different time points: 1) time of enrollment (T0) was at the time of cardiac catheterization but prior to any diagnostic (angiogram) or therapeutic intervention; 2) six hours after enrollment (T6), which was also after percutaneous coronary intervention (PCI) for all subjects that had a procedure; and 3) quiescent phase (>3 month follow-up from time of enrollment) in which subjects were in a stable state with no active acute illness. Samples were analyzed using ultra-performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS) in multiple ion modes and gas chromatography mass spectrometry (GC-MS). Reproducibility of measurements has been demonstrated by this laboratory. Instrume2-transformed intra-subject temporal fold change on indicators of study group. This allowed for the determination of metabolites with: (1) significant mean intra-subject fold change from acute states (T0 or T6) to the quiescent state (>3 months follow-up) in thrombotic MI, and (2) metabolites with significantly different intra-subject fold change in one versus any of the other groups   are reported for comparing the distributions of continuous variables across groups. As metabolites that are specific to thrombotic MI should exhibit temporal change between a quiescent state and the acute thrombotic MI state, and this change should differ from that observed in non-thrombotic MI and stable CAD, a linear model framework was used for testing such hypotheses. The framework consisted of regressing Logble CAD) . ConfounGiven the large number of hypotheses tested , q-values for preserving the False Discovery Rate (FDR) at or below a given threshold were reported for deteTo evaluate the diagnostic utility of the metabolites identified, a statistical classifier was constructed utilizing subject-level baseline abundance values of the metabolites identified in the fold change analysis at time point T0. The goal of constructing a statistical classifier was to determine if the set of metabolites identified as having unique change between acute (T0) and quiescent, or a reduced subset, could be used to discriminate thrombotic MI from non-thrombotic MI and stable CAD at the time of presentation. Given that a subject’s quiescent phase metabolite abundance would not be known at the time of evaluation for suspected thrombotic MI, only the abundance of metabolites at the time of enrollment (T0) were used in the construction of the classifier. Area under the receiver operating curve for discriminating thrombotic MI versus either non-thrombotic MI or stable CAD (AUC) was used to evaluate the performance of the final classifier. Since anEighty subjects were enrolled in this study. To ensure accurate assignment of subjects to study groups, subjects with indeterminate diagnosis were eliminated from the analysis. A total of 38 subjects: 11 acute thrombotic MI, 12 acute non-thrombotic MI, and 15 stable CAD subjects met the stringent analysis criteria . Of thesAn overview of the analytical approach is summarized in We executed a unique study design that leveraged specific outcomes (as opposed to heterogeneous or composite outcomes), used subjects as their own controls from time of acute disease to quiescent state , and selected controls by matching on the underlying disease (atherosclerosis) rather than an arbitrarily selected set of risk factors for the targeted disease state . This was accomplished by developing and applying stringent criteria to include only subjects with histologically confirmed thrombotic (type 1) MI, and comparing changes (time of acute MI versus quiescent state) in this group of subjects to change over the same time course in two unique control groups undergoing cardiac catheterization, non-thrombotic MI and stable coronary artery disease (CAD). The non-thrombotic MI group was employed to control for non-specific acute disease processes and ischemia / necrotic change specifically. Non-thrombotic MI was defined via novel criteria that included stringent troponin and blinded angiographic coronary flow / tissue perfusion analysis criteria . The staR2Y = 0.80 at T0 and R2Y = 0.77 at T6)  . Two-dim7 at T6) .From the set of 65 metabolites that demonstrated significant (FDR <10%) mean intra-subject change between the acute (T0) and quiescent state (>3 mo follow-up) in thrombotic MI subjects, in which the change was different across groups, a statistical classifier was constructed. Given that a subject’s quiescent phase metabolite abundance would not be known at the time of evaluation for suspected thrombotic MI, metabolite abundances at the time of enrollment only were utilized from all 11 thrombotic MI, 12 non-thrombotic, and 15 stable CAD subjects to construct a classifier. Ten-fold cross-validation was used to estimate the performance of the final classifier composed of 17 metabolites . Cross vTo identify changes in metabolites specific to thrombotic MI as compared with both non-thrombotic MI and stable CAD, two criteria were imposed. First, significant dynamic change in a metabolite at the time of acute thrombotic MI (quiescent versus acute phase) was required, using each subject’s quiescent abundance as a baseline. Second, to ensure that the observed intra-subject changes from a stable disease state to acute state were specific to thrombotic MI, these changes were contrasted to those observed in subjects undergoing the same procedure (cardiac catheterization) with acute non-thrombotic MI (ischemia and non-specific acute illness control) and stable CAD subjects with the same underlying disease state (atherosclerosis).Nineteen metabolites demonstrated a significant mean intra-subject change (FDR <5%) between acute (T0) and quiescent state in thrombotic MI subjects, in which the change was significantly different (FDR <5%) from changed observed in non-thrombotic MI and stable CAD subjects over the same time course . These 1This study identifies 19 metabolites with an intra-subject fold change from time of acute thrombotic MI presentation to the quiescent state, all of which are distinct from any change measured in both the non-thrombotic MI and stable CAD groups undergoing cardiac catheterization over the same time course. Therefore, the changes observed in these metabolites at the time of acute thrombotic MI are unlikely to be related to the cardiac catheterization procedure (performed in both control groups), non-specific stress of acute illness or myocardial necrosis (present in the non-thrombotic MI control), and by association are likely related to plaque disruption and / or coronary thrombosis resulting in acute MI. The unique metabolic changes associated with the acute phase of thrombotic MI, as compared to acute phase of non-thrombotic MI and stable CAD, consist mainly of lipids, steroid hormones (lysophospholipids in particular), 2-hydroxybutyrate, and amino acids including histidine, glycine, asparagine, n-acetylphenylalanine, n-acetylleucine, and n-acetylvaline. These findings warrant further investigation given the potential for leading to new biological understanding of the mechanisms underlying plaque disruption, resultant coronary thrombosis, ischemia, and the development of novel therapies for one of the world’s most common cause of death—acute MI.A candidate diagnostic model was produced from metabolites that demonstrated an intra-subject fold change from the time of acute MI (cardiac catheterization) to a quiescent state that differed between thrombotic MI, non-thrombotic MI, and stable CAD subjects over the same time course. The resultant 17 metabolite model consisted of multiple lipids (steroids in particular) and histidine, and it performed well via confusion matrix estimates for the differentiation of thrombotic from non-thrombotic MI and stable CAD, utilizing metabolite abundance at presentation alone. These metabolites hold promise and warrant further validation for more efficient diagnosis of acute thrombotic MI, prior to irreversible myocardial necrosis, and for the ability to differentiate thrombotic MI from non-thrombotic MI. Additionally, this set of metabolites may be representative of common biological processes, e.g., lipid / monoacylglycerol metabolism, that cause or are triggered by thrombotic MI.National guidelines state that cardiac troponins are the most sensitive and specific biomarkers for non-ST elevation myocardial infarctions, but theAlthough steroid hormones increase in the circulation after an acute emotional or physiological stress,, 27 bothIt is possible that the metabolites found to be different in plasma from thrombotic MI patients may contribute to the thrombotic response. Steroid hormones have been associated with a hypercoagulable state characterized by increased platelet activity, reduced fibrinolytic capacity, increased heart rate and blood pressure—all possible mechanistic links between stress, steroid hormones, and acute MI., 35–39 IAt the time of acute thrombotic MI, several amino acids were lower, while several N-acetylated amino acids were higher compared with the quiescent state. This change was distinct from the change in these metabolites over the same time course in non-thrombotic and stable CAD subjects. Acetylation of the amino acid after an N-terminal methionine occurs as part of protein catabolism., 43 The Thrombotic acute myocardial infarction (MI) is most commonly caused by thrombi overlying disrupted atherosclerotic plaques. However, even though plaque disruption often precipitates thrombosis, autopsy studies have shown that plaque rupture alone is not sufficient to cause an occlusive coronary thrombus. Nearly 80% of plaque ruptures do not result in occlusive coronary thrombosis, and are healed via a controlled hemostatic response. MoreoverOur study has several strengths. First, our methodology for the classification of subjects into study groups was designed to create “ideal” circumstances for discovering new biology related to acute thrombotic MI connected to atherosclerotic plaque disruption and the systemic milieu necessary for pathological thrombosis rather than myocardial necrosis alone. Our criteria for classification of acute thrombotic and acute non-thrombotic MI expands upon criteria we previously introduced, 16 and Our study has several limitations. Although our analysis identified over 1000 candidate metabolites, present estimates suggest that the human metabolome consists of over 20,000 endogenous metabolites. As the tWe report the discovery of multiple circulating metabolites specific to the acute thrombotic MI state in comparison with acute non-thrombotic MI and stable CAD. Changes in several lipids (steroids in particular) and amino acid metabolites were specific to acute thrombotic MI as compared to non-thrombotic MI and stable CAD states. These metabolite changes and baseline differences in abundance show promise for differentiating thrombotic versus non-thrombotic MI and stable CAD accurately by model testing. This study introduces research criteria for differentiation of thrombotic and non-thrombotic MI and substantiates the value of high-throughput discovery metabolomics to gain a greater understanding of plaque disruption, pathological thrombus formation, and myocardial ischemia. Rigorous validation of the metabolic differences and relative changes identified in this study has the potential to provide a new direction for the discovery of diagnostic and prognostic biomarkers, and may also hold promise for development of novel therapeutics for the prevention and treatment of acute MI.S1 File(DOCX)Click here for additional data file.S1 Fig(TIF)Click here for additional data file.S2 FigBox plot of metabolite that demonstrates a significant distributional change from acute to quiescent state in thrombotic MI subjects but has a similar pattern of change in non-thrombotic MI and stable CAD subjects. Therefore, such metabolites are likely related to the procedure at time of enrollment (coronary angiography), acute illness or ischemia—as opposed to thrombotic MI.(TIFF)Click here for additional data file.


Depression is a common mental illness with complex and heterogeneous progression dynamics. Risk grouping of depression treatment population based on their longitudinal patterns has the potential to enable cost-effective monitoring policy design. This paper establishes a rule-based method to identify a set of risk predictive patterns from person-level longitudinal disease measurements by integrating the data transformation, rule discovery and rule evaluation. We further extend the identified rules to create rule-based monitoring strategies to adaptively monitor individuals with different disease severities. We applied the rule-based method on an electronic health record (EHR) dataset of depression treatment population containing person-level longitudinal Patient Health Questionnaire (PHQ)-9 scores for assessing depression severity. 12 risk predictive rules are identified, and the rule-based prognostic model based on identified rules enables more accurate prediction of disease severity than other prognostic models including RuleFit, logistic regression and Support Vector Machine. Two rule-based monitoring strategies outperform the latest PHQ-9 based monitoring strategy by providing higher sensitivity and specificity. The rule-based method can lead to a better understanding of disease dynamics, achieving more accurate prognostics of disease progressions, personalizing follow-up intervals, and designing cost-effective monitoring of patients in clinical practice. Due to potential side effects of the medication, the U.S. Food and Drug Administration (FDA) has made recommendations to closely monitor patients taking antidepressants4, with the most stringent recommendation being 7 visits in 12 weeks for children and adolescent and one visit every 6 months or annually for adults5. Due to the lack of evidence from empirically designed monitoring strategy, studies have consistently documented far less monitoring in practice than the FDA recommendations, e.g. one study noted that only 23% of patients have received the FDA-recommended level of care at 12 weeks6. Furthermore, existing recommendations regarding follow-up intervals do not consider significant heterogeneity in depression treatment outcomes. Accurate prognostics of patients’ depression severities can provide evidence for identifying which patient should be closely monitored in clinical practice, holding promises for more efficient and cost-effective monitoring strategies.The extended use of Electronic Health Record (EHR) provides an abundance of clinical measurements that may help to predict patients’ disease progressions. Leveraging this rich information can accelerate the transition from one-size-fits-all monitoring guidelines to personalized monitoring strategies. For example, approximately 30 million Americans are using antidepressant medication for depression treatment with the goal of achieving complete remission and preventing relapse of depression7. In addition, since disease progression is a complex and dynamic process, understanding its etiology requires repeated clinical measurements over time rather than relying only upon a baseline profile8. Identifying the predictive factors for disease progression from time-varying and irregular clinical measurements poses a significant challenge. Furthermore, the widely reported heterogeneity on disease progression further increases this challenge. For instance, five broad trajectory patterns have been found in a depression treatment population from a U.S. EHR dataset9. Rather than examining subpopulations with distinct depression trajectory patterns, existing research on depression prognostics focuses on identifying risk factors that are associated with the outcome of interest, which are essentially global associations on the population level12. Logistic regression10, structural equation modeling11 and regression analysis12 are commonly used methods to model the association between depression progression and risk factors. Consequently, risk factors identified from these models only reflect the average effects over a population and are inadequate to be used for decision making on the individual patient level13.Statistical analysis of the EHR data has the potential to identify risk predictive factors for disease progression and provide accurate prognostics for patients’ health outcomes. Recent advances in machine learning have provided a variety of prognostic models, such as logistic regression, support vector machine (SVM), and random forest. However, these prognostic models for predicting individuals’ disease progressions from EHR data are inadequately implemented in practice due to the following challenges. First, machine learning models such as SVM and random forest deal with the complex interactions between predictive factors, but they lack interpretability to understand disease etiology and support medical decision making14. A rule describes the range of values on one or more risk factors that either indicates increased or decreased risk for disease progression. Rules generated on time-varying risk factors provide some natural semantics to define the risk predictive longitudinal patterns; since a rule may indicate a low-risk signal or warning signal for monitoring. By identifying the unknown rules from observations, a set of risk predictive rules can be considered as a set of medical signals, providing us with a risk estimation by looking into the risk patterns endorsed by each individual14. Next, by integrating the risk predictive rules with monitoring frequencies, a set of rule-based monitoring strategies can be developed to enable frequent monitoring of individuals with warning signals, and to allow less frequent monitoring of individuals with low-risk signals.The aim of this study is to establish a rule-based analytic framework to identify a set of risk predictive longitudinal patterns from the EHR data to personalize depression prognostics and adaptive monitoring. Rule-based analysis is particularly useful for identifying a set of risk patterns that segment the population into subgroups, with individuals in the same group share similar patterns and thus tend to develop similar outcomesSpecifically, our method integrates data transformation, rule discovery, and rule evaluation by following the steps in Fig. 15. The data include Patient Health Questionnaire (PHQ)-916 results between years 2007 and 2012. The data also include relative time between PHQ-9 measures, treatment status, type of providers , individuals’ age, sex, and the Charlson comorbidity score . These variables are likely to be available in most electronic health record or depression outcomes monitoring system.The Mental Health Research Network (MHRN) data are drawn from the EHR of four health systems participating in the MHRN 17, which has been widely used to avoid sampling bias. Then we validate the prediction capability and association with underlying disease severity of the identified rules on the remaining individuals (testing data).We focus our study on 1,762 individuals receiving ongoing treatment (defined as either psychotherapy visit in the prior 90 days or filled prescription for antidepressant in the prior 180 days). To capture the depression progression process, we focus on the individuals who are frequently monitored with no less than 3 measurements in the first 6 months and at least one measurement in the next half year. We randomly separate 1,200 individuals (68% data) into a training set to build the prognostic model and identify a set of predictive rules. The training set is selected using the simple random sampling methodThe study only included de-identified patients’ electronic health records and all analyses have been carried out in accordance with the approved guidelines by institutional review board through Kaiser Permanente Washington Health Research Institute (KPWRI) and MHRN. The data that support the findings of this study are available from the MHRN, but restrictions apply to the availability of these data. Data are however available from the authors upon reasonable request and with permission of MHRN.16. We define the outcome, Yi, as 1 if the patient i, i = 1, …, N, is in low-risk group in the following 6 months and 0 otherwise.We build a prognostic model based on measurements within 6 months to predict an individual’s depression severity in the following 6 months. For each individual, we transform the measurements in the first 6 months (referred as the predicting period) and the following 6 months (referred as the responding period) to predictive factors and depression severity, respectively. In total, we generate 45 factors . The PHQ-9 scores can be further segmented into five depression severity levels using the conventional classification, including minimum (0–4), mild (5–9), moderate (10–14), moderately severe (15–19) and severe (20–27)15. We also summarize the percentage of PHQ-9 scores in each depression level.For each individual, we summarize the longitudinal PHQ-9 scores, Charlson comorbidity scores and the 9et al.9, fluctuation of PHQ-9 scores can be predictive for depression progression. To extract these temporal patterns, we consider the deepest decrease and deepest increase between two consecutive PHQ-9 scores together with the volatility of the difference between nearby PHQ-9 scores. We further use the time stamp of the first observation, number of observations, observing density and the latest PHQ-9 score, to describe the irregularity and sparsity of the individual’s EHR data. The observing density is defined as the number of observations divided by the time span of all observations.As demonstrated in Lin 18 (NC) are used to capture the non-random patterns in the depression progression (details see eAppendix 1).In addition to the factors mentioned above, we adopt control theory to further extract the non-random longitudinal patterns in individuals’ PHQ-9 scores. The moving range control charts and the control rules including the Western Electronic rules (WE) and Nelson rules19 to discover these longitudinal patterns (represented as rules) predictive of depression severity. RuleFit is a high-dimensional computational algorithm for rule discovery from a large number of candidate risk factors. It generates the rules by first exhaustively searching for candidate rules over the potential risk factors in the “rule generation” phase and pruning out the redundant and irrelevant rules in the “rule pruning” phase19 (details see eAppendix 2).We assume individuals can be categorized into risk groups by a set of longitudinal patterns. Each pattern is characterized as a rule over factors (as described in data transformation) extracted from time stamped measurements. For example, individuals endorsing a rule consisting of the latest PHQ-9 score greater than 17 and the volatility of PHQ-9 score lower than 7 are predicted to have higher probability of depression in the next 6 months. We use the RuleFitYi = 1) in each rule endorsing group. Then, we select the rules that have high prevalence of either depressive patients or low-risk patients in their endorsing groups. In other words, the rules that lead to endorsing groups which have an equal mix of depressive and low-risk patients are not predictive and thereby discarded. We then name the rules as either increasing or decreasing risk rules. Specifically, the decreasing risk rules which have high proportion of low-risk patients in their endorsing groups indicate that patients satisfying these rules are less likely to have depression in the next 6 months. The increasing risk rules, on the other hand, have low proportion of low-risk patients in their endorsing groups and indicate higher risk of depression if these rules are endorsed.A set of predictive rules is discovered where each individual rule can segment the population into subgroups with distinct disease severities. We calculate the proportion of low-risk patients . The latent variable learned from IRT is normalized to the interval between 0 and 1 to represent the disease severity of each individual.Rules identified from the previous step are predictive of depression severity on the training population. To evaluate the statistical significance of these rules on the testing population, we first test whether the depression severities in the rule endorsing and un-endorsing groups are significantly different. We apply the Mann-Whitney-Wilcoxon test on each rule, which is a non-parametrical statistical test for deciding whether two independent samples come from the same distribution. We further build a rule-based prognostic model to predict individuals’ disease severities based on the rule endorsements. The rule-based prognostic model uses the item response theory (IRT)The endorsement of each risk predictive rule provides evidence for adaptive monitoring in the following 6 months by stratifying the individual’s depression severity into risk levels. Specifically, individuals endorsing the decreasing risk rules are less likely to have depression in the following period and may be less frequently monitored; while individuals satisfying the increasing risk rules should be closely monitored. Therefore, each rule can be extended to a rule-based monitoring strategy by applying both individual-rule based policies and multiple-rules based policies outlined in Table To evaluate the efficiency of rule-based monitoring, we compare several monitoring strategies by estimating the number of depressive patients (PHQ-9≥10) in the next 6 months that are correctly monitored (true positives). We assume under the status quo, all patients are monitored every 6 months, which may lead to unnecessary monitoring of low-risk patients. We also consider a PHQ-9 based strategy, which monitors the patient if his/her last-period PHQ-9 score is 10 or greater. Under rule-based monitoring, we consider both using individual rules and combining all top predictive rules.We apply the RuleFit model on the training data and identify 46 rules. By calculating the proportion of low-risk patients in each rule endorsing group, we select 12 top predictive rules listed in Table The disease severity of each patient is normalized between 0 and 1 with larger value indicating more severe depression. We evaluate the prediction capability of an individual rule by examining the distributions of depression severity in the rule endorsing and un-endorsing groups in Supplementary eFigure Next, we estimate the disease severity of each individual by applying the rule-based prognostic model  on the identified rules. To evaluate the goodness of prediction, we compare the prediction accuracy of the rule-based prognostic model using 12 identified rules with the RuleFit, logistic regression, and SVM models trained on all 45 factors or the factors included in the identified rules. The prediction accuracies of these models are evaluated using the average area under the curve (AUCs) on the testing population. The results are summarized in Table The monitoring outcomes of various strategies using testing data are shown in Fig. We establish a general rule-based analytic framework to identify the longitudinal patterns for predicting disease severity in a heterogeneous patient population, and provide actionable knowledge to support the design of adaptive monitoring strategy. We apply this framework on a depression treatment population and demonstrate that the rule-based method can efficiently identify risk predictive longitudinal patterns from sparse and irregular measurements of depression severity (i.e. PHQ-9 score), demographic factors (age and sex), and Charlson comorbidity scores within a 6-months monitoring period. 12 longitudinal patterns are found to be predictive of depression severity in the following 6 months.23, which provide validation for our rule-based analysis. However, existing studies only reflect the average effects of risk factors over the whole population and ignore their interactions. Rule-based analysis may be superior at capturing complex interactions between risk factors and further characterizing depression progression in each risk group. For instance, a two-year follow-up study on 352 patients responding to treatment of major depression (MD) found that females were more likely than males to experience a MD in any month of the study, and marginally more likely to experience a relapse24. In addition, depression trajectories characterized by historical measurements of the PHQ-9 questionnaire have been found predictive of depression progression26. However, studies considering the interactions between sex and depression trajectories are limited. Costello et al. used semi-parametric group-based modeling to explore risk factors associated with trajectories of depressed mood from adolescence to early adulthood, and found females were more likely to be classified into stable-low depressed mood, early-high depressed mood and late-escalating depressed mood patterns versus no depressed mood27. The associations discovered from this retrospective data analysis are inadequate to predict future depression progression. Complementary to evidence from the literature, our study finds males with a lower than 0.71 mean score on the 9th question and fewer than 38% observations being in moderately severe depression (15 ≤ PHQ-9 < 20) are less likely to have depression, which demonstrates sex has an impact on the depression trajectory. Furthermore, several studies examined depression onset in different age groups21. However, there is a lack of agreement on the relationship between age and depression onset. A study on the trajectory of depression symptoms across 2,320 adults’ life span has found that depressive symptoms were highest in young adulthood, decreased across middle adulthood, and increased again in older adulthood28. In our study, we find patients in young and middle adulthoods (age <65) having more than 23% severe depression measurements on their PHQ-9 records are more likely to be depressed in the future.The 12 identified rules include time-varying measurements such as PHQ-9 scores as well as time invariant measurements such as age and sex. These risk factors have been identified as significant predictors for depression progression in the literature27. Therefore, the multi-rule based method has promise to be more effective and universal than the latest PHQ-9 based strategy in a larger population in clinical practice. By integrating the rule discovery, rule evaluation, and rule-based monitoring strategy design into a unified framework, the rule-based method effectively translates the EHR data into actionable knowledge and enhances the efficient use of data-driven evidence in medical decision making.By evaluating the prediction capability of individual rules and the rule-based prognostic model, we further demonstrate that an individual rule is sufficient to segment population into groups with different risk levels; and the rule-based prognostic model is capable of providing accurate personalized risk assessment by utilizing the relationships between rules and the underlying disease severity. The identified rules also provide solid evidence for designing adaptive monitoring strategies in a treatment population by closely monitoring the patients with warning signals (i.e. endorse increasing risk rules) and less frequent monitoring of the patients with healthy signals (i.e. endorse decreasing risk rules). The advantage of multi-rule based strategy is not large compared with the latest PHQ-9 based strategy because the patients in this studied population has more stable depression severity. Unstable depression progression patterns such as early-high and late-escalating depressed mood have been discovered in clinical practice27. Thus, additional data such as providers’ notes or clinical text may lead to the discovery of more risk predictive patterns and improving the accuracy of depression prognostics. Second, due to the lack of knowledge on treatment response scenarios and the lack of adequate depression assessments that cover the whole life span of patients in the depression treatment population, it is challenging to estimate the long-term effectiveness of adaptive monitoring strategies.There are several limitations of our study. First, our depression EHR data provide limited information on patients’ socioeconomic and other clinical factors. As discovered in the literature, factors including race, education level, family structure, psychotic symptoms, and co-morbidities (e.g. anxiety disorder) are likely to be associated with depression progressionIn summary, we discovered 12 risk predictive rules from a depression treatment population that can segment individuals into risk subgroups based on their longitudinal patterns. We further developed and evaluated adaptive monitoring strategies based on these identified rules. We established a rule-based analytic framework to automatically leverage the sparse, irregular and time-varying measurements in EHR data to support the monitoring strategy design by integrating the data transformation, rule discovery and rule evaluation. More generally, the proposed method can lead to a better understanding of disease dynamics, more accurate prognostics of disease progressions, and efficient monitoring of a treatment population in clinical practice.Supplementary File


Neurogenesis is a very intensive process during early embryonic brain development, becoming dramatically restricted in the adult brain in terms of extension and intensity. We have previously demonstrated the key role of embryonic cerebrospinal fluid (CSF) in developing brain neurogenic activity. We also showed that cultured adult brain neural stem cells (NSCs) remain competent when responding to the neurogenic influence of embryonic CSF. However, adult CSF loses its neurogenic inductive properties. Here, by means of an organotypic culture of adult mouse brain sections, we show that local administration of embryonic CSF in the subventricular zone (SVZ) niche is able to trigger a neurogenic program in NSCs. This leads to a significant increase in the number of non-differentiated NSCs, and also in the number of new neurons which show normal migration, differentiation and maturation. These new data reveal that embryonic CSF activates adult brain NSCs, supporting the previous idea that it contains key instructive components which could be useful in adult brain neuroregenerative strategies. Neural stem cells (NSCs) in the adult mammalian brain are regarded as a useful tool to regenerate neurons in pathological conditions such as neurodegenerative diseases or acute brain injury. Neurogenic potential has been demonstrated in only two specific areas of the normal mammal brain in adults, namely, the subventricular zone (SVZ) of the lateral ventricle (LV) and the subgranular layer of the hippocampal dentate gyrus (DG). In mammals, both locations show neurogenic activity throughout life , as previously described  to detect cellular replication. Following culture, the samples were fixed in Carnoy’s solution for 1 h, embedded in paraffin and sectioned at 8 μm for immunohistochemistry. We performed a total of five different cultures, generating 30 SVZ cultured brain slices for experimental condition .Organotypic cultures were performed according to current protocols .To assess whether the NSCs population brings about an increase in SVZ neurogenesis, we evaluated the presence of BrdU co-localized with Sox2, a marker for NSCs in an undifferentiated state; Neurod1, a transcription factor widely used as an early stage neuron commitment marker; βIII-Tubulin (Tuj-1) a widespread young neurons marker; Doublecortin, a specific marker for migratory NSCs and Calretinin, a differentiated neuronal marker ; Anti Sox2 (D-17) ; Anti Neurod1 ; Anti β-III-Tubulin (Tuj 1) ; Anti Calretinin ; and Anti Doublecortin (DCX) . Secondary antibody for Anti Sox2 was Antigoat Ig G-Alexa 594 . Secondary antibody for the rest was Antimouse Ig G-Alexa 488 , 1/1000 dilution.Immunolabeling was performed following standard procedures and the antibodies used for immunostaining were as follows: Immunolabeled cells were photographed with a Leica TCS SPE confocal laser microscope.n = 10) for each experimental condition  and immunolabeling type. All images (which had a 0.0269 mm2 area) were carefully obtained in the selected zones: SVZ, striatum (ST) and RMS defined in Figure post hoc Bonferroni test, or alternatively we used the two-tailed Student’s t-test. In both cases the significance threshold was set as p ≤ 0.05 or p ≤ 0.001.To quantify the results, we randomly selected 20 laser confocal brain images taken from 10 different animals  the nuclear co-localization of BrdU and SOX2, which is a molecular marker of undifferentiated NSCs , there were also many cells (BrdU positive and Sox2 negative) in the SVZ (data not shown), which could be those in a more advanced state of differentiation. To assess whether the NSCs amplified population detected in E-CSF treated sections also brings about an increase in SVZ neurogenesis, we evaluated the expression of Neurod1 at an early stage in the neuronal commitment marker and βIII-Tubulin (Tuj-1) as a young neurons marker.As is shown in Figures In order to test whether the increase in neuronal commitment induced by E-CSF leads to an effective and persistent increase in new neurons, we evaluated the number of cells with co-expressed BrdU and βIII-Tubulin in the SVZ. Given the presence of two types of co-labeled cells in the striatum area studied in accordance with the amount of βIII-Tubulin in the cytoplasm and identified as low Figure , in E-CSIn order to evaluate the normal behavior of the new neurons induced by E-CSF, we studied their migratory capacity by the cytoplasmic expression of Doublecortin (DCX), a specific marker for migratory NSCs, co-labeling with nuclear BrdU expression as a cellular lineage marker. For this purpose we determined the co-labeled cells in areas located at the starting point of the RMS Figure , RMS, whThe results are shown in Figure The last part of our study sought to answer another relevant question: the evolution to mature neurons of the newly generated neurons in the SVZ niche under E-CSF influence.Despite not being able to follow the migratory neural precursors to their natural destination, the olfactory bulb, we assume that in our coronal section culture system migratory NSCs stop at the starting point of the RMS, where the maturation of new neurons start. Consequently, here we attempted to locate cells with co-labeling of BrdU  and Calretinin, a mature neurons marker. As can be seen in Figure in vitro”; this previous study only showed that E-CSF is able to induce the initial steps of neuronal differentiation in SVZ NSCs cultured “in vitro” (outside the niche). This is the first report of the influence of E-CSF in organotypic cultures of the SVZ neurogenic niche; moreover, here we show the influence of E-CSF on the main steps of the physiological behavior of SVZ NSCs including expansion of the un-differentiated NSCs population and also the increase of effective neuronal differentiation, migration and final neuronal maturation.We previously published  population. Our results have also shown that, under E-CSF influence, a significant number of proliferative SVZ NSCs initiate a process of neurogenesis (Neurod1 or βIII-Tubulin expression), which shows an “accelerated” neurogenic progress with respect to the controls. The results also suggest that A-CSF could have an inhibitory effect on neurogenesis from both the quantitative (reducing the number of new neurons) and qualitative (delaying the transition from newborn to young neuron) aspects. In our study, we used coronal sections of adult mouse brain; therefore, we were unable to track the final destination of these migratory neuroblasts (olfactory bulb). However, our data relative to Doublecortin positive cells, strongly suggest that E-CSF’s influence on NSC activity involves also an increase in normal migratory behavior. Finally, our study suggests that the final destination of the new neurons generated under the E-CSF influence in the SVZ could be transformation into mature neurons. Taking together the results reported here, E-CSF seems to activate the main steps of SVZ NSCs behavior, increasing neurogenesis.Here we show that the neurogenic instructive signals present in E-CSF are capable of increasing the physiological activity of adult brain NSCs. The CSF is deemed a key factor in contributing to the niche microenvironment diffusible signals in the developing brain  to the adult SVZ niche brings about complete activation of the NSCs including the two main steps in neurogenesis activity: an increase in mitotic activity leading to precursor population expansion and subsequent differentiation to young neurons able to migrate and progress to a mature state. This is in agreement with previous research showing the E-CSF neurogenic properties at the earliest stages of brain development (Zappaterra and Lehtinen, Taking into account these data together with the cellular lineage of the NSCs concept, we propose the theory that CSF is a changing brain component which evolves throughout life and plays a key role in neurogenesis.Further studies are necessary to better understand the molecular basis of embryonic CSF neurogenic properties and the changes induced by age, which probably limit adult neuroregeneration.About how E-CSF influences NSC behavior, E-CSF is a complex fluid involved in neurogenesis (Gato et al., This study supports the idea that the CSF could actually be considered an “inner lake” for brain cellular intercommunication, playing a key role in establishing the niche microenvironment in the developing and adult brain.Here we report new data relating to E-CSF properties. This represents the ability to induce global changes in adult mouse SVZ niche NSCs activity, promoting an expansive increase in the NSCs population, migratory activity and neurogenesis with neuronal maturation.in vivo” neurogenesis induction.Our main conclusion is that E-CSF could be a cocktail of instructive signals, with hidden key information on neurogenesis control from NSCs throughout life. Further studies are necessary to check the usefulness of these properties in adult brain “MIA, AG and FL designed and performed most of the experiments, analyzed data, prepared all figures and wrote the manuscript. EC, JAM contributed to the experiments. AM and JMFF collected the mouse embryonic fluid. MED helped to write the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Unilateral movement execution led to event-related desynchronization (ERD) followed by a rebound after movement termination event-related synchronization (ERS) of oscillatory beta activity in the STN and primary sensorimotor cortex of both hemispheres. Dopamine deficiency directly influenced movement-related beta-modulation, with greater beta-suppression in the most dopamine-depleted hemisphere for both ipsi- and contralateral hand movements. Cortical-subcortical, but not interhemispheric subcortical coherencies were modulated by movement and influenced by striatal dopaminergic innervation, being stronger in the most dopamine-depleted hemisphere. The data are consistent with a role of dopamine in shielding subcortical structures from an excessive cortical entrapment and cross-hemispheric coupling, thus allowing fine-tuning of movement.Activation of the basal ganglia has been shown during the preparation and execution of movement. However, the functional interaction of cortical and subcortical brain areas during movement and the relative contribution of dopaminergic striatal innervation remains unclear. We recorded local field potential (LFP) activity from the subthalamic nucleus (STN) and high-density electroencephalography (EEG) signals in four patients with Parkinson’s disease (PD) off dopaminergic medication during a multi-joint motor task performed with their dominant and non-dominant hand. Recordings were performed by means of a fully-implantable deep brain stimulation (DBS) device at 4 months after surgery. Three patients also performed a single-photon computed tomography (SPECT) with [ This abnormal activity and coupling is particularly strong in the beta frequency range (≈13–35 Hz) and is reduced by dopaminergic drugs or STN-DBS nortropane (FP-CIT) and single-photon computed tomography  that offers the possibility of recording LFPs in the STN in chronically-implanted patients months after surgery.Importantly, we determined the level of dopaminergic striatal innervation with a [® neurostimulation system . This system allows therapeutic DBS as well as on-demand LFP recordings from the implanted STN electrodes . All patients were diagnosed according to the UK Parkinson Disease Brain Bank criteria  compared to the preoperative improvement of the UPDRS motor score (UPDRS-III) during levodopa challenge  was then assessed using average regional uptake values from VOI analysis and the occipital cortex as the reference region  relative to the mean value of both striatum.SPECT data acquisition, reconstruction . The striatal dopaminergic loss exceeded 50% bilaterally in all but one subject . As previously reported  supervised the absence of any mirror movement or tremor in the hand not performing the task. The data presented refer only to the four subjects who completed the whole study protocol. Three patients were not able to complete the task in the required amount of time  with the right or left hand, and therefore their data were excluded from the analyses.For each movement, we measured: onset time , movement time , peak velocity (PV) and peak acceleration (PA) and path length .LFPs were recorded with a single bipolar contact configuration for each STN and amplified by 1000. The recording contacts were chosen according to the chronic stimulation setting as a bipolar montage of the two contacts surrounding the stimulation cathode  were removed by independent component analysis , normalizing the mean beta power by subtracting and dividing the average power of the whole task interval (from −3 s to 0 s) relative to the RT, multiplied by 100. For efficient spectral estimation of the relatively small number of trials, we used multitaper spectral analysis Thomson, . We estirebound in the beta band (13–35 Hz). The beta-rebound is the largest peak-to-peak difference between the minimum of the ERD during movement and the maximum of the post-movement ERS in beta band calculated, respectively, between −2 s and −1 s, and between 0 s and 1 s after RT.To study cortical beta variations, we assessed the Topological maps of these parameters showed two main clusters around CCP3h and CCP4h, as described also in previous studies  and for the beta rebound values. For each comparison , we computed the observed statistic (Tobs) as the difference between the event-related power changes or between the rebound, respectively. For constructing the surrogates (Tsurr), we shuffled the trials of each of above listed comparisons (separately), we then recomputed the surrogated event-related power changes and the surrogated beta rebound values. We performed the shuffling 10,000 times with replacement obtaining 10,000 Tsurr values for each test. The p value was computed as the Pr{Tsurr > Tobs}. The significance level was set at p < 0.05.Beta ERD and ERS were calculated computing the mean beta event-related power changes and then the mean beta p < 0.05. Multiple comparisons were corrected with false discovery rate method , we showed the smallest beta modulation in the corresponding cortical and subcortical areas , whereas the patient with the greatest striatal dopaminergic innervation loss  showed the strongest bilateral beta modulation Figure .L and MCR) and of the two STN , regardless of the moving hand  and subcortical  coherences are shown in Figure CohMC−/STN− was greater than CohMC+/STN− and CohMC+/STN+ irrespective of the moving hand. Of note, the patient with the greatest striatal DAT loss  showed the most persisting and strongest cortical-subcortical coherences, also in the MC+ hemisphere , although weaker than the ipsilateral cortical-subcortical ones. This subcortical coherence was not affected by movement  showed the greatest bilateral loss of striatal dopamine ” of the University Hospital Wuerzburg and by the “Fondazione Grigioni per il Morbo di Parkinson”. NGP was supported by a grant of the German Excellence Initiative to the Graduate School of Life Sciences, University of Wuerzburg and IRCCS “C. Mondino”, Pavia.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Three-dimensional (3D) printed templates can be designed to match an individual's anatomy, allowing surgeons to refine preoperative planning. In addition, the use of computer navigation (NAV) is gaining popularity to improve surgical accuracy in the resection of pelvic tumors. However, its use in combination with 3D printing to assist complex pelvic tumor resection has not been reported.A 36-year-old man presented with left-sided pelvic pain and a fast-growing mass. He also complained of a 3-month history of radiating pain and numbness in the lower left extremity.A biopsy revealed an osteochondroma with malignant potential. This osteochondroma arises from the ilium and involves the sacrum and lower lumbar vertebrae.Here, we describe a novel combined application of 3D printing and intraoperative NAV systems to guide hemipelvectomy for en-bloc resection of the osteochondroma. The 3D printed template is analyzed during surgical planning and guides the initial intraoperative bone work to improve surgical accuracy and efficiency, while a computer NAV system provides real-time imaging during the tumor removal to achieve adequate resection margins and minimize the likelihood of injury to adjacent critical structures.The tumor mass and the invaded spinal structures were removed en bloc.The combined application of 3D printing and computer NAV may be useful for tumor targeting and safe osteotomies in pelvic tumor surgery. During surgical resection, the entire lesion should be removed to minimize the chance of reoccurrence. Therefore, en-bloc resection is the treatment of choice for such lesions when feasible. However, the en-bloc resection of pelvic tumors is challenging due to the complex three-dimensional (3D) geometry of the pelvic bone, limited visibility, and the proximity to delicate organs and structures such as the bladder, sciatic nerve, and numerous vessels.The common clinical symptoms of osteochondroma with malignant potential are pain and mass enlargement.,4 Such systems have been developed to improve surgical accuracy with the goal of achieving adequate resection margins and better oncologic results. However, the virtual image seen on the NAV console is based entirely on bone anatomy. After surgical manipulation of the bone anatomy, the real-time anatomic location may not be accurately reflected.In recent years, the use of computer navigation (NAV) has been described in the surgical resection of musculoskeletal tumors such as those of the pelvis and sacrum. The model is able to accurately display tumor features, and the preoperative analysis of 3D printed models has resulted in surgeons reporting major improvements in their understanding of anatomic and surgical complexity.Three-dimensional (3D) printing is also emerging as a clinically promising technology for customizing anatomic models and allowing surgeons to examine patient anatomy in a more concrete way than traditional radiological images.We combined 3D printing and a computer NAV system for preoperative planning and intraoperative real-time localization of a complex pelvic tumor and were able to perform en bloc resection with adequate surgical margins.2A 36-year-old man presented with left-sided pelvic pain and a fast-growing mass. The pelvic pain had slowly worsened over 2 months. He also complained of a 3-month history of radiating pain and numbness in the lower left extremity. Magnetic resonance imaging (MRI), computed tomography (CT), and a 3D printed model revealed an iliac tumor invading the sacrum, left sacroiliac joint, left facet joint of L5/S1, left transverse process, pedicle, and partial L5 vertebral body  and the CT data sets were automatically transferred to the stereotactic NAV system . A drill with a tracking probe was then registered to enable the precise trajectory of the drill to be tracked in real time in the axial and sagittal planes during the tumor removal Fig. . Using N2.3Transforaminal lumbar interbody fusions (TLIFs) as well as pedicle screw instrumentation were placed from L4 to S1 on the right side to provide lumbosacral arthrodesis Figs.  and 6. NHistological analysis of the surgical specimen confirmed the diagnosis of osteochondroma with malignant potential, and the margins were negative for tumor in the final evaluation. The patient was neurologically intact. Although his postoperative course was complicated by gangliitis in the lower left extremity 1 week after the operation, symptom relief was achieved with a 2-week treatment course of pregabalin (75 mg p.o. b.i.d.).Signed written consent was obtained from the patient for the publication of this case report.3 To improve accuracy, the 3D model was autoclave-sterilized and used for intraoperative guidance during the surgical manipulation. Thereafter, we employed the O-arm coupled with stereotactic NAV to determine adequate tumor margins using intraoperative real-time localization. Experience has shown us that the virtual image seen on the NAV console may not be accurate after surgical manipulation of soft tissues and bone anatomy; therefore, we avoided using the NAV for the initial bone work.Here, we describe the combined use of 3D printing and a computer NAV system to enable the en bloc resection of a complex pelvic osteochondroma progressing toward malignancy. In this case, the tumor involved the ilium, sacrum, and left sacroiliac joint as well as the lumbar vertebrae. For the preoperative planning, the 3D printed model was used to accurately display the tumor features. The model provided an accurate depiction of the tumor and the adjacent critical structures to enable more direct and accurate preoperative planning. Intraoperatively, the 3D printed model also provided guidance for the surgical manipulation of the normal bone anatomy, while the intraoperative stereotactic NAV was used only for tumor targeting after the initial bone work. The initial bone removal is crucial to hemipelvectomy success. The bone work must be wide enough to allow preservation of the nerve roots and appropriate exposure for the subsequent tumor resection.,5,9 The use of a multi-dimensional anatomy imaging system improves surgeons’ visualization over conventional 2-dimensional imaging systems. However, previous CT-based computer NAV systems required patients to undergo another preoperative CT scan. In addition, CT images are acquired in the supine position, whereas spinal surgery is normally performed in the prone position. Thus, there is concern about NAV inaccuracies regarding movement at the intervertebral level in the mobile spine. The combination use of the O-arm and the StealthStation NAV offers a seamless NAV solution for spinal surgeries. This solution used with an intraoperative CT scan provides a 26-second scan time and automatic data transfer, eliminating the need for lengthy patient registration, reducing X-ray exposure for surgeons, and enabling NAV immediately after image acquisition. Because the CT images are acquired intraoperatively by the O-arm, they reveal the most current information about tumor extent. Furthermore, we found that the process was more effective when we checked real-time NAV images against the 3D printing model during the operation (380 minutes). Thus, this case demonstrates an effective en bloc tumor resection technique. However, this system's drawback is that the O-arm requires a radiolucent operative table for CT scanning, which incurs extra cost. Another unsolved problem is that the NAV system cannot simultaneously navigate both the soft tissues and the bone, while most tumors invade the surrounding soft tissue as well.Computer NAV-assisted surgery has been a useful method to increase accuracy in tumor resection of the pelvis since 2004. In this case, a drill with high-speed bur was registered and tracked in real time with reference to the patient's anatomy, making it possible to check every bur movement moment-by-moment during the osteochondroma removal. The drill holes were also marked on the bone surface for the planned osteotomy locations. When the osteotomy accessed the structures anterior to the sacrum and ilium, a diamond bur was supplemented to avoid injuring important vessels and organs using NAV and 3D model guidance. For pelvic tumor excisions, the use of an image-guided drill is more stable and convenient than that of an osteotome.The use of image-guided drills for tumor resection in computer-assisted spine surgery was recently reported. The approach has advantages over traditional procedures: surgical morbidity is reduced by avoiding the anterior approach; the horizontal incision provides additional lateral access for tumor resection; and appropriate lumbopelvic reconstruction can be performed simultaneously. Moreover, wound closure is important for pelvic tumors. Complex closures may also require skin grafting or pedicled flap reconstruction, while the posterior-only approach may be simpler and shows a lower wound infection rate. The wound healed uneventfully in our patient 2 weeks after the operation (Fig. In this case, we used the T-shape posterior-only approach to hemipelvectomy. The approach has been reported by the Mayo Clinic and Johns Hopkins University School of Medicine.ion Fig. . HoweverThe current case is the first reportofthe combined use of 3D printing and computer NAV for preoperative planning and intraoperative guidanceof vertebral osteotomies. The use of these useful tools ensures precise and safe osteotomies in pelvic tumor surgery via a posterior approach.


The nif genes of rhizobial and non-rhizobial Bradyrhizobium strains were compared in order to infer the evolutionary histories of nif genes. Based on all codon positions, the phylogenetic tree of concatenated nifD and nifK sequences showed that nifDK on symbiosis islands formed a different clade from nifDK on non-symbiotic loci (located outside of symbiosis islands and plasmids) with elongated branches; however, these genes were located in close proximity, when only the 1st and 2nd codon positions were analyzed. The guanine (G) and cytosine (C) content of the 3rd codon position of nifDK on symbiosis islands was lower than that on non-symbiotic loci. These results suggest that nif genes on symbiosis islands were derived from the non-symbiotic loci of Bradyrhizobium or closely related strains and have evolved toward a lower GC content with a higher substitution rate than the ancestral state. Meanwhile, nifDK on symbiosis plasmids clustered with nifDK on non-symbiotic loci in the tree representing all codon positions, and the GC content of symbiotic and non-symbiotic loci were similar. These results suggest that nif genes on symbiosis plasmids were derived from the non-symbiotic loci of Bradyrhizobium and have evolved with a similar evolutionary pattern and rate as the ancestral state.The nitrogen fixation ( DOA9 were located on a large-sized plasmid (approximately 736 kb), known as the symbiosis plasmid, providing the first evidence for symbiotic genes being located on a plasmid in Bradyrhizobium strains (nif–clusters in its genome. One is located on the symbiosis plasmid (symbiotic loci) and the other is located on the chromosome (non-symbiotic loci)  . The nucidentity . A phylond BTAi1 . These rnif genes of rhizobial and non-rhizobial Bradyrhizobium strains were compared to infer the evolutionary histories of nif genes using various Bradyrhizobium strains including newly genome-sequenced strains: Bradyrhizobium sp. DOA1  and Brat potato ).http://www.ige.tohoku.ac.jp/joho/gmProject/gmdownload.html). GC content and skew were calculated with sliding windows along the genome. The window size was set to 20,000 bases and the step size to 500 bases. The chromosome sequences of strains DOA1 and MAFF107635 containing runs of Ns that represent gaps were used as inputs. All predicted gap lengths were less than 5 kb. Organizations of nif–clusters were compared using GenomeMatcher version 1.861  genome sequences have been deposited in the NCBI genome database (last accessed April 2015). Since complete or high-quality draft genomes were limited to a narrow phylogenetic range of Bradyrhizobium species  with a lower GC content than the entire genome (nif genes are elucidated based on these criteria.A symbiosis island is defined as a DNA segment containing a cluster of symbiotic genes (e genome  10). Th. Thnif arpoB, recA, gyrB, and atpD).16S rRNA gene sequences were aligned using the CLUSTAL W program in MEGA 6.06 . The phynifD  and nifK (encoding the beta subunit of the nitrogenase molybdenum-iron protein) were analyzed in order to examine the phylogenetic relationships of nif genes in the tested strains. nifH was not used in the phylogenetic analysis because the tested strains, S23321, S58, ORS278, and BTAi1, possess two copies of nifH in the nif–cluster, which may affect the substitution rates of nifH sequences and lead to the incorrect estimation of phylogenetic relationships. The phylogenetic tree was constructed using nucleotide sequences by the maximum likelihood method as described above. The same analyses were performed using the 1st + 2nd codon positions. Rhodopseudomonas palustris CGA009 was used as an outgroup because it is phylogenetically close to Bradyrhizobium and possesses a nif-cluster, which may be descended from a common ancestor of the nif-cluster loci of Bradyrhizobium strains on non-symbiotic loci. The neighbor-joining method was also applied to confirm the tree topology. Evolutionary distances were computed using the Tamura 3-parameter method. Differences in the composition bias among sequences were considered in evolutionary comparisons.The concatenated sequences of nifDK using MEGA 6.06  without nodulation factor signals  approach  showed 70% bootstrap support for most branches . These results suggest two possible scenarios for nifDK on symbiosis islands. One scenario is that nifDK on symbiosis islands was inherited from a different ancestor from non-symbiotic loci with a low GC content. The other scenario is that nifDK on symbiosis islands was inherited from the same ancestor with non-symbiotic loci; however, it has since evolved toward a lower GC content with a higher substitution rate than nifDK on non-symbiotic loci. support , whereas support  and conc support  trees. Tlongated . These rtic loci . In addinifDK on symbiosis islands and non-symbiotic loci, the disparity index tests of substitution pattern homogeneity . Codon-based tests of positive selection showed that the number of synonymous nucleotide substitutions exceeds the number of non-synonymous nucleotide substitutions in all sequence pairs (P>0.05), which will lead to a higher substitution rate in the 3rd codon position containing a large number of synonymous sites. Although nifDK on symbiosis islands formed a polyphyly in the phylogenetic tree representing the 1st and 2nd codon positions    and high islands .nifDK on symbiosis plasmids are more closely related to nifDK on non-symbiotic loci than to those on symbiosis islands, suggesting different evolutionary histories for nifDK on symbiosis islands and plasmids. However, this hypothesis needs to be carefully evaluated using more strains because only one of the strains tested herein possessed a symbiosis plasmid (The disparity index  and nucl plasmid .nif–cluster in symbiosis islands is the non-symbiotic loci of Bradyrhizobium, we compared the nif–cluster of strains DOA1, USDA110, and USDA76 in the symbiosis island with those in the non-symbiotic loci of the other Bradyrhizobium strains MAFF107635, DOA9, S23321, S58, ORS278, and BTAi1. The organization of nif–clusters in symbiosis islands was very similar to those on non-symbiotic loci in other strains (Bradyrhizobium. In strain DOA1, nifH was located in an operon with nifDK, whereas in strains MAFF107635 and DOA9, nifH was located in an operon with nifQ (nifH was located in operons with nifDK and nifQ (nif–cluster in the symbiosis island of strain DOA1 was laterally transferred from a strain possessing only the nifHDK operon. However, none of the tested strains only possess the nifHDK operon in non-symbiotic loci. These results suggest two possibilities: a rearrangement of gene organization occurred in the nif–cluster of strain DOA1 or we were unable to identify the strains possessing only the nifHDK operon because of the limited number of strains tested. Strains USDA110 and USDA76 only possess the nifHQ operon (nif–clusters of strains MAFF107635 and DOA9 on non-symbiotic loci. The nif–cluster of strains DOA1 and USDA76 possesses nifV, which encodes homocitrate synthase (nifV, which is complemented by homocitrate provided from the host plant during nodule symbiosis (nifV in strains DOA1 and USDA76 partially supports the hypothesis that the nif–cluster on symbiosis islands was derived from the non-symbiotic loci of Bradyrhizobium or closely related strains. However, we cannot absolutely rule out the possibility that nif genes on symbiosis islands and plasmids serve as the donors of nif genes on non-symbiotic loci. This possibility needs to be carefully examined by studying more Bradyrhizobium strains that possess nif genes at non-symbiotic loci.In order to test the plausibility that the origin of the  strains , which sith nifQ . In straand nifQ . These rQ operon . This fesynthase . Homocitnif genes in symbiosis islands were acquired by lateral gene transfer because these genes are located in a symbiosis island, which is characterized by a lower GC content than the rest of the genome (nif genes in Bradyrhizobium strains. Our analyses indicate that nif genes on symbiosis islands and plasmids were derived from the non-symbiotic loci of Bradyrhizobium or closely related strains. Following gene transfer, nif genes on symbiosis islands evolved faster than those in the ancestral state toward a lower GC content; however, we cannot absolutely rule out the possibility that nif genes on symbiosis islands were transferred from bacteria with a low genomic GC content, closely related to Bradyrhizobium. In contrast, no apparent substitution bias was observed in the nif genes on symbiosis plasmids. Previous studies (nif genes across diverse taxonomic groups of α- and β-proteobacteria, and reported that rhizobial and non-rhizobial nitrogen-fixing strains were clustered in the same clade in phylogenetic trees. However, the nif genes of nodulation bacteria are closely related to those of non-rhizobial strains of the same or taxonomically-related groups. These results suggest that the translocation or lateral transmission of nif genes from non-symbiotic loci to symbiosis islands or plasmids occurred not only in Bradyrhizobium, but also in other rhizobial groups. Due to the limited number of complete or high-quality draft Bradyrhizobium genomes, our data set does not cover all Bradyrhizobium species. Further studies using diverse strains and genes will contribute to developing a greater understanding of the origin and evolution of legume–rhizobia symbiosis.The findings of previous studies suggest that e genome , 11. In  studies , 6, 8 an


Understanding dynamics on ultrafast timescales enables unique and new insights into important processes in the materials and life sciences. In this respect, the fundamental pump-probe approach based on ultra-short photon pulses aims at the creation of stroboscopic movies. Performing such experiments at one of the many recently established accelerator-based 4th-generation light sources such as free-electron lasers or superradiant THz sources allows an enormous widening of the accessible parameter space for the excitation and/or probing light pulses. Compared to table-top devices, critical issues of this type of experiment are fluctuations of the timing between the accelerator and external laser systems and intensity instabilities of the accelerator-based photon sources. Existing solutions have so far been only demonstrated at low repetition rates and/or achieved a limited dynamic range in comparison to table-top experiments, while the 4th generation of accelerator-based light sources is based on superconducting radio-frequency technology, which enables operation at MHz or even GHz repetition rates. In this article, we present the successful demonstration of ultra-fast accelerator-laser pump-probe experiments performed at an unprecedentedly high repetition rate in the few-hundred-kHz regime and with a currently achievable optimal time resolution of 13 fs (rms). Our scheme, based on the pulse-resolved detection of multiple beam parameters relevant for the experiment, allows us to achieve an excellent sensitivity in real-world ultra-fast experiments, as demonstrated for the example of THz-field-driven coherent spin precession. Pump-probe experiments employing accelerator-based 4th-generation photon sources and external lasers typically face a temporal jitter of the order of picoseconds (ps) between the accelerator- and laser-generated light pulses. Moreover, the coherent processes underlying the light generation in, e.g., self-amplified spontaneous emission (SASE), superradiant THz emission, or low-gain free-electron lasers (FELs), lead to significant fluctuations of the photon pulse intensities. This has significantly limited the temporal resolution and dynamic range of time-resolved experiments in the past. One approach to deal with this obstacle is to control the accelerator with active feedback loops. Recent pilot experiments lowered the temporal resolutions below 100 fs.In this article, we pursue a fundamentally different concept based on binning of the relevant pulse parameters and the postmortem or online correction of the taken experimental data. This approach relies on the accurate determination of arrival time and other parameters, such as intensity, for each individual photon pulse. Pulse-resolved detection schemes of this type have been successfully implemented at different XFEL's over the past 15 years.Here, we present the first demonstration of binning experimental parameters for every single pulse at the uniquely high repetition rates now available at accelerator-driven photon sources based on superconducting radiofrequency (SRF) technology. We first describe the developed scheme for measuring and subsequent binning of the arrival time and intensity of the accelerator-based photon pulses. Thereafter, we discuss the performance and the arising opportunities based on two benchmark THz pump laser probe experiments: (i) THz time-domain spectroscopy and (ii) transient Faraday probing of a THz driven coherent spin excitation in NiO.As we show in this article, the instabilities can then in many cases even be turned from a problem into an exciting opportunity for measuring multiple parameter dependencies in parallel. Most importantly, we demonstrate for the first time that ultra-fast accelerator-laser experiments can achieve an excellent dynamic range of up to 120 dB. Our scheme currently works at an unprecedented repetition rate of 100 kHz and with a demonstrated time resolution of better than 13 fs (rms) or 28 fs (FWHM). We achieve this result by a combination of pulse-resolved analysis and homodyne detection. We furthermore demonstrate that the concept of pulse-resolved detection in combination with the high repetition rates available from SRF accelerators allows a highly efficient study of nonlinear transient spectroscopy by determining temporal and fluence dependencies from a single sequence of measurements. Finally, we discuss the current limits of our scheme and show that a temporal resolution in the few-fs regime or better can in principle be achieved.supplementary material for details). The intensity stability of the TELBE THz sources depends on the accelerator settings. Fluctuations range from <10% (stable operation) to 50% (unstable operation), very similar to the case of SASE X-ray FELs.Figure In order to perform arrival time measurements at high repetition rates, single-shot electro-optic sampling by means of the spectral decoding techniqueOur arrival time monitor works as follows: The pulse in one branch of the laser beam is stretched by a grating-based stretcher to a duration of roughly 10 ps and overlapped with the single-cycle THz pulses from the diffraction radiator in a ZnTe crystal where its field is encoded in the spectrum. The spectrum of every second stretched laser pulse is detected by the CMOS line array, and the arrival time is deduced from the position of the main peak . Fourier-transformation yields the frequency-domain spectrum shown in Figure 6 or 120 dB for the maximum dynamic range in the frequency domain can be deduced.Figure The experiment shown in Figure An important aspect distinguishing pulse-resolved detection from the more common approach that averages over many pulses is the fact that one can correlate other relevant parameters in addition to the arrival time. This opens up the opportunity to also vary and detect multiple parameters  from one pulse to the next. Accounting for such variation in the data analysis offers the fascinating opportunity of performing simultaneous investigations in a multi-dimensional parameter space. This is particularly fruitful in combination with the high repetition rates provided by existing and future quasi-continuous 4th-generation light sources driven by superconducting radiofrequency (SRF) technology.6 in a time-domain spectroscopy measurement of our THz excitation pulse. These results show that our scheme is suitable for sensitive spectroscopy employing the accelerator-based THz pulses as a probe. A second class of experiments “pumps” a material with a strong THz excitation pulse and “probes” the response with an ultra-short pulse from the laser. In this case, an additional challenge lies in the fact that the detected signal, indicating the pump-induced ultra-fast change, can be minute. The performance of our scheme in this type of experiment has been benchmarked by performing a pump-probe experiment studying coherent spin precession driven by resonant THz excitation. In this experiment, multicycle, narrow-band THz pulses from the undulator source were tuned in resonance with the antiferromagnetic (AFM) mode of the prototypical antiferromagnet NiO at 1 THz. As shown previously,μm thick freestanding NiO crystal. The transient change of polarization is probed by a balanced detection scheme and plotted in Figure 4 (80 dB), thereby permitting the observation of minute polarization changes induced by the coherently deflected spins  that binning of photon pulse parameters at high repetition rate yields enormous opportunities for vastly improved data quality and (ii) that the substantially higher data rates can be handled in routine user-operation of a large scale photon facility for ultra-fast science. This finding is particularly crucial for the prospect of ultra-fast X-ray science at high-repetition-rate X-ray FELs that are based on quasi-cw SRF technologyThe technological limit of the temporal resolution in the current scheme of 12 fs (FWHM) or 5 fs (rms) is given by a combination of the probe-pulse bandwidth, the spectrometer, and the pixel size of the CMOS detector. A temporal resolution down to 1 fs or better can readily be achieved as is discussed in detail in the supplementary information. The technological limit of our approach in terms of the repetition rate is currently given by the available CMOS detector arrays. Purpose-built CMOS detector arrays operating at rates up to 900 kHz already exist,To summarize, we experimentally demonstrate that our pulse-resolved pump-probe detection technique presently provides a temporal resolution of 28 fs (FWHM) or 13 fs (rms) at a repetition rate of 100 kHz, which is close to the current instrumental limit. We achieved a dynamic range in ultra-fast experiments with synchronized external laser systems that is so far unprecedented for accelerator-based photon sources. The underlying pulse-to-pulse detection scheme allows us to perform parallel investigations of dependencies on multiple parameters in one experiment with excellent sensitivity.IV.supplementary material for (i) an analysis of the typical arrival time jitter at TELBE, (ii) a description of how the dynamic range and the SNR are derived in the THz spectroscopy measurement, and (iii) a detailed analysis on where the current limit of the time resolution originates from and how it can be improved in the future.See


An error was introduced in the PDF version of this manuscript during the preparation of this article for publication. Many of the figure references in the Results and Discussion portion do not contain the figure number. On page 4, the first sentence of the Results and Discussion should reference Figure 1. The correct sentence is: "The B. pseudomallei K96243 genome encodes nine predicted TAAs; the bpss1439 gene is located on chromosome two and has been predicted to be located in an operon with two downstream genes, bpss1442 and bpss1443  ."On page 6, there are multiple errors:1. The first sentence, which continues from page 5, should reference Figure 2a. The correct sentence is: "However, when these cultures were assessed for their ability to form biofilms, the bpss1439 mutant demonstrated a significant (p<0.001) reduction in biofilm formation at 48 hours in LB at 37°C compared to the wild-type strain ."2. The first sentence in the second paragraph should reference Figure 1. The correct sentence is: "To confirm that the observed reduction in biofilm formation is due to the disruption of bpss1439, rather than downstream genes in the operon , the bpss1439 mutant was complemented in trans."3. The third to last sentence of the third paragraph should reference Figure 2b. The correct sentence is: "However, biofilm production by the bpss1439 (pME-1439) strain was restored to wild-type levels in the presence of inducer ."4. Both figure references in the first paragraph of the subsection "The bbfA mutant demonstrated reduced adhesion and microcolony formation" should reference Figure 3a. The correct text is:"When grown under biofilm-inducing conditions, the bbfA mutant demonstrated reduced adhesion as seen by lower total bacterial numbers, as well as reduced microcolony formation as noted by the lack of clumping or autoagglutination of bacterial cells in comparison with the wild-type . As previously found with the crystal violet biofilm assay, the wild-type (pME) and the complemented bbfA (pME-1439) strains demonstrated wild-type levels of biofilm production with extensive microcolony formation while the bbfA mutant and bbfA (pME) strains displayed reduced adhesion and microcolony formation ."5. The first sentence of the second paragraph of the above section should reference Figure 3b. The correct sentence is: "Biofilm formation of the wild-type and bbfA strains was further examined by SEM ."6. The second sentence of the subsection "The bbfA mutant exhibited an attenuation for virulence in the BALB/c murine melioidosis model compared to the parent strain: should reference Figure 4. The correct sentence reads: "Groups of six BALB/c mice were challenged via the intra-peritoneal route and median time to death was determined by the Mantel-Cox log-rank test at 35 days post-infection ."