Breast tissue heterogeneity is related to risk factors that lead to more aggressive tumour growth and worse prognosis, yet such heterogeneity has not been well characterized. The aim of this study is to reveal the heterogeneous signal patterns of the apparent diffusion coefficient (ADC) of a tumour and its surrounding stromal tissue and to predict the Ki-67 proliferation status in oestrogen receptor (ER)-positive breast cancer patients. A dataset of 82 patients who underwent diffusion-weighted imaging (DWI) examination was collected. The ADC map was segmented into regions comprising the tumour and the surrounding stromal shells. To reflect correlations between each region in terms of its mean ADC value, a functional graph was constructed consisting of nodes as regions and edges as interactions between two nodes. Analysis of the graph revealed a higher average degree in samples over-expressing Ki-67 than in samples with low Ki-67 expression. In the low-Ki-67 group, most of the identified edges represented correlations between adjacent regions, whereas additional edges representing correlations between non-adjacent regions were found in the high-Ki-67 group. The ADC signal in various breast stromal regions surrounding the tumour showed a discriminative pattern and would be valuable for estimating the Ki-67 proliferation status by DWI. In ER-positive breast cancer, the Ki-67 index, which is a proliferation marker, has been used to distinguish luminal B from luminal A cancer2. Luminal B tumours are more often high grade and have higher Ki-67 index than luminal A tumours. Thus, patients with luminal B breast cancer often have worse prognosis than patients with luminal A breast cancer4. Therefore, it is of paramount importance to identify this subgroup of ER-positive patients with a relatively poor prognosis who may benefit from adjuvant chemotherapy.Breast cancer is known to be a heterogeneous disease, and the different subtypes can be defined by the immunohistochemical (IHC) approach based on oestrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) and Ki-67 expression levels. Approximately 70% of human breast cancer tumours are ER positive5. Recent data suggest that patients with a lower Ki-67 level more often undergo pathological complete response (pCR)8 and a high Ki-67 level (above 14%) defines a high-risk group in terms of prognosis9. Moreover, the Ki-67 proliferation index reflects the extent of proliferative activity, an indicator of tumour aggressiveness10, and is a reliable identifier of more aggressive growth in breast cancer9. During growth, a tumour constantly interacts with the surrounding microenvironment by releasing extracellular signals, thereby promoting increased angiogenesis and microvessel density and leading to a tumour-progression-related microenvironment12. The altered tumour microenvironment can in turn contribute to remodelling of the extracellular matrix (ECM), which changes stromal properties by altering matrix cross-linking, increasing collagen deposition and reorganizing fibres and consequently leads to a more rigid tumour-associated stroma13. Therefore, the tumour microenvironment is spatially heterogeneous, and the patterns and correlations between a tumour and its surrounding stromal tissue are contributed to the characterization of intrinsic features of breast cancer.A large population-based cohort study identified Ki-67 as an independent prognostic parameter for disease-free survival and overall survival14, and it is sensitive to changes in water diffusion in the intracellular and extracellular spaces13. As such, flow-insensitive apparent diffusion coefficients (ADCs) may provide a more accurate estimation of the cellularity of the tumour microenvironment by minimizing any vascular contribution15. Compared with benign tumours, lower ADC values, which reflect restrictions in water diffusion, are observed in malignant breast lesions17, and studies have identified that ADC values can be helpful for predicting response to neoadjuvant chemotherapy in breast cancer19. Related studies have reported a correlation between ADC values and pathologic factors24 as well as biomarkers such as tumour cellularity and Ki-67 expression levels in luminal-type breast cancer25, mucinous breast cancer26 and ER-positive breast cancer28. Conversely, another study found no association between ADC values and prognostic factors29.Diffusion-weighted imaging (DWI) is an approach that differs from conventional magnetic resonance imaging (MRI) techniques in that it measures the mobility of water within tissues, providing information about tissue cellularity and cell membranes integrity31 and the pathological status, to our knowledge, no study has investigated to analyse the heterogeneity of ADC patterns in stromal regions. In addition, no predictive models have been applied to differentiate Ki-67 status using these features within a given cohort. Here, we investigate features based on DWI in the peritumoural stroma region to evaluate this prognosis biomarker in breast cancer. Our approach is different from the work presented in previous studies31, in which statistical features were extracted based on the tumour and its surrounding stroma. In addition, we also examine correlations of mean ADC values between each peritumour stroma, which could help provide possible information for the differentiation of stromal connections between aggressive and non-aggressive cancers.Although previous studies have examined associations between stromal features surrounding the tumour regionIn this study, we analysed the heterogeneity of tumour and peritumoural sub-regions of breast stroma on DWI and evaluated the ability of MR features using a multivariate logistic classifier to distinguish ER positive breast cancers with low Ki-67 levels from those with high Ki-67 levels.This is a retrospective study approved by the Internal Research Review and Ethical Committee of the Zhejiang Cancer Hospital and informed consent was obtained from all patients. All methods were carried out in accordance with the relevant guidelines. We collected an initial dataset of 183 patients at Zhejiang Cancer Hospital, China. Of the total, 52 patients with no pathologic examination or incomplete pathology data were excluded. Additionally, 9 patients who underwent breast cancer treatment  prior to MRI were also eliminated from the cohort, and 40 ER-negative patients were removed from the dataset. Ultimately, 82 patients who met the selection criteria were included in our dataset for analysis.Table 2; flip angle 90°; TR (Repetition Time ms) 7000; TE (Echo Time ms) 85 ms; slice thickness 6 mm; spacing between slices 6.6 mm; b values 50 and 1000 sec/mm2; acquisition matrix 220 × 72; pixel bandwidth 1196 Hz per pixel; and in-plane resolution 1.45 × 1.45 mm2.All patients were scanned in the prone position using an MRI scanner with magnetic field strength of 3.0 T  and a dedicated eight-channel double-breast coil. A DWI sequence was obtained with the following parameters: field of view (FOV) 104 × 320 mm1/S0]/(b1 − b0), where S0 and S1 are the signal intensities in the image obtained by two gradient factors, b1 and b0 . To standardize the image analysis as much as possible, a representative ADC map that shows the largest dimension of the tumour was selected. Fibroglandular tissue was segmented on the DWI b = 50 image, excluding fat from the breast, based on a fuzzy C-means clustering procedure  an inner section of the tumour at least 2 pixels inside the tumour edge, i.e., SI; (2) the whole tumour (ST); (3) the boundary region of the tumour, from −2 to 2 pixels of the tumour edge (Sb); (4) peritumour stromal shell, with a 4 pixel width next to the tumour boundary, i.e., SP; (5) the intermediate stromal shell, with a 4 pixel width neighbouring the proximal stromal region (SM); and (6) the distant stromal shell (SD), with a 4 pixel width outside SM. For each region, eight histogram statistical features were calculated: mean, min, variance, mean, interquartile range, range, skewness, and kurtosis of ADC values.In our study, the resolution of DWI was 1.45 × 1.45 mm, and we selected stromal shell, with a 4 pixel width (1.45 mm × 4 pixel = 5.8 mm). We defined six regions similar to the procedure by Sin SB and SP, was calculated.We also calculated the ratio of statistical measurements between each of the two regions including the tumour and its surrounding stroma. For example, the ratio of the interquartile ranges of the tumour boundary and peritumoural proximal stromal regions, i.e., 32. A sample was scored as HER2 positive following the American Society of Clinical Oncology (ASCO)/College of American Pathologists (CAP) guideline recommendations for HER2 testing in breast cancer33. A Ki-67 level greater than 14% was considered positive, and a level below 14% was considered negative.Immunohistochemical staining was performed using streptavidin-peroxidase. The pathology report from the initial breast biopsy or surgery was used to record the ER, PR, and HER2 statuses of each invasive cancer. Samples were scored as positive for ER or PR by immunohistochemistry (IHC) when at least 1% of the tumour cell nuclei showed staining for ER or PR, respectively2 test or Fisher’s exact test when the expected value in any cell was less than five. Analysis of variance (ANOVA) was performed for comparison of ADC values between the high- and low-Ki-67 groups. A univariate logistic-based classifier was used to evaluate the performance of the individual features in differentiating high and low Ki-67 proliferation statuses. A multivariate logistic regression model was trained and validated to classify Ki-67 status using combined features. To evaluate the performance of the classifier, receiver-operating characteristic (ROC) analysis was performed, and the area under the ROC curve (AUC) was computed. Sensitivity and specificity were calculated using a Youden index determining the value that would maximize the average sensitivity and specificity34.Differences in tumour characteristics were assessed using a χA functional breast stromal region association network, i.e., an undirected unweighted graph, was built with tumour or stromal regions as nodes and associations as edges. More specifically, Pearson’s correlation coefficient (PCC) of mean ADC values between two nodes were calculated, and edges/associations were established if Bonferroni-corrected P values of PCC were less than 0.05. Therefore, a network with 6 nodes was generated representing similar tissue characteristics such as cellularity and water content.An evolutionary algorithm (EA)-based optimization method was applied to search for the optimal feature subset for classification. The mutation probability and crossover probability were set at 0.01 and 0.6, respectively. The EA chromosome population in each generation was 500, and the maximum number of generations was 200. The EA chromosome that achieved the highest AUC was selected to establish the optimal feature pool and build the optimal classifier. All statistical tests were two-tailed, and significance was set at a P value < 0.05. Statistical analyses were performed using Matlab R2013b (8.2).To avoid overfitting of classifiers to the available data, the leave-one-out cross-validation (LOOCV) test was employed. Specifically, at each iteration of the LOOCV process, one sample was used for testing and the other for training. This procedure was repeated for each sample. In each evaluation, we performed a Wrapper Subset Evaluator (WSE) feature selection using all training cases to search for optimal features from the optimal feature subset pool generated from EA. Using these features, a multiclass logistic classifier was trained based on the training set and was tested on the one independent test case omitted. The importance of the predictive imaging features in the classifier was evaluated by selection frequencies of features over all LOOCV loops.In our study, we compared tumour mean ADC values according to histopathological features, as shown in Table We first conducted comparison analyses of the mean ADC value within each subgroup in the whole tumour according to histological type, tumour diameter, tumour grade, and PR expression, as shown in Table 35. ANOVA revealed significant differences in mean ADC values among peritumoural stroma regions between the low and high Ki-67 proliferation statuses (p < 0.0001). An ascending order of median ADC values in the six regions is shown in Fig. In addition to examination of ADC values in breast tumours according to histopathological characteristics, further analysis was conducted to investigate ADC signal patterns in the tumour and peritumoural stroma. As shown in Table SP) had the highest degree, whereas that representing the inner tumour region had the lowest degree. Additionally, most edges in the low-Ki-67 group were found between nodes representing adjacent regions , though more edges representing correlations of non-adjacent regions were found in the high-Ki-67 group. These results suggest that the pattern for a tumour spreading to its surrounding region differed between breast cancers with greater versus lower proliferation. Supplementary Fig. As a tumour spreads its characteristics into breast tissue, we assumed that there should be similar patterns of ADC values between a tumour and its surrounding stroma. Therefore, exploratory analyses were conducted to evaluate correlations among these regions. A graph was thus established with nodes as tumour/stromal regions and edges as significant (corrected P < 0.05 for PCC) correlations of mean ADC values between two nodes, as displayed in Fig. Based on the above observations that ADC values and correlations among stromal regions differ between high- and low-Ki-67 groups, histogram features of ADC values in each stromal region and ratio features were further evaluated to discriminate Ki-67 status. We expected that different regions may have different performances in the classification of the Ki-67 expression level. The best two individual histogram features and ratio features in each stromal region, based on the AUC, accuracy, sensitivity and specificity, are provided in Table Further classification analysis using combined ADC features was performed for classifying Ki-67 proliferation status utilizing a multivariate logistic regression analysis model. To assess whether various stromal regions could have different performance, the classification models were applied in each region separately. Feature selection was performed for each model to produce the best subset of features for Ki-67 status discrimination. The performance of the classification models using features in the six proximal regions is illustrated in Table I, ST, SM and SP  are ratio features, indicating that the ratios of features between distinct regions contribute more to the classification model than statistical features.Analysis of the contribution (importance) of individual features to the classification was based on the frequency of each feature selected in LOOCV loops, as shown in Table In this study, we analysed patterns of ADC values in tumour and peritumoural stroma, and a predictive model was built to evaluate the performance of applying imaging features to predict Ki-67 proliferation status in patients with ER-positive invasive breast cancer. The tumour and its proximal regions were identified, and correlations between each region in terms of ADC values were established. A distinct pattern of connections between high- and low-Ki-67 proliferation status samples in the tumour and peritumour stroma was found. Further experiments showed that statistical features in the tumour boundary as well as in the proximal peritumoural stroma have higher predictive power than the other regions.31. Additionally, the features in the tumour boundary and the proximal peritumoural stroma showed higher performance in discriminating expression of Ki-67 compared to those in other regions. This result is in agreement with a recent study that identified more significant associations with Ki-67 status in the tumour boundary and proximal peritumoural stroma31. The results of our study indicate that the breast stromal band proximal to the tumour is important for the analysis of tumour characteristics.In the present study, ADC values were grouped in ascending order in breast stromal regions when the distance from the tumour edge was increased, which is in line with previous studiesBased on graph analysis, high total degrees representing correlations between stromal regions were observed for the high-Ki-67 expression group than the low-Ki-67 group. Moreover, higher degrees were observed for nodes representing the tumour boundary and proximal peritumoural stroma. The results suggest that compared to a low-Ki-67 tumour, a high-Ki-67 tumour, with more proliferation, may spread its characteristics to a greater distance in the proximal surrounding regions. Additionally, normal-appearing stroma surrounding a tumour would exhibit more abnormities in samples highly expressing Ki-67. This finding of our study is consistent with the fact that high-Ki-67 cases are more aggressive with regard to tumour cell proliferation than low-Ki-67 cases. Because the features of ADC values reflect the Ki-67 status, measuring patterns of ADC values may facilitate an understanding of patients’ clinical presentation. Therefore, DWI is a promising unenhanced tool for detecting differences in water mobility that reflect the tissue microenvironment.4. The possible explanation for this finding is that a high-Ki-67 tumour, with characteristics of more aggressive growth, might have a more heterogonous tumour microenvironment affecting its surrounding proximal stromal regions. Furthermore, the results of our study suggest that histogram analysis of pixel-based data in addition to mean ADC values could provide more predictive information in ER-positive breast cancer than conventional features such as the mean ADC value8.In our study, the two best individual features for predicting of Ki-67 status were the skewness and interquartile range on histogram analysis, which are regarded as a reflection of tumour heterogeneityOur study has several limitations. First, this is a retrospective study with limited statistical power due to the relatively small sample size. Thus, our study results must be confirmed in future studies with larger datasets. Second, this is a single-institution study without a validation cohort. As systematic fluctuations exist among different medical institutions caused by the use of different b value protocols for breast cancer patients, whether our image features of various peritumour stroma can be optimally applied to DW images acquired from other medical institutions must be tested in future studies. Third, only first-order histogram features were examined for prediction, whereas second-order spatial histogram features such as the textural features and the pathologic information, i.e., histologic grade and lymph node status, were not included in the model. Further analysis that uncovers more tumours and their surrounding stromal features should help to better understand the relationships between DWI and the prognosis of breast cancer.In conclusion, our results suggest that patterns of ADC values in tumours and stromal regions may be predictive of tumour aggressiveness indicators, i.e., Ki-67, in ER-positive patients. Statistical features of stromal regions according to the distance of the tumour boundary were correlated with tumour classification, which could provide additional information about tumour heterogeneity in such cases. This is the first exploratory study examining features of ADC values in tumours and stroma to reflect heterogeneous patterns in differentiation of Ki-67 proliferation status. A further prospective study with a large number of patients is needed to confirm our results.Supplementary Figure 1


A 58-year-old woman with no history of smoking was admitted to our hospital with exacerbation of cough. CT of the chest revealed a left upper lobe lung tumour. Bronchoscopic biopsy of the tumour revealed stage IV poorly differentiated adenocarcinoma  with epifigures 2After the failure of epidermal growth factor receptor-tyrosine kinase inhibitor (EGFR-TKI), rebiopsy of the tumour should be performed to determine the next treatment strategy.But it is unclear when to perform rebiopsy after EGFR-TKI failure.


Functional hemodynamic parameters such as stroke volume and pulse pressure variation (SVV and PPV) have been shown to be reliable predictors of fluid responsiveness in mechanically ventilated patients. Today, different minimally- and non-invasive hemodynamic monitoring systems measure functional hemodynamic parameters. Although some of these parameters are described by the same name, they differ in their measurement technique and thus may provide different results. We aimed to test the performance of seven functional hemodynamic parameters simultaneously in the same clinical setting.PiCCO, PPVPiCCO), LiDCOrapid , FloTrac (SVVFloTrac), Philips Intellivue (PPVPhilips) and Masimo pulse oximeter . Prediction of fluid responsiveness was tested by calculation of receiver operating characteristic (ROC) curves including a gray zone approach and compared using Fisher’s Z-Test.Hemodynamic measurements were done in 30 cardiac surgery patients that were mechanically ventilated. Before and after a standardized intravenous fluid bolus, hemodynamics were measured by the following monitoring systems: PiCCOplus  was observed: AUC-SVVAll functional hemodynamic parameters tested except for PVI showed that their use allows a reliable identification of potential fluid responders. PVI however, may not be suitable after cardiac surgery to predict fluid responsiveness.NCT02571465, registered on October 7th, 2015 (retrospectively registered). The assessment of the patient’s fluid status is a key element in perioperative care. The overall goal is to keep patients in a steady fluid state and avoid any disturbance like hyper- or hypovolemia. Inadequate fluid therapy is associated with impaired patient’s outcome. Therefore, fluids should only be given in well-defined protocols according to individual needs. Like any other medication, fluid therapy must neither be overdosed nor underdosed .Before any fluid administration, patients have to be clinically assessed. Obvious fluid deficits have to be immediately corrected in acute circulatory failure without any prior evaluation of preload responsiveness. In other clinical settings however, fluids should only be given to patients with a predicted positive fluid response – this means that patients should be tested before fluid administration . TherebyOver the last years, functional hemodynamic parameters have become well established to predict fluid responsiveness in mechanically ventilated patients. Positive pressure mechanical ventilation induces a cyclic reduction in left ventricular preload mainly through a decrease in venous return. The change in preload throughout the respiration cycle becomes significant in hypovolemia. Thereby, these cyclic changes in preload during respiration may result in variations of SV (SVV) and PP (PPV) .A major issue with fluid responsiveness is what exact values to measure and how, and many different technologies have been developed and tested over the last years in an attempt to find the ideal technique . Even leWith local ethics committee approval, adult patients scheduled for elective coronary artery bypass grafting (CABG) were included in this study after obtaining their written informed consent. The study was performed postoperatively in the intensive care unit (ICU). Patients undergoing emergent cardiac surgery, aged younger than 18 years, and those with with impaired left-ventricular function (EF < 45%), arrhythmias, intraventricular shunts, intra-aortic balloon counterpulsation or severe peripheral arterial occlusive disease were excluded.2 monitor. The monitor was calibrated using transpulmonary thermodilution according to manufacturer’s recommendations using triplicate injections of 15 ml ice-cold saline. An additional cannula was placed into the left radial artery that was connected with a FloTrac sensor attached to the Vigileo system  and an analogue pressure output module of the IntelliVue System was pluged with the LiDCORapid monitoring system . All systems were zeroed at mid-axillary. Signal quality was tested by visual arterial waveform assessment and square-wave flush test. A Masimo pulse oximeter  was attached with its finger cuff to the third or fourth finger on the hand opposite the side of the radial artery catheter. Detailed aspects of all monitoring systems releated to technical issues and especially algorithms used for assessment of stroke volume and functional hemodynamic parameters have been reported in detail elsewhere [.In all patients a standard monitoring  was used that included 5-lead ECG, pulse oximetry, and central venous pressure (CVP) via a central venous line inserted into the right internal jugular vein. Invasive arterial blood pressure was assessed via femoral artery access by a 5-French thermistor tipped catheter , which was connected to a PiCCO− 1 h− 1), remifentanil (2–5 μg kg− 1 h− 1) and rocuronium (0.2–0.5 mg kg− 1 h− 1) was given to allow complete mechanical ventilation. A volume-controlled mode was applied with a tidal volume of 8–10 ml kg− 1 and a respiratory rate of 12 min− 1 aimed at normoventilation (pCO2 at 4.0–4.5 kPa).Postoperative management was performed according to institutional standards. The patients remained sedated during the study period. Propofol (1–2 mg kgPhilips) and CVP were recoreded as well as data of the PiCCO2 system, i.e. cardiac output (CO), stroke volume (SV), stroke volume variation (SVVPiCCO) and PPVPiCCO. Moreover, data of the FloTrac/Vigileo (SVVFloTrac), the LiDCORapid monitor  and the Masimo device  were measured. All hemodynamic measurements were repeated after the administration of a standardized i.v. fluid bolus .After arrival in the ICU all monitoring devices were installed. Study measurements were started when the physician in charge made the decision to give i.v. fluids. The decision to give i.v. fluid was based on the presence of at least one clinical sign of acute circulatory failure or associated signs of hypoperfusion. Before fluid administration, hemodynamic data of the IntelliVue monitor – heart rate (HR), mean arterial pressure (MAP), pulse pressure variation .Statistical analysis was done using Microsoft Excel , Sigmaplot , SPSS® 10.0  and software R , a langu− 2, all patients had a preserved left ventricular function, and postoperative ejection fraction assessed by transesophageal echocardiography was 64.2 ± 6.5%.The study was performed in 30 patients in the postoperative period after elective CABG: 4 ± 1 by-passes were done, EuroSCORE  was 3.5Philips, SVVPiCCO, PPVPiCCO, SVVFloTrac, SVVLiDCO, PPVLiDCO and PVI) decreased  and PPVPhilips (14.1–18.0%), the largest gray zones were found for SVVLiDCO (9.6–20.6%) and PVI (9.3–26.2%)  between 0.87 and 0.92 for PPVely Fig. , Table 2Philips, SVVPiCCO, PPVPiCCO, and PPVLiDCC did not differ significantly in the cross-comparison. However, significant differences were observed for AUCs of PPVPhilips, SVVPiCCO, PPVPiCCO, and PPVLiDCO as compared with AUC of PVI  to predict fluid responsiveness was observed. SVV and PPV technologies differed in individual results but showed a good performance overall. The AUC’s to predict fluid responsiveness for SVV technologies were 0.78–0.91 and for PPV technologies 0.88–0.92. However, the non-invasive, functional parameter determined by the pulse oximeter, PVI showed only an AUC of 0.69.− 1. Moreover decreased right ventricular function has to be excluded. Most of the guidelines recommend measuring one of these functional hemodynamic parameters but don’t differentiate between different technologies. Here we could show that each single technology has its own precision determing fluid responsiveness  and un-calibrated  pulse-contour analysis to provide values for SVV and PPV. In addition, we measured PPV derived from routine intraarterial blood pressure monitoring (Philips) and PVI that was derived from pulse oximeter (Masimo). SVV and PPV of all systems are calculated from the minimal and the maximal SV  and the minimal and maximal PP  during the ventilator cycle using the following equations respectively: SVV (%) = (SVmax- SVmin) / SVmean and PPV (%) = (PPmax- PPmin) / PPmean. While all systems derive PP directly from the arterial pressure waveform, they differ in SV determination and the time window that is required for the assesment of these functional hemodynamic parameters [In our study, we used calibrated  that occur during the respiratory cycle. The pulse index reflects the amplitude of the pulse oximeter waveform and is calculated as the pulsatile infrared signal (AC or variable component), indexed against the non-pulsatile infrared signal (DC or constant component). The infrared signal is used because it is less affected by changes in arterial saturation than the red signal. A recent meta-anaylsis described PVI equally effective for predicting fluid responsiveness compared to SVV and PPV in ventilated adult patients. In contrast to our results, the pooled performance was much better: the AUC for identification of fluid responders was 0.85 . One posThe study has some limitations. This study was done in a small and selected group of cardiac surgery patients that were mechanically ventilated which precludes generalizability of our results. In addition, we used the ROC curve analysis to discriminate for fluid responsiveness. Although the ROC approach has been used in many studies dealing with fluid responsiveness, there is an unavoidable limitation since it artificially dichotomizes a continuous variable into a binary statistical index. Thereby, patients can only be categorized in fluid responders and non-responders according to their increase in SV after fluid loading. In the present study, our aim was to compare different technologies on how they perform predicting fluid response. This question can be answered by ROC curve anaylsis. However, in clinical reality, such a straight cutoff value (SV increase > 15%) is not always meaningful. Therefore, we additionally calculated the inconclusive zone (gray zone) where the accuracy of dynamic preload indices is limited in predicting positive fluid responsiveness , 26.Functional hemodynamic parameters of different invasive technologies, such as PPV and SVV performed well in predicting fluid responsiveness in mechanically ventilated patients after cardiac surgery except the non-invasive PVI technology. However, decision of fluid administration should not be based solely on the presence of preload responsiveness, but also on the presence of hemodynamic instability, peripheral hypoperfusion, clinical findings and the absence of high risk for fluid overload. All of the studied dynamic indices showed small but relevant differences between technologies.


Data regarding the effects of backpack carriage on children’s body strains while walking are limited. This study measured the body posture, muscle activation, and subjective discomfort scores of 12 male schoolchildren (age: 12.3 (range 12.1–13.0) y, height: 151.3 (range 144.2–154.6) cm, weight: 46.6 (range 43.6–49.7) kg) carrying backpacks weighing 5%, 10%, and 15% of their respective body weights (BWs) and walking for 10 min on a treadmill. For each load, three positions along the spinal column  were examined. Participants carrying a backpack weighing 15% of BW exhibited higher head flexion, trunk flexion, and corresponding muscle activation, and a lower lumbosacral angle compared with those carrying loads of 5% and 10% of BW. The waist received the highest discomfort scores when the backpacks were carried at the L3 position. Conversely, the discomfort rating for the neck and shoulders where the highest when the backpack was at the T7 position; this high backpack position also caused more head flexion than the other two positions. For the musculoskeletal health of children, the findings suggest that carrying a school backpack weighing 15% of BW should be avoided, and carrying at the T12 position may be recommended for schoolboys. Most schoolchildren in developed countries carry backpacks. Previous studies have demonstrated that daily physical stresses associated with carrying backpacks cause significant forward lean of the head and trunk –3 and chOne of the crucial factors of backpack carriage is the weight. In Australia, the average load carried by children was found to be 5.3 kg, or approximately 10% of their mean body weight (BW) . DockrelIn addition to backpack weight, attention has been given to the height of the backpack’s center of gravity (CG). The position of the backpack is crucial for determining the proper load-carriage method and for the ergonomic design of schoolbags. In general, a high position of the load is recommended . DevroeyAlthough backpack carriage is a dynamic activity, studies have typically performed backpack-carrying tests on subjects in static standing postures. Some studies have collected data from participants walking for short durations while carrying backpacks, but these studies either included college-aged adults  or did nThis study recruited 12 male schoolchildren and paid them at an hourly rate. Recruitment information was announced on the bulletin board of Ming Chi Elementary School  during January 1–20, 2013. The volunteers were then interviewed and informed of the details of the test procedure. The mean (range) age, height, and BW of the study participants were 12.3 (12.1–13.0) y, 151.3 (142.2–154.6) cm, and 46.6 (43.6–49.7) kg, respectively. All participants were right-hand dominant and were healthy with no reported musculoskeletal problems or back pain in the 12-month period prior to the study. The experimental procedures were approved by the Ethics Committee of Chang Gung Memorial Hospital , and informed and written consent was obtained from all the study participants and their parents prior to beginning the experiment.The backpack-carrying postures of the participants were recorded while they walked on a treadmill  for 10 min with various combinations of loads and positions. Because body joint angles in the sagittal plane were analyzed in this study, two-dimensional kinematics data were collected. Before data collection, six adhesive reflective markers were attached to each participant’s anatomical landmarks  on the dominant side . The ana by Chen .2; center-to-center electrode distance, 45 mm) parallel to the muscles following a standard preparation procedure; prior to electrode attachment on the skin, the area was shaved and cleaned with alcohol. Bipolar surface electrodes were attached to each participant’s skin with adhesive tape to avoid artifacts. The following electrode locations were used: trapezius, the midpoint between C7 and the acromion [In this study, a wireless electromyography (EMG) device  was used to measure activation in three muscle groups . Because the kinematics data were analyzed as the body posture was photographed in the sagittal plane, muscle group activation was measured on the left side of each participant. The EMG electrodes were attached on the opposite side of the joint markers because some participants felt relatively uneasy and unbalanced when all attachments were attached on the same side during walking. We placed pairs of Ag/AgCl surface electrodes  to normalize signals measured during backpack trials . Each muIn this study, subjective assessments were performed using a continuous visual analog scale. The scale was 100 mm in length and was modeled after the comfort scales developed by Mundermann et al. . The vis3, net weight 0.8 kg, No.1998-2002, UnMe, A-Star CO., LTD, Taiwan). Its CG was controlled using a custom-made wooden frame placed inside the backpack. The weights  were set using small lead blocks (dimensions 5 × 5 × 1.2 cm3). The weights for testing were obtained from previous studies [During the experiment, the participants wore clothing suitable for sports . Prior t studies ,32. The  studies . The len studies , which wBody posture and EMG data were synchronously collected during the last 10 s of the 10-min walking duration for the nine test combinations. We used a trigger signal to start collection of both motion and EMG data to ensure synchronization. Moreover, a test was also performed without a backpack by each participant for comparison. The average values for body posture and EMG data for the last 10 s were used for analysis. To prevent muscle fatigue, a minimum rest period of 20 min was allowed between successive trials, and each participant was tested for less than 2 h during each half day. We obtained 108 backpack data samples (12 participants × 3 backpack weights × 3 backpack positions) and an additional 12 samples of participants for the no-backpack condition. For each participant, backpack weight and position combinations were applied in a random order. During the test, a camera  positioned approximately 5 m from the participant and perpendicular to the sagittal plane, recorded the kinematic marker positions .A two-way repeated-measures analysis of variance (ANOVA) was used to investigate the effects of three backpack weights and three backpack positions, and the Duncan multiple range test (MRT) was used for post hoc comparisons. The Duncan MRT is a widely used procedure for comparing all pairs of means and is very effective at detecting differences between means . In the p < 0.001). Trunk flexion and lumbosacral angle were also affected by backpack weight, but not by backpack position (p < 0.001). All two-factor interactions were not significant. Tables Data of body posture, muscle activation, and discomfort score of all participants are provided for each backpack-carrying combination as supporting information . Table 1p < 0.05), whereas the other combinations (including two-factor interactions) were not significantly affected. Furthermore, the Duncan MRT results  and position (p < 0.001), whereas the back score was affected only by weight. No interaction effect of backpack weight and position was observed. The Duncan MRT result showed that the highest discomfort scores for the neck and shoulders, back, and waist were given when a weight of 15% of BW was carried , testing protocols , or assessment indices . To understand the most common backpack problems in schoolchildren, this study investigated body posture, muscle activation, and subjective discomfort scores in male schoolchildren who walked for 10-min periods carrying backpacks with various weight and position combinations.One study suggested that backpacks weighing between 10% and 20% of BW may be acceptable for school children ; anotherDevroey et al.  concludeStudies on the influence of backpack position have been somewhat inconclusive. Grimmer et al.  claimed One major limitation of this study is the relatively small and single-sex sample (12 schoolboys with a mean age of 12.3 y) recruited in the test. The results may not be applicable to the female population because the prevalence of lower-back pain caused by backpack carriage may be higher in girls than in boys , and chaThis study required participants to carry backpacks weighing 5%, 10%, and 15% of their total BW and walk for 10 min on a treadmill. Each carrying weight was also investigated at three backpack positions. The results show that differences in head and trunk flexion and lumbosacral angle between the no-backpack condition and carrying a load weighing 15% of BW were significant in comparison with differences between the other observed loads. Positioning the backpack near the T12 position may avoid extreme discomfort at the body sites investigated. This study suggests that carrying a load weighing no more than 10% of BW at the T12 position may be acceptable for schoolboys.S1 Dataset(XLS)Click here for additional data file.


Residence” should have appeared as “3.1.3. Residence.” “3.1.1. Socioeconomic risk factors” should have appeared as “3.1.4. Socioeconomic risk factors.”In the article, “Is rotavirus still a major cause for diarrheal illness in hospitalized pediatric patients after rotavirus vaccine introduction in the Saudi national immunization program?”,


A total of 56 mice were assigned to 3 studies. Study 1: Vessel permeability was evaluated using dynamic contrast-enhanced (DCE)-MRI (n = 3). Study 2: Tumour uptake of 131I-mIBG in excised lesions was evaluated by γ-counting and autoradiography (n = 28). Study 3: Tumour volume was assessed by longitudinal MR imaging and survival was analysed (n = 25). Tumour dosimetry was performed using Monte Carlo simulations of absorbed fractions with the radiation transport code PENELOPE.BALB/c nu/nu mice bearing SK-N-SH neuroblastoma xenografts were assigned to five treatment groups: 131I-mIBG and EBRT resulted in a seven-day delay in tumour regrowth. Following EBRT, vessel permeability was evaluated by DCE-MRI and showed an increase at 24 h post irradiation that correlated with an increase in 131I-mIBG tumour uptake, absorbed dose and overall survival in the case of combined treatment. Similarly, EBRT administered seven days after MRT to coincide with tumour regrowth, significantly decreased the tumour volume and increased overall survival.Given alone, both This study demonstrates that combining EBRT and MRT has an enhanced therapeutic effect and emphasizes the importance of treatment scheduling according to pathophysiological criteria such as tumour vessel permeability and tumour growth kinetics. The distinct advantage of combining different radiation therapy modalities lies in the ability to achieve higher radiation absorbed tumour doses without compromising the dose limiting organs of each individual therapy in vivo and to provide a framework for combination treatment protocols of neuroblastoma. To achieve this, three studies were undertaken. In study 1 dynamic contrast-enhanced (DCE)-MRI was used to assess the effect of EBRT on vessel permeability and its impact on 131I-mIBG uptake and distribution within the tumour. To address this phenomenon, in study 2, 131I-mIBG tumour uptake and radiation absorbed dose were analysed with or without prior EBRT treatment. In addition, the scheduling of EBRT before and after MRT was considered. For the latter, the temporal separation between EBRT and MRT was selected to coincide with the time-point at which tumour regrowth was observed after initial MRT. Finally, in study 3 the efficacy of the specific combined schedules of EBRT and MRT was evaluated by assessing tumour volume and overall survival with longitudinal MR imaging.The aim of this study was to explore the interaction between EBRT and MRT The SK-N-SH human neuroblastoma cell line  was cultured in Dulbecco’s Modified Eagle’s Medium  supplemented with 10% foetal calf serum (FCS) , 2 mM glutamine  and 100 U/mL penicillin/streptomycin .6) in 100 µL of matrigel were injected subcutaneously into the flanks of Nod scid gamma mice . In this mouse strain the success rate of xenograft development was 100%. Tumours were excised and homogenized when they reached a volume of 1 cm3 (at approximately five weeks). Tumour homogenates were injected into the right flank of recipient BALB/c nu/nu mice (n = 56) . Following this procedure the proportion of BALB/c nu/nu mice that developed xenograft tumours was 100%. Xenografts grew to an average volume of 500 mm3 (range 428–532 mm3) by 7 weeks following subcutaneous inoculation of SK-N-SH homogenate. The 56 recipient BALB/c nu/nu mice were then assigned to three sub-study groups as shown in n = 3; n = 28 and n = 25).All animal procedures were carried out in accordance with the UK Animals (Scientific Procedures) Act 1986 and with local Ethics Committee approval. In preliminary experiments the success rate of tumour development following subcutaneous injection of SK-N-SH cells into the flanks of BALB/c nu/nu mice was found to be low (10%). Therefore, SK-N-SH cells  preclinical imaging system as previously described EBRT treatments  were given using a small animal radiation research platform (SARRP) irradiator, . Dosimetric measurements for this collimator were performed using EBT3 film  that was calibrated against absolute measurements following the recommendations of the report of the American Association of Physicists in Medicine Task Group 61 n = 3) underwent DCE-MRI  was administered to two groups of xenograft-bearing mice (n = 28), the time schedule for tumour activity measurement is shown in 131I-mIBG and in the other group MRT was given alone. At set time points following injection, tumours were excised  and the accumulated radioactivity in tumours was measured in both groups using a gamma counter . Tumour cryo-sections from the two groups 24 h after 131I-mIBG injection were also prepared for autoradiography. Sections were left overnight to expose an image phosphor plate and analysed using a Cyclone® Plus Storage Phosphor System .S-values, were determined from event-by-event Monte Carlo (MC) transport with the general-purpose code PENELOPE 131I ρ = 1 g cm−3) for a sphere of equivalent tumour volume. For validation, PENELOPE simulation results were compared to OLINDA/EXM single sphere values The Medical Internal Radionuclide Dose (MIRD) schema was used to calculate the absorbed radiation dose to the tumour. Time activity curves for the 2 groups were generated from gamma counter radioactivity measurements of each tumour at the selected time points. Cumulated activity was assessed by numerical integration of the time activity curve per time point by using a trapezoidal integration method and assuming physical decay after the last data point. The dose to the target area per unit cumulated activity in the source region, i.e. n = 25): Control (n = 3), EBRT (n = 5), MRT (n = 6), EBRTt1 + MRT (n = 5) and MRT + EBRTt2 (n = 6). The group designated EBRTt1 + MRT received MRT 24 h following EBRT and the group designated MRT + EBRTt2 received MRT 6 days before EBRT. The scheduling of the MRT + EBRTt2 group was based on a prior study showing a regrowth of tumour 6 days after MRT 3, was reached. Mice were then euthanised. For animals with complete tumour regression, 53 days after the end of treatment was defined as an arbitrary censoring time for overall survival.For the main tumour growth inhibition study C, animalA single fraction of 5 Gy using the SARRP irradiator, based on previously published experience of the SK-N-SH model 131I-mIBG  was administered at a specific activity of 185 MBq/mL. Animals received intravenous (i.v.) tail vein injections of vehicle  or 131I-mIBG  ad libitum and potassium iodine  was added to the drinking water on the day of treatment and for 7 days thereafter to block thyroid uptake of the radiolabelled iodine.For all MRT treatments, ImageJ (version 1.50f) software was used for all image processing and analysis detailed below Change in vessel permeability, is presented as mean ± SD (Standard Deviation), and statistical significance is reported at the 5% level using a standard t-test. Analysis of variance F-tests were used to assess tumour volume differences between treatment groups. Kaplan–Meier curves were used to assess survival and a Weibull model was fitted to assess the effects of treatment on survival with quasi-variances. Statistical analyses were done using GraphPad Prism®  and R software  n = 3 animals (p < 0.05). By 72 h the mean signal enhancement has diminished compared to 24 h, although the difference between the 24 h and 72 h time points does not reach statistical significance  was slightly but not statistically significantly increased 2 and 4 h p.i. in the EBRT + MRT group (where MRT administration was preceded by EBRT 24 h earlier) in comparison to the MRT only group (%ID/g at 2 and 4 h: 13.50 ± 1.94 and 13.43 ± 2.37 versus 10.89 ± 4.06 and 10.94 ± 1.80 respectively) (131I-mIBG uptake was significantly greater in the EBRT + MRT group at 24 h (p < 0.01), 48 h (p < 0.001) and 72 h (p < 0.05) compared to the MRT only group . Autoradiography was performed on two tumours that were excised 24 h after administration of 131I-mIBG, one each from the EBRT + MRT and MRT groups. Autoradiography showed a marked increase in tumour-associated 131I when EBRT was administered prior to MRT compared to MRT alone (The percentage of injected ctively) A. HoweveRT alone B.Fig. 3ES-values can be ascribed to the different MC codes used 131I-mIBG 24 h after EBRT treatment in the EBRTt1 + MRT group.PENELOPE simulated absorbed fractions for unit density spheres were within 10% of OLINDA/EXM self-irradiation values . Differe3 in 7 days. As shown in 131I-mIBG alone caused a significant delay in tumour progression in comparison to the control group (p < 0.01) as tumour volume was stable during the first 5 days p.i. Regrowth of one tumour was observed at Day 7 and this tumour reached 800 mm3 by Day 13 (p < 0.001) (p < 0.001) in tumour volume compared with the MRT group at Day 13 was observed . As for the EBRTt1 + MRT group, animals in the MRT + EBRTt2 group developed tumour regrowth from Day 17 (3) was reached, as the time scale. For the EBRTt1 + MRT and the MRT + EBRTt2 groups, one mouse (1/5), and two mice (2/6), respectively, survived to the end of the observation period. As shown in p < 0.001). EBRT had a significant survival benefit compared to MRT (p = 0.03). Survival for each combined therapy group was significantly higher compared to the control group (p < 0.001) and to the EBRT and MRT alone groups  (There was no significant difference in tumour volume for the 5 treatment groups at baseline (Day 0) A. Tumoury Day 13 A. In con< 0.001) . Additioobserved C, the voRT alone A. The cor groups A–C. Simim Day 17 A. To evactively) B. There The potential benefits of combining EBRT and MRT are substantial as the dose limiting organs of the two modalities differ, thus allowing escalation of solid tumour doses without exceeding dose to the limiting organs 131I-mIBG could be used to inform the planning of subsequent EBRT 131I-mIBG. However, it is well documented that MRT given concurrently with EBRT is more effective than when given after a prolonged interval 131I-mIBG uptake and its effect on tumour volume as assessed by MRI imaging.It has been suggested that when MRT is given as the first treatment, the intratumoural distribution of 131I-mIBG treatment. The results show that for the SK-N-SH human neuroblastoma model, a significant increase in vessel permeability occurred 24 h after EBRT treatment. This finding allowed us to propose a schedule for the combined treatment of EBRT 24 h before MRT treatment (EBRTt1 + MRT) to potentiate the effect of both treatments. The combined treatment led to a more than two fold increase in 131I-mIBG uptake and radiation absorbed dose than that attributable to MRT only (8.03 and 3.61 Gy with or without EBRT beforehand). The increased vessel permeability was observed only in tumour tissue, as the normalized T1 signal in muscle remained the same over the selected time course  was observed. If EBRT is initiated at this point then there is no “wastage” of the cytocidal effect conferred by 131I-mIBG treatment. Despite the increased 131I-mIBG uptake after EBRT in the EBRTt1 + MRT group, this did not translate into a significant effect on survival when compared to the MRT + EBRTt2 group. Both treatment regimens (EBRTt1 + MRT and MRT + EBRTt2) significantly increased survival compared with either therapy alone and compared to the control group. Nonetheless, as previously reported, there was a heterogeneous treatment response to the combined therapies with 20% (1/5) and 33% (2/6) of the animals in the EBRTt1 + MRT and MRT + EBRTt2 groups respectively tumour free 53 days after the end of the respective treatments. The small number of animals used in each group could potentially limit the interpretation of results. In addition, there are a large number of biophysical factors that may contribute to the observed variability in the tumour response 131I-mIBG, and it is also plausible that the sensitising effect of low dose 131I-mIBG treatment before EBRT contributed to the efficacy of the MRT + EBRTt2 combination.The rationale for the schedule of the second combined treatment group (MRT first followed by EBRT at Day 7) was based on the differential between dose- and repair- rates of the tumour following In this study, we had not set out to establish dose equivalence between EBRT and MRT, primarily as the focus was the establishment of a combined schedule for EBRT and MRT informed by vessel permeability, MRT uptake and tumour growth. Dosimetry could be improved by quantitative small animal imaging which was not possible at the time of this study. Furthermore, for a curative intent, fractionation of both EBRT and MRT should be considered in the future, taking into consideration tissue toxicity.131I-mIBG therapy are more effective than either EBRT or MRT alone, with overall survival approximately equal to the sum of the two effects. In this study no statistical difference between the overall survival was observed when the order of treatments in the combined regimens was swapped. We propose that, with a prior knowledge of pathophysiological criteria such as vessel permeability and tumour repopulation kinetics, it would be possible to develop patient-specific EBRT and 131I-mIBG regimens that can promote long term local control of bulky tumours with focal EBRT augmented by 131I-mIBG. This strategy could potentially be used to develop a prospective protocol for multi-modality management of HRNB in paediatric patients.In conclusion, combinations of EBRT and The authors have no conflict of interest to disclose regarding this study.


This paper studies the heat transfer analysis caused due to free convection in a vertically oscillating cylinder. Exact solutions are determined by applying the Laplace and finite Hankel transforms. Expressions for temperature distribution and velocity field corresponding to cosine and sine oscillations are obtained. The solutions that have been obtained for velocity are presented in the forms of transient and post-transient solutions. Moreover, these solutions satisfy both the governing differential equation and all imposed initial and boundary conditions. Numerical computations and graphical illustrations are used in order to study the effects of Prandtl and Grashof numbers on velocity and temperature for various times. The transient solutions for both cosine and sine oscillations are also computed in tables. It is found that, the transient solutions are of considerable interest up to the times t = 15 for cosine oscillations and t = 1.75 for sine oscillations. After these moments, the transient solutions can be neglected and, the fluid moves according with the post-transient solutions. Energy transfer due to convection is of great importance and arises in many physical situations . AmongstIn contrast, the energy transfer due to convection flow in stationary or moving cylinder has numerous applications in engineering and geophysics, such as nuclear reactor cooling system and underground energy transport and hence attracted the attention of many researchers. However, this area of research is not as much studied as flow over a flat plate, channel flow, flow over sheets etc. Most probably, it is due to complex nature of these problems. Most of these studies were investigated in the absence of heat or heat and mass transfer, see for example the work of Fetecau et al. –14, JamiFree convection in cylindrical shape geometry is investigated in several earlier studies such as Goldstein and Briggs , in 1964The aim of this paper is to study the energy transfer in a vertically oscillating cylinder due to natural convection. Exact solutions are obtained by means of Laplace and Hankel transforms for velocity and temperature. The transient solutions for both cosine and sine oscillations of the cylinder are computed in tabular forms. Results of Prandtl and Grashof numbers for different times are shown in graphs and discussed.r0. The z-axis is considered along the axis of cylinder in vertical upward direction and the radial coordinate r is taken normal to it. Initially at time t ≤ 0, it is assumed that the cylinder is at rest and the cylinder and fluid are at the same temperature T∞. After time t = 0, the cylinder begins to oscillate along its axis and induces the motion in the fluid with velocity U0H(t)exp(iωt), where U0 is the characteristic velocity, H(t) is the unit step function and ω is the frequency of oscillation. At the same time, the cylinder temperature raised to Tw which is thereafter maintained constant =Tw,t>0.Introduc2 and using the initial condition  and q is the transform variable.Applying the Laplace transform to Eqs , 9)2 an2 an9)2 aondition 2, we obtrn, n = 0,1,… are the positive roots of the equation J0(x) = 0, J0 being the Bessel function of first kind and zero order.Applying the finite Hankel transform of order zero, to Eq , and usiTaking inverse Laplace transform of Eq , we obtaTaking inverse Hankel transform, we obtain1, and using the initial condition 1, a1, a9)1, ondition 1, we obtApplying finite Hankel transform to Eq  and usinWe considerApplying the inverse Laplace transform to Eqs , 20), ,  and 22)22), we oApplying the Laplace transform to Eq  and usinPr),Pr≠1.ApplyingFor cosine oscillations of cylinder, the velocity field is given as:For sine oscillations of cylinder, the velocity field is given as:In order to obtain some information on the fluid flow parameters and heat transfer, we have made numerical simulations using Mathcad software. The obtained results are presented in the graphs from Figs We were interested, to analyze the influence of the Prandtl number on the temperature, Nusselt number and on fluid velocity. Also, the influence of the Grashof number on the fluid velocity was studied.J0. These roots are generated by a numerical subroutine using Mathcad. All the parameters and profiles are dimensionless.To evaluate numerical values of the temperature, Nusselt number and of the fluid velocity, we need the positive roots of the Bessel function θ, are sketched versus the radial coordinate r, for different values of the time t and Prandtl number Pr. It is clear from the temperature expression (14) that, the exponential term tends fast to zero for large time or, for small values of the Prandtl number. This is due to the fast increasing values of the positive roots of the function J0(x). It is observed from The diagrams of t → ∞, the Nusselt number tends to zero).The influence of Grashof number Gr, on the fluid velocity is shown in uct, given by Eq , is of order 10−6, therefore, after this moment the transient solution can be neglected and, the fluid moves according with the post-transient solution.The decreasing of the transient solution en by Eq , is show−6 is lower for sine oscillations. For the same values of the system parameters, the transient solution for sine oscillations can be neglected after the value t = 1.75.Similarly, in The problem of heat transfer due to free convection in an oscillating vertical cylinder is studied. Exact solutions for temperature and velocity are determined by applying the Laplace and finite Hankel transforms. The velocity solutions are arranged in transient and post-transient parts. Obtained analytical results were plotted and discussed. Transient solutions were computed in tables. The main points of this study are listed below:Increasing Prandtl number Pr, the temperature decreases. The Nusselt number increases if the Prandtl number increases.For small values of the time t, the convection is dominant, while, for large values of time t the heat transfer by conduction is dominant.Fluid velocity increasing with Grashof number but decreasing with Prandtl number.−6, and thereafter the fluid moves according with the post-transient solutions.The transient solutions are significant up to the order of 10


Social media are an important source of information about the political issues, reflecting, as well as influencing, public mood. We present an analysis of Twitter data, collected over 6 weeks before the Brexit referendum, held in the UK in June 2016. We address two questions: what is the relation between the Twitter mood and the referendum outcome, and who were the most influential Twitter users in the pro- and contra-Brexit camps? First, we construct a stance classification model by machine learning methods, and are then able to predict the stance of about one million UK-based Twitter users. The demography of Twitter users is, however, very different from the demography of the voters. By applying a simple age-adjusted mapping to the overall Twitter stance, the results show the prevalence of the pro-Brexit voters, something unexpected by most of the opinion polls. Second, we apply the Hirsch index to estimate the influence, and rank the Twitter users from both camps. We find that the most productive Twitter users are not the most influential, that the pro-Brexit camp was four times more influential, and had considerably larger impact on the campaign than the opponents. Third, we find that the top pro-Brexit communities are considerably more polarized than the contra-Brexit camp. These results show that social media provide a rich resource of data to be exploited, but accumulated knowledge and lessons learned from the opinion polls have to be adapted to the new data sources. In recent years, the use of social media has increased dramatically in the private, business, and especially political communication. For example, 22% of online Americans used social networking or Twitter for politics in the 2010 United States elections campaign [Leave (supporting Brexit), Remain (opposing Brexit), or Neutral (uncommitted). The labeled tweets were used to train a classifier which then automatically labeled all the remaining tweets. Once each tweet had a label assigned, we can aggregate the tweets of each user to determine her/his prevailing stance about Brexit.The political issue investigated in this work concerns the United Kingdom (UK) European Union (EU) membership referendum, also known as Brexit, held in the UK on June 23, 2016. We focus on two aspects of Twitter activities: the leaning of the UK-based Twitter users for and against Brexit, and identification of the most influential users in both camps. In the weeks before the referendum, starting on May 12, we were continuously collecting the UK geo-located, Brexit-related tweets. We acquired around 4.5 million  tweets, from almost one million  users tweeting about Brexit. A large sample of the collected tweets  was manually labeled for the stance of their authors regarding Brexit: The first question addressed in this paper is the relation between the stance of the Twitter users in relation to the referendum outcome polls. We show that in the case of Brexit, the stance of Twitter users matches the polls surprisingly well, even with known demographic differences of the voting population and Twitter. We propose a demography-adjusted method which helps to determine the confidence bounds of opinion polls with the use of Twitter data.h-index) [h-index, we identified the influential Twitter users on both sides of the Brexit debate. Our analysis of the influential users in the Brexit campaign shows, on the one hand, a very active and organized Leave social media campaign, and on the other hand, a passive approach used by the Remain side.The second question addressed in this paper is how to formalize the notion of influence on Twitter. In this context, we adapt the Hirsch index (h-index) , an authThe paper is organized as follows: In "In this section, we give an overview of how social media, Twitter in particular, was used to predict election results. Then we focus on the Brexit referendum social media studies. We next relate the results of standard opinion polls to the experiments with Twitter. Finally, we give an overview of some approaches to measure user influence on Twitter.Elections usually stir a lot of attention and emotional response, and the election results are among better-documented reflections of public mood. There has been a lot of research on this topic, particularly on the question whether the analysis of social media can be used to predict the outcome of elections. A survey is given by Gayo-Avello . ConclusTumasjan et al.  showed tO’Connor et al.  analyzedBermingham and Smeaton  developeA study on the use of political bots during the Brexit referendum is presented by Howards et al. . They reAnother study by Khatua et al.  presentsBoth studies mimic a dictionary approach for assessing the stance of a tweet. They use sets of hashtags supporting either side of the debate and assign a tweet either to support or oppose Brexit based on simple frequency-based rules. No assessment of the quality of such labeling process is provided.The Facebook Brexit debate has also been analyzed by Del Vicario et al. . The autAn opinion poll is a research survey of public opinion from a selected sample. Opinion polls are usually designed to represent the opinions of a population by conducting a series of questions and then extrapolating generalities in ratio or within confidence intervals. A sample is drawn from a large panel of volunteers, and the results are weighted to reflect the demographics of the population of interest.Over time, a number of theories and mechanisms have been developed to explain erroneous polling results. Sources of errors in conventional polling include faulty demographic models by pollsters who weigh their samples by particular variables . Some of these reflect errors on the part of the pollsters; many of them are statistical in nature. Others blame the respondents for not giving candid answers  are under the age of 30 (vs. 22% for the rest of the online population) and 41% have a college degree (vs. 34% of other internet users). However, they look quite similar to the rest of the online population in their racial, gender, and income composition [The questions about the collection of demographic data on social media and their proper applications are still open. In the following section, we show a simple application of the age-adjusted prediction, based on the stance, automatically computed for the collected Twitter posts.Social influence is the behavioral change of individuals affected by relations with others in a network . It depeindegree influence , mention influence , and retweet influence .There are three main modalities in which users on Twitter interact: (1) the user follows posts of other users, (2) the user responds to other user’s tweets by mentioning them or replying to them, and (3) the user forwards interesting tweets by retweeting them. Based on these three interaction types, one can define three measures of influence of a Twitter user : indegreKwak et al.  compare The related work indicates that retweeting most closely reflects the intuitive notion of engaging others and getting support on Twitter. However, the retweet influence alone ignores the productivity of the Twitter user. Therefore, we combine the ability of the user to produce original contents about relevant topics with the contents spreading in the form of retweet influence. This combination resembles the scientific influence; therefore, we adapt the well-known Hirsch index to measure the social influence on Twitter.Remain a member, called Britain Stronger in Europe. There were two groups promoting exit which sought to be the official Leave campaign: Leave.EU , and Vote Leave (supported by Conservative Party Eurosceptics). The Electoral Commission announced on April 13, 2016 that Vote Leave was the official leave campaign. The UK government’s official position was to support the remain option. The referendum turnout was 71.8%, with more than 30 million people voting. Leave won by 51.9%, while Remain got 48.1% of the votes.The UK EU membership referendum, known as Brexit, took place on June 23, 2016 in the United Kingdom and Gibraltar. Its goal was to gauge support for the country either remaining a member of, or leaving, the EU. As of October 2015, there was a cross-party, formal group campaigning for Britain to Stance detection is the task of automatically determining whether the author of the text is in favor of, against, or neutral towards a target . The tarnegative, neutral, and positive standing for the stances Leave (−), Neutral (0), and Remain  a sample of tweets is manually annotated with stance, (ii) the labeled set is used to train and tune a classifier, (iii) the classifier is evaluated on an independent test set, (iv) the classifier is applied to the whole set of tweets, and (v) the stance of each user is assessed as the prevailing stance of her/his tweets.We collected 4.5 million  tweets, from almost one million  users posting about Brexit in the period from May 12, 2016 to June 24, 2016. 35,000 were randomly selected for manual annotation. We engaged six reliable English language students, interested in the Brexit discussions, to manually annotate the stance of the tweets about Brexit. The annotation was supported by a web-based platform GoldfinchThere are several measures to evaluate the inter-annotator agreement and performance of classification models. In order to compare the classifier performance to the inter-annotator agreement, we have selected four measures that incorporate to a different degree the idea that the misclassification of Neutral stance is less important than the misclassification of the extremes, i.e., Leave vs Remain: Alpha, fication , where \s Alpha   is a genTable orted in , we can We argue that the inter-annotator agreement provides an upper bound that the best classification model can achieve . In pracWe applied the stance classifier to our dataset of almost 4.5 million tweets. Figure When trying to infer the intentions of people on Twitter, it is not sufficient to look at the prevailing stance of the tweets only, since “less than 1% of the accounts generated almost a third of all the content”  in the TStanceScore of a user is computed from the number of the Leave tweets L, the number of the Neutral tweets N, and the number of the Remain tweets R, as follows:StanceScore has the range StanceScore around 0 have no clear leaning for or against Brexit. Therefore, we chose a band around 0 to clearly separate the pro- and contra-Brexit users. It is important that the same threshold is used on both sides of 0 to avoid any bias regarding the Leave and Remain classes. Setting a threshold on a signal is always somewhat arbitrary, and the most straightforward choice is to select the Neutral class of approximately the same size as the Leave and Remain classes. In our case, this is achieved by choosing a threshold of 0.2. The prevailing stance of the user is then computed asWe infer the stance of a Twitter user about Brexit from the prevailing stance of her/his tweets. If the user is balanced in the number of Leave and Remain tweets, or prevailingly neutral, her/his stance it assigned to be Neutral. More precisely, the nt score  which isFigure Since most of the users do not tweet regularly , we inspect the growth of users. Figure We use this result and compare it to YouGovThere are several steps to this procedure: first, we take as input the age distribution of Twitter users in Great Britain in May 2016, provided by the statistics portal StatistaSecond, we take the distribution of Brexit identified voters for each age group from the YouGov polls on June 22, 2016 (referendum eve) and June 23, 2016 (referendum day) (see row 1 of Tables Age-weighted poll results are considerably more (7–10%) in favor of Leave than our Twitter stance on both investigated days , they might not be as engaged in the political discussion as the adult Twitter population. There are several other assumptions in our demographic study that might, up to some point, influence the outcome: the usage of Twitter is assumed to be independent of the referendum stance; age is the only relevant demographic factor , and also party affinity.Our poll adjusted model (over) predicts Leave with 60.1% on referendum eve and 59.1% on the referendum day, while the actual referendum outcome was 51.9%. This is due to the violation of some of our assumptions. Likely, the major source of error is the difference between the “age distribution of Twitter users in Great Britain in May 2016” versus “the age distribution of Twitter users This kind of demographic correction can not be directly used for predicting referendum/election results or to adjust poll results. However, such an approach can suggest the direction of a poll error (in our case Brexit) and the upper bound of the error (in our case 7–10%).We consider retweeting as one of the most relevant activities for information diffusion on Twitter. In this section, we analyze two aspects of retweeting activities related to Brexit. First, we measure the social influence of Twitter users in terms of their posting activity and ability to engage their followers for support . Second, we construct a retweet network where Twitter users are linked when they retweet each other. We detect the largest communities in the network and the most central users. We show that the most central community users are typically not the most influential. Further, we compare the influence of Twitter users and polarization of the retweet communities in both camps (Leave and Remain): we show that the Leave users are more influential and that the Leave communities are more polarized.h-index) [h-index is a well-known author-level bibliometric indicator that quantifies the scientific output of a scholar by a single number. It combines both the productivity and citation impact of a scholar. A scholar with an index of h has published h papers, each of which has been cited in other papers at least h times. We adapt the h-index to Twitter data and argue that it is a well-suited measure of influence of individual Twitter users. A Twitter user with an index of h has posted h tweets, each of which has been retweeted at least h times. Let RT be the function that corresponds to the number of retweets of each tweet. The values of RT are ordered in decreasing order, from the largest to the lowest value, and i corresponds to the position in the ordered list. The h-index is then computed as follows:h-index computation to a set of one million  Twitter users collected until June 24, 2016. For comparison, we also compute the h-index on tweets collected until June 23 . The top ten polarized Twitter users (Leave and Remain) and some Neutral users are presented in Table We adapt the Hirsch index (h-index)  to rank ion 998,04 Twitterh-index is four times higher (297 compared to 70). Note, as a curiosity, that the Labour party has two influential Twitter accounts, one supporting Leave (@labourleave), and the other supporting Remain (@UKLabourIN). Additionally, the leader of the Labor party, Jeremy Corbyn (@jeremycorbyn), has a neutral stance regarding Brexit.The Leave group is also considerably more active regarding the generated content and retweets compared to the Remain group. The most influential Twitter user campaigning for Brexit (@vote_leave) has posted almost four times as many tweets as the most active Remain user (@guardian). The difference in terms of retweets is even higher: the Leave campaign user @vote_leave was retweeted 13 times as much as @guardian, and its Our analysis partly supports the results of Howard et al. . The auth-index . In this network, there are 520,516 Twitter users and 1,593,887 edges (retweets). The giant connected component of the network has 500,246 users and 1,581,538 edges. A retweet network can be regarded as linking users who agree on certain topics. Communities in such a network therefore identify groups of mutually agreeing users. When we apply the Louvain community detection, we get 12 communities with more than 5000 users each . The stance (or polarization) of a community is computed as the mean stance of its nodes (Twitter users). The communities and their polarization regarding the Brexit stance are in Fig. e and nodes u, v. A directed edge u to the user v indicates that contents of the user u have been retweeted by the user v. The outdegree centrality of u is the number of different users that have retweeted her/him at least once, and the indegree centrality is the number of different users that she/he retweeted. The degree centrality is the sum of the in- and outdegree centralities. We do not take the number of retweets into account; therefore, the graph is unweighted.For each community, we compute the degree centrality of all its nodes and identify the community by the top degree-central node. Let the retweet network be represented as a directed graph, with edges h-index values.Table h-index: @vote_leave, @PrisonPlanet, @BBCNews, @TheEconomist, and @jeremycorbyn. The rest have high outdegree, but did not produce many original tweets that were retweeted. The most interesting is the @iVoteLeave Twitter account that did not post any original tweet at all , but only retweeted posts by others. On the other hand, the remaining influential Twitter users from Table h-index is relatively stable and increases only slightly from June 23 to June 24, after the referendum day, despite the fact that the number of Twitter users increased considerably, from 620,000 to almost one million.There are several observations one can make when comparing Tables Another observation concerns the polarization and size of the retweet communities. On the one hand, the size of the communities leaning towards Remain  is considerably larger than the size of the Leave communities (negative stance score). This is surprising since the stance of Twitter users before the referendum was mostly balanced see Fig. . The reaOur analysis of influential Twitter users and polarized communities regarding Brexit shows, on the one hand, a very active and organized Leave social media campaign, and on the other hand, a passive approach by the Remain side. We observed a similar phenomena in the case of the European Parliament , where tIn this study, we analyze political tweets about the Brexit referendum. We developed a specialized stance classification model that can classify any Brexit-related tweet as pro-Brexit (Leave), contra-Brexit (Remain), or Neutral. The model differs from sentiment analysis methods by taking into account Brexit-specific vocabulary, and not merely positive and negative words. The model was developed by machine learning methods (as an extension of Support Vector Machines ) from a A naive application of our stance model predicts the outcome of the referendum as Remain. However, there are large differences in several aspect of demography between the Twitter users and eligible voters. We take into account just the age distribution, and adjust the outcome predicted by the model. This shows convincing win of the Leave supporters, even higher than the actual result. The conclusion from this experiment is the need for continuous monitoring of demographic distribution between the Twitter users, and careful adjustment of the predicted results.Another interesting result is an estimation of the Twitter user influence by the Hirsch index. We already showed that retweeting is a form of endorsement and can be used to identify real communities in retweet networks . The HirWe speculate that there might be a valuable lesson for all politicians who want to promote their agendas: do not underestimate the role of social media, invest in a long-term effort in building communities of supporters, and actively and continuously engage in distributing your contents.The methodology for stance and sentiment analysis used in this work is applicable to other domains, not just politics. We already applied the stance/sentiment analysis to different complex systems, and studied the relations between the social media on the one hand, and another complex system on the other hand. In financial markets, for example, we analyzed the effects of Twitter stance on stock prices (30 stocks from the Dow Jones index) , 42. We The political issue investigated in this study concerns the Brexit referendum, which was held on June 23, 2016, to decide whether the UK should leave or remain in the EU. The referendum turnout was 71.8%, with more than 30 million people voting. Leave won by 51.9%, while Remain got 48.1% of the votes.In the weeks before the referendum, we were continuously collecting the Brexit-related tweets from the UK in English language. Specifically, we collected the geo-located UK Twitter data resulting from a query: “Brexit OR EUref OR voteLeave OR leaveEU OR EUreferendum OR voteRemain,” in the period from May 12, 2016 to June 24, 2016. We collected around 4.5 million tweets, posted by almost one million Twitter users. To be precise, until June 24, we collected 4,508,440 tweets, posted by 998,054 different users. Until June 23, the referendum day, there were 3,463,163 tweets, posted by 623,100 users. By limiting our analysis to tweets from the UK, we restricted ourselves to about 25% of all Brexit-related tweets in English language.A large sample of the collected tweets posted before the referendum  was manually labeled for the perceived stance about the Brexit question: Leave, Neutral, or Remain (a member of the EU).Our approach to stance classification of tweets is based on supervised machine learning, where a sample of tweets is first manually annotated and then used to train and evaluate a classifier. The classifier can then be applied to the whole corpus of collected tweets or in real-time to the incoming Twitter stream.Leave, Neutral, or Remain, depending on the stance expressed by the Twitter user. Note that the labels are ordered: LeaveNeutralRemain. When two annotators are given the same tweet, they can either agree (both give the same label), or disagree (they give different labels). The annotators can disagree in two ways: one label is Neutral and the other is extreme (Leave or Remain), or both are extreme: one Leave and another Remain—this is considered severe disagreement.Annotators were asked to label each tweet with In general, the agreement can be estimated between any two methods of generating data. In our case, we want to estimate the agreement between humans when annotating the same tweets for stance. There are different measures of agreement, and to get a robust estimate, we apply four well-known measures.c and interval difference function. Note that the function attributes disagreement of 1 between the Leave (or Remain) and the Neutral stance, and disagreement of 2 between the Leave and Remain stance. The extreme disagreement is therefore four times larger.Krippendorff’s Alpha   is a gen0,+1}.In , this isc from two annotators into a k-by-k square matrix, where k is the number of possible values of c. In the case of stance annotations, we have a 3-by-3 coincidence matrix. The diagonal contains all the perfect matches, and the matrix is symmetrical around the diagonal. A coincidence matrix has the following general form:c and c and N(c) and N is the grand total.A coincidence matrix tabulates all pairable values of LeaveRemainNeutral, label is taken into account only indirectly. In general, c. In the case of a coincidence matrix, which is symmetric, the ‘precision’ and ‘recall’ are equal, and thus c.ssifiers . \documeAccuracy within n [n neighboring class values. In our case, Leave to Remain and vice-versa:Neutral; then within n . It assuThe four agreement measures are always computed from the same coincidence matrix. In the case of the annotator agreements, the coincidence matrix is formed from the pairs of stance labels assigned to a tweet by different annotators . In the case of a classification model, an entry in the coincidence matrix is a pair of labels, one from the model prediction, and the other from the “gold standard.”Data annotation is a process in which some predefined labels are assigned to each data point. In our case, a subset of 35,000 tweets about the Brexit debate was selected for manual stance annotation and later used to train a stance classifier. A user-friendly web-based annotation platform GoldfinchLeave (pro-Brexit), Neutral, or Remain (contra-Brexit). The guideline given to the annotators was to estimate how the author of the tweet would vote at the forthcoming referendum. During the annotation process, the annotator’s performance was monitored in terms of the inter-annotator agreement and self-agreement, based on 20% of the tweets which were intentionally duplicated.Six reliable English language students were engaged for the annotations. The annotation task was to label each tweet—isolated from its context—as The annotation quality is shown as a contingency table of inter-annotator agreement in Table The four evaluation measures described above were used to quantify the inter-annotator agreement in Table . Note thAs expected, the self-agreement measures are higher than the inter-annotator agreement measures. Compared to the extensive annotator study in ,5 we canRemain to Leave is worse compared to a misclassification from Remain to Neutral. Besides the usual quality metrics for multi-class classification, specific measures like Ordinal classification is a form of multi-class classification where there is a natural ordering between the classes, but no meaningful numeric difference between them . In thiscuracy±1  and \docF1¯  were defLeave-or-Neutral from the Remain tweets and another SVM model to distinguish Leave from Neutral-or-Remain tweets. The two SVM models partition the space around both hyperplanes into bins, and the distribution of the training examples in individual bins is computed. During classification, the distances from both hyperplanes determine the appropriate bin, but the class is determined as the majority class in the bin. Additionally, the classifier can also provide the confidence of the predicted class.We treat stance classification as an ordinal classification task with three ordered classes. We apply the wrapper approach, inspired by  and descThe stance classifier was trained and tuned on the training set of 37,048 annotated tweets . The tweets were preprocessed by applying Twitter-specific processing and then transformed into a standard Bag-of-Words (BoW) representation. The Twitter-specific preprocessing includes the following: replacing URLs, hashtags, happy emoticons, sad emoticons, different combinations of punctuation marks, and mentions of Twitter users with common tokens; appending common tokens, which reflect the tweet length or provide information that a tweet contains a stock symbol or a term in uppercase; removing repetitive letters and appending a common token, which represent that a term contains repetitive letters; and normalizing diacritical characters. The standard text preprocessing techniques consist of performing tokenization, stemming, unigram and bigram construction, removing terms which appear less than five times in the dataset, and constructing normalized TF-IDF feature vectors.The tenfold cross validation was performed to assess the quality of the classifier. The confusion matrix between the annotators  and the classifier are presented in Table We compare the performance of our machine learning stance classifier to a hashtag-based classifier proposed in Khatua et al. . The hasThe hashtag-based classifier categorizes almost all tweets (96.7%) into either class Mix or Uncertain . The 3.3% of the tweets that are classified as Leave or Remain are in 72.9% of the cases correctly classified. All the performance measures a very low for the hashtag-based classifier, except for