Detailed Signaling Research Articles

Exposure fusion using boosting Laplacian pyramid.

This paper proposes a new exposure fusion approach for producing a high quality image result from multiple exposure images. Based on the local weight and global weight by considering the exposure quality measurement between different exposure images, and the just noticeable distortion-based saliency weight, a novel hybrid exposure weight measurement is developed. This new hybrid weight is guided not only by a single image's exposure level but also by the relative exposure level between different exposure images. The core of the approach is our novel boosting Laplacian pyramid, which is based on the structure of boosting the detail and base signal, respectively, and the boosting process is guided by the proposed exposure weight. Our approach can effectively blend the multiple exposure images for static scenes while preserving both color appearance and texture structure. Our experimental results demonstrate that the proposed approach successfully produces visually pleasing exposure fusion images with better color appearance and more texture details than the existing exposure fusion techniques and tone mapping operators.

Seizure detection using wavelet decomposition of the prediction error signal from a single channel of intra-cranial EEG.

This paper presents a novel patient-specific algorithm for detection of seizures in epileptic patients from a single-channel intra-cranial electroencephaolograph (iEEG) recording. Instead of extracting features from the EEG signal, first the EEG signal is filtered by a prediction error filter (PEF) to compute a prediction error signal. A two-level wavelet decomposition of the prediction error signal leads to two detail signals and one approximate signal. Eight features are extracted every one second using a 2-second window with a 50% overlap. These features are input to two different types of classifiers: a linear support vector machine (SVM) classifier and an AdaBoost classifier. The algorithm is tested using the intra-cranial EEG (iEEG) from the Freiburg database. It is shown that the proposed algorithm can achieve a sensitivity of 95.0% and an average false positive rate (FPR) of 0.124 per hour, using the linear SVM classifier. The AdaBoost classifier achieves a sensitivity of 98.75% and an average FPR of 0.075 per hour. These results are obtained with leave-one-out cross-validation. In addition, for 13 out of 18 patients, the AdaBoost classifier requires only one feature, while it requires 4 features for the remaining 5 patients.

Statins as Bioactive Molecules to Support Bone Regeneration

Statins are currently used as an effective cholesterol-lowering medication through inhibition of the mevalonate pathway, but recent studies show their potential for bone repair. The bone anabolic effects of statins have been largely attributed to their ability to enhance BMP-2 expression in osteoblast cells. In vitro studies have demonstrated that statins can increase the expression of osteogenic and angiogenic markers such as alkaline phosphatase, vascular endothelial growth factor, and osteocalcin in cells. In vivo, statins have been shown to promote significant new bone growth when injected systemically or locally in combination with a scaffold. The potential anabolic effects of statins on bone make them attractive candidates to support bone regeneration. Since the molecular pathways by which statins induce osteoblast differentiation are still unclear, further investigations are required to elucidate the detailed cellular signaling mechanisms involved to determine the type of statin, optimal dose and mode of delivery to effectively utilize their anabolic effect. This also warrants the development of novel vehicles to locally deliver statins for the desired time periods to support optimal tissue regeneration in vivo.

A reward allocation method for reinforcement learning in stabilizing control tasks

Reinforcement learning is an area of machine learning that does not require detailed teaching signals by a human, which is expected to be applied to real robots. In its application to real robots, ...

The role of TGF-β1-miR-21-ROS pathway in bystander responses induced by irradiated non-small-cell lung cancer cells.

Background:Many studies have indicated an important implication of radiation-induced bystander effects (RIBEs) in cancer radiotherapy, but the detailed signalling remains unclear.Methods:The roles of tumour growth factor-beta1 (TGF-β1) and miR-21 in medium-mediated RIBEs in H1299 non-small-cell lung cancer cells were investigated using DNA damage, changes in proliferation and levels of reactive oxygen species (ROS) as end points. SB431542, a specific inhibitor of TGF-β type 1 receptor kinases, was used to inhibit TGF-β1 pathways in irradiated and bystander cells. Exogenous miR-21 regulation was achieved through inhibitor or mimic transfection.Results:Compared with relative sham-radiation-conditioned medium, radiation-conditioned medium (RCM) from irradiated cells 1 h post radiation (1-h RCM) caused an increase in ROS levels and DNA damage in bystander cells, while 18-h RCM induced cell cycle delay and proliferation inhibition. All these effects were eliminated by TGF-βR1 inhibition. One-hour RCM upregulated miR-21 expression in bystander cells, and miR-21 inhibitor abolished bystander oxidative stress and DNA damage. Eighteen-hour RCM downregulated miR-21 of bystander cells, and miR-21 mimic eliminated bystander proliferation inhibition. Furthermore, the dysregulation of miR-21 was attenuated by TGF-βR1 inhibition.Conclusions:The TGF-β1–miR-21–ROS pathway of bystander cells has an important mediating role in RIBEs in H1299 cells.

Research and Analysis of ECG Signal Feature Detection Based on Multi-Resolution Analysis and Baselines Wander

Electrocardiosignal feature extraction is the base of electrocard iologic automatic diagnosis. By using wavelet transform multi-resolution analysis, the noise in electrocard iosignal is removed; and by using proximity signals of wavelet transform the base linew ander is filtered. The high frequency noise is handled and eliminated with the default threshold; and the average value of the electrocardiosignals is set to zero. In detection of rpeak, because leak detection will occur when only 23 detail signals is considered, thus the 23 and 24 detail signals are integrated to avoid miss detection effectively. The methods avoiding error detection bring excellent effects. For calculating average cardiac electric axis, among the methods of area method, time voltage method and amplitude method, the area method offers the highest accuracy.

Dioscin induced activation of p38 MAPK and JNK via mitochondrial pathway in HL-60 cell line

Saponins have shown promise in cancer prevention and therapy; however, little is known about the detailed signaling pathways underlying their anticancer activities. In the present study, we examined the mechanisms of action of dioscin, a glucosides saponin isolated from Polygonatum zanlanscianense pump, in human myeloblast leukemia HL-60 cells. Dioscin suppressed HL-60 cell growth in a dose-dependent manner. This inhibition was due to the induction of apoptosis as revealed by the externalization of phosphatidylserine, and cleavages of lamin A/C and PARP-1. Treatment with dioscin induced apoptosis through activation of caspases 3, 7, 8, 9, and 10. Phosphorylation of p38 MAPK and JNK contributed to dioscin-induced apoptosis upstream of caspase activation. Using various inhibitors and antioxidant agents, we found that mitochondrial derived reactive oxygen species and depletion of mitochondrial transmembrane potential lead to the phosphorylation of p38 MAPK and JNK. Taken together, our results demonstrated that dioscin induces apoptosis by activation of p38 MAPK and JNK through the caspase-dependent mitochondrial death pathway. This work suggests that dioscin may be used as a drug lead for the treatment of myeloblast leukemia.

Neonatal maternal separation up-regulates protein signalling for cell survival in rat hypothalamus

We have previously reported that in response to early life stress, such as maternal hyperthyroidism and maternal separation (MS), the rat hypothalamic vasopressinergic system becomes up-regulated, showing enlarged nuclear volume and cell number, with stress hyperresponsivity and high anxiety during adulthood. The detailed signaling pathways involving cell death/survival, modified by adverse experiences in this developmental window remains unknown. Here, we report the effects of MS on cellular density and time-dependent fluctuations of the expression of pro- and anti-apoptotic factors during the development of the hypothalamus. Neonatal male rats were exposed to 3 h-daily MS from postnatal days 2 to 15 (PND 2–15). Cellular density was assessed in the hypothalamus at PND 21 using methylene blue staining, and neuronal nuclear specific protein and glial fibrillary acidic protein immunostaining at PND 36. Expression of factors related to apoptosis and cell survival in the hypothalamus was examined at PND 1, 3, 6, 9, 12, 15, 20 and 43 by Western blot. Rats subjected to MS exhibited greater cell-density and increased neuronal density in all hypothalamic regions assessed. The time course of protein expression in the postnatal brain showed: (1) decreased expression of active caspase 3; (2) increased Bcl-2/Bax ratio; (3) increased activation of ERK1/2, Akt and inactivation of Bad; PND 15 and PND 20 were the most prominent time-points. These data indicate that MS can induce hypothalamic structural reorganization by promoting survival, suppressing cell death pathways, increasing cellular density which may alter the contribution of these modified regions to homeostasis.

Application of Set Pair Analysis-Based Similarity Forecast Model and Wavelet Denoising for Runoff Forecasting

This study presents the application of a set pair analysis-based similarity forecast (SPA-SF) model and wavelet denoising to forecast annual runoff. The SPA-SF model was built from identical, discrepant and contrary viewpoints. The similarity between estimated and historical data can be obtained. The weighted average of the annual runoff values characterized by the highest connection coefficients was regarded as the predicted value of the estimated annual runoff. In addition, runoff time series were decomposed using wavelet transforms to acquire approximate and detailed runoff signals at various resolution levels. At each resolution level, threshold quantifications were performed by setting the values of a detailed signal below a fixed threshold to zero. The denoised runoff time series data were obtained from the approximation at the final resolution level and processed detailed signals using threshold quantification at all resolution levels of runoff by wavelet reconstruction. Instead of using the original annual runoff, the denoised annual runoff was applied to compute the similarity between estimated and historical data for model calibration. The original data were used for model calibration and validation; the denoised runoff data were used as input data to calibrate the model (obtaining different connection coefficients) that is then applied for validation purposes by using as benchmark the same original data. To verify the accuracy of the proposed method, the annual runoff data of six stations in Eastern Taiwan were analyzed. Based on a root mean square error (RMSE) criterion, the analytical results demonstrated that, for all six stations, the proposed method using denoised annual runoff outperformed the traditional SPA-SF model, using original annual runoff, because noise was effectively removed from the detailed data, using a constant threshold, thus enhancing the accuracy of the annual runoff forecasting for the SPA-SF model.

Wind Power Forecasting Based on Improved Biased Wavelet Neural Network

Wind power prediction is the key to grid-connected wind power system. In this paper, first of all, we decompose and reconstruct the power sequence by wavelet analysis, and reduce the noise of the detail signal, to obtain the strong-regularity subsequence. We adapt the biased wavelet neural network rolling forecast model for the processed sequence to obtain seven days of rolling forecast results through several amendments. For the sequence of 5 minutes interval the prediction accuracy is 98.63%, for the sequence of 15 minutes interval the prediction accuracy is 99.88%.

Interaction of the protease resistant proteins with the intestinal mucosa membrane proteins (1036.3)

Many studies have shown that some substances in soy, such as digested peptides or isoflavones from soy have effects on satiety control and nutrient absorption functions. However, it is still clearly not known how soy can control those functions. We have shown that some of the soluble soy proteins were resistant to proteases, such as pepsin and pancreatine, in the in vitro digestion analysis. We named these proteins as PRFPs (protease‐resistant food proteins), and postulated that PRFPs can interact with the mucosa membrane on the villi or microvilli in the small intestine to trigger the biological activities mentioned above. The interactor proteins on the intestinal membrane and soy proteins were identified by crosslinking and mass spectrometric analysis. The identified intestinal membrane proteins were mostly channel proteins and galectins. The soy partners to the membrane proteins were found to be glycinin and beta‐conglycinin, which are the majority proteins of soy. Phosphoproteomic changes in the intestinal cell line upon soy protein treatment were tested. After the soy protein treatment, phosphoproteins enriched from the cell extract by IMAC column were separated on 2D gels and compared for the up and down regulations. Interestingly, many of the proteins involved in protein folding or energy metabolism were significantly phosphorylated by soy protein treatment to the cell line.For further studies, detailed cell signaling pathway/mechanism by soy PRFPs treatment to the cells are under investigation in view of revealing the function of soy proteins in satiety, nutrient absorption, and others.

Advanced Eccentricity Fault Recognition in Permanent Magnet Synchronous Motors Using Stator Current Signature Analysis

In this paper, a novel index is introduced for static and dynamic eccentricity fault diagnosis in permanent magnet synchronous motors (PMSMs). The proposed index is a linear combination of the energy, shape factor, peak, head angle of the peak, area below the peak, gradient of the peak of the detail signals in wavelet decomposition, and coefficients of the autoregressive model, which are extracted from the stator current signature analysis. Principal component analysis is applied to the features as the linear transform for dimension reduction and elimination of linear dependence between the features. In order to demonstrate the capability of these indexes to estimate eccentricity type and degree, the fuzzy support vector machine is employed as a classifier. Classification of the results indicates that the nominated index can be utilized to detect eccentricity occurrence, recognize its type, and determine its degree precisely. Since extraction of efficient indexes closely depends on precise computation of necessary signals, the time-stepping finite element method is utilized to model the PMSM under eccentricity fault and calculate the stator currents as a proper signal for processing. Simulation results are verified by the experimental results.

Divergent signaling pathways cooperatively regulate TGFβ induction of cysteine-rich protein 2 in vascular smooth muscle cells

BackgroundVascular smooth muscle cells (VSMCs) of the arterial wall play a critical role in the development of occlusive vascular diseases. Cysteine-rich protein 2 (CRP2) is a VSMC-expressed LIM-only protein, which functionally limits VSMC migration and protects against pathological vascular remodeling. The multifunctional cytokine TGFβ has been implicated to play a role in the pathogenesis of atherosclerosis through numerous downstream signaling pathways. We showed previously that TGFβ upregulates CRP2 expression; however, the detailed signaling mechanisms remain unclear.ResultsTGFβ treatment of VSMCs activated both Smad2/3 and ATF2 phosphorylation. Individually knocking down Smad2/3 or ATF2 pathways with siRNA impaired the TGFβ induction of CRP2, indicating that both contribute to CRP2 expression. Inhibiting TβRI kinase activity by SB431542 or TβRI knockdown abolished Smad2/3 phosphorylation but did not alter ATF2 phosphorylation, indicating while Smad2/3 phosphorylation was TβRI-dependent ATF2 phosphorylation was independent of TβRI. Inhibiting Src kinase activity by SU6656 suppressed TGFβ-induced RhoA and ATF2 activation but not Smad2 phosphorylation. Blocking ROCK activity, the major downstream target of RhoA, abolished ATF2 phosphorylation and CRP2 induction but not Smad2 phosphorylation. Furthermore, JNK inhibition with SP600125 reduced TGFβ-induced ATF2 (but not Smad2) phosphorylation and CRP2 protein expression while ROCK inhibition blocked JNK activation. These results indicate that downstream of TβRII, Src family kinase-RhoA-ROCK-JNK signaling pathway mediates TβRI-independent ATF2 activation. Promoter analysis revealed that the TGFβ induction of CRP2 was mediated through the CRE and SBE promoter elements that were located in close proximity.ConclusionsOur results demonstrate that two signaling pathways downstream of TGFβ converge on the CRE and SBE sites of the Csrp2 promoter to cooperatively control CRP2 induction in VSMCs, which represents a previously unrecognized mechanism of VSMC gene induction by TGFβ.

Synthetic aperture radar image de-noising based on Shearlet transform using the context-based model

As the synthetic aperture radar (SAR) has been widely used in agriculture, forestry, hydrology, mining, marine, mapping and other fields, the method to improve the image quality and visual effect of the SAR image has become a hot research issue. The suppression and removal of the speckle of SAR image have become more and more important. This paper analyzes how the noises of the SAR image are generated and the models are appropriate for the characteristics of SAR images. Then based on the advantages of the Shearlet transform, we proposed an SAR image de-noising algorithm which combines the improved Shearlet transform with a cycle spinning de-noising algorithm by using an adaptive threshold method based on the context model. Simulation results show that the proposed algorithm can significantly suppress the speckle noise and improve the peak signal-to-noise ratio (PSNR) of the image; it also holds the characteristics of translational invariance (which can keep the edges of the image detail signal well and inhibit Gibbs phenomenon caused by noise reduction), and it can greatly improve the visual effect.

Short-term Prediction Method of Resources Occupancy for LBS Guidance System

Guidance system can improve the resources utilization of LBS (Location Based Service) system effectively, and the validity of guidance system depends on accurate predicting of the near-future system resources occupancy and its trend. Considering the strong randomicity of short-term characteristic of the trend, a multi-model fusion method integrating adaptive filter and ARMA (Auto Regressive Moving Average) model is proposed to predict the resources occupancy and trend accurately. With the method, observation series of recent resources occupancy is decomposed to different scales and reconstructed by wavelet transform firstly. According to the different features of series at different scale, for approximate signal, the adaptive filter algorithm is used to predict the trend of resources occupancy at coarse scale, and for detail signals in multiple fine scales, ARMA algorithms are adopted. Finally, integrating the prediction results of multi-model from different scales, the resources occupancy and trend with high prediction accuracy can be obtained. Experiment results show that the resources occupancy prediction accuracy of proposed method is higher than that of typical algorithms such as exponential smoothing and weighted Markov algorithms.

Applying Wavelet Decomposition Hydrological Time Series and the Optimization of Model Groups in Flood Forecasting

Applying Wavelet Decomposition Hydrological Time Series and the Optimization of Model Groups in Flood Forecasting This study applies a redundant wavelet transform (WT) and an optimization of model groups to accurately forecast the flood of a watershed. Effective rainfall and direct runoff can be decomposed into detailed signals and an approximation by using a redundant WT. The AutoRegressive model with exogenous Input (ARX), nonlinear ARX (NARX), time-varying ARX, and time-varying NARX models are implemented in parallel at each resolution level, and the optimal model is selected as the forecasting model. The summation of the forecasting results obtained at various resolution levels yields the overall flood forecasting by applying the inverse WT. The first-stage validation results indicate that the optimalforecasting model at each resolution level for six events is timevarying NARX. The second-stage validation results show that the proposed approach is appropriate for modeling the rainfall-runoff process at each resolution level and estimating the overall runoff for small watersheds in Taiwan. The analytic results also confirm that the proposed wavelet-based method outperforms the conventional method, which uses data only at the original resolution level, because of the multi-resolution analysis (MRA) property of the wavelet transform.

Classification of Faults on Transmission lines using EMTP and Wavelet Multiresolution Analysis

This paper presents a technique for classification of faults on single circuit transmission line using Wavelet Multiresolution Analysis.EMTP (Microtran) is used to perform simulations which generate time domain input signal on a 400KV, 300km transmission line fed from one end. Daubechies eight (D-8)wavelet transforms of the three phase currents on a transmission line fed from one end are used for fault analysis in this work. The summation of the 1 level output of MRA detail signals of current in each phase extracted from the original signals generated are employed in fault classification algorithm. Simulation results show that the proposed method is effective and simple and give accurate results irrespective of fault location, fault inception angle and fault impedance.

A network biology approach to discover the molecular biomarker associated with hepatocellular carcinoma.

In recent years, high throughput technologies such as microarray platform have provided a new avenue for hepatocellular carcinoma (HCC) investigation. Traditionally, gene sets enrichment analysis of survival related genes is commonly used to reveal the underlying functional mechanisms. However, this approach usually produces too many candidate genes and cannot discover detailed signaling transduction cascades, which greatly limits their clinical application such as biomarker development. In this study, we have proposed a network biology approach to discover novel biomarkers from multidimensional omics data. This approach effectively combines clinical survival data with topological characteristics of human protein interaction networks and patients expression profiling data. It can produce novel network based biomarkers together with biological understanding of molecular mechanism. We have analyzed eighty HCC expression profiling arrays and identified that extracellular matrix and programmed cell death are the main themes related to HCC progression. Compared with traditional enrichment analysis, this approach can provide concrete and testable hypothesis on functional mechanism. Furthermore, the identified subnetworks can potentially be used as suitable targets for therapeutic intervention in HCC.

Automatic seizure detection using diffusion distance and BLDA in intracranial EEG

Approximately 1% of the world's population suffers from epilepsy. An automatic seizure detection system is of great significance in the monitoring and diagnosis of epilepsy. In this paper, a novel method is proposed for automatic seizure detection in intracranial EEG recordings. The EEG recordings are divided into 4-s epochs, and then wavelet decomposition with five scales is performed to the EEG epochs. Detail signals at scales 3, 4, and 5 are selected to form a signal distribution. The diffusion distances are extracted as features, and Bayesian linear discriminant analysis (BLDA) is used as the classifier. A total of 193.75h of intracranial EEG recordings from 21 patients having 87 seizures are employed to evaluate the system, and the average sensitivity of 94.99%, specificity of 98.74%, and false-detection rate of 0.24/h are achieved. The seizure detection system based on diffusion distance yields a high sensitivity as well as a low false-detection rate for long-term EEG recordings.

Early PK-Analysis Predicts Molecular Response In Patients With Early Chronic Phase Chronic Myelogenous Leukemia (CML-CP) Treated With Frontline Nilotinib

IntroductionTyrosine kinase inhibitor (TKI) trough plasma level testing and pharmacodynamic evaluation (i.e. quantification of phosphorylated BCR-ABL downstream targets) may help to optimize patient-tailored dosing in CML-CP. For 2nd generation TKIs the clinical value of TKI plasma level and protein phosphorylation quantification remains unclear. The ENEST1st study (NCT01061177) is focused on examining the role of first-line nilotinib therapy in CML-CP. This ENEST1st substudy addresses the value of plasma-level testing and protein single cell phosphorylation pharmacodynamics as predictors of response and long-term outcomes. MethodsNilotinib levels were centrally quantified by means of mass spectrometry (HPLC-MS/MS) in 54 patients at 3 hours after first drug intake at day (d) 1 (Cmax) and at d7, d28 as well as at months (mo) 3, 6, 12 prior to morning drug intake (Cmin=trough level). Single cell quantification of intracellular protein phosphorylation was performed by flow cytometry (phosphoflow) in the myelocyte cell population after full blood fixation/cell permeabilization/erythrocyte lysis and analyzed 3 hours after first drug intake at d1 and on d7 and d28. Primary endpoint of the study was MR4 (BCR-ABL <0.01% IS) at 18 months. ResultsOur initial analysis focuses on correlation of drug levels with molecular response by correlating PK data to early molecular response, i.e. ≤10% BCR-ABL (IS) reduction at 3 mo as well as 0.1% IS (MMR), MR4 and MR5. At 3, 6, 12 and 18 mo, 24%, 56%, 74% and 77 % of the here investigated patients achieved MMR; 2%, 22%, 34% and 44% achieved MR4 and MR5 was detected in 0%, 5%, 15% and 14% of patients at the respective time points. Only 1 patient at 3mo had >IS 10% BCR-ABL. On d1 at 3 hours after intake of the first tablet the median peak plasma levels reached 408 ng/ml increasing to a median trough level of 834 ng/ml at d7, 888 ng/ml at d28, 919 ng/ml at 3mo, 1122 ng/ml at 6m and 1003 ng/ml at 12mo. Initial Cmax-levels at d1 significantly correlated with steady state levels thereafter (e.g. with trough levels at 12mo: r=0.56, p=0.004). Three mo drug levels were significantly negatively associated with lower BCR-ABL mRNA burden at 12m and 24m (r=-0.44, p=0.011 for m12 and r=-0.35, p=0.014 for m24). When median steady state drug levels were compared in different response categories (yes/no: MR4, MR5, MMR, >IS 10% at 3mo), significantly higher nilotinib trough levels at day 90 and mean trough level (day 28-180) were seen in patients achieving the primary endpoint of the study (MR4 at 18mo). Similarly, 3mo and mean through levels were also significantly higher in patients achieving MR5 at 12 or 18mo. As only one patient did not reach <10% at 3mo, we cannot evaluate if early PK data are able to predict this endpoint. The probability of achieving MR5 at 12mo and 24mo was significantly higher in the top third of the day 90 PK level (cumulative incidence of MR5 in the respective PK groups at 12mo: 5.6% vs. 5.6% vs. 44.4% and at 24mo: 5.6% vs. 22% vs. 44.4%). Cumulative incidence of MR4 was faster in the higher PK group (6mo: 6% vs. 18% vs. 42%; 12mo: 22% vs. 29% vs. 57% and at 24mo: 46% vs. 67% vs. 58%), which however did not reach statistical significance. Phosphoflow of myelocytes revealed that compared to baseline, phosphorylation of almost all investigated proteins decreases over time, including significant reduction of phospho-Gab2(Y452), Abl(Y245), STAT5(Y694), Erk1/2(T202/Y204), and p38(T108/Y182) at d28. Correlation of plasma levels with changes in phosphorylation status revealed a negative correlation of increasing drug levels with decreasing phospho-p38 and phospho-STAT5 status. A more detailed signaling analysis of cellular subsets related to molecular response criteria are in progress. ConclusionsSingle cell myelocyte phosphoprotein detection allowed monitoring of the BCR-ABL signalling network. Inter-individual plasma levels show a relatively wide variability and Cmax levels upon first drug exposure correlate with later trough levels pointing out to a potential individual pharmacogenetic background regulating drug availability. PK-based optimizing of nilotinib-dosing may help to further improve response depth (i.e. reaching MR4 or MR5), which is assumed to be a prerequisite for treatment discontinuation. Disclosures:Wolf:Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding. Mustjoki:Novartis: Honoraria, Research Funding. Greil:Novartis: Honoraria, Research Funding. Hjorth-Hansen:Pfizer, BMS: Honoraria, Travel expenses Other. Verhoef:Novartis: Research Funding. Mark:Novartis: Employment. Haenig:Novartis: Employment. Jurjonas:Novartis: Employment. Giles:Novartis: Consultancy, Research Funding. Hochhaus:Novartis: Research Funding. Porkka:Novartis: Honoraria, Research Funding. Ossenkoppele:Novartis: Consultancy, Research Funding; BMS: Consultancy. Gjertsen:Novartis: Honoraria, Research Funding.