TGF beta type II receptor signaling controls Schwann cell death and proliferation in developing nerves

During development, Schwann cell numbers are precisely adjusted to match the number of axons. It is essentially unknown which growth factors or receptors carry out this important control in vivo. Here, we tested whether the type II transforming growth factor (TGF)beta receptor has a role in this process. We generated a conditional knock-out mouse in which the type II TGF beta receptor is specifically ablated only in Schwann cells. Inactivation of the receptor, evident at least from embryonic day 18, resulted in suppressed Schwann cell death in normally developing and injured nerves. Notably, the mutants also showed a strong reduction in Schwann cell proliferation. Consequently, Schwann cell numbers in wild-type and mutant nerves remained similar. Lack of TGF beta signaling did not appear to affect other processes in which TGF beta had been implicated previously, including myelination and response of adult nerves to injury. This is the first in vivo evidence for a growth factor receptor involved in promoting Schwann cell division during development and the first genetic evidence for a receptor that controls normal developmental Schwann cell death

Hybrid dynamical model for reluctance actuators including saturation, hysteresis and eddy currents

A novel hybrid dynamical model for single-coil, short-stroke reluctance actuators is presented in this paper. The model, which is partially based on the principles of magnetic equivalent circuits, includes the magnetic phenomena of hysteresis and saturation by means of the generalized Preisach model. In addition, the eddy currents induced in the iron core are also considered, and the flux fringing effect in the air is incorporated by using results from finite element simulations. An explicit solution of the dynamics without need of inverting the Preisach model is derived, and the hybrid automaton that results from combining the electromagnetic and motion equations is presented and discussed. Finally, an identification method to determine the model parameters is proposed and experimentally illustrated on a real actuator. The results are presented and the advantages of our modeling method are emphasized

Integrated local control of active power and voltage support for three-phase three-wire converters

The derivation of a robust control algorithm is presented to provide decoupled active power regulation and local grid voltage support in three-phase three-wire grid-connected converters (GCCs). Unlike conventional control schemes, the proposed strategy is designed to be harmonic sequence asym-metric for the purpose of local voltage unbalance correction. A frequency-domain Norton equivalent model is derived to illustrate the working principle of the strategy. Accordingly, by following a frequency-domain decoupled method, the funda-mental positive-sequence, the harmonic symmetrical sequences and the fundamental negative-sequence components are regu-lated independently. Consistent to the model analysis, simulation results validate reduction of local voltage unbalance and total harmonic distortion. Since no external sensors are required for the implementation of the strategy, it is a local approach, applicable to already-existing GCC systems. Moreover, in view of the higher switching frequencies as attainable by devices from the next SiC generation, the accuracy and dynamic behavior of the control algorithms can be much enhanced, improving therefore the quality of the processed energy

Square wave operation to reduce pulsating power in isolated MMC-based ultrafast chargers

This paper presents an application of modular multilevel converters to reduce pulsating power, and therefore sub-modules in ultrafast chargers. The converter’s analysis and a control scheme were presented to realize bidirectional power transfer between a three-phase medium-voltage grid and a single-phase medium-frequency transformer with square-shaped voltage, successfully reducing power fluctuation

Extended operating region of modular multilevel converters using full-bridge sub-modules

This paper presents an application of modular multilevel converters to remove line-frequency transformers from ultrafast charging stations, reducing cost and volume. The converter analysis with full-bridge sub-modules enables an operating region, that converts a medium-voltage grid into a lower voltage DC-bus, ideal for charging batteries rapidly

Meta-analysis approach as a gene selection method in class prediction: Does it improve model performance? A case study in acute myeloid leukemia

Background: Aggregating gene expression data across experiments via meta-analysis is expected to increase the precision of the effect estimates and to increase the statistical power to detect a certain fold change. This study evaluates the potential benefit of using a meta-analysis approach as a gene selection method prior to predictive modeling in gene expression data. Results: Six raw datasets from different gene expression experiments in acute myeloid leukemia (AML) and 11 different classification methods were used to build classification models to classify samples as either AML or healthy control. First, the classification models were trained on gene expression data from single experiments using conventional supervised variable selection and externally validated with the other five gene expression datasets (referred to as the individual-classification approach). Next, gene selection was performed through meta-analysis on four datasets, and predictive models were trained with the selected genes on the fifth dataset and validated on the sixth dataset. For some datasets, gene selection through meta-analysis helped classification models to achieve higher performance as compared to predictive modeling based on a single dataset; but for others, there was no major improvement. Synthetic datasets were generated from nine simulation scenarios. The effect of sample size, fold change and pairwise correlation between differentially expressed (DE) genes on the difference between MA- and individual-classification model was evaluated. The fold change and pairwise correlation significantly contributed to the difference in performance between the two methods. The gene selection via meta-analysis approach was more effective when it was conducted using a set of data with low fold change and high pairwise correlation on the DE genes. Conclusion: Gene selection through meta-analysis on previously published studies potentially improves the performance of a predictive model on a given gene expression data

Pragmatic rules for comparability of biological medicinal products

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Factors affecting the accuracy of a class prediction model in gene expression data

Background: Class prediction models have been shown to have varying performances in clinical gene expression datasets. Previous evaluation studies, mostly done in the field of cancer, showed that the accuracy of class prediction models differs from dataset to dataset and depends on the type of classification function. While a substantial amount of information is known about the characteristics of classification functions, little has been done to determine which characteristics of gene expression data have impact on the performance of a classifier. This study aims to empirically identify data characteristics that affect the predictive accuracy of classification models, outside of the field of cancer. Results: Datasets from twenty five studies meeting predefined inclusion and exclusion criteria were downloaded. Nine classification functions were chosen, falling within the categories: discriminant analyses or Bayes classifiers, tree based, regularization and shrinkage and nearest neighbors methods. Consequently, nine class prediction models were built for each dataset using the same procedure and their performances were evaluated by calculating their accuracies. The characteristics of each experiment were recorded, (i.e., observed disease, medical question, tissue/cell types and sample size) together with characteristics of the gene expression data, namely the number of differentially expressed genes, the fold changes and the within-class correlations. Their effects on the accuracy of a class prediction model were statistically assessed by random effects logistic regression. The number of differentially expressed genes and the average fold change had significant impact on the accuracy of a classification model and gave individual explained-variation in prediction accuracy of up to 72% and 57%, respectively. Multivariable random effects logistic regression with forward selection yielded the two aforementioned study factors and the within class correlation as factors affecting the accuracy of classification functions, explaining 91.5% of the between study variation. Conclusions: We evaluated study- and data-related factors that might explain the varying performances of classification functions in non-cancerous datasets. Our results showed that the number of differentially expressed genes, the fold change, and the correlation in gene expression data significantly affect the accuracy of class prediction models

European regulatory agenices should employ full time statisticians

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