Parallel high-throughput microarray and sequencing experiments produce vast quantities of multidimensional data which must be arranged and analyzed in a concerted way. One approach to addressing this challenge is the machine learning technique known as self organizing maps (SOMs). SOMs enable a parallel sample- and gene-centered view of genomic data combined with strong visualization and second-level analysis capabilities. The paper aims at bridging the gap between the potency of SOM-machine learning to reduce dimension of high-dimensional data on one hand and practical applications with special emphasis on gene expression analysis on the other hand.The method was applied to generate a SOM characterizing the whole genome expression profiles of 67 healthy human tissues selected from ten tissue categories . SOM mapping reduces the dimension of expression data from ten of thousands of genes to a few thousand metagenes, each representing a minicluster of co-regulated single genes. Tissue-specific and common properties shared between groups of tissues emerge as a handful of localized spots in the tissue maps collecting groups of co-regulated and co-expressed metagenes. The functional context of the spots was discovered using overrepresentation analysis with respect to pre-defined gene sets of known functional impact. We found that tissue related spots typically contain enriched populations of genes related to specific molecular processes in the respective tissue. Analysis techniques normally used at the gene-level such as two-way hierarchical clustering are better represented and provide better signal-to-noise ratios if applied to the metagenes. Metagene-based clustering analyses aggregate the tissues broadly into three clusters containing nervous, immune system and the remaining tissues.The SOM technique provides a more intuitive and informative global view of the behavior of a few well-defined modules of correlated and differentially expressed genes than the separate discovery of the expression levels of hundreds or thousands of individual genes. The program is available as R-package 'oposSOM'. These resources pose a challenge how to best arrange and to visualize the huge heaps of data in a fashion that enables combination of sample- and gene-centered views on multidimensional expression data to capture the global picture of groups of samples while simultaneously presenting the specific expression pattern within each individual sample.DNA microarray and next generation sequencing technologies allow researchers to screen ten thousands of genes for differences in expression between up to hundreds of individuals or experimental conditions of interest. Not only the progressively increasing data throughput of newest array and sequencing technologies challenges data analysis methods but also the increasing availability of large data sets from public data repositories such as gene expression omnibus (Self-organizing map (SOM) machine learning was developed by Kohonen about thirty years ago . It projIt has been demonstrated that such SOM displays are featured by several important benefits -9: (i) tSOM-based gene expression analyses have been applied, for example, in studies on cell differentiation and development -12, orgaDespite its convincing advantages the SOM method is relatively infrequently applied to high-dimensional molecular data compared with alternative approaches such as hierarchical clustering. Possibly, interpretation of SOM mosaic patterns is less trivial and/or unusual for many researchers because it requires basic understanding of details of the method such as the way how expression of real genes transforms into expression profiles of the metagenes. The lack of availability of this information presumably hampers application of SOM-based methods in a wider number of applications. Moreover, standard analysis tasks such as feature selection, significance analysis of differential expression and functional gene set enrichment analysis require the availability of appropriate algorithms and of suited program tools to generate the desired information. Such approaches must consider the specifics of gene expression analysis  on one hand, but also the specifics of SOM-machine learning on the other hand.We strongly advocate in favor of the SOM method. The present publication aims at bridging the gap between the potency of SOM-machine learning to reduce dimension of high-dimensional data on one hand and its availability with special emphasis on gene expression analysis on the other hand. Our approach includes a series of analytical reports which might support interpretation of SOM metagene data (see below) and an available R-program package. Here we focus on the identification and functional interpretation of metagene clusters using gene set overrepresentation analysis, a novel aspect in the context of SOM analysis, and on the comparison of data analysis based on single and on metagenes.We apply our approach to expression data of human tissues which is well suited as an illustrative example: The selected 67 tissues provide a sufficient large data set of highly diverse expression pattern possessing a complex internal covariance structure. Moreover, the samples are well classified in terms of distinct tissues and tissue categories allowing the clear assignment of expression pattern. The discovery of this human body index data set is also motivated by the argument that tissue-specific RNA expression pattern indicate important clues to the physiological function of the coding genes, suitable as a reference for comparison with diseased tissues. Our analysis thus provides a first step towards a SOM atlas of gene activity in normal human tissues which complements previous work on this objective -18.We address selected methodical aspects of the SOM method which aim at extracting functional information about the expression pattern: Firstly, we complement the gallery of primary SOM images with a number of summary maps characterizing the covariance structure of the data after transformation into latent variables. These summary maps allow extraction of so-called spot-clusters which collect co-expressed metagenes together. This spot-clustering enables to significantly reduce the dimensionality of expression data to a handful of representative expression-modules in an unsupervised fashion. The results of SOM-clustering are compared with the results of alternative methods such as non-negative matrix factorization, hierarchical clustering and sets of correlated genes. Secondly, the detected spot-clusters are linked with biological knowledge to support functional interpretation of the data based on the 'guilt by association' principle. Particularly, we apply gene set overrepresentation analysis to visualization space which is a novel approach to our best knowledge. Thirdly, we analyze the capability of the SOM approach for data filtering and dimension reduction in terms of maintaining representativeness and reduction of noisiness of the data. Finally, we applied SOM analysis in a zoom-in step to a subensemble of tissues to increase the resolution of the method. We use the sample-centered second-level SOM representation to visualize similarity relations between the different tissues and compare the results with independent component analysis.The main paper is supplemented with additional files which provide the full gallery of SOM-images of human tissues, a detailed methodical section addressing issues such as calibration of microarray raw intensity data to minimize possible artifacts due to systematic biases caused by improper preprocessing , the conMicroarray expression data taken from the human body tissue index data set were input into the SOM machine learning algorithm after calibration and normalization of the raw probe intensities as described in the Methods section below and in Additional file The tissues are grouped into ten categories in accordance with the classification used in Hornshoj et al. . Most ofSome tissues combine the characteristic spot pattern of different tissue categories  predominantly group along the edges of the map whereas only a few highly populated tiles are found in its central area. A zone of 'empty' metagenes lacking real genes  clusters in four regions halfway between the centre and the edges of the map. The tile of maximum population  refers to genes with virtually invariant, mostly absent specific expression in all tissues studied  distribution. It provides an overrepresentation p-value for each metagene and each gene set considered. We visualize the distribution of p-values of each gene sets using the same two-dimensional mosaic as used for the original SOM images and appropriate color-coding. The obtained overrepresentation maps allow identification of metagenes containing an enriched fraction of genes from a selected gene set by visual inspection. Note that this map applies to all samples studied because each of the mini-clusters contains the same genes in all samples used to train the SOM. The overrepresentation map thus reflects the global enrichment pattern of a chosen set of genes in the experimental series studied.Figure Overrepresentation analysis is not restricted to single tiles but it can also be applied to the over- and underexpression spots detected in the previous subsection. Accordingly, overrepresentation of selected gene sets can be linked with additional properties of the expression profiles such as overexpression by combining spot selection with overrepresentation analysis. Particularly, the genes associated with each spot are analyzed for overrepresentation of genes taken from the collection of 1454 gene sets downloaded from the GSEA-homepage according to the GO-categories molecular function, biological process and cellular component (see methods section). The hypergeometrical distribution then provides an ordered list of gene sets ranked with decreasing significance of overrepresentation for each of the spots.Figure The heatmap in Figure It is noteworthy that the enriched areas in the overrepresentation maps of the gene sets 'nervous system development' and 'immune response' , then one ranks the data accordingly and finally selects a certain number of features on top of the list for further analysis. The length of the list can be cut by applying different criteria such as a fixed number of features or a significance threshold.SOM analysis enables alternative filtering based on the metagenes as representative features characterizing the expression profiles of miniclusters of single genes. In other words, the metagene profiles itself can serve as a filtered and compressed extract of the original data. Our SOM-method assigns the expression profiles of the N = 22,277 input genes measured in 67 tissues to 3,600 metagene clusters. Each metagene cluster consequently contains G/M = <nk > = 6.2 real genes on the average. Hence, complexity of transcriptome characterization is reduced to about one sixth by utilizing the metagenes instead of the 'real' genes.k = 0 (empty metagenes) and nk = 308  clusters genes of virtually invariant expression profiles. These essentially not-informative features comprise 1.4%  of all single genes but only 0.3%  of all metagenes. Hence, their contribution is effectively down-scaled by nearly a factor of ~1/5 if one uses the metagenes instead of real genes. In other words, SOM clustering itself can be viewed as a sort of selective compression filter reducing the number of features considered by condensing larger numbers of similar single gene profiles into one metagene profile with a profile-specific compression factor, Fkcompression = (nk⋅K/N)-1 .Moreover, the local G/M-ratio considerably varies between the different metagene clusters with minimum and maximum values of nMetagene filtering is expected to outperform single gene filtering in terms of representativeness and noisiness because the reduced number of metagenes not only preserves the diversity of single gene profiles but it also amends the resolution of downstream analysis due to the reduced noise of the metagene profiles. With the objective of proving this expectation we compare two options for data filtering by applying top-list selection either to the metagenes or to the single 'real' genes. We used three types of filters to reduce the number of single genes and metagenes, namely fold change (FC)-expression, variance and significance (FDR-) filtering  using either the expression values of a list of real genes or of a list of metagenes of selected lengths. Hierarchical cluster analysis was applied because this method is often routinely run as a first step of data summary in microarray data analysis .One way hierarchical cluster trees obtained from single gene and metagene FC-lists of length 3600, 1000 and 100 reflect similar properties showing that clustering is relatively robust with respect to the chosen conditions Figure . TissuesThe blue immune system tissue cluster splits for both, the single gene and metagene filters in the opposite limit of low stringency using FC-3600 lists. These lists obviously become too long with worse noisiness characteristics. Note, that the FC-3600 metagene list considers all available metagenes whereas the FC-3600 single gene list is still limited to only 16% of all available single genes. Longer single gene lists reduce the quality of the observed cluster structure due to the progressive inclusion of noisy genes (data not shown). In summary, metagene lists are more representative and less noisy than single gene lists of equal length in downstream cluster analysis. On the other hand, also the length of metagene lists is optimal in the intermediate range : shorter and longer lists are suboptimal in terms of representativeness and noisiness, respectively.The cluster trees generated on the basis of single gene and metagene lists reveal another interesting difference  provides still reasonably resolved clusters in the ICA-plot. In conclusion, metagenes are more robust with respect to the quality of secondary analysis than single gene lists owing to their better representativeness. Hence, the reduction of dimensionality provided by SOM analysis improves the performance of downstream hierarchical clustering and ICA analysis. The number of considered features can be reduced by about one order of magnitude without loss of information if one uses metagenes instead of real genes. Clustering and ICA characteristics obtained for the metagene and single gene lists after variance and FDR filtering virtually agree with the results of FC-filtering  illustrating Pearson correlation coefficients for all mutual combinations between the tissues. The PCM-heatmaps shown in Figure The expression profiles of nervous tissues strongly anti-correlate with essentially all the other tissue categories, i.e. a gene overexpressed in nervous tissues usually becomes underexpressed in non-nervous tissues and vice versa. The original expression SOM always reflect this property showing one characteristic overexpression spot in the top left corner , immune system tissues (blue) and the remaining ones. The substructures of the three groups were further disentangled by applying the zoom-in step for each of the three tissue clusters as described above.Guo et al. proposed a second-level SOM analysis step . It mapsnd level SOM provide a similar view on the samples, however with subtle differences. For example, the ICA algorithm distributes the sample points continuously in the coordinate system spanned by the two leading principal components of maximum information content. The mutual separation between the points linearly scales with their distance in units of these components. In contrast, SOM machine learning uses non-linear scale to distribute the sample points in the discrete space defined by the mosaic grid of metasamples. It enables to display differences between the samples with improved resolution in regions of high sample density. In consequence, the individual tissues effectively spread over a larger area in the SOM mosaics than in the respective ICA.In general, ICA and 2As noticed above, most of the samples group into linear clusters which orient along one of the coordinate axes in the two dimensional ICA plots. The orthogonal orientation of most of these clusters indicates that each of them is characterized by genes which vary mutually independently. For example, nervous and immune systems tissues aggregate into such linear and perpendicularly-oriented clusters in the original ICA of all 67 tissues. Note that nervous and immune systems tissues are characterized by their specific spots A and F, respectively. Recall that these spots contain genes which indeed vary virtually independently.Similar orthogonal clusters are found in the ICA plots after zoom-in of the nervous, immune system and diverse tissues. The obtained clusters reveal groups of tissues which are governed by independent sets of genes with enhanced resolution. For example one finds that telencephalon tissues (dark yellow circles in the ICA of nervous tissues) form one linear cluster which can be attributed to a category-specific spot in the zoom-in SOM images . Hence, although very similar, 2nd level SOM and ICA visualize partly complementary aspects of the data which can be studied more in detail using the spot-texture of the individual SOM of the samples studied. Tree-based similarity analysis provides an additional option to visualize the mutual relations between the samples  transform into a slightly more compact cluster in the 2The microarray expression data of 67 human tissues was used as an illustrative example to demonstrate the strengths of the SOM method in disentangling large sets of heterogeneous data. After suited preprocessing and training, the SOM method decomposes the original data into metagene expression profiles representing clusters of correlated single genes. Metagene expression values in the individual samples provide mosaic pictures visualizing tissue-specific over- and underexpression in terms of characteristic color-coded textures. They enable the direct comparison of the expression of individual samples in a simple and intuitive way.Particularly, the tissue-specific patterns of gene expression were readily discernable in the obtained gallery of individual tissue maps. They reveal a series of about one handful stable over- and underexpression spots which selectively characterize different tissue categories such as nervous, immune system, muscle, exocrine, epithelial or adipose tissues. Single tissues of mixed characteristics such as tongue  can be easily identified. Also anti-correlated expression spots are detected which, for example, are overexpressed in nervous tissues but underexpressed in the other tissues and vice versa.To extract the functional context of spot and metagene related lists of single genes we applied overrepresentation analysis with respect to pre-defined gene sets of basically known functional impact. The mapping of overrepresentation of a selected gene set into the SOM mosaic provides a 'functional' map showing areas which are potentially relevant for this function. Tissue related spots typically contain enriched populations of function-related gene sets well corresponding to molecular processes in the respective tissues. This result strongly supports the 'guilt-by-association' principle that coexpressed genes are likely to be functionally associated. It, in turn, implies the ability to define either new gene sets using selected SOM spots or to verify and/or to amend existing ones.The SOM method compresses the original set of high-dimensional data in two consecutive steps: Firstly, similar expression profiles of single genes are collected into metagene clusters, which reduces the number of relevant features nearly by one order of magnitude in our application. These metagene profiles can be understood as a sort of 'eigen-modes' characterizing the multitude of expression pattern inherent in the data. Secondly, the textures of the obtained SOM are decomposed into a few  spots of similarly (over- or under-) expressed metagenes. This 'double compression' sequentially applies global (similar profiles) and local (over-/underexpression in part of the samples) criteria.The use of metagene instead of single gene expression reduces the dimension of the data and leads to an increased discriminating power in downstream agglomerative analysis such as hierarchical clustering and independent component analysis owing to essentially two facts: Firstly, the set of metagenes better represents the diversity of expression pattern inherent in the data and secondly, it also possesses the better signal-to-noise characteristics as a comparable collection of single genes. Due to the better representativeness, metagene lists are less sensitive to downstream filtering than lists of single genes. Metagenes can be seen as a natural choice to detect context-dependent patterns of gene expression in complex data sets. SOM-spot clustering provides groups of genes of higher sample-specificity compared with selected alternative methods such as non-negative matrix factorization, hierarchical clustering and correlated gene set clustering.Our example shows that SOM cartography transforms large and heterogeneous sets of expression data into an atlas of sample-specific texture maps which can be directly compared in terms of similarities and dissimilarities. This global view on the behavior of defined modules of correlated and differentially expressed genes is more intuitive than ranked lists of hundreds or thousands of individual genes. Importantly, the dimension reduction of the data does not entail the loss of primary information in contrast to simple filtering approaches which irretrievably removes part of the data. Instead, the reduction of dimension is attained by the re-weighting of primary information in the aggregation step. The whole set of single gene expression profiles remains virtually 'hidden' behind the metagenes. This primary information together with the respective gene annotations can be extracted in later steps of analysis to interpret the observed SOM textures using concepts of molecular biological function.Finally, the software used in this publication is available as CRAN package 'oposSOM'.m = 1, 2... (m = 1...M) replicates were downloaded from the Gene Expression Omnibus repository as the 'human body index - transcriptional profiling' - data set  of M = 67 tissues each measured in RRaw probe intensities are calibrated and transformed into expression value using the hook method ,34. The 10 E) were transformed into differential expression values relative to the mean expression of the particular gene in the experimental series of tissues considered,Logged expression values of each gene (e ≡ logEq. (1) thus defines differential expression in units of the logged fold change, logFC ≡ Δe.In the next step, the preprocessed differential expression values of the series of tissue samples, Δe, are processed using the unsupervised machine learning method to train a self organizing map (SOM) representing information-rich diagrams. The SOM method applies a neural network algorithm to project high dimensional data onto a two-dimensional visualization space ,36. SOMskmeta =  (k = 1...K). It is trained such that the profiles of the metagenes capture the range of all individual expression pattern observed. Each individual expression profile of a 'real' gene is assigned to the metagene pattern of closest similarity using the minimum Euclidian distance as criterion. Each metagene thus serves as a sort of condensation nucleus for a minicluster of nk 'real' genes with similar expression profiles, Δek,i = , with i = 1...nk and We applied a home-made R-program  which usk,mmeta. This way one obtains a coherent mosaic pattern that is characteristic for each sample owing to the similarity of adjacent metagenes. Since the SOMs assign the same metagene to the same tile in all samples, they can be directly compared to each other allowing immediate identification of biologically interesting groups of genes.The metagenes are arranged in a two-dimensional grid with K = x⋅y tiles where x and y are the number of tiles per dimension. Most similar expression profiles of metagenes are located adjacent each to another. The correlation between metagene expression decreases with the mutual distance between the tiles on the mosaic. The degree of similarity between adjacent metagenes depends on the number of genes assigned to the respective metagenes being closer for larger populated metagenes and vice versa. For each condition m = 1...M a SOM mosaic pattern is constructed by color-coding the tiles k = 1...K according to its metagene expression, Δe2 - 104 with, on the average, nk = 5 - 100 genes per metagene. The obtained mosaic pattern is usually more homogeneous than typical gene clustering heatmaps containing typically about 102 clusters. This finer granularity of SOM-maps is associated with a fewer number of genes per unit (cluster/metagene) which in consequence gives rise to a more detailed expression pattern.Typically, the number of tiles to 'pixelate' the expression profiles is K = 10 × 10 - 100 × 100 = 10The number of tiles per SOM image and also the lattice-type  potentially affects the obtained cluster structure and color texture of the images. In a preliminary study we found that the number of tissue-specific 'spots' converges for x = y > 50 and weakly depends on the chosen lattice type. Under these conditions the number of tiles exceeds the number of relevant expression modules roughly by two-orders of magnitude which allows their resolution with high granularity. The contrast of the SOM images can be adjusted using different color-scales to attenuate different aspects of the expression profiles with the aid of pattern recognition, feature selection and/or data filtering. In the supplementary information we compare three options of contrast variation with the focus on strong-to-moderate differential expression , very strong overexpression  or weak-to-moderate differential expression .http://som.izbi.uni-leipzig.de. The program is also available as CRAN package via http://cran.r-project.org/.Details of these methodical studies are presented in Additional file We define the following supporting maps which provide additional information about the miniclusters defined by each metagene and the associated real genes:(i) The metagene expression profiling map uses a coarse grained mosaic to provide an overview of the courses of the metagene profiles. For visualization purposes we use a coarse grained  mosaic with considerably less tiles than the mosaic grid applied for the SOMs (60 × 60). The metagene profiles might be plotted together with the associated single gene profiles.k.(ii) The population map plots the number of real genes per metagene in logarithmic scale, log n(iii) The variance map illustrates the variability of the expression profile of each metagene in the samples studied,(iv) The integral over-/under-expression summary maps collect all over-/underexpression spots observed in the individual sample SOMs into one master map.An extended set of supporting maps visualizing the covariance and the Euclidian distance between the genes and metagenes in each tile, the maxima and minima of the metagene profiles in absolute scale and the correlations between the metagenes are given as supporting information . Particularly, we included in total 1454 gene sets in our analysis according to the GO terms 'biological process' (825 sets), 'molecular function' (396 sets) and 'cellular component' (233 sets). We use the term 'overrepresentation' to assign the probability to find members of a given set in a list compared with their random appearance independent of the values of their expression scores. We use the hypergeometric distribution to characterize overrepresentation in terms of a p-value which estimates the probability to find a stronger overlap between the list and the set by chance [Gene set analysis requires the knowledge of predefined gene sets to study their enrichment in gene lists which are obtained from independent differential expression analysis  FC-filtering: the genes/metagenes are ranked with decreasing absolute value of the fold change (FC) for each sample and a certain number  of the top-most features is selected; (ii) Variance filtering: the genes/metagenes are ranked with decreasing variance of their expression profiles and a certain number of top-most features is selected; (iii) FDR-filtering: only genes/metagenes with a local false discovery rate (FDR) smaller than a certain threshold  were selected. The local FDR estimates the probability of false positives in a list genes/metagenes. We used a shrinkage t-score statistics to assign p-values to each single gene the distribution of which then provides its FDR-values. The FDR of the metagenes is simply calculated as log-average of the single gene FDR of the respective metagene cluster. Details of the method will be published separately .The performance of metagene and single gene filters was compared using the following benchmarks  was used to estimate the quality of the clusters.Two-way hierarchical clustering: The percentage of genes/metagenes attributed to tissue-specific clusters for three tissue categories  was used to estimate the representativeness of the list.ICA: The percentage of the variance of the independent components IC1 and IC2 of one tissue category, % = (varIC1+varIC2)one_category/(varIC1+varIC2)three_categories, was used to judge the relative size of the respective cluster.The cluster-specificity estimates the degree to which the expression of a selected cluster differs from ubiquitous uniform expression of all clusters in a given tissue. It can be measured in terms of the entropy ,(3)c,m is the logged expression of the cluster which is calculated as mean value over the expression values of its members. The entropy is calculated for each tissue sample m = 1...M where the sum runs over all clusters c = 1...C. It has units of bits and ranges from zero for tissues with only one highly expressed cluster to 1 for tissues with uniformly expressed clusters.where eFor comparison with the SOM spot-clusters we applied selected alternative methods of dimension reduction: non-negative matrix factorization , hieHW and HB: Conceived and designed this study, performed data analysis and wrote the manuscript. HW: Wrote the R-programs and performed the calculations. ML and MB contributed to finalize the study. All authors read and approved the final manuscript.The additional text describes methodical issues such as the calibration of microarray data and the adjustment of the size and topology of the SOM, additional supporting maps which illustrate the covariance and correlation structure of the metagene clusters, alternative options of contrast of the SOM images, the filtering of metagenes/single genes and the interpretation of cluster trees. Further details of zooming-in of tissue subgroups are given together with the 3D-ICA plots of the tissues studied.Click here for fileWhole set of 67 SOM expression profiles of human tissuesClick here for fileExpression profiles of human tissues in alternative color scales.Click here for fileAgglomerative cluster analyses after single gene and metagene filtering using FDR and variance criteriaClick here for fileTable of samples studied The complete set of results of our SOM analysis of the human tissue dataset can be found on our website: http://som.izbi.uni-leipzig.deClick here for file


Nat Rev Genet 6: 451-464). In this paper, the probability transition rate and first-passage time in an autoactivating positive-feedback loop with bistability are investigated, where the gene expression is assumed to be disturbed by both additive and multiplicative external noises, the bimodality in the stochastic gene expression is due to the bistability, and the bistability determines that the potential of the Fokker-Planck equation has two potential wells. Our main goal is to illustrate how the probability transition rate and first-passage time are affected by the maximum transcriptional rate, the intensities of additive and multiplicative noises, and the correlation of additive and multiplicative noises. Our main results show that (i) the increase of the maximum transcription rate will be useful for maintaining a high gene expression level; (ii) the probability transition rate from one potential well to the other one will increase with the increase of the intensity of additive noise; (iii) the increase of multiplicative noise strength will increase the amount of probability in the left potential well; and (iv) positive (or negative) cross-correlation between additive and multiplicative noises will increase the amount of probability in the left (or right) potential well.A hallmark of positive-feedback regulation is bistability, which gives rise to distinct cellular states with high and low expression levels, and that stochasticity in gene expression can cause random transitions between two states, yielding bimodal population distribution (Kaern et al., 2005,  As predicted by their theoretical model, temperature-induced protein destabilization led to the existence of two expression states. The result of Isaacs et al. shows clearly the effects of varying the strength of feedback activation on population heterogeneity Click here for additional data file.


Interaction between calcium nanodomains of individual voltage-dependent calcium channels in triggering exocytosis in neurons and endocrine cells is of significant importance for understanding localized calcium signaling in general and synaptic physiology in particular. Such channel domain interaction (overlap) is often experimentally probed by measuring the sensitivity of release rate to the total presynaptic calcium current, which is varied by changing the number of open channels while the single-channel current is kept fixed. Variation in the number of activated channels is in turn achieved using appropriate voltage-clamp protocols, or by pharmacologically blocking a subset of channels. The resulting slope of the log-log regression line between release rate and total calcium current is termed calcium current cooperativity, and is believed to indirectly probe the number of channels contributing to the exocytosis of a single vesicle (reviewed in ). If forDue to the indirect nature of such current cooperativity assay, mathematical and computational modeling proved valuable in the analysis of this experimental protocol -7. Here


Synchronously firing groups of neurons, or cell assemblies, exhibit excess pair-wise synchronous spiking in parallel recordings of neuronal activity. Experimental work  indirectrial-by-trial basis. Using the difference of phases at which maximal rate changes are observed in two neurons we then relate the magnitude of individual rate steps to the degree of rate co-variation across neurons. By comparing the findings to theoretical calibrations tuned to the experimental rates, we find that although the firing rate modulation is related to LFP phase, the locking of significant spike synchrony does not preferentially occur with large co-varying changes of rate.In a first approach we propose an analysis to identify the largest rate changes in UE analysis windows under the assumption that they represent a rate step that is time-locked to a particular phase angle of the instantaneous LFP oscillation rhythm on a Based on these observations, we replace the parametric distribution of expected coincidences used in the original study  for eval


The key reference to work of Mortier and Patriarche had been inadvertently omitted from this article . The sta"Unfortunately, ever since the first report on oxyfluoride GCs, there have been much research on the properties of nano-particles in GCs but no publication has been reported about how to obtain free nano-particles in aqueous solution from the GC-host and how to apply it to the fields mentioned above, especially in biological field."The authors amend it as follows:Mortier and Patriarche  showed t


Post-axial polydactyly is a common congenital hand anomaly with a wide range of manifestations. We report here an unusual case of intra-natal torsion of duplicated small finger which presented as gangrene at birth. The duplication of the little finger is one of the most frequent congenital malformations of the hand. The extent of duplication varies from a soft tissue nubbin to a completely developed digit. The presented case probably represents the first reported case of intra-natal torsion of post-axial polydactyly presenting as gangrene at birth, as an extensive review of the literature and Pubmed search did not reveal a similar published case.A male child presented with gangrene of the accessory digit of the left hand at birth . The chiPost-axial polydactyly constitutes one of the most frequent congenital malformations of the hand and is thought to be the result of an excess longitudinal segmentation representing probably an increased folding of the apical ectodermal ridge. The true incidence of post-axial polydactyly is difficult to determine as many of them are removed by the obstetricians and paediatricians in the nursery. Post-axial polydactyly occurs in approximately 1 in 300 black births and 1 in 3000 in Caucasians and is more frequently seen in male children.1The spectrum of the deformity in post-axial polydactyly ranges from a simple soft-tissue problem to a complex completely developed accessory ray. Post-axial polydactyly has been classified into three types based on the degree of duplication. Type I iThe torsion of pedunculated accessory digit is sometimes seen in Type I duplication, however, intra-natal torsion of post-axial polydactyly has not been previously reported in the literature to the best of our knowledge. The intra-natal torsion of visceral or parietal organs per se is very rare. An extensive search of the literature in Pubmed revealed only one case of intra-natal torsion of a testicular tumour.Post-axial polydactyly is believed to be genetically determined. The Type I pattern is dominant and multifactorial and involves two genes with incomplete penetrance. Type II and III are inherited as dominant traits with marked penetrance. The blacPost-axial polydactyly requires detailed clinical, syndromic and imaging evaluation prior to the treatment. The treatment modalities vary from simple excision to complex skeletal surgeries depending upon the type. For Type I deformities, early excision followed by primary closure and occasionally z-plasty closure  under local anaesthesia is the preferred treatment.6 The use


In recurrent excitatory networks where the mean current caused neurons to fire, increasing chain connections led to higher network synchrony while increasing convergent connections decreased synchrony . In netwIn the present study, we systematically investigate the influence of second order network motifs on the dynamical state of recurrent networks of excitatory and inhibitory neurons in the fluctuation-driven regime. We simulate networks of sparsely coupled integrate-and-fire neurons, where the connection probabilities are given by the second order network model. We allow the probability of each second order motif to depend on the populations (excitatory versus inhibitory) of the neurons involved in the motif. We again find that divergent connections alone have little qualitative effect on the dynamics. However, both convergent connections and chain connections dramatically affect the onset and magnitude of synchronous network oscillations, either increasing or decreasing oscillations depending on the populations of the neurons in the motif. We explain these results through analysis of a mean-field model of the coupled populations.


Some have suggested the quality of reporting of network meta-analyses (a technique used to synthesize information to compare multiple interventions) is sub-optimal. We sought to review information addressing this claim.To conduct an overview of existing evaluations of quality of reporting in network meta-analyses and indirect treatment comparisons, and to compile a list of topics which may require detailed reporting guidance to enhance future reporting quality.An electronic search of Medline and the Cochrane Registry of methodologic studies (January 2004–August 2013) was performed by an information specialist. Studies describing findings from quality of reporting assessments were sought. Screening of abstracts and full texts was performed by two team members. Descriptors related to all aspects of reporting a network meta-analysis were summarized.We included eight reports exploring the quality of reporting of network meta-analyses. From past reviews, authors found several aspects of network meta-analyses were inadequately reported, including primary information about literature searching, study selection, and risk of bias evaluations; statement of the underlying assumptions for network meta-analysis, as well as efforts to verify their validity; details of statistical models used for analyses (including information for both Bayesian and Frequentist approaches); completeness of reporting of findings; and approaches for summarizing probability measures as additional important considerations.While few studies were identified, several deficiencies in the current reporting of network meta-analyses were observed. These findings reinforce the need to develop reporting guidance for network meta-analyses. Findings from this review will be used to guide next steps in the development of reporting guidance for network meta-analysis in the format of an extension of the PRISMA  Statement. Systematic reviews incorporating meta-analyses (SRMA) have long been used to derive summary comparison measures from multiple sources of evidence, most commonly randomized clinical trials (RCTs), to establish the effectiveness and safety of one treatment relative to another. This approach to evidence synthesis is now considered a standard in evidence based medicine. SRMAs have long been considered a scientifically rigorous means of comparing pairs of different medical interventions. To maximize their transparency, methodologic quality and consistency of reporting, the Quality of Reporting of Meta-Analyses (QUOROM) checklist for authors was developed in 1999 network meta-analysis  to simultaneously compare and rank a network of treatments, subsets of which have been compared in individual studies.Over time, increasingly large numbers of treatments for many medical conditions have provided clinicians with more choices from which to select a treatment strategy for their patients. Regulators have only required evidence of benefit over no treatment and a lack of evidence of harms for approval to market. The resulting absence of motivation for drug developers to compare their products against those of their competitors has promoted analytic methods to establish the relative benefits of new agents relative to existing therapies. Following work by Higgins and Whitehead The frequency of use of network meta-analysis has risen notably since the mid 2000s, The PRISMA statement As part of the development of a statement extending PRISMA to cover network meta-analyses, we set out to examine the findings of the peer reviewed literature on the key limitations in the reporting of network meta-analyses. This literature review will help assess the need for reporting guidance, as well as focus the guideline on those features of most importance to clear reporting of network meta-analysis.A brief protocol was developed prior to initiation of this review. It can be acquired by request from the corresponding author.No ethical approval was required for the performance of this work.indirect treatment comparison, mixed treatment comparison, network meta-analysis, and multiple treatments meta-analysis. The search was peer reviewed by members of the authorship team, as well as by an independent information specialist who employed the PRESS (Peer Review of Electronic Search Strategy) framework. An information specialist designed an electronic literature search to search for studies that assessed the quality of reporting of indirect treatment comparisons and network meta-analyses, as well as related key guidance documents for network meta-analysis. The search included a broad range of terms related to the concept of indirect treatment comparisons and network meta-analysis including We included studies, in full text or abstract form, which assessed one or more aspects of the quality of reporting of network meta-analyses or indirect treatment comparisons. Relevant aspects of reporting included (but were not limited to) the following elements: completeness of literature search description; completeness and adequacy of reporting of statistical methods used for analysis; statement and description of assumptions made for network meta-analysis . Stage 1 screening used a liberal strategy where only one individual had to consider a citation of potential relevance in order for it to be advanced to Stage 2 full text screening. For Stage 2, the same individuals reviewed all potentially relevant works in full text. Final inclusion was confirmed if both individuals felt the study was directly relevant to the objectives of this review. Planned involvement of a third party to deal with unresolved discrepancies was not required.Two members of the team extracted data from each retrieved paper . To enable description of the network meta-analyses reviewed in each study and the types of information collected, we gathered the following information from all studies: authorship list, inclusion criteria for types of meta-analyses, number of network meta-analyses reviewed, frequency of the various methods used, and primary authors’ results and conclusions drawn.Figure S1 presents the process of study selection.The literature search and recommendations from co-authors for additional papers identified a total of 775 citations for review. Stage 1 screening identified a total of 19 studies considered to be potentially relevant and whose full texts or supporting posters (where available) were obtained. Following Stage 2 screening, 9 of the studies were retained . The flow diagram in The six full publications assessed indirect treatment comparisons Narrative descriptions of the findings from the included papers have been stratified below according to the type of publication (full versus abstract) in which the research was described. Bafeta et al Nikolakopoulou et al Tan et al Coleman et al Donegan et al Song et al Three studies described in recent abstractsBased on findings from the included reviews and subsequent discussion amongst co-authors, we categorized the perceived key elements related to reporting of both methodology and findings which were judged most important for consideration in a future reporting guidance document. These elements are summarized in Network meta-analysis represents an increasingly prevalent extension of traditional meta-analysis. In addition to continued training needs, our findings suggest a need to develop and promote guidance for reporting these types of studies to maximize transparency and replicability, a corner stone of all science. Such guidance will benefit systematic reviewers with limited exposure to network meta-analyses. Current guidance from groups associated with NICEThis overview identified a total of nine publications which included some form of assessment of the reporting quality of past network meta-analyses or indirect treatment comparisons. This data has identified several limitations for which reporting guidance could be generated to improve reporting. These components included several aspects related to the replicability and validity assessment of NMAs, important aspects of the approach to data analysis, and the reporting of findings. While not meeting our inclusion criteria, we also reviewed a recent report by Lee There are limitations to note for this review. First, our sampling frame was not extant, which would require a primary search for and risk of bias evaluation of all such studies. In our view, conducting an overview provides an adequate rationale for the need to consider extending the PRISMA statement for reporting network meta-analyses. Second, we did not perform a comprehensive search of the literature to identify all methodologic articles related to the conduct of network meta-analysis, which could also serve as a source of topics possibly worthy of reporting guidance. We relied upon the expertise and experience of our authorship team to identify additional items for inclusion in a Delphi survey exercise which was implemented during the summer of 2013, and the survey participants were also provided the opportunity to do so in the context of the survey. Third, we did not perform a risk of bias assessment of the studies included in this report. However, as our primary objective was to compile a list of potential checklist items for inclusion in the development of reporting guidance for network meta-analysis, this did not detract from achievement of our goal.Currently available literature regarding the reporting of network meta-analyses is sparse. Based on the existing evidence, several deficiencies in the reporting of network meta-analyses are apparent and we believe extending the PRISMA statement to network meta-analyses is the best resolution. This overview provided an excellent basis for a Delphi panel survey held in the summer of 2013 and a subsequent face-to-face meeting of experts in the fall of 2013. Dissemination of work generated from this process will be pursued in the near future.Figure S1The flow diagram for study selection.(TIF)Click here for additional data file.Table S1The Medline search strategy used for this syetmatic review.(DOCX)Click here for additional data file.Checklist S1presents a PRISMA checklist assessment for this review article.(DOC)Click here for additional data file.Protocol S1(DOCX)Click here for additional data file.


Tissue microstructure, in particular the alignment of myocytes (fibre direction) and their lateral organisation into sheets, is fundamental to cardiac function. We studied the effect of microstructure on contraction in a computational model of rat left ventricular electromechanics. Different fibre models, globally rule-based or locally optimised to DT-MRI data, were compared, in order to understand whether a subject-specific fibre model would enhance the predictive power of our model with respect to the global ones. We also studied the impact of sheets on ventricular deformation by comparing: (a) a transversely isotropic versus an orthotropic material law and (b) a linear model with a bimodal model of sheet transmural variation. We estimated ejection fraction, wall thickening and base-to-apex shortening and compared them with measures from cine-MRI. We also evaluated Lagrangian strains as local metrics of cardiac deformation. Our results show that the subject-specific fibre model provides little improvement in the metric predictions with respect to global fibre models while material orthotropy allows closer agreement with measures than transverse isotropy. Nonetheless, the impact of sheets in our model is smaller than that of fibres. We conclude that further investigation of the modelling of sheet dynamics is necessary to fully understand the impact of tissue structure on cardiac deformation. The primary eigenvector of the diffusion tensor has been shown to correlate with the fibre direction in the tissue Cardiac tissue is arranged into a complex architecture. Due to their elongated shape, myocytes are highly anisotropic and their local orientation in cardiac tissue is usually called the fibre direction The laminar structure of myocardial tissue is also important for cardiac electromechanics. It has been established that sheets undergo shortening in the fibre direction, extension in the in-sheet/cross-fibre plane, thickening and shear, whose regional variability and transmural distribution are important for the distribution of strain and stress throughout the ventricle during cardiac motion Two motivations underlie our study. First, we aim to understand whether the use of subject-specific fibres would enhance the predictive power of our model, or whether it would be as informative as simple globally prescribed fibres within our modelling framework. To this purpose, globally defined rule-based fibres were compared with a subject-specific model of fibres that is locally optimised in order to minimise the discrepancy with DT-MRI data Our second motivation is the exploration of the often underestimated importance of sheet dynamics in the common electromechanical models. We want to test the impact of sheet orientation in the mechanics of the left ventricle. This was achieved by (a) comparing transverse isotropic with orthotropic material laws for cardiac tissue and (b) under the orthotropic material properties, testing two global models of transmural variation of the sheet orientation, namely a linear and a bimodal model. The aim of the latter comparison was to see whether the latest findings regarding the discontinuous multiple-population distribution of sheets In order to quantify the effect of the different models of fibre and sheet orientation on contraction, global and local metrics were considered. The global metrics are ejection fraction, wall thickening, and shortening in the base-to-apex direction. We compared their predicted values with those obtained from rat cine-MRI data. The local metrics considered are the Lagrangian circumferential strain, as a measure of contraction, circumferential-radial shear strain, as a measure of the relative twist of sub-endocardial and sub-epicardial layers, and circumferential-longitudinal shear strain, as a measure of torsion A preliminary study of the effect of fibre direction and material orthotropy on contraction had been performed with a simplified electromechanical model (without pressure boundary conditions) No new animal experiment was required for the results presented in this paper. The images used come from a dataset previously published and for which the ethics statement was duly reported In vivo cine-MRI and subsequent ex vivo DT-MRI of rat hearts were acquired on a 9.4T (400 MHz) MR system . The cine-MRI dataset used a 2D multi-frame gradient echo sequence, and consisted of a stack of eight contiguous short-axis slices covering the heart from apex to the atrioventricular separation. Each short-axis slice comprised of 32–36 frames acquired throughout the cardiac cycle with an in-plane resolution of GmshWe approximate the left ventricle with a thick-walled truncated ellipsoid. A first coarse tetrahedral mesh was generated with semi-axes and wall thickness estimated from the end-diastolic frame of one of the cine-MRI rat scans described , which whelix angle that defines the fibre direction sheet angle. The vector Tissue structure is defined at the centroid of each element of the mechanical mesh by assigning fibre, sheet and sheet-normal directions. This orthonormal local system of coordinates is assigned according to a well-established approach Different models for the fibre and sheet orientation are introduced in this tissue structure generation framework by modelling the transmural variation of the helix and sheet angle as described below.A set of rule-based globally defined fibres were generated by assigning to each element of the mesh a value of the helix angle The global fibre models are compared with a subject-specific fibre model developed by Karadag et al. The standard approach to the modelling of sheet orientation is that of a smooth variation in the transmural direction At the tissue level the monodomain equations  and (4))deformation gradient tensorGreen deformation tensorLagrangian strain tensor is The mechanical component is coupled to the electrical component via the transmembrane voltage. At the tissue level, the mechanical behaviour is modelled according to finite elasticity theory. Let us consider two systems of Cartesian coordinates, a reference system defined on the undeformed body, The Two models of material passive behaviour were tested representing transversely isotropic and orthotropic behaviour. The stiffness parameters for the orthotropic case are taken from The active stress at the tissue level, The temporal evolution of the intra-cavital blood pressure is approximated by means of a sigmoidal function ranging from Chaste, an open-source simulation package for computational biology developed in our group (http://www.cs.ox.ac.uk/chaste) All simulations were run using in situ, the movement is in the opposite direction, i.e. the atrio-ventricular valve plane moves ‘downwards’ in the direction of the apex, but in terms of changes in apico-basal length, this parameter offers a satisfactory approximation within the modelling framework used). Here The global measures used to evaluate the simulations are: ejection fraction In order to compare the predicted values with experimental data, we measured these metrics on the cine-MRI dataset used for building the ellipsoidal mesh by applying analogous methods. The measured values are As local metrics we considered the Lagrangian circumferential strain In this section we present the results of the series of electromechanical simulations we performed in order to (a) compare sixteen globally defined fibre models  ejection fraction Changing from the transversely isotropic to the orthotropic material law (with the linear sheet model) has the general effect of increasing the For The shortening in the base-to-apex direction  with peak contraction (systole), we believe that this is unlikely to have had a major effect on our findings related to steady-state comparisons. As for the mechanics, there are rat-specific biophysical contraction models. However, we preferred to use the model of Kerckhoffs et al. In the development of the model we tried to be as species-specific as possible in all the components, with the exception of the reduced range for the cavity pressure, as described in Methods and further discussed later on. The geometry has dimensions taken from rat cine-MRI scans, the parameters of tissue electrophysiology are based on measurements in rat. Regarding the AP model, we preferred the mouse model of Wang and Sobie Various measures were considered, grouped into global and local parameters, in order to study the effect of the fibre model, material law and sheet model on contraction. For the global measures, ejection fraction (case of  . We belidels see ), the exal range , are lowThe Lagrangian strains ad-hoc procedure to estimate the pre-P mesh prior to applying the pressure load: this step should be tackled in a more automatic and rigorous approach, by solving an inverse problem. (d) The reduced range of pressures applied in the model: this is probably the most important limitation of the model as pressure is one of the best characterised physiological parameters of the heart. If we compare the results of the preliminary study we carried out using the model without pressure It is important to discuss the limitations of our model. (a) The use of a simplified geometry: the natural geometrical variability of the left ventricle is not taken into account in this study, including the lack of a neighbouring contracting chamber (right ventricle), or of papillary muscles whose orientation is predominantly axial. Previous studies suggest this might not bring a significant improvement To conclude, we developed and tested an electromechanical model and performed a computational study of the impact of tissue microstructure on contraction of the left ventricle in rat. We showed that the model predicts values of global metrics that are within physiological ranges but, except for


Synaptic transmission efficacy transiently changes in a short period of time with generation of presynaptic spikes. Depending on changes in releasable neurotransmitters and calcium concentration in presynaptic terminals, the transmission efficacy of the dynamic synapses decreases (short-term depression) or increases (short-term facilitation) . DynamicWe investigate the properties of the network dynamics with the leaky integrate-and-fire based spiking neural network and the corresponding mean field model. Changes in the synaptic transmission efficacy can be modeled with variables that represent the releasable neurotransmitter and the utilization parameter reflecting the calcium concentration. In the case of the facilitation synapses, the transmission efficacy increases with the successive generation of presynaptic spikes as shown in Figure In the neural network with dynamic synapses, the effective network structure can be reorganized; this causes qualitative changes in the population dynamics (bifurcation). In the presentation, we discuss possible network functions on the basis of this mechanism e.g., generation of sequential actions.


In this experimental model, neurons are free of predefined constraints and thus able to re-create networks that exhibit complex and highly variable spatio-temporal patterns of activity composed of synchronized bursts, mixed with random spikes. Starting from this experimental evidence, here we address the questions: does a particular network architecture promote such dynamics? Is it possible to predict the activity of a neuronal network on the basis of its connectivity map?In this work we aimed at investigating the interdependence between connectivity and dynamics in large-scale cortical networks cultured neuronal avalanches  [We determined the dynamic state of the network according to the statistical distribution of ritical) ,2. Due tritical) . Networkritical) . Within ritical) . The maiin vitro. Our results showed that different topologies of connectivity may determine different dynamic states and suggest that a scale-free architecture may account for the variability observed in the experimental data by varying the number of hubs.We used a combined approach involving models and experiments to get an insight into the interplay of topology and dynamics in cortical networks cultured


Pharmacology has been known to medical students as a hard-to-study subject that includes learning by heart hundreds of receptors and pathways, mechanisms of action, substances and side effects in a very limited time. Once graduated, young MDs are confronted with applied pharmacology without being properly prepared choosing and prescribing the right drug in the proper dose to individual patients.My new approach in teaching pharmacology focuses on the application of pharmacology in up-to-date therapy, stressing information relevant for decision-making and prescription. Exam questions contain patient cases to test prescribing and decision-making abilities. My approach in teaching pharmacology and clinical pharmacology within the curriculum with special focus in therapy was honoured by the students with two “Professor of the Term” awards and excellent ranking by the students and with outstanding results in the evaluation programme conducted by Innsbruck Medical University. The interest of the students in pharmacology grew, 116 students chose to attend extra classes in pharmacology beyond the curriculum during the last semester.


Plant cells exhibit active movement of membrane-bounded materials, which is more pronounced in large cells but is also appreciable in medium-sized cells and in tip-growing cells (such as pollen tubes and root hairs). Trafficking of organelles  and vesicles is essential for plant cell physiology and allows a more or less homogeneous distribution of the cell content. It is well established that the long-range trafficking of organelles is dependent essentially on the network of actin filaments and is powered by the enzyme activity of myosins. However, some lines of evidence suggest that microtubules and members of the kinesin microtubule-based motor superfamily might have a role in the positioning and/or short-range movement of cell organelles and vesicles. Data collected in different cells (such as trichomes and pollen tubes), in specific stages of the plant cell life cycle  and for different organelle classes  encourage the hypothesis that microtubule-based motors might play subtle yet unclarified roles in organelle trafficking. In some cases, this function could be carried out in cooperation with actin filaments according to the model of “functional cooperation” by which motors of different families are associated with the organelle surface. Since available data did not provide an unambiguous conclusion with regard to the role of kinesins in organelle transport, here we want to debate such hypothesis. Chara that are considered the model par excellence in which to investigate this process  disperse in the cytoplasm or aggregate around the nucleus. Melanosomes are transported by kinesin-2, cytoplasmic dynein, and myosin-V, in a way that is dependent on the number of active motors . The rate of organelle transport is likely to depend both on the number of active motors bound to the organelle surface and on the aggregation state of motors . Uncertainty also exists for the single member of the kinesin-6 subfamily and for two putative members of the kinesin-8 subfamily. Analysis of identified kinesins suggests that there is no relationship between affiliation to a subfamily and specific function. The kinesin-4 subfamily contains members localized in the cortical microtubule array and putatively involved in regulating the organization of cellulose microfibrils. The kinesin-5 subfamily contains members that might dimerize allowing the sliding of anti-parallel microtubules; since the role of kinesin-5 in spindle structure/function is conserved between organisms, plant kinesins-5 may have evolved additional roles unique to plants. Some members of the abundant kinesin-7 subfamily might be involved in the regulation of phragmoplast assembly as well as in the interaction between chromosomes and microtubules. The kinesin-10 subfamily is likely to be responsible for the delivery of Golgi vesicles in the phragmoplast. The kinesin-12 members are capable of regulating the formation of the preprophase band while the kinesin-13 motor proteins mediate the interaction between Golgi membranes and cortical microtubules. The kinesin-14 subfamily is the most expanded one and contains members with a C-terminal motor domain likely involved in the sliding of adjacent microtubules as it occurs in the formation/functioning of the mitotic spindle and, putatively, in the assembly of the preprophase band. Recently, some plant kinesins have been shown to be unique because they might work as transcriptional activators during gibberellin biosynthesis and cell growth  was found to be expressed in interphase BY-2 tobacco cells  and possMost of plant kinesins are involved in cell division. The preprophase band is likely to be organized by a number of kinesins such as ATK1 , KCBP B, AtKRP12The involvement of plant microtubules in organelle transport is subtle and often difficult to decipher. Apart from the role of microtubules in chromosome movement, transport of organelle and vesicles along microtubules is documented only by sporadic reports and is limited to few cell types where membrane movement can be studied more easily. The directional grain-to-apex (and vice versa) movement of organelles, accomplished to the focusing of Golgi vesicles in the tip, makes the pollen tube an excellent system where to investigate the contribution of microtubules and microtubule-based motors to organelle trafficking . ConsequArabidopsis mitochondria. Those proteins contain an N-terminal mitochondrial targeting signal and might be a specific subclass of kinesins whose function is currently unknown , were supposed to work inside  unknown . More ge unknown . This su unknown . Bindingc oxidase activity  and a protein isolated from the organelle fraction exhibited immunological and biochemical properties of the kinesin family  was immunolocalized in the tobacco pollen tube and shown to partially overlap with the 58K protein (a marker of Golgi apparatus). However, the motor protein was not found in the pollen tube apex suggesting that it was not critical for the delivery of secretory vesicles  in root meristem cells of wheat. The antibodies localized the antigen in association with aggregates also labeled by Golgi markers  and chloroplasts tend to accumulate in the cortical area of plant cells, exhibiting a movement that is neither linear nor continuous. Unlike other organelles, the movement of chloroplasts is not dependent on the interaction between myosin XI and actin filaments but on the polymerization state of short actin filaments (cp-AFs) associated with the outer envelope of chloroplasts . Howeverin vivo (BC12) is a gene that encodes for a kinesin-4 protein. Like Fra1, bc12 mutants show a significant alteration in the orientation of cellulose microfibrils. Even BC12 is an authentic motor protein but unlike Fra1 is also present in the nucleus and may function as a transcriptional activator  and ending with the synthesis of cellulose microfibrils and the correct assembly of the wall. Historically, microtubules have always been involved in the process of cell wall assembly since first observations of cortical microtubules associated with the plasma membranes . The prein vivo . This apin vivo , the prein vivo . Currentin vivo . The proin vivo . The rolin vivo . Brittlectivator . On the ctivator  thus sugIf kinesins participate in the assembly of the cell wall, where is their contribution necessary? The movement of CesA in the plane of the plasma membrane is definitely independent of a kinesin-based motor process because CesA can move even in the absence of microtubules. In addition, it is likely that microtubules could direct the movement of CesA, but they do not participate in the making of CesA trafficking. Therefore, kinesins may participate in other steps of the cell wall assembly process, especially during the transport of CesA to the insertion points. We cannot exclude that kinesins may also participate in the assembly of the protein complex that makes CesA as active but the few evidence collected so far suggest that kinesins might determine the insertion of CesA in the plasma membrane. If both Fra1 and BC12 are true motors, it is possible that they carry cargoes containing CesA to the insertion points. Recent data helped to clarify the specific cargo carrying CesA toward the plasma membrane. A particular type of membrane compartment has been identified as critical in either transport or recycling of CesA, the so-called MASC  or SmaCCFigure 3). The importance of motor proteins during cell elongation is also documented by the microtubule-based movement of the endoplasmic reticulum in characean algae at a stage coinciding with the onset of cell elongation  isolated from tobacco pollen tubes can move along microtubules so slowly that are incompatible with cytoplasmic streaming . These vin vitro analysis of organelle/vesicle movement along both actin filaments and microtubules  direct the movement of organelles along microtubules while myosins often counteract this activity in order to position organelles precisely . In thisotubules  and by aotubules . As suppotubules . In thisotubules . In planotubules . Althougotubules , we cannotubules . Althougotubules . Althougotubules .Before the discovery of kinesins, organelle trafficking in plant cells was essentially assumed dependent on actin filaments and myosins . Now, weThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Publications from the group in Abidjan indicate the performance of the first 300 cases of open-heart surgery by 1983, the figure increasing to 850 by 1987. Senegal reportedly began performing open-heart surgery in 1995 and is currently a reference point for open cardiac procedures for francophone West Africa. The Ghanaian open-heart experience began in 1964 when surface cooling was used to achieve hypothermia for the successful closure of an atrial septal defect. However, it was not until 1989 that Ghana's National Cardiothoracic Center (NCTC) was established. The NCTC performs regular open-cardiac procedures covering almost the entire spectrum of cardiothoracic procedures including video-assisted thoracoscopic surgery (VATS). The NCTC is equipped with modern cardiovascular/thoracic facilities and has been accredited by the West African College of Surgeons as a center of excellence for the training of cardiothoracic surgeons and has performed creditably in this regard. It is emphasized that open-heart surgery has been practiced in West Africa for decades and continues to be practiced with excellence matching international standards at Ghana's National Cardiothoracic Center.We read with concern the paper of Budzee and colleagues in a recent issue of the Pan African Medical Journal. We wish to draw the attention of the authors and the readership of the journal to gross inaccuracies in the report. The first open-heart surgery in Nigeria is reported to have taken place on 1 We read with concern the paper of Budzee and colleagues in a recent issue of the Pan African Medical Journal . We wishThe West African sub-region is deficient in terms of healthcare facilities, especially those relating to cardiac surgery. However, open heart surgery is not new in the sub-region neither is coronary artery bypass graft, as Budzee and colleagues claim in their publication . It is dOur experience with open-heart surgery in Ghana began in 1964 when Charles Odamtten Easmon, a Ghanaian surgeon used surface cooling as a means of achieving hypothermia to close atrial septal defects in two patients. Subsequently, a myriad of problems stalled the open-heart program in Ghana until 1989 when Ghana's National Cardiothoracic Center (NCTC) was set up by Dr. Kwabena Frimpong-Boateng who was a pioneer of heart transplantation at the Medizinische Hochschule Hannover in Germany where he trained. The NCTC currently performs over 400 cardiothoracic operations a year, roughly a quarter of these are open-heart procedures performed using extracorporeal circulation. Operations performed include surgery for congenital heart disease, rheumatic heart disease, coronary artery bypass graft, and many others. In our 22 years of existence, the NCTC has received referrals from several West African nations including Cameroon and has reported results of open-heart procedures performed on patients from across the West African sub-region . We are It is surprising to us therefore when Budzee and colleagues claim that their institution is the only cardio-surgical center in Central / West Africa, equipped with ultra modern health technologies to offer diverse services. We have been in this service since 1989 and have made and continue to make significant contributions in the sub-region and beyond. We know other centers in the sub-region that have also done quite well until recently.We congratulate Budzee and coworkers for the effort they have made to bring open-heart surgery to Cameroonians. We are well aware of the many obstacles and challenges facing developing countries starting open-heart surgery programs but we consider their report overzealous and historically inaccurate. We wish to emphasize that theirs is not the only cardio-surgical center in West Africa; and coronary artery bypass grafts have been performed (and still being performed) by several indigenous West African teams starting two to three decades before their own.The authors declared no conflicts of interest.


With the use of specific cardiac markers with higher sensitivity, new perspectives have emerged on the nature of myocardial necrosis and injury, which are associated with acute coronary syndrome (ACS). The third universal definition of myocardial infarction3The clinical circumstances associated with elevated values of cardiac troponin (c-Tn) due to myocardial injury have been listed,6Guidelines on the use of high-sensitivity cardiac troponin (hs-cT) markers have recently been set in the consensus statement of the Ethics and Guidelines Standing Committee of the South African Heart Association.The committee recommends that high-sensitivity troponin assays be widely adopted as the preferred biomarker for the diagnosis of myocardial infarction, based on evidence of earlier diagnosis of MI, more reliable ruling out of MI, and shortening of the chest pain triage (to four hours compared to former assays). All cardiac troponin measurements are to be reported in ng/l. The first sample is to be collected on first assessment, followed by a second sample after three hours, should the first value be lower than the 99th percentile (URL) of a normal reference population for the specific assay, or between the URL and the WHO-defined URL for MI. Serial measurements are to be reported as percentage change. A specific algorithm for both hs-cTropT and hs-cTropI is proposed for the diagnosis of MI.The Expert Consensus document on the third universal definition of myocardial infarction4The guidelines defined for South Africa (SA)et al.,In the South African guidelines,The validity of the use of the URL7Furthermore, inter-assay differences concerning reference values for specific populations appear to impact on risk stratification.29et al. and others.27The 20% limit of defining a significant increase from the time of first assessment if baseline values are above the URL has been established within the National Academy of Clinical Biochemistry laboratory medicine practice guidelinesOther complexities of measurement, as eluded to by Thygesen,44et al.et al. on carry-over to subsequent samples with certain analysers, potentially leading to false-positive results.45In addition, Lippi The South African guidelines on the use of high-sensitivity cardiac troponins as biomarkers are timely and of great value, provided that clinicians take up the challenge of applying them clinically.


Limitations of known anatomical circuit rules necessitate the identification of supplementary rules. This is essential for explaining how associative sensory stimuli induce nervous system changes that generate internal sensations of memory, concurrent with triggering specific motor activities in response to specific cue stimuli. A candidate mechanism is rapidly reversible, yet stabilizable membrane hemi-fusion formed between the closely apposed postsynaptic membranes of different neurons at locations of convergence of sensory inputs during associative learning. The lateral entry of activity from the cue stimulus-activated postsynapse re-activates the opposite postsynapse through the hemi-fused area and induces the basic units of internal sensation  as a systems property. Working, short-term and long-term memories can be viewed as functions of the number of re-activatible hemi-fusions present at the time of memory retrieval. Blocking membrane hemi-fusion either by the insertion of the herpes simplex virus (HSV) glycoproteins or by the deposition of insoluble intermediates of amyloid protein in the inter-postsynaptic extracellular matrix (ECM) space leads to cognitive impairments, supporting this mechanism. The introduction of membrane fusion blockers into the postsynaptic cell cytoplasm that attenuates long-term potentiation (LTP), a correlate of behavioral motor activities in response to memory retrieval, provides further support. The lateral spread of activity through the inter-postsynaptic membrane is capable of contributing to oscillating neuronal activity at certain neuronal orders. At the resting state these oscillations provide sub-threshold activation to many neurons at higher orders, including motor neurons maintaining them at a low initiation threshold for motor activity. The integral of the resulting non-specific set of semblances was hypothesized to provide a framework for consciousness  or stabilized as hemi-fused inter-postsynaptic membranes for a long period of time (explaining long-term memories). What ideal properties can allow the inter-postsynaptic functional LINKs to operate between two postsynaptic membranes? Even though the spread of neurotransmitters to neighboring synapses  glycoproteins can induce the formation of membrane hemi-fusion by getting inserted into the host membranes  synapse with postsynapses (dendritic spines) of the neurons in the lateral amygdala. We anticipate functional LINK formation between the postsynapses of different lateral amygdala neurons. In patch-clamp experiments following fear conditioning, an example of associative learning, when thalamic afferents are stimulated to measure the EPSPs at the pyramidal neurons in the lateral amygdala, an increase in the amplitude of the AMPA current is observed  receptors of the excitatory neurotransmitter glutamate have been shown to be necessary for behavioral motor activities indicative of memory retrieval  such that the nervous system becomes capable of retrieving similar memories even when the hippocampi are removed. This explains the process of consolidation of memories  show slow oscillations  in the dendritic tree through the re-activation of a functional LINK by activity arriving from the cue stimulus. Similarly, sub-threshold motor neurons maintained by continuous oscillatory neuronal activity can have a significant role in central pattern generator functions at different locations of the nervous system and in the initiation and maintenance of locomotion.The formation of internal sensations depends on the nature of the semblions formed in response to the specificity of the cue stimulus. The lateral spread of activity through the inter-postsynaptic functional LINKs induces physiological oscillatory neuronal activity, which maintains large numbers of sub-threshold activated neurons at the higher orders. The latter are activated by the arrival of one or a few EPSPs and determine the neurons that are activated by the arrival of the cue stimulus  maintains the required molecular changes and may eventually convert them to near-structural LINKs, and (b) incorporates more new neurons in the circuitry to expand the number of functional LINKs from which a large number of combinatorial semblances can be induced for retrieving different memories. Exposure to rare combinations of sensory stimuli will lead to the formation of specific new sets of functional LINKs at higher neuronal orders. The cognitive abilities that depend on the capacity to associatively learn specific patterns of physical properties of various items in the environment are likely to depend on the available unique combinations of postsynapses that can be functionally LINKed. Many functional LINKs are expected to be formed by simultaneous inputs from the environment that depends on the fixed physical properties of the items. It is possible that the functional LINKs get evolutionarily preserved as structural LINKs and are maintained through genetic mechanisms. In a novice nervous system, synaptic neurotransmission, and the spread of activity through innate structural LINKs between postsynapses will be responsible for innate behavioral responses  required for basic survival needs.It is anticipated that successful stable memories for an item will have excess of net semblances beyond what is required, so that the system can afford to lose some of the functional LINKs without losing the required minimum net semblances for a specific memory (Vadakkan, The gold standard requirement for the operational mechanism of a complex system is an interrelated framework that can explain almost all its functions. The inter-postsynaptic functional LINKs can provide these requirements, which include the retrieval of memories at physiological time-scales, the consolidation of memories (Vadakkan, We have presented a supplementary circuit rule-set that can operate in unison with existing circuit rules and provides interconnected frameworks to explain various nervous system functions. It was imperative to make reasonable assumptions to view the formation of semblances as an emergent property of a system having oscillatory neuronal activity at certain neuronal orders. In such systems, the lateral entry of activity re-activating the inter-postsynaptic functional LINKs provides the horizontal component responsible for the neuronal oscillations along with the formation of basic units of internal sensations; namely, semblions. The concurrent formation of semblances and behavioral motor activity that depends on the frequency of neuronal oscillations provides a finely-regulated system. The present work highlights the importance of developing technologies to measure the summated EPSPs from the soma of the neurons, both at rest and during a cognitive operation, as an initial step followed by developing methods to trace the synapses from where they arrive. Verifying the wiring rules by examining the basic structural mechanisms of operations will help us understand additional information regarding the first-person perspective of different higher brain functions.The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The use of Casemix system as providers’ reimbursement method under social health insurance schemes has spread beyond high-income countries. Over the last decade many low and middle-income countries (LMIC) have started to implement Casemix system to replace the conventional yet inefficient fee-for-service as provider payment mechanism. UNU-IIGH launched an international grouper called UNU-CBG in 2010 to support the implementation of Casemix system in LMIC. To date, UNU-CBG has been introduced in 12 countries in different phases of implementation process. UNU-CBG was designed to function as an international grouper that can be customized and adapted to suit the local needs and norms of each country. However inability to provide robust groupings for sub-acute and chronic conditions is a major limitation in the use of Casemix system in these countries. Sub-acute and chronic diseases that account for more than two thirds of disease burden in LMIC should be categorized in mutually exclusive groups if the reimbursement were to be made using Casemix system. Researchers in UNU-IIGH and International Casemix and Clinical Coding Centre of UKM have been working very hard for the past two years to upgrade the capacity of UNU-CBG grouper to classify sub-acute and chronic cases into Casemix group. Additional variables included in the new grouping algorithm are extended length of stay, costly prostheses, drugs, procedures, investigations and scores of Activity of Daily Living. The new version of UNU-CBG launched during this conference provides an opportunity for social health insurance managers in LMIC to use Casemix system as a total solution for provider payment method.