BACKGROUND
the morbidity of bladder cancer is in the first place among the cancers of urinary system.
the bladder cancer cells can spread by breaking away from the original tumor.
they can spread through the blood vessels to the liver, lungs and bones.
however, its causes are not yet clear.
the bladder cancer is a heterogeneous disease that shows both superficial and invasive growth  <cit> .
superficial tumors frequently recur and may progress to invasive growth.
a part that warrants better treatment regimes bladder cancer is also a good model system to study tumor initiation and progression.
to gain insights into the molecular biology of these processes, we performed gene expression analyses to get some important information about bladder cancer-associated genes.
systems biology is an emerging approach applied to biomedical and biological scientific research.
it is a biology-based inter-disciplinary field of study that focuses on complex interactions within biological systems, using a holistic approach to biological and biomedical research  <cit> .
network biology is a new way of representation and analysis of biological information processing, which understands life as a network.
in fact, the network biology is a branch of the systems biology.
differential co-expression network  is one of biological networks.
a gene co-expression network has emerged as a novel holistic approach for microarray analysis  <cit> .
stuart et al.
<cit>  and bergmannet al.
<cit>  separately constructed the gene co-expression network that connected genes whose expression profiles were similar across different organisms.
a human network was analyzed by leeet al.
<cit>  with functional grouping and cluster analysis.
van noort et al.
<cit>  demonstrated the small-world and scale-free architecture of the yeast co-expression network.
they showed that functionally related genes are frequently co-expressed across organisms constituting conserved transcription modules.
we wanted to explore transcriptional changes in terms of gene interactions rather than at the level of individual genes.
in the end, we constructed two gene co-expression networks and sought to find cancer-induced changes in the network.
the identification of co-expressed pairs in tumor and normal tissues led to the construction of two distinct networks that represent tumor and normal states, respectively.
we expected that biological changes would be reflected in transcriptional changes, which could be identified by comparing the two co-expression networks.
in the transcriptome analysis, differential co-expression analysis  is emerging as a unique complement to traditional differential expression analysis.
dcea investigates differences in gene interconnection by calculating the expression correlation changes of gene pairs between two conditions.
the rationale behind differential co-expression analysis is that changes in gene co-expression patterns between two contrasting phenotypes  provide hints regarding the disrupted regulatory relationships or affected regulatory subnetworks specific to the phenotype of interest .
therefore, among the many growing directions of dcea, there is the so-called "differential regulation analysis", which integrates the transcription factor -to-target information to probe upstream regulatory events that account for the observed co-expression changes.
recently, many researchers have integrated differential co-expression and differential expression concepts to propose a novel regulatory impact factor  that can be used to prioritize disease-causative tfs  <cit> .
in addition, a lot of researchers have begun to perform differential co-expression analyses of micrornas  <cit> .
currently, some tools have been developed for differential expression analysis based on microarray, such as r packages "limma" <cit> , "samr" <cit> , "wgcna" <cit>  and so on.
in our study, we collected microarray datasets of bladder cancer from geo http://www.ncbi.nlm.nih.gov/geo/ to analyze the datasets by an integrated strategy including some functions of samr <cit> , wgcna <cit> , cytoscape <cit>  and other packages.
we selected some degs at two different state  and constructed two dcns.
through the comparisons between two dcns of two different states, we found some hub genes associated with the bladder cancer.
RESULTS
the datasets of gene expression affymetrix microarray of bladder cancer  were download from the geo database of ncbi <cit> .
it has  <dig> samples,  are from the normal tissues and the other  <dig> samples are from the cancer tissues.
the datasets were processed by an integrated strategy.
some degs were selected and constructed two dcns.
in the end, simple analysis was applied to the two dcns and some hub genes were found through comparing two dcns at different states.
normalization of the microarray datasets
in order to get high-quality and strong-expression genes for the convenience of the following data processing, we normalized the microarray dataset using medians.
after normalization, the expression values are in the better order.
we also discovered the distribution of the expression datasets .
selection and bioinformatical analysis of degs
after preprocessing the microarray datasets including the above normalization, some degs were selected using the r package "samr"  = 100; del  =  <dig> ).
<dig> up-regulated genes and  <dig> down-regulated genes were picked out .
next, we did bioinformatics analysis of those degs including go function enrichment using a online tool amigo <cit> .
a part of go enrichment results are showed in figure  <dig>  from the go function enrichment results, we can easily find that the main functions of the up-regulated degs are nitrogen compound metabolic process , heterocycle metabolic process  , cellular aromatic compound metabolic process   and regulation of metabolic process .
and the down-regulated degs mostly involve ion binding  ,multicellular organismal process, single-multicellular organisms process  and response to stimulus   etc.
we used a online tool gather <cit>  to do pathway enrichment .
the up-regulated degs mostly involve the pathways related to "metabolism".
however, the down-regulated degs are included in toll-like receptor signaling pathway, gamma-hexachlorocyclohexane degradation besides two metabolism-associated pathways.
we also investigated the clustering of the degs associated with the bladder cancer.
the heatmap of the degs is showed in figure  <dig>  from the heatmap, we can see the clustering results of the degs.
the clustering method can correctly divide the samples into two classes.
construction and analysis of two dcns
we first calculated a adjacency matrix of degs at the normal or cancer state using the method "pearson correlation" based on the gene expression values.
if the adjacency value of a gene pair is greater than  <dig> , the two genes will be connected to be an edge of a dcn.
we constructed a normal dcn and a cancer dcn  employing the package "wgcna".
we can easily see that in the cancer state, a few up-regulated degs take parts in the co-expression relations.
through the comparisons of two different dcns, we found that the shortest path length distribution  has a little changes from the normal to the cancer.
however, the average clustering coefficient distribution  and the topological coefficients  under the normal condition differ from those under the cancer conditions, which indicates that the modules in the two different dcns have a lot of changes.
next, we want to find differential links  between two different dcns.
at first, we set two thresholds t <dig> and t <dig>  if the correlation  of a gene pair is less than t <dig>  at normal state, but is bigger than t <dig>  at the cancer state, the link of the gene pair is defined as a differential link.
we computed the significance of the differential links using permuation test .
all the selected differential links can compose a differential co-expression subnetwork .
in figure  <dig>  the blue ellipses represent down-regulated degs and the red ellipses represent up-regulated degs.
the shapes of nodes  grow bigger with the degree of nodes.
the  <dig> biggest nodes represent the hub genes: "gdf9", "cyp1a2", "atf7", "trpm3", "cer1", "ptprj", "kcnip1", and "lrrc15".
these hub genes mainly involve the biological processes: cellular response to stimulus , regulation of biological process , response to stimulus , multi-organism process , and regulation of localization .
and they mostly take part in five pathways: gamma-hexachlorocyclohexane degradation , fatty acid metabolism), adherens junction, tryptophan metabolism, and wnt signaling pathway.
among of them, "cyp1a2" and "ptprj" have been reported that they are associated with the bladder cancer <cit> .
and then, we dectected the modules of a dcn.
there have been some methods using clustering algorithms <cit> .
here, we adopted another good approach  <cit> .
in order to detect the modules of two dcns under different conditions, we begun with calculating the topological overlap matrix  <cit>  of expression datasets.
the topological overlap of two nodes reflects their similarity in terms of the commonality of the nodes they connect to.
in order test and verify the inference, we clustered the tom similarities from two different conditions .
from the additonal file  <dig>  it is obvious that the modules at two different states change greatly.
for observing the module changes, we plotted the module heatmaps of degs at two different states and showed parts of the module heatmaps .
discussion
the development of molecular markers for tumor classification and expression signatures that predict outcome will greatly improve diagnosis treatment of bladder cancer.
we employed the r package "samr" to select  <dig> degs including  <dig> up-regulated and  <dig> down-regulated.
in the go function enrichment results , it is showed that the main functions of the degs are cellular physiological precess and cellular metabolism , which is reasonable for the bladder.
however, it is not clear why the difference between the number of the up-regulated and the down-regulated is so large.
we used the r packages "wgcna" to construct two dcns under different conditions.
and we used the tool "cytoscape" to visualize and analyze the two different dcns.
some hub genes were found and analyzed in view of bioinformatics.
the hub genes of the normal dcn mainly involve the neuroactive ligand-receptor interaction pathway and their go functions mostly are response to biotic stimulus,response to stimulus and response to external stimulus.
the hub genes of the cancer dcn involve the following three kegg pathways: gamma-hexachlorocyclohexane degradation, fatty acid metabolism and tryptophan metabolism.
their main go functions are cellular physiological process, surface receptor linked signal transport and signal transduction.
in addition, we found some difference of the two dcns in modules from the clustering plots and the heatmaps.
but it is unknown for us that the functions of the different modules, which is our future work.
we found several hub genes from the selected differential co-expression subnetwork of two different dcns.
two of them have been reported to be associated with the bladder cancer.
then whether are the other hub genes associated with the bladder cancer?
it need to be validated through biological experiments.
CONCLUSIONS
in the work, we adopted an integrated strategy to analyzing the bladder cancer-associated genes by combining several r packages, gene ontology and kegg.
in the experimental results, it shows that the bladder cancer results from the abnormal signaling pathways caused by many genes.
through the data mining for gene expression microarrays, we found differential co-expression subnetwork and the hub genes of the subnetwork.
through the main go functions and pathways of the hub genes, we can better understand the development of the bladder cancer, which will support the wet biological experiment and even further promote the prevention, treatment,diagnosis and cure of the bladder cancer in the future.

the morbidity of bladder cancer is in the first place among the cancers of urinary system. the bladder cancer cells can spread by breaking away from the original tumor. they can spread through the blood vessels to the liver, lungs and bones. however, its causes are not yet clear. the bladder cancer is a heterogeneous disease that shows both superficial and invasive growth. superficial tumors frequently recur and may progress to invasive growth. a part that warrants better treatment regimes bladder cancer is also a good model system to study tumor initiation and progression. to gain insights into the molecular biology of these processes, we performed gene expression analyses to get some important information about bladder cancer-associated genes. systems biology is an emerging approach applied to biomedical and biological scientific research. it is a biology-based inter-disciplinary field of study that focuses on complex interactions within biological systems, using a holistic approach to biological and biomedical research. network biology is a new way of representation and analysis of biological information processing, which understands life as a network. in fact, the network biology is a branch of the systems biology. differential co-expression network is one of biological networks. a gene co-expression network has emerged as a novel holistic approach for microarray analysis. stuart et al. and bergmannet al. separately constructed the gene co-expression network that connected genes whose expression profiles were similar across different organisms. a human network was analyzed by leeet al. with functional grouping and cluster analysis. van noort et al. demonstrated the small-world and scale-free architecture of the yeast co-expression network. they showed that functionally related genes are frequently co-expressed across organisms constituting conserved transcription modules. we wanted to explore transcriptional changes in terms of gene interactions rather than at the level of individual genes. in the end, we constructed two gene co-expression networks and sought to find cancer-induced changes in the network. the identification of co-expressed pairs in tumor and normal tissues led to the construction of two distinct networks that represent tumor and normal states, respectively. we expected that biological changes would be reflected in transcriptional changes, which could be identified by comparing the two co-expression networks. in the transcriptome analysis, differential co-expression analysis is emerging as a unique complement to traditional differential expression analysis. dcea investigates differences in gene interconnection by calculating the expression correlation changes of gene pairs between two conditions. the rationale behind differential co-expression analysis is that changes in gene co-expression patterns between two contrasting phenotypes provide hints regarding the disrupted regulatory relationships or affected regulatory subnetworks specific to the phenotype of interest. therefore, among the many growing directions of dcea, there is the so-called "differential regulation analysis", which integrates the transcription factor -to-target information to probe upstream regulatory events that account for the observed co-expression changes. recently, many researchers have integrated differential co-expression and differential expression concepts to propose a novel regulatory impact factor that can be used to prioritize disease-causative tfs. in addition, a lot of researchers have begun to perform differential co-expression analyses of micrornas. currently, some tools have been developed for differential expression analysis based on microarray, such as r packages "limma", "samr", "wgcna" and so on. in our study, we collected microarray datasets of bladder cancer from geo http://www.ncbi.nlm.nih.gov/geo/ to analyze the datasets by an integrated strategy including some functions of samr, wgcna, cytoscape and other packages. we selected some degs at two different state and constructed two dcns. through the comparisons between two dcns of two different states, we found some hub genes associated with the bladder cancer.  the datasets of gene expression affymetrix microarray of bladder cancer were download from the geo database of ncbi. it has samples, are from the normal tissues and the other samples are from the cancer tissues. the datasets were processed by an integrated strategy. some degs were selected and constructed two dcns. in the end, simple analysis was applied to the two dcns and some hub genes were found through comparing two dcns at different states. normalization of the microarray datasets in order to get high-quality and strong-expression genes for the convenience of the following data processing, we normalized the microarray dataset using medians. after normalization, the expression values are in the better order. we also discovered the distribution of the expression datasets. selection and bioinformatical analysis of degs after preprocessing the microarray datasets including the above normalization, some degs were selected using the r package "samr" = 100; del = ). up-regulated genes and down-regulated genes were picked out. next, we did bioinformatics analysis of those degs including go function enrichment using a online tool amigo. a part of go enrichment results are showed in figure from the go function enrichment results, we can easily find that the main functions of the up-regulated degs are nitrogen compound metabolic process, heterocycle metabolic process, cellular aromatic compound metabolic process and regulation of metabolic process. and the down-regulated degs mostly involve ion binding,multicellular organismal process, single-multicellular organisms process and response to stimulus etc. we used a online tool gather to do pathway enrichment. the up-regulated degs mostly involve the pathways related to "metabolism". however, the down-regulated degs are included in toll-like receptor signaling pathway, gamma-hexachlorocyclohexane degradation besides two metabolism-associated pathways. we also investigated the clustering of the degs associated with the bladder cancer. the heatmap of the degs is showed in figure from the heatmap, we can see the clustering results of the degs. the clustering method can correctly divide the samples into two classes. construction and analysis of two dcns we first calculated a adjacency matrix of degs at the normal or cancer state using the method "pearson correlation" based on the gene expression values. if the adjacency value of a gene pair is greater than, the two genes will be connected to be an edge of a dcn. we constructed a normal dcn and a cancer dcn employing the package "wgcna". we can easily see that in the cancer state, a few up-regulated degs take parts in the co-expression relations. through the comparisons of two different dcns, we found that the shortest path length distribution has a little changes from the normal to the cancer. however, the average clustering coefficient distribution and the topological coefficients under the normal condition differ from those under the cancer conditions, which indicates that the modules in the two different dcns have a lot of changes. next, we want to find differential links between two different dcns. at first, we set two thresholds t and t if the correlation of a gene pair is less than t at normal state, but is bigger than t at the cancer state, the link of the gene pair is defined as a differential link. we computed the significance of the differential links using permuation test. all the selected differential links can compose a differential co-expression subnetwork. in figure the blue ellipses represent down-regulated degs and the red ellipses represent up-regulated degs. the shapes of nodes grow bigger with the degree of nodes. the biggest nodes represent the hub genes: "gdf9", "cyp1a2", "atf7", "trpm3", "cer1", "ptprj", "kcnip1", and "lrrc15". these hub genes mainly involve the biological processes: cellular response to stimulus, regulation of biological process, response to stimulus, multi-organism process, and regulation of localization. and they mostly take part in five pathways: gamma-hexachlorocyclohexane degradation, fatty acid metabolism), adherens junction, tryptophan metabolism, and wnt signaling pathway. among of them, "cyp1a2" and "ptprj" have been reported that they are associated with the bladder cancer. and then, we dectected the modules of a dcn. there have been some methods using clustering algorithms. here, we adopted another good approach. in order to detect the modules of two dcns under different conditions, we begun with calculating the topological overlap matrix of expression datasets. the topological overlap of two nodes reflects their similarity in terms of the commonality of the nodes they connect to. in order test and verify the inference, we clustered the tom similarities from two different conditions. from the additonal file it is obvious that the modules at two different states change greatly. for observing the module changes, we plotted the module heatmaps of degs at two different states and showed parts of the module heatmaps. discussion the development of molecular markers for tumor classification and expression signatures that predict outcome will greatly improve diagnosis treatment of bladder cancer. we employed the r package "samr" to select degs including up-regulated and down-regulated. in the go function enrichment results, it is showed that the main functions of the degs are cellular physiological precess and cellular metabolism, which is reasonable for the bladder. however, it is not clear why the difference between the number of the up-regulated and the down-regulated is so large. we used the r packages "wgcna" to construct two dcns under different conditions. and we used the tool "cytoscape" to visualize and analyze the two different dcns. some hub genes were found and analyzed in view of bioinformatics. the hub genes of the normal dcn mainly involve the neuroactive ligand-receptor interaction pathway and their go functions mostly are response to biotic stimulus,response to stimulus and response to external stimulus. the hub genes of the cancer dcn involve the following three kegg pathways: gamma-hexachlorocyclohexane degradation, fatty acid metabolism and tryptophan metabolism. their main go functions are cellular physiological process, surface receptor linked signal transport and signal transduction. in addition, we found some difference of the two dcns in modules from the clustering plots and the heatmaps. but it is unknown for us that the functions of the different modules, which is our future work. we found several hub genes from the selected differential co-expression subnetwork of two different dcns. two of them have been reported to be associated with the bladder cancer. then whether are the other hub genes associated with the bladder cancer? it need to be validated through biological experiments.  in the work, we adopted an integrated strategy to analyzing the bladder cancer-associated genes by combining several r packages, gene ontology and kegg. in the experimental results, it shows that the bladder cancer results from the abnormal signaling pathways caused by many genes. through the data mining for gene expression microarrays, we found differential co-expression subnetwork and the hub genes of the subnetwork. through the main go functions and pathways of the hub genes, we can better understand the development of the bladder cancer, which will support the wet biological experiment and even further promote the prevention, treatment,diagnosis and cure of the bladder cancer in the future.

by comparing two dcns between the normal and cancer states, we found some great changes in hub genes and topological structure, which suggest that the modules of two different dcns change a lot. especially, we screened some hub genes of a differential subnetwork between the normal and the cancer states and then do bioinformatics analysis for them. through constructing and analyzing two differential co-expression networks at different states using the screened degs, we found some hub genes associated with the bladder cancer. the results of the bioinformatics analysis for those hub genes will support the biological experiments and the further treatment of the bladder cancer. bladder cancer is the most common malignant tumor of the urinary system and it is a heterogeneous disease with both superficial and invasive growth. and it is indispensable to find key genes or modules causing the bladder cancer. employing an integrated strategy, up-regulated differentially expressed genes and down-regulated degs were selected and main functions of those degs are cellular physiological precess and cellular metabolism.

Abstract: bladder cancer is the most common malignant tumor of the urinary system and it is a heterogeneous disease with both superficial and invasive growth. however, its aetiological agent is still unclear. and it is indispensable to find key genes or modules causing the bladder cancer. based on gene expression microarray datasets, constructing differential co-expression networks is an important method to investigate diseases and there have been some relevant good tools such as r package'wgcna','dcgl'.  employing an integrated strategy, up-regulated differentially expressed genes and down-regulated degs were selected and main functions of those degs are cellular physiological precess and cellular metabolism. the up-regulated degs are mainly involved in the the pathways related to "metabolism". by comparing two dcns between the normal and cancer states, we found some great changes in hub genes and topological structure, which suggest that the modules of two different dcns change a lot. especially, we screened some hub genes of a differential subnetwork between the normal and the cancer states and then do bioinformatics analysis for them.  through constructing and analyzing two differential co-expression networks at different states using the screened degs, we found some hub genes associated with the bladder cancer. the results of the bioinformatics analysis for those hub genes will support the biological experiments and the further treatment of the bladder cancer. 28- june 10th international symposium on bioinformatics research and applications zhangjiajie, china


Erwin is the best character


this is test 2


foo


Good shoe for office work. They will scuff very easy so be aware.


I have had the Patricia II wedge in black for about 1 year & wore them regularly in season.  When I saw the Patricia at a good price in navy (from 6pm), I purchased them because I thought they would fit just like my Patricia IIs.  I was wrong, and paid the price with return shipping that 6pm doesn't pay.

The crocs website says that crocs aren't suppose to fit like other sandals - they are suppose to be looser & thus more comfortable - I normally wear an 8-1/2, so have now tried both an 8 & a 9 in the Patricia shoe (I have an 8 in the Patricia II).  The Patricia 9 swims on my feet & they would be a hazard to walk around in.  The size 8 fits my left foot (which is my wider foot) but is too narrow on my right foot.  When I placed the shoes sole to sole, I did notice a slight difference in the width, which, apparently, my foot notices too.  I can only conclude a manufacturing defect.  But, it is this shoe specifically or the form for this shoe? (others have written the shoe is narrow).

Consequently, if you have a wider foot, order the Patricia II instead of this one & if you are a 1/2 size, order down, not up.


Width not right and size too small if width had been just little wider and ordered size larger would have been good. Loved the shoe look


I received these shoes and they weren't the same as the picture described them, they were a different color. When i tried to return them, the shipping wasn't paid for. So i had to pay $20 for shipping. A waste of time and money. I dont recommend anyone to buy from TheSmartBuy.


They began to split alone the mesh material after a month but loved the shoe and the feel of it


Excellent shoes ,  very confortable and litgthweight !


Usually love Ethnies product. In this case the raised arch area of one shoe is too far back and raised to the point of discomfort.
The other shoe fits fine.?? Had to wear em before I figured it out so im stuck with em.
Also, they do run a bit narrow(or at least one shoe did) ;)


Could not get my foot into the shoe.  Was disappointed and returned them.


Everything about the boot is great.


Nice looking shoe, okay for short-term wear.  Much narrower than other size 11's  that I have - tight fit!


theres not enough going on around here for air service none want to waste there time on this town


How about ZERO


Her name is still floating?  Might I suggest flushing twice?


Stop by So Cold at MFNW/Project Pabst! It'll make the top of any list.
socoldpdx.com


Nobody awake on the bridge, no lookouts ,,no radar tech. Never saw the ships lights ????  Oh boy !!


Pessimist's might say that we the US will have a Godzillion deficit in 10 years and bucks for Lo-Lo's going be gonahs real soon, however Optimist's might say; look on the bright side, if you work at the Pearl Harbor Ship Yard or for the DOD your career and future is secure braddahs and sistahs!


I'm sure Hawaiian can put Southwest out of business but the Southwest/Alaska combination may be too much to handle.


States don't grow economies, cities grow economy.
Cities that invest in their residents will encourage economic diversity and less reliance on government spending.
Look at what state legislatures in the sw and se are doing to kill econimic diversity by passing draconian social laws that discriminate against classes of tax paying citizens. Private sector businesses are threatening to pull out and/or reduce investment. Government money and religious conservatism stifles creativity and economic diversity. Less government is better for the economy.


What makes Magnitsky so special? Countless thousands of people around the world have been tortured and have died under suspicious circumstances throughout history but no country considered their deaths worthy enough to apply sanctions against offending governments and /or their officials. One must wonder what are the real motivations of those who are promoting a Canadian version of the Magnitsky Act.


The Mayor said we need "more skin in the game" . That explains the rash on my...


<sitename>Wikipedia</sitename>


<case>first-letter</case>


<namespace key="0" />


Question: anger

Nana came into the room with a puzzled look on her face.


Question: anticipation

Nana came into the room with a puzzled look on her face.


Question: disgust


Question: fear


Question: joy


Question: sadness


Question: surprise


Question: trust


Question: anger


Question: anticipation

Nana came into the room with a puzzled look on her face.


It has recently been shown that Craf is essential for Kras G12D-induced NSCLC.

It has recently become evident that Craf is essential for the onset of Kras-driven non-small cell lung cancer.


Previous studies demonstrated that the decrease level of 5 hmC in tumors was due to the reduced expression of TET1/2/3 and IDH2 genes or tumor derived IDH1 and IDH2 mutations.

In addition, genetic and functional studies suggest that neomorphic IDH mutations contribute to myeloid transformation, at least in part, by inhibiting TET enzymatic function.


More recently, IDH mutations and resultant 2-hydroxyglutarate (2HG) production in leukemia cells were reported to induce global DNA hypermethylation through impaired TET2 catalytic function.

It has also been recently reported that mutations of the isocitrate dehydrogenase genes IDH1 and IDH2 can lead to the aberrant production of 2-hydroxyglutarate (2-HG), a metabolite that inhibits TET2 enzymatic activity, resulting in a hypermethylated promoter phenotype in acute myeloid leukemia (AML) tumors carrying IDH1/2 mutations.


Recent in vitro studies using shRNA-based approaches have suggested a role for TET2 in regulating myeloid differentiation and in regulating stem/progenitor cell proliferation.

Two recent studies used RNAi-mediated Tet2 knock-down in vitro to suggest that TET2 depletion led to impaired hematopoietic differentiation and to preferential myeloid commitment.


Recently, it was reported that expression of IDH1R132H suppresses TET2 activity and the mutations of IDH1 and IDH2 genes occur in a mutual exclusive manner with that of TET2 gene in AML.

This large-scale study showed that IDH1/IDH2 mutations were mutually exclusive with inactivating TET2 mutations, suggesting that the two types of mutations had similar effects and were thus functionally redundant.


n view of the evidence that many tumors occurring in nature arise from the selection of cells with genetic and/or epigenetic changes.

The phenomena of neoplastic development and neoplastic transformation, whether they occur in vivo or in vitro, are thought to represent the accumulation of a series of genetic and epigenetic alterations that disrupt the normal processes of cell division and tissue integrity.


The phenomena of neoplastic development and neoplastic transformation, whether they occur in vivo or in vitro, are thought to represent the accumulation of a series of genetic and epigenetic alterations that disrupt the normal processes of cell division and tissue integrity.

Neoplastic development represents cumulative genetic and epigenetic events leading to the emergence of cells that can attain a tumorigenic phenotype.


Tumorigenesis is a multistage process that involves multiple cell types.

Previous studies have suggested a number of hallmark functions that need to be acquired for a cancer to generate, helping researchers understand the complexity in tumor progression in a way of logical, scientific manner.


Furthermore, transiently expressed exogenous LATS2 is localized to centrosomes.

LATS1 and LATS2 have been detected on interphase and mitotic centrosomes


Since in S. cerevisiae DBF2 was shown to be associated with anaphase and/or telophase progression, we examined whether the deletion of the kinase would also affect cell cycle progression in N. crassa

Taking into consideration the role that DBF2 homologs have been shown to play in cell cycle progression, predominant localization of DBF-2 in N.crassa is expected.


Proces privatizacije na Kosovu pod lupom

Case=Nom|Gender=Masc|Number=Sing Case=Gen|Gender=Fem|Number=Sing Case=Loc Case=Loc|Gender=Neut|Number=Sing Case=Ins Case=Ins|Gender=Fem|Number=Sing


She burns all over everywhere.


TAURISO


Now on her panting Breast his Head repose,


Datasets streaming + map doesn't work for `Audio`

bug

Something isn't working

duplicate

This issue or pull request already exists

This seems related to https://github.com/huggingface/datasets/issues/3505.

## Describe the bug
When using audio datasets in streaming mode, applying a `map(...)` before iterating leads to an error as the key `array` does not exist anymore.

## Steps to reproduce the bug

```python
from datasets import load_dataset

ds = load_dataset("common_voice", "en", streaming=True, split="train")

def map_fn(batch):
    print("audio keys", batch["audio"].keys())
    batch["audio"] = batch["audio"]["array"][:100]
    return batch

ds = ds.map(map_fn)

sample = next(iter(ds))
```

I think the audio is somehow decoded before `.map(...)` is actually called.

## Expected results

IMO, the above code snippet should work.

## Actual results

```bash
audio keys dict_keys(['path', 'bytes'])
Traceback (most recent call last):
  File "./run_audio.py", line 15, in <module>
    sample = next(iter(ds))
  File "/home/patrick/python_bin/datasets/iterable_dataset.py", line 341, in __iter__
    for key, example in self._iter():
  File "/home/patrick/python_bin/datasets/iterable_dataset.py", line 338, in _iter
    yield from ex_iterable
  File "/home/patrick/python_bin/datasets/iterable_dataset.py", line 192, in __iter__
    yield key, self.function(example)
  File "./run_audio.py", line 9, in map_fn
    batch["input"] = batch["audio"]["array"][:100]
KeyError: 'array'
```

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.17.1.dev0
- Platform: Linux-5.3.0-64-generic-x86_64-with-glibc2.17
- Python version: 3.8.12
- PyArrow version: 6.0.1


dataset-viewer

Related to the dataset viewer on huggingface.co

## Dataset viewer issue for '*name of the dataset*'

**Link:** *link to the dataset viewer page*

*short description of the issue*

Am I the one who added this dataset ? Yes-No


Add The Medical Segmentation Decathlon Dataset

dataset request

Requesting to add a new dataset

## Adding a Dataset
- **Name:** *The Medical Segmentation Decathlon Dataset*
- **Description:** The underlying data set was designed to explore the axis of difficulties typically encountered when dealing with medical images, such as small data sets, unbalanced labels, multi-site data, and small objects.
- **Paper:** [link to the dataset paper if available](https://arxiv.org/abs/2106.05735)
- **Data:** http://medicaldecathlon.com/
- **Motivation:** Hugging Face seeks to democratize ML for society. One of the growing niches within ML is the ML + Medicine community. Key data sets will help increase the supply of HF resources for starting an initial community.

(cc @osanseviero @abidlabs )

Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).


conll 2003 dataset source url is no longer valid

bug

Something isn't working

dataset bug

A bug in a dataset script provided in the library

## Describe the bug
Loading `conll2003` dataset fails because it was removed (just yesterday 1/14/2022) from the location it is looking for.

## Steps to reproduce the bug
```python
from datasets import load_dataset

load_dataset("conll2003")
```

## Expected results
The dataset should load.

## Actual results
It is looking for the dataset at `https://github.com/davidsbatista/NER-datasets/raw/master/CONLL2003/train.txt` but it was removed from there yesterday (see [commit](https://github.com/davidsbatista/NER-datasets/commit/9d8f45cc7331569af8eb3422bbe1c97cbebd5690) that removed the file and related [issue](https://github.com/davidsbatista/NER-datasets/issues/8)).

- We should replace this with an alternate valid location.
- this is being referenced in the huggingface course chapter 7 [colab notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/master/course/chapter7/section2_pt.ipynb), which is also broken.

```python
FileNotFoundError                         Traceback (most recent call last)
<ipython-input-4-27c956bec93c> in <module>()
      1 from datasets import load_dataset
      2
----> 3 raw_datasets = load_dataset("conll2003")

11 frames
/usr/local/lib/python3.7/dist-packages/datasets/utils/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token, ignore_url_params)
    610             )
    611         elif response is not None and response.status_code == 404:
--> 612             raise FileNotFoundError(f"Couldn't find file at {url}")
    613         _raise_if_offline_mode_is_enabled(f"Tried to reach {url}")
    614         if head_error is not None:

FileNotFoundError: Couldn't find file at https://github.com/davidsbatista/NER-datasets/raw/master/CONLL2003/train.txt
```

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:
- Platform:
- Python version:
- PyArrow version:


Unable to create a dataset from a parquet file in S3

bug

Something isn't working

## Describe the bug
Trying to create a dataset from a parquet file in S3.

## Steps to reproduce the bug
```python
import s3fs
from datasets import Dataset

s3 = s3fs.S3FileSystem(anon=False)

with s3.open(PATH_LTR_TOY_CLEAN_DATASET, 'rb') as s3file:
    dataset = Dataset.from_parquet(s3file)
```

## Expected results
A new Dataset object

## Actual results

```AttributeError: 'S3File' object has no attribute 'decode'```
```
AttributeError                            Traceback (most recent call last)
<command-2452877612515691> in <module>
      5
      6 with s3.open(PATH_LTR_TOY_CLEAN_DATASET, 'rb') as s3file:
----> 7     dataset = Dataset.from_parquet(s3file)

/databricks/python/lib/python3.8/site-packages/datasets/arrow_dataset.py in from_parquet(path_or_paths, split, features, cache_dir, keep_in_memory, columns, **kwargs)
    907         from .io.parquet import ParquetDatasetReader
    908
--> 909         return ParquetDatasetReader(
    910             path_or_paths,
    911             split=split,

/databricks/python/lib/python3.8/site-packages/datasets/io/parquet.py in __init__(self, path_or_paths, split, features, cache_dir, keep_in_memory, **kwargs)
     28         path_or_paths = path_or_paths if isinstance(path_or_paths, dict) else {self.split: path_or_paths}
     29         hash = _PACKAGED_DATASETS_MODULES["parquet"][1]
---> 30         self.builder = Parquet(
     31             cache_dir=cache_dir,
     32             data_files=path_or_paths,

/databricks/python/lib/python3.8/site-packages/datasets/builder.py in __init__(self, cache_dir, name, hash, base_path, info, features, use_auth_token, namespace, data_files, data_dir, **config_kwargs)
    246
    247         if data_files is not None and not isinstance(data_files, DataFilesDict):
--> 248             data_files = DataFilesDict.from_local_or_remote(
    249                 sanitize_patterns(data_files), base_path=base_path, use_auth_token=use_auth_token
    250             )

/databricks/python/lib/python3.8/site-packages/datasets/data_files.py in from_local_or_remote(cls, patterns, base_path, allowed_extensions, use_auth_token)
    576         for key, patterns_for_key in patterns.items():
    577             out[key] = (
--> 578                 DataFilesList.from_local_or_remote(
    579                     patterns_for_key,
    580                     base_path=base_path,

/databricks/python/lib/python3.8/site-packages/datasets/data_files.py in from_local_or_remote(cls, patterns, base_path, allowed_extensions, use_auth_token)
    544     ) -> "DataFilesList":
    545         base_path = base_path if base_path is not None else str(Path().resolve())
--> 546         data_files = resolve_patterns_locally_or_by_urls(base_path, patterns, allowed_extensions)
    547         origin_metadata = _get_origin_metadata_locally_or_by_urls(data_files, use_auth_token=use_auth_token)
    548         return cls(data_files, origin_metadata)

/databricks/python/lib/python3.8/site-packages/datasets/data_files.py in resolve_patterns_locally_or_by_urls(base_path, patterns, allowed_extensions)
    191     data_files = []
    192     for pattern in patterns:
--> 193         if is_remote_url(pattern):
    194             data_files.append(Url(pattern))
    195         else:

/databricks/python/lib/python3.8/site-packages/datasets/utils/file_utils.py in is_remote_url(url_or_filename)
    115
    116 def is_remote_url(url_or_filename: str) -> bool:
--> 117     parsed = urlparse(url_or_filename)
    118     return parsed.scheme in ("http", "https", "s3", "gs", "hdfs", "ftp")
    119

/usr/lib/python3.8/urllib/parse.py in urlparse(url, scheme, allow_fragments)
    370     Note that we don't break the components up in smaller bits
    371     (e.g. netloc is a single string) and we don't expand % escapes."""
--> 372     url, scheme, _coerce_result = _coerce_args(url, scheme)
    373     splitresult = urlsplit(url, scheme, allow_fragments)
    374     scheme, netloc, url, query, fragment = splitresult

/usr/lib/python3.8/urllib/parse.py in _coerce_args(*args)
    122     if str_input:
    123         return args + (_noop,)
--> 124     return _decode_args(args) + (_encode_result,)
    125
    126 # Result objects are more helpful than simple tuples

/usr/lib/python3.8/urllib/parse.py in _decode_args(args, encoding, errors)
    106 def _decode_args(args, encoding=_implicit_encoding,
    107                        errors=_implicit_errors):
--> 108     return tuple(x.decode(encoding, errors) if x else '' for x in args)
    109
    110 def _coerce_args(*args):

/usr/lib/python3.8/urllib/parse.py in <genexpr>(.0)
    106 def _decode_args(args, encoding=_implicit_encoding,
    107                        errors=_implicit_errors):
--> 108     return tuple(x.decode(encoding, errors) if x else '' for x in args)
    109
    110 def _coerce_args(*args):

AttributeError: 'S3File' object has no attribute 'decode'
```

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.17.0
- Platform: Ubuntu 20.04.3 LTS
- Python version: 3.8.10
- PyArrow version: 6.0.1


Bug in wiki bio load

wiki_bio is failing to load because of a failing drive link . Can someone fix this ?

![7E90023B-A3B1-4930-BA25-45CCCB4E1710](https://user-images.githubusercontent.com/3104771/149617870-5a32a2da-2c78-483b-bff6-d7534215a423.png)

![653C1C76-C725-4A04-A0D8-084373BA612F](https://user-images.githubusercontent.com/3104771/149617875-ef0e30b0-b76e-48cf-b3eb-93ba8e6e5465.png)
a


Add Text2log Dataset

Adding the text2log dataset used for training FOL sentence translating models


label information get lost after parquet serialization

Something isn't working

## Describe the bug
In *dataset_info.json* file, information about the label get lost after the dataset serialization.

## Steps to reproduce the bug
```python
from datasets import load_dataset

# normal save
dataset = load_dataset('glue', 'sst2', split='train')
dataset.save_to_disk("normal_save")

# save after parquet serialization
dataset.to_parquet("glue-sst2-train.parquet")
dataset = load_dataset("parquet", data_files='glue-sst2-train.parquet')
dataset.save_to_disk("save_after_parquet")
```

## Expected results
I expected to keep label information in *dataset_info.json* file even after parquet serialization

## Actual results
In the normal serialization i got
```json
"label": {
      "num_classes": 2,
      "names": [
        "negative",
        "positive"
      ],
      "names_file": null,
      "id": null,
      "_type": "ClassLabel"
    },
```
And after parquet serialization i got
```json
"label": {
      "dtype": "int64",
      "id": null,
      "_type": "Value"
    },
```

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.17.0
- Platform: ubuntu 20.04
- Python version: 3.8.10
- PyArrow version: 6.0.1


Add The Mexican Emotional Speech Database (MESD)

dataset request

Requesting to add a new dataset

## Adding a Dataset
- **Name:** *The Mexican Emotional Speech Database (MESD)*
- **Description:** *Contains 864 voice recordings with six different prosodies: anger, disgust, fear, happiness, neutral, and sadness. Furthermore, three voice categories are included: female adult, male adult, and child. *
- **Paper:** *[Paper](https://ieeexplore.ieee.org/abstract/document/9629934/authors#authors)*
- **Data:** *[link to the Github repository or current dataset location](https://data.mendeley.com/datasets/cy34mh68j9/3)*
- **Motivation:** *Would add Spanish speech data to the HF datasets :) *

Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).


Add PASS dataset

This PR adds the PASS dataset.

Closes #3043


Q:

StoryBoard API in Corona

I am trying to use StoryBoard API in Corona.

However, my button touch event is not working at all.

Can you look at my code and help me out?

main.lua :
     local storyboard = require "storyboard"

     storyboard.gotoScene( "scene1", "fade", 500 )

scene1:
     local storyboard = require( "storyboard" )
     local scene = storyboard.newScene()

     local image

     -- Touch event listener for background image
      local function onSceneTouch( self, event )
  if event.phase == "began" then

    storyboard.gotoScene( "scene2", "fade", 400  )

    return true
        end
      end

     -- Called when the scene's view does not exist:
    function scene:createScene( event )
local screenGroup = self.view

image = display.newImage( "bird.png" )
screenGroup:insert( image )

image.touch = onSceneTouch

print( "\n1: createScene event")
   end

   function scene:enterScene( event )

print( "1: enterScene event" )

  end

    function scene:exitScene( event )

    print( "1: exitScene event" )

   -- remove touch listener for image
image:removeEventListener( "touch", image )

  end

   -- Called prior to the removal of scene's "view" (display group)
   function scene:destroyScene( event )

print( "((destroying scene 1's view))" )
  end

   scene:addEventListener( "createScene", scene )

   scene:addEventListener( "enterScene", scene )

   scene:addEventListener( "exitScene", scene )

   scene:addEventListener( "destroyScene", scene )

   return scene

And my second scene is same like that.

However when i touch on the image, it does not go to second scene.

A:

Remember to add the event listener to your image.


Q:

SQL count Query retrieving 0

i want to list the amount of views per object_id, between the current date and the date year ago.

So, i have this Query:
SELECT count(*) FROM event_logs WHERE object_id=252 AND object_type='product' AND event_type='view' AND event_date BETWEEN 2014-02-14 AND 2015-02-14 GROUP BY month(event_date)

And the following table:
event_id | user_id | objectd_id | object_type | event_type | event_date
1        | 1       |    252     |   product   | view       | 2014-02-25 00:00:00
2        | 1       |    252     |   product   | view       | 2015-02-12 19:36:05
3        | 1       |    252     |   product   | view       | 2015-01-05 19:36:05

The problem is when i execute the query, show an amount of results of 0 (zero),
Could have i been doing wrong?

Please help and thank you all for your attention!


The present invention relates to an improved platen for use in an impact printer.

More particularly, the present invention relates to such a platen capable of reducing noise by absorbing vibrations that occur upon a print head of the printer striking a printing surface on the platen.

A conventional platen for an impact printer comprises a steel shaft having on an outer surface thereof a hard covering or layer of ebonite or other material.

However, such conventional platen is very heavy, and this requires that the printer include a large driving motor for rotating the platen.

Further, when a print head of the printer strikes the platen, substantial and large noise is generated.

In view of such problems of the conventional platen, various attempts have been made to devise improved platens that are lighter in weight and that reduce noise upon striking by the printer print head.

For example, one attempted improvement is to replace the steel shaft with a hollow steel pipe.

This arrangement reduces the weight of the platen, but the noise generated during printing is increased due to the resonance phenomenon of the hollow pipe.

One proposal to reduce such resonance phenomenon is disclosed in Japanese Patent Application Laid-Open No.

This arrangement fills the hollow pipe with liquid having a low viscosity.

This solution reduces noise from the resonance phenomenon, but other noise newly is generated.

Particularly, such platen cannot absorb efficiently vibrations imparted to the platen, and the platen acts as a transmitter of such vibrations.

Another solution, disclosed in Japanese Patent Application Laid-Open No.

204973/1987 fills approximately 50% of the interior of the hollow pipe with granular material such as sand.

This solution absorbs impact occurring on printing to reduce noise.

However, this solution does not remove noise due to the resonance phenomenon because a portion of the interior of the hollow pipe is not filled, i.e. there is a space therein enabling movement of the granular material.

As a solution for such problems, the present inventor has provided a platen, disclosed in Japanese Patent Application Laid-Open No.

Such platen provides that the interior of the hollow pipe is filled with liquid and sheet or granular fragments within the liquid that rub against each other.

This platen absorbs impacts during printing by means of inertia resistance of the mass of the many fragments and the liquid, frictional resistance occurring upon the fragments rubbing against each other through the liquid, and velocity resistance of the liquid.

Therefore, vibrations that occur upon the print head striking the printing surface on the platen can be prevented, thereby reducing noise of the platen as well as noise of the printer frame and other parts of the printer due to transmission of such vibration.

Such platen employs as the filler liquid for preventing vibrations an emulsion paint, such as Gelnac (trade name) made by Nippon Automatic Co., Ltd. However, the use of such material results in certain inherent disadvantages.

Thus, such filler requires a drying process involving a long period of time, and this adversely effects productivity.

Further, such filler is hard to treat and is high priced.

Due to such disadvantages, it is difficult to produce a low priced printer employing such platen.


0:30 Arsenal midfielder Aaron Ramsey is relishing Sunday's return to former club Cardiff, where he has challenged the Gunners to enter the international break on a high Arsenal midfielder Aaron Ramsey is relishing Sunday's return to former club Cardiff, where he has challenged the Gunners to enter the international break on a high

Aaron Ramsey says he is "excited and optimistic" for the future at Arsenal under Unai Emery.

The midfielder, who is entering the final year of his contract, has started two of the three Premier League matches Arsenal have played under Emery so far this season.

One of those was last weekend's 3-1 win over West Ham as the Gunners picked up their first points of the campaign.

Ramsey says he is 'optimistic' about Arsenal life under Unai Emery

And ahead of a trip to his former club Cardiff on Sunday, live on Sky Sports Premier League from 12.30pm, Ramsey said: "I am very excited and optimistic.

We are all very positive and optimistic and we are all on board with it.

"It's just a case of us sticking to it, becoming more comfortable, doing the things the manager wants us to do, and I am sure we will get stronger and stronger as the season goes on."

Ramsey speaks exclusively to Sky Sports ahead of the Super Sunday clash with Cardiff

Asked how Emery differs from previous Arsenal manager Arsene Wenger, Ramsey said: "He's a lot more detailed on the opposition and we are playing different styles and trying to press a lot higher up the pitch and trying to hold a higher line to win the ball back in more dangerous areas.

"We have done it well at times but we are very early on in the process.

Any time there is a change it takes a period of time to adapt and we are in that situation."

Wales international Ramsey started his career with Cardiff before leaving in 2008 to join Arsenal.

0:33 Watch as a phone call interrupts Unai Emery's press conference Watch as a phone call interrupts Unai Emery's press conference

He says he is relishing the opportunity to return, but expects a "battle" against the newly-promoted Bluebirds.

"I know how difficult it will be to win there," he said. "

They have a couple of clean sheets already this season.

"Hopefully we can get our passing game going early to settle us down.

It's a great opportunity to go into the international break with another win under our belts and hopefully then we can build when we come back and really kick on."

Watch Cardiff v Arsenal live on Sky Sports from 12.30pm on Sunday


                  IN THE SUPREME COURT OF PENNSYLVANIA
                             EASTERN DISTRICT

DARRYL A. BELL,                        :   No.

39 EAP 2014
                                       :
                   Appellant           :   Appeal from the Order of the
                                       :   Commonwealth Court entered on July 24,
                                       :   2014 at No.

181 MD 2013
            v.                         :
                                       :
                                       :
PENNSYLVANIA BOARD OF                  :
PROBATION AND PAROLE,                  :
                                       :
                   Appellee            :

                                    ORDER

PER CURIAM
     AND NOW, this 27th day of April, 2015, the Order of the Commonwealth Court is

AFFIRMED.


Calcitonin gene-related peptide increase in the rat spinal dorsal horn and dorsal column nucleus following peripheral nerve injury: up-regulation in a subpopulation of primary afferent sensory neurons.

Calcitonin gene-related peptide in sensory primary afferent neurons has an excitatory effect on postsynaptic neurons and potentiates the effect of substance P in the rat spinal dorsal horn.

It has been established that calcitonin gene-related peptide expression in dorsal root ganglion neurons is depressed, and the effect of calcitonin gene-related peptide on dorsal horn neurons is attenuated, following peripheral nerve injury.

We report here that a subpopulation of injured dorsal root ganglion neurons show increased expression of calcitonin gene-related peptide.

Using in situ hybridization and the retrograde tracer, FluoroGold, we detected an increased number of medium- to large-sized rat dorsal root ganglion neurons projecting to the gracile nucleus that expressed alpha-calcitonin gene-related peptide messenger RNA following spinal nerve transection.

Immunohistochemistry revealed a significant increase in calcitonin gene-related peptide immunoreactivity in the gracile nucleus and in laminae III-IV of the spinal dorsal horn.

These results indicate that a subpopulation of dorsal root ganglion neurons express alpha-calcitonin gene-related peptide messenger RNA in response to peripheral nerve injury, and transport this peptide to the gracile nucleus and to laminae III-IV of the spinal dorsal horn.

The increase of the excitatory neuropeptide, calcitonin gene-related peptide, in sites of primary afferent termination may affect the excitability of postsynaptic neurons, and have a role in neuronal plasticity following peripheral nerve injury.


Multi-source hypothesis

The Multi-source hypothesis is a proposed solution to the synoptic problem, holding that Matthew, Mark, and Luke are not directly interdependent but have each drawn from a distinct combination of earlier documents.

It encompasses a family of theories differing in the particulars of the nature and relationships of these earlier documents.

An early form of the theory was proposed by Marsh over two centuries ago.

More recently, Boismard proposed a structurally similar theory, which was further developed by Rolland and Burkett.

According to these theories, the common material among the three synoptic gospels ultimately derives from a proto-gospel somewhat like Mark.

This proto-gospel underwent two independent revisions, A and B. Mark was formed by recombining these two revisions.

Matthew built upon A and Luke upon B. Both Matthew and Luke also drew from a common source Q, as well as other source sources for their unique material.

See also
 Two-source hypothesis
 Source criticism

Notes

References
  (translated by Lorraine Caza, Robert Beck and Francis Martin)

Category:Synoptic problem
Category:Hypotheses


All we ever hear from you is blah blah blah
So, all we ever do is go ya ya ya
And we dont even care about what they say cause its
Ya ya ya ya
Blah blah blah blah
All we ever hear from you is blah blah blah
So, all we ever do is go ya ya ya
And we dont even care about what they say cause its
Ya ya ya ya
Blah blah blah blah
All we ever hear from you is blah blah blah
Blah blah blah
Blah blah blah
Blah blah blah
Blah blah blah
Blah blah blah
Blah blah blah.....
All we ever hear from you is blah blah blah
So, all we ever do is go ya ya ya
And we dont even care about what they say cause its
Ya ya ya ya
Blah blah blah blah
All we ever hear from you is blah blah blah
So, all we ever do is go ya ya ya
And we dont even care about what they say cause its
Ya ya ya ya
Blah blah blah blah
All we ever hear from you is blah blah blah
So, all we ever do is go ya ya ya
And we dont even care about what they say cause its
Ya ya ya ya
Blah blah blah blah
All we ever hear from you is blah blah blah
So, all we ever do is go ya ya ya
And we dont even care about what they say cause its
Ya ya ya ya
Blah blah blah blah


She walks all late at night
As she says her goodbyes
Shes close to sacrifice
She knows something aint right
It aint easy
And it aint pleasing
Shes just tryna find a way out there
She put our faith in
You know its chasing
Shes just tryna find her love out there
And she cries
I need you, hm
Like the wave needs an ocean
Like the love needs devotion
I need you
Like the desert pleads for water
Like a mission needs a martyr
I need you
Every minute, every hour
I lay broken like a flower
Cause I need you, I need you
Shes lost her way of life
Feels like a hole inside
Hmm, she wants to feel alive
And nothing satisfies
It aint easy
And it aint pleasing
Shes just tryna find a way out there
She put our faith in
You know its chasing
Shes just tryna find her love out there
And she cries
I need you, hm
Like the wave needs an ocean
Like the love needs devotion
I need you
Every minute, every hour
I lay broken like a flower
Cause I need you
Like the wave needs an ocean
Like the love needs devotion
I need you
Like the desert pleads for water
Like a mission needs a martyr
I need you
Every minute, every hour
I lay broken like a flower
Cause I need you


Im not saying Im not sorry
Just looking for another you
Its not getting any better
But what can I do
When theres something wrong
When we keep holding on
To the best days
Im not saying Im not sorry
Just looking for another you
Looking for another you
Just looking for another you
Another, another you
Just looking for another
Looking for another
Looking for another you
Looking for another you
Just looking for another
Looking for another you
I dont know any other way to show
Its not getting any better
I think we both know that
Theres something wrong
When we keep holding on
To the best days
Im not saying Im not sorry
Just looking for another you
Looking for another you
Looking for another you
Looking for another you
Looking for another you
Just looking for another
Looking for another you
Looking for another you
Looking for another you
Looking for another you
Looking for another you
Just looking for another you


She woke up in the morning
With the sunrise in her eyes
But all that she sees is darkness
She wont tell you why
No more butterflies cause they dont ever last
Stolen from the light by demons of the past
Its always raining
But she keeps on praying
Oh, sunny days
Lift me when Im down
Oh, sunny days
Breaking through the clouds
Oh, sunny days
Oh, oh, ooh
She keeps on praying
Oh, oh, ooh
She keeps on praying
Oh, oh, ooh
She keeps on praying
All she needs is shelter
Shelter for the night
And this one could be heaven
But shes looking down the line
No more butterflies cause they dont ever last
Stolen from the light by demons of the past
Its always raining
She keeps on praying
Oh, sunny days
Lift me when Im down
Oh, sunny days
Breaking through the clouds
Oh, sunny days
Oh, oh, ooh
She keeps on praying
Oh, oh, ooh
She keeps on praying
Oh, oh, ooh
She keeps on praying
Sunny days
Lift me when Im down
Oh, sunny days


I cant dance, cause my feet wont let me
And I cant love, cause my heart wont let go
I cant tell no one the way I really feel
Cause I dont feel nothing no more
And I came here
To remember what its like
To hold someone
And then I saw you dancing on your own and
You look like therapy
Exactly what I need
Youre where the darkness meets the light, yeah
The perfect remedy
To heal whats hurting me
Come be the therapy for me
And me for you, tonight
I can tell by the way that you move
Theres a cure for me somewhere in you
Your touch, your kiss, and your body next to mine
I just want a bit of your time
And I came here
To remember what its like
To hold someone
Now Im close enough to you to know that
You look like therapy
Exactly what I need
Youre where the darkness meets the light, yeah
The perfect remedy
To heal whats hurting me
Come be the therapy for me
And me for you, tonight
And I came here
Cause Im trying to numb my pain
And oh, my dear
I think you might be trying to do the same, yeah
You look like therapy
Exactly what I need
Youre where the darkness meets the light, yeah
The perfect remedy
To heal whats hurting me
Come be the therapy for me
And me for you, tonight
Come be the therapy for me
And me for you, yeah
Cmon, now-now
Therapy, therapy, therapy, yeah
You look like therapy
Exactly what I need
Youre where the darkness meets the light
Where the darkness meets the light, yeah
The perfect remedy, ooh
To heal whats hurting me
Heal whats hurting me
Come be the therapy for me
And me for you, tonight


Hello son, youre ready to come into the air
We cant wait to hold you and rock you to sleep in your rocking chair
Before you know it, youll be standing
Crashing round the house like bandits all day
You cant get into too much trouble
Nothings gonna stop me from loving you always
My wild, wild son, run free
Ooh, youll know it when youre where youre supposed to be
Ooh, my wild, wild son, run free
Oooh, youll know it when youre living out your dream
Someday when you leave the house
And the open road calls
Just know your heart wont lie to you
If you learn to listen close
When you meet your queen, go get her
Make sure that she knows youll love her always
Fore you know it, your own childs standing
You can crash around with them like bandits all day
My wild, wild son, run free
Ooh, youll know it when youre where youre supposed to be
Ooh, my wild, wild son, run free
Oooh, youll know it when youre living out your dream
Ooh, my wild, wild son, run free
Oooh, youll know it when youre living out your dream


I had a dream last night - a vision:
I saw a world full of people
Everybody was dancin and screamin loud;
They were just there to listen to the music
Some even had their eyes closed
Everybody was just smilin
It was deep
It was underground
Tranceparent
It was magical
It was a happy place
Celebrating music!
Celebrating life!
Men and women - free - without a worry
Then, when I woke up, I realized:
I wanna BE in that moment
The very essence of my existence is looking for that emotion
And when the weekend comes
I live for that energy


A man is spreading shreded cheese on a pizza.

A man is spreading shredded cheese on an uncooked pizza.


A man is playing on a guitar and singing.

A woman is playing an acoustic guitar and singing.


A person is throwing a cat on to the ceiling.


###############################################################################
##
##  Copyright (C) 2013-2014 Tavendo GmbH
##
##  Licensed under the Apache License, Version 2.0 (the "License");
##  you may not use this file except in compliance with the License.
##  You may obtain a copy of the License at
##
##      http://www.apache.org/licenses/LICENSE-2.0
##
##  Unless required by applicable law or agreed to in writing, software
##  distributed under the License is distributed on an "AS IS" BASIS,
##  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
##  See the License for the specific language governing permissions and
##  limitations under the License.
##
###############################################################################

from autobahn.asyncio.websocket import WebSocketClientProtocol, \
                                       WebSocketClientFactory

import asyncio

class MyClientProtocol(WebSocketClientProtocol):

   def onConnect(self, response):
      print("Server connected: {0}".format(response.peer))

   @asyncio.coroutine
   def onOpen(self):
      print("WebSocket connection open.")

      ## start sending messages every second ..
      while True:
         self.sendMessage(u"Hello, world!".encode('utf8'))
         self.sendMessage(b"\x00\x01\x03\x04", isBinary = True)
         yield from asyncio.sleep(1)

   def onMessage(self, payload, isBinary):
      if isBinary:
         print("Binary message received: {0} bytes".format(len(payload)))
      else:
         print("Text message received: {0}".format(payload.decode('utf8')))

   def onClose(self, wasClean, code, reason):
      print("WebSocket connection closed: {0}".format(reason))

if __name__ == '__main__':

   import asyncio

   factory = WebSocketClientFactory("ws://localhost:9000", debug = False)
   factory.protocol = MyClientProtocol

   loop = asyncio.get_event_loop()
   coro = loop.create_connection(factory, '127.0.0.1', 9000)
   loop.run_until_complete(coro)
   loop.run_forever()
   loop.close()


from itertools import chain

from django.utils.itercompat import is_iterable

class Tags:
    """
    Built-in tags for internal checks.
    """
    admin = 'admin'
    caches = 'caches'
    compatibility = 'compatibility'
    database = 'database'
    models = 'models'
    security = 'security'
    signals = 'signals'
    templates = 'templates'
    urls = 'urls'

class CheckRegistry:

    def __init__(self):
        self.registered_checks = set()
        self.deployment_checks = set()

    def register(self, check=None, *tags, **kwargs):
        """
        Can be used as a function or a decorator. Register given function
        `f` labeled with given `tags`. The function should receive **kwargs
        and return list of Errors and Warnings.

        Example::

            registry = CheckRegistry()
            @registry.register('mytag', 'anothertag')
            def my_check(apps, **kwargs):
                # ... perform checks and collect `errors` ...
                return errors
            # or
            registry.register(my_check, 'mytag', 'anothertag')
        """
        kwargs.setdefault('deploy', False)

        def inner(check):
            check.tags = tags
            checks = self.deployment_checks if kwargs['deploy'] else self.registered_checks
            checks.add(check)
            return check

        if callable(check):
            return inner(check)
        else:
            if check:
                tags += (check, )
            return inner

    def run_checks(self, app_configs=None, tags=None, include_deployment_checks=False):
        """
        Run all registered checks and return list of Errors and Warnings.
        """
        errors = []
        checks = self.get_checks(include_deployment_checks)

        if tags is not None:
            checks = [check for check in checks if not set(check.tags).isdisjoint(tags)]
        else:
            # By default, 'database'-tagged checks are not run as they do more
            # than mere static code analysis.
            checks = [check for check in checks if Tags.database not in check.tags]

        for check in checks:
            new_errors = check(app_configs=app_configs)
            assert is_iterable(new_errors), (
                "The function %r did not return a list. All functions registered "
                "with the checks registry must return a list." % check)
            errors.extend(new_errors)
        return errors

    def tag_exists(self, tag, include_deployment_checks=False):
        return tag in self.tags_available(include_deployment_checks)

    def tags_available(self, deployment_checks=False):
        return set(chain.from_iterable(
            check.tags for check in self.get_checks(deployment_checks)
        ))

    def get_checks(self, include_deployment_checks=False):
        checks = list(self.registered_checks)
        if include_deployment_checks:
            checks.extend(self.deployment_checks)
        return checks

registry = CheckRegistry()
register = registry.register
run_checks = registry.run_checks
tag_exists = registry.tag_exists


"""
The :mod:`sklearn.utils` module includes various utilites.
"""

from collections import Sequence

import numpy as np
from scipy.sparse import issparse
import warnings

from .murmurhash import murmurhash3_32
from .validation import (as_float_array, check_arrays, safe_asarray,
                         assert_all_finite, array2d, atleast2d_or_csc,
                         atleast2d_or_csr, warn_if_not_float,
                         check_random_state)
from .class_weight import compute_class_weight

__all__ = ["murmurhash3_32", "as_float_array", "check_arrays", "safe_asarray",
           "assert_all_finite", "array2d", "atleast2d_or_csc",
           "atleast2d_or_csr", "warn_if_not_float", "check_random_state",
           "compute_class_weight"]

# Make sure that DeprecationWarning get printed
warnings.simplefilter("always", DeprecationWarning)

class deprecated(object):
    """Decorator to mark a function or class as deprecated.

    Issue a warning when the function is called/the class is instantiated and
    adds a warning to the docstring.

    The optional extra argument will be appended to the deprecation message
    and the docstring. Note: to use this with the default value for extra, put
    in an empty of parentheses:

    >>> from sklearn.utils import deprecated
    >>> deprecated() # doctest: +ELLIPSIS
    <sklearn.utils.deprecated object at ...>

    >>> @deprecated()
    ... def some_function(): pass
    """

    # Adapted from http://wiki.python.org/moin/PythonDecoratorLibrary,
    # but with many changes.

    def __init__(self, extra=''):
        """
        Parameters
        ----------
        extra: string
          to be added to the deprecation messages

        """
        self.extra = extra

    def __call__(self, obj):
        if isinstance(obj, type):
            return self._decorate_class(obj)
        else:
            return self._decorate_fun(obj)

    def _decorate_class(self, cls):
        msg = "Class %s is deprecated" % cls.__name__
        if self.extra:
            msg += "; %s" % self.extra

        # FIXME: we should probably reset __new__ for full generality
        init = cls.__init__

        def wrapped(*args, **kwargs):
            warnings.warn(msg, category=DeprecationWarning)
            return init(*args, **kwargs)
        cls.__init__ = wrapped

        wrapped.__name__ = '__init__'
        wrapped.__doc__ = self._update_doc(init.__doc__)
        wrapped.deprecated_original = init

        return cls

    def _decorate_fun(self, fun):
        """Decorate function fun"""

        msg = "Function %s is deprecated" % fun.__name__
        if self.extra:
            msg += "; %s" % self.extra

        def wrapped(*args, **kwargs):
            warnings.warn(msg, category=DeprecationWarning)
            return fun(*args, **kwargs)

        wrapped.__name__ = fun.__name__
        wrapped.__dict__ = fun.__dict__
        wrapped.__doc__ = self._update_doc(fun.__doc__)

        return wrapped

    def _update_doc(self, olddoc):
        newdoc = "DEPRECATED"
        if self.extra:
            newdoc = "%s: %s" % (newdoc, self.extra)
        if olddoc:
            newdoc = "%s\n\n%s" % (newdoc, olddoc)
        return newdoc

def safe_mask(X, mask):
    """Return a mask which is safe to use on X.

    Parameters
    ----------
    X : {array-like, sparse matrix}
        Data on which to apply mask.

    mask: array
        Mask to be used on X.

    Returns
    -------
        mask
    """
    mask = np.asanyarray(mask)
    if np.issubdtype(mask.dtype, np.int):
        return mask

    if hasattr(X, "toarray"):
        ind = np.arange(mask.shape[0])
        mask = ind[mask]
    return mask

def resample(*arrays, **options):
    """Resample arrays or sparse matrices in a consistent way

    The default strategy implements one step of the bootstrapping
    procedure.

    Parameters
    ----------
    `*arrays` : sequence of arrays or scipy.sparse matrices with same shape[0]

    replace : boolean, True by default
        Implements resampling with replacement. If False, this will implement
        (sliced) random permutations.

    n_samples : int, None by default
        Number of samples to generate. If left to None this is
        automatically set to the first dimension of the arrays.

    random_state : int or RandomState instance
        Control the shuffling for reproducible behavior.

    Returns
    -------
    Sequence of resampled views of the collections. The original arrays are
    not impacted.

    Examples
    --------
    It is possible to mix sparse and dense arrays in the same run::

      >>> X = [[1., 0.], [2., 1.], [0., 0.]]
      >>> y = np.array([0, 1, 2])

      >>> from scipy.sparse import coo_matrix
      >>> X_sparse = coo_matrix(X)

      >>> from sklearn.utils import resample
      >>> X, X_sparse, y = resample(X, X_sparse, y, random_state=0)
      >>> X
      array([[ 1.,  0.],
             [ 2.,  1.],
             [ 1.,  0.]])

      >>> X_sparse                   # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
      <3x2 sparse matrix of type '<... 'numpy.float64'>'
          with 4 stored elements in Compressed Sparse Row format>

      >>> X_sparse.toarray()
      array([[ 1.,  0.],
             [ 2.,  1.],
             [ 1.,  0.]])

      >>> y
      array([0, 1, 0])

      >>> resample(y, n_samples=2, random_state=0)
      array([0, 1])

    See also
    --------
    :class:`sklearn.cross_validation.Bootstrap`
    :func:`sklearn.utils.shuffle`
    """
    random_state = check_random_state(options.pop('random_state', None))
    replace = options.pop('replace', True)
    max_n_samples = options.pop('n_samples', None)
    if options:
        raise ValueError("Unexpected kw arguments: %r" % options.keys())

    if len(arrays) == 0:
        return None

    first = arrays[0]
    n_samples = first.shape[0] if hasattr(first, 'shape') else len(first)

    if max_n_samples is None:
        max_n_samples = n_samples

    if max_n_samples > n_samples:
        raise ValueError("Cannot sample %d out of arrays with dim %d" % (
            max_n_samples, n_samples))

    arrays = check_arrays(*arrays, sparse_format='csr')

    if replace:
        indices = random_state.randint(0, n_samples, size=(max_n_samples,))
    else:
        indices = np.arange(n_samples)
        random_state.shuffle(indices)
        indices = indices[:max_n_samples]

    resampled_arrays = []

    for array in arrays:
        array = array[indices]
        resampled_arrays.append(array)

    if len(resampled_arrays) == 1:
        # syntactic sugar for the unit argument case
        return resampled_arrays[0]
    else:
        return resampled_arrays

def shuffle(*arrays, **options):
    """Shuffle arrays or sparse matrices in a consistent way

    This is a convenience alias to ``resample(*arrays, replace=False)`` to do
    random permutations of the collections.

    Parameters
    ----------
    `*arrays` : sequence of arrays or scipy.sparse matrices with same shape[0]

    random_state : int or RandomState instance
        Control the shuffling for reproducible behavior.

    n_samples : int, None by default
        Number of samples to generate. If left to None this is
        automatically set to the first dimension of the arrays.

    Returns
    -------
    Sequence of shuffled views of the collections. The original arrays are
    not impacted.

    Examples
    --------
    It is possible to mix sparse and dense arrays in the same run::

      >>> X = [[1., 0.], [2., 1.], [0., 0.]]
      >>> y = np.array([0, 1, 2])

      >>> from scipy.sparse import coo_matrix
      >>> X_sparse = coo_matrix(X)

      >>> from sklearn.utils import shuffle
      >>> X, X_sparse, y = shuffle(X, X_sparse, y, random_state=0)
      >>> X
      array([[ 0.,  0.],
             [ 2.,  1.],
             [ 1.,  0.]])

      >>> X_sparse                   # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
      <3x2 sparse matrix of type '<... 'numpy.float64'>'
          with 3 stored elements in Compressed Sparse Row format>

      >>> X_sparse.toarray()
      array([[ 0.,  0.],
             [ 2.,  1.],
             [ 1.,  0.]])

      >>> y
      array([2, 1, 0])

      >>> shuffle(y, n_samples=2, random_state=0)
      array([0, 1])

    See also
    --------
    :func:`sklearn.utils.resample`
    """
    options['replace'] = False
    return resample(*arrays, **options)

def safe_sqr(X, copy=True):
    """Element wise squaring of array-likes and sparse matrices.

    Parameters
    ----------
    X : array like, matrix, sparse matrix

    Returns
    -------
    X ** 2 : element wise square
    """
    X = safe_asarray(X)
    if issparse(X):
        if copy:
            X = X.copy()
        X.data **= 2
    else:
        if copy:
            X = X ** 2
        else:
            X **= 2
    return X

def gen_even_slices(n, n_packs):
    """Generator to create n_packs slices going up to n.

    Examples
    --------
    >>> from sklearn.utils import gen_even_slices
    >>> list(gen_even_slices(10, 1))
    [slice(0, 10, None)]
    >>> list(gen_even_slices(10, 10))                     #doctest: +ELLIPSIS
    [slice(0, 1, None), slice(1, 2, None), ..., slice(9, 10, None)]
    >>> list(gen_even_slices(10, 5))                      #doctest: +ELLIPSIS
    [slice(0, 2, None), slice(2, 4, None), ..., slice(8, 10, None)]
    >>> list(gen_even_slices(10, 3))
    [slice(0, 4, None), slice(4, 7, None), slice(7, 10, None)]
    """
    start = 0
    for pack_num in range(n_packs):
        this_n = n // n_packs
        if pack_num < n % n_packs:
            this_n += 1
        if this_n > 0:
            end = start + this_n
            yield slice(start, end, None)
            start = end

def tosequence(x):
    """Cast iterable x to a Sequence, avoiding a copy if possible."""
    if isinstance(x, np.ndarray):
        return np.asarray(x)
    elif isinstance(x, Sequence):
        return x
    else:
        return list(x)

class ConvergenceWarning(Warning):
    "Custom warning to capture convergence problems"


#!/usr/bin/python
# encoding: utf-8 -*-

# Copyright: (c) 2013, Matthias Vogelgesang <matthias.vogelgesang@gmail.com>
# GNU General Public License v3.0+ (see COPYING or https://www.gnu.org/licenses/gpl-3.0.txt)

from __future__ import absolute_import, division, print_function
__metaclass__ = type

ANSIBLE_METADATA = {'metadata_version': '1.1',
                    'status': ['preview'],
                    'supported_by': 'community'}

DOCUMENTATION = '''
---
module: kernel_blacklist
author:
- Matthias Vogelgesang (@matze)
version_added: '1.4'
short_description: Blacklist kernel modules
description:
    - Add or remove kernel modules from blacklist.
options:
    name:
        description:
            - Name of kernel module to black- or whitelist.
        required: true
    state:
        description:
            - Whether the module should be present in the blacklist or absent.
        choices: [ absent, present ]
        default: present
    blacklist_file:
        description:
            - If specified, use this blacklist file instead of
              C(/etc/modprobe.d/blacklist-ansible.conf).
'''

EXAMPLES = '''
- name: Blacklist the nouveau driver module
  kernel_blacklist:
    name: nouveau
    state: present
'''

import os
import re

from ansible.module_utils.basic import AnsibleModule

class Blacklist(object):
    def __init__(self, module, filename, checkmode):
        self.filename = filename
        self.module = module
        self.checkmode = checkmode

    def create_file(self):
        if not self.checkmode and not os.path.exists(self.filename):
            open(self.filename, 'a').close()
            return True
        elif self.checkmode and not os.path.exists(self.filename):
            self.filename = os.devnull
            return True
        else:
            return False

    def get_pattern(self):
        return r'^blacklist\s*' + self.module + '$'

    def readlines(self):
        f = open(self.filename, 'r')
        lines = f.readlines()
        f.close()
        return lines

    def module_listed(self):
        lines = self.readlines()
        pattern = self.get_pattern()

        for line in lines:
            stripped = line.strip()
            if stripped.startswith('#'):
                continue

            if re.match(pattern, stripped):
                return True

        return False

    def remove_module(self):
        lines = self.readlines()
        pattern = self.get_pattern()

        if self.checkmode:
            f = open(os.devnull, 'w')
        else:
            f = open(self.filename, 'w')

        for line in lines:
            if not re.match(pattern, line.strip()):
                f.write(line)

        f.close()

    def add_module(self):
        if self.checkmode:
            f = open(os.devnull, 'a')
        else:
            f = open(self.filename, 'a')

        f.write('blacklist %s\n' % self.module)

        f.close()

def main():
    module = AnsibleModule(
        argument_spec=dict(
            name=dict(type='str', required=True),
            state=dict(type='str', default='present', choices=['absent', 'present']),
            blacklist_file=dict(type='str')
        ),
        supports_check_mode=True,
    )

    args = dict(changed=False, failed=False,
                name=module.params['name'], state=module.params['state'])

    filename = '/etc/modprobe.d/blacklist-ansible.conf'

    if module.params['blacklist_file']:
        filename = module.params['blacklist_file']

    blacklist = Blacklist(args['name'], filename, module.check_mode)

    if blacklist.create_file():
        args['changed'] = True
    else:
        args['changed'] = False

    if blacklist.module_listed():
        if args['state'] == 'absent':
            blacklist.remove_module()
            args['changed'] = True
    else:
        if args['state'] == 'present':
            blacklist.add_module()
            args['changed'] = True

    module.exit_json(**args)

if __name__ == '__main__':
    main()