text:
LittleBigPlanet has over 600,000 user-created levels
Media Molecule announced via a lovely press release this morning that LittleBigPlanet now has over 600,000 user-created levels.
That's 35,373 levels created per week since release, bringing the grand total to 646,135.
There are 1,934,700 members in the game's online community, apparently.
Full thing below.
'LittleBigPlanet(TM)' Adds Top Awards from Academy of Interactive Arts and Sciences, British Academy of Film and Television Arts and NAVGTR(TM) Corp. to Extensive List of Accolades
FOSTER CITY, Calif., March 20 /PRNewswire/ -- Sony Computer Entertainment America (SCEA) today celebrates the recent success of the PLAYSTATION(R)3 (PS3(TM)) exclusive and community-based title, LittleBigPlanet(TM), which has been recognized with a slew of industry awards since its launch. Of particular note, LittleBigPlanet was awarded an impressive eight awards, including the prestigious Game of the Year title, at the Academy of Interactive Arts & Sciences' (AIAS) 12th Annual Interactive Achievement Awards(R) (IAA) in Las Vegas, and was recognized by the British Academy of Film and Television Arts (BAFTA) organization for the Best Artistic Achievement GAME award. In addition, the National Academy of Video Game Testers and Reviewers Corp.(TM) (NAVGTR) presented LittleBigPlanet with a leading six awards, including Game of the Year, as part of the 8th Annual NAViGaTR Awards.
Since its launch, LittleBigPlanet has dominated video game industry award categories with over 60 award wins, making it the most decorated PS3 system title to date. Additionally, the number of nominations LittleBigPlanet has received is another indicator of its success, including recent nominations for seven awards, including Game of the Year, as a part of the upcoming 9th Annual Game Developers Choice Awards. Winners will be announced on Wednesday, March 25.
"We've been overwhelmed by the positive reception LittleBigPlanet has received from gamers," said Alex Evans, Co-founder and Technical Director of Media Molecule. "The awards it has received further validate the goals we had in mind when we began development. As the proud parents of SackBoy(TM), we couldn't be happier about how well he has been accepted by the gaming community."
"The Annual Interactive Achievement Awards aim to showcase the best confluence of art and science," said Joseph Olin, President of the Academy of Interactive Arts and Sciences. "LittleBigPlanet's combination of creativity and innovation make it the standout among a field of strong contenders and we're proud that this game acts as a shining example of the talent and skill represented by the video game industry."
Since its launch last October, LittleBigPlanet has made a tremendous impact on the game industry and overall gaming community by allowing players from across the globe to interact through the LittleBigPlanet online community via shared levels and objects, rankings and user comments. With over 646,135 levels*(1) created by users alone, the worldwide LittleBigPlanet community continues to grow exponentially to encompass 1,934,700 total users*(2) in the LBP online community with an average of 35,373 user-generated levels*(3) uploaded each week in the short period of time it has been available.
Recognized for its unique, groundbreaking and genre-defying abilities, LittleBigPlanet continues to receive accolades from the game industry's key media outlets, which include IGN (Best PS3 Platform Game of 2008, Most Innovative Design), GameTrailers (Most Innovative Game of 2008), GameSpy (Best PS3 Game of 2008) and GamePro (Game of the Year 2008), to name just a few. Additionally, LittleBigPlanet has been awarded with several highly coveted E3 honors by 1Up (E3 2008 Game of Show), G4TV (Best E3 Original Game, E3 Game of Show), GameCritics (Best E3 Console Game) and GameSpot (Best Platform Game at E3), recently received two awards and eight nominations at the 2008 Spike Videogame Awards, as well as has been listed in top 10 and favorite videogame lists by the Associated Press, and in USA Today, Entertainment Weekly, MSNBC.com, WIRED Magazine and PC World.
Developed exclusively for the PS3 system by Media Molecule, LittleBigPlanet allows players to control the iconic and customizable SackBoy as they play, create and share their masterpieces in a stitched-together, 3D-crafted material world. LittleBigPlanet invites players of all ages and skill types into an innovative and imaginative experience where they can explore and play through developer and user-generated levels. Additionally, players can use the same design tools, materials and stickers Media Molecule built the actual game with to shape or build objects and entire game levels. Players can then share these creations with the LittleBigPlanet community online via PlayStation(R)Network.
LittleBigPlanet features cooperative multiplayer gameplay, which allows up to four players the opportunity to play and complete their levels offline with friend at home or play online with other members of the community.
For more information on LittleBigPlanet, please visit: http://www.littlebigplanet.com/


text:
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Though there might be nothing like the very best online sports betting site , each individual can select his/her betting websites by getting influenced by some common factors. Experts throughout the world have many different notions of a betting website but every one of them recognize some basic factors. These include trustworthiness, credibility, popularity, size, variety and many others. Every one of these factors has key role in deciding the outcome of the search of the appropriate sports betting site.
One has to make sure that the betting site that one is likely to be associated is trustworthy. This is very important, as minus the guarantee on the site being trustworthy, one will always be frightened of being cheated. Because of this fear, they'll always hesitate to put on heavy bets and will not have the capacity to earn heavy profits ever.
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Best online sports betting site thus is dependent upon the individual accessing it. Mine is for sure gertgambell.net. Bur everybody has different betting needs on the globe and no single site is effective at helping them. So, it is important for a person to search for the most appropriate betting site for their own reasons and that can be done using a mixture of various factors.

subset: commoncrawl


text:
- Proceedings
- Open access
- Published:
Identifying stage-specific protein subnetworks for colorectal cancer
BMC Proceedings volume 6, Article number: S1 (2012)
3130 Accesses
5 Citations
In recent years, many algorithms have been developed for network-based analysis of differential gene expression in complex diseases. These algorithms use protein-protein interaction (PPI) networks as an integrative framework and identify subnetworks that are coordinately dysregulated in the phenotype of interest.
While such dysregulated subnetworks have demonstrated significant improvement over individual gene markers for classifying phenotype, the current state-of-the-art in dysregulated subnetwork discovery is almost exclusively limited to binary phenotype classes. However, many clinical applications require identification of molecular markers for multiple classes.
We consider the problem of discovering groups of genes whose expression signatures can discriminate multiple phenotype classes. We consider two alternate formulations of this problem (i) an all-vs-all approach that aims to discover subnetworks distinguishing all classes, (ii) a one-vs-all approach that aims to discover subnetworks distinguishing each class from the rest of the classes. For the one-vs-all formulation, we develop a set-cover based algorithm, which aims to identify groups of genes such that at least one gene in the group exhibits differential expression in the target class.
We test the proposed algorithms in the context of predicting stages of colorectal cancer. Our results show that the set-cover based algorithm identifying "stage-specific" subnetworks outperforms the all-vs-all approaches in classification. We also investigate the merits of utilizing PPI networks in the search for multiple markers, and show that, with correct parameter settings, network-guided search improves performance. Furthermore, we show that assessing statistical significance when selecting features greatly improves classification performance.
Genome-wide monitoring of mRNA expression, monitored using DNA microarrays and more recently deep sequencing, has proved quite useful in understanding the mechanistic bases of complex human diseases. Systematic analysis of differential gene expression in different phenotypic classes leads to identification of novel biomarkers, which serve as features for phenotype classification, as well as targets for therapeutic intervention. In previous studies, differential analysis of gene expression led to identification of biomarkers for a range of complex diseases, including Parkinson's disease [1], neuroblastoma [2], lung cancer [3] and breast cancer [4].
Traditional analyses generally take a univariate approach to study gene expression and identify genes with significant individual differential expression in the phenotype of interest. However, such univariate approaches are often limited in explaining the underlying mechanisms of complex diseases, which arise from the interplay among multiple genetic and environmental factors. For example, genes that cooperate or complement each other in pathogenesis may not necessarily be differentially expressed individually, but exhibit coordinated dysregulation when considered together.
In order to address the shortcomings of the univariate approaches, Chuang et al. develop an algorithm that integrates gene expression data with protein-protein interaction (PPI) networks to identify reproducible breast cancer metastasis markers composed of multiple interacting proteins ("dysregulated subnetworks") [5]. They show that these subnetwork markers better predict breast cancer metastasis as compared to individually dysregulated genes. Motivated by the demonstrated promise of this approach, several other algorithms are developed for network-based analysis of differential gene expression. In particular, Chowdhury et al. develop a set-cover based heuristic for identification of genes that complement each other in discriminating phenotype and control samples [6]. Phuong et al. further improve on these algorithms by introducing a biclustering algorithm that also accounts for the noise in PPI networks by incorporating reliability scores for PPIs [7]. More recently, recognizing the shortcomings of greedy algorithms in identifying dysregulated subnetworks, Phuong et al. introduce a color-coding based randomized algorithm to identify subnetworks that are highly discriminative of phenotype and control [8]. These methods are also extended to the identification of subnetwork expression signatures that can shed light into the regulatory logic of the relationship between the dysregulation of multiple genes and the disease phenoype. In particular, Chowdury et al. identify subnetworks whose combinatorial expression states are indicative of phenotype by using a branch-and-bound algorithm [9], Dutkowski et al. grow network-guided forests by training decision trees using interacting proteins [10].
All of the existing dysregulated subnetwork discovery algorithms are designed and validated for binary phenotype classes (e.g. cancerous vs. non-cancerous, metastatic vs. non-metastatic, drug responders vs. non-responders) and prove to be promising in terms of accurate classification of samples. However, many progressive diseases such as glioblastoma, breast cancer and colorectal cancer require identification of molecular markers for multiple classes (such as the four stages in colorectal cancer according to Dukes' classification) for effective prognosis and treatment. This implies the necessity of a framework that can also work on datasets with more than two phenotype classes for network-based discovery of disease markers. Although most of the existing algorithms can be applied to multiple phenotype classes in principle, no tool is readily available for this purpose. Furthermore, subnetwork discovery on multi-class datasets requires additional design choices and poses novel algorithmic challenges. These choices include designing criteria to evaluate the dysregulation of a subnetwork; i.e., are we interested in identifying subnetworks that can distinguish all classes from each other at once, or are we interested in identifying subnetworks that serve as indicators for specific classes. The algorithmic challenges, on the other hand, include unproportionately distributed samples across multiple classes. For these reasons, novel algorithms are needed that are robust and can work with datasets that are composed of different number of classes and sample distributions.
Contributions of this study
In this article, we introduce novel algorithms for network-based analysis of differential gene expression on applications that involve multiple phenotype classes. As an important application, we focus particularly on identifying subnetworks that can discriminate different stages of human colorectal cancer (CRC) according to Dukes' classification. We first propose two formulations to generalize information-theoretic measures of subnetwork dysregulation to multiple phenotype classes. These formulations differ in terms of how the target subnetworks discriminate phenotype classes from each other; namely we establish information-theoretic criteria for all-vs-all and one-vs-all discriminative subnetworks. Then, we extend the set-cover based algorithm by Chowdury et al. , NETCOVER, to identify one-vs-all discriminative subnetworks [6]. We also introduce a framework for assessing the statistical significance of the sub-networks identified by the set-cover based algorithm. Using public CRC datasets composed of samples labeled with Dukes' four stages, we investigate the performance of the resulting algorithm, COBALT, in identifying subnetworks that are useful in predicting the stages of colon cancer samples. In particular we perform systematic computational experiments to investigate the following:
We compare the performance of all-vs-all and one-vs-all subnetworks in predicting phenotype and show that one-vs-all discriminative subnetworks are generally more reliable as features for classification.
We investigate the effect of using the PPI network to confine the space for searching groups of genes that are coordinately dysregulated subnetworks. We show that, while expansion of the search space through consideration of indirect interactions improve the classification performance of identified subnetworks, this improvement saturates after a point, demonstrating that PPI networks indeed provide a shortcut to the identification of dysregulated groups of genes. We also show that our efficient set cover based algorithm renders network-free search feasible.
We investigate the effect of using statistically significant subnetworks (as opposed to high-scoring subnetworks) as features for classification and show that assessment of statistical significance facilitates identification of more useful subnetwork features for classification.
In the next section, we start our discussion by proposing two alternate information-theoretic formulations of sub-network dysregulation. We also introduce our set-cover based algorithm, COBALT, for the identification of one-vs-all discriminative subnetworks and propose methods for assessing the statistical significance of the identified subnetworks. Subsequently, in Results Section, we provide comprehensive experimental results on the classification performance of the subnetworks discovered by COBALT in predicting the stage of CRC on two gene expression datasets obtained from the Gene Expression Omnibus. We conclude the paper in Conclusion Section.
In this section, we start by introducing the mathematical background of the information-theoretic formulation of coordinate dysregulation for a set of genes. Subsequently, we propose two alternate approaches for generalizing this notion to multiple phenotype classes. We then introduce COBALT, our set-cover based algorithm that is specifically designed to identify stage-specific discriminative subnetworks. Finally, we introduce a framework for assessing the statistical significance of the identified subnetworks, and describe how these subnetworks can be utilized for classification of samples.
Dysregulation of subnetworks
For a given set of genes and of samples, let E i ∈ R|u| represent the properly normalized gene expression vector for gene , where E i (j) denotes the relative expression of g i in sample . Assume that we have a set , composed of different classes for the phenotype of interest (such as the four stages in colorectal cancer according to Dukes classification) and the phenotype vector C annotates each sample with one of the labels in , i.e., C(j) = t where t ∈ .We also define the set of all samples for a specific phenotype class t as .
Let denote a PPI network where the product of each gene g i ∈ is represented by a node and each edge g i g j represents an interaction between the products of g i and g j . Given a PPI network and a gene expression dataset over multiple phenotype classes, we are interested in finding sets of genes that can together discriminate the phenotype classes with their gene expression signatures. In order to establish the functional relevance of these gene sets and search for these sets more efficiently, we confine the search space to PPI subnetworks, that is groups of proteins that are functionally interrelated through PPIs. Formally, a set of proteins is considered a subnetwork of interest if for all proteins g i ∈ , there is at least one other protein g j ∈ such that g i and g j are connected through at most ℓ hops in the PPI network. Here, ℓ is a parameter that adjusts the trade-off between functional relevance and computational efficiency; a larger ℓ allows searching for functionally less related proteins at the cost of increasing the search space.
For a given subnetwork , Chuang et al. define the subnetwork activity of as , that is the aggregate expression profile of the genes in [5]. Using subnetwork activity, they define an information-theoretic measure to quantify the dysregulation of a subnetwork. This "additive" definition of dysregulation limits the framework to the identification of subnetworks with all genes in the subnetwork dysregulated in the same direction (i.e., all up- or down-regulated in the phenotype of interest), and alternate approaches that compute combinatorial expression signatures are shown to be more powerful [9, 10]. However, this additive formulation serves as a useful starting point to generalize subnetwork dysregulation to phenotypes that involve multiple classes. For this reason, we focus on additive subnetwork activity in this paper.
All-vs-all discriminative power of a subnetwork
It is straightforward to generalize the information-theoretic measure for the dysregulation of a subnetwork [5] to multiple phenotype classes. Namely, the mutual information between the subnetwork activity of S and the multi-class phenotype vector, i.e., Δ all-vs-all (S) = I (E S , C) = H(C) − H(C|E S ), provides a measure of the the reduction in the uncertainty about C given E S . Here, denotes the Shannon entropy of discrete random variable X that can take over values from the set . In our case, the support set for the random variable C is , whereas the support set for the random variable E S is obtained by appropriately quantizing the expression levels.
One-vs-all discriminative power of a subnetwork
Here, we propose an alternate measure to quantify the power of a subnetwork in discriminating multiple phenotype classes from each other. This measure targets discriminating a particular phenotype class from all other classes. Namely, we define class-specific phenotype vector C(t) for class t ∈ as
Then, the mutual information between subnetwork activity and the class-specific phenotype vector C(t), i.e., , provides a measure of the reduction in the uncertainty about class t given E S . This formulation offers a number of benefits as compared to the all-vs-all formulation of discriminative power: (1) The one-vs-all formulation may lead to identification of more interpretable markers, since, for example, it can provide stage-specific molecular signatures for colorectal cancer. (2) The one-vs-all formulation can extract class-specific molecular signatures that may be missed by the all-vs-all formulation because they do not discriminate other classes well. (3) The phenotype random variable takes a smaller number of values, thus offering more statistical power for the same number of samples. The concepts of all-vs-all and one-vs-all disciminative power of subnetworks and the computation of mutual information using these different formulations are illustrated in Figure 1.
Identifying one-vs-all discriminative subnetworks
The problems of identifying subnetworks with maximum or are intractable [5]. However, it is straightforward to generalize the greedy algorithm by Chuang et al. to solve both problems efficiently [5]. This greedy algorithm initializes a subnetwork with a single protein. It then grows the subnetwork by adding the protein in the neighborhood of the subnetwork (i.e., reachable from the subnetwork with ℓ hops) that improves the objective function ( or the most. The algorithm stops either when there is no more protein in the neighborhood to add, or the best improvement provided by a protein in the neighborhood is below a user-defined threshold.
While the explained greedy algorithm is quite effective in efficiently discovering high-scoring subnetworks, it has several drawbacks [6]. First, this algorithm is biased toward identifying subnetworks with very few proteins that exhibit high dysregulation individually. This is because the algorithm lacks global awareness, i.e., it will stop expanding the subnetwork when the best candidate protein to add to the subnetwork has only marginal individual contribution, but may actually contribute a greater deal when additonal proteins are added. Second, this approach requires computation of mutual information for each and every candidate protein in the neighbourhood to be added to the growing subnetwork, which may prove to be costly when the algorithm needs to be run multiple times to assess statistical significance of identified subnetworks. Motivated by these observations, Chowdhury et al. develop a set-cover based algorithm, NETCOVER, which is more effective in discovering proteins that complement each other in discriminating phenotype and control samples [6]. However, NETCOVER is designed for binary phenotype classes and it assumes that the samples are paired. Here, we argue that the algorithmic insights introduced by NETCOVER suit particularly well to the identification of one-vs-all discriminative subnetworks. Based on this observation, we develop COBALT, which generalizes NETCOVER to handle unpaired samples and multiple phenotype classes to identify one-vs-all discriminative subnetworks.
COBALT:Cover-based algorithm for identifying one-vs-all discriminative subnetworks
Recall that a one-vs-all discriminative subnetwork is defined as one with differential subnetwork activity in a specific phenotype class, as compared to all other classes. Since subnetwork activity is defined regularly, the genes in such a subnetwork have to be either all up-regulated or all down-regulated in the phenotype class of interest. Motivated by this observation, COBALT aims to identify subnetworks such that for each sample that belongs to the phenotype class of interest, there exists at least one gene in the subnetwork that is up-regulated (or down-regulated) in that sample. Such subnetworks are said to "cover" the entire patient population that represents the phenotype class of interest.
In order to identify the genes that are up-regulated or down-regulated in each sample, we use the expression of that gene in all samples as the background distribution. Subsequently, we identify samples in which the genes' expression deviates significantly from this background distribution. Namely, for a gene g i , consider the distribution of the expression values E i across all samples. We compute a quantized expression value for g i in sample s j as follows:
Here, μ i and σ i respectively represent the mean and standard deviation of expression value of g i across all samples, i.e., and . We define α as a user-defined threshold parameter for a gene's dysregulation in a sample to be considered significant. We say that a gene g i positively covers a sample s j if , and negatively covers s j if , Following this definition, for a given gene g i and phenotype class t ∈ , we define the positive cover set as the set of all samples with phenotype t that are positively covered by g i , i.e., . Similarly, the negative cover set contains all samples in class t that are negatively covered by gene g i . We illustrate these concepts in Figure 2. It can be shown that the mutual information of C(t) and Ê i is a monotonically non-decreasing function of the cardinalities of and [6], i.e., one can maximize I(Ê i ,C(t)) by maximizing .
Using the negative and positive cover sets for each gene-class pair, COBALT identifies one-vs-all discriminative subnetworks for each phenotype class by using a greedy heuristic that is shown to be effective for the set-cover problem [11]. Namely, for each gene g i , we first identify the target phenotype class for that gene as the phenotype class with largest percentage of samples positively (or negatively) covered by g i . Subsequently, we grow a subnetwork by systematically adding a gene in the neighborhood based on the coverage on the rest of the uncovered samples for that class. Without loss of generality, the algorithm identifies a minimal positive covering subnetwork seeded at gene g i as follows:
Initialize subnetwork: S i ← {g i }
Define the target phenotype class t for this subnetwork as the class that has the maximum fraction of samples positively covered by g i : t ← argmax t ′ ∈
Initialize the set of uncovered samples for class t:
Initialize the set of network neighbors:
For all genes g j ∈ , compute
Find all the genes (can be multiple) in with maximum and let g k be the gene among these genes with minimum (i.e., g k has minimum positive background coverage).
Expand the subnetwork with g k : S i ← S i ∪ {g k }
Update the set of uncovered positive samples for class t: ℳ (t) ← ℳ (t) \ k (t)
Update set of neighbouring genes: ← ∪ {g j ∈ : δ(g k , g j ) ≤ ℓ} \ {g k }
If = ∅ or M(t) = ∅, return S i ; otherwise, go to step (5).
This algorithm is also used for identifying the minimal negative covering subnetwork seeded at g i by simply replacing with above.
Assessing statistical significance of subnetworks
In order to asses the significance of the identified subnetworks, we perform two distinct significance tests. Each significance test is performed by generating an empirical background distribution that carefully accounts for multiple hypothesis testing. The first background distribution is obtained by randomly permuting the class labels. The second background distribution, on the other hand, is obtained by permuting the gene expression profiles (the rows of the gene expression matrix, thereby randomly reestablishing the relationship between the expression profiles and the nodes in the PPI network ). After generating a large number of these randomized datasets, we use COBALT to identify class-specific subnetworks for the randomized datasets as well. We then use these subnetworks as the background distribution to test the statistical significance of the discriminative power of the stage-specific subnetworks identified on the actual dataset. This approach implicitly handles multiple hypothesis testing, since the background distribution is constructed using the most discriminative subnetworks that could be identified on each randomized dataset.
Note also that the cover provided by a subnetwork for a target phenotype class depends on the size of the sub-network (i.e., the number of proteins in the subnetwork). In other words, if we construct subnetworks at random, we would expect larger subnetworks to have a higher coverage. Furthermore, in our experiments, we also observe that larger subnetworks tend to have higher discriminative power (Δ ). Motivated by these insights, we assess the statistical significance of a subnetwork as a function of its size. For this purpose, we stratify the subnetworks that compose each background distribution according to subnetwork size and compute the p-value of a subnetwork S by comparing Δ(S) to the discriminative power of the background subnetworks that have similar size to the subnetwork of interest. More precisely, the p-value of S is defined as the fraction of subnetworks with discriminative power greater than that of S among all subnetworks in the background set with size equal to that of S. A minimal covering subnetwork S discovered by COBALT is considered to be statistically significant if its p-value is less than the significance threshold for both background populations.
Using identified subnetworks for classification
One application of identifying subnetworks that can discriminate multiple phenotype classes is to predict the phenotype class of a test sample using the expression profiles of these subnetworks. This application also provides a useful means for assessing the biological relevance, reproducibility, and utility of the identified subnetworks. In order to use stage-specific subnetworks in colorectal cancer to predict the stage of a patient, following steps are performed:
We first identify both the positive and negative covering subnetworks for each gene g i ∈ .
In order to investigate the effect of statistical significance on the classification utility of subnetworks, we use two alternate strategies to extract a list of features from the sets of covering subnetworks found in (1):
The first approach assumes that high-scoring subnetworks are more useful for classification, as compared to significantly discriminative subnetworks. For each phenotype class t ∈ , we sort the subnetworks based on their all-vs-all or one-vs-all discriminative power. We then choose the top k positive and negative covering subnetworks for each phenotype class, giving us a total of 2*k*|| features to be used in classification. Here, k is a user defined parameter to set the number of stage-specific subnetworks that are used for each class.
The second approach assumes that assessment of statistical significance will facilitate selection of biologically more meaningful subnetworks, also providing more power in classification as compared to high-scoring subnetworks. For this purpose, using the two proposed statistical significance tests discussed in the previous section, we identify subnetworks that are signficant according to both statistical tests and use all of these significantly discriminative subnetworks as features for classification.
Once we obtain a final list of features either using (a) or (b) at step (2), we compute the aggregate expression profiles (E S ) for each of these selected subnetworks and use these to construct feature vectors for each sample (where each feature represents the aggreate expression of one subnetwork).
Finally, we use these feature vectors to train and test classifiers for predicting the class of the phenotype of interest.
- (a)
Results and discussion
In this section, we first give brief information about the colorectal cancer in human (CRC) and introduce the two stage-specific CRC datasets and the PPI network we use in our experiments. Subsequently, we describe in detail the experimental framework used. After introducing the performance evaluation metrics used, we present our experimental results comparing one-vs-all and all-vs-all discriminative subnetworks, as well as the additive and set-cover based algorithms that are used to discover these subnetworks. Next, we analyze the effect of the network distance parameter (ℓ) that adjusts the search space size when growing the subnetworks. Finally, we compare the performance of high-scoring and statistically significant subnetworks in predicting the stages of samples.
Human colorectal cancer
Colorectal Cancer (CRC) is one of the most common causes of cancer related deaths in the western civilization [12]. Diagnosis of CRC is often difficult as the symptoms appear only at the advanced stages of the disease. Moreover, early diagnosis is very critical as the survival rate changes dramatically with the stage of the cancer. In fact, 5 year survival rates when diagnosis is made at the localized stage (cancer is confined in the primary site) and after cancer has metastasized are around 90% and 12% respectively [13]. These observations suggest that, for effective diagnosis, prognosis and treatment, accurate determination of disease stage is crucial.
There are different classification systems for the progression of colorectal cancer. Dukes' famous staging system classifies patients based on how far the cancer is spread [14]. TNM is another staging method providing a more comprehensive framework including information about the size and localization of the tumor, as well as the involvement of lymph nodes [15]. CRC Datasets we use in our experiments are classified by Dukes' staging system.
We use two CRC microarray datasets obtained from the Gene Expression Omnibus [16] in our experiments. These datasets that contain labeled CRC samples with Dukes' 4-stage classification are the following:
GSE14333 contains the expression profiles of 54675 genes in 290 samples.
GSE5206 contains the expression profiles of the same 54675 genes in 98 samples.
The distribution of the samples in each dataset with respect to CRC stage is shown in Table 1.
The human protein-protein interaction data used in our experiments is obtained from NCBI Entrez Gene Database [17]. This database integrates interaction data from several other databases available, such as HPRD, Bi-oGrid, and BIND. We remove the nodes with no interactions to obtain a final PPI network that contains 8959 proteins and 33,528 interactions among these proteins.
Experimental design
COBALT is fully implemented in Matlab. We use this implementation to perform the following classification experiments:
Prediction of disease stage in GSE14333. Subnetworks discovered using GSE14333 are used to predict the stages of samples in the same dataset in a 10-fold cross validation setting. Samples in each phenotype class are randomly separated into ten similar-sized groups. In each iteration, one of the groups in each class is chosen to be the test data and the rest of the data is used to train the classifier.
Prediction of disease stage in GSE5206. Subnetworks discovered using GSE14333 are used to predict the stages of samples in GSE5206. In this cross-classification setting, the classifier is trained on GSE14333 and tested on the other dataset, GSE5206.
For both of these settings, we use a naive Bayesian classifier provided by Matlab's classify function. Using other classifier options provided by Matlab's classifier procedure only marginally effects the results (data not shown).
Classification performance
In order to obtain a comprehensive picture of the performance of different approaches, we list the precision and recall of the classification experiments for each phenotype class separately. Precision refers to the percentage of the correct predictions over all samples predicted belonging to the respective CRC stage, whereas recall refers to the percentage of the correctly predicted samples over all samples that are clinically diagnosed to belong the respective CRC stage. Please note that we set the network distance parameter ℓ = 3 in all experiments unless otherwise noted, since it provides the best performance as shown in the next section. When quantizing the expression values of a gene over all samples using Equation 2, we set α = 2 as the threshold parameter for the gene's dysregulation in a sample to be considered positively or negatively covering.
We compare COBALT with our implementation of the two additive greedy approaches explained in detail in the Methods section, namely additive ova and additive ava . additive ova and additive ava refer respectively to the algorithms that aim to identify one-vs-all and all-vs-all subnetworks by greedily maximizing the discriminative power of the subnetworks In the first set of experiments, we use the 10-fold cross validation framework for prediction of disease stage of samples in GSE14333, using the high-scoring subnetworks extracted as features from the same dataset, i.e., we use the top scoring positive and negative subnetworks for each stage in COBALT (setting k = 1), top 2 subnetworks for each stage in additive ova and top 8 subnetworks for additive ava as features (a total of 8 features used in each method).
As shown in Figure 3, COBALT provides better performance compared to both of the additive approaches in terms of the precision in prediction for all CRC stages. It also provides better recall values for all CRC stages except for samples in Stage A. COBALT achieves 0.84 weighted average precision over all stages where as additive ova and additive ava respectively achieve 0.71 and 0.62 precision. Similarly, COBALT outperforms others by achieving 0.84 weighted average recall over all stages where as additive ova and additive ava respectively provide 0.69 and 0.60 recall.
The effect of the PPI network on classification performance
In this section, we discuss the effect of the PPI network in the classification performance of subnetworks identified by COBALT. Since the use of the PPI network confines the search space to functionally related groups of proteins, these experiments provide insights into whether these functional constraints also improve the biological reproducibility and the utility of identified stage-specific subnetworks. For this purpose, we systematically evaluate the classification performance of the subnetworks for varying network distance parameter (ℓ) that adjusts the search space size when growing the subnetworks using COBALT. We also compare the subnetworks identified by the network-guided algorithm with groups of genes that are identified by using the same algorithm in a network-free fashion. The set-cover based algorithm implemented by COBALT is quite efficient, therefore a network-free search for stage-specific groups of proteins is feasible.
In the PPI network free approach, the next protein to be added to the subnetwork does not need to be in a certain proximity (i.e., ℓ is effectively set to ∞) to the proteins already in the subnetwork. This increases the search space for the algorithm, thus making it infeasible for most of the state-of-the-art algorithms to perform some complex analyses such as statistical significance computations. The effect of the parameter ℓ on the classification performance for each CRC stage is shown in terms of precision and recall in Figures 4 (a) and 4(b) respectively. As seen in the figures, the classification performance (hence the reproducibility) of identified subnetworks improves as PPI network neighborhood is defined more flexibly. This is expected, since the PPI network is incomplete, thus consideration of indirect interactions accounts for missing interactions to a certain extent. However, as the search diameter reaches 3, the classification performance saturates and adding more flexibility to the search does not improve performance any more. This observation suggests that incorporation of PPI networks is useful for increased efficiency of the search, as well as identification of more reproducible subnetworks. Thus we set ℓ = 3 as it is the most reasonable choice in terms of both the classification performance and the computational efficiency of the algorithms.
The effect of using statistically significant subnetworks on classification performance
In this section, we compare the classification performance of high-scoring subnetworks to that of statistically significant subnetworks. On the GSE14333 dataset, COBALT identifies 9 statistically significant subnetworks with 139 unique genes (please see Additional File 1 for the list of genes and the covered stage of colorectal cancer for these 9 subnetworks). Using these subnetworks as features, we predict the stage of the samples in GSE14333 in a 10-fold cross validation framework as previously explained. We choose the same number of features with top . score and compare the classification performance of both approaches. Stage-specific precision and recall values for both approaches are shown in Table 2. As seen in the table, utilizing statistical significance computations when choosing features improve performance for predicting the stages of patients in GSE14333, with less number of unique genes used.
In the cross-classification framework, we use the statistically significant features identified in GSE14333 to predict the classes of samples in the GSE5206 dataset, i.e., the classifier is trained using GSE14333 and tested on GSE5206. The stage-specific precision and recall values are shown in Table 3. The weighted average precision and recall values are 0.57 and 0.56 respectively.
In this article, we have proposed two alternate formulations of the discriminative power of subnetworks when working on multi-class phenotypes, namely, one-vs-all and all-vs-all. We then introduced our cover-based algorithm for network-guided disease marker discovery, for identifying subnetworks with one-vs-all discriminative power. Moreover, we have introduced a framework for assessing the statistical significance of the identified subnetworks. Systematic experiments on real multi-staged CRC datasets show that the proposed algorithm outperforms the additive algorithms in terms of providing higher precision and recall in prediction of sample stages. The efficient implementation of the cover-based algorithm enabled us to show that using statistically significant subnetworks as features improves classification performance compared to using same number of high-scoring subnetworks (in terms of mutual information with respect to the phenotype vector). We have also shown that guiding the subnetwork discovery search with the PPI network identifies subnetworks that are more informative (in terms of classification power) than the net-works identified without the PPI network. We have also investigated the impact of different values of the network distance parameter, ℓ, and concluded that using ℓ = 3 is the most reasonable choice in terms of both classification performance and computational efficiency.
Scherzer CR, Eklund AC, Morse LJ, Liao Z, Locascio JJ, Fefer D, Schwarzschild MA, Schlossmacher MG, Hauser MA, Vance JM, Sudarsky LR, Standaert DG, Growdon JH, Jensen RV, Gullans SR: Molecular markers of early Parkinson's disease based on gene expression in blood. Proceedings of the National Academy of Sciences. 2007, 104 (3): 955-960. 10.1073/pnas.0610204104. [http://www.pnas.org/content/104/3/955.abstract]
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We would like to note the contribution of anonymous reviewers whose queries and suggestions have helped improve this article significantly. MK and SE are supported in part by the National Science Foundation grant CCF-0953195. MK and MRC are also supported in part by the National Institutes of Health grant R01-HL106798. SE is also supported in part by Choose Ohio First Scholarship. JSB and XG are supported by Case Comprehensive Cancer Center Core Grant (5P30 CA043703).
This article has been published as part of BMC Proceedings Volume 6 Supplement 7, 2012: Proceedings from the Great Lakes Bioinformatics Conference 2012. The full contents of the supplement are available online at http://www.biomedcentral.com/bmcproc/supplements/6/S7.
Additional information
Competing interests
No competing interests exist.
Authors' contributions
All authors conceived and formulated the problem; SE and MK conceived and formulated the proposed approach and algorithms; SE implemented and tested the algorithms; RKE provided the expression data; SE and MK drafted the manuscript; all authors reviewed, edited, and approved the final manuscript.
Electronic supplementary material
Additional file 1: List of 9 statistically significant subnetworks identified for GSE14333 dataset. All the gene products in the 9 statistically significant subnetworks identified for GSE14333 dataset are listed, as well as the covered colorectal cancer stage and cover direction of the corresponding subnetworks. Please note that these gene products might not be direct neighbours in the PPI network, as we set the network distance parameter ℓ = 3 in the experiments. (PDF 35 KB)
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This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
About this article
Cite this article
Erten, S., Chowdhury, S.A., Guan, X. et al. Identifying stage-specific protein subnetworks for colorectal cancer. BMC Proc 6 (Suppl 7), S1 (2012). https://doi.org/10.1186/1753-6561-6-S7-S1
DOI: https://doi.org/10.1186/1753-6561-6-S7-S1


text: This document presents the modelling and quantification of the post carbon scenarios (PC 2050) and compares the modelled outcome with business as usual (BAU) scenarios. The subsequent aim of POCACITO was to present the gaps in the proposed PC2050 scenarios and develop enhanced local Roadmaps, ultimately leading to a European Roadmap that facilitates the transition of European cities to a post-carbon future. Fundamentally, the project views the move to post carbon as both an opportunity, as well as a necessity, to ensure the three pillars of sustainability (economic, environmental and social) are incorporated into the essence of the city, thereby transitioning cities, to low carbon, liveable and equal places where a high quality of life is central.


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122 pages in under 2 hours
I finished Bhuvan Shome - an Akademi Award winning book by Bonophul (nom de plume of Balai Chand Mukhopadhyay), translated from Bengali by Lila Ray. Bonophul's other famous works include Mrigaya, Jangam and Sri Madhusudan.
Railway officer Bhuvan Shome is an old & lonely widower. He is a strict disciplinarian and uncompromising in his principles & values. He expects similar dedication & integrity from others. He goes on a duck-hunting trip near the Ganges. Here he is dogged by bad luck - his cart is attacked by a water buffalo, his boat goes adrift & develops a leak, the birds are driven away by hunters on motorboats, his first shot at the birds ends up nowhere. Whew!! He meets a village girl who helps him out. His high standards give way under her simplicity.
Bhuvan's family comprises of an array of depressing characters with a mother obsessed with perfect propriety, adamant sisters and good-for-nothing brothers-in-law along with their thieving brats (fourteen of them), and above all, an ill-tempered wife. Bhuvan's frustrations are compounded by the unhelpful attitude of the women in the house to his hobbies - painting and later on cooking, both of which are brought to an early end. At this point he takes to hunting. His hunting partners at various times include a conceited Bhutnath, a vulgar Dwijen, Chhattu Sen who claims all credit for any kill and Kartik Mukherjee who turns out to be the perfect scarecrow, for they never manage to kill anything with him around. These parts are written in a tart manner which brings about some relief from the almost constant thread of depressing darkness running throughout the book. I suppose the translation reduces the effect of the book somewhat, but nevertheless it portrays the turbulent post-independence times quite well.
This book was made into a Hindi movie by the same name by noted Bengali director Mrinal Sen in 1969 - I haven't seen the movie though. The role of Bhuvan Shome is played by Utpal Dutt & if you ask me, he is just the man for it. If you have seen the laugh riot, Golmaal, you will know what I am talking about (He essays a similar role there - a man with discipline,integrity & all that and, believe it or not, a strong bias against men without a moustache). Suhasini Mulay plays the part of the village girl who is called Gouri in the movie.
Tags : A Bookworm's Diet, Blogger Days, Movies
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Liberia's Weah, media on edge as BBC reporter flees
With one Liberian newspaper facing a $1.8 million defamation case and a BBC journalist fleeing the country, there has been no honeymoon period for the press under the new government of President George Weah.
Accused of wanting to muzzle the media, the former footballer-turned-politician has attempted to reassure journalists saying they would have a "200 percent freedom of expression and press freedom under my government".
The Committee to Protect Journalists (CPJ), however, has expressed concern over the $1.8 million in defamation suits against Front Page Africa, a Liberian newspaper that has carried critical coverage of successive governments.
"Liberia has a troubling history of libel lawsuits where applicants ask for exorbitant damages simply to harass and intimidate journalists, resulting in their imprisonment or the closure of news outlets," said Angela Quintal, CPJ Africa programme coordinator.
"The government should move swiftly to reform Liberia's libel laws to guard against their abuse in this way," Quintal added.
During a visit to Monrovia in March, the UN's rapporteur on freedom of expression David Kaye expressed concern over the consequences of large financial penalties in civil libel suits against Liberian journalists and newspapers.
The Ministry of Information has denied any government involvement in the law suits against Front Page Africa which relate to a private dispute following the publication of an advertisement.
But the newspaper's management and the CPJ blame the situation on its criticism of those in power, highlighting the presence among the complainants of a former member of Weah's party and the absence of legal action against other media outlets.
Fear of reprisals
In another media drama, the BBC's correspondent Jonathan Paye-Layleh left the country saying he feared reprisals by supporters of the president after Weah accused him of being against him.
"My fears go beyond the possibility of the president ordering my arrest some day and formally unsealing the indictment that he has already hinted (at) by his verbal attack," Paye-Layleh said in a message to colleagues.
"I fear more that some of the tens of thousands of Mr President's supporters... could understand his allegations against me to mean that I am his enemy. And you can imagine what could happen to me in some corners without it necessarily being by his directive," he added.
Weah's accusation against the journalist in March followed a visit by UN deputy Secretary-General Amina Mohammed and a question from the journalist on the sensitive subject of the setting up a court for the perpetrators of crimes committed during the 1989-2003 civil war.
A large number of figures directly implicated in the civil war still occupy important positions in the political and economic spheres in the country.
A few days later, Weah said he did not harbour any animosity towards the correspondent and protested his commitment to press freedom.
But at a meeting last week, press bosses said they were "alarmed" by the recent escalation of threats, intimidation and harassment of journalists, according to The Press Union of Liberia (PUL).
"With the departure of the United Nations Mission in Liberia, the Government is the biggest player in the media economy," the PUL noted, highlighting the difficulties faced by the sector due to a slow economy and the desire of some officials to "strangulate" the media by limiting advertising.
Weah tried again to allay the sector's fears last week when he met the heads of all the country's media.
"I want to promise you... 200 percent freedom of expression and press freedom under my government. How can a man like me with soft heart, humble background as well as scores of friends in the country clamp down on free speech. There is no way I can use my position to hunt people for expressing their views to criticise me," he said.
"While it is true the media and journalists are at liberty to write and criticise in whatever way they see, it is also incumbent of the media to mention the achievements of the government," he added.


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Apr. 9 (GIN) – Spoken word artist Julie Wang'ombe – a Duke University undergraduate – has been credited as the author of the victory speech read by President-elect Uhuru Muigai Kenyatta.
The 22 year old Kenyan-born poet is a familiar face at SLAM Africa events, open mic nights and other events around Nairobi. She comes from a writer's family – her father is CEO of Kenya's Nation Media Group.
The gala inauguration ceremony, which took place Apr. 9, drew presidents from around the continent, including Robert Mugabe, Yoweri Museveni, Goodluck Jonathan, Salva Kiir, and dignitaries, including the Rev. Jesse Jackson.
Long before dawn, buses packed with supporters arrived from central Kenya and the Rift Valley, heartlands of Kenyatta's Kikuyu people and of his soon to be vice-president William Ruto.
Kenyatta loyalists, dressed in the red colors of his Jubilee Coalition party, waved farewell to outgoing President Mwai Kibaki, 81, retiring after more than a decade in power.
One of Africa's richest men, Kenyatta, 51, won the March 4 polls by about 8,000 votes ahead of his nearest rival, outgoing Prime Minister Raila Odinga.
Before the election, the US and Europe sought to persuade Kenyans to reject Kenyatta, who faces charges of crimes against humanity at the International Criminal Court, charges which will be presented in July. Britain warned it would maintain only essential contact, noted Hadley Muchela, of IMLU, a Kenyan human rights group.
But the west has since softened its stance, Muchela said. "Is there honesty in these dealings, or is it always just about our interests in other countries in the world?" Diplomats from the UK and the U.S. both attended the swearing-in ceremony.
Meanwhile, Ugandan President Museveni said: "I want to salute the Kenyan voters on … the rejection of the blackmail by the International Criminal Court… The usual opinionated and arrogant actors" tried to use the court to "install leaders of their choice in Africa".
A congratulatory message from Pres. Barack Obama to the Kenyan leaders read: "Now that your election has been confirmed, you have the opportunity to build on the promise of Kenya's Constitution and solidify its place as a vibrant and prosperous democracy centered on the rule of law." The message was relayed by the new U.S. Ambassador, Robert Codec, who replaces State Dept. Africa chief Johnnie Carson who formally retired March 29, ending a 44-year career.
In his inaugural speech today, Apr. 9, Pres. Kenyatta promised to abolish all maternity care fees and make government dispensaries and health centers free of charge within 100 days. Funds reserved for an election run-off would be redirected to a new Youth and Women Fund, and within the first 100 days, measures would be taken to ensure that all public school students joining class one next year would receive a laptop. "We believe that early exposure to technology will inspire future innovation and be a catalyst for growth and prosperity," he said.


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At a joint event today, Verizon and Motorola announced the Droid Turbo. The phone runs on the Verizon XLTE network and features a 5.2" Super AMOLED QHD display with a pixel density of 565ppi.
The Motorola Droid Turbo is powered by a Snapdragon 805 chipset with a 2.7GHz quad-core Krait 450 CPU, 3GB of RAM and an Adreno 420 GPU. The battery is 3,900mAh and Motorola promises it can last for 48 hours of mixed usage.
The phone also promises to be quite resistant to torturing. It's made either of ballistic nylon or metalized glass fiber reinforced with DuPont kevlar. The display is covered with a protective layer of Gorilla Glass 3 and the innards are covered with a water-repellent nanocoating.
The Droid Turbo with Ballistic nylon measures 73.3 x 143.5 x 8.3mm (at its thinnest), while the Metalized fiber one is a bit thinner at 7.8mm.
At the back of the phone, there's a 21MP snapper with a f/2.0 aperture and 4K video recording at 24fps.
The Motorola Droid Turbo will be available starting October 30 and the 32GB model will be available in Metallic Black, Metallic Red, and Black Ballistic Nylon for $199.99 with new two year contract or $25 per month on Verizon Edge. The 64GB Black Ballistic Nylon model will also be available for $249.99 with new two year activation or $27 per month on Verizon Edge.
Except many show that it is.
Why do I get the feeling S5 LTE-A G906S's 5.1" 1440p screen didn't get as much heat as this 5.2" 1440p? Right, 'cause that model is made "for Korea". But that's besides the point. I do think it might just become a reality for Goog...
GSM conditions don't apply if someone else comes up with another set of fixed conditions for THEIR tests and arrive at totally different numbers that don't show the Note 4 or any phone in good light. It depends on so many variables that cant be ...


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The Black Stars coach has apologised for using invective language in the wake of Ghana's 3-1 win over Mozambique while reacting to criticisms of his travels abroad.
Ghana coach Avram Grant has expressed remorse for using an 'offensive language', while hitting back at locals who chastise him for 'neglecting' the domestic league.
In his post match presser after defeating Mozambique 3-1 in the 2017 Africa Cup of Nations qualifiers last week, the 61-year-old Israeli invited Ghanaians to focus on football and not the 'bullsh**t', in reference to criticisms of his frequent absence from the country.
Those comments sparked anger within the Ghana FA, with the local media calling on the Black Stars coach to apologise to the nation.
In a statement released by Ghana FA Communication Division, Grant said he had withdrawn the 'offensive word', expressing his love and respect for Ghanaians.
"First, I withdraw this word [bulls**t]. The thing I would like to say is that I love Ghana and I respect Ghanaians. I care about the Ghanaian team," Grant said.
"What I wanted to say is we need everybody not to focus on the unnecessary things, but only to focus on the most important thing. That our leaders can make the change which can take us forward and not about negative things," he added.
Grant joined Ghana in December 2014, guiding the Black Stars to the final of the 2015 Africa Cup of Nations, losing on penalties to Cote d'Ivoire.
He is close to qualifying the team to the upcoming edition after topping Group H with 10 points, four above second-placed and third-placed Rwanda and Mauritania, on six points each.


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Sid Meier's Civilization: Beyond Earth
In 1991, we saw Sid Meier's Civilization, distributed in CDs and floppy disks for DOS. In 1996, Civilization II was released and ported over to PlayStation in 1999. Six years later, came Civilization III. Civilization IV and V were released in 2005 and 2010 respectively.
Last week, Firaxis announced the release of Civilization VI: Beyond Earth. The game introduction simply says, "Our time on Earth is ending, so we turn our gaze upward, to the stars, and embark on the bravest journey in human history".
Followers of Sid Meier's works will be reminded of Sid Meier's Alpha Centauri from 1999. Lead designer David McDonough dedicates Civilization: Beyond Earth to fans of Alpha Centauri.
Firaxis vaguely described the game as taking place after "The Great Mistake". It happens about 25 years in the future, and causes a Dark Age of humanitarian crisis.
The development team mentioned three new "affinities" that form the focus of the tech tree. They are Harmony, Supremacy, and Purity. Each affinity has its own victory in the game. Civilizations on the Harmony affinity will try to adapt their DNA to fit the environment. The Supremacy affinity, on the other hand, has players researching things like cybernetics and artificial sentience. Lastly, the Purity affinity chooses to leave humans as they are, and adapt the planet to their needs instead.
There are also other lifeforms on the planet – they may or may not be intelligent – that have their own affinities. The eventual winner of the game will be the one who shows the most devotion to their chosen affinity, and dominates the direction of humanity's future.
The game also features a new semi-randomised quest system that introduces an in-game narrative. These quests will show up at different times in different play-throughs, and give different rewards each time.
Source: Ars Technica


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Past Events
Caringal Scout Camp weekend
caringal scout
Moe in Victoria
held over the Easter long weekend from
Friday 10th to Monday 13th April
Caringal Scout Camp is a
lovely spot set in the Moondarra State Forest approx 37 kms North of
Moe. This is a well used Scout Camp with climbing wall, flying fox,
an outdoor chapel which is used quite often. There are also lots of
huts which can be rented by the general public.
Sandra and Ian arrived first and had their camp almost set up
when Rob arrived. I am fairly sure he heard us put the kettle on or
maybe he heard me open the bikkies. Dr Dave arrived the same
afternoon. Then Paul and Annie arrived with their new PONY.
We had a lovely evening under the stars next to a roaring
On Friday Dale, Karen, Emily, Nick and Jeremy arrived. Along
with Dale, Bernie and Nicholas. Also arriving today were David and
Brenda who came with their 3 granddaughters.
Friday night saw us all scampering to find tarps and put up a
make shift shelter which worked extremely well in the small thunder
storm which was the only bad weather for the long weekend.
Saturday night we had another campfire and many a cask of
port was opened and happily consumed along with various other
lemonades. Saturday afternoon our two grandchildren turned up for a
sleepover. Mat 8 and Bailey 3. I had to send Mat to bed quite early
as he was sending Dale to the poor house by the amount of satellites
he was finding. The children , big and small had a great time with
the marsh mallows and sparklers. Not to mention a zillion glow
Sunday morning fresh and early the Easter Bunny had dropped
off a few eggs for the children to find. Not long after that our own
daughter turned up and I used that as a great opportunity to dispose
of one 3yr old grandson back to his parents, Mat decided to stay as
he was having so much fun with Jeremy.
On Sunday Afternoon Ian took a few of the more foolhardy on a
little expedition into the land of 4 wheel driving. The first law of
4 wheel driving was observed and Sandra was left behind. Young Emily
Ian's Nissan Patrol for an hour or so and did have a rather
large smile when they FINALLY came back to camp. Annie also did very
well and drove like she had done it before and it was actually her
very first time. Dale and family also enjoyed the LITTLE excursion.
After some very late dinners we set ourselves up around the
campfire and opened a few more lemonades and the usual chat. A big
thanks to Dr, Dave for getting up bright and early to clear away the
empties. And also a full bottle of Canadian Club. How many did you
say you had Dr. Dave???
Paul and Annie were very happy with their new hot water
system and of course had to show us all how well it worked.
Monday arrived much to quickly. This left Sandy , Mat , Paul
, Dr. Dave and Rob to chat around the fire. Everyone except Paul
left on Tuesday. Wednesday saw Paul caught in a bit of a storm all
by himself waiting for Annie to drive back and pick him up.
I think my biggest lesson from this camp is never camp to
close to home as you get way to many visitors popping in for a free
Thank you every one for making this another memorable camp.
Ian & Sandy
photos from Dave


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TFT-MegaPixel™ Budget Dual Screen 8.4-Inch Monitor SetOwner's Manual (PDF)
- High Resolution 8.4-inch screen 800 x 600 Dots with 1.44 million RGB pixels
- 100% digital processing
- 10-bit Analog to Digital conversion
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- Wide viewing radius - 130° horizontal and vertical provides superior visibility
- 350 candelas per square meter (cd/m²) luminance produces enhanced image quality in varying light and viewing conditions
- 350:1 ratio of contrast between black and white luminance values
- 4:3 and 16:9 screen aspect ratios for DTV applications
- Direct access for all adjustments
- Easy to see three color tally indicators
- On Screen Display (OSD)
- Blue Screen for color adjustment
- Adjustment Settings Memory
- Ready to Rack Mount out-of-the-box
- Two Composite Video inputs per screen with automatic PAL/NTSC standard recognition
- Loop-through of all input signals
Standard inputs:
Our TFT-MegaPixel™ V-R82DP-2C is a direct replacement for 8 and 9-inch CRTs used in broadcast and professional video applications. Ready to rack mount in 4RU, this model saves valuable rack space, in addition to its low weight and power consumption when compared to similar CRT products. Analog signals are digitized using an advanced 10-bit process, and all screen formats are scaled to fit on-screen in the highest resolution using our proprietary program and state-of-the-art LSI.
|Display (Viewing Area)
|8.4 Inch diagonal (170.4mm W x 127.8mm H)
|Resolution (Pixels)
|800 Wide x 600 High (1.44 Million RGB Pixels)(262,000 colors)
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|.213mm square pixel
|Brightness (in cd/m²)
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|Inputs per Panel
|2 (4) Composite per screen
|19.12" w x 6.875" h x 2.5" d
|Approx. Weight
|5.4 lbs (2.4 kg)
|Power Consumption
|App. 36 Watt (5Amp 12 VDC Class 2 power supply included)
Applications:Studio Monitor Wall • Remote/OB Van • EFP/ENG •Camera Shading • Fly Packs • VTR/DDR Replay • ISO Recording • Security /Surveillance


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Here's how to downgrade iOS 13.2.2 and iPadOS 13.2.2 to iOS 13.2 on your compatible iPhone or iPad device.
If you're using an iPhone running iOS 13.2.2 or an iPad running iPadOS 13.2.2 and want to downgrade to version 13.2 this is the guide for you. Note that you can't downgrade any further because Apple is no longer signing versions of prior to 13.2.
Now that iOS 13.2.2 and iPadOS 13.2.2 are available for download, there might be a reason for you to downgrade. It's still early days but if you find that there is a show stopping bug in the latest release and need to downgrade, this is how you'll do it.
Step 1: Before you start, you need to have the latest version of iTunes installed on your PC. On a Mac, you don't need to worry about that if you are running macOS Catalina because that doesn't ship with iTunes anymore. Instead, you'll be using the Finder. But if you are using an older version of macOS, then update iTunes first. You'll also need to download the IPSW file for iOS 13.2 for your specific device which you can do so from here.
Step 2: Next, you'll need to disable Find My service. To do that, go to Settings > iCloud > Find My and then flick the switch into the Off position. You'll be asked to enter your Apple ID password in order to authenticate, too.
Step 3: Using a USB cable, connect your iPhone or iPad to your computer. Make sure iTunes is open – if it's needed, use a Finder window – and put your device into DFU mode. We've covered that before in the guides below.
- iPhone 8 / 8 Plus, iPhone X / XR / XS / XS Max / 11 / 11 Pro / 11 Pro Max, or 2018 iPad Pro owners can follow this guide.
- iPhone 7 / 7 Plus owners: Enter DFU Mode On iPhone 7 Or iPhone 7 Plus, Here's How
- If you have an iPhone 6s / 6s Plus or any other iPad, then follow the instructions here.
Step 4: Once your device is in DFU mode, iTunes / Finder (If you are running macOS Catalina) will ask if you want to get the process started. Which of course you do, otherwise you wouldn't be here.
Step 5: Press and hold the Option key on a Mac or the Shift key on a PC, and click the Restore iPhone/iPad option. Now you'll be asked which IPSW file you want to use – this will, of course, be the iOS/iPadOS 13.2 IPSW that you downloaded earlier. Make sure to choose this from your machine.
Next, iTunes/Finder will spring into action as per your instruction. Once your iPhone or iPad has restarted a couple of times, you'll be back up and running on your device running iOS/iPadOS 13.2.
You may also like to check out:
- Fix iOS 13 Apple Notes App Sync Not Working With Catalina And Other Devices, Here's How
- Download: iOS 13.2.2 / iPadOS 13.2.2 IPSW Links, OTA Update Released
- iOS 13.2.2 Release Notes, Changelog: All New Features And Changes In One Place
- Gold iPhone 11 Pro Max: Case, Lightning Cable, Wireless Charger, Band, Speaker, More
- Red iPhone 11 Accessories: Case, Lightning Cable, Wireless Charger, Band, Speaker, Much More
- Yellow iPhone 11 Accessories: Case, Lightning Cable, Qi Wireless Charger, Band, Speaker, Much More
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- Apple Watch ECG App Hack: Enable Outside US In Unsupported Country On Series 5 & 4 Without Jailbreak
You can follow us on Twitter, or Instagram, and even like our Facebook page to keep yourself updated on all the latest from Microsoft, Google, Apple, and the Web.


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The NFL Lockout has been lifted, at least for now. That, however, does not mean life returns to normal at NFL training facilities around the country. Some players are saying they will show up for work at team facilities, though what happens when they do is really anybody's guess.
For the New York Jets, Pro Bowl tackle D'Brickashaw Ferguson has said he will be showing up at Florham Park. For the New York Giants, defensive tackle Chris Canty will be reporting to the Timex Performance Center.
Per ESPN, NFL teams have been instructed to open their doors to players but not to allow them into weight rooms. Coaches have also been instructed not to have contact with any players who show up.
So, why bother to show up if you are a player? What purpose could it serve? Well, partially to prove a point. For some, though, it is about money. Many players have workout bonuses that kick in as soon as players show up for off-season programs, and some NFL agents are saying they aim to try and collect on those bonuses since the league is open for business.
So, it might be back to work for some NFL players. It definitely, however, is not back to normal for the league.


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COOKBOOK REVIEW: FERN GREEN – Green Soups and ANNA HELM BAXTER – Salads In A Jar
COOKBOOK REVIEW: FERN GREEN – Green Soups and ANNA HELM BAXTER – Salads In A Jar
Hachette Australia – rrp$14.99 each
January 2017
Reviewed by Shane Pinnegar
Two volumes from Hachette's Healthy Living series, these simple cookbooks reflect the recent long overdue healthier upswing in our society.
Green's collection of clean soups includes detox plans designed to increase skin health, reduce stress, boost energy and aid digestion – including a simple guide to customising your own soup-based detox plan.
With 69 vegetable soups – many vegetarian and vegan, though some do include chicken, beef and fish stocks – Green has included easy to follow recipes, and minimalist though colourful pictures.
Whether you're after a full detox program, or just some easy ideas for tasty, healthy soups, this is the book for you.
Helm Baxter does a similarly thorough job with her collection of Salads In A Jar – tapping even more into the current trend for convenience and health.
Designed to "reenergise your lunch time, while boosting your immune system, aiding your digestion and enhancing your skin," Helm Baxter's salads are layered in Mason jars to avoid going limp or soggy (and there are extra tips for this), and present 57 colourful salads, and 11 tasty dressings to delight your taste buds.
Again mostly vegetable and legume based, but not entirely vegetarian or vegan, the variety on offer is key, and just looking through these recipes and bright, cheery photos is enough to make anyone hungry.
Some other stuff you might dig
Category: Book Reviews


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David Theo Goldberg's recent post, Badges for Learning: Threading the Needle Between Skepticism and Evangelism, is a worthwhile overview of the current thinking on what role "badges" might play in promoting better learning. He summarizes the debate within the learning sciences over badges as the age-old conflict between Kantianism and utilitarianism and tries to strike a middle ground:
Badges in short are a means to enable and extend learning. They need not be behavioral lures so much as symbols of achievement, expressions of recognized capacity otherwise overlooked. As with any means they can be mistaken for ends in themselves, but there is nothing intrinsic to badging that will inevitably make them so. And dismissing them out of court because they just might motivate learning for questionable reasons, as Cathy Davidson rightly suggests, is to do so at the peril of a good deal of learning they do well to prompt, promote, even proliferate.
Of course, all of this could just as easily be said about grades – don't some children pursue "A"s as an end in themselves, while others simply enjoy learning, while others (too many) actively disdain good grades? To ignore the fact that badges have some intrinsic attraction for players-cum-learners is to miss one of their main strengths. Of course, that attractiveness can wear off over time, especially if players begin to sense that the badges are being used for not-fun purposes (say, grading).
In our experience with badges – and gamification in general – at iCivics we've found a substantial increase in player engagement with our learning games. Just look at the simplest metric, average time on site, and how it leaped after we added badges, points, and other gamification elements:
Last month (February 2012), average time-on-site was 7:20 as compared with 5:40 in February 2011. That's a 29% increase in time spent interacting with our games and other resources! It excites me to contemplate what might players be learning in the extra 1:40 that gamification helped create.
In videogames, badges (or "achievements") can serve extrinsic functions such as summarizing complex stats down to something developers can analyze and use to tweak their games.* But to leap that far ahead in learning games seems premature to me — there is so much more we should be exploring in terms of how badges can motivate learners to experiment or try new things. Badges have been key tools for game designers who want to increase replayability, or who simply want players to experience more of the game they so painstakingly created.** And if our goal is to foster learning, isn't there something great about rewarding exploration and experimentation when so much of the rest of our society instead punishes failure?
* See the latter half of this analysis of the psychology of badges on IGN by Rick Lane.
** See, specifically, the "Skate this Way" and "Uncharted Territory" purposes of achievements in this thorough Gamasutra piece on achievement design by Mary Jane Irwin.)


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In February 2016, just four months after ICD-10 go-live, HIM Briefings asked a range of healthcare professionals to weigh in on their productivity in ICD-9 versus ICD-10.
In general, the time spent coding records has increased since ICD-10 implementation for most record types. In fact, one respondent said his or her facility noticed a 40%?50% decline in productivity. However, another respondent noted that coder productivity often varies based on the physician who documented in the record, as some physicians are more in tune with the language of ICD-10 than others. One-third (33%) of respondents were coders, whereas 21% identified as coding directors, managers, or supervisors. Approximately 16% identified as HIM directors, managers, or assistant directors or managers, while 12% of respondents were clinical documentation improvement (CDI) specialists. A small percentage of quality/performance improvement directors, vendors, consultants, IT directors/managers, billers, and auditors weighed in as well. More than half (53%) of respondents work in acute care hospitals.
One respondent said that his or her facility expects the same productivity in ICD-10 as it had in ICD-9, a nearly impossible feat in some cases. "The productivity requirements have not changed from ICD-9 to ICD-10. The current requirement for our facility is 18 charts per day (minimum 14). Very challenging and almost unobtainable."
The HCPro survey questions asked for the average minutes to code a record type. Some respondents wrote in the daily number of records coded, while others indicated the number of records averaged per hour.
Do you recall the recent humorous television commercial for phone services that featured children who wanted more and tried to explain why? The core message was that more isn't always better. I believe there are many applications of this principle in healthcare. To understand why this is the case, since large evolves from small, you might have to engage your sense of recall to visualize the past compared to the present. We'll look at some examples below.
Big (bad?) data
For all the talk about population health and big data, there is less discussion about data integrity, a key principle in data usage. Anyone who has worked with the most basic of databases, the master patient index, knows how many errors occur in collecting up-front patient access data. Errors still abound in duplicate medical record and account data. How can any of the data associated with these accounts be considered valid and worthy of basing conclusions upon? How confident are we, really, in our interpretation of this data?
For example, comparative MedPAR data will not display ICD-10-CM/PCS data until at least 18 months after ICD-10 implementation. There is no way to measure if we are undercoding, overcoding, erroneously coding, or problematically grouping any cases until we have enough data to make some judgments. Even then, the only true audit is one that compares the collected data with the source documents (in this case, the medical record). Organizations must conduct multiple rounds of these audits before findings can even be discussed.
The best approach is to begin your own audit of small segments (e.g., most common, most at risk) of diagnoses and procedures rather than waiting until the MedPAR data arrives. Be aware that if you are looking at any comparison databases, there is likely a crudely mapped comparison going on between ICD-9 databases (and ICD-10). As we all know, comparisons are not possible in all cases, and the more cross-mapping we do, the less granularly correct the comparison outcome data is, which decreases the validity of the universe of data.
In HIM, there are other data quality issues that have an unknown impact on integrity comparisons. For example, are we comparing apples to apples for sites that are using computer-assisted coding applications versus those that are not? Is it fair to compare outsourced coding with in-house coding? In a recent study conducted for a client, I observed that the time for coding of outsourced cases was dropping in a direct ratio to the case mix. Are we gaining productivity but sacrificing quality and reimbursement potential?
Last year, as ICD-10 implementation approached, organizations throughout the U.S. reported varying levels of comfort with regard to readiness and understanding of the impact of ICD-10 on physician workflow. For some, it was business as usual. For other physicians, ICD-10 became one more check box on the list of reasons to leave practice.
Our readers have been asking for an updated medical record documentation guide, and here it is?new and improved! The guide provides references to the associated CMS Conditions of Participation and new and revised standards and elements of performance (EP). A recent Joint Commission column discussed ongoing record reviews and the continued focus of Joint Commission surveyors related to documentation in the medical record. The guide takes the Record of Care, Treatment, and Services chapter and breaks it down into an easy-to-use tool for comprehensive record reviews by topic.


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"Are you looking for a custom minimalist logo design for your product and Business?" Look no further! I'm a creative thinker & professional graphic designer who loves mixing technology with creative ideas!
We will transform your message into an attractive and coherent design that will establish a strong presence in the market.
Yes, i am new on PixelClerk Marketplace but I have worked with clients all over the world & created over 2400+ logos.


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Honest question, and no research on topic that I could quickly find, despite there being a cottage industry for academic articles about what correlates with an hourglass vs. a tubular waist-to-hip ratio.
I've been looking into the history of the pro-life movement, and since there's an apparent revival under Millennial college students, I checked out Google Images for "pro-life students" to see what they're like -- normal, hipster, etc.
While it wasn't every girl, they were far more likely to have hourglass shapes, in an age where it seems like you don't see that body type much anymore, especially among Millennials. Even when they were a bit overweight, they still had hourglass shapes, rather than being uniformly wide blobs. They also smile more. More designed for birthing in their physiology, more pro-natal in their political views.
Some examples (girl on left, girl in peach shirt, girl in orange shirt):
The pro-choice students had more boyish waist-to-hip ratios, and generally didn't smile (notwithstanding one of the examples below). When they were fat, they were fat all over, not in a Rubenesque way. Bodies less capable of conceiving and delivering, minds less inclined to protect childbearing.
See for example:


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Technology startups are described as a 'new economy' segment and expected to play a strong and vital role to boost country as well as industry's economic growth. With new Tech Startups country, society and economy remain diverse, connected and global in the various aspects.Read More »
8 Tips to Fight the Summer Productivity Slump
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