The emba R package name stands for Ensemble (Boolean) Model Biomarker Analysis. It’s main purpose is to be used on a dataset consisted of an ensemble of boolean models. These models are usually (but not necessarily) different versions of the same initial model, parameterized in different ways (e.g. some boolean operators in the model equations have changed from OR to AND or vice-versa). A prerequisite for using this package, is that this model dataset must be tested in-silico (using some computational way) against a list of drug combinations, in order to assess which drugs combinations behave synergistically for which models. An example software that generates such boolean model ensembles and performs a comprehensive drug response analysis on them is the DrugLogics NTNU software pipeline (see respective documentation).
Given a list of gold-standard (lab-observed/verified) synergies 1, this package enables the easy grouping of the models into different classes based on a specific performance metric evaluation. This model classification enables the discovery and visualization of biomarkers - nodes whose activity and/or boolean model parameterization might affect either the prediction performance of those models or the manifestation of the predicted synergies.
In the next sections we will describe the main inputs and outputs of the general analysis functions (which group a lot of functionality into one) and provide some insights on the implementation behind. Biomarkers will be assessed and visualized using a test dataset generated from the DrugLogics software mentioned above.
The complementary R package usefun has various helpful functions that are used both inside the emba package and during the analysis below.
For further analyses using this package on boolean model ensemble datasets see this GitHub repository. See also an example that demonstrates all the intermediate steps included in the general analysis functions as well as other miscellaneous usages that this guide does not cover. Lastly, you might also want to check a nice presentation I made for a conference about this package.
The test dataset we will use has \(7500\) boolean models with \(139\) nodes each. It helps to think of each boolean model as a network of nodes where the edges represent either activation or inhibition of the corresponding target and the nodes activity can be either active (1) or inactive (0).
The models have been assessed for synergy against a total of \(153\) drug combinations.
data.list = readRDS(url("https://github.com/bblodfon/emba/blob/master/vignettes/data.rds?raw=true")) model.predictions = data.list$model.predictions models.stable.state = data.list$models.stable.state models.link.operator = data.list$models.equations observed.synergies = data.list$observed.synergies # (x,y) coordinates for visualization nice.layout = data.list$nice.layout # model network as an igraph object net = data.list$net # drug combinations drug.combos = colnames(model.predictions) # change model names (shorter names for readability) model.names = paste0("model", 1:7500) rownames(model.predictions) = model.names rownames(models.stable.state) = model.names rownames(models.link.operator) = model.names
This data represents the results of in-silico testing the boolean models against a drug combination dataset. More specifically, the model predictions is a data.frame
whose values (corresponding to a specific model-drug combination element) can be one of the following:
NA
(in case the model couldn’t be assessed for synergy, e.g. there were no stable states in either the drug combination perturbed model or in any of the two single-drug perturbed models).model.predictions[1:5, 77:84] %>% kable(caption = "Model predictions example")
| PI-JN | PI-D1 | PI-60 | PI-SB | PI-RU | PI-D4 | PI-F4 | PI-ST | |
|---|---|---|---|---|---|---|---|---|
| model1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NA |
| model2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NA |
| model3 | NA | NA | NA | NA | NA | NA | NA | NA |
| model4 | 0 | 1 | 0 | NA | 0 | NA | 0 | NA |
| model5 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | NA |
Each model must have a stable state configuration where the nodes have fixed to either 0 (inactive state) or 1 (active state). In other words, a fixpoint attractor. Of course, if a model has multiple attractors or other methods are used to derive a solution to the system of boolean equations that is the model itself, then continuous activity state values (in the \([0,1]\) interval) are also supported.
models.stable.state[1:5, 5:11] %>% kable(caption = "Model stable states example")
| MAP3K4 | MAP2K4 | IKBKG | IKBKB | AKT1 | BRAF | SMAD3 | |
|---|---|---|---|---|---|---|---|
| model1 | 0 | 1 | 1 | 1 | 1 | 0 | 1 |
| model2 | 0 | 1 | 1 | 1 | 0 | 0 | 0 |
| model3 | 0 | 1 | 1 | 1 | 1 | 0 | 1 |
| model4 | 0 | 1 | 1 | 1 | 0 | 0 | 1 |
| model5 | 0 | 1 | 1 | 0 | 0 | 0 | 1 |
This is a non-essential input for the functions we will use, but we include it here since the test dataset supports it. It is a way to represent the structure (parameterization) of the boolean models in the dataset.
If each boolean model is a list of boolean equations of the form:
T = (A1 OR A2 OR ...) AND NOT (I1 OR I2 OR ...)
, where the A
and I
nodes are the activating and inhibiting regulators respectively of the target node T
and the AND NOT
is the link (balance) operator, we can specify a data.frame
object whose values (corresponding to a specific model-target node element) can be one of the following:
AND NOT
link operator)OR NOT
link operator)models.link.operator[1:5, 1:10] %>% kable(caption = "Models link operator example")
| MAP3K4 | MAP2K4 | IKBKB | AKT1 | SMAD3 | GSK3B | RAF1 | GAB2 | CTNNB1 | NR3C1 | |
|---|---|---|---|---|---|---|---|---|---|---|
| model1 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 1 | 0 |
| model2 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 0 |
| model3 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 0 |
| model4 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 1 | 0 |
| model5 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 |
Note that in the test dataset, the nodes (columns of the models.link.operator
object) who didn’t have a link operator are pruned.
A list of gold standard (GS) drug combinations which have been termed as synergistic via experimental and/or other computational methods. These drug combinations must be a subset of the ones tested in the models (the column names of the model.predictions
data).
usefun::pretty_print_vector_values(observed.synergies, vector.values.str = "observed synergies")
17 observed synergies: AK-60, AK-BI, AK-D1, PI-D1, PD-G2, AK-G4, D1-G4, PI-JN, BI-P5, PD-P5, PI-P5, AK-PD, BI-PD, AK-PI, BI-PI, PD-PI, PK-ST
Performance biomarkers are nodes in our studied networks (boolean models) whose activity state and/or boolean model parameterization (link operator) affects the prediction performance of those models. These nodes can be thus used as indicators of either activity or structural changes that have a positive effect on the prediction performance of our models.
The model performance can be assessed via various ways. In this package we offer two ways to group the models to different classification categories: either based on the number of true positive (TP) predictions or on the Matthews correlation coefficient (MCC) score with respect to the drug combination dataset tested for synergy. The function emba::biomarker_tp_analysis()
is used for the former classification and the function emba::biomarker_mcc_analysis()
for the latter. Note that it’s generally better to use the MCC classification, since it’s a more robust performance evaluation metric compared to the number of TP predictions, since it takes into account all of the four confusion matrix values.
When the models have been grouped to different classification categories, their nodes activity or boolean model parameterization can be summarised in each group and compared to the others, obtaining thus the expected biomarkers using the methodology described below.
We use the emba::biomarker_tp_analysis()
function with the specified inputs:
tp.analysis.res = emba::biomarker_tp_analysis( model.predictions, models.stable.state, models.link.operator, observed.synergies, penalty = 0.1, threshold = 0.55)
The penalty
term is used to reduce the bias when model groups have different sizes. For example, if I were to compare the average activity of nodes between two groups of models, with respective group sizes 5 and 1000, then the result would be heavily biased towards the group with the larger size, making thus the quality of the results coming out of this comparison questionable. As such, with penalty
values closer to 0, more bias is introduced and we expect more biomarkers to be found. The default value of \(0.1\) is a good rule-of-thumb choice for minimizing such biases. See more info on emba::get_vector_diff()
.
As a first result, we get the predicted synergies - i.e. the drug combinations that are a subset of the observed ones and were predicted by at least one of the models in the dataset:
usefun::pretty_print_vector_values(tp.analysis.res$predicted.synergies, vector.values.str = "predicted synergies")
5 predicted synergies: AK-PD, BI-PD, BI-PI, PD-PI, PI-D1
The percentage of true positive predicted synergies is thus 29.4%. Such a low number might be a sign that the models quality is poor (need for a different parameterization) or other reasons like incorrect assessment of the gold standard synergies, etc.
The next informative barplot shows the distribution of models according to their true positive predictions:
pr(emba::make_barplot_on_models_stats(table(tp.analysis.res$models.synergies.tp), title = "True Positive Synergy Predictions", xlab = "Number of maximum correctly predicted synergies", ylab = "Number of models"))
Next result we get is the average activity differences per network node for all group classifications:
tp.analysis.res$diff.state.tp.mat %>% as.data.frame() %>% select(c("AKT","PTEN","PSEN1","STAT3","CEBPA")) %>% # show only part of the matrix kable(caption = "Average Activity Difference Matrix")
| AKT | PTEN | PSEN1 | STAT3 | CEBPA | |
|---|---|---|---|---|---|
| (0,1) | -0.0278105 | 0.4462906 | -0.0012761 | 0.2316447 | 0.0283239 |
| (0,2) | -0.0851093 | 0.3814671 | 0.0104114 | 0.0200058 | 0.0118586 |
| (0,3) | 0.0175886 | 0.2594653 | 0.4728458 | -0.3232749 | 0.4327603 |
| (1,2) | -0.0630977 | 0.0363188 | 0.0113038 | -0.1576796 | -0.0099449 |
| (1,3) | 0.0306321 | 0.0456991 | 0.4695673 | -0.4304678 | 0.4157744 |
| (2,3) | 0.0916724 | 0.0305575 | 0.6086029 | -0.4379904 | 0.5551273 |
STAT3
node). The more positive the activity difference value, the more active the node is in the better performance models (e.g. CEBPA
node).threshold
level, a node is declared as an activity biomarker if it’s highest absolute value surpasses that threshold (see emba::get_biomarkers()
for more info).In our case, threshold = 0.55
and thus CEBPA
and PSEN1
are returned as active biomarkers:
usefun::pretty_print_vector_values(tp.analysis.res$biomarkers.tp.active, vector.values.str = "active biomarkers")
2 active biomarkers: PSEN1, CEBPA
usefun::pretty_print_vector_values(tp.analysis.res$biomarkers.tp.inhibited, vector.values.str = "inhibited biomarkers")
0 inhibited biomarkers:
With the models initial network as an igraph object (see emba::construct_network()
on how to create such a net
object), we can visualize every row of the above matrix as follows:
pr(emba::plot_avg_state_diff_graph(net, tp.analysis.res$diff.state.tp.mat["(2,3)",], layout = nice.layout, title = "Bad models (2 TP) vs Good models (3 TP)"))
Note that with less penalty
, more bias would be introduced and thus more biomarkers would be found (even for a higher chosen threshold
):
tp.analysis.res.biased = emba::biomarker_tp_analysis( model.predictions, models.stable.state, models.link.operator, observed.synergies, penalty = 0, threshold = 0.7) usefun::pretty_print_vector_values(tp.analysis.res.biased$biomarkers.tp.active, vector.values.str = "active biomarkers")
13 active biomarkers: PSEN1, CEBPA, MAPK8IP1, MAPK9, JAK1, TYK2, JAK3, IFNGR2/INFGR1, IFNGR1, PTPN11, IFNGR2, IL2RB, IL10RA
usefun::pretty_print_vector_values(tp.analysis.res.biased$biomarkers.tp.inhibited, vector.values.str = "inhibited biomarkers")
17 inhibited biomarkers: MAP3K7, MAP2K6, MAP2K3, NLK, IKBKG, STAT3, RXRA, SOCS3, TGFB1, HSPA1A, SALL4, ROCK1, TGFBR1, TRAF6, RHOA, PIK3R1, CASP9
Last result we get is the average link operator differences per network node (whose boolean equation had a link operator) for all group classifications:
tp.analysis.res$diff.link.tp.mat %>% as.data.frame() %>% select(c("AKT","PTEN","PSEN1","STAT3","CEBPA")) %>% # show only part of the matrix kable(caption = "Average Link Operator Difference Matrix")
| AKT | PTEN | PSEN1 | STAT3 | CEBPA | |
|---|---|---|---|---|---|
| (0,1) | 0.2019689 | 0.4291810 | -0.0047786 | 0.2316447 | 0.0269599 |
| (0,2) | 0.1116284 | 0.3682466 | -0.1722642 | 0.0200058 | 0.0101784 |
| (0,3) | 0.1588424 | 0.2512846 | 0.2915064 | -0.3232749 | 0.4356235 |
| (1,2) | -0.0440675 | 0.0363188 | -0.1671873 | -0.1576796 | -0.0105660 |
| (1,3) | 0.0617622 | 0.0456991 | 0.2913783 | -0.4304678 | 0.4192610 |
| (2,3) | 0.1171370 | 0.0305575 | 0.5194769 | -0.4379904 | 0.5602202 |
STAT3
node). A value closer to \(1\) means that on average, the node’s boolean equation has mostly the OR NOT link operator in the better performance models (e.g. CEBPA
node).threshold
level, a node is declared as a link operator biomarker if it’s highest absolute value surpasses that threshold (see emba::get_biomarkers()
for more info).In our case, threshold = 0.55
and thus CEBPA
is returned as an OR
link operator biomarker:
usefun::pretty_print_vector_values(tp.analysis.res$biomarkers.tp.or, vector.values.str = "'OR' biomarkers")
1 ‘OR’ biomarker: CEBPA
usefun::pretty_print_vector_values(tp.analysis.res$biomarkers.tp.and, vector.values.str = "'AND' biomarkers")
0 ‘AND’ biomarkers:
We can also visualize every row of the average link operator differences matrix as follows:
pr(emba::plot_avg_link_operator_diff_graph(net, tp.analysis.res$diff.link.tp.mat["(2,3)",], layout = nice.layout, title = "Bad models (2 TP) vs Good models (3 TP)"))
Interpreting the result regarding the CEBPA
biomarker, we look back at its boolean equation and we see that the higher performance models must have the OR NOT
link operator in order for CEBPA
to be in an active (ON) state (an AND NOT
results mostly on an inhibited state for CEBPA
):
CEBPA = (GSK3B OR MAP2K1 OR MEK1/2) OR NOT CTNNB1
We use the emba::biomarker_mcc_analysis()
function with the specified inputs:
mcc.analysis.res = emba::biomarker_mcc_analysis( model.predictions, models.stable.state, models.link.operator, observed.synergies, threshold = 0.65, num.of.mcc.classes = 4, penalty = 0.2)
penalty
term is used to reduce the bias when model groups have different sizes (default value is \(0.1\)). See more info about this on the TP-based analysis above and on the documentation of the function emba::get_vector_diff()
.num.of.mcc.classes
parameter, with default value \(5\)). Internally, the function Ckmeans.1d.dp()
is used to perform an optimal univariate k-means clustering on the models MCC scores, i.e. it groups the models to different MCC classes, with higher classes having higher MCC scores (corresponding thus to better performance models).First result is the predicted synergies, which are the same as the ones found with the TP-based analysis (the model predictions did not change). As such, the drug combinations which were predicted by at least one of the models in the dataset are:
usefun::pretty_print_vector_values(mcc.analysis.res$predicted.synergies, vector.values.str = "predicted synergies")
5 predicted synergies: AK-PD, BI-PD, BI-PI, PD-PI, PI-D1
We can get a first idea of the range and distribution of the models MCC scores with the next barplot:
pr(emba::make_barplot_on_models_stats(table(mcc.analysis.res$models.mcc), title = "MCC scores", xlab = "MCC value", ylab = "Number of models", cont.values = TRUE))
We can also plot the MCC-model histogram, which in addition shows the estimated density (how many models) and width (MCC range) of each MCC class:
models.mcc = mcc.analysis.res$models.mcc num.of.mcc.classes = 4 res = Ckmeans.1d.dp(x = models.mcc, k = num.of.mcc.classes) models.cluster.ids = res$cluster pr(emba::plot_mcc_classes_hist(models.mcc, models.cluster.ids, num.of.mcc.classes))
Next result we get is the average activity differences per network node for all group classifications:
mcc.analysis.res$diff.state.mcc.mat %>% as.data.frame() %>% select(c("AKT","PPM1A","PTEN","PSEN1","PTK2","CEBPA")) %>% # show only part of the matrix kable(caption = "Average Activity Difference Matrix")
| AKT | PPM1A | PTEN | PSEN1 | PTK2 | CEBPA | |
|---|---|---|---|---|---|---|
| (1,2) | -0.0263106 | 0.5637404 | 0.5637404 | 0.0002462 | -0.5637404 | -0.0180814 |
| (1,3) | -0.0419764 | 0.6791224 | 0.6791224 | -0.0004583 | -0.6791224 | 0.0189502 |
| (1,4) | -0.0551735 | 0.6416618 | 0.6416618 | 0.0039844 | -0.6416618 | -0.0054756 |
| (2,3) | -0.0196324 | 0.1787135 | 0.1787135 | -0.0007511 | -0.1787135 | 0.0390053 |
| (2,4) | -0.0300506 | 0.1517566 | 0.1517566 | 0.0034388 | -0.1517566 | 0.0088853 |
| (3,4) | -0.0138152 | 0.0141918 | 0.0141918 | 0.0036747 | -0.0141918 | -0.0191041 |
PTK2
). The more positive the activity difference value, the more active the node is in the better performance models (e.g. PPM1A,PTEN
).threshold
level, a node is declared as an activity biomarker if it’s highest absolute value surpasses that threshold (see emba::get_biomarkers()
for more info).In our case, threshold = 0.65
and thus PTEN
and PPM1A
are returned as active biomarkers and PTK2
as an inhibited biomarker:
usefun::pretty_print_vector_values(mcc.analysis.res$biomarkers.mcc.active, vector.values.str = "active biomarkers")
2 active biomarkers: PTEN, PPM1A
usefun::pretty_print_vector_values(mcc.analysis.res$biomarkers.mcc.inhibited, vector.values.str = "inhibited biomarkers")
1 inhibited biomarker: PTK2
Note that looking at the respective boolean equations:
PPM1A = PTEN
PTK2 = not PTEN
we conclude that the only activity biomarker of interest is PTEN
as it’s the only regulator whose state directly influences the PPM1A
and PTK2
nodes.
With the models initial network as an igraph object (see emba::construct_network()
on how to create such a net
object), we can visualize every row of the above matrix as follows:
pr(emba::plot_avg_state_diff_graph(net, mcc.analysis.res$diff.state.mcc.mat["(1,4)",], layout = nice.layout, title = "Bad models (MCC Class 1) vs Good models (MCC Class 4)"))
Last result we get is the average link operator differences per network node (whose boolean equation had a link operator) for all group classifications:
mcc.analysis.res$diff.link.mcc.mat %>% as.data.frame() %>% select(c("AKT","PTEN","PSEN1","CEBPA","STAT3","JAK1")) %>% # show only part of the matrix kable(caption = "Average Link Operator Difference Matrix")
| AKT | PTEN | PSEN1 | CEBPA | STAT3 | JAK1 | |
|---|---|---|---|---|---|---|
| (1,2) | 0.2793768 | 0.5880847 | 0.0466807 | -0.0280618 | -0.3069948 | 0.2806183 |
| (1,3) | 0.3182545 | 0.6791224 | 0.0115062 | 0.0114039 | 0.0664054 | -0.0648736 |
| (1,4) | 0.2860364 | 0.6416618 | -0.0109073 | -0.0060220 | -0.6043843 | 0.5783232 |
| (2,3) | 0.0684637 | 0.1542205 | -0.0343283 | 0.0407584 | 0.3818048 | -0.3535848 |
| (2,4) | 0.0461114 | 0.1330813 | -0.0457409 | 0.0160442 | -0.3147733 | 0.3112794 |
| (3,4) | -0.0055788 | 0.0141918 | -0.0179237 | -0.0137708 | -0.5550172 | 0.5321063 |
STAT3
node). A value closer to \(1\) means that on average, the node’s boolean equation has mostly the OR NOT link operator in the better performance models (e.g. PTEN
node).threshold
level, a node is declared as a link operator biomarker if it’s highest absolute value surpasses that threshold (see emba::get_biomarkers()
for more info).In our case, threshold = 0.65
and thus PTEN
is returned as an OR
link operator biomarker:
usefun::pretty_print_vector_values(mcc.analysis.res$biomarkers.mcc.or, vector.values.str = "'OR' biomarkers")
1 ‘OR’ biomarker: PTEN
usefun::pretty_print_vector_values(mcc.analysis.res$biomarkers.mcc.and, vector.values.str = "'AND' biomarkers")
0 ‘AND’ biomarkers:
We can also visualize every row of the average link operator differences matrix as in the following example:
pr(emba::plot_avg_link_operator_diff_graph(net, mcc.analysis.res$diff.link.mcc.mat["(1,4)",], layout = nice.layout, title = "Bad models (MCC Class 1) vs Good models (MCC Class 4)"))
Overall, we note that using the more robust MCC score to classify the models according to their prediction performance on the drug combination dataset they were tested on, produces more reliable biomarkers compared to using the simple number of true positive predictions. In addition, the biomarker results are different between the 2 methods, e.g. the TP-analysis revealed CEBPA
as an active state performance biomarker whereas the MCC-based analysis showed PTEN
to be so.
Synergy biomarkers are nodes in our studied networks (boolean models) whose activity state and/or boolean model parameterization (link operator) affects the manifestation of synergies. These nodes can be thus used as indicators of either activity or structural changes that make the models predict specific drug combinations as synergistic.
The core idea behind the implementation is that the models are now classified to groups based on whether they predict or not each one of the predicted synergies (which for the test dataset are the same 5 as found with the previous analyses). Thus, by comparing the average node activity or boolean model parameterization from the group that predicted a drug combination as a synergy vs the group that predicted it to be an antagonism, we can derive biomarkers for that drug combination.
The function used to perform such an analysis is the emba::biomarker_tp_analysis()
:
synergy.analysis.res = emba::biomarker_synergy_analysis( model.predictions, models.stable.state, models.link.operator, observed.synergies, threshold = 0.5, calculate.subsets.stats = TRUE, penalty = 0.1)
Now in addition to the predicted synergies set, we get all the subsets for which a model predicted all drug combinations in that subset as synergistic. We can visualize this result with the emba::make_barplot_on_synergy_subset_stats()
function:
pr(emba::make_barplot_on_synergy_subset_stats( synergy.analysis.res$synergy.subset.stats, threshold.for.subset.removal = 1, bottom.mar = 9))
PI-D1
synergy is predicted by almost all the rest of the modelsBI-PD,PD-PI,PI-D1
which is the maximum number of predicted synergies by an individual modelNext result is the matrix of activity state differences vectors, one for each of the predicted synergies:
synergy.analysis.res$diff.state.synergies.mat[,1:8] %>% kable(caption = "Average State Differences per Synergy Predicted", digits = 3)
| MAP3K7 | MAP2K6 | MAP2K3 | NLK | MAP3K4 | MAP2K4 | IKBKG | IKBKB | |
|---|---|---|---|---|---|---|---|---|
| AK-PD | 0.104 | 0.104 | 0.104 | 0.104 | -0.016 | 0.054 | 0.104 | 0.016 |
| BI-PD | -0.790 | -0.790 | -0.790 | -0.790 | -0.026 | -0.416 | -0.790 | 0.003 |
| BI-PI | 0.045 | 0.045 | 0.045 | 0.045 | 0.004 | 0.044 | 0.045 | 0.018 |
| PD-PI | -0.314 | -0.314 | -0.314 | -0.314 | 0.005 | -0.147 | -0.314 | -0.033 |
| PI-D1 | 0.080 | 0.080 | 0.080 | 0.080 | 0.002 | 0.103 | 0.080 | -0.018 |
threshold
level, a node for a specific synergy is declared as an activity biomarker if it’s highest absolute value surpasses that threshold (see emba::get_biomarkers()
for more info).Every row of the above matrix can be also network-plotted. We show for example the average state difference graph for the PI-D1
synergy:
pr(emba::plot_avg_state_diff_graph(net, synergy.analysis.res$diff.state.synergies.mat["PI-D1",], layout = nice.layout, title = "Prediction of PI-D1 (Good Models: synergy, Bad Models: antagonism)"))
Given the user-defined threshold
(\(0.5\)) we also get as a result the activity state biomarkers:
# prune nodes (columns) that were not found as biomarkers for any predicted synergy biomarker.act.mat = usefun::prune_columns_from_df( df = synergy.analysis.res$activity.biomarkers, value = 0) biomarker.act.mat[, 4:12] %>% # show only part of the matrix kable(caption = "Activity State Biomarkers Per Synergy Predicted")
| NLK | IKBKG | CREB1 | STAT3 | PTEN | PtsIns(3,4,5)P3 | JAK1 | TYK2 | JAK3 | |
|---|---|---|---|---|---|---|---|---|---|
| AK-PD | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| BI-PD | -1 | -1 | 0 | -1 | 0 | 0 | 1 | 1 | 1 |
| BI-PI | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| PD-PI | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| PI-D1 | 0 | 0 | -1 | 0 | 1 | -1 | 0 | 0 | 0 |
# define your own threshold my.thres = 0.76 activity.biomarkers.new = as.data.frame(apply( synergy.analysis.res$diff.state.synergies.mat, c(1,2), usefun::get_ternary_class_id, my.thres))
Note that there were predicted synergies (rows in the above matrix), for which we couldn’t find activity biomarkers (row was all zeros). This is justifiable, since the number of models in the synergistic and antagonistic model groups can be fairly unbalanced and the penalty
term is used to correct this bias. For example, comparing the models that predict AK-PD
as synergistic vs those that predict it as antagonistic, we have:
drug.comb = "AK-PD" syn.models.num = sum(model.predictions[, drug.comb] == 1 & !is.na(model.predictions[, drug.comb])) ant.models.num = sum(model.predictions[, drug.comb] == 0 & !is.na(model.predictions[, drug.comb])) usefun::pretty_print_string(paste0("Number of models (AK-PD): #Synergistic: ", syn.models.num, ", #Antagonistic: ", ant.models.num))
Number of models (AK-PD): #Synergistic: 3, #Antagonistic: 6214
Lastly, the synergy.analysis.res$diff.link.synergies.mat
result is a matrix that contains the average link operator differences per network node (whose boolean equation had a link operator) when comparing the synergistic vs antagonistic model groups for each predicted synergy. The corresponding link operator biomarkers (based on the given threshold
) are given in the synergy.analysis.res$link.operator.biomarkers
output.
R version 3.6.3 (2020-02-29)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.5 LTS
Matrix products: default
BLAS: /home/john/soft/R-3.6.3/lib/libRblas.so
LAPACK: /home/john/soft/R-3.6.3/lib/libRlapack.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] Ckmeans.1d.dp_4.3.3 knitr_1.29 dplyr_1.0.2
[4] usefun_0.4.8 emba_0.1.7
loaded via a namespace (and not attached):
[1] Rcpp_1.0.5 cpp11_0.2.1 highr_0.8 compiler_3.6.3
[5] pillar_1.4.6 tools_3.6.3 digest_0.6.25 jsonlite_1.7.1
[9] evaluate_0.14 memoise_1.1.0 lifecycle_0.2.0 tibble_3.0.3
[13] pkgconfig_2.0.3 rlang_0.4.7 bibtex_<IP_ADDRESS> igraph_1.2.5
[17] rstudioapi_0.11 yaml_2.2.1 pkgdown_1.6.1 xfun_0.17
[21] stringr_1.4.0 htmlwidgets_1.5.1 hms_0.5.3 desc_1.2.0
[25] generics_0.0.2 fs_1.5.0 vctrs_0.3.4 systemfonts_0.3.1
[29] gbRd_0.4-11 rprojroot_1.3-2 tidyselect_1.1.0 rje_1.10.16
[33] glue_1.4.2 R6_2.4.1 Rdpack_1.0.0 rmarkdown_2.3
[37] tidyr_1.1.2 readr_1.3.1 purrr_0.3.4 magrittr_1.5
[41] backports_1.1.9 htmltools_0.5.0 ellipsis_0.3.1 assertthat_0.2.1
[45] ragg_0.3.1 stringi_1.5.3 visNetwork_2.0.9 crayon_1.3.4
Note that the assessment of these synergies based on experimental data (usually High-Throughput Screening data) is an analysis on its own↩


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Judge Dashboard
{% for team in teams %}
{{ team.name }}
{% endfor %}
Team
Review
1st review
2nd review
3rd review
Score
loading-b


Someone requested that the password for your NPS account be reset
Copyable link: [[url]]
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Donald Trump 'is the chosen one sent by God to do great things', aide claims
Donald Trump is ‘the chosen one’ who has been sent to Earth by God as part of a ‘divine plan’, a top aide claims.
The President of the United States’ outgoing Secretary of Energy Rick Perry made the glowing comments on Fox News Sunday.
Perry began by explaining his belief that God has ‘used imperfect people all through history,’ before naming Biblical examples King David, Saul and Solomon.
He said he’d given Trump a brief note on those Old Testament kings last month, and added: ‘I shared it with him and said, “Mr. President, I know there are people that say you said you were the chosen one and I said, ‘You were'”.
Advertisement Advertisement
‘I said, “If you’re a believing Christian, you understand God’s plan for the people who rule and judge over us on this planet in our government.”
The clip then cut back to the Fox and Friends studio, with host Ed Henry adding that Perry indeed believed Trump was sent ‘to do great things.’
He said that Perry had also told him how Barack Obama had previously been sent by God, and that the US gets the right person to lead it at the right time.
Trump has previously heaped similar praise on himself by retweeting gushing words from conservative commentator Wayne Allyn Root comparing his presidency to ‘the second coming of God.’
Writing in August, Trump wrote: ‘Thank you to Wayne Allyn Root for the very nice words. “President Trump is the greatest President for Jews and for Israel in the history of the world, not just America, he is the best President for Israel in the history of the world…and the Jewish people in Israel love him like he’s the King of Israel.
‘They love him like he is the second coming of God…
‘But American Jews don’t know him or like him.
‘They don’t even know what they’re doing or saying anymore. It makes no sense!
‘But that’s OK, if he keeps doing what he’s doing, he’s good for all Jews, Blacks, Gays, everyone.
‘And importantly, he’s good for everyone in America who wants a job.” Wow!’


ILOTYCIN - erythromycin ointment
Fera Pharmaceuticals, LLC
ILOTYCINTM Erythromycin Ophthalmic Ointment belongs to the macrolide group of antibiotics. It is basic and readily forms a salt when combined with an acid. The base, as crystals or powder, is slightly soluble in water, moderately soluble in ether, and readily soluble in alcohol or chloroform. Erythromycin ((3R*, 4S*, 5S*, 6R*, 7R*, 9R*, 11R*, 12R*, 13S*, 14R*) -4-[(2,6-dideoxy-3-C-methyl-3-0-methyl-α-L-ribo-hexopyranosyl)oxy]-14-ethyl-7, 12,13-trihydroxy-3, 5, 7, 9, 11, 13-hexamethyl-6-[[3, 4, 6-trideoxy-3-(dimethylamino)-β-D-xylo-hexopyranosyl]oxy]oxacyclotetradecane-2, 10-dione) is an antibiotic produced from a strain of Streptomyces erythraeus. It has the following structural formula:
Molecular Formula: C37H67NO13
Molecular Weight: 733.94
Each gram contains Erythromycin USP 5 mg in a sterile ophthalmic base of mineral oil and white petrolatum.
Erythromycin inhibits protein synthesis without affecting nucleic acid synthesis. Erythromycin is usually active against the following organisms in vitro and in clinical infections:
Streptococcus pyogenes (group A β-hemolytic)
Alpha-hemolytic streptococci (viridans group)
Staphylococcus aureus, including penicillinase-producing strains (methicillin-resistant staphylococci are uniformly resistant to erythromycin)
Streptococcus pneumoniae
Mycoplasma pneumoniae (Eaton Agent, PPLO)
Haemophilus influenzae (not all strains of this organism are susceptible at the erythromycin concentrations ordinarily achieved)
Treponema pallidum
Corynebacterium diphtheriae
Neisseria gonorrhoeae
Chlamydia trachomatis
For the treatment of superficial ocular infections involving the conjunctiva and/or cornea caused by organisms susceptible to erythromycin.
For prophylaxis of ophthalmia neonatorum due to N. gonorrhoeae or C. trachomatis.
The effectiveness of erythromycin in the prevention of ophthalmia caused by penicillinase-producing N. gonorrhoeae is not established.
For infants born to mothers with clinically apparent gonorrhea, intravenous or intramuscular injections of aqueous crystalline penicillin G should be given; a single dose of 50,000 units for term infants or 20,000 units for infants of low birth weight. Topical prophylaxis alone is inadequate for these infants.
This drug is contraindicated in patients with a history of hypersensitivity to erythromycin.
The use of antimicrobial agents may be associated with the overgrowth of nonsusceptible organisms including fungi; in such a case, antibiotic administration should be stopped and appropriate measures taken.
Avoid contaminating the applicator tip with material from the eye, fingers, or other source.
Two year oral studies conducted in rats with erythromycin did not provide evidence of tumorigenicity. Mutagenicity studies have not been conducted. No evidence of impaired fertility that appeared related to erythromycin was reported in animal studies.
Teratogenic effects -Pregnancy category B. Reproduction studies have been performed in rats, mice, and rabbits using erythromycin and its various salts and esters, at doses that were several multiples of the usual human dose. No evidence of harm to the fetus that appeared related to erythromycin was reported in these studies. There are, however, no adequate and well-controlled studies in pregnant women. Because animal reproductive studies are not always predictive of human response, the erythromycins should be used during pregnancy only if clearly needed.
The most frequently reported adverse reactions are minor ocular irritations, redness, and hypersensitivity reactions.
To report SUSPECTED ADVERSE REACTIONS, contact Fera Pharmaceuticals, LLC at<PHONE_NUMBER> Monday-Friday 9am-5pm EST or FDA at 1-800-FDA-1088 or www.fda.gov/medwatch.
In the treatment of superficial ocular infections, a ribbon approximately 1 cm in length of ILOTYCINTM Ophthalmic Ointment should be applied directly to the infected structure up to 6 times daily, depending on the severity of the infection.
For prophylaxis of neonatal gonococcal or chlamydial conjunctivitis, a ribbon of ointment approximately 1 cm in length should be instilled into each lower conjunctival sac. The ointment should not be flushed from the eye following instillation. A new tube should be used for each infant.
Sterile ILOTYCINTM Ophthalmic Ointment USP, 5 mg/g as follows:
1 g tamper-evident tubes
NDC 48102-016-11
Store at 20o to 25oC (68o to 77oF) [See USP Controlled Room Temperature].
Avoid excessive heat.
Protect from freezing.
Manufactured for:
Fera Pharmaceuticals, LLC
Locust Valley, NY 11560
PF016
Rev. 06/10
NDC 48102-016-11
STERILE Rx only
ILOTYCINTM
(ERYTHROMYCIN
OPHTHALMIC
OINTMENT USP)
0.5% (5mg/g)|
ILOTYCIN
erythromycin ointment | ||||||||||||||||||||
| ||||||||||||||||||||
| ||||||||||||||||||||
| ||||||||||||||||||||
| ||||||||||||||||||||
|
| Labeler - Fera Pharmaceuticals, LLC (831023713) |
| Registrant - Fera Pharmaceuticals, LLC (831023713) |


| Trees | Indices | Help |
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Removes leading zero(s). EX.025/250/025->25/50/25 or 00500->500 |
Lookup the city and state associated with a 5 or 9-digit zipcode. 5 digit will return a list of matches. >>> from eicpy.misc import zipAllCityStates >>> zipAllCityStates('100010000') [(u'New York', u'NY')] >>> zipAllCityStates('74334') [(u'Miami', u'OK'), (u'Lone Grove', u'OK')]
|
Send an email to the website administrators. DEPRECATED for Error Email use. Use the erroremail.py script instead. @param msg: the body of the message including subject line @param subject: the subject line of the email @param to: a list of email addresses @param frm: who the email should show as coming from |
Handles the log writing to EPAY.HITS
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DEFAULT_EMAIL_LIST
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| Trees | Indices | Help |
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| Generated by Epydoc 3.0.1 on Mon Oct 8 11:15:35 2012 | http://epydoc.sourceforge.net |


EMAIL:
{{ hrOnboard.view.email }}
MOBILE:
{{ hrOnboard.view.mobile }}
DATE OF BIRTH:
{{ hrOnboard.view.dob }}
ADDRESS:
{{ hrOnboard.view.address }}
STATE:
{{ hrOnboard.view.state.abv }}
POSTCODE:
{{ hrOnboard.view.postal_code }}
NEXT OF KIN NAME:
{{ hrOnboard.view.kin_name }}
NEXT OF KIN MOBILE:
{{ hrOnboard.view.kin_mobile }}
NEXT OF KIN RELATIONSHIP:
{{ hrOnboard.view.kin_relationship }}
MEDICAL CONDITIONS:
{{ hrOnboard.view.med_conditions }}
ALERGIES:
{{ hrOnboard.view.allergies }}
Evidence of Documentation
DRIVERS LICENSE
WHITE CARD
HIGH RISK WORK LICENSE
OTHER TICKIETS
UNION TICKET
SUPERANNUATION
LONG SERVICE LEAVE
MEDICAL DECLARATION
OTHER DOCUMENTS


DROWNING MONA A film review by Steve Rhodes Copyright 2000 Steve Rhodes RATING (0 TO ****): * 1/2
Phil Dearly (William Fichtner) and Rona, The Waitress (Jamie Lee Curtis) are having an affair. At their usual hourly motel for the week's assignation, they quickly start to strip for a hot game of Wheel of Fortune. Enjoy the moment for this is about as funny as Nick Gomez's DROWNING MONA ever gets.
Peter Steinfeld's script calls for a cast of thousands -- all suspects in the murder of Phil's wife, Mona (Bette Midler). Meant to be an Agatha Christie style mystery, the movie is constructed as a series of loosely-connected vignettes, frequently told in flashback and without much narrative drive or momentum. Sometimes cute, the film has trouble ever working up to funny.
Set in the fictional town of Verplanck, New York, where the first Yugos were tested, the movie's on-going joke is that almost all of the cars are old Yugos. In the first scene of the picture, Mona dies in a car accident in her son's Yugo, and foul play is suspected.
 When her family is told of her death, her son, Jeff (Marcus Thomas), has only one concern -- why she was driving his car. Jeff claims that his mom suffered from a "personality disorder" -- not exactly an original line. Like most of the local citizenry, Jeff has an IQ firmly in the double digits. The running sight gag with him, not that it's funny, is his stump in place of a hand.
At the center of the circle of suspects is boyishly blonde Bobby Calzone (Casey Affleck), Jeff's business partner. Bobby is planning to marry Ellen Rash (Neve Campbell), whose father is Wyatt (Danny DeVito), the local Chief of Police.
Although Wyatt seems rather lackadaisically to be trying to solve the crime, others show no interest whatsoever. As Wyatt's deputy (Peter Dobson) succinctly puts it, "Ding, dong the witch is dead -- end of story."
Like a firecracker that's packed with powerful ingredients but ends up just fizzling, the movie spurts along until the killer is rather arbitrarily chosen -- something that could be said of Neve Campbell's last picture (SCREAM 3).
Have you ever wished that movies featuring a large cast would have everyone wear name tags so that you could remember the names? Well, DROWNING MONA has a solution. Most of the characters have their names on the vanity plates on their Yugos. Cute, huh?
DROWNING MONA runs 1:30. It is rated PG-13 for some thematic elements, language and brief sexuality and would be acceptable for kids around 11 and up.
Email<EMAIL_ADDRESS>Web: http://www.InternetReviews.com
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


Simple Depreciation
Enter the facts about an item to calculate its salvage amount:
When did you purchase the item?
When was the item lost?
How much did you pay for the item?
Please enter the depreciation rate.*
Calculate Salvage Amount
The item's value is now $


"A special thanks to CodeUp, Geekdom and the Sanchez family for their generous contributions to SomatiQ's development."Jose Sanchez CEO and founder of EVOQ
Turn your 2D designs into high quality, 3D product shots in seconds using free Photoshop actions by PSDCovers! Visit their website to download some of their awesome, free photoshop actions!
Todays world calls for multiple formats and rich content. Whether your business is operating a webpage or an app, you can rest assured that the pulse of your business will beat strong from a PC, Laptop, Tablet, Android or iPhone.
"My website is amazing and I've received quality traffic from customers that are "seeking" my services. The SomatiQ team listened to my concerns and delivered a quality product. Check out my page at ENVI Waterless Auto Spa San Antonio"!


Inv.h
InvInt.h
InvMain.c
InvUtil.c
By: Freddy Mang and Shaz Qadeer
By: Freddy Mang and Shaz Qadeer
By: Freddy Mang and Shaz Qadeer
See AlsoInv.y
Inv.l
InvUtil.c
Inv.h
InvInt.h
By: Freddy Mang and Shaz Qadeer
This file contains routines for accessing the fields of the invariant data structure, for printing invariants, and for reading invariants from a file.


Thornton leads BC past Wake Forest 77-70 in schools’ opener
by KEN POWTAKBOSTON (AP) - Derryck Thornton scored a career-high 23 points and Nik Popovic had 19 to lead Boston College to a 77-70 victory over Wake Forest Wednesday night in the season opener for both schools.
Jay Heath, a 6-foot-3 freshman guard, added 18 points, five rebounds and four assists for the Eagles.
Brandon Childress had 24 points, and Isaiah Mucius 16 for the Demon Deacons.
Committed to running the floor on nearly every opportunity, combined with tough man-to-man defense, helped BC open a 42-27 lead at halftime.
The Eagles went on a 20-3 run during a 6:47 stretch of the opening half to make it 27-12. Popovic, Jared Hamilton and CJ Felder each nailed a 3 during the spree.
After Wake Forest sliced its deficit to eight late in the half, Heath sparked a quick 10-0 spurt, scoring eight of his points during the spree.
The Demon Deacons committed 10 turnovers the opening half, while BC had one.
Wake Forest, opening a season with its first true road game since 1993, sliced it to six with just under two minutes to play, but BC held on.
BIG PICTURE
Wake Forest: The Demon Deacons returned 77% of their scoring from last season, but they looked rattled, at times, offensively and turned the ball over way too much. They’ll needed to clean that up before they get to ACC play.
Boston College: Thornton, a graduate transfer from USC who started his collegiate career as a highly touted player at Duke before transferring, looked poised and ran the offense effectively. Certainly, having experience at the point should help a lot when things get tight, and with the addition of Heath and Felder, the Eagles look like they can be more balanced offensively this season.
SPLITS
Thornton made a quick cut near the foul line driving toward the lane and split the bottom of his left sneaker during the first half.
When the 6-foot-3 guard backed out and dribbled near midcourt, he looked down and the bottom of his sneaker was slightly hanging off.  He wasn’t injured.
INJURY
BC sophomore forward Jairus Hamilton came down awkwardly trying to make a steal in front of his team’s bench in the first half. He limped to the bench, was looked at by a trainer before walking slowly favoring the leg heading to the locker room. He didn’t return. . His brother, Jared, a senior, also went down hard in the final two minutes, favoring his left leg.
UP NEXT
Wake Forest: Hosts Columbia on Sunday.
Boston College: At South Florida on Sunday.
Sign up for Daily Newsletters
Copyright © 2019 The Washington Times, LLC.
Please read our comment policy before commenting.


OK, so previews are a little bit of a kludge… a more normal way might be to add a @:preview@ flag to each individual model class, and then have some kinda weird duplication/versioning system to handle it.
This is a mess.
The other normal way of doing this would be to store the model instance currently being edited in the session, which means you don’t have to involve mucking around in databases.
This is dirty, non-performant (because Rails sessions are stored in cookies), and it is very easy to exceed the session size limit… not good.
Instead, I’ve created a preview model which is instantiated and used to store the ‘preview’ state of the content being edited that we can replay it into a new instance of that content model at a later date.
Of course, I could have just changed session handling to use the database, but that seemed a little easy…
:D
- CLASS Preview


Here are your favorite sessions.
To add/remove one go to the session's page and play with the checkbox !


OAuth SSO Spotify Demo
Displaying playlist data for user: {{ username }}
{% for playlist_info, track_info in playlists%}
Playlist Name: {{ playlist_info[0] }}
Tracks:
{% for arr_track_names, arr_track_artists, arr_track_artist_links in track_info %}
Track Name: {{ arr_track_names }}
Track Artist: {{ arr_track_artists }}
Artist Link: Link
{% endfor %}
{% endfor %}


Before Change
documents = graph.nodes()
weights = _bm25_weights(documents)
for i in range(len(documents)):
for j in range(len(documents)):
if i == j or weights[i][j] < WEIGHT_THRESHOLD:
continue
sentence_1 = documents[i]
sentence_2 = documents[j]
edge_1 = (sentence_1, sentence_2)
edge_2 = (sentence_2, sentence_1)
if not graph.has_edge(edge_1):
graph.add_edge(edge_1, weights[i][j])
if not graph.has_edge(edge_2):
graph.add_edge(edge_2, weights[j][i])
// Handles the case in which all similarities are zero.
// The resultant summary will consist of random sentences.
if all(graph.edge_weight(edge) == 0 for edge in graph.edges()):
_create_valid_graph(graph)
After Change
if i == j or weight < WEIGHT_THRESHOLD:
continue
edge = (documents[i], documents[j])
if not graph.has_edge(edge):
graph.add_edge(edge, weight)


Here we will be talking about some of the key players on the current Minnesota Vikings roster so you are not completley clueless when fellow Vikings fans talk to you about them. Below we will also provide some of the twitter handles of a few of these key players.
Position: QB
College: Michigan State
Most Known for: "YOU LIKE THAT"
College: Minnesota
Most Known for: Going from an undrafted special teams player to one of the best receivers in the league
Position: WR
College: Maryland
Most Known for: Catching Minneapolis Miracle
Position: TE
College: Notre Dame
Most Known for: Being associated with Christmas.
Position: FS
College: Notre Dame
Most Known for: Best FS in the league
Position: CB
College: Florida State
Most Known for: Closing the Rhodes
Position: DE
College: USC
Most Known for: Jumping out of an ambulance
Position: LB
College: UCLA
Most Known for: Injuring Aaron Rodgers, causing a league wide rule change.
Position: DE
College: LSU
Most Known for: Just being a beast


Update: Serious Crash, Tuakau Bridge- Port Waikato Road
by New Zealand Police"Update: Serious Crash, Tuakau Bridge- Port Waikato Road"
The Tuakau Bridge- Port Waikato Road is now open following a serious crash earlier today.
Police were called to the two-vehicle collision at around 9.50am.
One person received serious injuries and was transported to hospital.
ENDS


beYOU-tiful
Sta. Mesa, Manila
NDC Campus, College of Engineering and Architechture
Rocket ship builder, Pizza Expert, Loves the Giants, Time Traveler ..


© Sept 2013 Kamil Ogórek
Game Engine by Solpeo [www.solpeo.com]
Art and Audio by Kenney Vleugels [www.kenney.nl]
© Sept 2013 Kamil Ogórek
Game Engine by Solpeo [www.solpeo.com]
Art and Audio by Kenney Vleugels [www.kenney.nl]
© Sept 2013 Kamil Ogórek
Game Engine by Solpeo [www.solpeo.com]
Art and Audio by Kenney Vleugels [www.kenney.nl]


For the majority of the 1980s, Nintendo dominated the video game market with its 8-bit NES console after it revived the gaming industry following the crash of 1983. The company paved the way for quality, hand-crafted games rather than the constant flood of shovelware being put out before Nintendo took over as the dominating company.
However, in 1988, Nintendo started to form a rivalry with company Sega, who put out a 16-bit machine, the Sega Genesis. The graphics on the Genesis looked better than anything Nintendo could've possibly put out on their flagship NES console. The Genesis was faster, sleeker, and was the hot new item of the late 80s and early 90s. It single-handedly beat down the giant that Nintendo was, and Sega was beginning to take over the spotlight.
Enter: The Super Nintendo Entertainment System
This was Nintendo's response to Sega--their first 16-bit console. It debuted strong in North America in 1991, selling out across stores everywhere. It was the only way to play games that took advantage of Mode 7 technology, which allowed for breath-taking pseudo-3D graphics, making launch games Pilotwings and F-Zero must-have titles of that holiday season.
The Super Nintendo had quite a few benefits over the Sega Genesis. Some of these include:


The SUI-Card component features a placeholder to portray a custom primary and a secondary content. So you can set an image, slider or any media component in one and some other content in the other.
The SUI-Card allows to pass custom content in the primary placeholder and also provides a sui-Card-primary class as a wrapper. This primary content is required. Secondary content might be passed or not to SuiCard component. If content is provided will be rendered inside a generic div sui-Card-secondary wrapper. If no content is provided this contained will not be rendered at all.
Use an array of Objects
const images = [{
src: 'https://scontent-mad1-1.cdninstagram.com/t51.2885-15/e15/11199623_633712610062793_1285693904_n.jpg',
type: 'image',
alt: 'Test',
link: 'https://www.instagram.com/p/TNUdPKpMgM/?taken-by=davecarter'
}];
ReactDom.render(
<SuiCard className={'myCustom-class'}
primary=[{

]}
secondary={[

This is the Card Title

This is the description

}
/>,
document.getElementById('main')
);
There is still room for improvement. If you are considering adding a feature don't hesitate to fork and pull request
Star and share SUI-card component to show your support. In addition you can follow the twitter account SUIEngineers to keep in touch.
This component is released under MIT license


Class IncomingPacketTests
Tests for packets that implement the IIncomingPacket interface.
Inheritance
System.Object
IncomingPacketTests
Inherited Members
System.Object.Equals(System.Object)
System.Object.Equals(System.Object, System.Object)
System.Object.GetHashCode()
System.Object.GetType()
System.Object.MemberwiseClone()
System.Object.ReferenceEquals(System.Object, System.Object)
System.Object.ToString()
Namespace: Fibula.Communications.Packets.Incoming.Tests
Assembly: Fibula.Communications.Packets.Tests.dll
Syntax
[TestClass]
public class IncomingPacketTests
Methods
| Improve this Doc View SourceAttackPacket_Initialization()
Checks AttackPacket initialization.
Declaration
[TestMethod]
public void AttackPacket_Initialization()
AutoMoveCancelPacket_Initialization()
Checks AutoMoveCancelPacket initialization.
Declaration
[TestMethod]
public void AutoMoveCancelPacket_Initialization()
AutoMovePacket_Initialization()
Checks AutoMovePacket initialization.
Declaration
[TestMethod]
public void AutoMovePacket_Initialization()
DefaultReadPacket_Initialization()
Checks DefaultReadPacket initialization.
Declaration
[TestMethod]
public void DefaultReadPacket_Initialization()
GameLogInPacket_Initialization()
Checks GameLogInPacket initialization.
Declaration
[TestMethod]
public void GameLogInPacket_Initialization()
GameLogOutPacket_Initialization()
Checks GameLogOutPacket initialization.
Declaration
[TestMethod]
public void GameLogOutPacket_Initialization()
GatewayLogInPacket_Initialization()
Checks GatewayLogInPacket initialization.
Declaration
[TestMethod]
public void GatewayLogInPacket_Initialization()
HeartbeatPacket_Initialization()
Checks HeartbeatPacket initialization.
Declaration
[TestMethod]
public void HeartbeatPacket_Initialization()
HeartbeatResponsePacket_Initialization()
Checks HeartbeatResponsePacket initialization.
Declaration
[TestMethod]
public void HeartbeatResponsePacket_Initialization()
LookAtPacket_Initialization()
Checks LookAtPacket initialization.
Declaration
[TestMethod]
public void LookAtPacket_Initialization()
ModesPacket_Initialization()
Checks ModesPacket initialization.
Declaration
[TestMethod]
public void ModesPacket_Initialization()
SpeechPacket_Initialization()
Checks SpeechPacket initialization.
Declaration
[TestMethod]
public void SpeechPacket_Initialization()
StopAllActionsPacket_Initialization()
Checks StopAllActionsPacket initialization.
Declaration
[TestMethod]
public void StopAllActionsPacket_Initialization()
ThingMovePacket_Initialization()
Checks ThingMovePacket initialization.
Declaration
[TestMethod]
public void ThingMovePacket_Initialization()
TurnOnDemandPacket_Initialization()
Checks TurnOnDemandPacket initialization.
Declaration
[TestMethod]
public void TurnOnDemandPacket_Initialization()
WalkOnDemandPacket_Initialization()
Checks WalkOnDemandPacket initialization.
Declaration
[TestMethod]
public void WalkOnDemandPacket_Initialization()


About me
Hard-working undergraduate studying Computers with Electronics in University of Essex who is fearless of new challenges and continuously ready to encounter new opportunities. Ambitious individual looking for a full-time job to show his own talent.
University Projects
Most projects available at my Github page - https://github.com/emeyva
Follow @emeyva
Contact
+939626990
<EMAIL_ADDRESS>Oeiras


{% extends 'watson/base.html' %} {% block body %}
Hi,
Your password has been reset to {{ password }}.
These credentials are stored securely and not in plain text. Please update your own records and then delete this email.


Functions to assess the calibration of logistic regression models with the GiViTI (Gruppo Italiano per la Valutazione degli interventi in Terapia Intensiva, Italian Group for the Evaluation of the Interventions in Intensive Care Units - see <http://www.giviti.marionegri.it/>) approach. The approach consists in a graphical tool, namely the GiViTI calibration belt, and in the associated statistical test. These tools can be used both to evaluate the internal calibration (i.e. the goodness of fit) and to assess the validity of an externally developed model.
| Version: | 1.3 |
| Imports: | alabama, rootSolve, grDevices, graphics, stats |
| Suggests: | testthat, knitr, rmarkdown |
| Published: | 2017-01-24 |
| Author: | Giovanni Nattino [cre, aut], Stefano Finazzi [aut], Guido Bertolini [aut], Carlotta Rossi [aut], Greta Carrara [aut] |
| Maintainer: | Giovanni Nattino <giovanni.nattino at marionegri.it> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| CRAN checks: | givitiR results |
| Reference manual: | givitiR.pdf |
| Vignettes: |
'givitiR' package: assessing the calibration of binary outcome models with the calibration belt |
| Package source: | givitiR_1.3.tar.gz |
| Windows binaries: | r-devel: givitiR_1.3.zip, r-release: givitiR_1.3.zip, r-oldrel: givitiR_1.3.zip |
| OS X binaries: | r-release: givitiR_1.3.tgz, r-oldrel: givitiR_1.3.tgz |
| Old sources: | givitiR archive |
Please use the canonical form https://CRAN.R-project.org/package=givitiR to link to this page.


Load event data to be able to display information about the collections here.
Currently supported event data files written in JSON and OBJ files. Supported file formats: .json, .obj


Id: 309
Name: Scrotian
Status: Alive
Species: Animal
Type: Scrotian
Gender: Male
OriginName: unknown
OriginUrl:
LocationName: Signus 5 Expanse
LocationUrl: https://rickandmortyapi.com/api/location/22
Image: https://rickandmortyapi.com/api/character/avatar/309.jpeg
Episode: ['https://rickandmortyapi.com/api/episode/13', 'https://rickandmortyapi.com/api/episode/16', 'https://rickandmortyapi.com/api/episode/21', 'https://rickandmortyapi.com/api/episode/25']
Url: https://rickandmortyapi.com/api/character/309
Created: 2018-01-05T14:26:50.679Z


Save: On the tool bar, click on , you can save the chromatogram to your disk as a PNG image.
Print: On the tool bar, click on , you can print the chromatogram as a PNG image.
Export: On the tool bar, click on , you can export the time and intensity values into a tab delimited file.
Zoom: To zoom in, you can left click and hold your mouse key, drag to highlight the area you want to zoom into. To zoom out, you can either right click your mouse in the main display area or on the tool bar, click on .
Show Grid: On the tool bar, click on , this will show/hide the grid in the main display area.


| Github ID | Points | References |
|---|---|---|
| HamishC |
Forks: 0 Bugs: 0 Docs: 0 |
Forks: Forked libpng (in review) Forked libssh (in review) Forked openssl (in review) Forked SQLite (in review) |
| Raggles |
Forks: 0 Bugs: 0 Docs: 0 |
Forks: Forked apr (in review) Forked expat (in review) Forked prce5 (in review) |
| ferzkopp | Forks: 0 Bugs: 0 Docs: 0 |
Forks: Forked SDL (in review) Forked SDK_gfx (in review) |
| ericschultz | Forks: 0 Bugs: 0 Docs: 0 |
Forks: Forked libiconv (in review) |
| PetterS | Forks: 0 Bugs: 1 Docs: 0 |
Forks: Forked pthreads (in review) Forked coin-cbc (in review) Forked freeglut (in review) Bugs: pkt (1) |
| hanrahat | Forks: 0 Bugs: 2 Docs: 0 |
Bugs: pkt (1) pkt (1) |
| riverar | Forks: 0 Bugs: 0 Docs: 0 |
Forks: Forked libjpeg (in review) |
| fearthecowboy | Forks: 0 Bugs: 0 Docs: 0 |
Forks: Forked zlib (in review) Forked grep (in review) |


The Vivo S1 Pro is for those young and defining their style
Combining unique phone design, stunning camera features, with in-display fingerprint scanning technology, S1 Pro is tailored specifically for the fashionable and youthful audience
by Rappler.comVivo is a leading global technology company committed to creating trendsetting smart mobile innovations and services. Vivo is devoted to forming a vibrant mobile internet ecosystem, and currently owns and operates an extensive network of research operations, with R&D centers focusing on the development of cutting-edge consumer technologies including 5G, AI, mobile photography and next-generation smartphone design.


We offer a different approach to creating a visual advertisement for your organization or company. We believe in structuring a sound piece of work that will emulate that which can be seen on a national level.
There is a great deal of freedom with a promo and we intend to use all of it. Whatever you need to promote, we will insure that the message gets to the viewer in a stylistic and classy way.
Our wedding videos are documentary style where we interview multiple guests from the wedding party, including the bride and groom.
Our music videos are fictional narratives structured to fit the song. The artist or artists actually become characters in their own short film.
Lorem ipsum dolor sit amet, consectetur adipisicing elit. Repellendus ex perferendis totam alias, corporis, ducimus impedit molestiae cum porro. Maxime repellat reiciendis facilis optio recusandae sapiente, beatae repellendus aperiam explicabo.


SafeMap
class is a Map
object, with
the implementation of the method SafeMap.generate()
which randomly
creates a game map with grass and at most 10% water tiles, and one treasure tile.super()
in Map
, to create a map of desired size.SafeMap
object used for testing.SafeMapTest
class tests the functionality of
methods in the SafeMap
class.Scanner
object for user input.Map.tiles
array.HazardousMap
instance for future
tests.Map
using MapCreator
for future testing.SafeMap
instance for future
tests.Team
instance for use in tests.size
was used as a fixed size of
the map for the tests.size
was used as a fixed size of
the map for the tests.start()
method takes care of the initial set up of the game.Subject
class is an abstract class used to implement the
observer design pattern.observers
ArrayList.


Academies (unless they’re only for pupils between 16 and 19 years old), maintained schools and pupil referral units should support children with medical conditions .
You can ask to see the school’s policy on supporting pupils with medical conditions.

If your child has a disability, the school must make ‘reasonable adjustments’ to make sure they aren’t discriminated against.
By law, schools have to provide a space for:
This is usually 2 separate rooms, both with a sink and access to a toilet.
If your child can’t attend because of illness or injury, your school and local council will provide support to make sure their education doesn’t suffer.
The school should:
If your child’s going to be away for a long time, the local council will make sure they get as normal an education as possible. This could include arranging:
The local council must make sure your child continues to get a full time education - unless part time is better for their health needs.
The local council should also:


24/7 CTF Hidden Painting
Description
We created a hidden painting which will lead you to the flag. Can you connect the dots and piece it back together?
Solution
We are given a secret_map.txt
file, which contains lines and lines of 2 hex values.
$ head secret_map.txt
0x4b 0x9d0
0x44 0x974
0x33 0x92
0x2f 0x660
0x48 0xe1
0x47 0x59c
0x5b 0x4cf
0x7c 0x489
0x32 0x5f0
0x41 0x62
My first idea was that they were coordinates: each line contained a first value for x and a second value for y.
With that in mind, I started a Python script to build a list of (x,y) values, so that we can try stuff on it. My initial goal was just to draw a dot for each of these coordinates and see what happens!
# secret_map.txt is a list of coordinates
# each line contains x, y encoded in hexadecimal
x_list = []
y_list= []
points = []
# parse numbers of secret_map.txt
# points = [ (x1, y1), (x2, y2), (x3, y3), ...]
# x_list = [ x1, x2, x3, ...]
# y_list = [ y1, y2, y3, ...]
with open("secret_map.txt", "r") as f:
for line in f.readlines():
x, y = line.split(" ")
x = int(x, 16)
y = int(y, 16)
x_list.append(x)
y_list.append(y)
points.append((x, y))
Next, we create a blank image using the maximum values as width and height:
# retrieve the width and height of the image by taking the highest x valuex and highest y value
width = max(x_list) + 10
height = max(y_list) + 10
im = Image.new('RGBA', (width, height), "white")
draw = ImageDraw.Draw(im)
# draw borders to the image
draw.rectangle([(0, 0), (width, height)], outline='black')
The next step is to draw the actual points:
# draw each point in red
draw.point(points, "red")
Looks like it was the right idea! We have a flag, but it’s mirrored and needs to be rotated!
# nearly there, let's put the image in the right orientation
im = im.rotate(90, expand=True)
im = im.transpose(Image.FLIP_TOP_BOTTOM)
Flag: censored according to the write-ups policy of 24/7
A quick fun challenge! The “rotation/mirror of the image” is the part that took me the most time. I didn’t knew about the expand
option of rotate
and ended up with weird cropped images…
Toodledoos!


Mesut Özil is a German footballer who plays for English club Arsenal and the German national team. Özil has been a youth national team member since 2006, and a member of the German national team since 2009.


Home Office
Digital Customer Programme Discovery Phase
14
Started applications
12 SME, 2 large
4
Completed applications
4 SME
Important dates
| Opportunity attribute name | Opportunity attribute value |
|---|---|
| Published | Tuesday 7 March |
| Deadline for asking questions | Tuesday 14 March |
| Closing date for applications | Tuesday 21 March |
Overview
| Opportunity attribute name | Opportunity attribute value |
|---|---|
| Summary of the work | Newham Council require a supplier with specialist capability and experience in transforming large scale, complex legacy technology and services into efficient modern solutions, to provide a team for a 10 week Discovery phase as part of its Digital Customer Programme, aligned to the Digital By Default Service Standard. |
| Latest start date | undefined NaN undefined |
| Expected contract length | 10 week Discovery Phase |
| Region | London |
| Organisation the work is for | Newham Council |
| Budget range | There is a fixed cost budget of £90,000.00 (exclusive of VAT) |
About the work
| Opportunity attribute name | Opportunity attribute value |
|---|---|
| Why the work is being done | Our aspiration is to pioneer change by creating a resilient council and community that shapes Newham as a place where people choose to live, work and stay. We are transforming the way we work to be more effective, commercial, innovative and pioneering in how we deliver the services our residents say matter most to them. Our vision is for services to be Digital by Default ; while saving money and improving the user experience. We require a specialist team to work with us on a Discovery phase as part of the Digital Customer Programme. |
| Problem to be solved | While many transactions have been moved online, the user experience is fragmented and often confusing, relying on inefficient, manual intervention for a number of transactions. There is an opportunity to make savings by improving the user experience in the broadest sense and to develop an evidenced based digital roadmap to deliver digital tools that meet user needs and support organisational goals for the future. |
| Who the users are and what they need to do | The discovery phase should identify who the users are and what they need to do. At a high level the users are Newham residents, partners, businesses and other organisations. Within the council the users are those who commission services. Newham Council provides a range of services to users including adult and children health and social care, advice and benefits, housing, community and living, environment and planning, education and learning, jobs and careers, leisure and culture, transport and streets.. A more complete list is available on the Council website www.newham.gov.uk. |
| Who the users are and what they need to do | The discovery phase should identify who the users are and what they need to do. At a high level the users are Newham residents, partners, businesses and other organisations. Within the council the users are those who commission services. Newham Council provides a range of services to users including adult and children health and social care, advice and benefits, housing, community and living, environment and planning, education and learning, jobs and careers, leisure and culture, transport and streets.. A more complete list is available on the Council website www.newham.gov.uk. |
| Any work that’s already been done | We recently carried out an analysis of all activity across the council to identify savings and service improvements, and our business case has now been approved. There are a number of projects and programmes which have an impact on or dependency with the Digital Customer Programme. Newham has a growing understanding and appreciation of the scope and management of Discovery phases, and has run agile projects and programmes in the past. |
| Existing team | |
| Current phase | Not started |
Work setup
| Opportunity attribute name | Opportunity attribute value |
|---|---|
| Address where the work will take place | London Borough of Newham, Newham Dockside, 1000 Dockside Road, London E16 2QU |
| Working arrangements | Monday to Friday on site. A professional working day, hours and working pattern as needed to deliver the service in an efficient way. Mainly based from our office in Dockside, London. |
| Security clearance |
Additional information
| Opportunity attribute name | Opportunity attribute value |
|---|---|
| Additional terms and conditions |
Skills and experience
Buyers will use the essential and nice-to-have skills and experience to help them evaluate suppliers’ technical competence.
| Opportunity attribute name | Opportunity attribute value |
|---|---|
| Essential skills and experience |
|
| Nice-to-have skills and experience |
|
How suppliers will be evaluated
| Opportunity attribute name | Opportunity attribute value |
|---|---|
| Maximum number of suppliers that will be evaluated | 5 |
| Proposal criteria |
|
| Cultural fit criteria |
|
| Payment approach | Fixed price |
| Assessment methods |
|
| Evaluation weighting |
Technical competence 60%
Cultural fit 20%
Price 20%
|
| Estimated date shortlisting ends | undefined NaN undefined |
| Estimated date evaluation ends | undefined NaN undefined |


Welcome to the statistical methods section of this website. We have generated a very brief set of tutorials for the statistical methods that we used for our paper. Hopefully this will make the article more accessible and be helpful to anyone who needs to replicate partially or completely some of the code we used.
Kallisto: read pseudo-alignment
We used Kallisto, an excellent of software from Lior Pachter’s group, to perform read pseudo-alignment for each mutant we analyzed. Although pseudo-alignment is initially not as accurate as complete alignment, the speed at which the algorithm completes means that it can be bootstrapped. The bootstrapping is what really makes it really great (well, that and the way in which they compute k-classes). We really love this software.
See the paper at Nature Biotechnology: Near-optimal probabilistic RNA-seq quantification
Sleuth: differential expression analysis
Sleuth is another great software from Lior Pachter’s group. This beautiful library was developed to optimally accept Kallisto processed reads, although it can work with other alignment tools. Sleuth performs the differential expression analysis by fitting a log-linear model to explain changes in expression between the different samples. Together, the combination of Sleuth and Kallisto are fantastic tools for processing RNA-seq data. Equally important, they are succinct. Kallisto is run via a single command using Terminal, and Sleuth requires about 20 lines of fairly standardized code.
See their paper at BioRxiv: Differential Analysis of RNA-seq incorporating quantification uncertainty
Bayesian robust regressions for interaction prediction
Jupyter Notebook on Bayesian Robust Regressions
Orthogonal distance regression
Orthogonal distance regression (ODR) is a method to fit the line of best fit when you have measurements that have errors in both the x- and the y-coordinates. Usually, when we have measurements with no error bars, we use a method called Least Squares to find the line of best fit. When the measurements have errors along the y-axis, we can modify this method and use Weighted Least Squares. Weighted Least Squares takes points with large errors and makes them less important, whereas points with smaller errors are more important. ODR is similar in the sense that it is takes into account errors in both x and y and weights points accordingly.
A major difference between usual Least Squares and ODR is the minimization we are performing. For Least Squares, we are usually minimizing the vertical distance of the points to the line. In other words, when we have minimized the sum of the squares of the residuals we have found the line of best fit. However, if there are errors on both X and Y, we need to minimize something else. In this case, it again makes sense to find the line that also minimizes the distance between the points and the lines. However, in this case, we will add the constraint that the ruler we use to measure this distance must always be at right angles to the line itself (hence the orthogonal).
The reasoning behind this has been explained previously. For further information on the mathematics behind regressions, we refer you to David Hogg’s excellent article: Data Analysis recipes: Fitting a model to data. In the next Jupyter notebook, we show quickly how ODR is considerably better than least-squares when errors in both coordinates are known.
Jupyter Notebook on Orthogonal Regression
Bootstrapping to test a hypothesis
Parametric Bootstrap
Jupyter Notebook on Parametric Bootstrapping
Non-parametric Bootstrap
Jupyter Notebook on Non-parametric Bootstrapping
Model Selection
Jupyter Notebook on Model Selection
Contact us
If there are broken links, or you have questions, you can contact us at<EMAIL_ADDRESS>or at


Short-form Crypto Fundamentals
-by Infominer
- What’s a Cryptographic Primitive?
- Cryptographic Hash Functions
- Proof of Work in a Nutshell
- Zero Knowledge Proofs
- David Chaum - The Father of Digital Cash
- The Birth of the Cypherpunks
- The Rise of the Cypherpunks
- /cypherpunks/ and the Crypto Wars
- Pre-Bitcoin /electronic-cash/
Timelines
https://github.com/peercoin/peercoin/wiki/history-of-cryptocurrency</a>
- Hobbes’ Blockchain Timeline 0.1 — Blockchain, Bitcoin, Distributed Ledgers, Smart Contracts and Cryptocurrencies
- Bitcoin’s Academic Pedigree
- Bitcoin and Markets - Bitcoin History - Timeline of important research and events since 1970.
Pre Digital Cryptography
- Pre-Digital Cryptography
- EFF - Crypto Is For Everyone—and American History Proves It
- “GET READY FOR A WORLD CURRENCY” Get Ready for the Phoenix- Economist; 01/9/88, Vol. 306, pp 9-10
- Before Babylon, Beyond Bitcoin: From Money that We Understand to Money that Understands Us
Digital Cryptography
- The Code Book: The Science of Secrecy from Ancient Egypt to Quantum Cryptography [ϟ]
“Simon Singh offers the first sweeping history of encryption, tracing its evolution and revealing the dramatic effects codes have had on wars, nations, and individual lives. From Mary, Queen of Scots, trapped by her own code, to the Navajo Code Talkers who helped the Allies win World War II, to the incredible (and incredibly simple) logisitical breakthrough that made Internet commerce secure, The Code Book tells the story of the most powerful intellectual weapon ever known: secrecy.”
Hash Algorithms
- History of Hash Algo’s - StackExchange
- New Directions in Cryptography ‘76
- DATA ENCRYPTION STANDARD (DES) approved as a federal standard in ‘76
Public Key Cryptography
- Publishing a new idea
by Ralph C. Merkle
In the Fall of 1974, as an undergraduate, I enrolled in CS244, the Computer Security course offered at UC Berkeley and taught by Lance Hoffman. We were required to submit two project proposals, one of which we would complete for the course. I submitted a proposal for what would eventually become known as Public Key Cryptography – which Hoffman rejected. I dropped the course, but kept working on the idea.
- Part 1 — Part 2 about the development of public-key cryptography.
For a more complete version, read: - CRYPTO— how the code rebels beat the government— saving privacy in the digital age -Steven Levy
- THE OPEN SECRET
—Public key cryptography - the breakthrough that revolutionized email and ecommerce - was first discovered by American geeks. Right? Wrong.
The story of the invention of public key cryptography is a cypherpunk sacred text: In 1976, an iconoclastic young hacker named Whitfield Diffie hooked up with Stanford professor Martin Hellman, and together they devised what experts hailed as the most important development in crypto since the invention of polyalphabetic ciphers during the Renaissance. The duo produced a system that allowed an unlimited number of people to communicate with total privacy.
David Chaum
- Chaum.com
- Everipedia - David Chaum
- Cryptography Legend - David Chaum
- The Birth of Digital Cash
- THE FATHER OF ONLINE ANONYMITY HAS A PLAN TO END THE CRYPTO WAR 2016
- What Everybody Misunderstands About Privacy Pioneer David Chaum’s Controversial Crypto Plan
- How David Chaum’s eCash spawned a Cypherpunk Dream - 2018
- David Chaum: Godfather of Digital Currency - 2018
Distributed Systems
- The Origin of Blockchains - What is a Distributed System?
- FLP Impossibility (1985)
- CAP Theorem (1999)
- Paxos (1989)
- Practical BFT (1999)
- Byzantine Paxos (2010)
- Raft (2014)
- PoW - Bitcoin
- The Eureka Moment That Made Bitcoin Possible [ϟ]
Cypherpunks
- Bitcoin and the rise of the Cypherpunks
- 1980s, Dr David Chaum
- late 1992, Eric Hughes, Timothy C May, and John Gilmore founded cypherpunks
- The Cypherpunks mailing list was formed, and a few months later, Eric Hughes published “A Cypherpunk’s Manifesto“.
“Privacy is necessary for an open society in the electronic age. Privacy is not secrecy. A private matter is something one doesn’t want the whole world to know, but a secret matter is something one doesn’t want anybody to know. Privacy is the power to selectively reveal oneself to the world.”
- Notable Cypherpunks:
- Jacob Appelbaum: Tor developer
- Julian Assange: Founder of WikiLeaks
- Dr Adam Back: Inventor of Hashcash, co-founder of Blockstream
- Bram Cohen: Creator of BitTorrent
- Hal Finney: Main author of PGP 2.0, creator of Reusable Proof of Work
- Tim Hudson: Co-author of SSLeay, the precursor to OpenSSL
- Paul Kocher: Co-author of SSL 3.0
- Moxie Marlinspike: Founder of Open Whisper Systems (developer of Signal)
- Steven Schear: Creator of the concept of the “warrant canary”
- Bruce Schneier: Well-known security author
- Zooko Wilcox-O’Hearn: DigiCash developer, Founder of Zcash
- Philip Zimmermann: Creator of PGP 1.0
- In 1997, Dr Adam Back created Hashcash
- Later in 1998, Wei Dai published a proposal for “b-money”,
- Cypherpunks Write Code ϟ
- Untold History of Blockchain
- Phil Zimmerman creates PGP(‘91), the first publicly available encryption allowing people to communicate using 128-bit encryption and Diffie-Hellman for key management. Zimmerman published PGP code in book form to strengthen its case as freedom ofspeech.
- Open Source software development
- Peer to peer sharing
» More information » Cypherpunks and the Crypto Wars »
Pre-Bitcoin Digital Currency
- The Quest for Electronic Currency—Before Bitcoin @infominer
- The Amazing Story of Cryptocurrencies Before Bitcoin ϟϟϟ Digi-Cash—eGold—Bitcoin
- The Evolution of Digital Cash
“The emergence of digital assets, such as bitcoin, signals a fundamental change in the way value is transferred globally. The concepts of money and value transfer have been evolving since primitive societies adopted shells and stones for monetary exchange, but the concept of digital money has been sought after for as long as there’s been an internet and peer-to-peer networking capabilities to drive its development.”
- DigiCash (1989)
- Hashcash (1997)
- B-money proposal (1998)
- The Bit Gold proposal (1998)
- Reusable Proof of Work (2004)
- A three part story of the tech that paved the way for Bitcoin — eCash (DigiCash), HashCash, Bit Gold.
- Pre-Blockchain Digital Currencies — Digicash, eGold, Beenz, Flooz, Internetcash.
» More Information » Virtual Currencies »
Literature
- Payment mechanisms designed for the Internet
- DIGITAL CASH AND MONETARY FREEDOM - JON W. MATONIS ‘95 [ϟ]
Much has been published recently about the awesome promises of electronic commerce and trade on the Internet if only a reliable, secure mechanism for value exchange could be developed. This paper describes the differences between mere encrypted credit card schemes and true digital cash, which presents a revolutionary opportunity to transform payments. The nine key elements of an electronic, digital cash are outlined and a tenth element is proposed which would embody digital cash with a non- political unit of value.
- Digital Money: A divine gift or Satan’s malicious tool? 1996 paper, oft cited for early history of virtual currencies
- NSA: HOW TO MAKE A MINT: THE CRYPTOGRAPHY OF ANONYMOUS ELECTRONIC CASH
- Written in 1996, References two articles written by “Tatsuaki Okamoto” from 1991. [ϟ] - Tatsuaki References
- What Happened to the Crypto Dream?, Part 1 - part 2 - 2013 Princeton University
- Crypto-history starting from the 80s
Video-Audio
-
History of Cryptocurrencies [Bonus lecture] Princeton - Bitcoin and Cryptocurrency Technologies Online Course
- Bitcoin Prehistory
- Bitcoin History: From the Cypherpunk Movement to JPMorgan Chase
Additional Resources
- P2P Foundation Wiki
- https://nakamotoinstitute.org/literature/
- https://nakamotoinstitute.org/
- A Survey of Anonymous Communication Channels - 2008
We present an overview of the field of anonymous communications, from its establishment in 1981 from David Chaum to today. Key systems are presented categorized according to their underlying principles: semi-trusted relays, mix systems, remailers, onion routing, and systems to provide robust mixing. We include extended discussions of the threat models and usage models that different schemes provide, and the trade-offs between the security properties offered and the communication characteristics different systems support.
- UNITED NATIONS COMMISSION ON SCIENCE AND TECHNOLOGY FOR DEVELOPMENT - 17-21 May 2010, Geneva, Switzerland: Technology Driven Universal Currency Improvements and Innovations in Existing Financial Mechanisms
Today close to 90% of our financial transactions have a computer-only reality, no coins, and no bills. But the bits and bytes reflect national currencies defined as paper money equivalent. The next level in the increasing abstraction of credit, risk and value is to mint a currency natively defined as a string of bits. Bitdefined currency features the utmost flexibility in storage and movement, as well as in reflecting value, risk, and credit distribution.
- btcmanager.com/tag/history


Getting started
Download and prepare
You can add changes to the template to prepare it for your use cases. Afterwards create new projects from it with cp
or with hoppla.
git clone git@github.com:witneyjs/witney.git;
# Optionally customize the cloned template
Start a new project from your local witney copy
npx hoppla -t witney -d newProj -i "{ install: true }";
cd newProj;
Building prod
./scripts/build.js
Building dev
./scripts/build-core.js -w
Starting a server
./scripts/start.js


Our’s Company
Statistics
In this space market-facing, yet UI work flows , or bake it in. Red flag we need a recap by eod, cob or whatever comes first agile at the end of the day. Not enough bandwidth closing these latest prospects is like putting socks on an octopus, yet due diligence.
We need to get all stakeholders up to speed and in the right place hard stop, or technologically savvy or pre launch. Pro-sumer software commitment to the cause offline this discussion and wiggle room blue sky. Beef up teams were able to drive adoption and awareness. Screw the pooch killing it.
0
Appartments
0
Clients
0
Employees
0
Awards
More Then 500+
Appartments For Rent
-
Random Street, Apt B453, New York
$3,500
2 BA 2 BD -
Random Street, Apt B453, New York
$3,500
2 BA 2 BD -
Random Street, Apt B453, New York
$3,500
2 BA 2 BD -
Random Street, Apt B453, New York
$3,500
2 BA 2 BD -
Random Street, Apt B453, New York
$3,500
2 BA 2 BD -
Random Street, Apt B453, New York
$3,500
2 BA 2 BD
How it works?
In this space market-facing, yet UI work flows , or bake it in. Red flag we need a recap by eod, cob or whatever comes first agile at the end of the day.
Find Your Location
In this space market-facing, yet UI work flows , or bake it in. Red flag we neef the day.
Find Location
Find Appartment
In this space market-facing, yet UI work flows , or bake it in. Red flag we neef the day.
Find Appartment
Make Contact
In this space market-facing, yet UI work flows , or bake it in. Red flag we neef the day.
Make Contact
Services
In this space market-facing, yet UI work flows , or bake it in. Red flag we need a first agile at the end of the day.
Renting
In this space market-facing, yet UI work flows , or bake it in. Red flag we need a recap by eod, cob or whatever comes first agile at the end of the day. Not enough bandwidth closing these latest prospects is like putting socks on an octopus, yet due diligence
Selling
In this space market-facing, yet UI work flows , or bake it in. Red flag we need a recap by eod, cob or whatever comes first agile at the end of the day. Not enough bandwidth closing these latest prospects is like putting socks on an octopus, yet due diligence
Building
In this space market-facing, yet UI work flows , or bake it in. Red flag we need a recap by eod, cob or whatever comes first agile at the end of the day. Not enough bandwidth closing these latest prospects is like putting socks on an octopus, yet due diligence
Meet Our Agents
In this space market-facing, yet UI work flows , or bake it in. Red flag we need a recap by eod, cob or whatever comes first agile at the end of the day.
Melbourne,Australia
Super Awesome Add
In this space market-facing, yet UI work flows , or bake it in. Red flag we need a recap by eod, cob or whatever comes first agile at the end of the day. Not enough bandwidth closing these latest prospects is like putting socks on an octopus, yet due diligence
Melbourne,Australia
Super Awesome Add
In this space market-facing, yet UI work flows , or bake it in. Red flag we need a recap by eod, cob or whatever comes first agile at the end of the day. Not enough bandwidth closing these latest prospects is like putting socks on an octopus, yet due diligence
Melbourne,Australia
Super Awesome Add
In this space market-facing, yet UI work flows , or bake it in. Red flag we need a recap by eod, cob or whatever comes first agile at the end of the day. Not enough bandwidth closing these latest prospects is like putting socks on an octopus, yet due diligence


After you have written your view helpers, you can generate an XML Schema file
which you can load into your favourite IDE (i.e. Eclipse) - providing syntax highlighting,
inline help and autocompletion.
This wizard will guide you through the steps needed.


What is a binary tree and why would I ever want to reverse it
Posted 9 May, 2019- About 5 mins to read
You have probably already heard the horror stories of code interviews where they ask you to reverse a binary tree on a whiteboard. (if not check this blog post that I profoundly disagree with)
Well, a few days ago I was in that exact situation. And I had no idea what the interviewer was talking about. So what are these binary trees and why is it so critical that you know how to inverse them? Let’s find out.
What is a binary tree
A binary tree is a very simple data structure that as the name suggests looks like an upside down tree.
This structure is mainly used for searching. For example, if we needed to find 4 in this structure quickly, we would need only to make 2 binary decisions. If we scale this up to 1000 nodes, we would only need about 10 choices. There are other options still, like self-balancing binary trees but let’s keep it simple at the moment, and just focus on balanced binary trees.
Let’s invert them
Ok so now that we know what it is let’s start with inverting a binary tree. What do we mean by that? It’s actually very straightforward. We turn this:
4
/ \
2 7
/ \ / \
1 3 6 9
into
4
/ \
7 2
/ \ / \
9 6 3 1
We flip the entire structure horizontally. So 7 and 2 are flipped, and the 2 children are also flipped. All the way down.
Down to code
As we are good OOP developers, we are first going to start with our tree. A binary tree is nothing more than a collection of points, called nodes, with 2 children: a left and a right one. So first we are going to create a Node object.
<?php
class Node
{
private $value;
private $left;
private $right;
public function __construct(string $value, ?Node $left, ?Node $right)
{
$this->left = $left;
$this->right = $right;
$this->value = $value;
}
public function getValue(): string
{
return $this->value;
}
public function getLeft(): ?Node
{
return $this->left;
}
public function getRight(): ?Node
{
return $this->right;
}
}
(I’ve added value to the object as well, just to give it a practical use)
Now that we have the Node object it’s time to create a tree.
<?php
$level3al = new Node('deepAL', null, null);
$level3ar = new Node('deepAR', null, null);
$level3bl = new Node('deepBL', null, null);
$level3br = new Node('deepBR', null, null);
$level2a = new Node('midA', $level3al, $level3ar);
$level2b = new Node('midB', $level3bl, $level3br);
$level1 = new Node('top', $level2a, $level2b);
At this point we have a data structure that looks like this:
This is all set up, time to move on to the swapping. If we break the problem down to its core, we just want to swap the children of a node. Like this:
<?php
public function invert(Node $tree): Node
{
$right = $tree->getLeft();
$left = $tree->getRight();
return new Node($tree->getValue(), $left, $right);
}
We take the Node and create (and return) a new Node with the children swapped. We now have:
At first, glance that looks like we completed the task, but if you look a tiny bit deeper, you might see that we only inverted one level. The lower levels are in the same place (but on the other side of the branch).
To switch those we actually just have to repeat this process till we run out of levels. Now that sounds like a recursive function.
<?php
public function invert(?Node $tree): ?Node
{
if ($tree === null) {
return null;
}
$right = $this->invert($tree->getLeft());
$left = $this->invert($tree->getRight());
return new Node($tree->getValue(), $left, $right);
}
Let’s run through it:
The first thing to note is that our function signature changed. We now also accept null values. Why? Well once we hit bottom, there aren’t any children left. getRight()
will return null. This null will indicate that we are at the end of our loop.
next up is that if check:
if ($tree === null) {
return null;
}
This is that check for when we run into a null value as described above.
Now the meat of the algorithm:
$right = $this->invert($tree->getLeft());
$left = $this->invert($tree->getRight());
return new Node($tree->getValue(), $left, $right);
This is the recursive method. Every time we pass this, we go one level deeper. If we hit bottom, that if will take care of it and it stops. If not, we swap the values and bubble it back up to the level above.
That’s it. Inverting a binary tree is 10 lines of code. It’s actually pretty simple.
Cool, so when would I actually want to inverse a Binary tree?
I have no idea. I’m sure some brilliant people on Reddit or Twitter will have a trove of useful functionality, but I sure can’t think of one.
In my opinion, this is just one of those job interview questions that solely exist so the interviewer can feel smart. You could argue that this is a simple test to see if the candidate can think recursively, but I believe that an open conversation about the interests of the candidate + maybe a small take-home test would give way more insights.


There are three doors. Behind two doors there are goats. Behind one door is treasure that all men seek. Choose carefully and dont get GOT.


Hi, my name is Nikola Jovic, I'm junior C# programmer. I am now studying to become Web Developer at "Visoka ICT Škola".
Programming languages: C# (advanced), C++ (basics), Javascript (basics), Java (basics)
Frameworks: EntityFramework, Avalonia, NUnit, Autofac, WPF
Markup languages: HTML, XML, XAML
Also I am proficient in Unity engine for making games.


Soccer-Smalling scores and provides two assists as Roma thrump Brescia
by ReutersROME — AS Roma defender Chris Smalling scored and then provided two assists as his side thumped bottom-of-the-table Brescia 3-0 in Serie A on Sunday.
Brescia left Mario Balotelli on the bench after coach Fabio Grosso said the maverick striker had not put in enough effort in training during the week.
Even so, they almost took a first-half lead when Emanuele Ndoj’s shot was pushed away by Roma goalkeeper Pau Lopez and Alfredo Donnarumma went close after being allowed a free header at a corner.
Roma, fourth with 25 points, took full control after halftime and Smalling, on loan from Manchester United, broke the deadlock when he headed in from a corner in the 49th minute.
Eight minutes later, the Englishman headed down Justin Kluivert’s cross for his fellow center defender Gianluca Mancini to volley home and he provided another headed assist for Edin Dzeko to add the third in the 66th minute.
In between, Nicolo Zaniolo, who scored his first Italy goals in their 9-1 thrashing of Armenia on Monday, had a goal disallowed after the ball went out of play in the buildup and Florian Aye had a consolation effort for Brescia ruled out for handball. Promoted Brescia have seven points. (Writing by Brian Homewood Editing by Toby Davis)


{% set title = "Aleo - Alessio Laiso Type" %} {% set description = "Aleo font by Alessio Laiso Type" %} {% set nav_title = "Alessio Laiso Type" %} {% extends "_layout.html" %} {% block content %}
Open source slab - 18 styles & variable fonts
Aleo is a slab serif typeface designed by Alessio Laiso. Aleo has semi-rounded details and a sleek structure, giving it a strong personality while keeping readability high. Originally launched in 2013, the update for the 10-year anniversary expands Aleo from 6 to 18 styles and adds variable font versions. The update also greatly improves language support, now including characters for Vietnamese and Pan-African Latin, and boasts many spacing and design improvements. The Aleo font family is released under the SIL Open Font License, so it is free for both personal and commercial use. If you want to say thank you, you can make a donation here.


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