text:
Sean McVay: I don't want to run away from adversity, I want to run through it
Josh Alper
The question surrounding Sean McVay for most of last week was whether he'd be leaving his post as the Rams head coach, but Friday brought word that he's decided to stay with the team for the 2023 season.
With that out of the way, the question moved to why he flirted with the idea of leaving for the second straight season before opting back in with the team. McVay provided an answer to that question to Jay Glazer of Fox Sports and it focused on not wanting to walk away after the team stumbled to a 5-12 record after winning the Super Bowl last season.
"I wanted to take some time to really know I could restore and renew the passion and zest I need, and after leaning on family and friends and taking time to reflect it became clear to me. I don't want to run away from adversity, I want to run through it," McVay said.
Saturday brought word that the Rams are going to pick up quarterback Matthew Stafford's option, so there will be a lot of familiar faces back as the Rams try to move things back in the right direction in 2023.
Sean McVay: I don't want to run away from adversity, I want to run through it originally appeared on Pro Football Talk
Novak Djokovic riles crowd en route to yet another Australian Open final
Patrick Reed: 'It's not like I tried to assassinate Rory McIlroy'
In less than a day Patrick Reed went from calling Rory McIlroy "an immature child" to praising the world No 1 for alerting him to the fact he could return to bed for a few more hours of sleep because of a weather delay to the first round of the Dubai Desert Classic.
Geno Auriemma: Lou Lopez Senechal has been 'more important' to UConn than anyone else has to their team
UConn coach Geno Auriemma gave high praise to his senior guard Lou Lopez Senechal after her 26-point performance against Tennessee on Thursday night.
Lakers may be looking to upgrade backcourt
Brian Windhorst reported the Lakers may be looking for an upgrade in their backcourt instead of looking for another frontcourt player.

redpajama_set_name: RedPajamaCommonCrawl


Work began on schedule the first launch day.
Satellite launch «Glonass-M» moved to the day.
Delivered to the Baikonur cosmodrome 7 spacecraft «Globalstar-2».
Operations and experiments on the International Space Station August 11, 2011.
General assembly has started the carrier rocket «Proton-M» to run the Russian spacecraft «Express-AM4».
Federal Space Agency has given «Electro» meteorologists.
Launched the second phase of flight testing of the spacecraft, «Spektr-R».
Flight testing of the spacecraft, «Electro-L» completed.
Spacecraft «KvettsSat-1» ready for testing.
The preparation of cargo transport ship (SCC) of «Progress M-12M».
Operations and experiments on the International Space Station August 9, 2011.
Spacecraft «KvettsSat-1» delivered to Baikonur.
On Baikonur completes assembly of SHM to launch satellites «Express-AM4».
Operations and experiments on the International Space Station, August 8, 2011.
NASA launched a mission to Jupiter.
Delivered to the Baikonur upper stage «Fregat».
Taken the first signals of a spacecraft «Cedar».
Work has begun on preparing for the launch of a spacecraft, «GLONASS».
Operations and experiments on the International Space Station, August 4, 2011.
Hatch docking module «Pirs» closed — spacewalk successfully completed.


I recently purchased a very nice condition Juno 106 nearly mint and thought of removing the epoxy coating from all the six voice chips and three wave chips before they fail. I know many sites that teach one how to remove the coating and restore the chips, but anyone know of a very good site? Would this be a good idea to remove the coating before the chips fail? Any advice on this process is appreciated.
NCChao1268 wrote: I recently purchased a very nice condition Juno 106 nearly mint and thought of removing the epoxy coating from all the six voice chips and three wave chips before they fail. I know many sites that teach one how to remove the coating and restore the chips, but anyone know of a very good site? Would this be a good idea to remove the coating before the chips fail? Any advice on this process is appreciated.
I think there might be some confusion about the potting on the 80017A VCA/VCF modules which is being perpetuated across the internet.
Epoxy potted ceramic subtrate modules are somewhat notorious for long term reliability issues and not just limited to those found in the 106. In many cases it is not the potting that causes the issue but bad solder joints on the components and in some cases failed components. With the correct tools although removing the potting may allow the module to be repaired (and in some cases even appear to magically fix it!) it often doesn't for very long.
Stripping off the epoxy is in itself a risky task to do properly and involves the use of copious amounts of hydrogen peroxide based solvents which is a really messy job. Before this can even be undertaken the modules need to be removed from the voice board which requires considerable care because the tracks and pads on the 106's pressed paper/resin pcbs are rather fragile compared to modern FR4 plated-thru pcbs.
But frankly by the time you've done all of that - you might as well replace them.
My stance with the 106 et al is simply to wait until a module fails and then replace it with one of the better clones such as the Analogue Renaissance modules. It only takes an hour or so for a good tech to remove the old module, fit a clone and recalibrate. The 106 in my studio has suffered from 2/6 failed 80017A VCA/VCF and 1/3 failed D5534A wave gen over the past 20 years.
BTW.. being pedantic - people keep calling these things chips.. strictly speaking they are ceramic hybrid modules which contain several chips.
HideawayStudio wrote: I think there might be some confusion about the potting on the 80017A VCA/VCF modules which is being perpetuated across the internet.
But frankly by the time you've done all of that - you might as well replace the them.
The reason I wanted to do this overhaul of the chips is to ensure a long life for my Juno 106 and an increased investment price, many people use acetone to remove the coating as well as buying better pins to make remounting the chips more efficient and long lasting with much success in either two just described. The way I am thinking of going is a least costly method that still preserves the original parts in it without any calibration needed.
I personally don't like service techs too much as of now because they ended up being very expensive in the end, and the job needed to be accomplished is something someone can do if they have experience with PCB's and electronics.
I agree that any electronics tech/eng could undertake this task but its perhaps a little unfair to say a synth tech will be more expensive unless the engineer is a friend who is doing a favor for you.
I'm also far from convinced anyone would pay more for a 106 with its original filter modules in place. The bulk of the labour to change the modules is the very same labour you'd undertake to remove the originals prior to removing the epoxy resin. There really is no special voodoo about these modules - its not like the infamous KORG35 filters (which are extremely sensitive to component selection due to their remarkably minimal design) - the components on the 106's modules are a mixture of bread and butter parts and the same Roland OTAs used in the previous Juno models.
As for calibration - ideally it is periodically needed regardless of what modules are in there - especially if using self-resonant filters as a second oscillator is important.
I still think the safest/more inexpensive route is to replace them as and when.


text:
Mike Littwin: Breaking new ground, Trump declares a national emergency by conceding there is no national emergency
Opinion | February 24, 2019
Mike Littwin
Fair and Unbalanced
I know it seems early in the game, but the argument over whether there is a real national emergency on the southern border — hint: there isn't — is already over.
Done. Finished. Finito.
Don't take my word or the word of any number of experts. For once, and this is the real shocker, you can take Donald Trump's word.
In a rare moment of Trumpian candor, Trump told the truth about his bogus emergency declaration — with the surprising reveal that there is no emergency at all. In telling the truth, he didn't just undercut his declaration. He put a knife through the heart of it. He basically conceded that he was, uh, appropriating $7 billion that Congress had appropriated for other uses, much of it from the Pentagon. And he was doing this because Congress — a co-equal branch of government, which constitutionally controls the purse — had overwhelmingly voted that he couldn't.
That's the emergency in total. Congress wouldn't give him his wall, and so, ignoring the Constitution and a bunch of other norms, he just decided to take the money and run. He said he expected to be sued. He said he expected Congress to try to stop him. None of it mattered to him in the least. It was just another day in Trumpworld, although with not quite as much executive time, but with the usual number of lies.
Here's how it played out. Trump came to the Rose Garden to announce his emergency declaration. Generally when when you have an emergency to announce, you start with the actual emergency, particularly if it's an emergency that could well lead to a constitutional crisis.
But, no. First Trump talked about China. Then about Great Britain. Then about North Korea. Then about the economy and then about Wall Street and then how "we have all the records." He talked about everything but his latest physical.
Then, finally, eventually, he got around to the bogus emergency declaration. It's like calling 911 and first discussing with the operator how your garden is doing, then the funny thing your kid said that morning, before, you know, reporting that your house is burning down.
I'll just let Trump talk here, in his typical can-you-believe-I'm-the-freaking-president ramble, so you can get the full flavor:
"We're going to be signing today and registering national emergency. And it's a great thing to do, because we have an invasion of drugs, invasion of gangs, invasion of people, and it's unacceptable. And by signing the national emergency, something signed many times by other presidents, many, many times, President Obama, in fact, we may be using one of the national emergencies that he signed having to do with cartels, criminal cartels. It's a very good emergency that he signed. … And what we really want to do is simple. It's not like it is complicated. It's very simple. We want to stop drugs from coming into our country. We want to stop criminals and gangs from coming into our country. Nobody has done the job that we have ever done."
And then we get to the money shot: "I can do the wall over a longer period of time. I didn't need to do this. I would rather do it much faster."
I didn't need to do this. I would rather do it much faster.
Tell me, where do you see emergency there? The law, we're told, is not entirely clear on this, but can you have an emergency when we've been arguing about the border for decades and when the number of people illegally crossing the border is at its lowest point in 40 years? More to the point, can you declare an emergency when there is no emergency and even if there were one, Trump's wall would in no way resolve it?
The only emergency is that we have Trump as president. It's an ongoing emergency. Let me tell you how desperate the situation is: I'm going to quote a tweet from Ann Coulter to describe it.
I really am. I hate to do it, but she's got it right when she says "the goal of a national emergency for Trump is to scare the stupidest people in his base for two more years."
She later tweeted that the No. 1 trending topic on Twitter was the 25th Amendment. This is Ann Coulter. Come on.
Trump already tried a record-breaking government shutdown, and when that failed, he went for a Constitution-breaking money grab. Any Republican who now votes to sustain this latest piece of Trump madness — and the votes are coming, as the Democratic House will surely vote to overrule Trump's declaration and the Senate will, according to the rules of the National Emergencies Act of 1976, have to follow — has basically forfeited whatever legitimacy he or she has ever claimed.
We can start with Mitch McConnell, who told Trump he would support his declaration if only Trump wouldn't shut down the government again on McConnell's watch. This is what you call a party-over-country move, which is the hallmark of McConnell's sad tenure as majority leader.
Meanwhile Cory Gardner, who wants you to think he is upset by the Trump moves, says he is "reviewing" the matter, which is Cory-speak for "I just announced my support for this clown and now you're asking me to say out loud that he's an authoritarian, Constitution-defying head case."
Noted presidential historian Douglas Brinkley stopped just short of calling Trump a head case. He prefers "wild-eyed imperial presidency."
This is where we are. Trump declares a bogus emergency in order to raid the Pentagon to pay for an unneeded wall that he once promised Mexico would pay for. Congress could stop him, but would need to come up with a veto-proof vote to do it. In any case, the money for the wall will likely be tied up in court for months. Eventually the Supreme Court justices will probably get a chance to stop Trump. The fact that we have no idea whether they would says everything you need to say about the situation.
The situation is, of course, dire. Trump admits he didn't need to do this, but at this point in his presidency, even his most devoted followers won't believe him.
Mike Littwin runs Sundays in the Aspen Times. A former columnist for the Rocky Mountain News and Denver Post, he currently writes for ColoradoIndependent.com
Meredith C. Carroll: Shame on the climate shamers
Guest commentary: The benefits of Basalt's whitewater park
She Said, He Said: Stepping into a blended family can be tough … for the kids as well as adults
John Colson: Impeachement? Fine, but remember to vote
Guest commentary: More than actions, money speaks louder in fight against climate issue
Destination Residences Snowmass
Reservations/Front Office at Destination Residences Snowmass in SNOWMASS VILLAGE
Reservations/Front Office The Laurelwood/ Aspenwood Condominiums in Snowmass Village are looking for a reservations/front office…
Timberline Condominiums
Front Desk & Bell Staff at Timberline Condominiums in SNOWMASS VILLAGE
Timberline Condominiums in Snowmass Village is looking for a front desk and bell staff, full…


Definition - What does Parent Partition mean?
A parent partition is an instance of partition within the Windows Hyper V virtualization environment that is responsible for running the virtualization stack and creating child partitions. The parent partition is the second layer of partition after the root partition. It directly interfaces with hardware and logical virtualization resources.
A parent partition primarily provides a logically isolated space for executing the hypervisor's virtualization-specific processes. The parent partition has its own share of storage, memory and computing resources. Each instance of a Hyper V virtualization environment can have only one parent partition. It interfaces the primary hypervisor component stored in the root partition using the hypercall API to create one or more child partitions.
A parent partition is generally considered the root partition; however, parent partitions are logically distributed partitions for hypervisor-specific operations.


Solid Opal Copper and Silver Ring. A solid Opal from Lightning Ridge Australia which has been free form hand cut by ourselves and formed with a textured copper setting on a handmade sterling silver ring band.
The opal is approximately 0.5 x 0.4 cms at its widest points. It is a dainty looking ring. The opal is a darker tone of blue, green and yellow.
UK ring size Q 1/2.


text:
Q: How to align td of my table with th? The table's first columns are personal data, the second part is about operations for employees. I want to create cells within the row. It works without conditional repeats of the data of all the employees, but if I put the condition it causes me this problem.
This information is part of the tr.
  <?php
        if( !empty($obtOpera)) {
         foreach($obtOpera as $fi) {
           if( !empty($consult) ) {
             foreach($consult as $fil) {
               if($fil->operation==$fi->operation && $fila->cveEmp ==$fil->cveEmp)  {

                echo "color'
 $fil->operation
 X";
               }#Close if
            elseif ($fi->operation!=$fil->operation) {
             echo "N/A	";

          }#Close Else if
        }#Close consult
      }#Close if that compare operation
     }#Close foreach fi
   }#Close if de obtOpera  ?>

Here image for table
Table

A: You are missing the closing 	 in the foreach loop.
it should be:
echo "color'
 $fil->operation
 X	";

A: Here answer of my question:
<?php
          if(!empty($obtOperation))
          {
           foreach($obtOperation as $fi)
           {
              $this->load->model('Reports_model');
              $fil=$this->Reports_model->consult($fil->idBlock, $date1, $date2, $fila->cveEmp, $fi->idOperation);

            if(!empty($fil))
            {
             foreach($fil as $fil)
             {
               if($fi->idOperation==$fil->idOperation)
               {
                 echo "color'>
 $fil->operation
 X	";
                }#close else
           }#close consult
         }
         else
         {
          echo "N/A	";
          }#Close if
        }#Close foreach fi
    }#Closeif de obtOperation
  ?>

redpajama_set_name: RedPajamaStackExchange


> Daddy's wallet. And we users should not so readily forget that.
> Sadly there are times when I too am blatantly forgetfull of that.
> Thanks to all the people that make debian happen.
creating a loop that might be snagged.
to look, so are unable to say, thank you for your efforts.
on this list who share information.
frustration that those who volunteer for Debian might feel.
wish, or as it did in the past. But know in Testing this does happen.
Debian GNU/Linux - Magic indeed.


text:
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Hollis + Miller partners with Rocky Mountain Athletic Conference, expands western presence
By Hollis + Miller Architects
Castle Rock, Colo. – Hollis + Miller Architects is excited to announce the formation of a new corporate sponsorship with the Rocky Mountain Athletic Conference (RMAC). The multi-year agreement establishes Hollis + Miller as the exclusive architect partner of the RMAC.
Marking Hollis + Miller's second partnership with student athletic conference organizations, the new agreement comes on the heels of announcing a local sponsorship with the Kansas City-based Mid-America Intercollegiate Athletics Association (MIAA). With the RMAC's presence throughout several western states, Hollis + Miller's partnership with the conference signals the latest expansion of the firm's geographical footprint outside of Kansas City.
The RMAC is a premier NCAA Division II conference with 15 member schools, and is located in Colorado Springs, Colorado. The RMAC sponsors 21 varsity NCAA sports and has produced 62 NCAA Division II national champions and 52 national runners-up since 1992.
"The RMAC and MIAA are incredible groups to partner with, and we could not be more excited to have the opportunity to expand our capability to support students, staff and communities associated with these conferences," said Hollis + Miller Partner Chris Heinz. "Our team at Hollis + Miller is passionate about designing environments where students can develop mentally, physically and socially, both on and off the field, to prepare for a successful future."
Hollis + Miller Architects, headquartered in Kansas City, Missouri, with an office in Castle Rock, Colorado, is one of the top learning environment specialists in the country providing comprehensive services for their clients, including interior design, graphic design, furniture integration and structural engineering, as well as community engagement and capital planning.
Hollis + Miller brings a specialized team dedicated to and passionate about collegiate athletic design. The team understands that spaces are more than just bricks and mortar and serve as the foundation for a program's performance, recruiting, brand, and overall identity associated with a university and community. Hollis + Miller designs for the entire student-athlete experience, from performance (both mentally and physically) to the classroom.
"The partnership forged with Hollis + Miller will provide opportunities and solutions to our member institutions, both within athletics and across the campus community," said RMAC Commissioner Chris Graham. "The breadth of their services, coupled with their sincere commitment to improving the experience of our student-athletes, makes this a very beneficial relationship."
Hollis + Miller Architects joins an impressive and growing list of RMAC corporate partners such as Under Armour, Samsung, FloTrack, NASO and Legacy Lockers, and accompanies MIAA corporate partners such as Adidas, Mammoth Turf, McCownGordon Construction and Jostens.
About Hollis + Miller
Founded in 1950, Hollis + Miller is an integrated architecture firm designing the future of learning environments, including public, private, K-12 and higher education. Learn more at hollisandmiller.com.
Meet the Team: Hillary L'Ecuyer, Interiors
Hollis + Miller's 2021 Learnscape Recipient Announced
Hollis + Miller Architects honored with IIDA, AIA KC Design Awards for Boyd Elementary School
Announcing Hollis + Miller's First Higher Education Learnscape
Hollis + Miller Awarded John Shaw Regional Award for Second Straight Year from Association for Learning Environments
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Understanding the molecular mechanisms plants have evolved to adapt their biological activities to a constantly changing environment is an intriguing question and one that requires a systems biology approach. Here we present a network analysis of genome-wide expression data combined with reverse-engineering network modeling to dissect the transcriptional control of Arabidopsis thaliana. The regulatory network is inferred by using an assembly of microarray data containing steady-state RNA expression levels from several growth conditions, developmental stages, biotic and abiotic stresses, and a variety of mutant genotypes.
We show that the A. thaliana regulatory network has the characteristic properties of hierarchical networks. We successfully applied our quantitative network model to predict the full transcriptome of the plant for a set of microarray experiments not included in the training dataset. We also used our model to analyze the robustness in expression levels conferred by network motifs such as the coherent feed-forward loop. In addition, the meta-analysis presented here has allowed us to identify regulatory and robust genetic structures.
These data suggest that A. thaliana has evolved high connectivity in terms of transcriptional regulation among cellular functions involved in response and adaptation to changing environments, while gene networks constitutively expressed or less related to stress response are characterized by a lower connectivity. Taken together, these findings suggest conserved regulatory strategies that have been selected during the evolutionary history of this eukaryote.
Living organisms have evolved molecular circuitries with the aim of promoting their own development under dynamically changing environments. In particular, plants are not able to evade those changes and have had to evolve robust methods to cope with environmental stress and recovery mechanisms. Genomic sequences specify the context-dependent gene expression programs to render cells, tissues, organs and, finally, organisms. Then, at any moment during the cell cycle and at each stage of an organism's development, and in response to environmental conditions, each cell is the product of specific and well defined programs involving the coordinated transcription of thousands of genes. Thus, the elucidation of such programs in terms of the regulatory interactions involved is pivotal for the understanding of how organisms have evolved and what environments may have conditioned evolutionary trajectories the most. However, we still have little understanding of how this highly tuned process is achieved for most organisms, and the surface of the problem is only just being scratched for a handful of model organisms, such as the bacterium Escherichia coli , the yeast Saccharomyces cerevisiae , the nematode Caenorhabditis elegans , the plant Arabidopsis thaliana [4, 5], and, to a lesser extent, humans .
Meta-analyses of microarray data collections may now be used to construct biological networks that systematically categorize all molecules and describe their functions and interactions. Networks can integrate biological functions of cells, organs, and organisms. During recent years, there has been a tremendous effort in the development and improvement of techniques to infer gene connectivity. Clustering approaches [7–11] and information theory methods [12–16] have been used to infer regulatory networks. Bayesian methods [17–20] can give accurate networks with low coverage but at a high computational cost.
The analysis of the expression of the A. thaliana transcriptome offers the potential to identify prevailing cellular processes, to associate genes with particular biological functions, and to assign otherwise unknown genes to biological responses. Previous attempts to model the A. thaliana gene network used methods such as fuzzy k-means clustering , graphical Gaussian models , and Markov chain graph clustering [5, 15]. The inconvenience of the first approach is that clustering describes genes based on a characteristic property common to all genes, but it is difficult to deduce a pathway structure from this property alone because pathways would have to be concerned with co-expression features that transcend such cluster structure. The second approach assumes that the number of microarray slides should be much larger than the number of genes analyzed or approximations must be taken (for example, empirical Bayes with bootstrap re-sampling or shrinkage approaches). The last approach is based on Person's correlations and, therefore, strongly sensitive to outliers and to violations of the implicit assumption of linear relationships among genes. In this article, we present a predictable genome model from a regulatory scaffold inferred by using probabilistic methods  and estimate the corresponding kinetic parameters using linear regression [22–25]. We analyze the topological properties and predictive power of the inferred regulatory model. We evaluate the performance of the network by predicting already known transcriptional regulations and assess the functional relevance and reproducibility of the co-expression patterns detected. Finally, we discuss the evolutionary implications of transcriptional control in plants.
High-throughput technologies combined with rigorous and biologically rooted modeling will allow understanding of how simple genetic or environmental perturbations influence the dynamic behavior of cellular genetic and metabolic networks . However, transcriptomic data need to be properly integrated to formulate a model that can be used for making quantitative predictions on how the environment interacts with cellular networks to affect phenotypic responses. At the end, the accurate prediction of this quantitative behavior will open the possibility of re-engineering cellular circuits. To reach this end, we have attempted the integration of experimental and computational approaches to construct a predictive gene regulatory network model covering the full transcriptome of the model plant A. thaliana.
In the present work, we have applied a recently developed inference methodology, InferGene , to obtain a gene regulatory model suitable for analyzing optimality and allowing study of the transcriptional control response under changing environments in A. thaliana. For this, we have considered the Affymetrix chip for the A. thaliana genome, from which we selected 22,094 non-redundant genes, of which about 1,187 are putative transcription factors (TFs; see Materials and methods). The data used for the inference procedure were a compendium of 1,436 Affymetrix microarray hybridization experiments publicly available at The Arabidopsis Information Resource (TAIR) website; these were normalized using the robust multi-array average method . Here we used the whole expression set (1,436 experiments) to construct the model. In Figure 1 we show the inferred transcriptional regulatory network of A. thaliana drawn using the Cytoscape viewer ; Table 1 collates some parameters describing the topology of the network.
Plot of the inferred regulatory network of A. thaliana visualized using Cytoscape. Nodes only represent TFs.
which is the harmonic mean of precision and sensitivity. Indeed, precision and sensitivity are necessarily negatively correlated performance statistics, and these two values were set up so they maximize global performance (F) by selecting values > 5 (Figures S1 and S2 in Additional data file 1) for the z-score used as threshold to predict the transcriptional regulations. Figure S3 in Additional data file 1 shows P, S and F as a function of the z-score threshold. Sensitivity is maximized (S = 100%) for z = 0 (that is, a high number of regulations but very low confidence) while precision is maximized (P = 100%) for z = 11 (that is, high confidence but a very low number of regulations). The optimum value is reached for z = 5, a value for which F = 26% (P = 40% and S = 20%). In a recent study, a smaller network topology has been proposed for A. thaliana . This network contains 18,625 regulations and an F = 3.7% (P = 88% but S = 1.8%), relative to the AtRegNet reference dataset.
InferGene predicts that more than half of the genes are controlled by constitutive promoters (17.89%) or by promoters regulated by less than three TFs (Table 1). Also, from a purely topological perspective, the inferred transcriptional network of A. thaliana is weakly connected directed, containing 18,169 connected genes (Table 1), while the size of the largest strongly connected component contains only 730 nodes, all of which are TFs. In addition, it has a high density (0.078%; Table 1); this parameter is the normalized average connectivity of a gene in the network in comparison to values reported in similar studies on other organisms. For example, Lee et al.  suggested a network density of 0.0027% for S. cerevisiae, while we previously reported a value of 0.036% for the inferred network for E. coli . The characteristic path length  of the network follows a Gaussian distribution, with an average value of 5.065 edges (Table 1; Figure S4 in Additional data file 1) and, specifically, the distance between two genes for which a path exists ranges from 1 to 13 edges. In a previous study, we estimated that the characteristic path length for the E. coli network was 1 , much smaller than that for A. thaliana. Furthermore, the E. coli inferred network did not contain any strongly connected components and its largest weakly directed subnetwork contained only four TFs.
Other relevant statistical properties of networks are the stress distribution (Figure S5 in Additional data file 1) - that is, the number of paths in which a gene is involved - and the betweenness centrality distribution (Figure 2d) - that is, the number of shortest pathways in which a particular gene is involved. Both distributions are highly asymmetrical, with many nodes having low betweenness centrality and only a few cases with high betweenness centrality (Figure 2d), and with the number of shortest paths per gene smoothly increasing until reaching a maximum of approximately 105 short paths per gene followed by a drastic drop, with very few genes (around 5) having 107 short paths (Figure S5 in Additional data file 1). Ten genes (At1g32330, At4g26930, At1g24110, At4g24490, At2g36590, At1g01030, At1g76900, At2g19050, At2g03840, and At3g19870) are connected among themselves but remain isolated from the rest of the main network (Figure 1); the number of shortest paths for these genes ranges from 1 to 3 (Figure S5 in Additional data file 1). All these genes but the last are involved in several and apparently loosely related Gene Ontology (GO) functional categories that include regulation of transcription, transportation and signal transduction, and development and senescence.
Analyses of the regulatory network of A. thaliana. Distributions for the transcriptional network of: (a) outgoing connectivity showing the master regulators from Table 2 in gray; (b) incoming connectivity; (c) clustering coefficient; and (d) betweenness centrality. Distributions for the non-transcriptional network of: (e) outgoing connectivity; and (f) incoming connectivity.
Next, we sought to explore whether the inferred regulatory network has scale-free properties. It has been suggested that the distribution of outgoing connections should belong to the class of scale-free small-world networks, representing the potential of TFs to regulate multiple target genes, whereas the distribution of incoming connectivities would be more exponential-like because regulation by multiple TFs should be less common than regulation of several targets by a given TF . Figure 2a shows the distribution of outgoing connectivities per TF, whereas Figure 2b shows the same distribution but only for incoming connectivities per gene. As expected, the outgoing connectivity is best fitted by a truncated power-law (that is, the Weibull distribution) with exponent γ = 0.902 and cutoff k c = 99.093 (Table S1 in Additional data file 2; R2 = 0.949; Akaike's weight over a set of 10 competing models > 99.99%). This distribution indicates that outgoing connectivity has a scale-free behavior in the range 1 ≤ k <k c but deviates from this for connectivities over the cutoff. According to Barabási and Oltvai , scale-free properties arise when hub genes are related in a hierarchical way, with the hub receiving most links being connected to a small fraction of all nodes. In the case of incoming connectivity, the model that better describes the data is a restricted exponential, the half-normal distribution (Table S1 in Additional data file 2; R2 = 0.983; Akaike's weight > 99.99%). Taken together, these two observations suggest that the A. thaliana transcriptional network contains a few highly connected regulators (Table 2) that play a central role in mediating interactions among a large number of less connected genes. Notice that 88.4% of the TFs regulate more than 10 genes, 36.3% regulate more than 100 genes and just 2.6% control over 500 genes. For the sake of comparison, it is worth mentioning that, in the case of S. cerevisiae, the critical exponents estimated for the outgoing connectivity distribution (γ = 0.96 [2, 32]) are quite similar to that reported here. However, the estimate obtained for E. coli was smaller (γ = 0.87), a result that suggests that hubs are more important in bacteria than in the two eukaryotes .
We have validated the set of predicted targets for the 25% most highly connected TFs using AtRegNet, recovering 80% of known interactions for the regulatory model and up to 85% for the effective model (that is, the one containing both gene-gene and gene-TF interactions). Figure 2c shows that the scaling of the average clustering coefficient with the number of genes with k-connections is approximately linear in a log-log scale in the range 1 to 10,000 for neighbors with slope -1.05 (R2 = 0.850). Barabási and Oltvai  and Ravasz and Barabási  have suggested that whenever clustering scales with the number of nodes with slope -1, as in our case, it has to be taken as a strong indication of hierarchical modularity - that is, genes cluster in higher-order units of different modularity - a finding that has been suggested as general for system-level cellular organization in plants . Similarly, when the effective model is analyzed, it shows similar results to those for the regulatory model. The outgoing connectivity per gene follows a truncated power law with scale-free behavior up to k c = 21.341 connections per gene and with an exponent γ = 0.765 (Table S1 in Additional data file 2; R2 = 0.998, Akaike's weight > 99.99%; Figure 2e). Figure 2f shows that the incoming connectivity per gene does not present scale-free properties as it fits to a normal distribution (Table S1 in Additional data file 2; R2 = 0.998, Akaike's weight > 99.99%).
The environment significantly influences the dynamic expression and assembly of all components encoded in the A. thaliana genome into functional biological subnetworks. We have computed the clustering coefficient for all subnetworks with the largest normalized index of connectivity between genes involved in the subnetwork. The subnetworks were then ranked according to these numbers and the top 12 networks are shown in Table 3. Interestingly, four of these highly connected subnetworks are involved in responses to external influences - for example, responses to pathogens and other processes related to abiotic stresses (heat, salinity, light, reduction/oxidation). For the sake of illustration, Figure 3 shows the inferred subnetworks for three abiotic and three biotic responses. In particular, we have made a comprehensive analysis for the subnetwork of systemic acquired resistance (Figure 3d) and found that the fraction of predicted interactions is P = 33%. Not surprisingly, all genes involved in this subnetwork are associated with GO categories related to responses to stress, such as defense against pathogens, responses to other organisms such as fungi, bacteria and insects, and responses to cold.
Transcriptional subnetworks with high clustering coefficients corresponding to the following GO pathways: (a) auxin metabolic process; (b) response to other organism; (c) response to heat; (d) systemic acquired resistance (experimentally verified regulations are represented with thick edges); (e) response to salt stress; and (f) immune response.
*The clustering coefficient for the random subnetworks is 0.005, as computed from 10 subsets of 100 genes each.
The basic premise of our approach is to use transcriptomic data from multiple perturbation experiments (either genetic or environmental) and quantitatively measure steady-state RNA concentrations to assimilate these expression profiles into a network model that can recapitulate all observations. We also developed a test model that excludes 10% of experiments to quantify prediction power. This dataset was randomly split into two subsets. The first, larger subset contained 1,292 experiments and was used as a training set for inferring a transcription network containing 128,422 regulatory interactions. The second, smaller subset contained 144 array experiments and was used for validation purposes.
where N c = 144 is the number of microarray experiments included in the random tester dataset. Figure 4a shows the distribution of Δ g for all genes included in the predicted A. thaliana transcriptional network. The distribution of errors has a median value of 3.66% and is significantly asymmetrical (skewness 1.709 ± 0.017, P < 0.0001), with most genes having a relatively low error but with some genes whose expression is estimated having errors > 10% and in a few instances even > 16%. How does this predictive performance compare to that obtained for other organisms, for example, E. coli? In a previous study, we constructed a transcriptional network containing 4,345 genes and 328 TFs from E. coli  using a dataset containing 189 experimental conditions. For this network, the average error over the training set was similar (3.68%) to the values reported above but with the error distribution being even more asymmetrical (skewness 2.314 ± 0.017, P < 0.0001). The average error over the E. coli test set (4.80%) was larger. Figure 4b shows the distribution of Δ g for gene-gene and gene-TF interactions, which is also significantly asymmetrical (skewness 1.455 ± 0.017, P < 0.001), although in this case the median error is reduced to 2.71% and, in all cases, the error was < 9%. Both distributions significantly differ in shape (Kolmogorov-Smirnov test P < 0.001) and location (Mann-Whitney test P < 0.001), with the latter being narrower and centered around a lower expression error.
Histogram of the relative gene expression error in (a) the transcriptional test model (with an average error of 0.0402) and (b) the effective model (with an average error of 0.0280). Errors were obtained from the comparison of the predicted model obtained from the training dataset and the experimental determinations contained in the random test dataset.
One may ask whether the predictability of our model was driven by TFs and not by non-TF genes. To test this possibility, we proceeded as follows. First, we selected a random set of 1,187 non-TF genes and used them to construct the corresponding pseudo-transcriptional network. Then we evaluated its performance as described above. The level of precision reached was undistinguishable from that of the previous model, with the distribution of relative expression error obtained fully overlapping with thar shown in Figure 4b (data not shown). We conclude from this analysis that TFs do not have stronger predictive power than other genes. This could be rationalized because, in terms of mathematical equations, genes that are coexpressed with the TFs have a priori equal chances to work as regulatory elements. On the other hand, we have also constructed an effective model excluding the TFs from the set of predictors and observed that the relative expression error decreased proportionally to the number of excluded TFs.
As a second step for the predictability of our test model, we computed Pearson correlation coefficients (r) between the experimental and predicted gene expression levels for all microarray experiments and observed that, as expected, genes having high r also have low Δ g (Figure S6 in Additional data file 1). In addition, we noticed that the predictability of the expression of those genes with high r depends on a reduced set of TFs (Figure S7a in Additional data file 1 shows that the critical mass of points concentrates in a region with high r and a low number of predictors), suggesting that a selective pressure exists to introduce indirect regulations as a way to increase robustness of genetic systems to dynamic environments. Figure S7a in Additional data file 1 also shows that the model does not tend to add large numbers of regulations as a way to minimize expression error and, by contrast, the highest density of values corresponds to a rather low number of regulations (between 0 and 30). The average incoming connectivity values estimated for E. coli  and S. cerevisiae  were 1.56 and 2.26 regulators, respectively. The comparison of these figures with the data reported here suggests that r does not significantly increase beyond a given number of regulations.
Nonetheless, a few genes were predicted to have more than 60 regulations. Looking at just the 20 most extremely regulated genes in Figure S7a in Additional data file 1, the results are interesting: the two most extreme cases correspond, respectively, with gypsy- and copia-like retrotransposons (89 and 83 connections to TFs, respectively), nine genes are annotated as unknown proteins, two are annotated as belonging to the F-box family but without any assigned biological process, one has been assigned as a putative protein kinase, five have been loosely assigned to transcription, translation, transport and secondary metabolism, and the only one with a well defined function is the At2g26330 locus, which encodes the ERECTA receptor of protein kinases involved in several developmental roles as well as in response to bacterial infections. Moreover, Figure S7b, c in Additional data file 1 shows a histogram of r per gene over 1,292 experiments in the training set and 144 conditions in the test set, respectively. The average r for the training set was 0.767 and was very similar for the test set (0.759). These values are in the same range as those reported in a study inferring the regulatory network (1,934 genes; including 81 regulators) for Halobacterium salinarum NRC-1  using 266 experimental conditions for the training model and 131 extra experiments as the test set. In this case r = 0.788 for the training set and r = 0.807 for the test set.
For illustrative purposes, Figure 5 shows the expression predicted for the five best cases for the transcriptional network; each dot in the scatter plots represents a value obtained from a different hybridization experiment. The left column shows the prediction obtained using the whole dataset (1,436 experiments) as both training and tester sets, whereas the right column shows, for the same five genes, the correlation between the prediction obtained using the test model (inferred from the reduced training set of 1,292 experiments) and that obtained using the tester set (144 experiments). It is remarkable that the quality of the prediction does not change by using a reduced training set, in good agreement with the results reported for E. coli . Similarly, Figure S8 in Additional data file 1 shows the three best and worst predicted cases for the effective gene-gene interaction model inferred from the whole dataset. In this case, the R2 for the poorly predicted genes ranged widely, with gene At2g02120 (encoding a pathogenesis-related protein belonging to the defensin family) having the lowest determination coefficient observed.
Predictive power for gene expression of the transcriptional model of A. thaliana inferred from the whole dataset (1,436 conditions) and the test model from 1,292 microarray experiments used as a training set. The left column shows the regression coefficient (R2) between the model and experimental profiles across the whole dataset for the five best predicted genes. The right column shows R2 between the test model and the 144 experimental profiles used as the test set for the same five genes. In either case, correlation coefficients were highly significant.
Organisms have a high capacity for adjusting their metabolism in response to environmental changes, food availability, and developmental state . On the one hand, we have detected that GO pathways (Table 4) related to response to diverse environmental (for example, defense against diverse pathogens, response to radiation, temperature, light intensity, or osmotic stress) and internal (development, secondary metabolism, porphyrin biosynthesis, and so on) stimuli consist of sets of genes with high incoming connectivity, that is, genes regulated by many different TFs. Therefore, this high degree of interconnection among different stimulus-related pathways allows the cell to rapidly adjust its homeostasis in response to changing environments. On the other hand, functional GO pathways associated with biological functions that are unaffected by external stresses (for example, glycerophospholipid and glycerophospholipid metabolic process, sulfur amino acid biosynthetic process, indole and derivative metabolic process, membrane lipid biosynthetic process, sulfured compounds biosynthetic, and Golgi vesicle transport (Table 4)), have low incoming connectivity. Notice that some GO pathways indirectly related to external stresses, such as indole derivatives (for example, camalexin, which is involved in response to the bacterium Pseudomonas syringae) or lipid biosynthesis pathways (playing a role in defense) do not have high connectivity scores and do not have a high number of feed-forward loops (FFLs). Furthermore, the predicted master regulators of A. thaliana listed in Table 2 are associated with biological functions related to transcription and regulation of cellular metabolic processes (containing 812 TFs each) or RNA metabolic processes (536 TFs) that are stimulated by environmental and developmental stresses. After all, the regulatory network of A. thaliana governs intracellular processes and modulates and determines the expression of the different programs encoded in the genome.
*Only GO pathways involving more than 20 genes and less than 300 from all levels were selected. †Total number of TFs that regulate the genes of the GO pathway. ‡Relative number of TFs. §Total number of FFLs involved in the GO pathway.
Networks can be decomposed into subnetworks, which can be seen as building blocks of the former. These building blocks, generally known as motifs, are defined in terms of their frequency and are typically composed of several promoter regions of genes expressing TFs that regulate each other in a number of well known patterns (for example, bifans, forward, feed-forward, or negative feedback loops) . Certain regulatory network motifs have been described as conferring robustness to perturbations in individual edges, the coherent FFL being the prototypical example of such a robustness-conferring motif [37–40]. Therefore, we sought to characterize our inferred complex network in terms of the presence and abundance of regulatory network motifs. An exhaustive list of detected three- and four-element motifs for both transcriptional regulations as well as gene-gene interactions, together with their observed frequency and whether this frequency significantly deviates from the expected value from a random network, is given in Tables S2 to S5 in Additional data file 2. Some of the overrepresented motifs are shown in Figure 6. The third most abundant motif found is the FFL (third row in Figure 6a). Indeed, the FFL motif is overrepresented among GO categories involved in stress response compared to non-stress response categories (Table 4; Fisher's exact test, P < 0.001).
Network motifs of three (a) and four (b) genes found in the transcriptional network of A. thaliana. Here we plot the most statistically significant motifs (see Additional data file 2 for a complete list of motifs). (c) The FFL, a motif significantly overrepresented, where an external factor inhibits gene A thereby limiting expression of gene B, but this is compensated for by indirect regulation through gene C. (d) The evolution of the qualitative development of a plant with motifs (dashed line) and without motifs (solid line) under changing environments. We note that there is an evolutionary optimization, including topological units such as FFLs, that provides robustness under external factors despite decreasing system fitness (areas I and II) due to excess expression of those genes providing indirect interactions. (e) Distribution of normalized robustness coefficients (ρ*) computed for all interactions between TFs and genes.
Next, we sought to test whether the presence of FFLs indeed contributes to increase the robustness of the gene expression of the involved genes. To do so, we have computed a score, ρ*, quantifying the robustness of gene expression for all predicted TF-gene interactions involving three nodes (Figure 6c). Figure 6e shows the distribution of the robustness score computed from the inferred regulatory network. Although it may not be apparent after visual inspection of Figure 6e, the distribution is asymmetrical (skewness 1.881 ± 0.007, P < 0.001) and strongly leptokurtic (1,294.051 ± 0.014, P < 0.001), suggesting that there are more data points in the tails than close to the mean. The data points in the upper tail correspond to the more robust interactions and, if coherent FFLs are involved in such types of interactions, they may be over-represented in this tail. This is, indeed, the case. If we look at the top 1% of values, 90.7% of them correspond to a coherent FFL. By contrast, if we look at the 1% of interactions around the mean value, only 5.7% correspond to FFLs. Interestingly, 90.2% of motifs within the bottom 1% of the distribution correspond to incoherent FFLs.
We have discussed a reverse-engineered model of the A. thaliana gene regulatory network that will aid future research focused on distinguishing, for example, the molecular targets of a plant virus from the hundreds to thousands of additional gene products that may have modified levels of gene expression as a side-effect. We have used a recent methodology to infer the global topology of transcription regulation from gene expression data to produce a kinetic model able to predict the alterations in gene expression in plants subjected to different external stimuli. Moreover, we have concluded that the A. thaliana inferred transcriptional network presents a hierarchical scale-free architecture where biological functions cluster in modules. We have identified biological functions that are highly controlled by predicted master regulators that could change their operating points in response to dynamic external factors to produce a consistent and robust response upon different stresses at the expense of decreasing the cellular replication rate. We have successfully applied the inferred model to predict the transcriptomic response of A. thaliana under all experimental conditions included in the whole dataset, and also applied the test model to predict the response in a reduced test set, producing errors of 2 to 10% relative to the experimental value (averaging across all test experiments). Thus, we believe this modeling-validation approach constitutes an important step towards understanding an organism's large-scale mode of action to cope with a generally changing environment. The network model suggests that A. thaliana promoters are regulated by multiple TFs (Table 1), a feature that has been shown to be characteristic of eukaryotic gene regulation .
We have discussed a first gene regulatory model based on a transcriptional layer and a second model that enhances this by including gene-gene interactions that provide an even more accurate prediction of gene expression. Future work will consider just the interactions between tissue-specific genes. We have also quantified the presence of network motifs and found that FFLs are overwhelmingly common, thus supporting the above notion that robustness against perturbation has been a major driving force during the evolution of plant lineages. Furthermore, we have confirmed that coherent FFLs are overwhelmingly over-represented among interactions that are robust against the knockout of regulatory TFs (Figure 6e), while incoherent FFLs are among the most sensitive interactions. Figure 6c illustrates a possible mechanism by which FFLs would confer robustness. Imagine that the B product is relevant for cell survival. On one hand, regulatory flow through C is costly because it implies producing a redundant element; on the other hand, if perturbations disrupt the direct edge between A and B, the existence of C still allows the cell to obtain the precious B without incurring a major penalty (Figure 6d). Whether a given regulatory network may be selected to contain this sort of regulatory element depends on the balance between the fitness costs and benefits associated with redundancy [41, 42]. The fact that A. thaliana network topology seems to be rich in these transcriptional regulatory elements suggests that it has been evolutionary optimized to allow rapid responses to changes in external conditions while maintaining cellular homeostasis, and hence maximizing fitness.
The reconstruction of genome-scale regulatory models constitutes a major step towards understanding cellular behavior, but it is also useful in Synthetic Biology, where predictive models can be applied to engineer synthetic systems for biotechnological applications. InferGene  provides a means to predict changes in biological processes when perturbing a cell in order to identify the effects of drugs, viral infection and herbicides on plant interactomes. It may also facilitate optimization of cellular processes for biotechnology applications that utilize the complex regulatory properties of genetic networks.
In this study, we have shown that the A. thaliana regulatory network is scale-free and clustered, both characteristic properties of hierarchical networks. We also used our model to analyze the robustness of expression levels conferred by network motifs such as the coherent FFL. Hence, the meta-analysis presented here has allowed us to identify regulatory and robust genetic structures. These results suggest that A. thaliana has evolved a high connectivity in terms of transcriptional regulations among cellular functions involved in responses and adaptation to changing environments, while gene networks constitutively expressed or less related to stress responses are characterized by a lower connectivity. We successfully applied our quantitative network model to predict the full transcriptome of the plant for a set of microarray experiments, and the quality of the predictions was evaluated by several methods.
where α i is its constitutive transcription rate, β ij the regulatory effect that gene j has on gene i and δ i the degradation coefficient. If j has no effect on the expression of i, then β ij = 0. No cooperation between genes for regulation has been assumed. Time was conveniently scaled such that δ i = 1 and the model is assumed in steady-state (y i = α i + Σ j β ij y j ), since fitting the appropriate mRNA degradation constant would require time series data .
Steady-state mRNA expression profiles derived from transcriptional perturbations collected from the TAIR website  were used in this study. We found 1,187 TFs by looking for the motif 'transcription factor' in the functionally annotated A. thaliana genome from TAIR (version 7). The dataset contains pre-processed expression data from 1,436 hybridization experiments using the 22,810 probe sets spotted on Affymetrix's GeneChip Arabidopsis ATH1 Genome Array . For this study, we consider 22,094 genes. The arrays were obtained from NASCArrays  and AtGenExpress . Data were normalized using the robust multi-array average method .
The inference procedure consisted of two nested steps. In the first step, the global network connectivity was inferred using the InferGene algorithm . This method uses mutual information with a local significance (z-score computation) to obtain the genome regulations . Hence, the potential interaction between a regulator and a gene is z-scored, constituting an estimator of the likelihood of mutual information. This approach allows some false correlations and indirect influences to be eliminated . Subsequently, we selected a z-score threshold for a cutoff. In a second step, multiple regressions were obtained to estimate the kinetic parameters of a regulatory model based on ordinary differential equations. Multilayer models were constructed to account for different types of regulations between genes and TFs. We have constructed two different models, one for transcription regulations and another to account for effective (transcription and non-transcription) regulations. In the case of non-transcriptional interactions, Lasso's method was used to avoid over-fitting  and the effective interactions between genes giving the non-transcriptional layer were unveiled. To this end, we applied a simple and efficient algorithm based on the Gauss-Seidel method  that reduces the number of regulators that exceeded the z-score threshold for a given gene. Note that the Lasso method enriches in TFs among the predictors of the target for 33.21% of the non-constitutive genes of A. thaliana (that is, the ratio between the number of TFs selected and the total number of predictors of a given gene above a threshold defined as 1,187/22,094 = 0.0537). Finally, one systems biology markup language (SBML)  file containing the transcriptional model and a plain text file containing the effective model were constructed and are available as supplementary files in Additional data file 3. These files can be viewed using the Cytoscape viewer for further analysis. Notice that the transcriptional model was embedded within the effective one. Networks are constructed by placing genes as nodes and regulations as edges. For the transcriptional model, edges only go from TFs to genes (including those encoding other TFs). For the non-transcriptional model, edges connect two genes, the regulator and the target and, thus, the resulting network is directional.
The performance of the inferred model topology was evaluated using a reference network including genes with known transcriptional regulation. For this, the AtRegNet platform  linking cis-regulatory elements and TFs into regulatory networks was used. Only those interactions among genes included in that reference set were evaluated. The fraction of interactions that were correctly predicted by the model (precision, P) and the fraction of all known interactions that were discovered by the model (sensitivity, S) were used to compute a performance statistic defined as F = 2PS/(P + S) . We have to note that the number of transcriptional regulations experimentally confirmed and compiled in AtRegNet is quite limited, containing only 448 reported interactions between TFs and genes. Therefore, it is difficult to obtain an accurate value for the performance of the model.
To validate the predictive power of the methodology, we constructed two transcription models. The first was obtained by using the 1,436 microarrays for training. For the second model (the test model), 1,292 of these 1,436 microarrays were used as a training set (90%) and 144 randomly chosen ones (10%) were retained for validation studies.
The FANDOM program  was used to detect motifs of three and four genes in the predicted A. thaliana regulatory model. Statistically significant motifs have z-scores > 2.
which is now contained in the interval [-1, 1]. Values of close to 1 would correspond to maximally robust motifs, whereas values close to zero correspond to motifs not contributing to the robustness of the network. Values close to -1 correspond to incoherent motifs, that is, gene circuits implementing antagonistic regulations .
The following additional data are available with the online version of this paper: Figures S1 to S8 (Additional data file 1); Tables S1 to S5 (Additional data file 2); the effective and transcriptional models (Additional data file 3).
Javier Carrera, Guillermo Rodrigo contributed equally to this work.
We thank J Forment for help with computer resources and MA Blázquez for critical reading of the manuscript and useful suggestions. This work was supported by grants BFU2006-14819-C02-01/BMC and TIN2006-12860 from the Spanish Ministerio de Ciencia e Innovación to SFE and AJ, respectively; FP6-NESTs 043340 (BioModularH2) and 043338 (Emergence), FP7-KBBE-212894 (Tarpol), the Structural Funds of the European Regional Development Fund (ERDF), the 91-A3405-ATIGE Genopole/UEVE and the MIT-France grants to AJ. JC, GR and AJ acknowledge the HPC-Europa program (RII3-CT-2003-506079). GR was supported by a graduate fellowship from the Generalitat Valenciana and an EMBO Short-term fellowship (reference ASTF-343.00-2007). SFE also acknowledges support from the Santa Fe Institute.
SFE conceived the study. JC, GR and AJ performed all the computations. All authors analyzed the data and contributed to writing the manuscript.


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THIS DAY IN HIP-HOP...THE ART OF WAR
On this day in hip-hop history, Bone Thugs-N-Harmony released their third studio album, The Art of War.
On this day in 1997, Bone Thugs-N-Harmony released their third studio album The Art of War on Ruthless Records. The multi-platinum double album discs were titled "World War I" and "World War II" and were produced by DJ U-Neek.
The Art of War featured a guest appearance by 2Pac on the track "Thug Luv" and contained the #1 R&B song, platinum-selling single "Look Into My Eyes." Another single, "If I Could Teach the World" reached #3 on the Rap chart. The album itself reached #1 on both the Billboard 200 and the R&B chart.
Also on this day...
2016 - DJ Khaled released his ninth album Major Key on Epic.


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Posted on December 15, 2014 December 23, 2015 by Matt Williams
Just in Time for the Holidays – Galactic Encounter Puts on Stunning Display
At this time of year, festive displays of light are to be expected. This tradition has clearly not been lost on the galaxies NHC 2207 and IC 2163. Just in time for the holidays, these colliding galaxies, which are located within the Canis Major constellation (some 130 million light-years from Earth,) were seen putting on a spectacular lights display for us folks here on Earth!
And while this galaxy has been known to produce a lot of intense light over the years, the image above is especially luminous. A composite using data from the Chandra Observatory and the Hubble and Spitzer Space Telescopes, it shows the combination of visible, x-ray, and infrared light coming from the galactic pair.
In the past fifteen years, NGC 2207 and IC 2163 have hosted three supernova explosions and produced one of the largest collections of super bright X-ray lights in the known universe. These special objects – known as "ultraluminous X-ray sources" (ULXs) – have been found using data from NASA's Chandra X-ray Observatory.
While the true nature of ULXs is still being debated, it is believed that they are a peculiar type of star X-ray binary. These consist of a star in a tight orbit around either a neutron star or a black hole. The strong gravity of the neutron star or black hole pulls matter from the companion star, and as this matter falls toward the neutron star or black hole, it is heated to millions of degrees and generates X-rays.
The core of galaxy Messier 82 (M82), where two ultraluminous X-ray sources, or ULXs, reside (X-1 and X-2). Credit: NASA
Data obtained from Chandra has determined that – much like the Milky Way Galaxy – NGC 2207 and IC 2163 are sprinkled with many star X-ray binaries. In the new Chandra image, this x-ray data is shown in pink, which shows the sheer prevalence of x-ray sources within both galaxies.
Meanwhile, optical light data from the Hubble Space Telescope is rendered in red, green, and blue (also appearing as blue, white, orange, and brown due to color combinations,) and infrared data from the Spitzer Space Telescope is shown in red.
The Chandra observatory spent far more time observing these galaxies than any previous ULX study, roughly five times as much. As a result, the study team – which consisted of researchers from Harvard University, MIT, and Sam Houston State University – were able to confirm the existence of 28 ULXs between NGC 2207 and IC 2163, seven of which had never before been seen.
In addition, the Chandra data allowed the team of scientists to observe the correlation between X-ray sources in different regions of the galaxy and the rate at which stars are forming in those same regions.
The Mice galaxies, seen here well into the process of merging. Credit: Hubble Space Telescope
As the new Chandra image shows, the spiral arms of the galaxies – where large amounts of star formation is known to be occurring – show the heaviest concentrations of ULXs, optical light, and infrared. This correlation also suggests that the companion star in the star X-ray binaries is young and massive.
This in turn presents another possibility which has to do with star formation during galactic mergers. When galaxies come together, they produce shock waves that cause clouds of gas within them to collapse, leading to periods of intense star formation and the creation of star clusters.
The fact that the ULXs and the companion stars are young (the researchers estimate that they are only 10 million years old) would seem to confirm that they are the result of NGC 2207 and IC 2163 coming together. This seem a likely explanation since the merger between these two galaxies is still in its infancy, which is attested to by the fact that the galaxies are still separate.
They are expected to collide soon, a process which will make them look more like the Mice Galaxies (pictured above). In about one billion years time, they are expected to finish the process, forming a spiral galaxy that would no doubt resemble our own.
A paper describing the study was recently published on online with The Astrophysical Journal.
Further Reading: NASA/JPL, Chandra, arXiv Astrophysics
CategoriesAstronomy, galaxies, X-Ray Astronomy TagsarXiv, Canis Major Constellation, Chandra Observatory, galactic mergers, Hubble Space Telescope, IC 2163, Mice Galaxies, NGC 2207, Spitzer Space Telescope, ultra-luminous x-ray sources, ULX
One Reply to "Just in Time for the Holidays – Galactic Encounter Puts on Stunning Display"
Ps8 says:
" this x-ray data is shown in pink, which shows the SHEER PREVALENCE of x-ray sources within both galaxies."
The resulting picture is stunning; but what an INHOSPITABLE place for LIFE!
Its nice to know that it is expected to calm down once the merger is complete "in about a billion years".
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Q: Simple remote access to another Mac or PC I frequently get asked by various poeple to fix or help them with their computers (Windows XP, Windows 7, OSX Leopard, OSX Snow Leopard, OSX Lion). What is currently the best secure, free (or cheap) and most imporantly simple way to remotely access the desktop of another Mac or PC over the internet from my Mac (OSX Lion).
It should be especially simple for the person at the other end asking for help because often they are not too computer-savvy. It should invlove nothing more than starting an application or enabling a checkbox in the settings. It would also be really nice if I could do remote access from my iPhone while I am on the go. It should also be possible to exchange files.

A: You could look at TeamViewer and it is free for non-commercial users.

A: You should have a look @ LogMeIn

A: You should have a look @ RealVNC, but it will be difficult for end user to set up permission on there router...

A: www.join.me
it's free and works with both macs and windows.


Thread: Can someone give me some tips for printing out art?
Im trying to print some artwork of mine but all of the pics keep coming out pixelated.......some images I have to scale to fit because they show up small on the print preview, even though the image is of a large enough size to print out at almost full 8 1/2 by 11 scale paper. If anyone can tell me their methods for printing or any tips that I should consider before printing, I would greatly appreciate it!!!!!
Do you need a Web Designer? Contact me!
To have your images print well, you need to be sure you create them at a resolution of 300 px per inch, instead of the monitor display default of 72 ppi. That is why they look pixelated, because you are stretching those 72 pixels into 250-300, and so all the "jaggies" show up clearer.
...some images I have to scale to fit because they show up small on the print preview, even though the image is of a large enough size to print out at almost full 8 1/2 by 11 scale paper.
To be more clear about this, if you are preparing the artwork for publication, in a commercially printed magazine for instance, the artwork needs to be at the actual size in inch or pixel dimensions and saved at 300 ppi (pixels per inch). If you are instead printing the artwork on photo paper from your inkjet printer or from a color laser printer from a service like Kinkos, it still needs to be the actual size it will print but you could get by with 150-200 ppi.
Can someone please give me some tips for sketching hair in charcoal?
Can Sum1 Please Give Me Paint Tips Please!!!
Art: Tips that you can give me?


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THE ACME FACTORY
Sam and Ralph:1960
"Ready, Woolen and Able" shows that Sam and Ralph are apparently moving up the corporate ladder, for they now have reserved parking spaces at thier job site. Sam, in his old jalopy, is left in the dust by Ralph in his dragster. Points go to Sam though for using the appropriate hand signal when turning.
In no hurry to start work, the two casually wait for the whistle to blow. As usual, Ralph's first attempt at thievery is to simply grab a sheep in plain view, and all it takes is an object, in this case a rake, dropped from above by Sam to foil the wolf.
Several gags that follow could easily have been placed in a Road Runner cartoon, such as Ralph's barrel of TNT conveniently bouncing over it's intended target and rather showering a storm of boulders upon the wolf. That tiny umbrella looks very familiar and if I recall, it ain't gonna save you, Ralph.
The final sequence takes the idea of Sam being able to apprear everywhere at once to an extreme and Ralph finds that not even a whale's mouth is to be refuge from the ever-watchful canine.
Ralph is finally driven insane, but as he is hauled away to the looney bin (pun intended I guess), he remains confident that he will be back to work tomorrow. Now that is job dedication.
Posted by Coyote at 7:42 PM No comments:
Labels: Looney Tunes
In "Steal Wool", we get a glimpse into the home lives of Sam and Ralph, who are now neighbors. Sam shares his newspaper with Ralph before they head off to work, exchanging the usual pleasantries. At the job site, Ralph forces himself to change his facial expression to one of villainy, driving home the fact once more that this is simply his job.
Ralph usually tries the direct approach first...simply grabbing a sheep and trying to walk off with it. Then his schemes get a bit more elaborate. Of course when they don't work, he simply tries to murder Sam. Gotta love it.
One of my favorite gags of the entire series involves Ralph's giant rubber band. His expressions are just as comical as those of his coyote counterpart in the Road Runner series.
Maximum pain is inflicted on Ralph and, unlike the norm in which cartoon characters are injured but then are perfectly fine in the next scene, Ralph staggers home bandaged in a sling with a black eye.
Friends and neighbors once more, Sam shows concern and offers to let Ralph have a day off tomorrow. "I can handle both jobs." Now that I would love to see.
Posted by Coyote at 11:30 PM No comments:
All of the elements of this series begin to fall into place in "Double or Mutton". Firstly, Sam is finally Sam and Ralph is finally Ralph. The two arrive at work, punch in on the timeclock and exchange those monotone greetings that we all know from our own jobs.
Another day on the job begins and within seconds Ralph has received a sledgehammer blow to the head compliments of Sam. Ralph dips into some heavy artillary such as a cannon and a rocket in attempts to dispose of Sam.
My favorite gag involves Ralph flying a helicopter, trying to nab sheep with a metal claw dangling from a rope. Sam strolls by below and nonchalantly yanks the rope, bringing the copter crashing to the ground. Ralph deploys his parachute just a little too late.
Sam begins to prove that he is a bizarre entity, transcending time and space. After dropping one end of Ralph's highwire, he instantly appears holding the opposite end.
Ralph's Little Bo Peep disguise seems to work until it is revealed that the sheep is actually Sam in disguise, when only moments before both Sam and the sheep that Ralph lures away were in the same space together.
The whistle blows during another thrashing of Ralph, signaling the end of the work day. The two punch out and, now that they are off the clock, resume being civil to one another. "See you tomorrow, Sam."
First things first: the characters names are still a jumble here. Sam is Fred, Ralph is Sam. All things in due time, I suppose.
"Sheep Ahoy" finds the wolf awaiting the shift change so he can nab some sheep but of course he isn't quick enough for the sheepdog. This cartoon features a more stationary Sam. He definetely doesn't move around as much as in the first film. I love the fact that not only can the Wolf/Wile E. Coyote not capture a fast character, but he also can't outwit an immobile one.
Here we see the wolf not only trying to aquire a sheep, but also turning to murderous plots as he tries to kill Sam, for example, with a boulder. For all of Sam's lethargy, he still packs a mean whallop.
As with the coyote, the wolf defeats himself a lot of the time, such as with a balloon/fishing pole scheme. The film ends with the inevitable shift change, but there's a new element that comes into play this time. Not only does Sam have to stop his thrashing of the wolf to allow his co-worker to take over, but it's quitting time for Ralph as well, who has a co-worker of his own take his place for the resumed beating.
One point that I always find amusing is this: I can see why someone would pay Sam to protect the sheep, but who in the world is paying Ralph to steal them (and most likely eat them)?
Chuck Jones' Sam and Ralph first appeared in 1953's "Don't Give Up the Sheep". As with most cartoon series, all of the elements didn't come together right from the start. The first obvious point is that Sam is named Ralph!
The series revolves around a sheepdog whose job is, of course, to guard a flock of sheep. The job aspect is amusingly highlighted by the dog punching a time clock and relieving his co-worker during a shift change. A hungry wolf creeps along, anxious for dinner. Another aspect of the series that would not emerge just yet is that of the wolf also punching the timeclock on his way into "work". For now he is just a predator trying to score a meal.
The sheepdog thwarts the wolf's attempts at sheep-nabbing in several "black-out" gags similar to the Road Runner series. After all, the wolf is just Wile E. Coyote with a red nose, although his design in this first cartoon is a bit more scraggly.
The sheepdog is a bit more active here than in later cartoons. Sure, he is always quick with a fist to the wolf's face, but the dog would eventually become much more stationary.
Messing with the timeclock, unleashing a wild cat (from Acme of course), and disguising himself as the Greek God, Pan, are some examples of the wolf's schemes.
The end of the cartoon sees another shift change occur during the sheepdog's beating of the wolf. The canine co-worker takes over the thrashing with barely missing a beat.
Mornin' Sam...Mornin' Ralph
Next up in the spotlight here at the Factory are Sam Sheepdog and Ralph Wolf.
When I was a kid, the Roadrunner/Coyote series were my favorite Looney Tunes. I think it was the lack of dialogue that made the cartoons so easy to follow. So to find "the Coyote" in two other series of cartoons always made it worth it to get up at the crack of dawn on Saturday mornings.
Hearing Wile E. Coyote speak in the cartoons with Bugs Bunny was never really appreciated until I was older, but the Wolf/Sheepdog films were right up my alley and I appreciate them still, but on a completely different level.
Once I entered the world of labor, I began to appreciate the antics of Sam and Ralph all the more. We all have had to do things that we really didn't want to, but we had to because it was our job. Our business lives are completely different from our social lives. There are the themes of the Wolf/Sheepdog cartoons.
An advantage to such a timeline is that I won't have to choose just one film per year, for there was only one Wolf/Sheepdog cartoon released in any given year. Not only will every Sam/Ralph film be highlighted but I get to glorify one of my favorite cartoon series. Stay tooned!
PD Warehouse:1934
It's time for another edition of PD warehouse. Where cartoons are "breathtakingly restored" after "hundreds of hours of detailed restoration work". How do these companies sleep at night?
Sarcasm aside, today's cartoon comes from the Fleischers in two-strip technicolor for all fans of red and green.
"An Elephant Never Forgets" features a group of jungle animals as children heading to school. The Fleischer's use of a rotating tabletop background behind the cels creates the three-dimensional effect in the opening scenes. A lazy hippo never makes it inside the school, but rather falls asleep just outside the door.
A messy pig is physically disciplined with a smack on the head by the goose teacher. After a musical role call, a bullying gorilla abuses the elephant and the other children mock him for not remembering what two plus two equals.
A turtle is left in charge of the classroom who thwarts a giraffe's cheating attempt. While the teacher's back is turned, violence erupts and many a head is bonked with a book.
When class is dismissed, the elephant exacts revenge on the gorilla, proving that an "elephant never forgets"...to hold a grudge, apparently.
Labels: Fleischer
Tom and Jerry:2005
We end our trip through time with Tom and Jerry with "The Karate Guard". Joseph Barbera returned one last time to co-direct this cartoon. He passed away soon after.
The cartoon takes the idea of Spike being Jerry's bodyguard and puts a martial arts spin on it. Jerry's karate skills are no match for Tom's flyswatter (there's that darn flyswatter again). The mouse is offered help by a mystical mentor who gives a magical gong to Jerry.
Whenever Jerry is in trouble with Tom, he simply bangs the gong to summon Spike the dog, who pummels Tom in classic violent fashion. Even when Tom attaches pillows to Spike's ears to block the sound of the gong, the cat's good fortune is short-lived.
In one impressive scene, Tom is launched directly at the camera, passes it and hits the roof of a house, all in one continuous shot, where he then claws frantically at the roof as he slides down. Tom enlists the help of Butch and his cronies and paintballs prove to be Spike's kryptonite. However, soon the cats are mere bowling pins to be knocked down by a balled-up Tom.
Tom is reduced to a servant, waiting hand and foot on Spike and Jerry, who gorge themselves on popcorn.
As with most cartoon series, the later, lackluster efforts of Tom and Jerry should not diminish the films of thier glory days, when fast-paced action, great music and violent mayhem ruled the screen with hilarious results.
Thanks to everyone who visits here for toughing it out through what became the longest timeline to complete due to my gaps in posting.
Coming up next will be another installment of PD Warehouse. From there...well, I have some ideas for characters who deserve the timeline treatment but I am open to any suggestions as well. Drop me a comment and let me know of any characters you would like to see highlighted here and I will see what I can do. Happy Thanksgiving!
Labels: MGM
When I made my last post, I was so grateful to reach the end of the Deitch/Jones films that I actually implied this timeline was over. Not true, as Tom and Jerry would not go away so quietly.
In 1975, H-B made some new cat and mouse cartoons for television, but the violence was turned down to an extreme and the duo were more buddies than enemies.
I hate to even mention the Filmation cartoons of the early '80s, which make the Deitch cartoons seem like animation masterpieces.
H-B took another crack at Tom and Jerry in the 90's before the rights were turned over to Turner and more specifically, Warner Bros.
I will spare you any mention of "The Tom and Jerry Movie".
Plenty of direct-to-video releases have resulted, as well as today's highlighted cartoon, "The Mansion Cat".
Not much to say here really. Amidst very simplified backgrounds, Tom and Jerry wreck havok in the house while thier owner is away.
While trying to watch his own performance in "Muscle Beach Tom", the cat is forced into the VCR by Jerry and ejected in cassette form. In a moment resembling a scene from "Terminator", Tom is frozen, shattered and reassembled and the slow-paced chase resumes.
A water bed is punctured, fireplace soot is tracked everywhere, and an out-of-control vacuum cleaner sucks up everything it gets near. Tom gets a plunger stuck on his rear and drives a lawnmower through the house.
Finding his home destroyed doesn't seem to effect Tom's owner much, who just makes a dry observation on Tom's worth.
Tom's shark troubles continue in "Cannery Rodent". Why did I pick this cartoon? I actually dig the music over the opening titles. There, I admitted it.
Now that I have gotten that shameful revelation out of the way, let's move on. The cat and mouse chase leads our heroes onto a conveyor belt and into a cannery where they are sealed within properly-labeled cans.
Tom escapes easily with the aid of his claws while Jerry is left trapped in his can until he is captured and released in the same manner. Jerry repays Tom with a bite to the finger.
Tom repeatedly ends up in the water below where a hungry shark awaits. In yet another "friendship" moment, Jerry rescues Tom from the shark with some conveniently-placed pepper. Tom doesn't have such an easy change of heart, as evidenced by his devil horns, which quickly pop Jerry's halo.
Jerry earns his own devil horns, however, when he uses the classic "fake shark fin" gag to send Tom swimming for his life into the horizon.
This would be the final year for theatrical Tom and Jerry cartoons, but is this the end? Nope. The mandatory wrap-up/conclusion post is forthcoming.
Zzzzz...huh? Wha? Sorry, I must have dozed off there. "Filet Meow" has Jerry once more in charge of protecting a goldfish from Tom's starving belly. A simple needle to the feline's rear thwarts his first attempt.
Tom discovers that by wearing a trashcan, he can now be protected from any future needle attacks. Bear with me, folks. Tom attacks Jerry with an axe but (as I have pointed out before) Tom apparently has no feeling in his tail, which allows Jerry to place it on the chopping block without his knowledge. Luckily it screws right back in place.
Still wearing the trashcan, Tom is mistaken by the garbage man for actual trash and is thrown out. Tom returns and rigs up a hose system beneath the fishbowl to suck out his prey.
In order to accept the ending of this cartoon, you would have to accept the fact that Jerry finds a pet store that delivers a man-eating shark to his bathtub. Tom unknowingly sucks the shark through the tube and is forced to flee for his life.
Oh...and the shark apparently falls in love with the goldfish...or something.
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Beginning in mid-June, Arizona State University will host 50 of Africa's brightest emerging leaders for six-week academic and leadership institutes sponsored by the U.S. Department of State.
The Mandela Washington Fellowship, the flagship program of President Obama's Young African Leaders Initiative (YALI), empowers young African leaders through academic coursework, leadership training, mentoring, networking, professional opportunities, and support for activities in their communities. Fellows are young leaders from Sub-Saharan Africa who have established records of accomplishment in promoting innovation and positive change in their organizations, institutions, communities, and countries.
ASU has been a host institution for the program since its inception in 2014. Led by the College of Public Service and Community Solutions, two cohorts of Fellows—one in Civic Leadership and one in Public Management—will come to Arizona as part of a larger group of 1,000 Mandela Washington Fellows hosted at institutions across the United States this summer. The highly competitive program attracted more than 40,000 applications.
"We are excited to expand the program this year with two cohorts. Each year has been inspiring, and we truly learn as much from the fellows as they do from us," says Jonathan Koppell, dean of the College of Public Service and Community Solutions.
Working closely with the U.S. Department of State's Bureau of Educational Affairs and its implementing partner, IREX, host institutions have designed academic programs that will challenge and empower these inspiring young leaders from Africa.
"Our college has diverse, cross-sector expertise in civic leadership, social entrepreneurship, public policy and urban management. Coupled with our strong community partnerships, we are creating connections that will result in collaborative solutions," Koppell added.
Fellows will meet with faculty from across the university, as well as community advocates and practitioners. The College has partnered with dozens of institutions across Arizona to provide this group a comprehensive experience: City of Phoenix, Local First Arizona, International Rescue Committee, the Hopi tribal community, Grand Canyon Trust, Terra BIRDS, City of Tucson, City of Flagstaff, National Parks Service, and more.
As part of the institute, Fellows will travel to Flagstaff, Tucson and Sedona, visiting landmarks such as the Grand Canyon and Kartchner Caverns. They will work on community service projects, visit with elected officials, local agencies and nonprofits.
At the end of the institutes, all of the Fellows will gather in Washington, D.C. for a Presidential Summit; select Fellows will also spend six weeks in professional development training with U.S. non-governmental organizations, private companies and government agencies.


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Poe Valley State Park
Poe Valley State Park is a 620-acre (251 ha) Pennsylvania state park in Penn Township, Centre County, Pennsylvania in the United States. The park is surrounded by Bald Eagle State Forest. Poe Paddy State Park is 4 miles (6.4 km) to the west. The forests of the park surround the 25-acre (10 ha) Poe Lake. Poe Valley State Park is in isolated Poe Valley which lies between Potters Mills on U.S. Route 322 and Millheim on Pennsylvania Route 45. The park will be closed for the 2008 and 2009 season while the lake is drained for dam repairs and the park facilities are upgraded.
Poe Valley State Park and nearby Poe Paddy State Park were built by the Civilian Conservation Corps during the Great Depression of the 1930s. The young men of the CCC also worked to clear the regrowing forests of brush to prevent forest fires. They constructed roads throughout the forests, built state park facilities, constructed bridges on the state roads, planted trees for reforestation, and cleaned streams.
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