Korg EA-1

The Korg Electribe A (EA-1) is an analogue modeling synthesizer in the Korg Electribe series first produced in 1999.

Functions
The EA-1 has many functions, such as the ability to build analog-style sounds by using its waveform, filter, effect knobs.

It is possible to create two-part sequenced phrases using the two analog modelling tone generators. External audio sources can also be input, and processed with its resonant filter or effects knobs, and then synched to a sequence. The oscillator can be modulated using Ring modulation, Sync, or Decimator.

The EA-1 can store a total of 256 (64 x 4 banks) phrases or patterns plus 16 songs consisting of multiple patterns.

The EA-1 can be synchronised to other MIDI devices using tap tempo or MIDI clock, or by tapping in the beat and synchronising to a turntable or other non-MIDI sound sources.
The Tempo delay, chorus/flanger, and distortion effects can also be used to manipulate sounds and patterns.
The EA-1 can be controlled from an external sequencer and used as a MIDI expansion tone generator.

Specifications
 Type: Synth/ module/
 Synthesis type: Digital analog modeling
 Polyphony:
 Max: 2
 Typical in use: 2
 Multi-timbral (number of parts): 2
 Oscillators per voice :
 Min : 1
 Max : 2
 Controllers : 11 knobs, 1 data wheel, 21 buttons, 3 transport buttons, 16 keyboard buttons
 Effects :
 Number of FX units : 2
 Number of different effects : 2
Keyboard :
 Number of keys : 16
 Can send on simultaneous MIDI channels
 Responds to : Note on/off, MIDI control change
 Sounds can be split by : Cannot be split
 Inputs and outputs :
 Number of audio outs (excluding phones) : 2
 Number of audio ins : 1
 Number of MIDI Outs (excluding thru) : 1
 Number of MIDI Ins : 1

See also
 Roland TB-303
 Korg ER-1
 Korg Electribe series

References

Further reading

External links
 Korg EA1 Support Page

E


Posttesticular antifertility action of triptolide in the male rat: evidence for severe impairment of cauda epididymal sperm ultrastructure.
A variety of active diterpene epoxides, including the triptolide (isolated from Tripterygium wilfordii) have been reported to cause infertility in male rats. Previously, we showed that oral administration of triptolide at a dosage of 100 microg/kg per body weight for 70 days completely inhibited fertility in male rats, with little or no demonstrable detrimental effect on spermatogenesis and Leydig cell function as determined by testicular light microscopic appearance and serum and intratesticular testosterone levels. Despite the apparent absence of effects on the testes, cauda epididymal sperm were abnormal, with complete cessation of sperm motility and some reduction in sperm numbers. This study was undertaken to provide additional insight into the subcellular sites and possible mechanisms of action of this compound using ultrastructural analysis of the testes and epididymidis. The most striking effect of triptolide treatment was observed in sperm in the epididymis. In rats rendered infertile with 100 microg/kg per body weight of triptolide daily for 70 days, virtually all cauda epididymal sperm exhibited complete absence of plasma membrane over the entire middle and principal piece, premature decondensation of the nuclei, and disorganization of the mitochondrial sheath with many vacuolated mitochondria. No ultrastructural differences in the epididymal epithelium were observed between control and triptolide-treated rats. The testes appeared to be mildly affected after triptolide treatment but exhibited only subtle ultrastructural defects in the germ cells. The findings of severe impairment of cauda epididymal sperm ultrastructure, along with minimal discernible abnormalities in the fine structural cytology of the testes, further suggest that the site of action of this compound is posttesticular and may be confined to the cauda epididymal sperm. However, we cannot rule out an effect of triptolide that occurs during germ cell maturation but is delayed in its manifestation or triggered at the rete testis and epididymal level.


Sensorly: 4G Coverage and Speedtests

See coverage maps and speedtest maps of your carrier's 3G, 4G, or LTE network based on actual signal and speed readings provided by other users ! See how much time your phone really is connected to your 4G network.

With Sensorly you get : ·up-to-date coverage maps and speedtest maps for 4G, LTE, 3G, and 2G networks worldwide. Maps update daily.·A speedtest tool: instantly get to know your data transmission speed and share your results on Facebook, Twitter, Google + or Pinterest! ·The ability to map your neighborhood: use your daily trips to complete your carrier's coverage map. Check if there is 4G or LTE or 3G in your area! ·Signal and speedtest comparison info to help you compare carriers: who has got the best coverage for you? Make your choice among all networks available around you.

Sensorly is a free crowdsourced service: the more you use the app, the more precise and complete a coverage map and speedtest map get. So join us and get real info on your coverage and speed!

Join us on social networks: Twitter : http://twitter.com/sensorlyFacebook : http://www.facebook.com/sensorly


Q:

How to make configslurper delegate method call to caller class

I want to create a groovy class og script like this:
//...
def slurper = new ConfigSlurper().parse(someFile)
//...
//The exact method declaration
def methodCall(def arg){
   //Whatever i want to do
}
//Maybe it is easier with methodMissing
def methodMissing(def args) {
   //Whatever i want to do
}

The file to slurp could look like this:
some {
   property = methodCall("with arg")
}

The question is how i can delegate the "methodCall" to the class or script that parses with the configslurper? At the moment it will give you a methodMissing.

A:

Thanks to Sérgio Michels link I found a solution:
public class ScriptWithMethods extends Script {

   String scriptText;

   public ScriptWithMethods(File file) {
      scriptText = file.text
   }

   public void run() {
      GroovyShell shell = new GroovyShell();
      Closure closure = shell.evaluate("{it->$string}");
      closure.resolveStrategy = Closure.DELEGATE_FIRST
      closure.delegate = this
      closure.call()
   }

   def methodCall(def arg){
      //Whatever i want to do
   }
}

//...
def script = new ScriptWithMethods(scriptText:someFile)
def slurper = new ConfigSlurper().parse(script)

Of cause you could also use "method missing", but this works in my usecase


Abstract

Emotion regulation has primarily been studied either experimentally or by using retrospective trait questionnaires. Very few studies have investigated emotion regulation in the context in which it is usually deployed, namely, the complexity of everyday life. We address this in the current paper by reporting findings of two experience-sampling studies (Ns = 46 and 95) investigating the use of six emotion-regulation strategies (reflection, reappraisal, rumination, distraction, expressive suppression, and social sharing) and their associations with changes in positive affect (PA) and negative affect (NA) in daily life. Regarding the relative use of emotion-regulation strategies, a highly similar ordering was found across both studies with distraction being used more than sharing and reappraisal. While the use of all six strategies was positively correlated both within- and between-persons, different strategies were associated with distinct affective consequences: Suppression and rumination were associated with increases in NA and decreases in PA, whereas reflection was associated with increases in PA across both studies. Additionally, reappraisal, distraction, and social sharing were related to increases in PA in Study 2. Discussion focuses on how the current findings fit with theoretical models of emotion regulation and with previous evidence from experimental and retrospective studies.


Engineered and wild-type L. lactis promote anti-inflammatory cytokine signalling in inflammatory bowel disease patient's mucosa.
Dysbiosis of intestinal microbiota and aberrant inflammatory responses in gastrointestinal mucosa plays important roles in the development of inflammatory bowel disease (IBD). The purpose of this study was to demonstrate the probiotic activity of Lactococcus lactis and the ability of TNF-α-binding by recombinant L. lactis bearing TNF-α-binding affibodies. Various concentrations of recombinant L. lactis were exposed to TNF-α and its binding measured by ELISA. Mucosal biopsies of patients with active IBD were incubated with various L. lactis strains or E. coli DH5α strain and concentrations of TNF-α, IL-23, and IL-10 in the supernatants determined by ELISA. Recombinant L. lactis, at 1 × 109 and 1 × 108 CFU/mL, bound 22.6% and 18.4%, respectively of TNF-α (p < 0.05). When IBD-mucosa was incubated with any L. lactis strain at 1 × 109 CFU/mL, levels of TNF-α and IL-23 were significantly decreased and that of IL-10 increased relative to that for the sterile culture. Opposite trends were observed with E. coli cultures. Recombinant L. lactis at 1 × 108 CFU/mL bound as much as 62.8% (p = 0.026) of TNF-α in IBD-mucosa supernatants compared with the control strain. L. lactis strains are reported, for the first time, to induce an ex vivo anti-inflammatory cytokine profile in IBD inflamed mucosa. L. lactis could therefore constitute a promising alternative approach for treating IBD.


Charlize Theron Gets Feisty – and Tipsy! – at Harvard

She may have dropped out of high school, but Charlize Theron still went to Harvard – for the day, at least.

The actress headed to the esteemed university in Cambridge, Mass., Thursday to receive their Hasty Pudding Woman of the Year award. So what is she going to do with her prize, the coveted Pudding Pot?

"I'm going to make a stew [in it], and then I'm going to eat it," she told reporters at the event, hosted by Hasty Pudding Theatricals (the nation's oldest collegiate theater group, known for burlesque musicals featuring cross-dressing men). "I'm going to look at it and remember today and how much fun I had."

Fun, indeed. Her Harvard hosts gave her apple cider – in honor of her role in The Cider House Rules – and, she joked, "I think it was laced with something, which made it much better."

Still, the actress seemed torn when asked which was the biggest honor, the Pudding Pot or her Academy Award for Monster.

"I know you want me to say that the Oscar sucks and this is better," she said. "There are no men in drag at the Oscars like there are here. I've never been surrounded by so many fake breasts."

Then again, considering Hollywood's penchant for plastic surgery, she added, "Actually, I think I was at the Oscars."

'Sexiest' Label Favorite

In fact, the 32-year-old said, it's the title of Esquire magazine's Sexiest Woman Alive that means the most to her.

"So you can tell everyone that I'm the sexiest woman alive, and I'm going to vote for Barack [Obama]," the outspoken star said at the press conference. "And you can tell them I told you all of this while I was drunk!"

The South-African-born actress did remain serious – almost – when asked about working with longtime boyfriend Stuart Townsend, who's directing her in the upcoming Battle in Seattle. "It was great," she said. "I'm not allowed to say anything else, because I sleep with the director."

After almost seven years together, she added, "I love going home with him."


[Effects of extracts of Chinese medicines on Ganoderma lucidum in submerged culture].
Effects of water and ethanol extracts of 10 Chinese medicines, such as Astragalus membranaceus, Coix lachryma-jobi, etc., on biomass and exopolysaccharide of Ganoderma lucidum were studied by submerged culture. The results showed: water extracts of all medicines can improve the culture of G. lucidum except of A. membranaceus, ethanol extracts of C. lachryma-jobi, Dioscorea opposita, Codonopsis pilosula, and Achyranthes bidentata( < 187.5g Medicine/L substrate) can also increase the biomass of G. lucidum, but the ethanol extracts of Angelica sinensis, Dendrobium nobile check the growth of G. lucidum. The production of exopolysa-ccharide can be improved by all the Chinese medicines and their dosage used in this experiment, Although A. sinensis, D. Nobile check the growth of G. lucidum, they could stimulate the secretion of exopolysaccharide in lower dosage. It is concluded that some Chinese medicines, such as C. lachryma-jobi, D. opposita, C. pilosula, etc. can be processed by the fermentation of G. lucidum, and bio-active compound can be produced by adding appropriate Chinese medicine in the substrate to culture G. lucidum.


A conscious and ongoing effort to reduce drag in vehicular structures is seen in the prior art and the reduction of drag or resistance in vehicles is of increasing importance where fuel economy is an increasingly large consideration in automotive and vehicular design. Current application of aerodynamic concepts shows that many prior approaches to streamlining were of more optical and retinal satisfaction than the achievement of minimal drag. While this is true in automobiles, it is particularly true where trucks and trailers are considered because of the substantial surfaces involved and the enormous increase in reliance upon trucks and trailers for haulage and heavy transport. This is accentuated by the need for maximum cargo space and the use of relatively sharp corners and boxy appearance at the front of trucks and trailers.
Early efforts at minimization of drag were found in changing body styles; in bridging between tractor and trailer as in U.S. Pat. No. 3,945,677 to Ronald A. Servais, et al; in the modification of the front wall of trailers as in U.S. Pat. No. 3,415,566 to W. H. Kerrigan; and in the spaced foils or shields as seen in U.S. Pat. No. 2,964,352 of J. Werner. More lately, the direction taken by persons seeking to minimize drag in such vehicles is exemplified by the use of corner plates having gradual and concave arcuate rise from a plane transverse to the principal direction of vehicle travel and then reversing in an arc closing on the corner and in parallel side contact with the vehicle body. This work is seen in the recent U.S. Pat. Nos. 3,934,922 and 4,057,280 to Paul B. MacCready, Jr. and Peter B. S. Lissaman.
By contrast, the present invention provides a distinctly different approach to reduction of drag by the utilization of a drag attenuating corner cover similar to a subsonic airfoil comprising an elongate strip having a surface which abruptly arises from a transverse front surface or plane; the strip then wraps around the corner in a convex or bulbous arch and returns or reverses direction outboard of the flank plane of the vehicle body and tapers generally back toward the flank plane of the vehicle body in one or more undulations or ripples formed by alternate concavities and convexities until the surface fairs into contact against the flank plane of the body well rearward of the projected intersection of the front and flank planes. "Front" as used here has reference to the surface first engaging air or wind flow and which surface is generally perpendicular to the airflow. "Flank" as used here means the surface plane as, for example, side and top of a vehicle which is generally parallel to the airflow in moving vehicles. The undulating or ripple surface commencing with a discontinuity outboard of the flank plane and diminishing rearwardly to closure against the flank plane discourages and delays boundary layer separation as the air flows across the flank plane thereby reducing aerodynamic drag.
Alternatively the elongate stip may wrap the corner in a convex or bulbous arch lying below the flank plane of the vehicle body and extending rearward asymptoticly joining the flank plane at its leading edge.
Accordingly, the principal object is to provide a wholly new edge surface useful in reducing drag in vehicles.
Another object is to provide an edge surface which may be added onto or which may cover existing corner or edge contours and which is economical to form and install as either an original or an "after market" add-on construction.
Another object is to apply aerodynamic boundary layer concepts for attached flow in solving the problems of resistance or drag in vehicles at the corners thereof in a manner compatible with present day vehicle construction.
Another object is to provide a device for enshrouding any forward facing corner that is itself too sharp to maintain attached flow and may be applied to any corner which faces into the oncoming airstream.
Another object is to provide an "add-on" device which exhibits attached flow around a previously separated flow corner.
Still other objects, including applicability to various flow facing surfaces and corners, will be better appreciated as the description proceeds.


Q:

Dynamically construct field of type URL from fixed +  elements

I have a field defined as a url, however the URL is actually made up of a standard URL to our document managment system and the document number which is what the user enters.  How do I get the formula for this field to concatenate the standard URL part with the 6 character number field the user types in which represents the document nuber to be retrieved from our system?  So in the end the URL looks like
'http://www.ourURL.com/applications/DMSRouter/3596560'  Where the number at the end is what the user types into the field.  I am trying to use the concatenate function but how do I reference the current value of the field (ie the numebr part)?

A:

Actually there is a way - using a workflow rule. The workflow rule can update the field that triggers it. So by using this formula in the update step of the workflow rule I get a close to deisred effect;
"http://www.ourURL.com/applications/DMSRouter/" & right(Trim(DMS_Number__c),7)
However the editing experience could be prone to typos as the edit window only shows the 1st 3rd of the field and the user has to navigate to the end of the line to change the number.


Effects of noise in a cortical neural model.
Recently Phys. Rev. E 64, 011920 (2001)]; Phys. Rev. Lett. 88, 118102 (2002)]] made long-term observations of spontaneous activity of in-vitro cortical networks, which differ from predictions of current models in many features. In this paper we generalize the excitatory-inhibitory cortical model introduced in a previous paper [Neural Comput. 14, 2371 (2002)]], including intrinsic white noise and analyzing effects of noise on the spontaneous activity of the nonlinear system, in order to account for the experimental results of Segev et al. Analytically we can distinguish different regimes of activity, depending on the model parameters. Using analytical results as a guide line, we perform simulations of the nonlinear stochastic model in two different regimes, B and C. The power spectrum density (PSD) of the activity and the interevent-interval distributions are computed, and compared with experimental results. In regime B the network shows stochastic resonance phenomena and noise induces aperiodic collective synchronous oscillations that mimics experimental observations at 0.5 mM Ca concentration. In regime C the model shows spontaneous synchronous periodic activity that mimics activity observed at 1 mM Ca concentration and the PSD shows two peaks at the first and second harmonics in agreement with experiments at 1 mM Ca. Moreover (due to intrinsic noise and nonlinear activation function effects) the PSD shows a broad band peak at low frequency. This feature, observed experimentally, does not find explanation in the previous models. Besides we identify parametric changes (namely, increase of noise or decreasing of excitatory connections) that reproduces the fading of periodicity found experimentally at long times, and we identify a way to discriminate between those two possible effects measuring experimentally the low frequency PSD.


Q:

How to get KNIME workflow name within a workflow

Which node do I need to get a KNIME workflow name within the workflow?  I am using KNIME version 2.11, and would like to add this information to the table being created.

A:

For others who came across this in Google search, you can use "Extract Context Properties" under IO>Other categories.  This will give you the flow variable port, which will contain the workflow name.
I've used "Variable to Table Column" then "Unpivoting" nodes to turn it into a table.


Playing for Team Human today, activist trainer and executive director of theRules.org, Alnoor Ladha.

Alnoor will help us understand the interplay between political organization, system thinking, storytelling, technology, and the decentralization of power. In a conversation spanning a wide range of topics including anarchism, collective organizing, local economies, psychedelics, and even spirituality, Ladha and Douglas Rushkoff underscore the multifaceted and necessary work of building a resilient and just society. In today's monologue Rushkoff addresses the deleterious effects of our algorithmically programmed cyber experience. Are Facebook and Google a threat to the health of civil society? How can we restore human agency and critical thinking to our digital lives?

Learn more about Alnoor and his work at the rules at https://therules.org/

Team Human is supported by listeners like you. Please visit our support page to help keep us going.

Header image by Alice Donovan Rouse via Unsplash


---
abstract: 'The NewtonWigner basis of orthonormal localized states is generalized to orthonormal and relativistic biorthonormal bases on an arbitrary hyperplane in spacetime. This covariant formalism is applied to the measurement of photon location using a hypothetical 3D array with pixels throughout space turned on at a fixed time and a timelike 2D photon counting array detector with good time resolution. A moving observer will see these detector arrays as rotated in spacetime but the spacelike and timelike experiments remain distinct.'
address: 'Department of Physics, Lakehead University, Thunder Bay, Canada'
author:
- Margaret Hawton
title: Photon location in spacetime
---

Introduction
============

In nonrelativistic quantum mechanics the localized states at all positions in space at a fixed time form a basis of Dirac delta-functions. In relativistic quantum mechanics particle localization is a difficult and controversial concept. Photons are always in the relativisitic regime so the problems are especially severe in their case. However, in spite of the theoretical difficulties, localized photon states are a useful concept. The projection operators onto these localized states define a positive operator valued measure (POVM) that has been used to describe photon position in imaging and photon counting experiments [@Teich; @Tsang; @HawtonPOVM].

While the probability density basis describes the position of a particle at a fixed time, an experimenter might use a photon counting array detector and record arrival time at one of its pixels. In the nonrelativistic context a spatial volume element is a scalar. In relativity space and time form the four dimensional Minkowski spacetime in which the volume of ordinary space is a spacelike hyperplane with its normal parallel to the time axis. The elements of a photon counting array have normals in a spacelike direction and thus are timelike. Here both possibilities will be combined to give a covariant theory of photon location.

Newton and Wigner (NW) derived bases of localized states at a fixed time for massive particles and zero mass particles with spin zero and one-half [@NW]. These states are orthogonal and hence localized in the sense that the invariant inner product of states centered at different positions equals zero. The NW procedure failed for photons because spherical symmetry was assumed. However a basis of localized photon states with axial symmetry can be constructed using the NW procedure [@HawtonPositionOp], thus extending the concept of NW localization to photons.

Particle density is usually expressed in terms of the positive frequency part of the field. According to Hegerfeldt’s theorem this leads to instantaneous spreading and possible causality violations [@Hegerfeldt]. Recent work on the Klein Gordon (KG) equation shows that it is possible to include negative frequency terms and this approach will be applied to the photon here.

The plan of this paper is as follows: Some recent work on the KG particles will be reviewed in Section 2 and extended to the photon in 3. Probability density, photon counting and the perspective of a moving observer will be examined 4 and we will conclude. Rationalized natural units ($\hbar
=c=\varepsilon_{0}=\mu_{0}=1$) and covariant notation will be used throughout. An event in spacetime will be described by the four-vector $x=x^{\mu}=\left(  t,\mathbf{x}\right)  $. With the metric signature $\left(
-,+,+,+\right)  $, $x_{\mu}=\left(
-t,\mathbf{x}\right)  $. The four-wavevector is $k^{\mu}=\left(
k^{0},\mathbf{k}\right)  $. Repeated indices in products of four-vectors will imply summation and a contraction such as $kx=k_{\mu}x^{\mu}=-k^{0}t+\mathbf{k\cdot x}$ is an invariant. Sums over polarization and positive and negative flux directions will be written out explicitly.

Klein-Gordon particles
======================

Recent work motivated by attempts to reconcile quantum mechanics with general relativity has lead to a better understanding of the localization of KG particles. A norm that is positive definite for both positive and negative frequencies and zero for their cross terms can be defined as [@HenneauxTeitelboim; @HalliwellOrtiz; @KoksmaWestra]$$\left\langle \mathbf{k},\varepsilon\mathbf{|k}^{\prime},\varepsilon^{\prime
}\right\rangle =2\omega\delta^{3}\left(  \mathbf{k}-\mathbf{k}^{\prime
}\right)  \delta_{\varepsilon,\varepsilon^{\prime}} \label{kNormalization}%$$ where $\omega=\sqrt{\mathbf{k\cdot k}+m^{2}}$ and $\varepsilon=\pm$. The KG field can then be written as$$\psi_{\varepsilon}\left(  x\right)  =\int_{k^{0}=\varepsilon\omega}\frac
{d^{3}k}{2\omega}\frac{\exp\left(  ikx\right)  }{\left(  2\pi\right)  ^{3/2}%
}\psi_{\varepsilon}\left(  k\right)  \label{KGWaveFunction}%$$  where $\left\vert \psi\right\rangle $ is a one-particle state vector, $\psi_{\varepsilon}\left(  k\right)  =\left\langle k,\varepsilon
|\psi\right\rangle $ and $k^{0}=\varepsilon\omega$. The invariant inner product defined in [@HalliwellOrtiz] is$$\left\langle \phi|\psi\right\rangle =i\sum_{\varepsilon=\pm}\varepsilon
\int_{\Sigma}d\sigma^{\mu}\phi_{\varepsilon}^{\ast}\left(  x\right)
\overleftrightarrow{\partial}_{\mu}\psi_{\varepsilon}\left(  x\right)
\label{KGInnerProduct}%$$ where$$f\left(  x\right)  \stackrel{\leftrightarrow}{\partial}_{\mu}g\left(  x\right)
\equiv f\left(  x\right)  \partial_{\mu}g\left(  x\right)  -g\left(  x\right)
\partial_{\mu}f\left(  x\right)  \label{Derivative}%$$ and $\Sigma$ is a hyperplane with normal surface elements $d\sigma^{\mu}$. The integrand in (\[KGInnerProduct\]) is the KG particle flux across $\Sigma$.

Photons
=======

Although (\[kNormalization\]) is invariant, it will be generalized to an arbitrary hyperplane to allow consistent evaluation of the equations derived here. Also, for photons it is necessary to include polarization $\lambda$. For the special case of planes the reciprocal or $k$-space normals are the same as those in $x$-space so the hyperplanes will still be referred to as $\Sigma$ in $k$-space. Since $m=0$ for photons the components of $k$ are related by the dispersion relation $k^{\mu}k_{\mu}=0$ in vacuum. The invariant $\int d^{4}k\delta^{2}\left(  k^{\mu}k_{\mu
}\right)  $ can be integrated over the component of $k$ normal to $\Sigma$ to give $\int_{\Sigma}d\kappa/2\left\vert k_{\Sigma}\right\vert $ where $d\kappa$ is a $k$-space hyperplane element, $$k_{\Sigma}=k^{\mu}n_{\mu}=\varepsilon\left\vert k_{\Sigma}\right\vert
\label{kSigma}$$ and $n^{\mu}$ is a unit normal to $\Sigma$. The $k$-space orthonormality relation then becomes$$\left\langle k\mathbf{,}\lambda,\varepsilon\mathbf{|}k^{\prime},\lambda
^{\prime},\varepsilon^{\prime}\right\rangle =2\left\vert k_{\Sigma}\right\vert
\delta^{3}\left(  k-k^{\prime}\right)  _{\Sigma}\delta_{\lambda,\lambda
^{\prime}}\delta_{\varepsilon,\varepsilon^{\prime}}
\label{kPhotonNormalization}%$$ where the subscript $\Sigma$ on the $\delta$-function indicates that it is valid only on the hyperplane $\Sigma$. The one-photon four-potential in vacuum can be written as$$\psi_{\varepsilon}^{\mu}\left(  x\right)  =\sum_{\lambda}\int_{\Sigma,k_{\Sigma}=\varepsilon|k_{\Sigma}|}
\frac{d\kappa}{2\left\vert k_{\Sigma}\right\vert }e_{\lambda}^{\mu}\left(
k\right)  \frac{\exp\left(  ikx\right)  }{\left(  2\pi\right)  ^{3/2}}%
\psi_{\lambda,\varepsilon}\left(  k\right)  \label{PhotonWaveFunction}%$$ where $e_{\lambda}^{\mu}\left(  k\right)  $ form a basis of polarization unit vectors and $$\psi_{\lambda,\varepsilon}\left(  k\right)  =\left\langle k,\lambda
,\varepsilon|\psi\right\rangle . \label{kAmplitude}$$ With the generalized orthonormality condition (\[kPhotonNormalization\]), $\varepsilon$ now denotes the direction of photon flux across $\Sigma$. The components of $k$ on $\Sigma$ take continuous values from $-\infty$ to $\infty$ and the dispersion relation then requires that the normal component of $k$ take two values, $\pm\left\vert k_{\Sigma}\right\vert $.

Since the electric and magnetic field operators form the second rank tensor $F^{\mu\nu}=\partial^{\mu}A^{\nu}-\partial^{\nu}A^{\mu}$, contraction with the four-potential $A_{\mu}$ in the Lorenz gauge gives the four-vector $J^{\mu
}=F^{\mu\nu}A_{\nu}$. The positive frequency four-flux operator was derived in [@HawtonNumberOp]. Using the Coulomb gauge in vacuum for simplicity [@HawtonNumberOp] gives $$J_{\varepsilon}^{\mu}\left(  x\right)  =\left\langle \phi\left\vert i\hat{\mathbf{A}}^{\dagger}_{\varepsilon}\cdot\overleftrightarrow{\partial}_t\hat{\mathbf{A}}_{\varepsilon}
,-i\hat{\mathbf{A}}^{\dagger}_{\varepsilon}\times\overleftrightarrow{\nabla}
\times\hat{\mathbf{A}}_{\varepsilon}\right\vert\psi\right\rangle. \label{PhotonFlux}$$ By inspection it can be seen that the timelike component involves the electric field while the spacelike components require the magnetic field. In the latter the difference in (\[Derivative\]) becomes a sum due to the asymmetry of the cross product. Eq. (\[PhotonFlux\]) is the four-vector generalization of the KG flux. The four-potential $\psi_{+}^{\mu}\left(
x\right)  \propto \left\langle 0\left\vert \hat{A}^{\mu\left(  +\right)  }\left(
x\right)  \right\vert \psi\right\rangle $ replaces the positive frequency part of the invariant KG field.

Since photon and KG flux satisfy the same continuity equation, by analogy (\[KGInnerProduct\]) should replaced with$$\left\langle \phi|\psi\right\rangle =\sum_{\varepsilon=\pm}%
\varepsilon\int_{\Sigma}d\sigma_{\mu}J_{\varepsilon}^{\mu}\left(  x\right)  .
\label{FluxIntegral}%$$ According to the definitions adopted here, when counting photons their direction of crossing is irrelevant and the factor $\varepsilon$ ensures that the particle density on $\Sigma$ is positive regardless of this direction. The sum over $\varepsilon$ in the $J_{\varepsilon}^{0}$ term is a sum over forward and backward in time but propagation of a photon backward in time can be reinterpreted as propagation of an antiphoton forward in time. Negative frequency photon absorption will be seen as photon emission so that each pixel can act as a detector or a source.

Integration of (\[FluxIntegral\]) over $d\sigma$ gives $$\left\langle \phi|\psi\right\rangle =\sum_{\lambda,\varepsilon}\int_{\Sigma,k_{\Sigma}=\varepsilon|k_{\Sigma}|
}\frac{d\kappa}{2\left\vert k_{\Sigma}\right\vert }\phi_{\lambda,\varepsilon
}^{\ast}\left(  k\right)  \psi_{\lambda,\varepsilon}\left(  k\right)  .
\label{PhotonInnerProduct}%$$ The inner product can be evaluated in $k$-space or $x$-space as discussed [@HawtonLorentz], but evaluation in $k$-space using (\[PhotonInnerProduct\]) is simpler.

Spacetime location
==================

Only three of the four components of $k$ can be treated an independent variables. Usually the spacelike components, $\mathbf{k}$, are taken to be independent and a localized basis is defined at a fixed time for all points in space and each polarization $\lambda$. However, a moving observer will not agree that localization of these states is simultaneous so this basis is not invariant. Here covariance is achieved by defining a localized basis on an arbitrary hyperplane.

The generalized NW localized state on $\Sigma$ at $x'$ with polarization $\lambda '$ and flux direction $\varepsilon '$ so that $k_{\Sigma}=\varepsilon ' |k_\Sigma|$ is $$\chi_{x',\lambda',\varepsilon ';\lambda,\varepsilon}\left(  k\right)  =\sqrt{2\left\vert k_{\Sigma
}\right\vert } \frac{\exp\left(  -ikx'\right)}{\left(  2\pi\right) ^{3/2}}
\delta_{\varepsilon,\varepsilon '} \delta_{\lambda,\lambda '}.
\label{LocalizedPhoton}%$$ Here the primed indices are fixed while the unprimed indices are summed over in (\[PhotonInnerProduct\]). According to the inner product (\[PhotonInnerProduct\]) these basis states satsify $$\left\langle \chi_{x'',\lambda '',\varepsilon ''}|\chi_{x^{\prime},\lambda^{\prime
},\varepsilon^{\prime}}\right\rangle =\delta_{\varepsilon '',\varepsilon^{\prime
}}\delta_{\lambda '',\lambda^{\prime}}\delta^{3}\left(  x''-x^{\prime}\right)
_{\Sigma}. \label{Orthogonality}%$$ This implies that these states are localized in the sense originally defined by NW [@NW]. The projection of an arbitrary state vector onto the localized state (\[LocalizedPhoton\]) is $$\left\langle \chi_{x',\lambda ',\varepsilon '}|\psi\right\rangle =\int_{\Sigma,k_{\Sigma}=\varepsilon '|k_\Sigma|
}d\kappa\frac{\exp\left(  ikx'\right)  }{\left(  2\pi\right) ^{3/2}}\frac
{\psi_{\lambda ',\varepsilon '}\left(  k\right)  }{\sqrt{2\left\vert k_{\Sigma
}\right\vert }}. \label{ProjectionOntoLocalizedPhoton}$$ The $x$-space completeness relation is $$\sum_{\lambda,\varepsilon}\int_{\Sigma,k_{\Sigma}=\varepsilon |k_\Sigma|}d\sigma\left\langle \phi|\chi
_{x,\lambda,\varepsilon}\right\rangle \left\langle \chi_{x,\lambda
,\varepsilon}|\psi\right\rangle =\left\langle \phi|\psi\right\rangle
\label{Complete}%$$ as can be verified by substitution of (\[ProjectionOntoLocalizedPhoton\]) and integration over $d\sigma$ to give (\[PhotonInnerProduct\]). Eq. (\[Complete\]) is equivalent to the partition of the identity operator$$\hat{1}=\sum_{\lambda,\varepsilon}\int_{\Sigma,k_{\Sigma}=\varepsilon |k_\Sigma|}d\sigma\left\vert
\chi_{x,\lambda,\varepsilon}\right\rangle \left\langle \chi_{x,\lambda
,\varepsilon}\right\vert \label{Identity}$$ that defines a projection valued measure (PVM) which is a special case of a POVM.

Substitution of (\[LocalizedPhoton\]) in (\[PhotonWaveFunction\]) gives $$\chi_{x',\lambda',\varepsilon ';\varepsilon}^{\mu}\left(  x\right)  =\int_{\Sigma,k_{\Sigma}=\varepsilon ' |k_\Sigma|}
\frac{d\kappa}{\sqrt{2\left\vert k_{\Sigma}\right\vert} }e_{\lambda '}^{\mu}\left(
k\right)  \frac{\exp \left[ ik (x-x')\right] }{\left(  2\pi\right) ^3}
\delta_{\varepsilon,\varepsilon '}
\label{NotLocalized}%$$ demonstrating that the potential describing a particle localized at $x'$ is not itself localized as noted by NW.

Eqs. (\[PhotonInnerProduct\]) to (\[NotLocalized\]) are the central results of this paper. In the following subsections they will be applied to probability density and photon counting measurements as seen by stationary and moving observers. The equations can be generalized to allow counting of $N$ photons [@HawtonPOVM].

Probability density
-------------------

In quantum field theory (QFT) probability density is usually defined at a fixed time $t,$ so that the integrals should be evaluated on a spacelike hyperplane with timelike normal $n^{\mu}=\left(  1,0,0,0\right)  .$ To measure probability density one can imagine an array of transparent detectors throughout space turned on at $t=a$ as sketched as in Fig.1. In this case (\[ProjectionOntoLocalizedPhoton\]) reduces to$$\left\langle \chi_{x,\lambda,\varepsilon}|\psi\right\rangle =\int d^{3}%
k\frac{\exp\left(  ikx\right)  }{\left(  2\pi\right)  ^{3/2}}\frac
{\psi_{\lambda,\varepsilon}\left(  k\right)  }{\sqrt{2\omega}}.
\label{ProbabilityAmplitude}%$$ The probability density is $\sum_{\lambda,\varepsilon}\left\langle \psi
|\chi_{x,\lambda,\varepsilon}\right\rangle \left\langle \chi_{x,\lambda
,\varepsilon}|\psi\right\rangle $ and the probability to detect a photon is its integral over volume. In spacetime this volume is a hyperpixel with a timelike normal.

\[ptb\]

[Figure1.eps]{}

Photon counting
----------------------

Since space and time are on an equal footing in a covariant theory, it is equally valid to fix a spatial coordinate. In fact, this choice describes a planar photon counting array detector with good time resolution. It will be assumed that this detector occupies the plane $x_{3}=b$ with normal $n^{\mu
}=\left(  0,0,0,1\right)  $ parallel to the $x_{3}$-axis as also sketched in Fig. 1. Projection of $\left\vert \psi\right\rangle $ onto this basis gives$$\left\langle \chi_{x,\lambda,\varepsilon}|\psi\right\rangle =\int dk_{1}dk_{2}dk^{0}
\frac{\exp\left(  ikx\right)  }{\left(2\pi\right)  ^{3/2}} \frac{\psi
_{\lambda,\varepsilon}\left(  k\right)  }{\sqrt{2\left\vert k_{3}\right\vert
}}.\label{PhotonCounting}%$$ The probability to detect a photon is $\sum_{\lambda,\varepsilon}\left\langle
\psi|\chi_{x,\lambda,\varepsilon}\right\rangle \left\langle \chi
_{x,\lambda,\varepsilon}|\psi\right\rangle $ integrated over pixel area and measurement time, that is integrated over a hyperpixel with a spacelike normal. If the “wrong” spacelike probability density basis is used to calculate the flux across a timelike hypersurface an addition factor $cos\theta=k_{3}/ \omega$ arises and the formalism is not covariant [@Mostafazadeh2]. The result derived here is more like Fleming’s covariant generalization of the NW basis [@Fleming].

If an ideal detector is positioned appropriately relative to the source, the photon will be detected in some hyperpixel of the array with certainty [@HawtonPOVM]. In writing (\[PhotonCounting\]) it was assumed that the $k^{0}$ integral extends from $-\infty$ to $\infty$. For an optical pulse whose line width is small in comparison with its center frequency the negative frequency contributions are negligible, but negative frequencies can be included as discussed previously. The wavevectors $k_{1}$ and $k_{2}$ range from $-\infty$ to $\infty$ but $k_{3}$ takes only the two values $\pm
\sqrt{\omega^{2}-k_{1}^{2}-k_{2}^{2}}$. The wavevector component $k_{3}$ can be imaginary. For two points in the plane of the detector $k_{3}$ does not appear in $\left\langle \chi_{x'',\lambda '',\varepsilon ''}|\chi_{x^{\prime},\lambda^{\prime
},\varepsilon^{\prime}}\right\rangle ,$ but the transition amplitude between arbitrary points does depend on $k_{3}$. The mathematical properties of this Angular Spectrum Representation that includes both propagating and evanescent waves have been studied and it have been applied in optics and acoustics [@AngularSpectrumRepresentation].

Moving observer
---------------

In Fig. 2 the observer is travelling with velocity $-\beta$ relative to the detectors. According to this observer the spacelike photodetector pixels are not turned on simultaneously and the timelike photon counting array is in motion. The detector coordinates are Lorentz transformed to $x'_{3}=\gamma (x_{3}+\beta t )$ and $t'=\gamma (t+\beta x_{3})$ so that $t'=\gamma^{-1}a+\beta x'_{3}$ and $x'_{3}=\gamma^{-1}b+\beta t'$. In the spacelike experiment the observer sees its normal rotated to $n^{\mu}=\left(  \gamma,0,0,\gamma\beta\right)  $ while the timelike detector is rotated to $n^{\mu}=\left(  \gamma\beta,0,0,\gamma\right)  $. As $\beta$ approaches unity the light cone $t'=x'_{3}$ at $\alpha=\tan^{-1}\beta=\pi /4$ is approached so no observer sees a timelike experiment as spacelike and vice versa, but any $n$ has a physical interpretation.

\[ptb\]

[Figure2.eps]{}

Conclusion
==========

In this paper it was found that it is possible to define an orthonormal NW basis or a relativistic biorthonormal basis on an arbitrary hyperplane in spacetime. These bases are localized in the sense defined by Newton and Wigner in 1949 [@NW]: the invariant inner product of two different basis states equals zero. In physical terms projection of the photon state vector onto one of these bases describes a spacelike probability density measurement or a timelike photon counting experiment in which a hyperpixel can act as either a detector or a source. Either family of bases is covariant since a moving observer will just see the hyperplane of the at-rest measurement as rotated in spacetime. Because the observer is limited by the speed of light, the spacelike and timelike experiments are distinct.

Suppose that a source of single photons is available and the location of each photon is to be measured. The basis of localized states at all points in spacetime is vastly overcomplete. The importance of hyperplanes is discussed in [@HalliwellOrtiz] and emphasized by Fleming [@Fleming]. Here it was demonstrated that a basis can be defined on any fixed hyperplane and that such a basis is complete and exclusive. If a photon exists it should be found somewhere in space at any fixed time (a spacelike experiment). On the other hand an experimentalist is rather more likely to set up a photon counting array detector on some plane in space and wait for the photon to arrive. This is the timelike experiment. In either case it is logical to look for the photon on a fixed hyperplane. Here it was demonstrated that the general case incorporating both these possibilities is a covariant generalization of the NW basis that describes both of these experiments.

*Acknowledgements:* The author thanks the Natural Sciences and Engineering Research Council for financial support and thanks Juan Leon for valuable discussions.

[99]{}

A. F. Avouraddy, B. E. A. Saleh, A. V. Sergienko, and M. C. Teich, Phys. Rev. Lett. **87**, 123601 (2001).

M. Tsang, Phys. Rev. Lett. **102**, 253601 (2009).

M. Hawton, Phys. Rev. A **82**, 012117 (2010).

T. D. Newton and E. P. Wigner, Rev. Mod. Phys. **21**, 400 (1949).

M. Hawton, Phys. Rev. A **59**, 954 (1999); M. Hawton and W. E. Baylis, Phys. Rev. A **71**, 033816 (2005).

G.C.Hegerfeldt, Phys. Rev D **10**, 3320-3321 (1974).

M. Henneaux and C. Teitelboim, Annals Phys. **143**, 127 (1982).

J. Halliwell and M. Ortiz, Phys. Rev. D **48**, 748 (1993).

J. Koksma and W. Westra, arXiv:1012.3473v1 \[hep-th\] (2010).

M. Hawton and T. Melde, Phys. Rev. A **51**, 4186 (1995).

M. Hawton, Phys. Rev. A **78**, 012111 (2008).

A. Mostafazadeh, J. Mod Phys. A **21**, 2553 (2006).

M. Hawton, Phys. Rev. A **75**, 062107 (2007).

A. Mostafazadeh and F. Zamani, Annals Phys. **321**, 2183 (2006).

G. N. Fleming, Philosophy of Science **67**, 515 (2000).

E. Wolf, J. Opt. Soc. Am. A **4**, 1920 (1986).


Isozymes of glycogen synthase.
Antisera to rat skeletal muscle glycogen synthase failed to recognize liver glycogen synthase by electroblot analysis. The antisera recognized the enzyme in skeletal muscle, heart, fat, kidney, and brain. The results support the hypothesis that there are at least two isozymes of glycogen synthase, and that most tissues contain a form similar or identical to the skeletal muscle type. There is a virtual absence of the muscle-type enzyme in adult rat liver.


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What Is Proven—and What Is Not—Regarding Benefits and Risks of Cannabis

What Is Proven—and What Is Not—Regarding Benefits and Risks of Cannabis

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{
    // 使用 IntelliSense 了解相关属性。
    // 悬停以查看现有属性的描述。
    // 欲了解更多信息，请访问: https://go.microsoft.com/fwlink/?linkid=830387
    "version": "0.2.0",
    "configurations": [
        {
            "type": "node",
            "request": "launch",
            "name": "测试 vue-observer",
            "program": "${workspaceFolder}/codes/vue/__test_observer.js"
        }
    ]
}


Q:

How to pass arguments to kapt compiler dynamicly inside my gradle plugin?

I'm currently writing a Gradle plugin to inject some arguments to an annotation processor. It works fine when using annotationProcessor in a Java based Android project. When I tried kapt in a Kotlin based project, it didn't work since kapt collected args from its own configurations.
What I did so far:
val args = variant.javaCompileOptions.annotationProcessorOptions.arguments
args["key"] = "value"

What I knew for kapt args passing is that you can set them via its extensions in the build.gradle, link:
kapt {
    arguments {
        arg("key", "value")
    }
}

So how to pass arguments to kapt compiler dynamicly inside my gradle plugin? Thanks.

A:

Unfortunately it's not well documented and I have run into this particular issue before. Here is what I did
in your plugin apply function do this (target is the gradle project that the plugin is being applied to). If you  are applying the plugin into your root project then simply put the same code into target.subProjects{...}
target.afterEvaluate{
    target.plugins.all {
        when (it) {
            is AppPlugin -> {
                extensions[AppExtension::class].run {
                    configureJavaCompilerArguments("option1", "option2")
                }
            }
            //if your project has a library that requires/use kapt also do this
            is LibraryPlugin -> {
                extensions[LibraryExtension::class].run {
                    configureJavaCompilerArguments("option1", "option2")
                }
            }
         }
    }
}

For simplicity I have declared a fancy extension function to add the arguments
fun BaseExtension.configureJavaCompilerArguments(option1:String, option2: String) {
    with(defaultConfig.javaCompileOptions.annotationProcessorOptions) {
        includeCompileClasspath = true
        arguments(mapOf(
                "some.key.in.your.ap.1" to option1,
                "some.key.in.your.ap.2" to option2
        ))
    }
}

and also this fancy function that I took from Butter-knife source code
internal operator fun <T : Any> ExtensionContainer.get(type: KClass<T>): T {
    return getByType(type.java)
}

now in your annotation processor just declare interest to these options specifying the same keys!! that is so important.
Happy coding!


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Introduction {#Sec1}
============

Hydrocarbons are major components of crude oil, but they are harmful to human health, especially polycyclic aromatic hydrocarbons (PAHs), which are mainly generated by incomplete combustion (Yan et al. [@CR73]). PAHs exist not only in fossil fuels but also in biomass because the complex aromatic polymer lignin accounts for a quarter of the land-based biomass on Earth (Kirk and Farrell [@CR31]), therefore the burning of sugarcane also generates PAHs (Ferreira et al. [@CR14]). More recently, it is reported that PAHs take part in the formation of urban haze (Guo et al. [@CR21]).

Great efforts are made to eliminate PAHs from the environment through physical, chemical, and biological approaches (Floehr et al. [@CR15]). Biodegradation of PAHs by microorganisms is promising (Peng et al. [@CR57]; Kanaly and Harayama [@CR26]) because many bacteria have the ability to use hydrocarbons as their sole carbon and energy sources for growth, e.g., *Alcaligenes faecalis* AFK2 (Kiyohara et al. [@CR32]), *Arthrobacter sp.* P1--1 (Seo et al. [@CR62]), *Burkholderia sp.* RP007 and C3 (Laurie and Lloyd-Jones [@CR38]; Seo et al. [@CR63]), *Comamonas testeroni* GZ42 (Goyal and Zylstra [@CR20]), *Mycobacterium vanbaalenii* PYR-1 (Kweon et al. [@CR36]), *Nocardioides sp.* KP7 (Iwabuchi and Harayama [@CR24]), *Rhodococcus sp*. NCIMB12038, P200 and P400 (Kulakov et al. [@CR33], [@CR34]), *Sphingobium yanoikuyae* B1 (Zhao et al. [@CR75]), and *Terrabacter sp.* DBF63 (Habe et al. [@CR22]). Besides these bacteria, ligninolytic and nonligninolytic fungi, e.g., *Phanerochaete chrysosporium* (Bogan et al. [@CR5]), *Phanerochaete laevis* (Bogan and Lamar [@CR4]), and *Bjerkandera adusta* (Wang et al. [@CR67]), are also able to mineralize PAHs (Cerniglia [@CR8]).

PAHs can be classified as low-molecular weight and high-molecular weight PAHs, and their catabolism in bacteria is extremely diverse and complicated because of numerous PAHs, which numbered 922 not long ago (Sander and Wise [@CR59]). In *M. vanbaalenii* PYR-1, for example, the degradation of pyrene needs 27 enzymes (Kim et al. [@CR30]) whereas the degradation of fluoranthene requires 53 enzymes (Kweon et al. [@CR35]). So far, many degradation pathways have been elucidated (Seo et al. [@CR63]; Warhurst and Fewson [@CR68]; Meckenstock et al. [@CR45]), and their corresponding genes have been identified (Ghiorse et al. [@CR18]; Xia et al. [@CR72]). For instance, the aromatic ring-cleaving dioxygenase opens the aromatic ring of single-ring aromatic compounds by incorporating two atoms of oxygen via the β-ketoapidate pathway (Frazee et al. [@CR16]).

*Pseudomonas aeruginosa* is a Gram-negative bacterium and has an extremely important implication in a series of diseases, and currently serves as a model organism in quorum sensing studies (Lee and Zhang [@CR39]). *P. aeruginosa* can decompose hydrocarbons (Patel et al. [@CR56]; Liu et al. [@CR41]), and lives in oil fields (Gai et al. [@CR17]). In fact, many species of *Pseudomonas* have such ability, e.g., *P. aeruginosa* (Gai et al. [@CR17]), *P*. *alcaligenes* PA-10 (Gordon and Dobson [@CR19]), *P. fluorescens* ACB (Kamerbeek et al. [@CR25]), *P. putida* NCIB 9816 (Yang et al. [@CR74]), *P. resinovorans* (Nojiri et al. [@CR51]), *P. veronii* (Onaca et al. [@CR54]; Nam et al. [@CR49]), and *P. stutzeri* (Bosch et al. [@CR6]). As a matter of fact, *P. paucimobilis* EPA505 was isolated in 1990 because it uses fluoranthene as its sole source of carbon and energy (Mueller et al. [@CR48]). Genetically, PAH degradation genes cluster together in *Pseudomonas* strains (Yang et al. [@CR74]; Menn et al. [@CR46]; Li et al. [@CR40]; Balashova et al. [@CR1]), whereas those genes scatter in *Sphingomonads* (Basta et al. [@CR3]).

Interestingly, when pyrene was catalyzed in *M. vanbaalenii* PYR-1, as many as 142 proteins were upregulated although its degradation needs 27 enzymes (Kim et al. [@CR30]). This not only indicates the existence of a complex network among genes, but also suggests that each gene dynamically and actively responds to different stresses. Indeed, different experimental conditions and different stresses should stimulate the genes to respond differently. Therefore, we are particularly interested in how PAH degradation genes in *P. aeruginosa* behave under different conditions with different stresses since this knowledge will render us a whole picture on how PAH degradation genes work.

Microarray analysis is certainly helpful because it simultaneously and globally examines the complete transcriptome in response to a stimulus by comparing mRNA levels before and after the stimulus, whose significance can be evaluated using statistical tests, absolute fold changes, and marginal call. In a broader sense, a stimulus not only regulates mRNA levels up or down, but also changes the relationship between any two gene expressions. The correlation between two genes indicates whether two genes act more coherently under the stimulus, but both genes do not necessarily have significance in up/down-regulation. Subsequently, the genes with high correlation are more likely to encode interacting proteins, or to have a similar biological function, or to belong to the same biological pathway (Braun [@CR7]). Gene Expression Omnibus (GEO) (Edgar et al. [@CR13]; Barrett et al. [@CR2]) provides the opportunity to explore correlations between gene expressions. Doubtless, the study combining numerous coexpression data of *P. aeruginosa* from GEO is a big data study in accordance with BD2K (Big Data to Knowledge) proposed by National Institutes of Health (NIH) (Ohno-Machado [@CR53]).

Hence, this study is designed to use network analysis with coexpression data of *P. aeruginosa* from GEO to investigate how PAH degradation genes behave under different circumstances with different stresses because technically network analysis can easily detect clusters; and thus, is an excellent tool to examine how PAHs genes work under different conditions with different stresses. In this context, network analysis produces the following novelties: (i) comparing the number of clusters before and after stresses, (ii) comparing the membership in each cluster before and after stresses, (iii) defining which gene changed its membership together with PAH degradation genes before and after stresses, (iv) classifying membership-changed-genes in terms of category in *Pseudomonas* Genome Database, (v) postulating unknown gene's function, and (vi) proposing new mechanism on genes of interests.

Materials and methods {#Sec2}
=====================

Data {#Sec3}
----

Publicly available microarray data were obtained from GEO with raw datasets performed in platform GPL84, which is the \[Pae_G1a\] Affymetrix *Pseudomonas aeruginosa* Array (<http://www.affymetrix.com/support/technical/byproduct.affx?product=paeruginosa>). This platform was used to study the transcriptional regulation and antimicrobial agent response of *P. aeruginosa* strain PAO1. GPL84 platform lists 5549 *P. aeruginosa* genes, which can be categorized according to the category in *Pseudomonas* Genome DataBase (Winsor et al. [@CR70], [@CR71]).

The following stresses and conditions are included in this study: (i) the exposure of *P. aeruginosa* PAO1 to hydrogen peroxide because microorganisms continuously encounter various reactive oxygen species during their lifetime (Miller and Britigan [@CR47]) and *P. aeruginosa* has a defense system against reactive oxidants (Ochsner et al. [@CR52]), and 223 and 1854 genes were found statistically changed in coexpression experiments (accession number in GEO: GSE3090) (Chang et al. [@CR9]; Palma et al. [@CR55]); (ii) the exposure of *P. aeruginosa* PAO1 to sodium hypochlorite, which is the most widely used disinfectant, led to 457 and 625 genes upregulated and downregulated (accession number in GEO: GSE7402) (Small et al. [@CR64]); (iii) the exposure of *P. aeruginosa* PAO1 to high and low oxygen concentrations because *P. aeruginosa* PAO1 can adapt to alternation of availability of oxygen in environment (accession number in GEO: GSE52445) (He et al. [@CR23]); (iv) the exposure of *P. aeruginosa* PAO1 to ortho-phenylphenol because the exposure leads to transition to anaerobic respiration and swarming motility, and 509 genes were statistically found changed after exposure for 20 and 60 min (accession number in GEO: GSE10604) (Nde et al. [@CR50]); (v) the expression of TpoN molecular roadblock in *P. aeruginosa* PAO1, which could potentially be used in any bacterium (accession number in GEO: GSE35632); (vi) subpopulations of biofilms of *P. aeruginosa* PAO1 because biofilms are spatially distinct and the top subpopulation is different from the bottom one (accession number in GEO: GSE34762) (Williamson et al. [@CR69]); (vii) the exposure of *P. aeruginosa* PAO1 biofilms to ciprofloxacin and tobramycin because 340 and 683 genes were up- and down-regulated in the biofilm, 78 and 15 genes were up- and down-regulated with ciprofloxacin treatment, and 111 and 70 genes were up- and down-regulated with tobramycin treatment (accession number in GEO: GSE65882) (Stewart et al. [@CR66]); (viii) the comparison of Australian epidemic strain-1 and 2 (*P. aeruginosa* AES-1 and AES-2) in planktonic culture and biofilm not only because these two strains prevail in Austrian cystic fibrosis patients (accession number in GEO: GSE6122 and GSE10304) (Manos et al. [@CR43], [@CR44]) but also because their transcriptome analyses were done using *P. aeruginosa* PAO1 Affymetrix array; and (ix) the comparison between wild-type *P. aeruginosa* PAO1 and PA2449-null mutant *P. aeruginosa* PW5126 (accession number in GEO: GSE39044) (Lundgren et al. [@CR42]) because phenazines, redox active compounds, are polyaromatic secondary metabolites controlled by PA2449.

Genes for PAH degradation {#Sec4}
-------------------------

In the past, studies sometimes included alkane degradation genes, which are numbered 19 in *P. aeruginosa* PAO1, together with PAH degradation genes in analysis (Liu et al. [@CR41]; Gai et al. [@CR17]). However, this study excludes alkane degradation genes in order to get true insight into the network of PAH degradation genes under various conditions. Based on Kyoto Encyclopedia of Genes and Genomes (Kanehisa and Goto [@CR27]; Kanehisa et al. [@CR28], [@CR29]), 46 PAH degradation genes were analyzed in this study as follows: PA0153, PA0154, PA0230, PA0231, PA0232, PA0247, PA0480, PA0817, PA0865, PA0880, PA1205, PA1210, PA1253, PA1966, PA2009, PA2024, PA2083, PA2085, PA2217, PA2418, PA2507, PA2508, PA2509, PA2512, PA2513, PA2514, PA2515, PA2516, PA2517, PA2518, PA2546, PA3240, PA3389, PA3629, PA3935, PA4091, PA4092, PA4121, PA4122, PA4123, PA4124, PA4125, PA4190, PA4486, PA4515, and PA5427.

Gene network {#Sec5}
------------

The genes with similar expression profiles are more likely (i) to encode interacting proteins, (ii) to have a similar biological function, and (iii) to belong to the same biological pathway (Braun [@CR7]). In network analysis, a gene corresponds to a node and the correlation between any two gene expression profiles corresponds to an edge between two nodes, and a network can be thus constructed.

Network analysis, heatmap and dendrogram {#Sec6}
----------------------------------------

Network analysis was conducted using iGraph R package (<http://igraph.org>/) and Pajek (de Nooy et al. [@CR11]). Heatmap and dendrogram were constructed using R package.

Results {#Sec7}
=======

Figure [1](#Fig1){ref-type="fig"} shows the network of 5548 genes from *P. aeruginosa* PAO1 including 46 PAH degradation genes before (panel A) and after exposure (panel B) to hydrogen peroxide. In these two gene networks, a symbol represents a gene with its locus tag, of which larger symbols represent PAH degradation genes. A line between two symbols indicates a good correlation between the coexpression profiles of these two genes. A cluster aggregates the symbols that more densely connect each other within their cluster but sparsely connect with the symbols in another cluster so the genes in a cluster are more likely to work together. Reorganization of genes is clearly visible in panel C, which can be read as follows, PA0153 together with 26 genes were found in a new cluster ([Supplementary materials](#MOESM1){ref-type="media"}) according to the category in *Pseudomonas* Genome Database (Winsor et al. [@CR70], [@CR71]).Fig. 1Network of 5548 genes from *P. aeruginosa* PAO1 including 46 PAH degradation genes before (panel A) and after exposure (panel B) to hydrogen peroxide. Each symbol represents a gene with its code: circles represent non-PAH degradation genes and large triangles represent PAH degradation genes. Each edge represents a good correlation between two genes coexpression profiles. Because of space limitation, a cluster may not appear to contain many genes, but in reality, there are 521 genes in cyan cluster, 506 genes in yellow cluster, 398 genes in lime green cluster, 368 genes in red cluster, 361 genes in blue cluster, 356 genes in pink cluster, 343 genes in white cluster, 321 genes in orange cluster, 302 genes in purple cluster, 280 genes in cadet blue cluster, 253 genes in teal blue cluster, 252 genes in olive green cluster, 245 genes in gray cluster, 243 genes in black cluster, 225 genes in maroon cluster, 181 genes in light green cluster, 168 genes in light yellow cluster, 140 genes in magenta cluster, and 85 genes in midnight blue cluster in panel A. Panel C shows the genes together with PAH degradation genes found in new clusters after exposure to hydrogen peroxide in *P. aeruginosa* PAO1. Each pair of the following PAH degradation genes (PA0865 and PA4091, PA1205 and PA2217, PA2512 and PA4122, PA2515 and PA5427) have the same profiles

Figure [2](#Fig2){ref-type="fig"} displays the genes together with PAH degradation genes in new clusters after exposure to sodium hypochlorite. Actually, this study used the same control as panel A in Fig. [1](#Fig1){ref-type="fig"} (Chang et al. [@CR9]). Network analysis found that after the exposure to sodium hypochlorite the 5548 genes formed several huge clusters with the largest cluster containing 2047 genes, which is really different from the exposure to hydrogen peroxide in panel C in Fig. [1](#Fig1){ref-type="fig"}, where no cluster contains more than 200 genes. Thus, the number of genes associated with PAH degradation genes for regrouping in Fig. [2](#Fig2){ref-type="fig"} is clearly higher than that in panel C in Fig. [1](#Fig1){ref-type="fig"}.Fig. 2Genes together with PAH degradation genes found in new clusters after exposure to sodium hypochlorite in *P. aeruginosa* PAO1. Each pair of the following PAH degradation genes have the same profiles: PA0230 and PA2518, PA0865 and PA0880, PA2083 and PA4486, PA2508 and PA2509, PA2514 and PA2546, PA3240 and PA4092, PA4121 and PA4122

Figure [3](#Fig3){ref-type="fig"} examines the gene networks of *P. aeruginosa* PAO1 after oxygen exposure from high to low oxygen concentration (upper panel) and from low to high oxygen concentration (lower panel) (He et al. [@CR23]). At first glance, there is little difference between upper and lower panels, but the difference is that network finds 170 and 113 clusters for oxygen exposing from high to low oxygen concentration (upper panel) and from low to high oxygen concentration (lower panel). Cyan colored symbols at the bottom in both panels are 2334 and 2523 isolated genes for oxygen exposing from high to low oxygen concentration (upper panel) and from low to high oxygen concentration (lower panel), i.e., they have no connection with any other genes.Fig. 3Gene networks of *P. aeruginosa* PAO1 after oxygen exposure from high to low oxygen concentration (upper panel) and from low to high oxygen concentration (lower panel)

Figure [4](#Fig4){ref-type="fig"} delineates the reorganizations of *P. aeruginosa* PAO1 genes after the exposure to ortho-phenylphenol for 20 and 60 min (Panels A and B) with respect to PAH degradation genes. For 5547 genes, network analysis discovers 18 clusters before the exposure and 15 and 16 clusters after the exposure for 20 and 60 min, indicating that the genes did reorganize after the exposure. At 20 min, unclassified genes, translation, ribosomal structure, and biogenesis genes and transcription genes reorganized strongly (last three rows along category axis in panel A), which is explainable because the initial response of *P. aeruginosa* PAO1 should increase its synthesis of defending protein. Two PAH degradation genes PA2514 and PA4123 associated with 121 genes at 20 min (panel A), among them 28 genes are associated with the transport and metabolism of amino acid or carbohydrate or inorganic ion. At 60 min, many genes are associated with PAH degradation gene PA5427 (last column along PAH degradation gene axis in panel B), including 38 genes having functions of translation, ribosomal structure, and biogenesis. Among them, PA4263 is a heat shock protein involved in stress response, and PA4266 is an elongation factor whose activity reflects a cellular protective response (Nde et al. [@CR50]).Fig. 4Genes together with PAH degradation genes found in new clusters before and after exposure to ortho-phenylphenol in *P. aeruginosa* PAO1 for 20 min (A) and for 60 min (B). In panel A, each pair of the following PAH degradation genes (PA0232 and PA4091, PA0247 and PA0817, PA0880 and PA2518, PA2507 and PA4121, PA2513 and PA4122, PA2514 and PA4123, PA3935 and PA4486) have the same profiles. In panel B, each pair of the following PAH degradation genes (PA0232 and PA4123, PA2217 and PA2516, PA2514 and PA4124, PA2518 and PA4486, PA2546 and PA4190) have the same profiles

Figure [5](#Fig5){ref-type="fig"} demonstrates the genes together with PAH degradation genes in new clusters before and after the expression of RpoN (PA4462) molecular roadblock. For 5549 genes, network analysis discovers 20 clusters before the expression and 19 clusters after the expression. As can be seen, RpoN roadblock did not lead many genes to regroup with PAH degradation genes compared with other figures. Actually, nine genes from PA2134 to PA2192 were found to have the relationship with PAH degradation genes, PA0154 vs PA2145, PA1253 vs PA2174, PA2508 vs PA2182, PA2514 vs PA2177, PA2516 vs PA2176, PA3240 vs PA2149, PA4121 vs PA2169, PA4122 vs PA2173, and PA5427 vs PA2143, whereas PA2173 and PA2176 are regulated by sigma factor σ22 (AlgU, PA0762).Fig. 5Genes together with PAH degradation genes found in new clusters before and after expression of RpoN (PA4462) molecular roadblock in *P. aeruginosa* PAO1. PA0817 and PA2217 have the same profiles

Figure [6](#Fig6){ref-type="fig"} exhibits gene networks of the top 30 μm (upper panel) and bottom 30 μm (middle panel) of biofilm because *P. aeruginosa* PAO1 biofilms are spatially distinct and form two subpopulations (Williamson et al. [@CR69]). The top subpopulation, whose cell activities were high (Williamson et al. [@CR69]), was composed of 89 clusters (upper panel), while the bottom subpopulation, whose cell activities were low (Williamson et al. [@CR69]), was composed of 21 clusters (middle panel). The lower panel shows the percentage of the genes that changed their membership in five clusters, where each pie is a cluster in the bottom subpopulation of biofilms and its compositions are the genes belonging to the clusters in the top subpopulation of biofilms. For example, the first cluster in the bottom subpopulation (pie I in lower panel) is composed of 14.57% from cluster 0 of the top subpopulation, 17.46% from cluster 1 of the top subpopulation, and so on.Fig. 6Gene networks of the top 30 μm (upper panel) and bottom 30 μm (middle panel) of the *P. aeruginosa* PAO1 biofilms with PAH degradation genes (big symbols). Lower panel is gene compositions of the first five clusters (cluster 0 contains 1764 genes, cluster 1 contains 1730 genes, cluster 2 contains 1516 genes, cluster 3 contains 398 genes, cluster 4 contains 90 genes) in the bottom subpopulation of biofilms in terms of gene clusters in the top subpopulation of biofilms

Figure [7](#Fig7){ref-type="fig"} highlights the genes together with PAH degradation genes in new clusters after 12 h treatments of *P. aeruginosa* PAO1 biofilms with ciprofloxacin (left-hand panel) and tobramycin (right-hand panel) (Stewart et al. [@CR66]). The first row in both panels shows the number of upregulated genes in biofilms with reference to PAH degradation genes. The second row in both panels shows the number of downregulated genes in biofilms with reference to PAH degradation genes. The third and fourth rows in both panels show the upregulated and downregulated genes with ciprofloxacin and tobramycin treatment, respectively.Fig. 7Genes together with PAH degradation genes found in new clusters after 12 h treatments of the *P. aeruginosa* PAO1 biofilms with ciprofloxacin (left panel) and tobramycin (right panel). In left panel, each pair of the following PAH degradation genes (PA0232 and PA0880, PA0865 and PA2514, PA2024 and PA4486, PA2418 and PA3629) have the same profile, and three genes PA2083, PA2515, and PA4121 have the same profile. In the right panel, each pair of the following PAH degradation genes (PA0247 and PA2418, PA0865 and PA1205, PA2085 and PA5427) have the same profiles, and three genes PA0232, PA0880, and PA2517 have the same profile

Figure [8](#Fig8){ref-type="fig"} illustrates the genes together with PAH degradation genes in new clusters of *P. aeruginosa* AES-1 in biofilm (panel A) (Manos et al. [@CR43]), and *P. aeruginosa* AES-2 in biofilm (panel B) (Manos et al. [@CR44]). For 5549 genes, network analysis finds 16 and 18 clusters in AES-1 planktonic culture and biofilm, and 17 clusters in AES-2 planktonic culture and biofilm, suggesting biofilm influences AES-1 more than AES-2.Fig. 8Genes together with PAH degradation genes found in new clusters of *P. aeruginosa* AES-1 (A) and AES-2 (B) in biofilm. In panel A, each pair of the following PAH degradation genes (PA2009 and PA3240, PA2514 and PA2517, PA2515 and PA3629, PA4123 and PA5427) have the same profiles, and the genes PA0154, PA0247, PA4121, and PA4122 have the same profiles. In panel B, each pair of the following PAH degradation genes (PA0153 and PA0865, PA0880 and PA2516, PA2508 and PA2509, PA3935 and PA4092, PA4091 and PA4122) have the same profiles

Figure [9](#Fig9){ref-type="fig"} pictures gene networks of wild-type *P. aeruginosa* PAO1 and PA2449-null mutant *P. aeruginosa* PW5126 (Lundgren et al. [@CR42]) because phenazines, redox active compounds, are polyaromatic secondary metabolites controlled by PA2449 and serve as virulence factors in *P. aeruginosa*. Network analysis found 13 clusters in wild-type *P. aeruginosa* PAO1 (upper panel) but 14 clusters in *P. aeruginosa* PW5126 (lower panel). Clearly, this difference is due to a mutation at PA2449.Fig. 9Gene networks of wild-type *P. aeruginosa* PAO1 (upper panel) and PA2449-null mutant *P. aeruginosa* PW5126 (lower panel)

Figure [10](#Fig10){ref-type="fig"} summarizes how PAH-degradation genes reorganized with association of gene category in response to nine different stresses and conditions in terms of heatmap and cluster analysis. The color in heatmap begins from red to violet (red, orange, yellow, green, blue, indigo, and violet) representing the number of genes in terms of gene category increases from 0 to 112, i.e., the bottom row has the highest number but two rows up to the bottom row have the lowest number. So the color indicates the degree of PAH degradation genes associated with different genes, which is in good agreement with the classification of the dendrogram on the left-hand side. With respect to columns, the dendrogram on the top depicts the hierarchical cluster tree of PAH degradation genes, from which we can trace different functions of PAH degradation genes.Fig. 10Heatmap and cluster analysis of PAH degradation genes and their association with gene category in response to nine different stresses and conditions. Different colors in heatmap represent different numbers of genes in terms of gene category, and trees of dendrogram classify PAH degradation genes and gene category

Discussion {#Sec8}
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In this study, we attempted to use network analysis to uncover how PAH degradation genes work under nine different conditions. Ideally, it would be far better to have a PAH condition as a "positive control", unfortunately no such data are currently available. Moreover, the number of PAHs has already reached 922 (Sander and Wise [@CR59]), and microarray on all PAHs would be very costly and time-consuming, but the microarray on a single PAH appears less attractive. In this situation, integrative analysis of available microarrays in different conditions appears more reasonable. Therefore, our analysis could render the knowledge on how PAH degradation genes work under different conditions. As mentioned in the Introduction, network analysis produces six novelties, which provide new information on PAH degradation.

Comparing cluster numbers before and after stresses {#Sec9}
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Network analysis groups the genes obtained from microarray into different clusters according to their correlation, and in this way the genes in a cluster have similar expression profiles, indicating that they encode interacting proteins, have a similar biological function, and belong to the same biological pathway (Braun [@CR7]). Comparing cluster numbers before and after stresses or between different stresses, we can trace the co-actions of different genes. For example, network analysis discovers 19 clusters before the exposure (panel A), but 35 clusters after the exposure (panel B) in Figs. [1](#Fig1){ref-type="fig"}, and 38 clusters in Fig. [2](#Fig2){ref-type="fig"}, which is in good agreement with the experiment, where the exposure to sodium hypochlorite led to more changes in genes than the exposure to hydrogen peroxide (Small et al. [@CR64]). As another example, network analysis uncovers the difference between top and bottom subpopulations of biofilms in Fig. [6](#Fig6){ref-type="fig"} because there are more clusters in the upper panel than in the middle panel, which implies a higher activity in the top subpopulation of biofilms than in the bottom subpopulation. Similar observations can be found in other experimental conditions. Also, different strains can influence biofilm formation, and this feature can be remarkably presented in Fig. [8](#Fig8){ref-type="fig"}, where it is seen that the number of reorganized genes are bigger in panel A than in panel B. Indeed, it was observed that biofilm formed by AES-1 is larger and thicker than usual (Manos et al. [@CR43]).

Comparing the membership in each cluster before and after stresses {#Sec10}
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Network analysis provides insights on gene membership in each cluster and how it changes between control and treated groups, and between two subpopulations. For example, the lower panel in Fig. [6](#Fig6){ref-type="fig"} displays such analysis, from which we can see how many genes changed their membership spatially in biofilms. Various reasons were given to explain why cells in biofilms spatially differ (Stewart and Franklin [@CR65]), thus it is intriguing to find out whether PAH degradation genes follow such trend. Of 46 PAH degradation genes, 12 genes (PA0154, PA0817, PA0865, PA0880, PA1205, PA1210, PA1966, PA2024, PA2085, PA2217, PA2509, and PA3935) stay in their original clusters, whereas the other 34 genes changed their memberships. In addition, those 34 PAH degradation genes are likely to be associated with high mRNA abundances including the genes PA4463, PA0059, PA2146, and PA3531. Taking Fig. [8](#Fig8){ref-type="fig"} as another example, PA0634 and PA0635 in panel A are associated with a PAH degradation gene PA0865, and PA0639 is associated with two PAH degradation genes PA4123 and PA5427. This suggests the initiation of PAH degradation genes could go through the toxin-antitoxin principle (Dziewit et al. [@CR12]) because the genes PA0632-PA0639 are identical to the tail proteins of bacteriophage N15 (Ravin et al. [@CR58]), which acts along this line. A motility gene PA1094, which can change flagellar shape affecting motility, is associated with a PAH degradation gene PA4092. Also, an ABC transport system gene PA1247 is found to be associated with a PAH degradation gene PA4125. Four PAH degradation genes PA0154, PA0247, PA4121, and PA4122 have the same profiles in panel A, and they are associated with PA2386, a virulence factor pyoverdine gene that plays an important role in cystic fibrosis (Lamont et al. [@CR37]).

Defining which gene changed its membership together with PAH degradation genes {#Sec11}
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On the one hand, many genes changed their membership together with PAH degradation genes when genes reorganized themselves under a certain condition. These associations are clearly demonstrated in panel C of Fig [1](#Fig1){ref-type="fig"}, and Figs. [2](#Fig2){ref-type="fig"}, [4](#Fig4){ref-type="fig"}, [5](#Fig5){ref-type="fig"}, [7](#Fig7){ref-type="fig"}, and [8](#Fig8){ref-type="fig"}. For instance, PA0576 (RpoD) is a housekeeping gene because of its stability (Savli et al. [@CR60]), and it changed its membership with a PAH degradation gene (PA1966) and 20 other genes to cluster purple from cluster olive green in Fig. [1](#Fig1){ref-type="fig"}. Of the 30 most downregulated genes in the exposure to hydrogen peroxide (Chang et al. [@CR9]), two genes PA5479 and PA5446 appeared in two new clusters with PAH degradation genes PA2508 and PA4121, respectively, so network analysis does penetrate into different mechanisms. Of highly up/down-regulated genes in response to oxidative stress, PA0182 encoding 3-ketoacyl-ACP reductase and PA2288 were associated with a PAH degradation gene PA0230, PA3008 encoding cell division inhibitor SulA was associated with the PAH degradation gene PA0231, PA5479 encoding glutamate/aspartate:proton symporter was associated with the PAH degradation gene PA2508. Notably, PAH degradation gene PA4124 was associated with R2/F2 pyocin gene locus (PA0612-PA0648) because ten genes (PA0615, PA0616, PA0617, PA0620, PA0634-PA0638, and PA0641) from this locus together with PA4124 changed to cluster olive green from cluster cyan, implying that PA4124 works with R2/F2 pyocin gene locus. In biofilm, attention is paid to drug efflux pumps, of which PA4206 is associated with PAH degradation gene PA0154 (Fig. [7](#Fig7){ref-type="fig"}), therefore the initiation of PA0154 could relate to PA4206. In ciprofloxacin treatment (left-hand panel in Fig. [7](#Fig7){ref-type="fig"}), 18 PAH degradation genes (PA0231, PA0232, PA0480, PA0865,PA0880, PA1210, PA1966, PA2024, PA2507, PA2513, PA2514, PA2518, PA3240, PA3935, PA4091, PA4092, PA4122, and PA4125) are only associated with upregulated genes, whereas five PAH degradation genes (PA0247, PA1205, PA2516, PA2546, and PA5427) are only associated with downregulated genes. As ciprofloxacin stimulates the genes for SOS response, non-membrane-bound organelles, cytolysis and bacteriocin biosynthesis, so the first 18 PAH degradation genes would relate to these responses. In tobramycin treatment (right-hand panel in Fig. [7](#Fig7){ref-type="fig"}), 11 PAH degradation genes (PA0230, PA0480, PA1253, PA2083, PA2217, PA4121, PA4122, PA4123, PA4125, PA4190, and PA 4515) are only associated with upregulated genes, whereas 11 PAH degradation genes (PA0153, PA2024, PA2085, PA2509, PA2514, PA2516, PA2546, PA3935, PA4092, PA4486, and PA5427) are only associated with downregulated genes. As tobramycin stimulates the genes for ribosome biosynthesis and RNA metabolism, and inhibits the genes for energy production, PAH degradation genes could be extrapolated along these directions. These findings once again demonstrate the importance of network analysis because the genes upregulated by ciprofloxacin and tobramycin did not overlap upregulated genes in biofilms (Stewart et al. [@CR66]) but they were found to be associated with PAH degradation genes. In biofilm and quorum sensing studies, attention was given to the role of stationary-phase sigma factor RpoS (PA3622) (Schuster et al. [@CR61]), which together with a PAH degradation gene PA2009 was found in a new cluster, suggesting their association. Also, a quorum sensing gene (PA1432, *lasI*) was found in a new cluster with PA0232, PA0880, and PA2517, which have the same profiles in both panels. In fact, a PAH degradation gene PA5427 was upregulated in several biofilm studies (Stewart et al. [@CR66]). Those studies illustrated the function of some genes, and this study highlighted their association with PAH degradation genes. For instance, PA1555 encoding for a cytochrome c oxidase appears with a PAH degradation gene PA1966, and PA2386 encoding L-ornithine N^5^-oxygenase was associated with a PAH degradation gene PA4122.

On the other hand, some conditions did not lead many genes to regroup with PAH degradation genes, such as the exposure to RpoN roadblock (Fig. [5](#Fig5){ref-type="fig"}). This means that RpoN roadblock is very specific and does not interrupt many processes per se although sigma factor RpoN (σ54) controls a regulon that is composed of around 20% *P. aeruginosa* PAO1 genome (Damron et al. [@CR10]), of which 59 genes from PA2134 to PA2192 are often studied. Intriguingly, if we look at genes PA2850, PA4613, PA4763, and PA5530 in panel C in Fig. [1](#Fig1){ref-type="fig"}, which were most highly regulated in the exposure to hydrogen peroxide (Chang et al. [@CR9]), they do not appear in any new clusters with PAH degradation genes, this is the evidence that network analysis reveals different aspects of gene dynamics. Notably, PA0153, PA1210, PA1253, PA4125, and PA4190 in the left panel of Fig. [7](#Fig7){ref-type="fig"}, and PA0865, PA1205, PA1210, PA2518, PA3240, PA4091, and PA4123 in the right-hand panel are independent of the influence of upregulated genes in biofilms because none of the 340 upregulated genes in bioifilms appear in the first row for these PAH degradation genes. Furthermore, PA1201, PA4190, and PA5427 in the left-hand panel of Fig. [7](#Fig7){ref-type="fig"}, and PA0480 and PA1210 in the right-hand panel are independent of the influence of downregulated genes in biofilms because none of the 683 downregulated genes in bioifilms appear in the second row for these PAH degradation genes. In biofilms, the reduction of anabolic and catabolic activities is common, so the PAH degradation genes, which are independent of the influence of downregulated genes, should not be associated with catabolic pathways such as tricarboxylic acid cycle, NADH dehydrogenase, and cellular respiration. Because the location of PA2449 is close to the cluster of genes that are involved in glycine and serine metabolism (PA2442, PA2446, PA2493, and PA4315), however, these genes did not regroup together with PAH degradation genes (Fig. [9](#Fig9){ref-type="fig"}), so we can exclude that PAH degradation genes have any association with glycine and serine metabolism.

Classifying membership-changed-genes {#Sec12}
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*Pseudomonas* genes were classified into different categories in *Pseudomonas* Genome Database (Winsor et al. [@CR70], [@CR71]), by which we can further analyze the membership-changed-genes to summarize their functions, as panel C in Fig. [1](#Fig1){ref-type="fig"}, and Figs. [2](#Fig2){ref-type="fig"}, [4](#Fig4){ref-type="fig"}, [5](#Fig5){ref-type="fig"}, [7](#Fig7){ref-type="fig"}, and [8](#Fig8){ref-type="fig"} show such analysis. The exposure of hydrogen peroxide led *P. aeruginosa* PAO1 to have 223 genes significantly up/down-regulated, which were considered to relate to pyocin system genes and iron regulation (Chang et al. [@CR9]). Taking panels A and B in Fig. [4](#Fig4){ref-type="fig"} as an example, an interesting trend of gene reorganization can be observed along the time course. The genes with transport and metabolism functions are activated at the early time response to a stimulus, while many genes having functions of translation, ribosomal structure, and biogenesis are activated at the later time. Considering different stimuli or stresses, an integrated view can be found from top to bottom in Fig. [10](#Fig10){ref-type="fig"} where PAH degradation genes reveal 1) strong connections with the genes of amino acid transport and metabolism (the 3rd row), 2) more connections with the genes of inorganic ion transport and metabolism, energy production and conversion, lipid transport and metabolism, and transcription (the 4th to 7th rows), 3) moderate connections with the genes of posttranslational modification, protein turnover, chaperones, coenzyme transport and metabolism, signal transduction mechanisms, cell wall-membrane-envelope biogenesis, and carbohydrate transport and metabolism (the 8th to 12th rows), 4) fewer connections with the genes of replication, recombination and repair, nucleotide transport and metabolism, cell motility, secondary metabolites biosynthesis, transport and catabolism, and translation, ribosomal structure and biogenesis (the 13th to 17th rows), and 5) fewer connections with the genes of defense mechanisms, intracellular trafficking, secretion, and vesicular transport, cell cycle control, cell division, chromosome partitioning, chromatin structure and dynamics, and RNA processing and modification (the 18th to 22nd rows).

Postulating unknown gene's function {#Sec13}
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Actually, many *P. aeruginosa* PAO1 genes have yet to define their functions. The detailed analysis of the associations of genes when changing their membership can get an idea on unknown gene's functions. Network analysis uncovered some unknown function genes having association with PAH degradation genes. For example, the unknown function genes PA1746, PA2782 (Stewart et al. [@CR66]), and PA3572 had association with PAH degradation genes PA3629, PA0817, and PA2516, respectively. An unknown function gene PA1673 and a phenazine biosynthesis protein PA4211 were associated with a PAH degradation gene PA3389. An unknown function gene PA0713 and PA4765 encoding outer membrane lipoprotein OmlA precursor were associated with a PAH degradation gene PA4486.

Furthermore, the gene PA0247 in panels A and B in Fig. [1](#Fig1){ref-type="fig"} encodes a 4-hydroxybenzoate 3-monooxygenase, and belonged to the light green cluster before the exposure (panel A) but changed to the black cluster after the exposure (panel B). Interestingly, it did not act alone, but with eight other genes (PA0470, PA0863, PA1417, PA2494, PA2635, PA3502, PA4649, and PA4660), i.e., these eight genes changed their membership from the light green cluster to the black one ([Supplementary materials](#MOESM1){ref-type="media"}). Of these eight genes, PA0470 encodes ferrichrome receptor FiuA, PA0863 encodes an oxidoreductase, PA2494 encodes resistance-nodulation-cell division (RND) multidrug efflux transporter MexF, and PA4660 encodes a deoxyribodipyrimidine photolyase, while the functions of PA1417, PA2635, PA3502, and PA4649 are unknown. Therefore, we can not only postulate that PA0247 initiation needs the transporter, receptor, oxidoreductase and photolyase, but also have the clue for the function of undefined genes. Taking a hypothetical protein PA4486 as another example, it located on the far right-hand side of Fig. [10](#Fig10){ref-type="fig"}, branched together with PA2024 (ring-cleaving dioxygenase) and then branched with PA2513 (*antB*, anthranilate dioxygenase small subunit), suggesting that these three genes work together. Moreover, PA4486 was found to be involved in the degradation of aromatic compounds for cleavage of a ring in reaction from 4-carboxymuconolactone to 2-oxo-2,3dihydrofuran-5-acetate and in the degradation of benzoate for cleavage of a ring in reaction between γ-carboxymuconolactone and 3-oxoadipateenol-latone. Therefore, these three genes, PA2024, PA2513, and PA4486, are more likely to be initiated by the genes in blue, indigo, and violet colors in the right-upper corner of Fig. [10](#Fig10){ref-type="fig"} for those two reactions. Similar deduction and reasoning can be applied to all the PAH degradation genes in question.

Proposing a new mechanism on genes of interests {#Sec14}
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The comparison between control and experimental groups in each experiment does generally provide a way to understand how PAH degradation genes behave under different conditions. In this study, the selection of PAH degradation genes are based on KEGG (Kanehisa and Goto [@CR27]; Kanehisa et al. [@CR28], [@CR29]) by examining metabolic pathways of PAHs in *P. aeruginosa* PAO1, and then the selected PAH degradation genes are checked against *Pseudomonas* Genome Database (Winsor et al. [@CR70], [@CR71]). It is interesting to look at gene reorganization along metabolic pathways in KEGG. In Fig. [2](#Fig2){ref-type="fig"}, genes PA2637-PA2649 relate to NADH dehydrogenase I, and their association with PAH degradation genes are as follows: PA2638 with PA1210, PA2639 with PA0231, both PA2641 and PA2647 with PA4125, PA2643 and PA2644 with PA2513, both PA2645 and PA2646 with both PA2009 and PA4091, and PA2648 with PA0247. These associations are reasonable because NADH dehydrogenase I is involved in oxidative phosphorylation. On the other hand, their associated PAH degradation genes are closely related to oxidation, i.e., PA0231 encodes 3-oxoadipate enol-lactonase, PA0247 encodes 4-hydroxybenzoate 3-monooxygenase, PA1210 encodes quercetin 2,3-dioxygenase, PA2009 encodes homogentisate 1,2-dioxygenase, PA2513 encodes anthranilate dioxygenase small subunit, PA4091 encodes 4-hydroxyphenylacetate 3-monooxygenase large subunit, and PA4125 encodes 5-carboxymethyl-2-hydroxymuconate isomerase. So the close relationship between PAH degradation genes can be established through this metabolic pathway.

In this study, we analyzed just the PAH degradation genes although alkane degradation genes were sometimes analyzed together with PAH degradation genes (Liu et al. [@CR41]; Gai et al. [@CR17]) because PAH is a polycyclic aromatic hydrocarbon while hazardous alkanes are generally linear structured, i.e., methane, ethane, propane, and butane. Naturally they have different metabolic pathways. For PAHs, the opening of the aromatic ring is important, for example, PA0817 encodes an aromatic ring-cleaving dioxygenase. When considering the influence of different oxygen concentrations in Fig. [3](#Fig3){ref-type="fig"}, it is interesting to find that 17 PAH degradation genes (PA0230, PA0247, PA0480, PA0817, PA1205, PA1210, PA2024, PA2418, PA2509, PA3389, PA3935, PA4092, PA4123, PA4125, PA4486, PA4515, and PA5427) were not associated with any other genes when oxygen concentration went from high to low (upper panel), while six PAH degradation genes (PA4091, PA4123, PA4125, PA4486, PA4515, and PA5427) were not associated with any other genes when oxygen concentration went from low to high (lower panel). In fact, the isolated genes included an important well-known oxygen responding gene PA5171, whereas the other three well-known oxygen responding genes PA5170, PA5172, and PA5173 were located in the lime green cluster (lower panel), and they were associated with seven PAH degradation genes PA0153, PA0480, PA0817, PA2009, PA2517, PA2518, and PA3240. Of the 85 genes identified as essential oxygen-availability response genes (He et al. [@CR23]), PA0230 and PA4515 were PAH degradation genes.

In Fig. [8](#Fig8){ref-type="fig"}, two quorum-sensing genes PA3049 and PA3922 are associated with two PAH degradation genes PA0865 and PA2418, respectively. Again, eight genes (PA0958, PA1432, PA1713, PA1723, PA1777, PA1947, PA4922, and PA5253) were focused in AES-2, however, they are not associated with any PAH degradation genes to change their membership ([Supplementary materials](#MOESM1){ref-type="media"}). AES-2 biofilms had upregulated genes in the type III secretion system, but PA1723, PA1713, and PA1705 are not associated with any PAH degradation genes (panel B), so PAH degradation genes would not interact with the type III secretion system.

Conclusions {#Sec15}
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In this study, network analysis was used to analyze how 46 PAH degradation genes reorganized among 5549 genes in *P. aeruginosa* PAO1 under nine different conditions and stresses. The results demonstrate the heterogeneity of gene networks and regrouping of genes under different conditions. The hierarchical structures between PAH degradation genes and *P. aeruginosa* PAO1 genes with different functions were stratified by dendrogram trees. Six aspects of novelties produced through network analysis are discussed in detail. To our knowledge, this is the first study to use network analysis to investigate synthetically how PAH degradation genes reorganized under different conditions. Such studies can shed lights on understanding gene interactions and reorganizations under various conditions and environmental stresses, which pave the way to conduct similar studies on other genes.

Electronic supplementary material
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 {#Sec16}

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**Electronic supplementary material**

The online version of this article (doi:10.1007/s13353-017-0402-9) contains supplementary material, which is available to authorized users.

Funding {#FPar1}
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This study was partly supported by research grants from National Natural Science Foundation of China (31460296 and 31560315), and Special Funds for Building of Guangxi Talent Highland.

Conflict of interest {#FPar2}
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Authors declare that they have no conflict of interest.

Ethical approval {#FPar3}
================

This article does not contain any studies with human participants or animals performed by any of the authors.

[^1]: Communicated by: Agnieszka Szalewska-Palasz


Oscars 2018: the winners in full

Guillermo del Toro’s fantasy romance The Shape of Water has won best picture, with del Toro also named best director after a close night at the Academy Awards. Del Toro’s film, which first screened to UK audiences at the BFI London Film Festival, also won for its production design and score.

Martin McDonagh’s drama Three Billboards Outside Ebbing, Missouri, which was closing night gala of the 61st LFF, won a brace of acting awards, with Frances McDormand getting best actress and Sam Rockwell named best supporting actor.

As expected, Gary Oldman won best actor for his turn as Winston Churchill in Darkest Hour. The film also won best makeup and hair, repeating its BAFTA double.

Rounding out the acting prizes was a win for Allison Janney in the figure-skating drama I, Tonya.

Following 13 unsuccessful prior nominations, British director of photography Roger Deakins finally triumphed with a win for best cinematography for Blade Runner 2049, which also won for its visual effects. Another double winner was Pixar’s Coco, which took best animated film and best song (‘Remember Me’).

The Chilean film A Fantastic Woman, another LFF premiere, was named best foreign language film, while LFF gala title Call Me by Your Name won the award for best adapted screenplay for James Ivory. The Oscar for original screenplay went to Jordan Peele for Get Out.

Both A Fantastic Woman and Call Me Your Name screen at BFI Flare: London LGBTQ+ Film Festival later this month.