text:
Grown-ish Is Cute, But Yara Shahidi Wants You to Get Politically Involved
Filed to:black girl magic
Screenshot: Sami Drasin (The Hollywood Reporter)
Don't ask Yara Shahidi to choose between being an ingenue and a serious intellectual; the 18-year-old is already one of several young black actresses breaking the mold when it comes to young Hollywood talent—not only for being non-messy, but for being socially conscious and civically-minded.
In a cover story for the August 8 issue of The Hollywood Reporter (THR), the soon-to-be Harvard freshman dispels the myth that being a celebrity should be her sole source of fulfillment, pushing back against what she says is "this idea that people perpetuate that you have to choose between education and entertainment."
It was the theme of one of her college essays, which, along with a perfect GPA and a letter of recommendation from no less than Michelle Obama, resulted in admission to not only Harvard, but every school she applied to, including Stanford, Yale and Spelman. But despite her high-profile acting career, Shahidi's post-college forecast is surprisingly not centered around entertainment; instead, she tells THR she envisions a future career that is "policy adjacent."
And that's unsurprising, given that even as a newly minted eligible voter (as of her 18th birthday on February 10, which she celebrated with a voting themed party, complete with a registration booth), Shahidi is deeply politically active, launching the Eighteen x 18 platform to inform and encourage her peers in Generation Z to engage in the political process.
"My passion really stemmed from having gone through the 2016 election, where myself and many of my peers were unable to vote," she tells THR. "[T]here was that feeling of being lost. Like, 'What can we do to contribute to our sociopolitical landscape?'"
Activist and mentor Michael Skolnik seems sold on the 18-year-old's activism, telling THR, "If Yara wants to be president one day, she will be."
But for now, Shahidi seems content to be balancing a deeply curious mind, a very public and influential platform and a very hot career—though she initially balked when asked to star in Grown-ish, a spinoff of Black-ish (which she also still appears on).
"I knew being in almost every scene of a show that could go multiple seasons would require a level of investment that I've never needed to make before," she says.
But Shahidi took on the challenge, and it paid off: filming of the second season of the hit spinoff begins next month, which the ridiculously well-read freshman will balance with her (semi-remote) course load at Harvard. She's also launching an NPR podcast with the working title 18 x 18 with Yara Shahidi, where she'll feature and interview young women like herself who are changing and challenging the prototype in their respective fields.
But while it's easy to imagine that Shahidi's seemingly boundless energy is a luxury of the very young, she urges us all not to count Gen Z out when it comes to their engagement, interest or impact, telling THR:
"The thing that people have yet to fully understand about my generation is just how multifaceted we are ... Oftentimes, we like to oversimplify what my generation believes in or does, I feel like there's so much more to the picture and what I've seen from my peers is that we're socially engaged and curious about the world."


Source: International Journal of Information Management 13 iii (1993): 205-219.
Source: Journal of Muamalat and Islamic Finance Research (JMIFR) 5 i (2008): 1-15.
Source: Journal of Islamic Banking & Finance 24 i (2007): 102-119.


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text:
package main

import (
	"bytes"
	"encoding/hex"
	"fmt"
	"strconv"

	"github.com/btcsuite/btcd/chaincfg/chainhash"
	"github.com/btcsuite/btcd/txscript"
	"github.com/btcsuite/btcd/wire"
	keychain "github.com/btcsuite/btcutil/hdkeychain"
	cli "github.com/urfave/cli"
	addrs "github.com/whyrusleeping/hdkeyutils/addrs"
	mktx "github.com/whyrusleeping/mktx"
)

var msigCmd = cli.Command{
	Name:  "msig",
	Usage: "manipulate HD multisig wallets",
	Subcommands: []cli.Command{
		msigRedeemScriptCmd,
		msigAddrCmd,
		msigMkSpendTxCmd,
		msigSignTxCmd,
		msigFinishTxSigCmd,
	},
}

var msigRedeemScriptCmd = cli.Command{
	Name:  "redeem-script",
	Usage: "create a multisig redeem script for the given keys",
	Flags: []cli.Flag{
		cli.IntFlag{
			Name:  "m",
			Usage: "'m' parameter for m of n",
		},
		cli.IntFlag{
			Name:  "n",
			Usage: "'n' parameter for m of n",
		},
		cli.IntFlag{
			Name:  "index",
			Usage: "hd wallet index of pubkeys to use",
			Value: -1,
		},
	},
	Action: func(c *cli.Context) error {
		msigscr, err := cmdMsigScript(c)
		if err != nil {
			return err
		}

		fmt.Printf("%x", msigscr)
		return nil
	},
}

func cmdMsigScript(c *cli.Context) ([]byte, error) {
	index := c.Int("index")
	if index < 0 {
		return nil, fmt.Errorf("must specify an index")
	}

	m := c.Int("m")
	if m <= 0 {
		return nil, fmt.Errorf("must specify a value for m")
	}
	n := c.Int("n")
	if n <= 0 {
		return nil, fmt.Errorf("must specify a value for n")
	}

	if n != len(c.Args()) {
		return nil, fmt.Errorf("must pass 'n' pubkeys")
	}

	var keys [][]byte
	for _, keystr := range c.Args() {
		k, err := keychain.NewKeyFromString(keystr)
		if err != nil {
			return nil, err
		}
		child, err := k.Child(uint32(index))
		if err != nil {
			return nil, err
		}

		pubk, err := child.ECPubKey()
		if err != nil {
			return nil, err
		}

		keys = append(keys, pubk.SerializeCompressed())
	}

	return mktx.MakeMultisig(m, n, keys), nil
}

var msigAddrCmd = cli.Command{
	Name:  "addr",
	Usage: "create an HDM wallet address",
	Flags: []cli.Flag{
		cli.IntFlag{
			Name:  "m",
			Usage: "'m' parameter for m of n",
		},
		cli.IntFlag{
			Name:  "n",
			Usage: "'n' parameter for m of n",
		},
		cli.IntFlag{
			Name:  "index",
			Usage: "hd wallet index of pubkeys to use",
			Value: -1,
		},
		cli.StringFlag{
			Name:  "format",
			Usage: "output format for key (btc or zec)",
			Value: "btc",
		},
		cli.BoolFlag{
			Name:  "testnet",
			Usage: "print testnet addrs",
		},
	},
	Action: func(c *cli.Context) error {
		msigscr, err := cmdMsigScript(c)
		if err != nil {
			return err
		}
		if c.Bool("testnet") {
			addrs.BitcoinP2SHPrefix = addrs.BitcoinTestnetP2SHPrefix
			addrs.ZcashP2SHPrefix = addrs.ZcashTestnetP2SHPrefix
		}

		var prefix []byte
		switch c.String("format") {
		case "btc":
			prefix = addrs.BitcoinP2SHPrefix
		case "zec":
			prefix = addrs.ZcashP2SHPrefix
		default:
			return fmt.Errorf("unsupported format: %s", c.String("format"))
		}

		hash := addrs.HashSha256Ripe160(msigscr)
		fmt.Println(addrs.Base58Check(hash, prefix))
		return nil
	},
}

var msigMkSpendTxCmd = cli.Command{
	Name:      "mktx",
	Usage:     "create a bare multisig spend transaction",
	ArgsUsage: "<redeem-script> <txhash> <target addr> <value>",
	Flags: []cli.Flag{
		cli.IntFlag{
			Name:  "prevoutindex",
			Usage: "specify the index of the previous output",
		},
	},
	Action: func(c *cli.Context) error {
		if len(c.Args()) < 4 {
			return fmt.Errorf("must pass four arguments")
		}

		redeemscr := c.Args()[0]
		txhash := c.Args()[1]
		target := c.Args()[2]
		svalue := c.Args()[3]
		value, err := strconv.ParseInt(svalue, 10, 64)
		if err != nil {
			return err
		}

		prevtx, err := chainhash.NewHashFromStr(txhash)
		if err != nil {
			return err
		}

		redeem, err := hex.DecodeString(redeemscr)
		if err != nil {
			return err
		}

		pkscript := mktx.PayToPubkeyScript(target)

		outp := wire.NewOutPoint(prevtx, uint32(c.Int("prevoutindex")))
		txin := wire.NewTxIn(outp, redeem, nil)
		tx := wire.NewMsgTx(1)
		tx.AddTxIn(txin)

		txo := wire.NewTxOut(value, pkscript)
		tx.AddTxOut(txo)

		buf := new(bytes.Buffer)
		tx.Serialize(buf)
		fmt.Println(hex.EncodeToString(buf.Bytes()))

		return nil
	},
}

var msigSignTxCmd = cli.Command{
	Name:  "sign",
	Usage: "sign a multisig transaction",
	Flags: []cli.Flag{
		cli.IntFlag{
			Name:  "index",
			Usage: "index of HD key to sign with",
			Value: -1,
		},
	},
	Action: func(c *cli.Context) error {
		if len(c.Args()) < 2 {
			return fmt.Errorf("must pass private key file and txdata to sign")
		}
		keyfi := c.Args()[0]
		txdata := c.Args()[1]

		index := c.Int("index")
		if index < 0 {
			return fmt.Errorf("must specify an index")
		}

		priv, err := loadPrivKey(keyfi)
		if err != nil {
			return err
		}
		sigkey, err := priv.Child(uint32(index))
		if err != nil {
			return err
		}
		ecpriv, err := sigkey.ECPrivKey()
		if err != nil {
			return err
		}

		rawtx, err := hex.DecodeString(txdata)
		if err != nil {
			return err
		}

		rawtx = append(rawtx, 1, 0, 0, 0)
		dhb := chainhash.DoubleHashB(rawtx)

		sig, err := ecpriv.Sign(dhb)
		if err != nil {
			return err
		}

		fmt.Printf("%x\n", sig.Serialize())
		return nil
	},
}

var msigFinishTxSigCmd = cli.Command{
	Name:      "finish",
	Usage:     "complete a multisig spend tx with signatures",
	ArgsUsage: "<txdata> <sig1> <sig2> ...",
	Action: func(c *cli.Context) error {
		if len(c.Args()) < 2 {
			return fmt.Errorf("must specify txdata and signatures")
		}
		txdata := c.Args()[0]

		var sigs [][]byte
		for _, sig := range c.Args()[1:] {
			rawsig, err := hex.DecodeString(sig)
			if err != nil {
				return err
			}

			sigs = append(sigs, rawsig)
		}

		rawtx, err := hex.DecodeString(txdata)
		if err != nil {
			return err
		}

		tx := wire.NewMsgTx(1)
		if err := tx.Deserialize(bytes.NewReader(rawtx)); err != nil {
			return err
		}

		sb := txscript.NewScriptBuilder()
		sb.AddOp(txscript.OP_0)
		for _, sig := range sigs {
			sb.AddData(append(sig, 1))
		}

		redeem := tx.TxIn[0].SignatureScript
		sb.AddData(redeem)

		outscript, err := sb.Script()
		if err != nil {
			return err
		}

		tx.TxIn[0].SignatureScript = outscript

		buf := new(bytes.Buffer)
		tx.Serialize(buf)
		fmt.Println(hex.EncodeToString(buf.Bytes()))

		return nil
	},
}

redpajama_set_name: RedPajamaGithub


Im a funloving individual, you will love me once you get to know me.
i am a hard working woman who have a very grate personality fun to be around an very jovial kind and loving person.
IM VERY NICE LOVE TO SOCIALIZE N LOVE TO PARTY N ENJOY LIFE TO THE FULLEST.
Young and adventurous,educated and bold.
I am an outgoing person who loves life and is always seeking knowledge. I am ambitious and puts no limit on what I can achieve in life,i am young,but wise and my charismatic respect often draws respect and I give respect to everyone.
I am a down to earth individual, a believer of true love. I like taking long walks on the beach,going new places and getting to knw new people.
i am a lover of flowers spend much time in the garden and with my birds , a caring person a workolcholic and at times i am moody.
i am pretty and attractive,a very humble girl who very much love to party and hang out with friends. And lol like every other girl i love fashion and i love my money!!!!!
I'm a honest fun loving person.
I love God. I am honest, caring, athletic. I am easy going for the most part. Can be stubborn at times. But overall a good person.


We are working with Keep Truckee Meadows Beautiful (KTMB) to help remove noxious weeds from the area around the Michael D. Thompson trailhead. KTMB is running this event, but Renee will be on hand to help guide and educate volunteers and promote volunteer opportunities further up the trail in the wilderness that Friends of Nevada Wilderness will be hosting in June. Come out and remove weeds from the trailhead and in turn help keep the weeds out of the Mount Rose Wilderness area. Find more information at http://ktmb.org.


It's new research in Gordon-Conwell's Center for the Study of Global Christianity's Christianity in its Global Context, 1970-2020. Missiologist Todd M. Johnson and his team found that 20 percent of non-Christians in North America really do not "personally know" any Christians.
The NYT highlights two efforts to document reprisal attacks on Christian targets, currently estimated to be 37 churches and 14 schools. USA Today offers an interactive map of such attacks in nine provinces.
While there is some truth to that description, we've also selectively forgotten a lot about the immigrants who passed through Ellis Island. They didn't all learn English the day that they stepped off the boat: like today's immigrants, most learned over time, but they started with enough "survival English" to get by. They (or their children) eventually did integrate into American society, but many initially lived in ethnic-specific tenements, isolated from the larger society not only because they felt more comfortable amongst those who could understand their languages but also because there were many American citizens that did not want them in their neighborhoods.
The Bible is not just one text — a large, user-unfriendly quotebook. The Bible contains many texts that have been edited, combined, and arranged in particular ways. Understanding the place of Micah 6:8 within the whole book of Micah, indeed within the larger prophetic proclamation, is the only way to make sense of that (highly quotable) statement. I would suggest that if you do not know what leads up to and follows Micah 6:8, you do not understand that verse.
It can be easy to point out the weaknesses of a generation while overlooking the strengths. But, as William Strauss and Neil Howe pointed out in their book Millennials Go to College, Gen Y has many positive characteristics. And just like any individual or generation, there are aspects of Millennial's generational stereotypes that, in a small part, reflect characteristics of Jesus. Here are four reasons I think Jesus would be proud to roll with Millennials.
It seems our culture loves to watch mayhem and destruction unfold on the big screen. The sheer amount of death and destruction in Hollywood led comic author Mark Waid to note that even in the newest Superman movie, we don't get "a win so much as a stop-loss." Our heroes don't save the day anymore, they simply limit the death toll.
I've never really thought of it as "evangelism" no doubt because like many folks who grew up in the American south, I saw the concept of evangelism abused so much that I've grown rather allergic to the word, let alone the concept. But if we can shake off the baggage and simply see evangelism (or, as Catholics prefer to say, evangelization) as a vulnerable sharing of joy and yearning for God, rather than a manipulative attempt to control another's religious behavior — then, I think, this idea of contemplative evangelism makes all sorts of sense.
The post warns that 911 should be used for life-threatening emergencies only and misuse of the 911 system may result in an arrest.
This entry was posted in links and tagged links on August 20, 2013 by Tim Archer.


This is a placeholder page for Obed Sadivar, which means this person is not currently on this site. We do suggest using the tools below to find Obed Sadivar.
You are visiting the placeholder page for Obed Sadivar. This page is here because someone used our placeholder utility to look for Obed Sadivar. We created this page automatically in hopes Obed Sadivar would find it. If you are not Obed Sadivar, but are an alumni of Lopez High School, register on this site for free now.


text:
Q: Reading a file on python my_file = open('file.txt')
next_line = my_file.readline()
while next_line != "":
    print(next_line)
    next_line = my_file.readline()

This code is correct and it reads one line in a file at a time. My question is why do they use  next_line!= '' what does this mean? Then they also say next_line = my_file.readline, what is the purpose of doing that, i don't understand the entire loop.
My other question is how can I change this code so it doesn't skip a line when it prints the lines on a file?

A: while next_line != "" means "while the line is not empty". So, the loop means "print next_line and set next_line to the next line until next_line is empty. When next_line is empty, exit the loop".
Calling .readline() on a file object leaves a "\n" at the end. So you're actually printing the line + \n, which is why it appears to skip a line.
Try using print(next_line[:-1]). This will print every character except for the last one (\n).

redpajama_set_name: RedPajamaStackExchange


text:
Q: How to centre my display math overfull hboxes? I have an overfull hbox which I've decided to live with.  Actually, several.
However, since they're in \[…\], it would look better if they could remain centered, i.e. overflowing the margins equally on both sides, rather than just overflowing to the right.  Is it possible to ask LaTeX to do this?  (I'm using pdflatex and the amsbook class, fwiw.)
(Yes, I know overfull hboxes make Donald Knuth cry!  But I've considered other options—splitting up these lines of maths, scaling them down a little, changing the document margins, etc—and this seems to me the least worst compromise; so please don't flame me too hard for doing this…)

A: Use \mathclap from the mathtools package:
\documentclass{amsbook}
\usepackage[margin=2in]{geometry}
\usepackage{mathtools}
\usepackage{lipsum}

\begin{document}
\lipsum[1]
\[\mathclap{%
  f(x) = \int\frac{\sin x}{x}\,\mathrm{d}x%
       = \int\frac{\sin x}{x}\,\mathrm{d}x%
       = \int\frac{\sin x}{x}\,\mathrm{d}x%
       = \int\frac{\sin x}{x}\,\mathrm{d}x%
       = \int\frac{\sin x}{x}\,\mathrm{d}x%
       = \int\frac{\sin x}{x}\,\mathrm{d}x%
}\]
\lipsum[2]
\end{document}


Modelling N2 fixation related to Trichodesmium sp.
Modelling N2 fixation related to Trichodesmium sp.: driving processes and impacts on primary production in the tropical Pacific Ocean Modelling N2 fixation related to Trichodesmium sp. Cyril Dutheil et al.
Dinitrogen fixation is now recognized as one of the major sources of bio-available nitrogen in the ocean. Thus, N2 fixation sustains a significant part of the global primary production by supplying the most common limiting nutrient for phytoplankton growth. The "Oligotrophy to UlTra-oligotrophy PACific Experiment" (OUTPACE) improved the data coverage of the western tropical South Pacific, an area recently recognized as a hotspot of N2 fixation. This new development leads us to develop and test an explicit N2 fixation formulation based on the Trichodesmium physiology (the most studied nitrogen fixer) within a 3-D coupled dynamical–biogeochemical model (ROMS-PISCES). We performed a climatological numerical simulation that is able to reproduce the main physical (e.g. sea surface temperature) and biogeochemical patterns (nutrient and chlorophyll concentrations, as well as N2 fixation) in the tropical Pacific. This simulation displayed a Trichodesmium regional distribution that extends from 150∘ E to 120∘ W in the south tropical Pacific, and from 120∘ E to 140∘ W in the north tropical Pacific. The local simulated maximuma were found around islands (Hawaii, Fiji, Samoa, New Caledonia, Vanuatu). We assessed that 15 % of the total primary production may be due to Trichodesmium in the low-nutrient low-chlorophyll regions (LNLC) of the tropical Pacific. Comparison between our explicit and the often used (in biogeochemical models) implicit parameterization of N2 fixation showed that the latter leads to an underestimation of N2 fixation rates by about 25 % in LNLC regions. Finally, we established that iron fluxes from island sediments control the spatial distribution of Trichodesmium biomasses in the western tropical South Pacific. Note, this last result does not take into account the iron supply from rivers and hydrothermal sources, which may well be of importance in a region known for its strong precipitation rates and volcanic activity.
Dutheil, C., Aumont, O., Gorguès, T., Lorrain, A., Bonnet, S., Rodier, M., Dupouy, C., Shiozaki, T., and Menkes, C.: Modelling N2 fixation related to Trichodesmium sp.: driving processes and impacts on primary production in the tropical Pacific Ocean, Biogeosciences, 15, 4333-4352, https://doi.org/10.5194/bg-15-4333-2018, 2018.
Nitrogen is known to be the most common limiting nutrient for phytoplankton growth in the modern world ocean (Moore et al., 2013), especially in the low-nutrient low-chlorophyll (LNLC) ecosystems (Arrigo, 2005; Gruber, 2005). Characterizing the processes governing nitrogen sources and sinks to and from the ocean is therefore central to understanding oceanic production, organic matter export, and food-web structure. Atmospheric dinitrogen (N2) dissolved in seawater is by far the dominant form of N present in the ocean, i.e. the N2 : NO3- ratio typically exceeds 100 in surface waters. However, most phytoplankton species cannot assimilate N2, and only grow using reactive forms of nitrogen such as nitrate, ammonium, and dissolved organic compounds. Some planktonic prokaryotic microorganisms, called "diazotrophs", use an enzyme, the nitrogenase, to fix N2 and convert it into ammonia (NH3) and ultimately ammonium (NH4+). At the global scale, they provide the major external source of reactive nitrogen to the ocean (Gruber, 2008), and support up to 50 % of new production in tropical and subtropical (LNLC) regions (Bonnet et al., 2009; Capone, 1997; Deutsch et al., 2007; Karl et al., 1997; Moutin et al., 2008; Raimbault and Garcia, 2008). These organisms are physiologically and taxonomically diverse including cyanobacteria, bacteria, and archaea (Zehr and Bombar, 2015; Delmont et al., 2018).
Autotrophic diazotrophs have been far more intensively studied than heterotrophic diazotrophs, whose contribution to global N2 fixation remains unclear (Turk-Kubo et al., 2014; Bombar et al., 2016; Moisander et al., 2017). Autotrophic diazotrophs have been characterized both in the field and through laboratory experiments and their physiology is consequently better known (Bergman et al., 2013; Küpper et al., 2008; Mulholland and Capone, 2001, 2000; Ohki et al., 1992; Ramamurthy and Krishnamurthy, 1967; Rubin et al., 2011). Cyanobacterial (autotrophic) diazotrophs are composed of three main groups: (1) the filamentous diazotrophs including the colonial, non-heterocyst-forming Trichodesmium, (2) the heterocyst-forming symbionts associated with diatoms (diatom–diazotroph associations; DDAs), and (3) the unicellular cyanobacterial diazotrophs (UCYN, phylogeneticaly divided into three groups: UCYN-A, -B, and -C). It has been established that autotrophic diazotroph growth rates are typically 1 order of magnitude lower than those of non-diazotrophs (Breitbarth et al., 2008; Falcón et al., 2005; Goebel et al., 2008; LaRoche and Breitbarth, 2005). This can be related to the high energetic demand (Postgate, 1982) required to convert N2 to NH3 as compared to that necessary to assimilate nitrate or ammonia. This low growth rate (compared to other phytoplankton species) mainly constrains their ecological niches to nitrate-poor regions, where they can be competitive. Moreover, their geographical distribution is constrained by nutrient availability in the photic layer (mainly iron and phosphate; Berman-Frank, 2001; Bonnet et al., 2009; Mills et al., 2004; Moutin et al., 2005, 2008; Rubin et al., 2011; Rueter, 1988) and temperature (Staal et al., 2003). Trichodesmium sp. are present only in water where the temperature is above 20 ∘C (Capone, 1997; LaRoche and Breitbarth, 2005; Montoya et al., 2004), while some UCYN can be found in colder and deeper waters (Bonnet et al., 2015; Church et al., 2005; Moisander et al., 2010).
The spatial distribution and rates of N2 fixation have been inferred at the global scale using several tools. Deutsch et al. (2007) have introduced the tracer P* which represents the excess of P relative to the standard N quota. A decrease in this tracer is then interpreted as N2 fixation, since N2 fixation extracts PO4 alone. More recently, Luo et al. (2014) developed a multiple linear regression that relates N2 fixation from the MAREDAT database (Luo et al., 2012) to environmental conditions (nutrients; sea surface temperature, SST; irradiance; mixed layer depth (MLD), etc.) in order to build a statistical model for global N2 fixation distribution.
Numerical models have also been used as they allow us to overcome the scarcity of observations that may limit the implementation of the two previous approaches (Aumont et al., 2015; Bissett et al., 1999; Dutkiewicz et al., 2012; Krishnamurthy et al., 2009; Monteiro et al., 2011; Moore et al., 2006, 2013; Tagliabue et al., 2008). They can notably be used to investigate the spatial and temporal variability in dinitrogen fixation and to study its controlling environmental factors. In these models, N2 fixation has been implemented in various ways. Some models use implicit parameterizations (Bissett et al., 1999; Maier-Reimer et al., 2005; Assmann et al., 2010; Aumont et al., 2015) to derive N2 fixation from environmental conditions (mainly nitrate, phosphate, and iron concentrations; temperature; and light) without explicitly simulating any nitrogen fixing organisms. Alternatively, other models rely on the explicit descriptions of diazotrophs (Moore et al., 2004; Dunne et al., 2013) that have mainly been developed from the knowledge derived from laboratory experiments focused on Trichodesmium sp. (Fennel et al., 2001; Hood et al., 2001; Moore et al., 2001). Noticeably, several modelling studies have been especially focused on the role of iron in controlling the distribution of diazotrophs and N2 fixation (Krishnamurthy et al., 2009; Moore et al., 2004, 2006; Tagliabue et al., 2008). Indeed, a realistic representation of marine iron concentrations has been stressed as a key factor to adequately simulate the habitat of diazotrophs (Monteiro et al., 2011; Dutkiewicz et al., 2012).
Moreover, among the full set of studies focusing on the spatial distribution of N2 fixation, some studies (Berthelot et al., 2017; Bonnet et al., 2009, 2015; Garcia et al., 2007; Shiozaki et al., 2014) based on oceanographic campaigns have reported high N2 fixation rates in the western tropical South Pacific (WTSP), which has been recently identified as a globally important hotspot of N2 fixation with rates >600 µmol N m−2 d−1 (Bonnet et al., 2017). Very high abundances of Trichodesmium have been historically reported in this region (Dupouy et al., 2000, 2011; Moisander et al., 2008; Neveux et al., 2006; Shiozaki et al., 2014; Stenegren et al., 2018) and have recently been identified as the major contributor to N2 fixation in this region (Berthelot et al., 2017; Bonnet et al., 2018). However, the reasons for such an ecological success of diazotrophs in this region are still poorly understood.
In this study, we aim at bringing new insights into this known, but poorly understood, "N2 fixation hotspot". This study aims to understand the spatial and temporal distribution (i.e. seasonal variability) of Trichodesmium and to evaluate the potential impact of Trichodesmium fixers on the biogeochemical conditions of the WTSP. We will specifically address the following overarching questions: (i) what are the mechanisms that structure the Trichodesmium distribution in the WTSP, particularly around the southwest Pacific islands, and (ii) what is the biogeochemical impact of N2 fixation in this region? Note, this study also takes advantage of the sampling done during the "Oligotrophy to UlTra-oligotrophy PACific Experiment" (OUTPACE), which nicely complements the data coverage in the southwest Pacific, and allows a better characterization of the processes responsible for the spatial and seasonal variability in N2 fixation.
To fulfill our objectives, we have implemented an explicit representation of the nitrogen fixers, based on the Trichodesmium physiology, in a biogeochemical model. The first section of this study describes the experimental design and the observations used in our study, while the second part of the paper provides a validation of our reference simulation with an analysis of the Trichodesmium compartment and its impacts on the biogeochemical conditions of the tropical Pacific. In the discussion, the impact of iron from island sediment on dinitrogen fixation is considered as well as the added value of an explicit dinitrogen fixer compartment rather than a simpler implicit representation of N2 fixation. Finally, implications for and limits of our modelling exercise are detailed in the conclusion.
In this study, we used a coupled dynamical–biogeochemical framework based on the regional ocean dynamical model ROMS (Regional Oceanic Modeling System, (Shchepetkin and McWilliams, 2005) and a new version of biogeochemical model PISCES (Pelagic Interactions Scheme for Carbon and Ecosystem Studies). The ocean model configuration is based on the ROMS-AGRIF (Penven et al., 2006) computer code and covers the tropical Pacific region (33∘ S–33∘ N, 110∘ E–90∘ W). It has 41 terrain-following vertical levels with 2–5 m vertical resolution in the top 50 m of the water column, then 10–20 m resolution in the thermocline and 200–1000 m resolution in the deep ocean. The horizontal resolution is 1∘. The turbulent vertical mixing parameterization is based on the non-local K profile parameterization (KPP) of Large et al. (1994). Open boundary conditions are treated using a mixed active/passive scheme (Marchesiello et al., 2001). This scheme is used to force our regional configuration with monthly climatological large-scale boundary conditions from a 0.5∘ ORCA global ocean simulation (details available in Kessler and Gourdeau, 2007), while allowing anomalies to radiate out of the domain. The use of similar ROMS configurations (e.g. vertical resolution, mixed active/passive scheme, turbulent vertical mixing parameterization) in the WTSP is largely validated through studies demonstrating skills in simulating both the surface (Jullien et al., 2012, 2014; Marchesiello et al., 2010) and subsurface ocean circulation (Couvelard et al., 2008).
To compute the momentum and fresh water and/or heat fluxes, we also use a climatological forcing strategy. Indeed, documenting the interannual to decadal variability is beyond the scope of our study, which justifies using climatological forcing fields. A monthly climatology of the momentum forcing is computed from the 1993–2013 period of the ERS1-2 scatterometer stress (http://cersat.ifremer.fr/oceanography-from-space/our-domains-of-research/air-sea-interaction/ers-ami-wind, last access: 12 July 2018). Indeed, the ERS-derived forcing has been shown to produce adequate simulations of the Pacific Ocean dynamics (e.g. Cravatte et al., 2007). A monthly climatology at 0.5∘ resolution computed from the Comprehensive Ocean–Atmosphere Data Set (COADS; Da Silva et al., 1994) is used for heat and fresh water forcing. In our set-up, ROMS also forces on line a biogeochemical model using a WENO5 advection scheme (i.e. five order weighted essentially non-oscillatory scheme; Shchepetkin and McWilliams, 1998). After a 1-year spin-up, we stored 1-day averaged outputs for analysis.
In this study, we use a quota version of the standard PISCES model (Aumont and Bopp, 2006; Aumont et al., 2015), which simulates the marine biological productivity and the biogeochemical cycles of carbon and the main nutrients (P, N, Si, Fe). This modified model, called PISCES-QUOTA, is extensively described in Kwiatkowski et al. (2018). Our version is essentially identical to Kwiatkowski's version that included an added picophytoplankton group, except that this latter group has been removed and replaced by a Trichodesmium compartment. Here we only highlight the main characteristics of the model and the specifics of our model version. Our version of PISCES-QUOTA has 39 prognostic compartments. As in the standard PISCES version, phytoplankton growth is limited by the availability of five nutrients: nitrate as well as ammonium, phosphate, silicate, and iron. Five living compartments are represented: three phytoplankton groups corresponding to nanophytoplankton, diatoms, and Trichodesmium and two zooplankton size classes that are microzooplankton and mesozooplankton. The elemental composition of phytoplankton and non-living organic matter is variable and is prognostically predicted by the model. On the other hand, zooplankton are assumed to be strictly homeostatic, i.e. their stoichiometry is kept constant (e.g. Meunier et al., 2014; Sterner and Elser, 2002). Nutrient uptake and assimilation as well as limitation of growth rate are modelled according to the chain model of Pahlow and Oschlies (2009). The P quota limits N assimilation which in turns limits phytoplankton growth. The phosphorus to nitrogen ratios of phytoplankton are described based on the potential allocation between P-rich biosynthesis machinery, N-rich light harvesting apparatus, a nutrient uptake component, the carbon stores, and the remainder (Daines et al., 2014; Klausmeier et al., 2004). This allocation depends on the cell size and on the environmental conditions.
Nutrients are delivered to the ocean through dust deposition, river runoff, and mobilization from the sediment. The atmospheric deposition of iron is derived from a climatological dust simulation (Tegen and Fung, 1995). The iron from sediment is recognized as a significant source (Johnson et al., 1999; Moore et al., 2004). This iron source is indeed parameterized in PISCES as, basically, a time-constant flux of dissolved iron (2 µmol m−2 d−1) applied over the whole sediment surface and modulated depending only on depth. A detailed description of this sedimentary source is presented in Aumont et al. (2015). The initial conditions and biogeochemical fluxes (iron, phosphorus, nitrate, etc.) at the boundaries of our domain are extracted from the World Ocean Atlas 2009 (https://www.nodc.noaa.gov/OC5/WOA09/woa09data.html, last access: 12 July 2018).
In this equation, TriC is the carbon Trichodesmium biomass, and the seven terms on the right-hand side represent, respectively, growth, biosynthesis costs based on nitrate and ammonium, mortality, aggregation, and grazing by micro- and mesozooplankton.
Table 1Models parameters for Trichodesmium (Tricho.) and nanophytoplakton (Nano.).
where μFixN2 denotes growth due to dinitrogen fixation, μNO3Tri and μNH4Tri represent growth sustained by NO3- and NH4+ uptake, respectively. Moreover, a fraction of fixed nitrogen is released back to seawater, mainly as ammonia and dissolved organic nitrogen, by the simulated Trichodesmium compartment. Berthelot et al. (2015a) estimated this fraction to be less than 10 % when considering all diazotrophs. We set this fraction at 5 % of the total amount of fixed nitrogen. For the other nutrients (i.e. iron and phosphorus), the same fraction is also released.
Dinitrogen fixation is limited by the availability of phosphate, iron, and light and is modulated by temperature. Loss processes are natural mortality and grazing by zooplankton. Natural mortality is considered to be similar to the other modelled phytoplankton species. Grazing on Trichodesmium is rarely described, but it is admitted that Trichodesmium represents a poor source of food for zooplankton (O'Neil and Romane, 1992) especially because they contain toxins (Hawser et al., 1992). On the other hand, many species of copepods have been shown to be able to graze on Trichodesmium despite the strong concentrations of toxins (O'Neil and Romane, 1992). For these reasons we applied two different coefficients for the grazing preference by mesozooplankton and microzooplankton (Table 1). For microzooplankton, grazing preference is halved to account for Trichodesmium toxicity, and for mesozooplankton the grazing preference is similar to that of the other phytoplankton species. The complete set of equations of Trichodesmium is detailed in Appendix A. Table 1 presents the parameters that differ between nanophytoplankton and Trichodesmium.
This set-up reproduces dinitrogen fixation through an explicit representation of the Trichodesmium biomass (to be compared with the often used implicit parameterizations (Assmann et al., 2010; Aumont et al., 2015; Dunne et al., 2013; Maier-Reimer et al., 2005; Zahariev et al., 2008) that directly link environmental parameters to nitrogen fixation without requiring the Trichodesmium biomass to be simulated).
Below the set of experiments that have been performed in this study is summarized (Table 2). All climatological simulations have been run for 20 years from the same restart and only the last 19 years are considered in our diagnostics. We chose the above-described simulation explicitly modelling the Trichodesmium to be our reference experiment (hereafter referred to as "TRI"). In a second experiment called "TRI_NoFeSed", the model set-up is identical to the reference experiment, except that iron input from the sediments is turned off between 156∘ E and 120∘ W. In a third experiment "TRI_imp", the explicit dinitrogen fixation module is replaced by the implicit parameterization described in Aumont et al. (2015), where fixation depends directly on water temperature, nitrogen, phosphorus, and iron concentrations and light (no nitrogen fixers are simulated). Finally, a fourth experiment "Wo_N2" corresponds to a model set-up in which no explicit nor implicit description of dinitrogen fixation is activated.
Table 2List and description of the different experiments.
Comparison between TRI and TRI_NoFeSed experiments enables us to estimate the impact of iron input from island sediments on the dinitrogen fixation, while the impact of dinitrogen fixation on the biogeochemical conditions in the Pacific Ocean can be investigated by comparing TRI and Wo_N2. Finally, the TRI and TRI_imp experiments are used to evaluate the added value of an explicit description of dinitrogen fixation relative to an implicit inexpensive parameterization.
Figure 1Annual mean concentrations in µmol L−1: (a) PO4 data from CARS (b) PO4 simulated by the ROMS-PISCES model (c) NO3 data from CARS (d) NO3 simulated by the ROMS-PISCES model. On panels (a) and (b), the black contours show the annual mean patterns of the temperature preferendum from observations (a) and the model (b). The red contours display the 25 ∘C isoline in austral winter (plain) and in austral summer (dash). On panels (c) and (d) the red boxes represent the LNLC regions (defined as region where [NO3-]<1 µmol L−1 and [Chl]<0.1 mg Chl m−3).
Several different databases have been used to evaluate the model skills. For nitrate and phosphate, the 0.5∘ global CSIRO Atlas of Regional Seas (CARS; http://www.marine.csiro.au/~dunn/cars2009/, last access: 12 July 2018) has been used. Iron has been evaluated with the global database from Tagliabue et al. (2012) complemented with the dissolved iron data from the OUTPACE cruise (Guieu et al., 2018). This database is a compilation of 13 125 dissolved iron observations covering the global ocean and encompassing the period 1978–2008. The global MARine Ecosytem DATa (MAREDAT; https://doi.pangaea.de/10.1594/PANGAEA.793246) database of N2 fixation has been expanded with data from recent cruises performed in the WTSP: MOORSPICE (Berthelot et al., 2017), DIAPALIS (Garcia et al., 2007), NECTALIS (http://www.spc.int/oceanfish/en/ofpsection/ema/biological-research/nectalis, last access: 12 July 2018), PANDORA (Bonnet et al., 2015), OUTPACE (Bonnet et al., 2018), and Mirai (Shiozaki et al., 2014). This database contains 3079 data points at the global ocean scale, of which ∼ 1300 are located in our simulated region (Luo et al., 2012). Finally, we have used surface chlorophyll concentrations from the GLOBCOLOUR project (http://hermes.acri.fr, last access: 12 July 2018) which spans the 1998–2013 time period.
In this subsection, we aim at validating our reference simulation "TRI" with the data previously presented. In the Pacific, phosphate and nitrate concentrations show maxima in the upwelling regions, i.e. along the western American coast, and in the equatorial upwelling (Fig. 1a, c), and minima in the subtropical gyres. First, phosphate patterns show modelled values and structures in qualitatively good agreement with observations, despite an underestimation in the areas of high concentrations as within the Costa Rica dome and along the Equator. In contrast, the nitrate structure shows larger biases. We observe concentrations higher than 1 µmol L−1 all along the Equator in CARS, while the model nitrate concentrations are lower than this value west of 170∘ W. More generally the model tends to underestimate nitrate concentrations.
Figure 2(a) Box plots of the 0–150 m averaged Iron (nmol Fe L−1) data (blue) and the equivalent for the model (red) co-localized with the observations in space and time. The coloured box represents the 25–75 % quartile of the distribution, the whiskers the 10–90 % percentile distribution. The line inside the coloured box is the median. (b, c) Iron concentrations (nmol Fe L−1) as observed (b) and as simulated by the model (c). Iron concentrations have been averaged over the top 150 m of the ocean. Model values have been sampled at the same location, the same month, and the same depth as the data.
The regions most favourable for Trichodesmium can be defined by temperatures within 25–29 ∘C (Breitbarth et al., 2007). The model reproduces relatively well the spatial distribution of this temperature preferendum. This distribution exhibits a significant seasonal variability, mainly as a result of the variability of the 25 ∘C isotherm. The latter moves by ∼ 7∘ latitudinally between summer and winter in the WTSP, and by ∼ 15∘ in the western tropical North Pacific (WTNP; Fig. 1a). This displacement is well reproduced in the TRI simulation (Fig. 1b). By contrast, along the Equator the mean position of the 25 ∘C isotherm is shifted eastward in the TRI simulation (120∘ W) compared to the observations (125∘ W; Fig. 1a vs. b), but its seasonal displacements are well reproduced except in the southeastern Pacific. Overall, this temporal variability is well reproduced by the model (Fig. 1b), despite this bias. In contrast, nitrate and phosphate seasonal variability remains low (not shown).
Figure 3(a, b, c) Annual mean surface chlorophyll concentrations (in mg Chl m−3) from (a) GLOBCOLOUR data (b) TRI simulation and (c) TRI_imp simulation. Panel (d) shows the annual mean surface chlorophyll concentrations of Trichodesmium in the TRI simulation.
Another important feature that needs to be properly reproduced by the model is the iron distribution in the upper ocean. We have sampled the modelled values at the same time and same location as the data. The median value, as well as the dispersion of the iron surface concentrations over the tropical Pacific, are displayed for both the data and the model in Fig. 2a. The Mann–Whitney test reveals that these two normalized distributions are not significantly different (p value = 0.26). Figure 2b, c display the observed iron field and the modelled values, respectively. The best sampled area is the central Pacific Ocean where simulated iron concentrations are low (0.1 to 0.3 nmol Fe L−1, Fig. 2c), which is consistent with the observations (Fig. 2b). The southwest Pacific is characterized by relatively high surface iron concentrations, between 0.4 and 0.8 nmol Fe L−1, both in the data and in the model. Large scale patterns are thus well represented by the model. Nevertheless, the model tends to overestimate iron levels in the South Pacific Gyre, between 180 and 140∘ W at about 20∘ S.
Figure 4N2 fixation rates (µmol N m−2 d−1) as observed (a, c) and as simulated by TRI simulation (b, d). In panels (a) and (b), N2 fixation rates have been integrated over the top 150 m of the ocean. In panels (c) and (d), the vertical integration has been restricted to the top 30 m of the ocean. Model values have been sampled at the same location, the same month (climatological month vs. real month), and the same depth as the data.
Figure 3 displays a comparison between surface chlorophyll (Chl) concentrations from GLOBCOLOUR data (a), from TRI (b), and TRI_imp (c) simulations. High chlorophyll concentrations are found in the eastern equatorial Pacific upwelling and along Peru in both the observations and our two simulations, with mean values higher than 0.3 mg Chl m−3. However, the equatorial chlorophyll maximum simulated by the model (Fig. 3b, c) is too narrow compared to the observations, especially in the Northern Hemisphere. Similarly, the model is unable to simulate the elevated chlorophyll levels around the Costa Rica dome and the localized enhanced chlorophyll off Papua New Guinea. In TRI (Fig. 3b), chlorophyll values in the southwest Pacific region vary between 0.1 and 0.2 mg Chl m−3, with maxima located in the vicinity of the Fiji and Vanuatu islands. These values are within the range of the data, even if the data tend to be slightly higher (up to 0.3 mg Chl m−3 near the coasts). The spatial structure is well represented, with maxima simulated around the islands. In the subtropical gyres, the simulation predicts chlorophyll concentrations of ∼0.05 mg Chl m−3 which are higher than in the observations (<0.025 mg Chl m−3). In contrast to TRI_imp (Fig. 3c), chlorophyll values in the southwest Pacific and in the Northern Hemisphere are too low in comparison with the ocean colour data (Fig. 3a).
Part of the surface chlorophyll in Fig. 3b is associated with Trichodesmium. Figure 3d shows the annual mean spatial distribution of surface Trichodesmium chlorophyll in the TRI simulation. This distribution displays two zonal tongues in the tropics, one in each hemisphere. Maximum values are located in the southwest Pacific (around Vanuatu archipelago, New Caledonia, Fiji, and Papua New Guinea) and around Hawaii, where they reach 0.06 mg Chl m−3. In the South Pacific, high chlorophyll biomass extends eastward until 130∘ W. Further east, concentrations drop to below 0.02 mg Chl m−3. It is important to note that, in the observations, Trichodesmium have never been observed eastward of 170∘ W. This bias in the model could be explained by the overestimated iron concentrations in the South Pacific Gyre. In the Northern Hemisphere, between the coasts of the Philippines (120∘ E) and Hawaii (140∘ W), Trichodesmium chlorophyll concentrations are greater than 0.03 mg Chl m−3. In the northeast Pacific, Trichodesmium chlorophyll is lower, yet significant (<0.03 mg Chl m−3). Otherwise the equatorial Pacific and southeast Pacific oceans are overall poor in Trichodesmium.
In Fig. 4, the dinitrogen fixation rates predicted by the model in TRI are compared to the observations from the MAREDAT expanded database. Evaluation of the model behaviour remains quite challenging because of the scarcity of the observations. Some large areas are not properly sampled such as the northwest tropical Pacific and the eastern Pacific. In addition, some areas are sampled only in the surface layer (0–30 m), while others have been sampled deeper. This non-homogeneous sampling may alter the distribution of the N2 fixation rates and undermine the comparison with model outputs. To overcome this sampling bias we compared the observations with N2 fixation rates simulated and integrated over two different layers (0–30 and 0–150 m). Despite their scarcity, some regional patterns emerge from the observations. Maximum fixation rates (600 to 1600 µmol N m−2 d−1; Fig. 4a) are observed around the southwest Pacific islands, in the Solomon Sea, around the Melanesian archipelagos, and around Hawaii, four well-known "hotspots" of N2 fixation (Berthelot et al., 2015b, 2017; Bonnet et al., 2009, 2017; Böttjer et al., 2017). The modelled regional patterns of strong fixation are coherent with the observations (Fig. 4b), showing values in the same range. In the South Pacific, the TRI simulation is able to reproduce the strong east–west increasing gradient of N2 fixation (Shiozaki et al., 2014; Bonnet et al., 2018; Fig. 4c, d). In the equatorial central Pacific, modelled values of mean fixation are negligible (<0.5 µmol N m−2 d−1) in contrast to the observations which suggest low but non-negligible fixation rates (between 1 and 2 µmol N m−2 d−1) (Bonnet et al., 2009; Halm et al., 2012). On the whole modelled domain, and for both integration layers, dinitrogen fixation rates are overestimated by 70 % in TRI compared to the data. Some recent studies have shown that the 15N2 tracer addition method (Montoya et al., 2004) used in most studies reported in the MAREDAT database may underestimate N2 fixation rates due to an incomplete equilibration of the 15N2 tracer in the incubation bottles. Thus, this overestimation may be an artifact arising from methodological issues (Großkopf et al., 2012; Mohr et al., 2010). However, some other studies performed in the South Pacific (Bonnet et al., 2016b; Shiozaki et al., 2015) compared the two methods, and did not find any significant differences.
Figure 5Relative contribution (in percentage) of Trichodesmium to total primary production.
Figure 6Trichodesmium biomass (mmol C m−2) in (a) austral summer and (b) austral winter, integrated over the top 100 m of the ocean.
We evaluated the direct relative contribution of Trichodesmium to primary production (PP; Fig. 5). The spatial distribution of this contribution is very similar to the spatial distribution of Trichodesmium chlorophyll, with two distinct tongues located on each side of the Equator in the tropical domain. In the Northern Hemisphere, the tongue extends from the coast of the Philippines (120∘ E) to Hawaii (140∘ W) longitudinally and between 10 and 25∘ N latitudinally. The maximum contribution (35 %) is reached near Hawaii while in the rest of the tongue, values are close to 20 %. In the Southern Hemisphere, the region of elevated contribution extends from PNG (140∘ E) to about the centre of the South Pacific subtropical gyre at 130∘ W, and between 5 and 25∘ S latitudinally. Maximum values are predicted in the vicinity of Vanuatu and Fiji Islands, where they can reach 35 %. Part of this elevated contribution is explained by the very low PP rates simulated in this region for both nanophytoplankton and diatoms (less than 0.03 mol C m−3 yr−1). Furthermore, the island effect seems to represent an important factor for explaining the spatial distribution of Trichodesmium growth rates. Indeed, maximum Trichodesmium chlorophyll concentrations and the largest contribution of Trichodesmium to PP are achieved near the islands. Finally, in LNLC regions (red boxes; Fig. 1c, d), we assess that Trichodesmium contribute to 15 % of total PP, which is in accordance with biogeochemical studies performed in these areas (Bonnet et al., 2015; Berthelot et al., 2017; Caffin et al., 2018).
Figure 7(a) Depth-integrated (0 to 125 m) rates of N2 fixation (µmol N m−2 d−1) at ALOHA for the data (blue) and TRI simulation (red). (b) Depth-integrated (from 0 to 150 m) rates of N2 fixation (µmol N m−2 d−1) in the South Pacific (red box, Fig. 1c) in the data (blue) and in the TRI simulation (red). The blue curve is the average of all the model points inside the South Pacific zone (red box, Fig. 1c), whereas the green curve corresponds to the average of the model points where data are available.
Trichodesmium biomass (Fig. 6) and simulated dinitrogen fixation rates (Fig. 7) display a seasonal variability that is driven by the seasonal variability of the environmental conditions (light, temperature, currents, nutrients). The regional maxima of Trichodesmium biomass (exceeding 3 mmol C m−2; integrated over the top 100 m of the ocean) are found in both hemispheres during the summer season (Fig. 6a, b) even if locally, maxima can be attained during other periods of the year than summer. In the South Pacific, the area of elevated Trichodesmium biomass moves by 3∘ southward from austral winter to austral summer. Along Australia and in the Coral Sea, Trichodesmium biomass exhibits a large seasonal variability with a very low winter biomass that contrasts with elevated values in summer. A similar important variability, which is shifted by 6 months, is simulated in the Northern Hemisphere in the Micronesia region and in the Philippine Sea.
Unfortunately, due to the scarcity of N2 fixation data, this seasonal cycle cannot be properly assessed at the scale of the tropical Pacific Ocean. This is only feasible at the time series station ALOHA located in the North Pacific Gyre at 22∘45′N, 158∘ W, where seasonal data of dinitrogen fixation are available from 2005 to 2012 (Böttjer et al., 2017). They proved that vertically integrated dinitrogen fixation rates are statistically significantly (one-way ANOVA, p<0.01) lower from November to March (less than 200 µmol N m−2 d−1) than from April to October (about 263 ± 147 µmol N m−2 d−1) as highlighted in Fig. 7a (blue dots). In the model (red dots; Fig. 7a), the maximum amplitude of the seasonal cycle appears to be underestimated relative to the observations (i.e. respectively ∼170 and ∼250 µmol N m−2 d−1). Dinitrogen fixation peaks 1 month earlier in the model than in the data (August for the model and September for the data). Indeed, the simulated dinitrogen fixation rates are minimum between December and May (averaging ∼241 ± 27 µmol N m−2 d−1) and maximum the rest of the year (averaging ∼347 ± 52 µmol N m−2 d−1). These values are comparable to the data even if they are slightly higher.
Figure 8Seasonal cycle of the limitation terms of Trichodesmium production in (a) the South Pacific and (b) the North Pacific. The right scale (in brown) represents the total limitation.
In order to assess the seasonal cycle of N2 fixation rates in the South Pacific (red box Fig. 1c; 160–230∘ E, 25–14∘ S), we have extracted the available data for each month from our database (blue dots; Fig. 7b), and the corresponding model values in TRI (red dots; Fig. 7b). In July no observations are available and in January, April, and August only one data point is available for the entire region. The predicted seasonal cycle is broadly consistent with the observations. Minimum dinitrogen fixation rates (239±205 µmol N m−2 d−1) occur during austral winter and autumn. Maximum rates are reached in February and March, where they exceed 600 µmol N m−2 d−1 in the observations. The increase in dinitrogen fixation rates occurs 1 month earlier than in the observations, in December instead of January, and remains 2 to 3 fold higher from April to June. It is important to note here that the sampling spatial and temporal distribution may distort the seasonal cycle. Using the model, it is possible to evaluate how well the seasonal cycle is captured by the sampling (red dots compared to green dots; Fig. 7b). The general structure of the seasonal cycle remains relatively unaltered. However, the amplitude is significantly impacted since it reaches 1100 µmol N m−2 d−1 if sampled at the observed stations, whereas it is about twice as low at 600 µmol N m−2 d−1 if all the model data points are considered. We can conclude that the TRI simulation reproduces well the seasonal cycle of N2 fixation rates at the Pacific scale, even though more data are needed to improve the evaluation of the model skills.
To further investigate the mechanisms that drive the seasonal variability in Trichodesmium in the Pacific, we examined the factors that control Trichodesmium abundance in the TRI simulation (not shown). This decomposition shows that the physical terms (advection and mixing) are negligible compared to biological terms. In addition, the seasonal cycles of grazing and mortality are in phase with the production terms but their sign is opposite. In conclusion, this analysis indicates that this seasonal variability is mainly controlled by the levels of PP, the other terms of tracer evolution dampen its amplitude but do not change its shape. Hence we further examine the limitation terms of PP (Fig. 8) in two representative regions characterized by elevated levels of N2 fixation rates (red boxes; Fig. 1c). A detailed description of these limitation terms is given in Appendix A. A limitation term reaching 1 means that growth is not limited, whereas a limitation term equal to 0 means that growth ceases.
Trichodesmium growth sustained by nitrate and ammonia is very slow in LNLC regions due to their very low availability and is therefore not considered further. Thus, our analysis is restricted to dinitrogen fixation. Trichodesmium growth can be limited by iron and phosphate and is inhibited when reactive nitrogen (nitrate and ammonia) is available. In the WTSP, the model suggests that iron is the sole nutrient that modulates Trichodesmium growth (red curve; Fig. 8a, b). The other limiting factors of Trichodesmium growth are light (green curve; Fig. 8a, b) and temperature (purple curve; Fig. 8a, b). The product of these three limiting factors gives the limiting coefficient of dinitrogen fixation (brown curve; Fig. 8a, b). The limiting factors vary according to the season and the hemisphere. In the South (North) Pacific, temperature and light are less limiting during the austral summer (winter) than during the austral winter (summer). The limiting factor associated with temperature varies from 0.8 to 1, and the light limiting factor varies from 0.15 to 0.3. Unlike light and temperature, iron is less (more) limiting in the South (North) Pacific during winter (summer) than during the austral summer (winter) with values varying between 0.4 and 0.7. Finally, Trichodesmium growth is more limited during austral winter (summer) in the South (North) Pacific. The seasonal variability is forced by light and temperature, and iron mitigates its amplitude. Indeed, nutrients and iron inputs brought to the euphotic zone by the seasonally enhanced vertical mixing are counterbalanced by the related inputs (e.g. temperature) of these water masses.
Figure 9(a, b) Minimum, mean and maximum in the South box (Fig. 1c) of (a) the iron concentrations (in nmol Fe L−1), and (b) of the chlorophyll concentrations of Trichodesmium (in mg Chl m−3). (c, d, e, f) Annual mean iron concentrations (shading; in nmol Fe L−1) and current velocities (vectors; in m s−1) for (c) the TRI_NoFeSed simulation and (d) the TRI simulation. Annual mean chlorophyll concentrations of Trichodesmium (mg Chl m−3) for (e) the TRI_NoFeSed simulation and (f) the TRI simulation. The concentrations have been averaged over the top 100 m of the ocean. The current velocities are identical on the panels (a) and (b).
Monteiro et al. (2011) performed a sensitivity study and found that a 5-fold increase in the solubility of aeolian iron improves the biogeographical distribution of N2 fixation in the southwest Pacific. In the meantime, a recent study has challenged this view by showing no increase in N2 fixation in response to increased dust deposition (Luo et al., 2014). In any cases, the sedimentary and hydrothermal sources were not taken into account in those studies, although they are likely significant sources (Bennett et al., 2008; Johnson et al., 1999; Moore et al., 2004; Tagliabue et al., 2010; Toner et al., 2009). In parallel, Dutkiewicz et al. (2012) evaluated the sensitivity of the biogeographical distribution of N2 fixation to the aeolian source of iron in a model which takes into account the iron sediment supplies, and conclude for minor changes in the southwest Pacific, while in the North Pacific the change was larger. Indeed, there are many islands with a marked orography that could deliver significant amounts of iron to the ocean (Radic et al., 2011) in the southwest Pacific.
Figure 10Percentage increase in primary production between the TRI simulation and the Wo_N2 simulation (a, b) and the TRI_imp simulation (c, d); panels (a) and (c) show total primary production including the contribution of Trichodesmium whereas in panels (b) and (d), primary production only includes the contribution of diatoms and nanophytoplankton.
To assess the impact of the sediment source of iron on the Trichodesmium production, we used the "TRI_NoFeSed" experiment in which this specific source of iron has been turned off for the islands between 156∘ E and 120∘ W (Table 2). In this simulation, iron and Trichodesmium chlorophyll decrease by 58 and 51 %, respectively (Fig. 9a, b), in the WTSP (red box Fig. 1c). Figure 9c displays the iron distribution simulated in TRI_NoFeSed, and shows that the maxima around the islands disappear. Furthermore, in the South Pacific, iron decreases due to the reduction of the zonal advection of iron downstream of the islands. The iron flux from the sediments around the islands also affects the spatial structure of Trichodesmium chlorophyll (Fig. 9e, f), most noticeably in the South Pacific, with maxima shifted from the South Pacific islands region (e.g. Fiji, New Caledonia, Vanuatu) in the TRI simulation to the coastal regions near Australia and Papua New Guinea in the TRI_NoFeSed simulation. In the Northern Hemisphere, the effects of the sediment flux of iron are less important with a shift of the Trichodesmium chlorophyll maxima towards the Philippine Sea and a localized effect near Hawaii. This sensitivity test demonstrates that Trichodesmium are highly sensitive to the iron distribution in our model and hence that the spatial patterns of Trichodesmium chlorophyll in the southwest Pacific are tightly controlled by the release of iron from the coastal sediments of the Pacific islands.
One of the questions we want to address is the quantification of the Trichodesmium impact on PP, at the Pacific scale with a focus on the WTSP region. In the oligotrophic waters of the South Pacific, dinitrogen fixation can be an important source of bio-available nitrogen in the water column through Trichodesmium recycling which can feed other phytoplankton. To evaluate that impact, we calculated the relative increase in PP between the TRI simulation and the Wo_N2 simulation in which no N2 fixation is considered (Fig. 10a). As expected, the spatial structure of the PP differences between both simulations matches the N2 fixation spatial distribution in the TRI simulation (two tongues, one in each hemisphere). In the North Pacific the maximum increase in the PP due to the N2 fixation is located around Hawaii, where it exceeds 120 %. In the remaining part of the Northern Hemisphere tongue, PP increases by 50 to 100 %. In the Southern Hemisphere, values are more homogeneous in the tongue (from 80 to 100 %), even though there is a local maximum around Fiji and Vanuatu (up to 120 %). Out of these northern and southern tongues, the increase in PP is less than 20 %. In average on our domain, the increase in PP is 19 %, and in LNLC regions it reaches approximately 50 %.
From total PP only, it is not possible to disentangle the increase in PP directly due to Trichodesmium themselves and the indirect increase due to the impact of N2 fixation on the other phytoplankton groups (nanophytoplankton and diatoms). Indeed, as mentioned in the Methods section, Trichodesmium also releases a fraction of the recently fixed N2 as bio-available nitrogen (in our model Trichodesmium releases ammonia and dissolved organic nitrogen, but only ammonia is directly bio-available). Figure 10b displays the difference in PP due to diatoms and nanophytoplankton only. The main large-scale patterns constituted of the northern and southern tongues persist, but the intensity of the differences contrasts with those found when considering total PP (Fig. 10a). Indeed, the increase in total PP (Fig. 10a) in those two tongues is twice as high as when the direct effect of Trichodesmium is excluded. This analysis stresses the importance of the bio-available nitrogen released by diazotrophs as we attribute about half of the total production increase to this release. Indeed, recent isotopic studies tracing the passage of diazotroph-derived nitrogen into the planktonic food web reveal that part of the recently fixed nitrogen is released to the dissolved pool and quickly taken up (24–48 %) by surrounding planktonic communities (Berthelot et al., 2016; Bonnet et al., 2016b, a).
With the simulation TRI_imp, we aim at comparing an implicit N2 fixation formulation to the explicit formulation used in TRI. Figure 10c displays the relative change of total PP between the TRI and the TRI_imp simulations (see Table 2). The implicit formulation displays a similar spatial distribution to that of the explicit distribution but it is predicting a lower total PP, especially in the southern Pacific where explicit formulation leads to an increase of about 45 % in total PP compared to the one related to the implicit formulation. On average across our domain, total PP is about 9 % higher when nitrogen fixation is explicitly modelled relative to an implicit formulation.
This difference becomes even weaker (2 %) if only PP by nanophytoplankton and diatoms is considered, with noticeable differences restricted to the areas of maximum N2 fixation in the Southern Hemisphere (around the islands). PP sustained by the release of bio-available nitrogen is thus similar in the TRI and TRI_imp simulations, but an explicit representation of N2 fixation allows for a better description of N2 fixation patterns. Indeed, the areas of intense N2 fixation rates cannot be properly simulated in the vicinity of the islands, especially in the Southern Hemisphere, by the tested implicit parameterization. We also assessed the carbon export (under the euphotic layer, mmol C s−2 d−1; Fig. S1 in the Supplement) and the N2 fixation rate (integrated over the top to 150 m; Fig. S2: panel (a) in µmol N m−2 d−1 and panel (b) in percentage) difference between TRI and TRI_imp simulations. We observe a carbon export greater in the TRI simulation, the average across the Pacific of this difference is 0.1 mmol C m−2 d−1 or 4 %, and in LNLC regions the increase varies between 6 and 10 %. The N2 fixation rates are greater in TRI simulation except in the warm pool, in the equatorial upwelling, and in the Peruvian upwelling.
In this study, we simulate N2 fixation through the explicit representation of only one type of diazotrophs, the Trichodesmium sp. This choice has been motivated by evidences that it represents one of the main nitrogen fixers in the western tropical Pacific (Bonnet et al., 2015; Dupouy et al., 2011; Shiozaki et al., 2014) and by the relatively good knowledge (compared to other dinitrogen fixers) we have about its physiology (Ramamurthy and Krishnamurthy, 1967; Ohki et al., 1992; Mulholland and Capone, 2000; Mulholland et al., 2001; Küpper et al., 2008; Rubin et al., 2011; Bergman et al., 2013). However, our model remains simple and some of the mechanisms that drive the behaviour of Trichodesmium have not been implemented in our model. As an example, the ability of Trichodesmium to group in colonies and to vertically migrate (Kromkamp and Walsby, 1992; Villareal and Carpenter, 2003; Bergman et al., 2013) is well documented. The reason for these mechanisms remains unclear, but several hypotheses have been put forward such as avoiding nitrogenase exposition to di-oxygen (Carpenter, 1972; Gallon, 1992; Paerl et al., 1989), or maximizing light (on the surface) and nutrient (at depth) acquisition (Letelier and Karl, 1998; Villareal and Carpenter, 1990; White et al., 2006), or even increasing the efficiency of the uptake of atmospheric iron (Rubin et al., 2011). Our model does not represent those processes nor does it model the resulting vertical migration of Trichodesmium. Moreover, the release of fixed dinitrogen as reactive nitrogen bioavailable to other phytoplanktonic organisms has been set to a constant value of 5 %. This percentage is known to be highly variable and therefore this value is in the lowest range of the observations. An increase in this value would increase the PP due to nanophytoplankton and diatoms in the TRI simulation, and thus decrease the relative contribution of Trichodesmium to total PP, which would be closer to the last observations (Berthelot et al., 2017; Bonnet et al., 2017).
Some studies, mostly based on extrapolated in situ data, aimed at assessing the potential of N2 fixation at global or regional scale (Codispoti et al., 2001; Deutsch et al., 2001; Galloway et al., 2004). In the south western tropical Pacific, Bonnet et al. (2017) have estimated total N2 fixation at 15 to 19 Tg N yr−1. For the same region, N2 fixation is predicted to amount to ∼7 Tg N yr−1 in the TRI simulation. As already mentioned, this rather low predicted estimate might be explained by the sole representation of Trichodesmium as nitrogen fixing organisms, which dominate in the western tropical South Pacific (Dupouy et al., 2011; Stenegren et al., 2018). It has to be noted that other diazotroph groups such UCYN-B and DDAs are abundant in the WTSP, representing 10–20 % of the overall diazotroph community (Stenegren et al., 2018). Moreover, the contribution of heterotrophic diazotrophic organisms is poorly studied and may account for a significant fraction of N2 fixation (Moisander et al., 2017). Our model estimation has also been computed from monthly averages and is thus not taking into account the high-frequency variability that may explain at least some of the very high rates of N2 fixation found in the study by Bonnet et al. (2017). Our assessment based on a model could thus be seen as a lower limit for N2 fixation in the western tropical Pacific. Moreover, our model also shows a good qualitative agreement with the studies based on observations that focus on the impact of N2 fixation in tropical oligotrophic waters (Raimbault and Garcia, 2008; Shiozaki et al., 2013). Indeed, in agreement with those studies, our reference simulation predicts that diazotrophs support a significant part of total PP (15 %) in LNLC regions.
This study describes the spatial and temporal distribution of Trichodesmium at the scale of the tropical Pacific Ocean, and investigates the impact of a major diazotroph species (e.g. Trichodesmium sp.) on the biogeochemistry of this region. Towards this end, we performed a first 20-year simulation with the coupled 3-D dynamical–biogeochemical model ROMS-PISCES in which we embedded an explicit representation of N2 fixation based on Trichodesmium physiology. This simulation was shown to be able to reproduce the main physical (SST) and biogeochemical (nutrient) conditions of the tropical Pacific Ocean. This includes the spatial distribution of surface chlorophyll and N2 fixation.
The validation of this simulation allows us to confidently assess the Trichodesmium distribution. The model predicts that areas favourable to Trichodesmium growth extend from 150∘ E to 120∘ W in the South Pacific, and from 120∘ E to 140∘ W in the North Pacific, with local optimal conditions around the islands (i.e. Hawaii, Fiji, Samoa, New Caledonia, Vanuatu). This broadly corresponds to the LNLC regions where Trichodesmium are predicted to be responsible for 15 % of total primary production (PP). The seasonal variability of the Trichodesmium habitat is dominantly controlled by SST and light, while iron availability modulates the amplitude of the seasonal cycle.
In our study we also assess the role played by iron released from the island sediments, and show that this iron source partly controls the spatial structure and the abundance of Trichodesmium in the western tropical South Pacific. However, this region is in the centre of the South Pacific Convergence Zone, which is the largest convective area of the Southern Hemisphere, with rainfall exceeding 6 mm d−1, hence it would be interesting to assess the impact of river iron supply on the diazotroph activity. In addition, the Vanuatu archipelago and Tonga are located on the ring of fire, hence hydrothermal sources could have a strong impact on N2 fixation. These two iron sources are not yet implemented in our configuration but may improve simulations of N2 fixation in the southwestern tropical Pacific region. Finally, our explicit simulation of N2 fixation has proven to be higher by 25 % (while still in the lower end of estimations from observations) than the more commonly used implicit parameterization.
where 0.25 d−1 is the maximum observed growth rate (Breitbarth et al., 2007). Hence, at 17 ∘C the growth rate is zero and maximum growth rate is reached at 27 ∘C. The Trichodesmium light limitation is similar to nanophytoplankton (Aumont et al., 2015).
From Eq. (2), we distinguish two cases for the growth rate due to N2 fixation.
In this equation, the first right-hand side term corresponds to photosynthesis, the second term corresponds to respiration, and the third term estimates nitrate and nitrite reduction. The parameters used in this equation are directly taken from Flynn and Hipkin (1999).
The supplement related to this article is available online at: https://doi.org/10.5194/bg-15-4333-2018-supplement.
We thank the ship captains, the scientists, and funding agencies of all the projects (OUTPACE, BIOSOPE, MOORSPICE, DIAPALIS, NECTALIS, PANDORA, Mirai) that allowed data collection without which we could not validate our model. The authors thank the Institute of Research for Development for supporting all authors. Cyril Dutheil is funded by European project INTEGRE.


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Language programs abroad
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Course Plus sport: Language courses abroad for 7-17 year olds
Sport is the perfect complement to language learning. After the morning language classes, there's nothing more stimulating or relaxing (this is up to you!) than exercising in the great outdoors. Moving, running, swimming, riding, gliding… sport is the best way to refresh body and mind!
A great thing about sport is the integration factor: physical activity helps you enjoy the natural landscapes, become one with nature, experience each moment of your trip more intensely and see things from a different angle. Even better, sport allows you to meet other likeminded locals and other students as well. This is the perfect opportunity for speaking the language you're studying in a natural setting.
With ESL, there are countless possibilities to combine sport with language classes abroad. Here are a few options among our range of Course Plus Sport courses:
Course Plus Football
Football is a sport that has spread worldwide, making its way into countries and cultures alike. Taking part in a football summer camp in English or French is your way of setting foot on some of the most legendary stadiums in Europe and re-living football history while learning a language. You'll be able to breathe in the magic of the famous 1998 World Cup in the Stade de France in Saint-Denis or visit the hallowed ground of the FA Cup at Wembley stadium in London. After your program, you'll return home with a better appreciation of the sport you love alongside some new and improved skills
English at London City Campus (13-17), London, England
French at Paris Passy Campus (13-17), Paris, France
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Have the summer of your life with a language tennis camp in the USA, England, France, Spain, Germany or Switzerland! You'll enjoy lessons in the language of your choice, plus hours of professional tennis coaching so you can perfect your backhand, serve and footwork! And, you'll be surrounded by other students who love tennis just as much as you do.
English at King's School Campus (13-17), Canterbury, England
English at Junior King's School Campus (8-12), Canterbury, England
English at Noosa College (13-17), Noosa, Australia
English at Central-Résidence Campus (8-14), Leysin, Switzerland
German at Waldsee Campus (13-17), Freiburg, Germany
German at Am Wannsee Campus (13-17), Berlin, Germany
German at Frankfurt-Lahntal Campus (10-17), Lahntal, Germany
German at Theresianische Akademie Campus (12-17), Vienna, Austria
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French at Central-Résidence Campus (8-14), Leysin, Switzerland
French at Central Résidence Campus (13-17), Leysin, Switzerland
French at Nice Campus (13-17), Nice, France
Spanish at Colegio Calasanz (11–17), Salamanca, Spain
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If you want to take advantage of your summer to perfect both your English language expertise and your skills on the court, an English basketball camp is the perfect choice! Spend time learning English in the classroom, and then get the basketball vocabulary you need to work on your lay ups, jump shots and dribbling.
English at LVS Ascot Basketball summer Campus (10-17) Berkshire, England
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If you're looking for a horse riding summer camp in English, Spanish, French or German, you're in the right place. Take fun and exciting lessons in the language of your choice in the mornings, and then in the afternoons, it's time to ride! You'll gain valuable horsemanship knowledge and spend precious hours in the saddle at the walk, trot and canter, all in total linguistic immersion.
English at Bath University Campus (13-17), Bath, England
English at Brighton Campus (10-17), Brighton, England
English at Central-Résidence Campus (13-17), Leysin, Switzerland
French at Campus Biarritz (13 - 17 years), Biarritz, France
French at Central-Résidence Campus (13-17), Leysin, Switzerland
German at Berlin Werbellinsee German Summer Camp (10-17), Berlin, Germany
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En-garde! If you love fencing and languages, then an English fencing camp is for you! Work on your English language skills in the classroom and then suit up in your fencing uniform to practise your swordsmanship skills under the instruction of professional experts. Fun is guaranteed!
English at King's school campus (8-12), Canterbury, England
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An English summer camp with watersports is a fantastic idea if you're looking to improve your language skills while having the time of your life! You can also choose from Spanish, German, French or Italian summer camps with watersports if you're interested in another language. Keep cool with activities like sailing, kayaking, windsurfing, paddle boarding and more!
English at St Paul's College (10-16), St. Paul's Bay, England
French at Montreux College (13-17), Montreux, Switzerland
French at Campus Biarritz (13-17), Biarritz, France
French at Campus Antibes (8-17), Antibes, France
Spanish at Summer Camp (14-17), San Sebastian, Spain
Spanish at Colegio Benalmadena (13-17), Malaga, Spain
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If you want to spend your school holidays at a diving summer camp in English or French, you've made a great choice! Learn all about your diving equipment, the buddy system, breathing underwater and dive safety practices. Get all the skills you need to explore the world under the sea in full linguistic immersion!
English at St Martin's College (12-17), Salina Bay, Malta
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If you're a snow addict and you're interested in learning German, this ski camp and snowboard camp is for you! In the mornings, you'll work on your language skills in fun and fast-paced German classes, while the afternoons will be dedicated to hitting the slopes on your skis or snowboard. Conquer the black slopes and the German language with this exciting winter camp!
German at Campus Lindenberg (13-17), Lindenberg, Germany
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If you're looking for adventure summer camps, you've made a great choice! Get your adrenaline pumping with an exciting adventure summer camp in total immersion in English, French or German. In the mornings you'll work on your language skills, while the afternoon will be filled with activities like ropes courses, bobsledding, mountain biking and go karting. You'll have the time of your life!
English at Central Résidence Campus (13-17), Leysin, Switzerland
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redpajama_set_name: RedPajamaCommonCrawl


Dental implants have changed completely the therapeutic approach of tooth loss and edentulism in the last 30 years.
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Dental implants are made out of pure Titanium. Titanium has the ability to become one with the bone, this ability is coined as osseointegration.
In modern confrontation of tooth loss the best therapeutic approach is implant placement, versus the conventional bridge that requires healthy teeth to be drilled. An implant can hold the bone as mastication forces that are transferred through it and to the bone stimulate a physiological reconstructive procedure.
Improved speech in implant supported removable dentures.
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Comfort in implant supported removable dentures.
Implants become one with the mouth and there is no need for support with an acrylic palate neither movement in speech, moreover there is no injury during mastication, all kinds of food can be chewed. Thus, self confidence, socialization and quality of life are significantly improved.
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Anyone who has lost one,more or all teeth, is more than 18-20 years old and his general health allows for the performance of a minor surgery.
If there are caries or periodontal disease. some therapies should be made prior implant placement, in order to eradicate any sources of infection that could harm the implants.
When implants are not the solution.
When in the medical and dental history there are reports of congenital blood pathologies, uncontrolled diabetes, some immune conditions and systematic diseases.
Another issue that has risen is a patient under biphosphonate therapy, which is common in women with osteopenia or osteoporosis. The disease itself is not an issue (though special approach is necessary) but the drugs are, so a patient which is under biphosponate therapy should report it, and consulting with the patients doctor possibly stop the therapy for some time before and after implant placement.
Attention in smoking is needed since it favors periodontal and periimplant disease.
We are in a time where big operations give their place to as possibly minimal intervention techniques that will reduce surgical stress.
Necessary prerequisite of success is the correct study of each case.
Our dental office is the only center in Greece recognized from the National Biotechnical Institute.


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Q: Django migrations not pushing to Heroku I have updated a Django blog to use slug urls <slug:slug>/ instead of id <int:pk>/. I also added a slugField to the Article model, and converted all hrefs to article.slug. Ran migrations and everything worked fine locally. However, when I pushed to Heroku I got an error.
This is what happens when I attempt to save a new article.

ProgrammingError at /admin/articles/article/
column articles_article.slug does not exist
LINE 1: ...ticles_article"."id", "articles_article"."title", "articles_...
                                                             ^
Request Method: GET
Request URL:    https://***********.herokuapp.com/admin/articles/article/
Django Version: 2.1.4
Exception Type: ProgrammingError
Exception Value:
column articles_article.slug does not exist
LINE 1: ...ticles_article"."id", "articles_article"."title", "articles_...
                                                             ^

I checked my Heroku Postgress database and I found that the new slug column hadn't been added even though I did migrations. I'm not exactly sure what to do next. I'm currently searching for ways to manually update the heroku postgress, but if there's a less invasive way to solve this problem I'm all ears.

A: first verify that the migration was executed (heroku)
verify that the last migration of the article model is in the django_migrations table

In case you can't find it, make sure you have updated migrations (local)
python manage.py makemigrations

run the migration in db production (heroku)
heroku run python manage.py migrate

A: Did you happen to make migrations first then migrate? It's noted backward in your comment above.  Also, are you sure you applied the migrations to your production database?  It requires a settings flag added to the migrate command to point to your production settings/db location.


Does solar really make sense in New York where the sun hides behind 8′ walls of snow for months at a time? The short answers is YES!! The Long Answer is..
Despite the amazing wind resource in New York, the contours of the land and lack of space in urban areas pose serious siting challenges to wind. Added to that, advancements in the solar technology and a worldwide drop in panel prices have made solar PV (Photo Voltaic) systems very realistic and cost effective.
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Radhakrishnan Nair has been appointed to ICICI Bank's board as an Additional (Independent) Director.
Radhakrishnan Nair is a former IRDAI (Insurance Regulatory and Development Authority of India) member.
His appointment will be for a period of five years and is subject to shareholders' approval.
Radhakrishnan Nair has around 40 years of experience in the banking industry and in the field of securities and insurance regulation.
Question 1. Who has been appointed as the Independent Director of ICICI Bank?
Question 2. Who has been recently appointed as the Chairman of IRDAI?
Question 3. Who is the current Chairman of SEBI?
Question 4. Who is the Chairman of ICICI Bank?


From Goodreads: Glory is from a family of witches. She is desperate to develop the 'Fae' and become a witch herself.
Lucas is the son of the Chief Prosecutor for the Inquisition and his privileged life is very different to the world of witches that he lives alongside.
And then on the same day, Glory and Lucas both develop the Fae. In one fell stroke, Glory and Lucas's lives are inextricably bound together, whether they like it or not . . .


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Cultivating Understanding and Enlightenment
How You Can Help/Contact Us
Up until 2010 we had no utilities on the property except for electricity at the old dairy shed. We would take bottles of water for drinking and cleaning up, but with no toilet facilities we had to resort to the woods — an interesting challenge considering most of the property was open pasture!
At least the Trees for Survival co-ordinator graciously provided us with a porta-loo on the TFS planting days
As you can imagine, it was very inconvenient for us and for the volunteers and staff working on the property with no toilet facilities and no clean water to wash up. This greatly limited the scope of our planting efforts because it was not possible to work for more than a few hours a day. We investigated various options and finally decided in 2009 to build a simple, functional cottage to provide basic amenities to people visiting and volunteering at CUE Haven.
Our cottage was ready for occupation in March 2010. We use the cottage to host volunteers who come to help at CUE Haven, for weekend retreats in the native bush and for community activities.
About Cue Haven
Celebration Plantings
Register of Celebration Plantings
Memorial Tree Plantings
Register of Memorial Plantings
Westpac–1 December 2022
ANZ – Auckland North Business Banking—22 November 2022
Orewa & Whangaparaoa College International Students—21 November 2022
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NorthTec Ecology Students Research —September 22-25 2022
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Unitec Ecology Students – Educational Field Trip – 30 August 2022
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Spark—17 June 2022
Westlake Boys High School—7 & 8 June 2022
The Warehouse Group—3 June 2022
Air New Zealand Data Analytics—27 May 2022
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Barry Cryer and Colin Sell's new comedy show, which was set to take place at The Capitol, Horsham, tonight (November 22), has been cancelled.
"We have just been advised that Barry Cryer, one of the stars of tonight's show Strictly Come Joking, has had a fall and broken his hip," said a Capitol spokesperson.
Visit The Capitol website at www.thecapitolhorsham.com or call the venue on 01403 750220.
Les Miserables, The Capitol, Horsham, review: Ariel performers put on a stunning musical. Click here to read our full review.


Bus accidents can involve complicated state and federal regulations, multiple defendants and insurers, damage caps, shortened statutes of limitations in which to bring a claim and a number of other important legal considerations.
Many bus accident victims are also taken in by so-called "fair settlement" offers soon after the incident, because they do not know what their legal rights are or how serious their injuries may turn out to be. For all these reasons and more, seek the advice and representation of an experienced law firm as soon as possible.
At NordstrandBlack PC, we represent people who have been injured in Southern California bus accidents involving bus company negligence, reckless drivers and a wide range of other common causes. You deserve to have an experienced Santa Barbara injury attorney - not a junior associate - representing you at a time like this. At our firm, you will. We know that this makes a difference.
Bus companies owe a higher duty of care to their passengers than they do to the other drivers and pedestrians they share the road with. If you were injured as a passenger on a bus, talk to our attorneys about your unique legal rights and options for recovery.
Collisions involving a school bus create a number of special concerns that city-bus accidents do not. When a school bus crashes, it is important to look at how the crash occurred, why it happened, how it might have been avoided, and if someone was negligent in creating the situation that caused it.
The driver of the school bus may be to blame, but the cause can be far more complex than that. For example, consider a scenario in which a school bus driver strikes a wall, injuring students on the bus. The immediate reaction would be to blame the driver – but what if the bus driver had to swerve to avoid another vehicle that made an illegal maneuver in front of the bus? What if the brakes failed, and the bus driver did his best to slow down before striking the wall?
These kinds of accidents can be complicated, and emotions often run high. An experienced personal injury attorney can provide an objective point of view to determine who is at fault. Once liability is established, it is easier to get compensation for the victims. There are shorter deadlines to file a claim against a school district, which may have government immunity. Don't wait to talk to a lawyer.
Tour buses are usually owned and operated by private companies. When a city bus or school bus is involved in a collision, the city or school district, both public entities, will investigate what happened. When a tour bus crashes, there may be less incentive for the company to perform an exhaustive investigation.
While there are still statutes of limitations in a tour bus crash claim, a lawsuit against a tour bus operator is typically easier to pursue than one against the city or state. If the company was negligent in any way, perhaps through poor hiring standards or insufficient maintenance, the company is legally responsible to the injury victims.
Bus driver negligence: Bus drivers are susceptible to fatigue, mind-wandering, distraction, and poor judgment while driving this large vehicle. If they did not show due caution for their passengers' safety, they (and their employers) should be liable.
Other driver negligence: A driver in a car who swerves in front of a bus and slams on the brakes without enough space for the bus to stop could cause a serious collision.
Defective parts: Failing parts on a bus, such as the brakes, can also cause an accident. When this occurs, it may be the manufacturer of that part was negligent and is responsible for the crash.
Improper maintenance: Bus operators, whether private or public, are expected to perform necessary inspections and maintenance on all buses to keep them running safely. When this is not done, mechanical failures or defective parts can cause accidents.
Bus company negligence: Even when maintenance is performed, negligence by the company or agency that operates the bus can still cause a collision. If a bus operator fails to perform background checks and hires someone with multiple DUI arrests, who then crashes the bus, it was a foreseeable consequence of their negligence.
Over 25 years of experience and success with vehicle accident lawsuits has proven to us, time and time again, that nothing should be taken for granted - that no two accidents are exactly the same and that no two people are affected by even the same injury in exactly the same way.
Our attorneys work closely with injured victims and families, with medical specialists and with other professionals to ensure that when the time comes - we can tell their story to defense counsel, to a judge or to a jury in clear, compelling terms and see to it that our clients receive appropriate compensation for all of their injuries and losses.
If you or any member of your family has recently been injured in a Metro bus, private charter or school bus accident - call our law offices directly or contact us online. The consultation is free and does not obligate you to hire us. There are no legal fees to pay unless we win.
Santa Barbara Bus Accident Attorney Disclaimer: The legal information presented at this site should not be construed to be formal legal advice, nor the formation of a lawyer or attorney client relationship. Any results set forth herein are based upon the facts of that particular case and do not represent a promise or guarantee. Please contact a lawyer for a consultation on your particular accident matter. This web site is not intended to solicit clients for matters outside of the State of California.


text:
Q: How to find a file containing a string?
Possible Duplicate:
How can I find a file whose name includes a given string, such as "abcde"?

How to find files in terminal that contains the string "foo"?
I tried the following but doesn't work.
find *foo*.*  // any character before or after "foo" of any extension.

I tried googling UNIX or bash wild card but doesn't provide me any useful result.

A: sorry I didn't understand the question.
You should try
find . -name '*foo*'

as specified above

A: Comment from @ire_and_curses would be almost correct, but it will not find any files like Foo,fOo, ... This would be better in your case:
from find man page:
-iname pattern
     Like -name, but the match is case insensitive.

find <path> -iname '*foo*'