Well, first, let me say that my first mistake was mixing psychoactives, when I knew that it was not wise. The night would have happened alot differently if I had of exercised better judgement.
I live in a university dorm, and that is where my experience took place. It was a Sunday night. I don't have monday classes, and I decided that I would purchase some mushrooms, and eat 1/16 of an ounce. I also had beer stocked up in my room, as well as marijuana.
At around 5pm, I purchased 1/2 ounce of mushrooms from a guy I knew and had bought from before. At 6pm, I dropped my car off at it's designated off campus parking spot, and ate roughly 1/16 ounce of the mushrooms (they were weighed out into 'eighths' in seperate bags) while I waited for a shuttle bus to arrive to take me back to campus. By 7pm, I was feeling the beginnings of a normal 'trip.' I was hanging out in my room with my roommate, and we had a few beers each. A friend of mine from down the hall asked if I was willing to go down there and smoke with him and his roommate if they paid me, since they were out of pot and could not find anymore.
 As well as bringing these items, I had the remainder of my 1/2 ounce of shrooms in my pocket. By this point I was tripping pretty good, had a bit of an alcohol induced haziness. I drank the beer I had brought, and part of one that they had in their room, and after hanging out for around 30 minutes (so this would be around 8:30pm) I handed my buddy my pot and told him to pack up a bowl. We continued smoking for about two hours, on an occasional basis. Needless to say, I became intensely 'high' from the marijuana, and this altered my trip.
At this point, my trip felt like the greatest thing I had ever experienced, and I wanted it to just keep going. At some point I reasoned that I should start eating a bit more, to add to it, or something. Obviously, my judgement was a bit warped. So, we were sitting around hanging out, and I was casually eating a shroom every now and then. By midnight or so, I reached into my pocket, and realized that there were no more shrooms, and realized (in a sort of passive manner) that I had eaten all of the 1/2 ounce. I thought on it for only a moment, because I was feeling great still, and then in a sort of 'oh well' way, shrugged it off and kept on watching tv.
By around 1am, things started to get pretty intense, and I was thinking in sort of paranoid ways. We were watching 'Anchorman' and I remember suddenly having a profound 'realization' that it was a brainwashing tool, and then I started to suspect that all of the people in the room were in on it. I knew it wasn't logical, as I was still somewhat tied to reality, so I didn't say anything about that, but I told my friends that I was a bit burnt out on the movie, and was heading back to my room. I walked down the hall and noticed some difficulty controlling my motor functions (I was leanin against the wall as I walked down the hall).
I got back into my room, and my roommate had been tripping also, on the normal dosage of 1/16 ounce of mushrooms. I told him I was feeling a bit odd, that I had eaten a half of an ounce, and that I was just going to lay down. While talking, I noticed a sense of being seperate from myself, and my talking seemed involuntary, and muffled, like I was underwater. My roommate said something or another about me looking pale and like I'd jumped out of the shower (talking to him after the fact, and from other people I encountered when things really went bad, I was pale white, and my hair was soaked with sweat as were my clothes). I didn't really reply.
On the way over to my bed, I fell down. I wasn't able to control my body it seemed, and I felt like I was several different entities discussing the situation and observing. Apparently, my roommate was talking to me, and I was just repeating 'help' in a quiet, monotone voice, like a robot. I felt really, really frightened. Yet at the same time, it was almost as if I was watching the fright, and seperate from myself. After a few minutes, I reached a point of clarity where it seemed as if the several entities I was split into were logically evaluating the situation (the conclusion was that my body was dying) and we (I) were ok with that, sad, but ok.
At some point I began to ramble off in the robotic voice instructions to my roommate, telling him to get sober people to get me out of the dorm, and then call an ambulance. My roommate was reasonably upset, and tripping, and he didn't remember those instructions. He couldn't figure out 911 (I know, funny and cliche, what's the number to 911?) but somehow managed to call the campus police number. So, the campus police arrived, and called an ambulance because I was going in and out of conciousness. I remember vaguely being carried into the ambulance and hospital.
At this point, I obviously wasn't aware of the time too much, but I peaked in the hospital. I ceased to really exist mentally in this world. It is hard to explain, and makes no logical sense, but I was in some other 'world', that seemed more or less like this one, but cleaner, better. Language... I just knew, felt what they were communicating.
After a couple of hours (real world) I became peaceful somehow. Not only was it a different place, but I did not have a body, I just seemed to exist as a part of this place. Also, my perception of time there seemed as if I had been there for many years. I remember a seperate life. I was convinced that I had died, and this was just the place that I was supposed to go. Throughout it all, entities would communicate with me.
I began to slip out of this other world, and it was an equally frightening experience. I felt like I was being ripped out of my afterlife. I eventually came to realize I was in a hospital, and was very unsure of how long I had been there, because to my mind, years had literally come to pass. I was very confused, and I had trouble communicating. I returned to the normal state of tripping gradually, and eventually it did end. Around 11am I was able to stand up and move about, and I checked out of the hospital.
It was a terribly frightening experience, and I don't feel like I'm the same person I was before. Also, this was only 3 weeks ago, and I find myself from time to time sliding back into a sense that this is not real, or that I am perhaps still tripping, because mentally, it seems like I was in the mushroom induced reality many, many times longer than I have been back in this reality. I would never repeat the experience, and would not suggest it to others, but I don't completely regret it either. I appreciate life so much more, and am optimistic about everything, because I remember what it felt like to 'know' I was dying/dead. I realized how much of my personal potential I have been wasting.
The event carried several real world consequences. One, I paid $100 dollars for that 1/2 ounce of mushrooms, and that was supposed to last for several different occassions, usually that will last me atleast a month, if not much longer. Two, I am suspended from the university for a semester because neither I or my roommate were in a sound enough state of mind to handle the situation better. Three, hospitals are expensive, and I have a $1000 dollar deductible, plus 20% of the costs are my responsibility beyond that initial $1000. I will be paying off this summer.
Also, if the campus police had wanted to, they could have royally fucked me, since they did 'search the room,' but did not document anything except the fact that I had shrooms and had a bowl in one of my desk drawers. They ignored the fact that I had marijuana in that same drawer, Adderall in my pocket, at least two cases of beer (all of this was left in the room too, oddly enough), and a vaporizer sitting out in the middle of the room. They did me a huge favor by not involving the state police, because then I would be dealing with a Federal legal process at some point.
I'm pretty sure that had they known (they were fairly ignorant about the severity of mushroom possesion and use) mushrooms were Schedule II, they wouldn't have even reported me that. I say that, because the next day, I called the main officer I had dealt with to get the details of what I was being charged with by the school, and what punishments they carried, and he was like 'Well, the shrooms will be a fine and community service, just like you would get for that marijuana if I had of reported it.' So, while I would rather that I hadn't dealt with the police at all, the officers did help me as much as they could, since I previously have a clean record. I could have been expelled from the university for the mushrooms, but because I cooperated, have great grades and agreed to drug counseling and a piss test prior to my reentrance to the university, I was only suspended for next semester, and will be allowed to finish this semester out.
Overall, the consequences were not worth it. The experience was not fun at all, even once I started to come down (because an emergency room with hurt and dying people is not what you want to be around when you're tripping). It was enlightening in many senses of the word, and I do value that, but it is not anything I would recommend to anyone, because I still feel some sense of uncertainty about even the basic aspects of who I am that have arisen from the experience. I'm trying to make the best of the aftermath, and learn from my mistake, but it was a very emotionally, spiritually breaking experience, and it'll be a good while before I put my life back together.
Zero - Tuesday, April 26, 2005
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


--- layout: post title: Collaborative Artifacts as Code date: 2012-11-12 17:42:19.000000000 +01:00 type: post published: true status: publish categories: - Bonnes pratiques de dév - Programmation tags: - Alberto Brandolini - artifact - bdd - collaboration - devops - diagram - documentation - impact mapping - infrastructure as code - markdown - module - plain text - project - readme - scm - software factory meta: _edit_last: '1' _activeshortener: tiny _twitterrelated_short_url: http://tinyurl.com/c84v9rz _twitterrelated_short_urlHash: b8967247e7640db792818d8848f44beb sfw_pwd: GYZXHHwXlbMU author: login: cyrille martraire email<EMAIL_ADDRESS>display_name: Cyrille first_name: Cyrille last_name: Martraire ---
A software development project is a collaborative endeavor. Several team members work together and produce artifacts that evolve continuously over time, a process that Alberto Brandolini (@ziobrando) calls Collaborative Construction. Regularly, these artifacts are taken in their current state and transformed into something that become a release. Typically, source code is compiled and packaged into some executable.
The idea of Collaborative Artifacts as Code is to acknowledge this collaborative construction phase and push it one step further, by promoting as many collaborative artifacts as possible into plain text files stored in the same source control, while everything else is generated, rendered and archived by the software factory.
Collaborative artifacts are the artifacts the team works on and maintains over time thanks to the changes done by several people through a source control management such as SVN, TFS or Git, with all their benefits like branching and versioning.
The usual way of storing documentation is to put MS Office documents into a shared drive somewhere, or to write random stuff in a wiki that is hardly organized.
Either way, this documentation will quickly get out of sync because the code is continuously changing, independently of the documents stored somewhere else, and as you know, “Out of sight, out of mind”.
Over the last few years, there has been changes in software development. Github has popularized the README.md overview file written in Markdown. DevOps brought the principle of Infrastructure as Code. The BDD approach introduced the idea of text scenarios as a living documentation and an alternative for both specifications and acceptance tests. New ways of planning what a piece of software is supposed to be doing have appeared as in Impact Mapping.
All this suggests that we could replace many informal documents by their more structured alternatives, and we could have all these files collocated within the source control together with the source.
In any given branch in the source control we would then have something like this:
Nobody creates software by editing directly the executable binary that the users actually run at the end, yet it is common to directly edit the MS Word document that will be shipped in a release.
Collaborative Artifacts as Code suggests that every collaborative artifact should be text-based to work nicely with source control, and to be easy to compare and merge between versions.
Text-based formats shall be preferred whenever possible, e.g. .csv over xls, rtf or .html over .doc, otherwise the usual big PPT files must go to another dedicated wiki where they can be safely forgotten and become instantly deprecated…
My colleague Thomas Pierrain summed up the benefits of this approach, for a documentation:
This approach is nothing really new (think about LateX…), and many of the tools we need for it already exist (Markdown renderers, web site to organize and display Gherkin scenarios…). However I have never seen this approach fully done in an actual project. Maybe your project is already doing that? please share your feedback!


By design view proxies must not return temporary objects. They return reference to an argument they take by (const) reference, cast to reference of unspecified type that is not copyable. Because of this, the return value of a view proxy can not be captured by auto:
auto tr=transposed(m); //Error: the return type of transposed can not be copied.
The correct use of auto with view proxies is:
auto & tr=transposed(m);
many view proxies are not read-only, that is, changes made on the view proxy operate on the original object. This is another reason why they can not be captured by auto by value.
The operator overloads in namespace boost::qvm are designed to work with user-defined types. Typically it is sufficient to make these operators available in the namespace where the operator is used, by using namespace boost::qvm. A problem arises if the scope that uses the operator is not controlled by the user. For example:
namespace ns1
{
struct float2 { float x, y; };
}
namespace ns2
{
using namespace boost::qvm;
void f()
{
ns1::float2 a, b;
a==b; //OK
ns1::float2 arr1[2], arr2[2];
std::equal(arr1,arr1+2,arr2); //Error: operator== is inaccessible from namespace std
}
}
In the std::equal expression above, even though boost::qvm::operator== is made visible in namespace ns2 by using namespace boost::qvm, the call originates from namespace std. In this case the compiler can't bind boost::qvm::operator== because only namespace ns1 is visible through ADL, and it does not contain a suitable declaration. The solution is to declare operator== in namespace ns1, which can be done like this:
namespace ns1 { using boost::qvm::operator==; }
Boost QVM does not call standard math functions (e.g. sin, cos, etc.) directly. Instead, it calls function templates declared in boost/qvm/math.hpp in namespace boost::qvm. This allows the user to specialize these templates for user-defined scalar types.
Boost QVM itself defines specializations of the math function templates only for float and double, but it does not provide generic definitions. This is done to protect the user from unintentionally writing code that binds standard math functions that take double when passing arguments of lesser types, which would be suboptimal.
Because of this, a call to e.g. rot_mat(axis,1) will compile successfully but fail to link, since it calls e.g. boost::qvm::sin<int>, which is undefined. Because rotations by integer number of radians are rarely needed, in QVM there is no protection against such errors. In such cases the solution is to use rot_mat(axis,1.0f) instead.
Due to a MSVC 2013 bug, expressions of the form (v,A<I>) used to access vector elements as well as (m,A<R,C>) used to access matrix elements do not parse correctly. A workaround is to add parentheses, for example (v,A<I>()). It is unknown if other versions of MSVC have the same issue.
The corresponding non-template syntax used to access specific elements, e.g. (m,A31) or (v,Y) is not affected by this issue.


In addition to control by major nutrient elements (nitrogen, phosphorus, and silicon) the productivity and species composition of marine phytoplankton communities are also regulated by a number of trace metal nutrients (iron, zinc, cobalt, manganese, copper, and cadmium). Of these, iron is most limiting to phytoplankton growth and has the greatest effect on algal species diversity. It also plays an important role in limiting di-nitrogen (N
Life in the ocean is dependent on fixation of carbon (C) and nitrogen (N) by planktonic microalgae, ranging in diameter from <1 to >100 μm. These “phytoplankton” consist of eukaryotic algae, which photosynthetically fix carbon dioxide (CO
In this review, I will discuss interactions between trace metal nutrients [iron (Fe), zinc (Zn), cobalt (Co), manganese (Mn), copper (Cu), nickel (Ni), cadmium (Cd), and molybdenum (Mo)] and phytoplankton in the ocean. These interactions involve not only the effect of the metals on the growth and species composition of phytoplankton communities, but also the profound effect of marine plankton on the distribution, speciation chemistry, and biological availability of these nutrient metals (Figure 1). There are many aspects to consider in these interactions, including (1) the distribution of metal nutrients in the ocean on various temporal and spatial scales; (2) the sources, sinks, and cycling of metals; (3) metal speciation and redox cycling, (4) the influence of these metals on phytoplankton metabolism and growth at different levels of biological organization (molecular, cellular, population, community, ecosystem, ocean/earth system), and (5) the influence of phytoplankton and the planktonic community as a whole on the chemistry and cycling of metal nutrients in the ocean.
Conceptual diagram of the mutual interactions between trace metal nutrients (Fe, Mn, Zn, Co, Cu, Mo, and Cd) and phytoplankton in the sea . In these interactions the chemistry of trace metal nutrients (their concentrations, chemical speciation, and redox cycling) regulate the productivity, species composition, and trophic interactions of marine phytoplankton communities. These communities in turn regulate the chemistry and cycling of the trace metals through cellular uptake and assimilation, vertical transport of biogenic particles (intact cells and fecal pellets), mediation of metal regeneration processes (by grazers, bacteria, and viruses), production of organic chelators, and biological mediation of metal redox cycling.
The distribution patterns of trace metal nutrients in the ocean have a profound influence on phytoplankton communities. Concentrations of filterable Fe and Zn (that which passes through a 0.2 or 0.4 μm-pore filter) are often extremely low (0.02–0.1 nM) in surface open ocean waters (Bruland, 1980 ; Martin et al., 1989 ; Johnson et al., 1997 ). Filterable concentrations of Cd, a nutrient analog for Zn, can reach values as low as 0.002–0.004 nM in surface waters of the North Pacific and Atlantic Oceans (Bruland, 1980 ; Bruland and Franks, 1983 ; Table 1 ). Filterable levels of these and other trace metal nutrients can increase by orders of magnitude in surface transects from the open ocean to coastal and estuarine waters owing to metal inputs from continental sources: rivers, ground water, aeolian dust, and coastal sediments (Bruland and Franks, 1983 ; Sunda, 1988/89 ). Filterable Fe can reach micromolar levels in estuaries and 10–20 μM in high humic rivers, 1000 to 10,000-fold higher than concentrations in surface ocean waters. Filterable iron in rivers occurs largely as colloidal particles (0.02–0.4 μm diameter), which are rapidly lost from estuarine and coastal waters via salt-induced coagulation and particulate settling (Boyle et al., 1977 ). Because of this efficient removal, much of the iron in rivers is deposited in estuarine and coastal sediments, and little directly reaches the open sea. However, reducing conditions in organic-rich shelf and margin sediments can re-mobilize some of the river-derived particulate Fe via reduction to soluble Fe(II), and thereby provide an important Fe source to the ocean (Moore and Braucher, 2008 ). Another equally if not more important source of iron to the ocean is aeolian deposition of iron-containing mineral dust transported by the wind from arid regions (Duce and Tindale, 1991 ; Jickells et al., 2005 ). These aeolian inputs change seasonally with variations in rainfall and prevailing winds and are highest in waters downwind of deserts (Measures and Vink, 1999 ; Jickells and Spokes, 2001 ). Regions far removed from aeolian and sedimentary continental sources, such as the South Pacific and Southern Ocean receive low external iron inputs and are among the most iron-limited areas of the oceans (Martin et al., 1990 ; Behrenfeld and Kolber, 1999 ; Coale et al., 2004 ). Other external sources such as glaciers/iceberg melt, seasonal sea ice melting, island wakes, volcanism, and hydrothermal activity can provide important local inputs of iron and other metals (Boyd and Ellwood, 2010 ).
Organic complexation of Fe, Cu, Zn, and Cd in filtered (0.4 μm) surface and deep waters of the Northeast Pacific Ocean (n.d. – not detected, n.c. – not computed) .
| Metal | Depth (m)(Obs.) | Total M (nM) | L1 (nM) L2 (nM) | Log K
L1,M′ Log K
L2,M′
|
−log [M′] | Percent total metal | Reference | ||
|---|---|---|---|---|---|---|---|---|---|
| (M′ | ML
1
|
ML
2
|
|||||||
| Fe | 20–300 | 0.22 ± 0.07 | 0.48 ± 0.07 | 13.04 ± 0.16 | 13.2 ± 0.2 | 0.03 ± 0.01 | 86.5 ± 2.7 | 13.5 ± 2.7 | Rue and Bruland ( 1995 ) |
| (6) | 1.47 ± 0.07 | 11.49 ± 0.10 | |||||||
| 500–2000 | 0.72 ± 0.05 | n.d | n.d. | 12.0 ± 0.2 | 0.15 ± 0.06 | n.d | 99.8 ± 0.1 | Rue and Bruland ( 1995 ) | |
| (3) | 2.57 ± 0.21 | 11.58 ± 0.16 | |||||||
| Cu | 25–120* | 0.53 ± 0.07 | 1.77 ± 0.56 | 11.58 ± 0.30 | 11.9 ± 0.2 | 0.31 ± 0.24 | 98.8 ± 0.9 | 0.9 ± 0.7 | Coale and Bruland ( 1990 ) |
| (18) | 5.7 ± 2.8 | 8.72 ± 0.46 | |||||||
| Zn | 22–200 | 0.23 ± 0.07 | 1.15 ± 0.19 | 10.66 ± 0.13 | 11.3 ± 0.3 | 2.6 ± 1.0 | 97.4 ± 1.0 | n.d. | Bruland ( 1989 ) |
| (9) | |||||||||
| 600 | 4.77 | n.c. | n.c. | 8.5 | 73.4 | 26.6 | n.d. | Bruland ( 1989 ) | |
| (1) | |||||||||
| Cd | 22–100 | 0.003 ± 0.001 | 0.08 ± 0.03 | 10.40 ± 0.22 | 12.0 ± 0.2 | 36.6 ± 12.5 | 63.4 ± 12.5 | n.d. | Bruland ( 1992 ) |
| (8) | |||||||||
| 600 | 0.78 | n.d. | n.d. | 9.0 ± 0.0 | 100 | n.d. | n.d. | Bruland ( 1992 ) | |
| (2) |
*Near-surface values only are given because of a potential problem with the differential pulse anodic stripping titration data in the deep water samples (Moffett and Dupont, 2007 ).
Because of the much lower trace metal concentrations in the open ocean relative to those in coastal waters, oceanic algal species have evolved the ability to grow at much lower external and intracellular concentrations of Fe, Zn, and Mn (Brand et al., 1983 ; Brand, 1991 ; Sunda and Huntsman, 1986 , 1992 , 1995a , b ). In doing so they have been forced to rearrange their metabolic architecture (e.g., by decreasing the abundance of iron-rich protein complexes [photosystem I and the cytochrome b
Residence times of trace metal nutrients in the ocean vary over a wide range – from 20 to 40 years for Mn (Landing and Bruland, 1987 ; Bruland et al., 1994 ) to ∼800,000 years for Mo (Emerson and Huested, 1991 ; Morford and Emerson, 1999 ). Concentrations of Zn, Cd, Ni, and Cu, which have intermediate to long residence times (3000–100,000 years; Bruland and Lohan, 2003 ) relative to the average ventilation time of ocean water (∼1000 years for deep ocean water), increase with depth, similar to increases observed for major nutrients (nitrate, phosphate, and silicic acid; Figures 2 and 3). In the northeast Pacific, filterable concentrations of Zn and Cd increase by 80-fold and 400-fold, respectively, between the surface and 1000 m (Bruland, 1980 ). The similarity between vertical profiles of trace metal nutrients and those of major nutrients suggest that both sets of nutrients undergo similar biological uptake and regeneration processes in which each is taken up by phytoplankton in sunlit surface waters and are thereby efficiently removed from solution. Much of these assimilated metals and major nutrients are recycled within the euphotic zone by the coupled processes of zooplankton grazing and excretion, viral lysis of cells, and bacterial degradation of organic materials (Hutchins et al., 1993 ; Hutchins and Bruland, 1994 ; Poorvin et al., 2004 ; Strzepek et al., 2005 ; Boyd and Ellwood, 2010 ). However, a portion of the assimilated metals and major nutrients is continuously lost from the euphotic zone by vertical settling of biogenic particulate matter, including intact algal cells, zooplankton fecal pellets, and organic detritus. The macro- and micro-nutrients contained in these settling biogenic particles are then returned to solution in non-lit deeper waters by bacterial degradation processes, with the rate of this regeneration decreasing exponentially with water depth. Over time the uptake, settling, and regeneration processes deplete trace metal and major nutrients in sunlit surface waters to low levels and increase concentrations at depth at elemental ratios equal to average values in phytoplankton. This set of processes also transfers CO
Depth profiles for major nutrients (A–C) [nitrate (Pacific only), phosphate, and silicic acid] and filterable concentrations (that passing a 0.4-μm filter) of trace metal nutrients (D–H) (Zn, Cd, Ni, Cu, and Mn) in the central North Pacific ( , 32.7°N, 145.0°W, September 1977) and North Atlantic ( , 34.1°N, 66.1°W, July 1979; Bruland and Franks, 1983 ) . Manganese concentrations in the Pacific were analyzed in acidified, unfiltered seawater samples.
Depth profiles for (A) phosphate and filterable concentrations of trace metal nutrients (B–D) (Fe, Zn, and Co) in the subarctic North Pacific Ocean (Ocean Station Papa, 50.0°N, 145.0°W, August 1987; Martin et al., 1989 ) and the Northeast Atlantic (40°N, 20°W, May 1989; Martin et al., 1993 ) .
Based on the above dynamics, the plots of trace metal nutrients with longer residence times (Cu, Zn, Ni, and Cd) vs those of major nutrients (e.g., phosphate) should have slopes equal the average ratios of trace metals to major nutrients in marine plankton. Such behavior was previously demonstrated for depth dependent plots of nitrate vs phosphate concentrations in which the slope of these relationships (16 mol mol −1) equaled the average N:P measured in ocean plankton (Redfield et al., 1963 ). Similar behavior has been observed for plots of Zn, Cd, Ni, and Cu vs phosphate, but with several caveats (Martin et al., 1976 ; Sunda and Huntsman, 1992 , 1995c , 2000 ; Croot et al., 2011 ; Figure 4). In depth profiles for the northeastern Pacific (Figure 2), the concentrations of three of the metals (Ni, Cu, Cd) within the nutricline are linearly related to those of phosphate, and for Cu and Cd, the metal:P slopes (or equivalent metal:C ratios) agree well with values measured in net plankton samples or in algae cultured at the concentration of unchelated metal [M′] (or other controlling metals in the case of Cd) observed in the sunlit surface layer (Figures 4B,C; Table 2 ). However, unlike N vs P plots, these relationships have positive y-intercepts for Cu and Ni, indicating that these metals are not completely “used up” biologically in N- and P-depleted surface waters. By contrast, the Zn:P relationship exhibits increasing slopes with increasing Zn concentrations and a negative y-intercept for a linear regression of Zn vs P (Figure 4A; Table 2 ). Here the Zn:P slope (and associated Zn:C molar ratio) in the productive surface layer (0–185 m) agrees with Zn:P and Zn:C values for marine algae grown at the measured [Zn′] range within the surface layer (Tables 1 and 2 ; Figure 5) and the Zn:C ratio (3.7 μmol mol −1) in phytoplankton growing in near-surface seawater in the northeast Pacific (Lohan et al., 2005 ). However at greater depths (185–800 m), the Zn:P slope and associated Zn:C ratio (22 μmol mol −1) is similar to average values for phytoplankton growing at the much higher [Zn′] range observed at depth (Sunda and Huntsman, 1992 ; Table 2 ; Figure 5).
Comparison of slopes of nutrient metal to phosphate plots for station H-77 (Bruland, 1980 ) in the northeast Pacific with metal:P ratios in net plankton (Martin et al., 1976 ) and metal:C ratios in cultured marine algae .
| Metal | Depth range (m) | P range (μM) | M vs P slope (mmol mol −1) | Intercept (nM) | R 2 ( n) | M:P of net plankton (mmol mol −1) | M:C from slopes* (μmol mol −1) | M:C in cultured algae |
|---|---|---|---|---|---|---|---|---|
| Zn | 185–780 | 0.81–3.27 | 2.32 ± 0.06 | −1.62 ± 0.14 | 0.998 (4) | 1.9 ± 1.6 | 22.2 ± 0.6 | |
| 0–185 | 0.06–0.81 | 0.33 ± 0.04 | 0.07 ± 0.02 | 0.987 (3) | 3.1 ± 0.4 | 3.7(2.2–5.5) † | ||
| Cd | 75–975 | 0.18–3.33 | 0.32 ± 0.01 | −0.06 ± 0.03 | 0.994 (6) | 0.43 ± 0.14 | 3.0 ± 0.1 | 2.5–4.1 ‡ |
| Ni | 0–780 | 0.06–3.27 | 1.93 ± 0.10 | 2.36 ± 0.20 | 0.989 (6) | 0.47 ± 0.26 | 18.2 ± 1.0 | |
| Cu | 0–975 | 0.06–3.33 | 0.44 ± 0.02 | 0.57 ± 0.04 | 0.992 (7) | 0.41 ± 0.13 | 4.2 ± 0.2 | 4.4 ± 0.6 § |
*Based on a C:P for marine plankton of 106 (Redfield et al., 1963 ) .
†Mean and range based on mean and range of [Zn′] computed from the Zn chelation data of Bruland( 1989 ; see Table 1 ) and the Zn:C vs [Zn′] relationship for the oceanic diatom Thalassiosira oceanica (Figure 5) .
§Based on the mean Cu:C in the diatoms T. oceanica and T. pseudonana, and the coccolithophore Emiliana huxleyi at the mean log [Cu′] in northeast Pacific ocean water (−11.8, see Table 1 ; Sunda and Huntsman, 1995c ) .
Plots of concentrations of (A) Zn, (B) Cd, (C) Ni, and (D) Cu vs phosphate within the nutricline of the northeast Pacific (upper 800–1000 m) at stations H-77 and C-I (Bruland, 1980 ) .
Cellular Zn:C vs log [Zn′] in the oceanic diatom Thalassiosira oceanica, the coastal diatom T. pseudonana, and the oceanic coccoclithophore Emiliania huxleyi in seawater at 20°C based on data from Sunda and Huntsman ( 1995a ) . These results are compared with the mean and range (errors bars) of Zn:C measured at the same temperature and a single [Zn′] in 15 different species of marine eukarotic phytoplankton from five major algal groups (Ho et al., 2003 ). The log [Zn′] range for ocean water is shown based on data of Bruland ( 1989 ) (Table 1 ).
The non-linearity of the Zn vs P plots likely reflects the very large variation in [Zn′] in ocean waters and the associated variation in Zn:P and Zn:C ratios in phytoplankton growing in these waters (Sunda and Huntsman, 1992 ). Although waters with high [Zn′] are usually found below the photic zone, and thus, cannot influence the removal of metals by algal growth and uptake, thermohaline and wind-driven upwelling can bring these high-nutrient, high-zinc waters to the surface, especially in polar regions. As a consequence of these processes, filterable Zn in surface waters of the Atlantic sector of the Southern Ocean increased from 0.5 nM at a latitude of 46°S to 4.5 nM at 66°S along with increases in phosphate and nitrate (Croot et al., 2011 ). The [Zn′] in surface waters increased to as high 1.8 nM, as the zinc concentration exceeded that of strong Zn binding ligands (Baars and Croot, 2011 ), a value ∼400-fold higher than mean surface [Zn′] in the North Pacific (Table 1 ). Subsequently, the lateral changes in nutrient and metal concentrations in these near-surface waters caused by algal uptake, biogenic particulate settling, and net regeneration in deeper waters are transposed to deeper depths and more northern latitudes with downwelling and lateral advection, which is most intense during winter (Redfield et al., 1963 ). In this way near-surface processes of algal uptake, settling, and shallow water regeneration can influence the composition and vertical structure of major and trace metal nutrients in deep ocean waters worldwide. Indeed, at depths at and just above the phosphate maximum in the Atlantic Ocean (600–800 m; see Figures 2B and 3A), about half of the phosphate occurs as “preformed phosphate,” which was present when the water originally subducted from the surface to form the Antarctic Intermediate water layer, while the remainder occurs as regenerated phosphate (Redfield et al., 1963 ). Likewise, only a portion of the trace metal nutrients at depth are likely derived from regeneration of settling biogenic particles, while the remainder must occur as “preformed metal nutrients.”
The deep water concentrations of both major nutrient elements (N, P, and Si) and many longer-lived metal micro-nutrients (Zn, Cd, Ni, and Cu) are two- to fivefold higher in deep waters of the North Pacific than the North Atlantic (Figures 2 and 3) because of large scale differences in the thermohaline circulation patterns between the Atlantic and Pacific oceans. In the Pacific the major water inflow occurs at depth where concentrations of nutrients and longer-lived nutrient metals are high (Broecker, 1991 ). Consequently, the Pacific acts as a nutrient trap and concentrates high levels of nutrients and longer-lived nutrient metals in its deeper waters (Broecker, 1991 ). The Atlantic, by contrast, has the opposite circulation pattern, with its major inflow at the surface where nutrients and metals are depleted, and its major outflow at depth, the North Atlantic Deep water. As might be expected, this circulation pattern tends to lower deep water concentrations of major nutrients and longer-lived nutrient metals.
Two trace metal nutrients with short ocean residence times (Mn and Co), show the opposite pattern to Cu, Zn, Cd, and Ni, and have highest concentrations at or near the surface and lower deep water concentrations in North Pacific than in the North Atlantic (Figures 2H and 3D; Landing and Bruland, 1987 ; Jickells and Burton, 1988 ; Martin et al., 1989 ). The short residence times and low concentrations in the older deep waters of the North Pacific are due to bacterially catalyzed oxidation of soluble Mn(II) and Co(II) to insoluble Mn(III and IV) and Co(III) oxides, and subsequent removal via particulate aggregation and settling as the water advects along its flow path. Likewise, iron is scavenged from deep ocean waters by oxide formation and adsorption onto particles, but it is also avidly taken up by phytoplankton in surface waters. It has moderately short residence times (∼70–200 years) and shows similar deepwater concentrations in the North Atlantic and Pacific (Johnson et al., 1997 ; Figure 3B). It exhibits surface depletion and increasing concentrations with depth in iron-limited regions such as the subarctic Pacific and northeast Atlantic during spring (Figure 3B), but can show pronounced surface maxima in stratified ocean waters receiving high aeolian inputs (Bruland et al., 1994 ; Measures and Vink, 1999 ).
In contrast to the other nutrient metals, Mo occurs at essentially the same concentration (100–110 nM) independent of depth or ocean basin (Collier, 1985 ). Its concentration is roughly proportional to salinity and its lack of depletion in surface waters indicates minimal removal of Mo by marine plankton relative to its seawater concentration, which is orders of magnitude higher than surface levels of other trace metal nutrients (Figures 1 and 2).
Trace metal nutrients in the ocean exist as a variety of different chemical species and forms, which strongly influences their biological uptake and biogeochemical cycling. All but Mo occur as cationic metal ions that are complexed to varying degrees by inorganic and organic ligands or are adsorbed onto or bound within various abiotic and biotic particles. Many of these metals (Fe, Cu, Mn, and Co) occur in different oxidation states, which have different solubilities, binding strengths with organic ligands, ligand exchange kinetics, and biological availabilities. Consequently, the redox chemistry of these metals has a major influence on their chemical behavior, biological uptake, and biogeochemical cycling.
Ni, Zn, and Cd exist in oxygenated seawater as soluble divalent cations that are complexed to varying degrees by inorganic ligands (Cl −, OH −, and
Manganese exists in seawater in three oxidation states: Mn(II), Mn(III), and Mn(IV). Insoluble Mn(III) and Mn(IV) oxides are the stable redox forms of this metal in oxygenated seawater, although Mn(III) also occurs as soluble chelates with organic ligands in some environments (e.g., hypoxic waters; Trouwborst et al., 2006 ). These oxides can be reduced chemically and photochemically to dissolved Mn(II), which is highly soluble and is not bound appreciably by organic ligands (Sunda and Huntsman, 1994 ). Although Mn(II) is unstable with respect to oxidation by molecular oxygen (O
The chemical behavior of iron, biologically the most important trace metal nutrient, is arguably the most complex. In oxygenated seawater its stable oxidation state Fe(III) forms sparingly soluble iron hydroxide and oxide precipitates, whose solubility, lability, and biological availability decreases with oxide aging (Wells et al., 1991 ; Kuma et al., 1996 ; Lui and Millero, 2002 ; Yoshida et al., 2006 ). This oxide formation and the tendency of ferric ions to adsorb onto particle surfaces results in low deep ocean concentrations (0.4–0.8 nM; Johnson et al., 1997 ; Boyd and Ellwood, 2010 ) despite iron’s high crustal abundance (it is the fourth most abundant element by weight). Most (>99.9%) of the filterable Fe(III) in seawater is strongly bound to complex mixtures of organic ligands (Gledhill and van den Berg, 1994 ; Rue and Bruland, 1995 ; Buck and Bruland, 2007 ; Gledhill and Buck, 2012 ). This organic complexation minimizes iron adsorption and precipitation, and thus reduces iron removal from seawater by particulate scavenging processes (Johnson et al., 1997 ). Titrations utilizing ligand competition/cathodic stripping voltammetry reveal two classes of iron chelating ligands in near-surface seawater, a high-affinity class (L
The identity of the two ligand classes is yet to be determined (Vraspir and Butler, 2009 ; Gledhill and Buck, 2012 ). Many of the high-affinity ligands may be bacterial siderophores, strong ferric chelators produced by bacteria to solubilize iron and facilitate its intracellular uptake (Macrellis et al., 2001 ). Individual siderophores, largely in the hydroymate class, have been identified in ocean waters, but their concentrations account for only a few percent of the strong iron-binding ligands (Mawji et al., 2008 , 2011 ; Velasquez et al., 2011 ). The low measured abundance of identifiable siderophores, however, may largely reflect our inability to quantitatively isolate other more-abundant classes of marine siderophores (e.g., catecholates and carboxylates) or reflect the presence of a large number of yet to be identified siderophores released by marine bacteria (Velasquez et al., 2011 ; Gledhill and Buck, 2012 ). There is also evidence that humic substances comprise at least some of the iron-binding ligands in coastal and deep ocean water and that many of these ligands may be derived from rivers and sedimentary sources (Laglera and van den Berg, 2009 ; Laglera et al., 2011 ). Other iron ligands with weak to moderate binding strengths are released from biological sources (e.g., grazing, viral lysis, and algal or bacterial secretion) and include porphyrins (Hunter and Boyd, 2007 ; Vong et al., 2007 ), acidic polysaccharides (Hassler et al., 2011a , b ), and secreted chelating compounds such as domoic acid (Rue and Bruland, 2001 ).
Ferric iron can be reduced in seawater to highly soluble Fe(II) (ferrous iron) by several processes, including, biological reduction at cell surfaces (Maldonado and Price, 2001 ; Shaked et al., 2005 ), direct photolysis of ferric chelates (Kuma et al., 1992 ; Barbeau et al., 2001 ; Barbeau, 2006 ), reduction by photochemically or biologically produced superoxide radicals (
Photo-redox cycling of ferric chelates (Fe(III)L), such as photoactive siderophore complexes . The cycle is initiated by the absorption of light by the ferric chelate and a subsequent photolytic reaction in which the iron is reduced to Fe(II) and the ligand is oxidized. The Fe(II) dissociates from the degraded chelate to give dissolved inorganic ferrous species (Fe(II)′) which are then rapidly oxidized to dissolved inorganic ferric hydrolysis species (Fe(III)′) by molecular oxygen and hydrogen peroxide. The Fe(III)’ is then re-chelated by the ligand to reform the ferric chelate. The cycle increases the uptake rate of iron by algal cells by increasing the steady state concentrations of biologically available Fe(II)′ and Fe(III)′. The transport system T directly accesses Fe(II)′ and indirectly accesses Fe(III)′ by reduction to Fe(II)′. Once inside the cell, much of the iron is used for synthesis of cytochromes (Cyt) and Fe-S redox centers, needed in high amounts in photosynthesis.
Thus, iron undergoes a dynamic redox cycling in surface seawater, which can greatly enhance its biological availability to phytoplankton by increasing concentrations of highly available dissolved inorganic Fe(II)′ and Fe(III)′ species (Anderson and Morel, 1982 ; Sunda, 2001 ; Maldonado et al., 2005 ; Fan, 2008 ; Figure 6). The photochemical enhancement of iron uptake by phytoplankton increases with decreasing temperature because the photolysis of Fe(III) chelates should be largely insensitive to temperature while the oxidation rates of photochemically produced Fe(II) to dissolved Fe(III)′ slows as does the re-chelation of Fe(III)′ by organic ligands (Sunda and Huntsman, 2011 ; Figure 6). These slower reoxidation and re-chelation rates at lower temperatures increases the steady state concentrations of the highly biologically available Fe(II)′ and Fe(III)′ species in the presence of sunlight, as shown for ferric complexes with the synthetic chelator EDTA (Sunda and Huntsman, 2003 , 2011 ). This enhanced photochemical effect should increase the availability of iron to phytoplankton in iron-limited polar regions such as the Southern Ocean, and without this effect, these regions might experience even more severe iron limitation.
Other micronutrient metals such as Cu and Co also exist in different oxidation states and are heavily chelated by organic ligands. Copper can exist in seawater as thermodynamically stable Cu(II), or as instable Cu(I) (Moffett and Zika, 1988 ). Most (>99%) of the Cu in near-surface seawater is heavily chelated by strong organic ligands (log K
Cu(II) can be reduced to Cu(I) by photochemical processes (Moffett and Zika, 1988 ), reduction at cell surfaces (Jones et al., 1987 ) or by reaction with chemical reducing agents, such as sulfur containing organic ligands (Leal and van den Berg, 1998 ). In surface waters, the resulting Cu(I) is re-oxidized by reaction with O
The chemistry of cobalt is also highly complex. Cobalt exists in seawater as soluble Co(II) or as Co(III), which forms insoluble oxides at the pH of seawater. The formation of these oxides appears to be microbially mediated and may be largely responsible for the removal of this metal from deep ocean waters (Tebo et al., 1984 ; Moffett and Ho, 1996 ; Figure 3D). Much of the filterable Co in seawater (up to100%) is strongly bound to organic ligands (Ellwood and van den Berg, 2001 ; Saito and Moffett, 2001 ; Saito et al., 2005 ). The conditional stability constant(s) for the Co complexes is extremely high (10 15.6 to ≥10 16.8 M –1), consistent with Co binding as kinetically inert Co(III) chelates (Saito et al., 2005 ).
Trace metal nutrients, like major nutrients, are taken up intracellularly by specialized transport proteins on the cytoplasmic membrane of algal cells. Consequently, uptake rates generally follow Michaelis Menten enzyme kinetics
as observed for Mn, Fe, Zn, and Cu (Sunda and Huntsman, 1986 , 1998a ; Hudson and Morel, 1990 ; Lane et al., 2008 ; Guo et al., 2010 ). In Eq. 1 V
Although Eq. 1 is relatively simple, its application is not entirely straight forward. One complicating factor is that the relevant substrate concentration, [M], is that at the surface of the cell membrane, which can be much less than that in bulk seawater in cases where the uptake rate approaches the maximum rate of diffusive flux of available metal species to the cell surface (Hudson and Morel, 1993 ). This indeed occurs for uptake of Zn and Co(II) by small diatoms and coccolithophores (3.5–6 μm diameter) at low Zn′ and Co′ concentrations (Sunda and Huntsman, 1995a ; Figure 7). Since diffusive flux per unit of cell biovolume varies with the inverse square the cell diameter, diffusion limitation of uptake intensifies substantially as the cell size increases, and can be a major impediment for the uptake of some nutrient metals (Zn, Co, Fe) in larger cells (Hudson and Morel, 1993 ; Sunda and Huntsman, 1995a ; Sunda, 2001 ). Another complicating factor is that the V
Cellular uptake rates for Zn, Co, and Cd (normalized per mol of cell carbon) for the
oceanic diatom
Thalassiosira oceanica plotted as a function of the log
Uptake systems are highly variable and range from simple to highly complex depending on the chemical speciation of the metal, its biological demand (requirement) relative to its seawater availability, and the range of concentrations of available metal species. Uptake systems appear to be simplest for dissolved Mn(II), which is taken up in phytoplankton by a single high-affinity transport system ( K
Uptake systems for Zn, Cd, Co, and Cu are more complex. Like that of Mn(II), algal uptake of these metals is believed to be related to the concentration of dissolved inorganic metal species (M′ = Zn′, Cd′, Co′, and Cu′) and metal chelates are generally not directly available for metal uptake, with the exception of some Cu chelates at low Cu′ levels (Hudson, 1998 ; Sunda and Huntsman, 1998b ; Guo et al., 2010 ). The phytoplankton species examined to date have at least two separate Zn transport systems, a low-affinity system whose V
Co and sometimes Cd can metabolically substitute for Zn in many Zn enzymes such as carbonic anhydrase (Price and Morel, 1990 ; Lane and Morel, 2000 ; Xu et al., 2008 ). To facilitate this substitution, the uptake of these divalent metals is increased by over 100-fold in diatoms and coccolithophores with decreasing [Zn′] in the external medium and decreasing cellular Zn (Figure 7; Sunda and Huntsman, 1995a , 2000 ). Uptake of Cd and Co by this inducible transport system (or systems) is down-regulated at high [Zn′] and intracellular Zn levels (Figure 7). Under these conditions, Cd is taken up into the cell by the cellular Mn(II) transport system (Sunda and Huntsman, 1996 , 2000 ) or a putative Fe(II) transport system (Lane et al., 2008 ), and consequently, is inversely related to concentrations of Mn(II)′ and Fe(II)′. Thus, cellular uptake of Cd in the ocean is regulated by complex interactions among dissolved concentrations of Cd′, Zn′, Mn(II)′, and Fe(II)′ (Sunda and Huntsman, 2000 ; Cullen and Sherrell, 2005 ; Lane et al., 2009 ). Likewise, since Co uptake is repressed at high [Zn′], biological removal of Co often does not occur until after Zn is depleted, as observed in the subarctic Pacific (Figure 8; Sunda and Huntsman, 1995a ).
Plots of filterable Zn and Co vs phosphate concentrations at two stations in the subarctic Pacific (Station T-5, 39.6°N, 140.8°W and Station T-6, 45.0°N, 142.9°W, August 1987) . The decrease in zinc with decreasing phosphate is caused by the simultaneous removal of both metals via cellular uptake and assimilation by phytoplankton. Cobalt decreases with decreasing phosphate only after zinc concentrations drop to very low levels (<0.2 nmol kg −1). This pattern is consistent with metabolic replacement of Co for Zn, as observed in phytoplankton cultures (see Figure 5). Data plots after Sunda and Huntsman ( 1995a ).
Iron is the most limiting trace metal nutrient, and its chemistry is arguably the most complex. As noted earlier, iron is highly bound in seawater as ferric chelates and ferric ions associated with various particulate phases such as Fe(III) oxyhydroxides. Early evidence suggested that cyanobacteria and eukaryotic marine algae utilized fundamentally different uptake systems to access this bound iron (Wilhelm and Trick, 1994 ; Hutchins et al., 1999 ). Both cyanobacteria and heterotrophic bacteria were thought to utilize primarily siderophore uptake systems, in which high-affinity Fe(III)-binding ligands (siderophores) were released extracellularly, followed by intracellular uptake of the resulting ferric-siderophore chelates (Martinez et al., 2000 ; Sandy and Butler, 2009 ). Siderophore uptake systems are widespread in terrestrial and enteric bacteria, and may also be common in sedimentary and particle-associated marine bacteria (Sandy and Butler, 2009 ). However, recent genomic data from cultures and natural communities shows little evidence for proteins involved in siderophore biosynthesis or for cellular uptake of ferric-siderophore chelates in the picocyanobacteria ( Synechococcus and Prochlorococcus) that often dominate open ocean phytoplankton communities, or by major oceanic N
Eukaryotic phytoplankton do not appear to produce siderophores and there is little evidence for the direct cellular uptake of ferric-siderophore chelates (Sunda, 2001 ). Instead there is mounting evidence for the utilization of a high-affinity transport system that accesses a variety of Fe(III) coordination species (including Fe(III)′ and ferric chelates) via reduction to Fe(II; Maldonado and Price, 2001 ; Kustka et al., 2007 ; Amin et al., 2009 ; Strzepek et al., 2011 ). The released Fe(II)′ binds to Fe(II) receptors on specific transmembrane proteins, which transport the iron into the cell. This intracellular transport involves the reoxidation of bound Fe(II) to Fe(III) by a copper protein (a multi-Cu oxidase; Maldonado et al., 2006 ). The ability of this transport system to access iron is dependent on the ease of reduction of ferric complex species, which is inversely related to the stability of the Fe(III) coordination complex (Maldonado and Price, 2001 ). Weakly complexed ferric hydrolysis species Fe(III)′ are reduced at orders of magnitude higher rates than strongly bound Fe-siderophore chelates, and are thus much more accessible for cellular uptake (Shaked et al., 2005 ; Morel et al., 2008 ). Likewise Fe(III) colloids are less available because of their slow diffusion kinetics within the cell’s diffusive boundary layer and because interior ferric ions within the colloid are not readily accessible for reduction and subsequent release as dissolved Fe(II). As a consequence, iron uptake by this system is highly dependent on the chemical speciation of iron in seawater, and increases in dissolved Fe(II)′ or Fe(III)′ concentrations can considerably increase the biological availability of iron (Hudson and Morel, 1990 ; Morel et al., 2008 ). Photo-reductive dissociation of ferric chelates increases iron uptake by diatoms and other eukaryotic algae by increasing steady state Fe(II)′ and Fe(III)′ concentrations, as shown in culture experiments with photolabile ferric complexes with synthetic chelators (e.g., ethylenediaminetetraacetic acid, EDTA; Anderson and Morel, 1982 ; Sunda and Huntsman, 2011 ), marine siderophores (Barbeau et al., 2001 ; Amin et al., 2009 ), and sugar acids (Ozturk et al., 2004 ; Figure 6). Similarly, in an incubation experiment in the Southern Ocean, natural planktonic assemblages exhibited higher iron uptake rates from an added photoactive siderophore ( 57Fe-aerobactin) in the presence of sunlight than from a non-photoactive one ( 57Fe-desferrioximine b; Buck et al., 2010 ).
In other recent iron uptake experiments in Southern Ocean waters, the pre-equilibration of 55Fe-labeled iron with the monosaccharide glucuronic acid increased the uptake of the radiolabeled iron by ∼2-fold compared to the uptake observed when iron was added by itself (Hassler et al., 2011b ). Similar enhanced uptake was observed with the addition of polysaccharides. These effects may be attributed to the formation of more biologically available weak organic chelates (Hassler et al., 2011b ; Benner, 2011 ). However as noted above, dissolved ferric chelates and coordination complexes of adsorbed sugar acids and polysaccharides on the surfaces of iron oxyhydroxides can undergo photolysis and subsequent iron redox cycling, which should increase Fe(II)′ and Fe(III)′ concentrations (Kuma et al., 1992 ; Ozturk et al., 2004 ; Steigenberger et al., 2010 ), thereby providing a plausible alternative explanation for the observed results. The influence of mono and polysaccharides on iron chemistry, photochemical redox cycling, and bioavailability needs further investigation given the high abundance of these compounds in dissolved and colloidal marine organic matter and the widespread production of extracellular polysaccharides by marine phytoplankton and bacteria (Steigenberger et al., 2010 ; Benner, 2011 ; Hassler et al., 2011a , b ).
The trace metal concentration in algal cells normalized to cell volume or to cell carbon is not only dependent on the rates of intracellular metal uptake by transport systems (as discussed above) or adsorption on cell surfaces (Tovar-Sanchez et al., 2003 ), but also by the rate of biodilution by new biomass or cell carbon (Sunda and Huntsman, 1998b ). The rate of change in cellular metal per mole of cell carbon (d Q/dt) equals the cellular uptake rate V
At steady state d Q/ dt = 0 and the equation collapses to:
Based on these equations, any factor that decreases growth rate but has no or a lesser effect on the metal uptake rate will increase the cellular metal:C ratio. This is seen in the response of cell Fe:C, Mn:C, and Zn:C ratios in diatoms and dinoflagellates to light limitation of growth rate, where a 60–70% decrease in specific growth rate with a decrease in light intensity from 500 to 50 μmol quanta m −2 s −1 increased the cell metal:C ratios by two- to threefold for a given [M′] (Sunda and Huntsman, 1997 , 1998c , 2005 ). For Mn and Fe, the higher Mn:C and Fe:C values helped the cells to photoacclimate to the low light conditions (see Fe and Mn sections below and Figure 9), and for Zn, the higher Zn:C ratio lowered the [Zn′] needed to achieve maximum growth rate. However, as discussed previously, decreasing light can also decrease Fe and Mn uptake rates by decreasing [Fe′] and [Mn(II)′] levels, so the overall effect of lower light may still be to lower cell Fe:C and Mn:C ratios (Sunda and Huntsman, 2011 ). Temperature, another major growth-controlling factor, could potentially also affect cellular M:C ratios, but in the one case examined to date, a temperature decrease from 20 to 10°C caused similar decreases in V
Effect of light on cellular growth requirements for (A) iron and (B) manganese in the coastal diatom Thalassiosira pseudonana at 20°C . (A) Relationships between specific growth rate and Fe:C molar ratio for cells growing under a 14:10 h light:dark cycle at light intensities of 500 (black triangles), 85 (green triangles), and 50 (blue triangles) μmol photons m −2 s −1. Open diamonds give data for cells growing at the highest light intensity (500 μmol photons m −2 s −1) but a 50% shorter daily photoperiod (7 h). (B) Relationships between specific growth rate and cellular Mn:C molar ratio for cell growing under a 14:10 h light:dark cycle at light intensities of 500 (black circles), 160 (red circles), and 90 (green circles) μmol photons m −2 s −1.
Equations 2 and 3 also have important implications for diel changes in cellular M:C ratios. Carbon is photosynthetically fixed only during the day (and a portion is respired at night), but metal uptake can continue during both the light and dark period (Sunda and Huntsman, 2004 ). Consequently, cellular M:C ratios should decrease during the light period and increase at night. In agreement with this prediction, the cell Fe:C ratio in an iron-limited diatom ( Thalassiosira pseudonana) growing at a specific rate of 1.6 day −1 decreased by 60% (from 63 to 25 μmol Fe mol C −1) from the beginning to the end of the 14 h light period (Sunda and Huntsman, 2004 ). This result was for a metal chelate system (Fe-nitrilotriacetic acid) with no photochemical enhancement of [Fe′] during the day, and the diel effect can be less or even reversed in marine systems with substantial photochemical redox cycling of iron (Sunda and Huntsman, 2004 ). For Zn, Co, Cd, and Ni, where photochemical cycling does not occur, the cellular M:C ratios should also decrease during the light period. This effect was observed in the diatom T. pseudonana where cell Zn:C decreased by twofold over the course of the light period (Sunda and Huntsman, 2004 ).
Metal uptake rates invariably increase with the concentration of available metal as observed for Zn uptake in the oceanic diatom Thalassiosira oceanica (Figure 7). For this diatom and the coccolithophore Emiliania huxleyi the specific growth rate is unaffected over most the oceanic [Zn′] range (log [Zn′] = −11.8 to −8.7). Consequently, the cellular Zn:C is proportional to the cellular Zn uptake rate, and increases with increasing [Zn′] (see Figures 5 and 7).
Cellular metal:C ratios can vary substantially among species. The cell Zn:C was two- to fivefold higher in the coccolitiophore E. huxleyi than the diatoms T. oceanica and T. pseudonana, depending on the [Zn′] in the culture medium (Sunda and Huntsman, 1995a ; Figure 5). Data of Ho et al. ( 2003 ) are consistent with those of Sunda and Huntsman ( 1995a ) and show a 10-fold variation in Zn:C ratios in 15 different marine algal species representing five major algal groups (Figure 5). Even larger variations of 13-, 71-, and 50-fold, respectively, were observed for Cu:C, Co:C, Cd:C ratios in these same algal species grown at a constant set of [M′] values (Ho et al., 2003 ).
Recent advances in synchrotron X-ray fluorescence microscopy has allowed measurement of metal:P and metal:S ratios in single cells for a wide array of trace metals. As in the above culture data, large intriguing variations were observed in different algal cells and cell types present in the same water samples (Twining et al., 2004 , 2010 , 2011 ). In a recent study in the equatorial Pacific, diatoms had sevenfold higher Ni:P ratios and fourfold higher Fe-P and Zn:P ratios than autotrophic flagellates (e.g., coccolithophores and dinoflagellates), but had 2.5-fold lower Co:P ratios (Twining et al., 2011 ).
Cells have the capacity to take up iron and other nutrient metals in far excess of that needed to support growth and metabolism, which helps them to take advantage of pulsed inputs, such as episodic increases in aeolian iron deposition associated with large desert dust storms (Sunda and Huntsman, 1995b ; Marchetti et al., 2006 ). This “luxury” uptake also protects cells from future declines in metal availability as blooms develop or to increases in metabolic demand for iron and other metals linked to decreases in light or increases in temperature (Sunda and Huntsman, 2011 ). In addition, because of a certain degree of non-specificity of transport systems, many reactive trace metals (including metal nutrients, e.g., Cu and Cd), can leak into cells via the transport systems for other metal nutrients such as Mn and Zn (Sunda and Huntsman, 1996 , 1998c ). Within the cell unchelated redox active metals such as iron and copper can mediate the formation of toxic reactive oxygen species (e.g., hydroxyl radicals via Fenten chemistry) and reactive metals such as Cu, Cd, and Zn can adventiciously bind with coordination sites of proteins or displace nutrient metals from their active sites in metalloproteins (Hartwig, 2001 ; Valko et al., 2005 ). Consequently, it is essential that “excess” concentrations of these metals be sequestered within algal cells to prevent metal toxicity or inhibition of metabolism (Finney and O’Halloran, 2003 ). Excess iron not occurring in metabolic proteins is bound within the iron storage protein ferritin in the pennate diatom Pseudo-nitzschia (Marchetti et al., 2009 ), and in Dps protein (another member of the ferritin protein family) in the N
High intracellular levels of Cd or Cu in a variety of eukaryotic phytoplankton are bound by phytochelatins, a set of sulfhydryl containing chelating ligands found in terrestrial plants, microalgae, and fungi. These ligands consist of small polymers of glutathione with the structure (γ-glutamate-cysteine) n-glycine, where n = 2–11 (Ahner et al., 1995 ; Ahner and Morel, 1995 ; Ahner et al., 1997 ). Although low basal levels of these cellular chelators exist in cells, the cellular synthesis of phytochelatins is up-regulated by exposure to high concentrations of toxic nutrient or non-nutrient metals (e.g., Cd, Cu, or Hg) or low levels of competing metal nutrients such as Zn or Mn, which promote increased cellular Cd or Cu uptake (Ahner et al., 1998 ; see Figure 7).
Trace metal nutrients are essential for the metabolism, growth, and reproduction of all marine phytoplankton. They play essential roles in photosynthetic C-fixation, respiration, N
The major requirement for iron in phytoplankton and likely all phototrophs is in the primary photochemical reactions of photosynthesis and associated photosynthetic electron transport (Raven, 1990 ; Strzepek and Harrison, 2004 ). To acclimate to low, subsaturating light conditions, phytoplankton increase the cellular concentration of photosynthetic pigments, reaction centers, and electron transport proteins and protein complexes, and to do this they need an increased amount of cellular iron (Raven, 1990 ). These iron-containing photosynthetic components include photosystem II (2–3 Fe), the cyt f/ b
Iron is also needed for the assimilation of important chemical forms of nitrogen, which next to carbon is the second most abundant nutrient element in the cell. Nitrogen limits the growth and biomass of phytoplankton in roughly 60% of the ocean (Moore et al., 2002 , 2004 ) and the overwhelming majority of biologically available fixed nitrogen in deep ocean reservoirs occurs as nitrate (Figure 2A). Nitrate is supplied to sunlit surface waters by upwelling and vertical mixing of deep ocean waters, but to utilize this substrate algal cells must first reduce it to ammonium, a process catalyzed by two iron-containing enzymes: nitrate reductase and nitrite reductase. In addition, both enzymatic reactions require the input of cellular energy (adenosine triphosphate, ATP) and reductant molecules (NADPH), and large amounts of cellular iron are needed for their photosynthetic production. Based on model calculations (Raven, 1988 ) and empirical measurements (Maldonado and Price, 1996 ), algal cells growing on nitrate require ∼50% more iron to support a given growth rate than do cells growing on ammonium. Consequently, iron can be especially limiting in oceanic upwelling systems (such as the equatorial and subarctic Pacific) where waters containing high nitrate concentrations, but low-iron, are advected to the surface (Martin and Fitzwater, 1988 ; Coale et al., 1996 ; Boyd et al., 2007 ). The low-iron concentrations favor the growth of small phytoplankton, which are rapidly grazed by microzooplankton, preventing blooms from developing, whose formation ultimately must be supported by inputs of upwelled nitrate (Price et al., 1991 , 1994 ). The rapid grazing also supplies recycled ammonium which is more efficiently assimilated by the iron-limited cells. Thus the net population growth of such systems can be viewed as being co-limited by iron and nitrate due to the high iron requirement for nitrate utilization (Price et al., 1994 ).
A more important iron-nitrogen linkage is provided by the large metabolic requirement for iron in N
Ocean N
In the mid-ocean waters of the subtropical and tropical Pacific, continental inputs of iron are low as are iron concentrations, and here N
Due to iron limitation of C-fixation and N
Manganese may influence the growth and species composition in certain low-Mn environments such as the subarctic Pacific and Southern Ocean, where Mn additions have been observed to stimulate algal growth in bottle incubation experiments (Buma et al., 1991 ; Coale, 1991 ). Mn occurs in the water oxidizing complex of photosystem II (which contains four Mn atoms), and thus is essential for oxygenic photosynthesis. Consequently, like iron, it is needed in higher amounts for algal growth at low light intensities (Raven, 1990 ; Sunda and Huntsman, 1998d ; Figure 9B), such as the bottom of the photic zone where Mn concentrations are often lower than at the surface (Figure 2H). Mn also occurs in superoxide dismutase, an antioxidant enzyme that removes toxic superoxide radicals, produced as byproducts of photosynthesis (Peers and Price, 2004 ; Wolfe-Simon et al., 2006 ). Because it has fewer metabolic functions, its cellular growth requirement is less than that for iron (Figure 9A, B).
Quantitative requirements for Zn in marine phytoplankton are similar to those for Mn (Sunda and Huntsman, 1995a , 1998d ). Zn is needed in a variety of essential proteins needed for cell growth and replication (da Silva and Williams, 1991 ). It occurs in carbonic anhydrase (CA), an enzyme that catalyzes the reversible reaction:
Thus CA is critical to intracellular CO
Co and sometimes Cd can substitute for Zn in CA, alkaline phosphatase and other Zn enzymes, leading to complex interactions among the three metals in marine phytoplankton (Price and Morel, 1990 ; Sunda and Huntsman, 1995a ; Xu et al., 2007 ; Jakuba et al., 2008 ; Saito and Goepfert, 2008 ; Figure 7). The presence of Cd in CA appears to explain its nutrient-like distribution in ocean waters (Figure 2E), and the identification of a unique Cd-CA enzyme in zinc-limited marine diatoms means that it functions as a micronutrient in these microalgae (Park et al., 2007 ; Xu et al., 2008 ). However, the substitution of Zn for Cd in this protein yields a more catalytically active enzyme (Xu et al., 2008 ), suggesting that Zn may have been evolutionarily the original metal center for this enzyme.
Co also occurs in vitamin B
Copper is also an essential micronutrient. It occurs along with iron in cytochrome oxidase, the terminal protein in respiratory electron transport that reduces O
Nickel has only a minimal known usage in marine phytoplankton. It serves as the active metal center in urease, which hydrolyzes urea to ammonium. Consequently, it is essential for utilization of urea, an important nitrogen source in N-limited oceanic waters (Price and Morel, 1991 ). Recently a Ni-superoxide dismutase (Ni-SOD) was found to occur in marine cyanobacteria, a dominant group of picophytoplankton in open ocean waters (Dupont et al., 2008a , b ). This finding may explain the unusually high Ni:P ratios in picoplankton (which were presumed to be cyanobacteria) in the equatorial Pacific (Twining et al., 2011 ). The Ni-SOD findings may be of significance evolutionarily as other forms of this critical antioxidant enzyme, which contain other metals in their catalytic centers (i.e., Fe-SOD, Mn-SOD, and Cu/Zn-SOD), are the main SODs in eukaryotic algae and virtually all land plants and animals. Thus, the occurrence of Ni-SOD in oceanic cyanobacteria may help to reduce the biochemical demand for other micronutrient metals (Fe, Zn, Cu, and Mn) which generally occur at lower concentrations than Ni in surface ocean waters (see Figures 2 and 3).
Mo occurs with Fe in several key N-assimilation enzymes, including nitrogenase and nitrate reductase (da Silva and Williams, 1991 ). Thus, it is important in the ocean’s nitrogen cycle; however, it is unlikely to limit algal growth due to the high concentration of molybdate ions in ocean water (105 nM; Collier, 1985 ). In addition to the Fe-Mo enzyme, some N
The interactions between trace metal nutrients and marine phytoplankton are reciprocal. While trace metals clearly influence the productivity and species composition of planktonic algae and bacteria, these microorganisms in turn have a profound effect on the chemistry and cycling of these metals in the ocean on a variety of time and spatial scales (Figure 1). One example of this interaction is the effect of algal uptake, particulate settling, and regeneration cycles on the vertical distribution and inter-ocean transfer of nutrient metals, as described earlier (Figures 2 and 3). In addition marine biota influence the chemical speciation of metals through a variety of processes: (1) the release of metal chelates and metal chelating ligands through zooplankton grazing, digestion, and defecation, and through viral lysis of cells and microbial degradation processes (Poorvin et al., 2004 ; Strzepek et al., 2005 ); (2) the active release of chelating compounds such as siderophores (Butler and Theisen, 2010 ) and polysaccharides (Hassler et al., 2011a ), and (3) the reduction and oxidation of redox active metals (Fe, Mn, Co, and Cu) by various biological processes. These processes include the reduction of iron and copper by cell surface reductases (Strzepek et al., 2011 ) or by biologically produced reducing agents (Rose et al., 2005 ), reduction of metals within particulate reducing microzones generated by microbial respiration, and oxidation of Mn(II), Co(II), and Fe(II) to their respective oxides by specific groups of bacteria (Moffett and Ho, 1996 ).
One of the most important effects of microorganisms on trace metal chemistry is in mediating the production of organic ligands that chelate Fe, Cu, Zn, and Co, especially in oceanic environments where the supply of terrestrial humic materials and other land-derived chelators is minimal. This chelation minimizes the loss of these essential metals by abiotic (and biotic) particulate scavenging and thereby minimizes their loss from ocean waters. The organic carbon needed for the in situ formation of these ligands is ultimately derived from algal photosynthesis, and thus the production of these ligands is at least indirectly dependent on marine algal productivity. However, many of these ligands appear to directly result from biological processes, and at least some directly facilitate biological uptake of metals. The release of siderophores clearly facilitates biological uptake of iron, as the releasing bacteria have specific transport systems for active uptake of iron-siderophore chelates (Butler and Theisen, 2010 ). However, most iron-siderophore chelates are orders of magnitude less available for algal uptake than is unchelated Fe′ (Shaked et al., 2005 ), and thus, in facilitating their own iron uptake, the bacteria could inadvertently lessen the uptake of iron by phytoplankton. Such an effect in turn could lessen photosynthetic production of organic carbon on which the bacteria depend for growth and survival. To avoid this conundrum, nature may have devised a clever mechanism to allow the bacteria to sequester iron for their own use, and at the same time make it more available to support photosynthetic fixation of carbon in sunlit surface ocean waters. Unlike the siderophores produced by soil and enteric bacteria, whose iron-siderophore chelates are unaffected by exposure to sunlight, the iron chelates of most siderophores produced by near-surface marine bacteria undergo photolysis owing to the presence of alpha hydroxy-carboxylate functional groups (Barbeau et al., 2003 ; Vraspir and Butler, 2009 ; Butler and Theisen, 2010 ). By contrast, these photoactive functional groups are relatively rare in terrestrial siderophores (Barbeau et al., 2003 ). The photolysis of the siderophore chelates can substantially increase the steady state concentrations of biologically available Fe(II)′ and Fe(III)′ species, and thereby increases the uptake of iron by phytoplankton, as directly shown in culture experiments with marine diatoms and dinoflagellates (Barbeau et al., 2001 ; Amin et al., 2009 ). In this way the bacteria are not only able to increase the availability of iron to themselves, but to phytoplankton in sunlit surface waters, thereby promoting the photosynthetic production of organic carbon needed for bacterial growth and reproduction, and ultimately for the production of siderophores. It is noteworthy that the marine bacteria found in close mutualistic association with marine dinoflagellates and cocolithophores produce a weaker iron-siderophore (vibrioferrin; log K
There is also evidence that the strong ligands that tightly chelate Cu in seawater are produced by phytoplankton, especially cyanobacteria. In stratified waters of the North Pacific and Atlantic such strong Cu-binding ligands often occur at highest levels near the deep chlorophyll maximum where cyanobacteria are abundant (Coale and Bruland, 1988 , 1990 ; Moffett, 1995 ). The chelators appear to perform an important function of detoxifying copper. In the presence of the Cu-chelators the average Cu′ in surface Northeast Pacific waters occurred at a non-toxic concentration (1.2 pM; Table 1 ) while in their absence Cu′ levels would have been 500 pM, a level toxic to many marine phytoplankton (Brand et al., 1986 ; Coale and Bruland, 1988 ). Ligands having similar Cu-binding strength (log K
There is also evidence from laboratory and field experiments that the strong ligands that chelate Co in seawater are also produced by cyanobacteria, and that these ligands, like siderophores, facilitate the uptake of the chelated metal (Co) by these organisms (Saito et al., 2002 , 2005 ).
Thus, trace metal nutrients and planktonic communities comprise an interactive system in the ocean in which each exerts a controlling influence on the other (Figure 1). On longer geological time scales, the feedback interactions between microorganisms and the chemistry and availability trace metals have been profound, and have substantially influenced the overall chemistry and cycling of major biological elements (C, O, H, N, and S) in the ocean and the earth as a whole, and have had a major impact on the evolution of life on this planet (da Silva and Williams, 1991 ). Currently, the air we breathe and virtually the entire ocean contain high concentrations of dioxygen molecules (O
Given the utility of Fe as a versatile redox catalyst and its abundance in the earth’s crust and early ocean, it is perhaps not surprising that this metal was utilized in the evolution of many of the central redox catalysts of life (Mauzerall, 2007 ). It occurs in high amounts in the redox centers of nitrogenase responsible for N
The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
I thank Mark Vandersea for help in formatting the figures and Yeala Shaked, Christel Hassler, and Ken Bruland for useful comments.


What is Neuroplasticity?
Neuroplasticity relates to how the brain isn’t static and can form new connections or pathways through things such as practice and learning. The more you do something, the stronger those connections in the brain become and your ability to recall information or preform actions becomes greater in the process.
Knowing this is a great motivator to continue learning and practicing writing code everyday, as over time the pathways in the brain responsible for this will improve.
Practice is a great way to improve neuroplasticity, especially when it comes to things such as learning a language or a musical instrument. Sleeping is another great way to “sweep out the cobwebs” in your brain. A good nights sleep helps to make you more engaged and focused so you’re better able to absorb new knowledge.
Other things like reading or travelling are great as they help to stimulate the brain. Even things like meditation, intermittent fasting and regular exercise are great tools for improving neuroplasticity.
Further Reading
- https://positivepsychology.com/neuroplasticity/
- https://reset.me/story/neuroplasticity-the-10-fundamentals-of-rewiring-your-brain/
What is a Growth Mindset?
Having a Growth Mindset (versus a Fixed Mindset) is a belief that you’re able develop your talents, to solve problems and eventually understand something rather than believing that you’re not smart enough. Having a fixed mindset can hinder your learning as a result of your brain having a tendency to avoid problems and not giving them a proper go.
Learning about the growth mindset has been quite interesting to me as I have a bit of a fixed mindset, if something isn’t interesting to me, and I struggle with it, I’ll avoid it. Only if I’m really invested in something will I ever give it the time required to improve. This is something that I want to change about myself, and it’s great knowing that I’m capable, I just need to put in the effort and have patience in myself.
Through things like positive affirmation and meditation and even knowing about the growth mindset can have a positive impact and help you better engage with problems. A key thing I took away from my reading was that you should form good habits, be kind to yourself, and allow yourself to fail, so you can improve.


At this point you have checkout the previous tutorial and you have your algorithm or computations ready. Remember that you can ask for the encrypted data multiple times but you can only ask once for the final result. To purchase access, follow these steps:
To handle the organization's data you would need to interact with your library functions (to install it check this tutorial). To import the library you can execute the following command in a terminal or inside a Python .py file:
import hesystem.client as cl
In this step you would need: your account address, private key (you can follow this tutorial to get them) and your URL endpoint (you can follow this tutorial to
get it). Remember to have enough funds in your account to execute the purchase. In the same terminal or file from the previous step, execute the following command to enter your information:
User = cl.initialize()
Get the package details URL that you wish you access (you can find it here). In the same terminal or file from the previous step, execute the following command to enter the URL and request the data:
data, public_key = cl.request_data(User)
The variable data from the previous step contains the encrypted data from the organization. You can interact with it in different ways. If you need specific help to do this, visit the library documentation here.
result = data.transpose().mean(1)[0]
When you algorithm or compuations have finished, you would have an encrypted result. Check out the conditions that your result should follow here. To request the decrypted result execute the following command in the same terminal or file from the previous step:
final_result = cl.request_result(User, result, public_key)


PHP: HYPERTEXT PREPROCESSOR
Created by: Rasmus Lerdorf in 1994.
When PHP parses a file, it looks for opening and closing tags which tell PHP to start and stop interpreting the code between them.
PHP has types mainly:
and many more if you would like more information just click the learn more button.


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java.lang.Object uchicago.src.sim.math.CEquation
public class CEquation
A user specifiable evaluatable equation. This class will evaluate a specified equation replacing the variables in the equation with values from a specified object. The result of the equation evaluation will then be written to the object.
When evaluated a CEquation will replace the variables in the specified equation with those read from a specified object. For example, if the equation is "x + 1 * z", then evaluation will replace x and z in the equation with the results of calling getX() and getZ() on the object. The result of this evaluation will be assigned to the a resultVar in a similar manner. For example, if the resultVar is "y", then on assignment the result of the equation evaluation will be assigned to the object via the object's setY(double) method.
The variable "t" can also be used. "t" is replaced by the value of the current tick count. Similarly, the variable "dt" can be used. It will be set to the delta of the current tick count and the tick count at which the last evaluation occured. Evaluation is performed using the
evaluate
method and assignment is
performed using the assign
method.
| Constructor Summary | |
|---|---|
CEquation(java.lang.Object target,
TickCounter counter,
java.lang.String equation,
java.lang.String resultVar)
Creates a CEquation from the specified parameters. |
|
CEquation(java.lang.Object target,
TickCounter counter,
java.lang.String equation,
java.lang.String resultVar,
double initialDTValue)
Creates a CEquation from the specified parameters. |
| Method Summary | |
|---|---|
void |
assign()
Assigns the result of the last call to evaluate to the result variable of the
target specified in the constructor. |
void |
evaluate()
Evaluates the equation encapsulated by this CEquation. |
void |
evaluateAndAssign()
Evaluates the currently encapsulated equation and assigns the result to the result variable. |
java.lang.String |
getEquation()
|
void |
setEquation(java.lang.String equation)
Sets the equation to evalute. |
| Methods inherited from class java.lang.Object |
|---|
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
| Constructor Detail |
|---|
public CEquation(java.lang.Object target, TickCounter counter, java.lang.String equation, java.lang.String resultVar)
The variable "t" can also be used. "t" is replaced by the value of the current tick count. Similarly, the variable "dt" can be used. It will be set to the delta of the current tick count and the tick count at which the last evaluation occured. dt defaults to a value of 0 on the first evaluation. Use the alternate constructor to specify a different initial value. Evaluation is performed using the
evaluate
method and assignment is
performed using the assign
method.
target
- the target from which the equations variable values are read and to which the
result is assignedcounter
- a TickCounter from which the value of the "t" variable can be setequation
- the equation to evaluate.resultVar
- the name of the variable to assign the result topublic CEquation(java.lang.Object target, TickCounter counter, java.lang.String equation, java.lang.String resultVar, double initialDTValue)
The variable "t" can also be used. "t" is replaced by the value of the current tick count. Similarly, the variable "dt" can be used. It will be set to the delta of the current tick count and the tick count at which the last evaluation occured. Evaluation is performed using the
evaluate
method and assignment is
performed using the assign
method.
target
- the target from which the equations variable values are read and to which the
result is assignedcounter
- a TickCounter from which the value of the "t" variable can be setequation
- the equation to evaluate.resultVar
- the name of the variable to assign the result toinitialDTValue
- the initial value of dt to use on the first evaluation when
it would otherwise be set to 0| Method Detail |
|---|
public void evaluate()
evaluate()
call with
a call to assign()
or alternatively call evaluateAndAssign()
.
assign()
,
evaluateAndAssign()
public void assign()
evaluate
to the result variable of the
target specified in the constructor.
public void evaluateAndAssign()
public java.lang.String getEquation()
public void setEquation(java.lang.String equation)
equation
-
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Tributes made to 27-year-old Ribble Valley nurse and hockey player who has died unexpectedly
by Matthew CalderbankTributes have been made to a 27-year-old woman who has died unexpectedly at her Ribble Valley home.
Katy Marie Blezard, affectionately known as 'Blez' to family and friends, died at home in Chipping overnight on Friday, November 8.
Miss Blezard's family said it has yet to be established how she died.
"Katy was a happy and bubbly character with a huge heart", said her family.
"She will be lovingly remembered and deeply missed by all who knew her."
Katy had worked as a nurse at Royal Preston Hospital since 2013, where she began her career on the neurosurgery ward.
In recognition of her hard-work and leadership skills, Katy was promoted to the position of Sister.
In September 2019, Katy joined the hospital's critical care unit (CCU), providing essential care and treatment to severely ill patients.
Sarah Cullen, nursing director at Lancashire Teaching Hospitals NHS Trust said "We are saddened to hear of our colleague Katy Blezard’s death. Our thoughts are with Katy’s family at this difficult time.
"Katy will be remembered with great fondness by her friends and colleagues."
When not at work caring for her patients, her family said Katy could usually be found playing in her role as 1st team keeper for Longridge Hockey Club, out with her dog "Zippo", or in the Sun Inn pub in the village of Chipping.
Her sister, Lucy, said: "Katy played hockey from a very young age. She was talent spotted and trained by Kay Hannon, an England Hockey coach when she was in her early teens."
A well-respected hockey keeper, Katy served as captain of the girls' Lancashire hockey squad for three years running, and was selected to play for the North of England ladies' hockey team.
A one-minute silence was held ahead of hockey matches across Lancashire last weekend.
Nicki Wilkinson, captain of Longridge Hockey Club, said: "Our beautiful, bubbly, crazy 1st team keeper Katy has been taken from our lives much too soon.
"Our club has been torn apart and our hockey family devastated. Katy will leave a big hole in all our hearts as well as our net."
Just days after her death, Katy was named in the England Hockey Team of the Week for her outstanding performance in her team's recent 2-1 victory over local rivals Garstang.
In a moving tribute to their team mate, and loyal friend, Longridge Hockey Club held a minute's applause before last weekend's game, with Katy's favourite tipple, a can of Fosters, sitting in the goal.
A funeral service to say goodbye to Katy will be held at 12pm on Thursday, November 21 at St Bartholomew's Church, Chipping.
The service will be followed by a celebration of Katy's life at the Sun Inn, Chipping.
Katy's family are inviting friends, colleagues and team mates to wear hockey kits, uniforms and bright colours at the service.
Donations in memory of Katy can be made to the family's chosen Endometriosis charity and Longridge Hockey Club .


mat-* components use cdk-expandable-input as a base and expand on top of that using @angular/material components.


Rupee vs Dollar: Rupee falls to 71.73 per dollar amid rising crude oil prices
by PTIAt the interbank foreign exchange, the rupee opened strong at 71.68, showing a rise of 3 paise over its previous closing
The rupee on Monday fell 2 paise to 71.73 against the US dollar in early trade, amid increased demand of the greenback by banks and importers.
Strengthening crude oil prices also impacted the rupee movement in the market, forex dealers said.
However, a higher opening in domestic equity markets and persistent foreign fund inflows restricted the rupee's fall, they added.
At the interbank foreign exchange, the rupee opened strong at 71.68, showing a rise of 3 paise over its previous closing.
The domestic unit, however, paired all its early gains to trade lower at 71.73 against the dollar.
The rupee on Friday had closed at 71.71 against the US dollar.
Meanwhile, brent crude futures, the global oil benchmark, surged 0.21 per cent to USD 63.52 per barrel.
Foreign investors have continued with their bullish outlook and bought equities worth Rs 305.72 crore on Friday, according to provisional data.
Domestic bourses opened on a positive note on Monday with benchmark Sensex was trading 143 points up at 40,502.82 in the opening session and Nifty rose 0.44 per cent to 11,967.20.
The dollar index, which gauges the greenback's strength against a basket of six currencies, fell 0.04 per cent to 98.25.
The 10-year government bond yield was at 6.50 per cent in morning trade.
Sensex Today LIVE: Share market rises further, Sensex climbs 220 points, Nifty above 11,965


modulepreload
"The modulepreload
keyword may be used with
link
elements. This keyword creates an external resource link. This
keyword is body-ok.
The modulepreload
keyword is a specialized alternative
to the preload
keyword, with a processing model geared toward
preloading module scripts. In particular, it uses the specific
fetch behavior for module scripts (including, e.g., a different interpretation of the crossorigin
attribute), and places the result into the
appropriate module map for later evaluation. In
contrast, a similar external resource link using the preload
keyword would place the result in the preload cache, without
affecting the document's module map.
Additionally, implementations can take advantage of the fact that module scripts declare their dependencies in order to fetch the specified module's
dependency as well. This is intended as an optimization opportunity, since the user agent knows
that, in all likelihood, those dependencies will also be needed later. It will not generally be
observable without using technology such as service workers, or monitoring on the server side.
Notably, the appropriate load
or error
events will occur after the specified module is fetched, and
will not wait for any dependencies.
The appropriate times to fetch and process the linked resource for such a link are:
When the external resource link is created on a link
element
that is already browsing-context connected.
When the external resource link's link
element becomes
browsing-context connected.
When the href
attribute of the link
element of an external resource link that is already browsing-context
connected is changed.
Unlike some other link relations, changing the relevant attributes (such as as
, crossorigin
, and
referrerpolicy
) of such a link
does not trigger a new fetch. This is because the document's module map has already been populated by a previous
fetch, and so re-fetching would be pointless.
The fetch and process the linked resource algorithm for modulepreload
links, given a link
element
el, is as follows:
If the href
attribute's value is the empty string,
then return.
Let destination be the current state of the as
attribute (a destination), or "script
" if
it is in no state.
If destination is not script-like, then queue an element
task on the networking task source given the link
element to
fire an event named error
at the link
element, and return.
Parse the URL given by the href
attribute, relative to the element's node
document. If that fails, then return. Otherwise, let url be the resulting
URL record.
Let settings object be the link
element's node
document's relevant settings object.
Let credentials mode be the CORS settings attribute credentials
mode for the crossorigin
attribute.
Let cryptographic nonce be the current value of the element's [[CryptographicNonce]] internal slot.
Let integrity metadata be the value of the integrity
attribute, if it is specified, or the empty string
otherwise.
Let referrer policy be the current state of the element's referrerpolicy
attribute.
Let options be a script fetch options whose cryptographic nonce is cryptographic
nonce, integrity metadata is
integrity metadata, parser
metadata is "not-parser-inserted
", credentials mode is credentials
mode, and referrer
policy is referrer policy.
Fetch a modulepreload module script graph given url, destination, settings object, and options. Wait until the algorithm asynchronously completes with result.
If result is null, then fire an event
named error
at the link
element, and
return.
Fire an event named load
at the link
element.
The following snippet shows the top part of an application with several modules preloaded:
<!DOCTYPE html>
<html lang="en">
<title>IRCFog</title>
<link rel="modulepreload" href="app.mjs">
<link rel="modulepreload" href="helpers.mjs">
<link rel="modulepreload" href="irc.mjs">
<link rel="modulepreload" href="fog-machine.mjs">
<script type="module" src="app.mjs">
...
Assume that the module graph for the application is as follows:
Here we see the application developer has used modulepreload
to declare all of the modules in their module graph,
ensuring that the user agent initiates fetches for them all. Without such preloading, the user
agent might need to go through multiple network roundtrips before discovering helpers.mjs
, if technologies such as HTTP/2 Server Push are not in play. In
this way, modulepreload
link
elements can be
used as a sort of "manifest" of the application's modules.
The following code shows how modulepreload
links can
be used in conjunction with import()
to ensure network fetching is done ahead of
time, so that when import()
is called, the module is already ready (but not
evaluated) in the module map:
<link rel="modulepreload" href="awesome-viewer.mjs">
<button onclick="import('./awesome-viewer.mjs').then(m => m.view())">
View awesome thing
</button>