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## If History Rhymes, Bitcoin (BTC) May Retrace To High-$5,000s What a week it’s been for Bitcoin (BTC). In the past 72 hours, the asset has rallied from the low-$6,000s to $7,500, then to$6,900 (current), driven by a newfound sense of hype, passion, and belief in the cryptocurrency cause.

However, as the market has cooled — temporarily at least — analysts have begun to get more level-headed, doing their utmost to figure out where cryptocurrencies could head next. This time for pondering has led to some staggering revelations.

## Bitcoin May See Retracement

Popular trader Josh Rager recently suggested that there are amazing similarities between 2015’s recovery out of a bear market.

During that cycle, BTC bottomed around $200, accumulated around$300 for months, went parabolic to tap $500, then saw a pullback. What’s more, the three-day Super Guppy, a key long-term trend indicator, didn’t signal a “buy” (green) until after the pullback.$BTC – 3D chart Super Guppy

Amazing similarity between the last bear market prior to uptrend

Bitcoin had a similar parabolic push out of accumulation, followed by a pullback and uptrend

Watching for a potential pullback where I'll add more to the stack pic.twitter.com/JABErMhlMq

— Josh Rager  (@Josh_Rager) May 12, 2019

Sound familiar? Well, that’s because Bitcoin is seemingly doing effectively the exact same thing, but in a different price region. If history is followed to a tee, Bitcoin may fall a further ~20% from current levels, and may then begin the second phase of accumulation, which will last up to seven months. From there, BTC should begin to rally parabolic into the block reward halving event, setting the stage for the next fully-fledged bull run.

$BTC The last parabola that broke us out of a bear market resulted in a near 7 month consolidation. With alts at their ATL supports against USD, if this sort of consolidation happens again on BTC after it tops (likely), we could be in for the REAL #altseasonpic.twitter.com/c3WwncLyda — HornHairs (@CryptoHornHairs) May 11, 2019 ## Price Action Still ‘Hella’ Bullish It is still important to note that the current price action seen with cryptocurrencies has been surprisingly bullish. Per previous reports from NewsBTC, as BTC tapped year-to-date highs on Saturday, the daily volume at BitMEX, the foremost crypto futures platform, hit a jaw-dropping nominal$10 billion. Potentially equally as importantly, Grayscale’s Bitcoin Trust surmounted $50 million on Friday, signaling interest in cryptocurrency from accredited investors, most of which are purported to be institutions. The most important part, this volume surge has been catalyzed by those already in the space. As NewsBTC’s Joseph Young postulated on Twitter, “existing money in the crypto market [is] coming back due to overall growth in confidence/comfort.” And as researcher Alex Krüger adds, amid the recent price action, the “Bitcoin” search term has seen not too much of an increase in volume. This directly implies that once consumers wake up to the latest cryptocurrency rally, the run will be even larger and more violent than it is now. But that begs the pressing question — will consumers down the red pill once again? This$BTC rally is happening without the general public yet involved en masse. This is very bullish. pic.twitter.com/H0wMEn56ND

— Alex Krüger (@krugermacro) May 11, 2019

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# Revision history [back]

1 . If there are multiple files referring to one node, which code is executed when we run that node?

The code that is part of the main entry point, which is by default: int main(int argc, char *argv[]).

Note that this is no different from how you'd specify a target in (GNU) Make: it's perfectly valid for binaries to consist of multiple object files.

2 . Why do we add the header files to the add_executable? I never saw this before.

This is often done for CMake generators that generate projects for tools / IDEs that don't show all files by default (in a project explorer view fi), but only those files that are "part of" a target. Qt Creator is one of those, as is Visual Studio.

Note: these questions are not ROS specific: your first question is basic CMake / Make, the second is a 'trick' (and listed as one of the CMake antipatterns (but read the Errata section at the bottom of the page).

1 . If there are multiple files referring to one node, which code is executed when we run that node?

The code that is part of the main entry point, which is by default: int main(int argc, char *argv[]).

Note that this is no different from how you'd specify a target in (GNU) Make: it's perfectly valid for binaries to consist of multiple object files.

2 . Why do we add the header files to the add_executable? I never saw this before.

This is often done for CMake generators that generate projects for tools / IDEs that don't show all files by default (in a project explorer view fi), but only those files that are "part of" a target. Qt Creator is one of those, as is Visual Studio.

Note: these questions are not ROS specific: your first question is basic CMake / Make, the second is a 'trick' (and listed as one of the CMake antipatterns (but read the Errata section at the bottom of the page).

Edit:

But I don't understand. Like when we call that node , which file is executed? are they all executed? which order?

Please read the link to the wikipedia article about entry points. The question "which file is executed" doesn't really make sense in the context of compiled languages and object code.

1 . If there are multiple files referring to one node, which code is executed when we run that node?

The code that is part of the main entry point, which is by default: int main(int argc, char *argv[]).

Note that this is no different from how you'd specify a target in (GNU) Make: it's perfectly valid for binaries to consist of multiple object files.

2 . Why do we add the header files to the add_executable? I never saw this before.

This is often done for CMake generators that generate projects for tools / IDEs that don't show all files by default (in a project explorer view fi), but only those files that are "part of" a target. Qt Creator is one of those, as is Visual Studio.

Note: these questions are not ROS specific: your first question is basic CMake / Make, the second is a 'trick' (and listed as one of the CMake antipatterns (but read the Errata section at the bottom of the page).

Edit:

But I don't understand. Like when we call that node , which file is executed? are they all executed? which order?

Please read the link to the wikipedia article about entry points. The question "which file is executed" doesn't really make sense in the context of compiled languages and object code.

code (as there isn't necessarily a 1-to-1 correspondence).

format: plain


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# I'm Optimistic With The Results!

Probability Level 2

If 8 unbiased coins are tossed simultaneously, what is the probability of getting at least 6 heads?

×

Problem Loading...

Note Loading...

Set Loading...


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# January, 2012

## Questions with only one answer

There are many questions with only one answer. Or at least, one sensible answer. If someone asks you “Are you dead?”, the only possible answer you can give is “no”. “Does my bum look big in this?” “Of course not, dear, it looks lovely”. And if you say “Is there

## Free for all Friday

Well… it’s not really a free-for-all Friday as I’m on a train to Bristol, but you can still leave me questions here and I’ll respond when I’m back in front of a computer. Any A-level maths topics – including Further Maths – you’re struggling with, I’ll be happy to help

## The Line of Best Fit (Linear Regression)

Have a look at this picture. What do you notice? “It’s a straight line, Colin!” Very good. You could get a ruler out and draw a straight line through the points. Why would you bother doing such a thing? Well, the idea is that if you can model a data

## How to do calculus with $a^x$

Every time you’re asked to differentiate $a^x$ and you say $xa^{x-1}$, a kitten dies. A kitten that had been raised lovingly by an orphan, her only friend in the whole wide word – DEAD, just because you couldn’t be bothered to learn how to do calculus properly. I hope the

## Free for all Friday

It’s Friday, which means… It’s free for all Friday time! Any questions you’ve got, about maths or anything else, pop them in the comments. I’m in the middle of the deadline doldrums (the next Dummies book won’t write itself, you know!) but I’ll pop in once in a while to

## Why radians rock (and degrees don’t)

If I could wave a magic wand and overhaul just one thing to make the world a better place, I’d have a tough choice. Would I get rid of the QWERTY keyboard in favour of a more sensible layout? Would I make the English language fonetik? Would I take maths

## How to do… R-sin-alpha questions

There’s a very typical question in C3 papers that looks something like: “Express $3 \sin (x) + 5 \cos (x)$ in the form $R \sin (x + \alpha)$.” (Sometimes it’s a different trig function, or it may have a minus sign in it, but the same principle works for any

## Free-for-all Friday

It’s C1 day (and C2 for many)! I hope it’s gone well if you were sitting it today. Anything that bothered you about the paper? It’s your chance to pick my brain about whatever’s on your mind about maths. Got a question you’ve been struggling with? Got a topic you

## Aces in the pack

Today’s big question is about poker. For some reason, statistics books shy away from gambling, reasoning that it’s somehow harmful or evil. It’s true, it can be addictive (although so can, for instance, using the computer or reading books) but it’s actually the whole reason statistics exists. (This Italian lad,

## Core 1 graphs: all you need to know

Part I: Basic shapes of Core 1 graphs There are a handful of basic shapes of graphs you need to know about for C1, namely: – reciprocal graphs ($y = \frac{1}{x}$) – reciprocal-square graphs ($y = \frac{1}{x^2}$) – straight line graphs ($y = x$) – you know this one, right?

No spam ever, obviously.

##### Where do you teach?

I teach in my home in Abbotsbury Road, Weymouth.

It's a 15-minute walk from Weymouth station, and it's on bus routes 3, 8 and X53. On-road parking is available nearby.


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# Use AES-256 Or AES-CTR-256 For One Block?

I am have one block of data to encrypt that is 32-bytes long. Is it better to use the AES-256 primitive for this single block (not a stream) or AES-256-CTR, and also would there be any cons or pros to using one and not the other such as to security or something else I am not thinking about.

This question relates to this question: AES-CTR-256 Sub Keys With Encrypted Sub Key Index

In relation I was wondering if I should just use the AES-256 to encrypt the sub-key with the master key instead of using the nonce and XOR operation, or maybe it would be better to use AES-256-CTR and use a nonce for the master key prefixed to the file.

-
AES256 refers to the key size. If I'm not wrong the block size is constant at 128 bits, isn't it? – srgblnch Jul 21 '14 at 15:12
@srgblnch - indeed. – hunter Jul 21 '14 at 15:13

Like said in the comments, a 256-bit message is two blocks of AES, no matter the keysize.

The main issue with ECB mode (i.e. using AES directly on 128-bit blocks) is that you leak whether two blocks are equal. When encrypting perfectly random data, that means there's a $2^{-128}$ chance the two parts of the key are equal, and the attacker knows that. The probability you see a collision between halves of any two keys becomes significant once you reach about $2^{63}$ keys, but that probably doesn't help the attacker much unless he knows one of the keys already.

In contrast, with CTR mode the chance that you get two equal ciphertext blocks is the same, but that only leaks the fact that the two halves are not equal, which helps the attacker a minuscule amount – and is always leaked by ECB. So CTR is obviously better. (Assuming you can ensure unique nonces.)

However, lets get realistic. In CBC mode, with random and unequal IVs, the attacker could instead learn the XOR of two key blocks with the same probabilities as above. Knowing that helps an attacker about as much as knowing an equality. Yet, we consider CBC a secure enough mode to use.

So I wouldn't really worry about it, but if you can easily use CTR it only costs you the IV storage. Even 128-bit keys would be secure, though.

Note: The above is only the case when you encrypt purely random data. With non-random data ECB is almost always broken. The above also assumes the attacker can't get a chosen plaintext encrypted or anything similar.

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I really appreciate the very well written and informative answer. – kmcguire Jul 22 '14 at 18:14


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# Correlations in Ising mean-field theory

I am reading the book "Critical Dynamics - A Field Theory Approach to Equilibrium and Non-Equilibrium Scaling Behavior" (sections 1.1.2 and 1.1.3) and have been somewhat confused about the meaning of a mean-field theory (related questions: 1, 2).

To summarise, in deriving the Ising mean-field theory, the spin variable at site $$i$$ is written as $$\sigma_i = m + (\sigma_i - \langle \sigma_i \rangle),$$ where $$m$$ represents the mean magnetisation, and then the Ising Hamiltonian, $$H = -\frac{1}{2} \sum_{i,j}J \sigma_i \sigma_j - h \sum_i \sigma_i$$ ($$h$$ is a magnetic field) is expanded by neglecting the term $$(\sigma_i - \langle \sigma_i\rangle)(\sigma_j - \langle \sigma_j \rangle)$$. Then the partition function is computed from which a consistency equation for $$m$$ is derived, namely $$m = \tanh \big(\beta (h+J m) \big).$$ So far so good.

Next, the author proceeds to compute the correlation function via the response function by considering a site-dependent magnetic field by writing $$\chi_{ij} = \frac{\partial m_i}{\partial h_j} = \beta \langle (\sigma_i - \langle \sigma_i\rangle)(\sigma_j - \langle \sigma_j \rangle )\rangle.$$ The response function $$\chi$$ on the lhs can be computed by taking appropriate derivatives from the consistency relation for $$m$$, from which the so-called Ornstein-Zernicke form for the correlation funciton on the rhs is obtained. This form is also later obtained from the coarse-grained Landau free energy.

It appears to me that this is an inconsistent approach, since we first ignore the correlations between spatial fluctuations (by omitting the $$(\sigma_i - \langle \sigma_i \rangle)(\sigma_j - \langle \sigma_j \rangle)$$ ) in order to obtain the partition function, from which free energy and $$m$$ are evaluated; and then we go back to compute the correlations that we had assumed to be zero and get a non-zero value for it!

What am I missing here?

PS: I also wonder whether "mean-field" theories, in general, should fully ignore any correlations between the variables, or do they still account for the correlations in some sense?


text:
My Math Forum Inequality constraint NLP problem

 Calculus Calculus Math Forum

 February 15th, 2015, 03:28 PM #1 Newbie   Joined: Feb 2015 From: London Posts: 1 Thanks: 0 Inequality constraint NLP problem Hello, Regarding subject, I have a difficulty with following NLP problem. Max f(x) = X1 / (X2+1) s.t. X1 - X2 <= 2 X1>=0 X2>=0 Actually, I could get the answer with simple graphical way that optima is 2 at X = [2;0] However, I could not get the solution with Lagrange multiplier method. After partial differentiation of L(X1, X2, lamda), I could get 3 equations and 3 variables, but two equations are parallel so it does not give me a solution. Could anyone solve this problem? And also, could I get Kuhn-Tucker conditions (KKT) of this solution? In addition, can't we say this problem is convex? Thank you. Last edited by skipjack; February 15th, 2015 at 11:26 PM.
 February 15th, 2015, 06:27 PM #2 Math Team   Joined: Dec 2013 From: Colombia Posts: 7,675 Thanks: 2655 Math Focus: Mainly analysis and algebra To maximise a quotient, you minimise the denominator and maximise the numerator. Thus we want $x_2 = 0$ and then $x_1 - x_2 \le 2 \implies x_1 = 2$. Of course, you might suggest that we could add $a \gt 0$ to each of these values and still satisfy the inequalities. This would work if$${x_1 + a \over x_2 + 1 + a} \gt {x_1 \over x_2 + 1}$$ Since we have that $x_2 = x_1 - 2$ we then have \begin{aligned}{x_1 + a \over x_1 - 1 + a} &\gt {x_1 \over x_1 - 1} \\ (x_1 - 1)(x_1 + a) &\gt x_1(x_1 - 1 + a) \\ x_1^2 + (a-1)x_1 -a &\gt x_1^2 + (a - 1)x_1 \\ a \lt 0\end{aligned} But we have already stated the $a \gt 0$, and indeed $a \lt 0$ violates the minimum value of $x_2$. So $(x_1, x_2) = (2,0)$ is the only solution.
 February 15th, 2015, 11:51 PM #3 Global Moderator   Joined: Dec 2006 Posts: 20,942 Thanks: 2210 As 0 $\leqslant$ X1 $\leqslant$ 2 + X2, one must take X1 = 2 + X2 to maximize X1/(X2 + 1). That gives f(x) = (2 + X2)/(X2 + 1) = 2 - X2/(X2 + 1). As X2 $\geqslant$ 0, the maximum value of f(x) is 2, achieved when X2 = 0 (and X1 = 2). It would have been better to write f(X1, X2) instead of f(x).

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text:
WOMBAT – A program for Mixed Model Analyses by Restricted Maximum Likelihood

## Fitting X-linked genetic effects

xlink_example.par
   PED pedigree.dat  +SEX
DAT xldata.dat
fixeff 2
animal
xlink
record
end
ANAL UNI
MODEL
RAN animal NRM
RAN xlink SEX
FIX fixeff
TR record
END
VAR animal 1
16
VAR xlink 1
4
VAR error 1
20

WOMBAT has an in-built facility to allow for X-linked genetic effects. In brief, this is fitted as an additional random effect and WOMBAT can calculate the appropriate inbreeding coefficients and set up the inverse of the corresponding relationship matrix directly from the list of pedigree information.

The box on the right shows an example for the parameter file for a univariate analysis fitting X-linked genetic effects. The specific requirements are:

1. The pedigree file needs to have a fourth column with a code of 1 or 2, specifying the number of X chromosomes for this individual (Usually 1=males, 2=females).
The code +SEX after the name of the pedigree file tells WOMBAT to look for this additional column.
2. The data file needs to have a column, here named xlink, which contains the identity of the individual, i.e. which is a duplicate of the column for animal.
3. In the model part of the parameter file xlink needs to be specified as an additional random effect with covariance option SEX.
The latter instructs WOMBAT to set up the inverse of the X-linked relationship matrix from the list of pedigree information.
4. Finally, the parameter file needs to contain a VAR statement for the xlink effect, together with a starting value for the pertaining variance component.
bib
@ARTICLE{fern90,
author = {Fernando, R. L. and Grossman, M.},
title = {Genetic evaluation with autosomal and X-chromosomal inheritance},
journal = {Theor. Appl. Genet.},
volume = {80},
year = {1990},
pages = {75--80},
url = {http://dx.doi.org/10.1007/bf00224018}
}


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# Cumulative impact of anti-sea lice treatment (azamethiphos) on health status of Rainbow trout (Oncorhynchus mykiss, Walbaum 1792) in aquaculture

## Abstract

Despite its widespread use in aquaculture, the impact of chemical anti-sea lice treatment on salmonids following application in a commercial farm has not been previously reported. This work reports the cumulative effect of three consecutive anti-sea lice treatments using azamethiphos on the health status of aquaculture reared rainbow trout through the investigation of clinical chemistry, histopathology and proteome expression. The serum biomarkers showed decreasing trends in total protein, albumin and potassium concentrations and an average increase of total bilirubin and phosphate concentration towards the end of the treatment period. Principal component analysis clearly distinguished correlated pairs of biomarkers and also demonstrates a shift from acute to chronic effects as treatment progresses. Proteomic analysis confirmed alterations of proteins involved in clot formation, immune reaction and free heme binding. Tissue damage after the series of delousing treatments, exhibited increased deposits of hemosiderin. Results from this study suggest an impact of azamethiphos on trout health through intravascular haemolysis and consequently from pathophysiologic process of haemoglobin metabolism and its products, causing chronic kidney injury from iron deposits. This is the first report to demonstrate in fish the impact of active iron accumulation in different organs from physiological processes that can seriously impair normal function.

## Introduction

Large marine trout aquaculture is a rather new but significant food industry in Europe with a production of 153,954 tonnes in 20161, roughly a tenth the size of European salmon production (1,488,434 tonnes). This is why today, the various aquaculture industries are recognized as major forms of primary production having important economic, social and environmental impact. Like in all other farm animal husbandry, disease represents an important barrier to production that must be addressed. However, in the marine environment, sea lice infestations with caligid copepods Lepeophtheirus salmonis and Caligus elongatus are commonly found in open sea cage systems and represent the primary cause of economic loss and impaired animal welfare2,3. The total costs including anti-sea lice treatments costs and losses resulting from the decline in fish growth, were estimated to be USD $480 M annually for the global salmonid aquaculture industry in 20064. More recently, the impact on Norwegian aquaculture alone for 2011 is estimated at USD$436 M, excluding the costs from Chile, UK, Canada, Ireland, Faeroes and USA salmonid aquaculture5. Therefore, vigorous attempts are underway to reduce sea lice numbers by various treatment methods based on chemical medication, mechanical removal or environmental manipulation.

Chemical measures to control sea lice populations include the application of chemical compounds such as organophosphates (azamethiphos in particular), synthetic pyrethroids (deltamethrin and cypermethrin), enamectin benzoate and hydrogen peroxide in the form of a bath treatments or medicated feed6. Azamethiphos (AZA) is an organophosphate insecticide and the active ingredient in the powder formulation Salmosan® used for bath treatments against sea lice, with a recommended dosage of 0.1 to 0.2 mg L–1 for up 1 h exposure. Azamethiphos is registered for use in salmon aquaculture in Norway, Ireland, Scotland and Chile. Although commonly used in salmon aquaculture it was also shown to be affective against sea lice in trout7,8. Organophosphorus compounds are not effective against juvenile stages, with only mature sea lice (stages pre-adult to adult) affected during treatment. Therefore, in order to ensure complete eradication of the lice, treatments may need to be repeated after 10 to 20 days and again after a further 14 days. Azamethiphos is a neurotoxic agent, causing acetylcholinesterase (AChE) inhibition9. The function of AChE is to mediate the hydrolysis of the nurotransmitter acetylcholine, which subsequently terminates transmission through the synaptic membrane. AChE is also present in membranes of red blood cells, but its function is unknown10. The reported acute 96-h LD50 for rainbow trout exposed to azamethiphos is 0.2 mgL−111. Atlantic salmon tolerates a single 1-hour exposure of 0.5 mgL−1 and three 1-hour doses of 0.3 mgL−1 repeated at weekly intervals. However, some mortalities were detected in fish exposed to 1.0 mgL−112. Therefore, we hypothesized that repeated minor exposures may cause a cumulative poisoning effect due to acetylcholinesterase inhibition, which may be reflected in the fish health status. Acetylcholinesterase levels have been found to return to normal several weeks following exposure12.

Fish welfare has become increasingly important in aquaculture and continuous health monitoring during sea lice treatments is essential to ensure fish wellbeing. Clinical chemistry analysis has been previously undertaken in rainbow trout13 and has proven a useful tool to analyze the health status of fish following pollution exposure14, feed trials15,16, disease monitoring and diagnosis17,18, toxicological studies19,20 and to investigate pathophysiology21. There is therefore a need to investigate the clinical impact of Azamethiphos under commercial aquaculture conditions to detect the specific organ injury, potentially leading to organ impairment.

Depending on their usage, different sets of biomarkers can be used to monitor an impact on a specific target organ. The only biomarker previously used to measure the impact of sea lice treatment is the AChE assay, which is specific for organophosphate toxicity and is measured in brain tissue homogenates22. We hypothesize that consecutive AZA treatments cause a cumulative effect on fish health through a cascade of red blood cell intravascular degradation and accumulation of oxidized ferric (Fe3+) in different tissues of the rainbow trout. This can lead to kidney damage, as nephrotoxicity is evident in animal and human cell models following exposure to organophosphates23. Therefore, the objectives of the present study were to investigate the possible causes of impaired fish health after consecutive azamethiphos sea lice treatments under commercial aquaculture conditions using a variety of clinical chemistry assays. Moreover, the goal was to link specific organ injury response with in-depth tissue structure analysis. Finally, by using rapid methods of blood biochemistry analyses combined with histopathology and proteome expression, our aim was to investigate the possible pathophysiological mechanisms behind the impacts observed after repeated anti sea lice treatments.

## Materials and Methods

### Study location, sea lice treatments, sampling regime and water quality

Samples were provided by a commercial trout farm (Kames Fish Farming Ltd.) based in Loch Melford, western Scotland. A population of all-female diploid (AquaGen) Rainbow trout from a 70 m pen were routinely monitored for the presence of sea lice Lepeophtheirus salmonis and Caligus elongatus. The cage contained approximately 22 000 fish, with a mean body weight of 2.5 kg at a stocking density of 11.7 kg/m3. In summer 2017, after a sea lice infestation (>3 lice/fish), the pen under investigation was treated using Salmosan®Vet (azamethiphos, 500 mg/g powder) bath for three consecutive treatments, with 12 days between first and second treatment and 21 days between the second and third treatment. An enclosed plastic skirt (1800 m3) was used, and fish were treated with 0.2 ppm of azamethiphos for 45 min with additional oxygen injection as suggested by the manufacturer. Sampled fish were randomly selected by hand-netting and anesthetized with 100 mg/L MS-222 (Sigma) following manufacturer’s instructions. A total of 30 fish were sampled for sea lice counts and blood collected for husbandry health analysis immediately before the bath treatment (representing reference values) and 24 hours, 48 hours, 4 days and 10 days post-treatment. Pre-treatment fish were sampled from the pool of crowded fish, in order to eliminate the effect of stress. Blood samples were taken from the caudal vein by using lateral approach. After withdrawal, bloods were placed in an empty Eppendorf tube and were allowed to clot for 3 hours at +4 °C. Following this time, the bloods were centrifuged for 10 minutes at 1300 × g, the serum was separated and stored at −80 °C until analysis within three months. Fish (n = 5) were selected for routine histopathological analysis of gills, liver, spleen and kidney tissue before the first treatment and 10 days after the third azamethiphos treatment. Daily values of hydrographic parameters were measured at the site using a HI 9828 Multiparameter probe (Hanna Instruments Srl, Italy) for salinity (psu), dissolved oxygen (DO, %), seawater temperature (T, °C), and pH.

### Serum clinical chemistry analysis

All serum samples were analysed on the Daytona RX (Randox Laboratories Ltd., Crumlin, UK) clinical chemistry analyser at 37°C, using commercial kits (Randox Laboratories Ltd., UK) following the producer’s instructions. Biochemical analysis was undertaken for 13 biomarkers (alkaline phosphatase (ALP), alanine aminotransferase (ALT), total protein (TP), albumin (ALB), total bilirubin (TBIL), lactate dehydrogenase (LDH), creatinine (CREA), inorganic phosphorus (P), potassium (K), iron (Fe), copper (Cu), haemoglobin (HB) and glucose-6-phosphate dehydrogenase (G-6-PD). Haptoglobin (HP) concentrations were quantified using the Fish Haptoglobin ELISA Kit (My BioSource, USA).

### Histopathological analysis

Routine histological analyses was undertaken. In brief, tissues were preserved in 4% buffered formalin for 24–72 h, transferred to 50% ethanol before being dehydrated through 70%, 96% and absolute ethanol, cleared in xylene and soaked in paraffin wax using an automatic Excelsior AS processor (Thermo Fisher Scientific Inc., USA). Paraffin block sections were cut at 2 μm using a microtome Microm HM 355 S (Thermo Fisher Scientific Inc., USA), dewaxed overnight in at 60 °C and stained with modified Harris haematoxylin and Young’s eosin stains (Thermo Fisher. Scientific Inc., USA). To demonstrate ferric iron in tissue sections, Pearls Prussian blue histochemical method was used (Merck, Germany). After staining, sections were dehydrated in increasing concentrations of ethanol (70–100%), cleared in xylene and mounted in Biomount DPX (Biognost, Croatia). Microphotographs were taken using Axio Scan.Z1 scanning light microscope (Zeiss, Germany) and were edited using ZEN 2.3 imaging software. Severity of histopathological alterations was assessed using semi-quantitative approach as control (0), mild (+), moderate (++) and severe (+++).

### Serum proteome analysis

Proteomic analysis was undertaken on six serum samples consisting of two pooled response replicates from 5 fish, sampled before, 48 h and 4 days after the second azamethiphos treatment. The serum was depleted of highly abundant albumin using salt-ethanol precipitation, alkylated and digested by trypsin following the procedure described in Bilić et al.24. Next, one-hundred micrograms of peptide from each sample was labelled with Amine-Reactive Tandem Mass Tag Reagents (Thermo Fisher. Scientific Inc., USA) according to the protocol supplied by the manufacturer. The labelled peptides were solubilized in 2% acetonitrile with 0.1% trifluroacetic acid, combined and fractionated on a nanoflow uHPLC system (Thermo RSLCnano) with online analysis by electrospray ionisation (ESI) mass spectrometry on an Orbitrap Elite mass spectrometer (Thermo Fisher Scientific Inc., USA). Samples were combined at equal amounts into 2 sets, the first consisting of 1 internal standard, 2 pooled samples before and 2 pooled samples 48 h after the treatment and second consisting of 1 internal standard and 2 pooled samples 4 days after the treatment. Acquired MS/MS spectra were analyzed for protein identification and quantification using Proteome Discoverer software 2.1 (Thermo Fisher Scientific Inc., USA). Protein identifications were assigned using the Mascot algorithm against the teleost taxonomical group in the NCBI database, allowing a mass tolerance of 10 ppm for the precursor and 0.6 Da for MS/MS matching. Quantification was performed using abundances of reporter ions based on signal to noise ratio values or intensity. Abundances ratios were obtained for each protein by comparing with values of the corresponding internal standard and then used to calculate average fold change ratio groups. To investigate the relationship among different time-points and different proteins with respect to abundance, the dataset was analysed using hierarchical cluster analysis based on Euclidean distance as previously described in Braceland et al.25 and the results were illustrated in the form of a heat map using ArrayStar software (DNASTAR, USA).

### Data processing and statistical analysis

All statistical analyses was performed using multiple linear regression operating on R language package (version i386 3.0.3, R Foundation for Statistical Computing, Austria) and divided into univariate and multivariate parts. The univariate part included analysis of variance with post – hoc tests on each variable, while multivariate analysis was performed by principal component analysis (PCA) on selected variables. A Box–Cox method was conducted to find the best transformation of the variables in terms of the normal distribution. In addition, this kind of transformation homogenizes the variances of different groups of data, linearizes the associations between skewed variables and reduces the problem of extreme outliers found within the data. The comparison of biochemical parameters in trout serum between different time points before and after the treatment was performed using a Welch’s ANOVA and post-hoc multiple comparisons using nonparametric Games – Howell test with level of significance p < 0.05. Groups that were not significantly different were assigned the same letter of the alphabet, as reported in tables and diagrams. To test the differences between overall averages of three treatments for each endpoint, more conservative p < 0.001 level of significance was used. Outliers were detected in multivariate fashion using Mahalanobis distance with robust covariance matrix estimation (MCD – minimum covariance determinant) and a 0.05 cut-off value, with outlying cases removed listwise. Principal component analysis (PCA) was performed on clinical chemistry endpoints with the first two principal components, accounting for 42.7% of total variance (PCA1–25.3%, PCA2–17.4) retained for interpretation.

### Ethical statement for animal experimentation

This study was approved by University of the West of Scotland ethics committee. Samples were collected by the fish farm as part of routine fish husbandry practice and therefore not regulated by the Home Office. Fish were anaesthetised before a blood samples was taken and euthanized before tissue samples were taken using MS-222 (Sigma) following the manufacturer’s instructions. All methods were performed in accordance with the relevant guidelines and regulations covered by EC directive 2010/63/EU “Protection of animals used for scientific purposes”.

## Results

### Seawater conditions and fish general health

The seawater temperature ranged between 14.1 °C and 15.0 °C during the study period with an average salinity was 33 ppt at the surface and 34 ppt at 5 m depth. The pH values were between 7.8 and 8.2 and the dissolved oxygen concentration was in range of 8.1 to 9.0 mg/L. The trout used in the study had an average fork length of 54.0 ± 4.5 cm and body weight of 2.5 ± 0.42 kg. Adult female lice numbers were >3 per farmed fish prior to treatment. Before the first treatment, trout were assessed by the farmers to be in good nutritional condition and vigorous. At the end of the third exposure, trout displayed obvious weakened condition with lower appetite and behavioural changes observed by the farmers. There were no immediate mortalities after the first two azamethiphos exposures, whereas mortalities occurred at the rate of 1% after the third exposure.

### Serum clinical chemistry

The complex relationship between clinical chemistry biomarker expression before, immediately after, 24 h, 48 h, 4 days and 10 days after each of the three individual 45 min exposures to azamethiphos (as Salmosan®) is illustrated in Fig. 1. This graph uses violin plots (mean ± SE) with different letters to indicate significant differences (p < 0.05) and shows all three consecutive treatments over a time line. Differences in biomarker expression following treatment in each exposure are compared with the data collected before the treatment and between the different sampling points.

In summary, in an early acute reaction, significant increases in TP, CREA and HB expression were observed immediately after the first treatment (Fig. 1d,g,l), with TBIL, TP, ALB and CREA (Fig. 1c,d,e,g) also significantly increased immediately after the third treatment. At 24 h and 48 h after the first treatment, in a late acute reaction, median activity of ALT and Cu concentration was significantly lower than before the treatment (Fig. 1b,k), while lower activity of ALP and LDH occurred only 24 h after the treatment (Fig. 1a,f). The same time points in the second treatment showed significant increases in TBIL, CREA, Fe and HB (Fig. 1c,g,i,l). After the third treatment higher concentrations of TP, ALB, CREA, Fe and HB were present at both late acute reaction time points (24 h and 48 h) (Fig. 1d,e,g,i,l).

The time points 4 and 10 days post treatment represent a prolonged reaction. Following the first treatment, ALT activity was significantly higher after 4 days and significantly lower 10 days post treatment (Fig. 1b). Concentrations of TP, CREA and P were statistically higher when compared to pre-treatment samples (Fig. 1d,g,j), while K concentrations were higher at 4 days and lower after 10 days post treatment (Fig. 1h). Concentrations of TBIL, CREA and P were significantly higher after the second treatment (Fig. 1c,g,i). Following the third treatment, ALB levels were significantly higher in both time points (Fig. 2a), while concentrations of CREA, K and Fe were higher 4 days post treatment (Fig. 1g–i) and ALT activity and concentrations of TBIL and TP were higher 10 days post treatment (Fig. 1b–d).

In addition, the data from all time points per treatment for each biomarker were pooled to enable comparisons between treatments presented as a mean line with the level of significant difference set as p <  0.001, presented as letters in a box. From this pooled data it is can be seen that ALT activity significantly decreased following the third treatment and concentrations of TP, ALB and K significantly decreased after the second and third treatments (Fig. 1b,d,e,h). Concentrations of TBIL and P were significantly increased after the second and third treatments (Fig. 1c,j), while concentrations of Fe and Cu were significantly lower following the second treatment (Fig. 1i,k).

Azamethiphos exposure had a significant impact on G-6-PD activity and HP concentration (results show 3 time points per treatment) (Fig. 2). During the second treatment, a significant increase in G-6-PD enzyme activity was seen from before (165.15 ± 12.94 U/L) to 4 days post treatment (399.9 ± 30.48 U/L), while no significant change was observed during the first and third treatments (Fig. 2a). Similarly, during the second treatment HP concentration showed a significant decrease from 63.96 ± 7.65 μg/mL before the treatment to 22.78 ± 6.36 μg/mL at 48 h post treatment (Fig. 2b).

Principal Component Analysis (PCA) bi-plot results of serum endpoints are displayed in Figs 3, 4. Depicting clinical chemistry endpoints as arrows in the space spanned by the first two PCs, relations between variables can be discerned (Fig. 3a). Arrows that point along the same line imply high correlations between corresponding endpoints: pointing the same direction means highly positive correlation, while opposite direction stands for a negative correlation. Arrows that are approximately perpendicular imply low or no correlation between endpoints. Also, length of the arrow denotes the quality of representation for the corresponding variable in the space of first two PCs: arrows that extend close to the unit circle are very well explained by the two PCs, while short arrows imply that the variable does not have a good representation in the first two dimensions. In this analysis, the first PC is dominantly composed of variables TP, ALB and Cu concentrations, where ALT activity also has some contribution. The second PC is dominated by concentrations of P and TBIL and ALP activity, with some contribution from concentrations of Fe and K, but in the opposite direction. ALT activity also contributes to this dimension. These two PCs roughly define two groups of variables that are highly correlated within the group but have low correlations between the two groups. ALT activity is the variable that has moderate correlation with both PCs. PCA diagram Fig. 3b with data corresponding to measurements taken before administering the chemical, shows that from the first treatment to the last, there was a shift in the negative direction of PC1. PCA diagram Fig. 3c shows all clinical chemistry data grouped by the treatment criteria, with confidence ellipses drawn for each treatment defining a confidence region for the two-dimensional mean of the treatment, with all time points. Higher separation of ellipses, as shown on the graph, implies that the means of those groups of data are significantly different. This diagram with data grouped by treatments shows that there is a significant shift of location between the overall mean of the first treatment and the means of the other two. The shift is mostly along the PC2 with some change in PC1. This corresponds with an increase in variables TBIL and P concentrations and a decrease in concentrations of TP and ALB.

PCA diagram (Fig. 4a) from the first azamethiphos treatment showed well separated time point groups, with measurements after treatment, up to 48 h later, showing a significant shift in the negative direction of PC2 compared to measurements before treatment. After 4 days, there is a large increase, mostly in variables aligned with PC1, including ALT (Fig. 3a). After 10 days, location is shifted closer to measurements recorded before the treatment. PCA diagram (Fig. 4b) from the second azamethiphos treatment showed very poor separation of time point groups, where ellipses for measurements up to 48 h are mostly overlapped. Only at 4 days after the treatment there was some significant separation, mostly in the positive direction of PC2. The PCA diagram (Fig. 4c) from the third treatment showed a similar pattern to the second treatment. However, at 4 days after the treatment, there was a more pronounced location shift in the negative direction of PC2, while after 10 days this location has shifted closer to measurements recorded before the treatment.

### Histopathological analysis

The histological findings observed in the gill, liver, spleen and kidney of trout before (representing reference appearance) and after the series of azamethiphos treatments are shown in Figs 5 and 6. The gills from trout before the treatment showed normal anatomy of primary and secondary lamellae, with some mild alterations (+) (Fig. 5a). After the treatment, histopathological screening revealed moderate structural and functional alterations in gill tissue, extensively to the secondary lamellae with presence of severe haemorrhages (+++) (Fig. 5b). Liver from the fish before the start of azamethiphos treatments showed good condition with hepatocytes filled with glycogen (0) (Fig. 5c), whereas in the liver after the treatment, mild necrosis of hepatocytes (++) with severe dilatation of sinusoids (+++) and glycogen deposits were scarce (Fig. 5d). The spleen tissue from trout before the treatment showed normal appearance (0) (Fig. 5e), while after a series of delousing treatments excessive hemosiderin deposits (+++) and incidence of granulomatous lesions could be seen (++) (Fig. 5f). Kidney sections of the trout before the treatments showed a normal structure such as glomeruli and Bowman’s space with uniform renal tubules and interstitial hematopoietic tissue (0) (Fig. 6a). However, tissue damage of kidney after delousing treatments showed glomerular atrophy (++) and necrosis (++) (Fig. 6b), with an increased deposit of hemosiderin in proximal and distal tubules revealed using Pearls blue histochemistry method (+++) (Fig. 6c).

### Serum proteome analysis

Trout serum protein profiling yielded a total of 184 proteins. Potential indicators of azamethiphos induced impairment were identified from this list based on 3 criteria: (1) proteins that were present in each of the pooled sample; (2) representative proteins from each protein group (same name but different accession number); (3) proteins that were significantly altered between time point before, immediately after and 4 days after the azamethiphos treatment. A total of 47 (25.5%) proteins metal.l 3 criteria. This rigorous approach ensures that only the potential effects of the azamethiphos were identified and were present at higher or lower levels in azamethiphos exposed fish, with fold-changes ranging from 1.1 to 3.8. Protein identification following a MASCOT search is provided in Table 1. The major biological functions of altered proteins are involved in clot formation (e.g. fibrinogen, plasminogen, heparin, vitamin K-dependent protein), immune reaction (e.g. complement factors, histone), free heme and haemoglobin binding (e.g. haptoglobin, heme binding protein, beta globin) and lipid binding (e.g. apolipoproteins). Less representative processes affected are antimicrobial function (e.g. lg kappa chain, lysozyme) and body development (e.g. retinol, protein LEG1).

Peak intensities at each time-point were analysed using Hierarchical Cluster Analysis to more clearly identify whether an association with treatment over time could be identified and to group proteins which possessed similar expression profiles (Fig. 7). The dendrogram at the top of Fig. 7 displays the connection of the protein abundances at the three time points and illustrates a clear distinction between those recorded before the treatment and those recorded immediately and 4 days after the treatment. Comparison of the proteome from before and immediately post treatment indicates a direct impact of azamethiphos. While comparison between before and 4 days post treatment indicates either a prolonged reaction pattern or homeostasis restoration. Table 1 shows the overall fold rise or decline in protein abundance for upregulated and downregulated (respectively) proteins before and after the treatment. Half of selected proteins (53%) were upregulated by the azamethiphos treatment in both sampling points post treatment. However, proteins such as complement factors, fibrinogen gamma chain, apolipoprotein, serum albumin, venom factor-like, antiplasmin, Ig kappa chains, protein-methionine sulfoxide oxidase, saxitoxin and tetrodotoxin-binding protein, macroglobulin, retinol-binding protein, catechol O-methyltransferase domain-containing protein, protein LEG1, leukocyte cell-derived chemotaxin precursor, heparin, plasminogen, haptoglobin like and heme-binding like protein showed a homeostasis restoration pattern. In contrast, only type-4 ice structuring protein and C-type lectin showed decreased abundancy in both time points after the exposure.

## Discussion

It is well established that treatments with anti-sea lice chemicals induces stress in rainbow trout26,27,28. Behavioural changes, as well as exhaustion and increased mortalities after repeated treatments were frequently reported by farmers. Sievers et al.29 reported 15% mortality in salmon 24 h after the treatment with 1 ppm AZA. Based on data collected from a commercial fish farm, we hypothesized that the cause of impaired fish health lies in the cumulative effect of repeated chemical anti-sea lice treatments. There is currently very little information on the toxicological impact on fish of the most commonly used anti-sea lice chemical (azamethiphos-AZA). To fill this knowledge gap a comprehensive investigation of the cumulative impacts of three consecutive treatments of azamethiphos in farmed rainbow trout was undertaken, to obtain an in-depth understanding of the effects on fish health through serum clinical chemistry changes with supportive histopathological alterations of fish tissues and serum proteome analysis.

Serum clinical chemistry has recently been described as a potential biomarker of chemical exposures in fish, particularly for organophosphates30,31,32. Serum proteome analysis is a relatively new method, and is now being frequently used in fish toxicological studies33,34. Moreover, negative effects of acute organophosphate exposure in fish can be detected at tissue level35,36,37, and novel findings in this area reveal the effects of organophosphates to haematology and red blood cell morphology38,39,40. This study is the first to investigate the effects of AZA in salmonids exposed to three treatments under farm conditions, where AZA was administered using short-term exposure by bath (45 min) at the recommended dose (0.2 ppm). Bioaccumulation of azamethiphos in salmon tissue is known to be very low and total azamethiphos is rapidly depleted after 10-degree days41. However, residue depletion was faster in muscle than in other tissues such as liver and skin42. After oral administration, azamethiphos is promptly excreted, primarily in the urine in form of 2-amino-3-hydroxy-5-chloropyridine as metabolite, which is then conjugated to glucuronic or sulfuric acid prior excretion. It has also been suggested that hepatic and serum carboxylesterases detoxify azamethipos43. No information has been found dealing with concentration of azamethiphos in fish blood after the treatment. In this study consecutive AZA treatments produced significant changes in the serum biochemical endpoints (Fig. 1). Higher concentrations of TP, ALB and CREA were observed in the early acute reaction immediately following the first and third treatments, as markers of increased liver function and impaired kidney tissue30.

In the late acute reaction (24 h and 48 h post treatment) a decrease in Cu concentration after the first treatment, and an increase in concentrations of CREA, Fe and HB during the second and third treatments were observed, along with a notable increase in TBIL concentration after the second treatment. Haemoglobin (HB) is the main protein in erythrocytes and it can be used as a marker of haemolysis, by measuring free HB in serum. The body’s primary source of Fe is from the turnover of haemoglobin in red blood cells. This is accomplished by spleen and liver macrophages which phagocytize RBC in pathophysiologic states such as haemolytic anaemia44. Once taken up by the macrophages, RBC are degraded in a phagolysosome which liberates haemoglobin, consisting of a heme group (porphyrin ring + iron) and globin chains. Once released from the heme group, iron must be converted to the Fe2+ state, which is facilitated by the copper-dependent enzymes45. This process is thought to be linked to the observed decreased Cu concentration during early acute reaction in first treatment and at 4 days after the second and third treatment. Bilirubin is the end product of heme metabolism and is released into the plasma, where it binds to albumin and is taken up by hepatocytes46. In the case of intravascular haemolysis, the released haemoglobin binds to haptoglobin and the complexes of haemoglobin-haptoglobin are ultimately cleared by spleen and liver macrophages47. For this reason, we measured the concentrations of haptoglobin and activity of glucose-6-phosphate dehydrogenase in serum at time points before, 48 h after and 4 days after the treatment. G-6-PD is an enzyme in the RBC hexose monophosphate pathway designed to produce reduced glutathione, that is used to avoid oxidative damage to haemoglobin and other intracellular structures48. In this study, we detected a significant increase in the antioxidant activity of G-6-PD, 4 days after the second treatment, which occurred after significant decrease of haptoglobin concentration 48 h after the same treatment, suggesting that haptoglobin was taken up and removed from circulation by free haemoglobin (Fig. 2b). This response is specific to the second treatment, occurring only 12 days after the first one.

The prolonged reaction period (4 and 10 days post treatment), showed a significantly higher concentration of TBIL and CREA in all three treatments and higher concentrations of Fe and P in first and second treatment. These biomarkers provide evidence of a moderately reduced glomerular filtration rate30 and further confirm the previously mentioned pathophysiological reaction following excessive red blood cell breakdown. Haptoglobin protective systems are saturated during severe serious haemolysis and glomerular filtration becomes the main pathway removing HB from circulation. Acute kidney injury, therefore remains as a significant complication after intravascular haemolysis49. Once released from the heme group iron can be stored within cells as ferritin, and over time becomes oxidized and degrades to form hemosiderin50. Hemosiderin can be visualized within macrophages as a dusky blue-grey pigment and can be definitively stained with Prussian blue (turning hemosiderin blue). In our study, fish kidneys showed tubule degeneration (cloudy swelling and hyaline droplets) which is the most common alteration found in the kidney of fishes exposed to water contaminants51. The presence of iron positive granules in proximate and distal tubules, coupled with the glomerular atrophy in this study indicates that the kidneys suffered from the damage caused by iron deposits at the end of the three consecutive AZA treatments (Fig. 6c). During these circumstances, renal damage mechanisms are regarded multifactorial and may result in direct damage of renal tubular cells through glomerular-filtered HB. Excessive hemosiderin deposits were also seen in spleen tissue after the treatments (Fig. 5f). Similar findings have been reported by Khan and Nag52 in plaice (Pleuronectes platessa) during haemolytic anaemia. These tissue changes observed in the liver correspond to previous findings from rainbow trout exposed to organophosphate chlorpyriphos37.

A possible explanation for the intravascular haemolysis caused by AZA arises from the fact that organophosphates inhibit erythrocyte cholinesterase activity and are also known to cause swelling, deformity and breakdown of the RBC38,40. Significant inhibition in erythrocyte acetylcholinesterase activity was observed in an 8-week lab based study in which groups of juvenile trout were exposed to sub-lethal concentrations (25 μg/L) of carbosulfan53. After one week of treatment, a 30% decrease in erythrocyte acetylcholinesterase activity was recorded. In a similar study with juvenile trout treated with biocide Chloramine-T, reduced activity of red blood cells AChE was evident following exposure to 30 mg of Chloramine-T after 3, 9 and 15 days22. Changes in the erythrocyte profile resulting from the acute impact of insecticide formothion in Heteropneustes fossilis54 and organophosphates such as quinalphos in silver barb55, malathion in Cyprinus carpio56 and trichlorphon in Piaractus mesopotamicus57 were reported. Still, evidence for AZA causing intravascular haemolysis in fish is scarce, despite a variety of studies reporting a toxic impact on marine invertebrates. Canty et al.58 suggest that AZA is relatively toxic to mussels following an exposure to 0.1 mg/L for periods up to 24 hours that resulted in a significant reduction in the activity of acetylcholinesterase in both haemolymph and gill, cell viability changes and decreased phagocytic index. Long-term (48 h) exposure of adult lobsters to azamethiphos concentration of 5 µg/L caused a significant change in haemolymph plasma biochemistry59,60.

The basic role of Principal Component Analysis (PCA) is to extract the main dimensions in the correlation structure of the analysed endpoints, with all endpoints having the same contribution to the overall variation. The first two dimensions encompass the main bulk of total variation, but does not necessarily include all of the most important endpoints. For this reason, despite CREA concentration showed a significant and systematic pattern on a univariate level, it is the least represented variable in the first two PCs. When looking at all three treatments together, CREA is not highly correlated with any of the other variables, so it is not significant when trying to explain the total variation in all endpoints (Fig. 3a). Interestingly, CREA is highly loaded on the third PC dimension, meaning that it is still important as a biomarker. In a similar way, variation in TBIL showed a pronounced shift in the second and third treatment, which is not well represented in the two PCA dimensions as these shifts do not correlate well with other variables. From the PCA analysis it was easy to distinguish well correlated pairs of biomarkers such as ALP and P, ALT and LDH, Cu and TP, Fe, HB and K. However, the mean data collected from the sampling point before each treatment (Fig. 3b) and for all sampling points for each treatment (Fig. 3c) show the cumulative effects on blood biochemistry, with weak overlap and complete segregation of the first treatment with other two treatments, respectively. This reduced separation of time points in later treatments (particularly following the second treatment) could be explained as a chronic effect following the more pronounced acute effect seen after the first treatment (Fig. 4a,c). Differences between biomarker average values for each treatment are illustrated by the mean lines in Fig. 1, with a significant decreasing trend seen in TP, ALB and K concentrations, and an increase in TBIL and P concentrations towards the end of the treatment period.

Serum protein analysis reveals an increase in beta-globin, haemoglobin subunits alpha, alpha-4 and beta, fibrinogen beta and alpha occurring immediately after treatment and persisting up to 4 days post treatment. Contrary to this, haptoglobin-like protein and heme-like binding protein were significantly decreased immediately after treatment and 4 days later were restored to pre-treatment levels (Fig. 7, Table 1). Proteomic analysis proved useful as a sensitive confirmatory method to support the previously mentioned hypothesis on excessive intravascular haemolysis in fish during repeated AZA treatments. Due to the expense involved in using this technique, proteome analysis was performed during the second (middle) treatment only. These techniques are not commonly used in aquaculture research, resulting in a lack of comparative results from this kind of analysis. The separation and identification of 47 proteins from trout serum that were altered following anti-sea lice treatment, will make a significant contribution to the use of these techniques in aquaculture research and the further understanding of the impact of sea lice treatment on salmonids.

## Conclusions

This is the first study to examine serum clinical chemistry in salmonids subjected to consecutive AZA treatments, enabling the investigation into activated biochemical and tissue pathways in trout. We also searched for alterations in the renal cortical tissue histology associated with haemoglobin (HB) excretion. Cumulatively, our results indicate that renal HB exposure has resulted in the accumulation of large amounts of oxidized ferric (Fe3+) in tissues and that these circumstances can foster heme-related cell damage pathway in tubule epithelial cells, characterized by an oxidative stress response. This study demonstrated that free heme is a probable cause for tubular barrier deregulation and oxidative cell damage and strengthened the hypothesis of uncontrolled free heme causing kidney injury, becoming observable with an increase in creatinine and phosphorus concentration after the treatments. These results also indicate that consecutive AZA treatments change the biochemical profile of trout, which could be explained through the physiological cascade of RBC destruction and elucidate potentially useful serum biomarkers for fish health monitoring during the anti-sea lice treatments. However, further studies are required to identify the particular mechanisms for AZA inhibitory action towards RBC acetylcholinesterase. In addition, the development of new and less aggressive substances to substitute the usage of organophosphates should become a priority for aquaculture research, together with usage of some nephron-protective agents as suggested by Georgiadis et al.23. We conclude that consecutive AZA treatments are causing cumulative impacts on fish health through a cascade of red blood cells intravascular degradation and accumulation of oxidized ferric (Fe3+) in different tissues of the rainbow trout. Therefore, we suggest, when possible, to use less invasive nonchemical anti-sea lice treatments and to secure sufficient intervals between repeated treatments to allow fish to recover from the previous treatment. Additionally, during recovery from the treatment it is important to keep the fish supplied with oxygen.

## Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

## Change history

• ### 20 May 2020

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

## References

1. 1.

FEAP. European Aquaculture Production Report 2008-2016. Prepared by the FEAP secretariat (November 2017). Available from http://blancchamp.be/wp-content/uploads/2018/05/production-report-2017_web.pdf.

2. 2.

Berg, A. G. T. & Horsberg, T. E. Plasma concentrations of emamectin benzoate after SliceTM treatments of Atlantic salmon (Salmo salar): Differences between fish, cages, sites and seasons. Aquaculture 288, 22–26 (2009).

3. 3.

Rosenberg, A. A. Aquaculture: The price of lice. Nature 451, 23–24, https://doi.org/10.1038/451023a, (2008).

4. 4.

Costello, M. J. The global economic cost of sea lice to the salmonid farming industry. J. Fish Dis. 32(1), 115–8, https://doi.org/10.1111/j.1365-2761.2008.01011 (2009).

5. 5.

Abolofia, J., Asche, F. & Wilen, J. E. The Cost of Lice: Quantifying the Impacts of Parasitic Sea Lice on Farmed Salmon. Mar. Resour. Econ. 32(3), 329–49, https://doi.org/10.1086/691981 (2017).

6. 6.

Aaen, S. M., Hamre, L. A. & Horsberg, T. E. A screening of medicinal compounds for their effect on egg strings and nauplii of the salmon louse Lepeophtheirus salmonis (Krøyer). J. Fish Dis. 39(10), 1201–12, https://doi.org/10.1111/jfd.12462 (2016).

7. 7.

Helgesen, K. O. & Horsberg, T. E. Single-dose field bioassay for sensitivity testing in sea lice, Lepeophtheirus salmonis: Development of a rapid diagnostic tool. J. Fish Dis. 36(3), 261–72, https://doi.org/10.1111/jfd.12053 (2013).

8. 8.

Arriagada, G. et al. Evaluating the effect of synchronized sea lice treatments in Chile. Prev. Vet. Med. 136, 1–10, https://doi.org/10.1016/j.prevetmed.2016.11.011 (2017).

9. 9.

Intorre, L. et al. Safety of azamethiphos in eel, seabass and trout. Pharmacol. Res. 49, 171–176 (2004).

10. 10.

Leal, J. K. F., Adjobo-Hermans, M. J. W., Brock, R. & Bosman, G. J. C. G. M. Acetylcholinesterase provides new insights into red blood cell ageing in vivo and in vitro. Blood Transfus. 15(3), 232–238, https://doi.org/10.2450/2017.0370-16 (2017).

11. 11.

Tomlin, C. The Pesticide Manual; Incorporating the Agrochemicals Handbook: A World Compendium. British Crop Protection Council, Farnham, Surrey, https://doi.org/10.1006/pest.1996.0019 (2011).

12. 12.

Roth, M., Richards, R. H. & Sommerville, C. Current practices in the chemotherapeutic control of sea lice infestations in aquaculture: a review. J. Fish Dis. 16, 1–26 (1993).

13. 13.

Hille, S. A literature review of the blood chemistry of rainbow trout, Salmo gairdneri Rich. J. Fish Biol. 20, 535–569 (1982).

14. 14.

Bernet, D., Schmidt-Posthaus, H., Wahli, T. & Burkhardt-Holm, P. Effects of wastewater on fish health: an integrated approach to biomarker responses in brown trout (Salmo trutta L.). J. Aquat. Ecosyst. Stress Recover. 8, 143–151 (2000).

15. 15.

Ferri, J. et al. The effect of artificial feed on blood biochemistry profile and liver histology of wild saddled bream, Oblada melanura (Sparidae). Mar. Environ. Res. 71, 218–224 (2011).

16. 16.

Adel, M., Abedian Amiri, A., Zorriehzahra, J., Nematolahi, A. & Esteban, M. Á. Effects of dietary peppermint (Mentha piperita) on growth performance, chemical body composition and hematological and immune parameters of fry Caspian white fish (Rutilus frisii Kutum). Fish Shellfish Immunol. 45(2), 841–7, https://doi.org/10.1016/j.fsi.2015.06.010 (2015).

17. 17.

Řehulka, J. Haematological analyses in rainbow trout Oncorhynchus mykiss affected by viral haemorrhagic septicaemia (VHS). Dis. Aquat. Organ. 56, 185–193 (2003).

18. 18.

Floyd-Rump, T. P., Horstmann-Dehn, L. A., Atkinson, S. & Skaugstad, C. Effect of ichthyophonus on blood plasma chemistry of spawning chinook salmon and their resulting offspring in a yukon river tributary. Dis. Aquat. Organ. 122(3), 223–236, https://doi.org/10.3354/dao03077 (2017).

19. 19.

Steinbach, C. et al. The sub-lethal effects and tissue concentration of the human pharmaceutical atenolol in rainbow trout (Oncorhynchus mykiss). Sci. Total Environ. 497–498, 209–218 (2014).

20. 20.

Javed, M., Ahmad, M. I., Usmani, N. & Ahmad, M. Multiple biomarker responses (serum biochemistry, oxidative stress, genotoxicity and histopathology) in Channa punctatus exposed to heavy metal loaded waste water. Sci. Rep. 7, 1765, https://doi.org/10.1038/s41598-017-01749-6 (2017).

21. 21.

Benfey, T. J. & Biron, M. Acute stress response in triploid rainbow trout (Oncorhynchus mykiss) and brook trout (Salvelinus fontinalis). Aquaculture 184, 167–176 (2000).

22. 22.

Boran, H. & Altinok, I. Impacts of chloramine-T treatment on antioxidant enzyme activities and genotoxicity in rainbow trout, Oncorhynchus mykiss (Walbaum). Journal of Fish Diseases 37(5), 431–41 (2014).

23. 23.

Georgiadis, G. et al. Nephrotoxicity issues of organophosphates. Toxicology 406-407, 129–136, https://doi.org/10.1016/j.tox.2018.07.019 (2018).

24. 24.

Bilić, P. et al. Serum proteome profiling in canine idiopathic dilated cardiomyopathy using TMT-based quantitative proteomics approach. J. Proteomics 15(179), 110–121, https://doi.org/10.1016/j.jprot.2018.03.007 (2018).

25. 25.

Braceland, M. et al. The serum proteome of Atlantic salmon, Salmo salar, during pancreas disease (PD) following infection with salmonid alphavirus subtype 3 (SAV3). J. Proteomics 94, 423–436 (2013).

26. 26.

Cárcamo, J. G., Aguilar, M. N., Barrientos, C. A., Carreño, C. F. & Yañez, A. J. Emamectin benzoate treatment alters the expression and activity of CYP1A, FMO and GST in different tissues of rainbow trout (Oncorhynchus mykiss). Aquaculture 434, 188–200 (2014).

27. 27.

Cárcamo, J. G. et al. Consecutive emamectin benzoate and deltamethrin treatments affect the expressions and activities of detoxification enzymes in the rainbow trout (Oncorhynchus mykiss). Comp. Biochem. Physiol. Part - C Toxicol. Pharmacol. 191, 129–137 (2017).

28. 28.

Olsvik, P. A., Ørnsrud, R., Lunestad, B. T., Steine, N. & Fredriksen, B. N. Transcriptional responses in Atlantic salmon (Salmo salar) exposed to deltamethrin, alone or in combination with azamethiphos. Comp. Biochem. Physiol. - C Toxicol. Pharmacol. 162, 23–33 (2014).

29. 29.

Sievers, G., Palacios, P., Inostroza, R. & Dölz, H. Evaluation of the toxicity of 8 insecticides in Salmo salar and the in vitro effects against the isopode parasite, Ceratothoa gaudichaudii. Aquaculture 134, 9–16, https://doi.org/10.1016/0044-8486(95)00026-X (1995).

30. 30.

Banaee, M. et al. Biochemical and histological changes in the liver tissue of rainbow trout (Oncorhynchus mykiss) exposed to sub-lethal concentrations of diazinon. Fish Physiol. Biochem. 39, 489–501, https://doi.org/10.1007/s10695-012-9714-1 (2013).

31. 31.

Narra, M. R., Rajender, K., Reddy, R. R., Murty, U. S. & Begum, G. Insecticides induced stress response and recuperation in fish: Biomarkers in blood and tissues related to oxidative damage. Chemosphere 168, 350–357 (2017).

32. 32.

Slaninová, A., Smutna, M., Modra, H. & Svobodova, Z. A review: Oxidative stress in fish induced by pesticides. Neuroendocrinol. Lett. 30, 2–12 (2009).

33. 33.

Alderman, S. L., Dindia, L. A., Kennedy, C. J., Farrell, A. P. & Gillis, T. E. Proteomic analysis of sockeye salmon serum as a tool for biomarker discovery and new insight into the sublethal toxicity of diluted bitumen. Comp. Biochem. Physiol. - Part D Genomics Proteomics 22, 157–166 (2017).

34. 34.

Karim, M., Puiseux-Dao, S. & Edery, M. Toxins and stress in fish: Proteomic analyses and response network. Toxicon 57, 959–969 (2011).

35. 35.

Banaee, M., Sureda, A., Mirvaghefi, A. R. & Ahmadi, K. Biochemical and histological changes in the liver tissue of rainbow trout (Oncorhynchus mykiss) exposed to sub-lethal concentrations of diazinon. Fish Physiol. Biochem. 39(3), 489–501, https://doi.org/10.1007/s10695-012-9714-1, (2013).

36. 36.

Nataraj, B., Hemalatha, D., Rangasamy, B., Maharajan, K. & Ramesh, M. Hepatic oxidative stress, genotoxicity and histopathological alteration in fresh water fish Labeo rohita exposed to organophosphorus pesticide profenofos. Biocatal. Agric. Biotechnol. 12, 185–190 (2017).

37. 37.

Topal, A. et al. In vivo changes in carbonic anhydrase activity and histopathology of gill and liver tissues after acute exposure to chlorpyrifos in rainbow trout. Arh. Hig. Rada Toksikol. 65, 377–385 (2014).

38. 38.

Jindal, R. & Kaur, M. Phenotypic Alterations in Erythrocytes of Ctenopharyngodon idellus (Cuvier & Valenciennes) Induced by Chlorpyrifos: SEM Study. Int. J. Fish. Aquac. Sci. 4, 2248–9975 (2014).

39. 39.

Mishra, A. Toxic impact of pesticides on the morphological characteristics of blood cells of fish Channa punctatus (Bloch). Indian J. Sci. Res. 12(2), 68–72 (2017).

40. 40.

Mishra, A. et al. Effect of an organophosphate insecticide, chlorpyriphos on mortality and haematology of freshwater butter catfish Ompok bimaculatus (Bloch, 1794). La Pensée. 76, 452–461 (2014).

41. 41.

Rawn, D. F. K. et al. Canadian total diet study in 1998: Pesticide levels in foods from Whitehorse, Yukon, Canada, and corresponding dietary intake estimates. Food Addit. Contam. 21(3), 232–50, https://doi.org/10.1080/02652030310001655470 (2004).

42. 42.

European Medicines Agency. European public maximum residue limit assessment report (EPMAR)—Azamethiphos—Extension to fin fish. EMA/CVMP/88291/2011. London, UK, (2011).

43. 43.

Richardson, J. Azamethiphos. In Encyclopedia of Toxicology. Elsevier, https://doi.org/10.1016/B978-0-12-386454-3.00345-6 (2005).

44. 44.

Satchell, G. H. Physiology and form of fish circulation. Cambridge University Press, https://doi.org/10.1017/CBO9780511983719.001 (1991).

45. 45.

Bury, N. Iron acquisition by teleost fish. Comp. Biochem. Physiol. Part C 135(2), 97–105, https://doi.org/10.1016/S1532-0456 (2003).

46. 46.

Sakai, T. et al. Hyperbilirubinemia of Coho salmon Oncorhynchus kisutch infected with Erythrocytic inclusion body syndrome (EIBS). Virus. Fish. Sci. 60, 519–521 (1994).

47. 47.

Wicher, K. B. & Fries, E. Haptoglobin, a hemoglobin-binding plasma protein, is present in bony fish and mammals but not in frog and chicken. Proc. Natl. Acad. Sci. 103(11), 4168–73, https://doi.org/10.1073/pnas.0508723103 (2006).

48. 48.

Ciftci, M. Effects of some drugs on the activity of glucose 6-phosphate dehydrogenase from rainbow trout (Oncorhynchus mykiss) erythrocytes in vitro. J. Enzyme Inhib. Med. Chem. 20, 485–489 (2005).

49. 49.

Deuel, J. W. et al. Hemoglobinuria-related acute kidney injury is driven by intrarenal oxidative reactions triggering a heme toxicity response. Cell Death Dis. 7, e2064 (2016).

50. 50.

Walker, R. L. & Fromm, P. O. Metabolism of iron by normal and iron deficient rainbow trout. Comp. Biochem. Physiol.–Part A Physiol. 55, 311–318 (1976).

51. 51.

Yancheva, V., Velcheva, I., Stoyanova, S. & Georgieva, E. Histological biomarkers in fish as a tool in ecological risk assessment and monitoring programs: A review. Appl. Ecol. Environ. Res. 14, 47–75 (2016).

52. 52.

Khan, R. A. & Nag, K. Estimation of hemosiderosis in seabirds and fish exposed to petroleum. Bull. Environ. Contam. Toxicol. 50, 125–131 (1993).

53. 53.

Altinok, I., Capkin, E. & Boran, H. Mutagenic, genotoxic and enzyme inhibitory effects of carbosulfan in rainbow trout Oncorhynchus mykiss. Pestic. Biochem. Physiol. 36, 80–87, https://doi.org/10.1016/j.pestbp.2011.10.011 (2012).

54. 54.

Singh, N. N. & Srivastava, A. K. Formothion and propoxur induced alterations in biochemical constituents of catfish, heteropneustes fossilis. Toxicol. Environ. Chem. 48(3-4), 149–153, https://doi.org/10.1080/02772249509358162 (1995).

55. 55.

Mostakim, G. M., Zahangir, M. M., Mishu, M. M., Rahman, M. K. & Islam, M. S. Alteration of blood parameters and histoarchitecture of liver and kidney of silver barb after chronic exposure to quinalphos. J. Toxicol. 2015, 1–8 (2015).

56. 56.

Yonar, S.M., Ural, M. S., Silici, S. & Yonar, M. E. Malathion-induced changes in the haematological profile, the immune response, and the oxidative/antioxidant status of Cyprinus carpio carpio: Protective role of propolis. Ecotoxicol. Environ. Saf. 102, https://doi.org/10.1016/j.ecoenv.2014.01.007202-9 (2014).

57. 57.

Tavares-dias, M., Martins, M. L. & Kronka, N. Evaluation of the haematological parameters in Piaractus mesopotamicus Holmberg (Osteichthyes, Characidae) with Argulus sp. (Crustacea, Branchiura) infestation and treatment with organophosphate. Rev. Bras. Zool. 16, https://doi.org/10.1590/S0101-81751999000200019 (1999).

58. 58.

Canty, M. N., Hagger, J. A., Moore, R. T. B., Cooper, L. & Galloway, T. S. Sublethal impact of short term exposure to the organophosphate pesticide azamethiphos in the marine mollusc Mytilus edulis. Mar. Pollut. Bull. 4(4), 396–402, https://doi.org/10.1016/j.marpolbul.2006.11.013 (2007).

59. 59.

Dounia, D., Andrea, B., Lefort, N. & Van Geest, J. L. Repeated sublethal exposures to the sea lice pesticide Salmosan® (azamethiphos) on adult male lobsters (Homarus americanus) causes neuromuscular dysfunction, hypoxia, metabolic disturbances and mortality. Ecotoxicol. Environ. Saf. 134, 106–115, https://doi.org/10.1016/j.ecoenv.2016.08.019 (2016).

60. 60.

Urbina, M. A., Cumillaf, J. P., Paschke, K. & Gebauer, P. Effects of pharmaceuticals used to treat salmon lice on non-target species: Evidence from a systematic review. Sci. Total Environ. 649, 1124–1136, https://doi.org/10.1016/j.scitotenv.2018.08.334 (2019).

## Acknowledgements

The financial support by Scottish Aquaculture Innovation Centre (SAIC) is acknowledged. The analysis was carried out as a part of SAIC project: “The development of diagnostic techniques to assess fish health and the impact of sea lice treatment in aquaculture”. Special thanks are due to Kames fish farm Ltd. for providing the samples used in this study. Proteomic analysis was carried on at Glasgow Polyomics, with the use of licenced software.

## Author information

Authors

### Contributions

Each author participated in the research and article preparation equaly. J.B., S.C. and B.Q. conceived the basic idea, interpreted the data and designed the study; S.C. organised the field experiment and samplings; J.B. carried out all the analysis; B.Q. coordinate the main project and foundation; all together wrote the manuscript and approved the final version and concur with the submission.

### Corresponding author

Correspondence to Brian Quinn.

## Ethics declarations

### Competing interests

The authors declare no competing interests.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

## Rights and permissions

Reprints and Permissions

Barisic, J., Cannon, S. & Quinn, B. Cumulative impact of anti-sea lice treatment (azamethiphos) on health status of Rainbow trout (Oncorhynchus mykiss, Walbaum 1792) in aquaculture. Sci Rep 9, 16217 (2019). https://doi.org/10.1038/s41598-019-52636-1

• Accepted:

• Published:

• ### Sea lice infestation of salmonids in Chile between 2011 and 2017: Use of regulatory data to describe characteristics and identify risk factors

• Emma Zalcman
• , Amy Burroughs
• , Anne Meyer
• , Alison Hillman
• , Catriona Mackenzie
• , Michael P. Ward
• , Mark Stevenson
• , Jonathan Happold
• , Jennifer Hutchison
• , Alicia L. Gallardo Lagno
• , Angus Cameron
•  & Brendan Cowled

Aquaculture (2020)

• ### Evaluation of azamethiphos and dimethoate degradation using chlorine dioxide during water treatment

• Marija V. Pergal
• , Igor D. Kodranov
• , Biljana Dojčinović
• , Viacheslav V. Avdin
• , Dalibor M. Stanković
• , Branka B. Petković
•  & Dragan D. Manojlović

Environmental Science and Pollution Research (2020)


text:
# The best “risk measure” for an investor who does not want to lose any of his seed money

## Question

• There is an investor who is afraid of losing any of his seed money (initial investment).
• Variance of investment returns is not a problem to him. He is willing to take variance as long as he does not lose his seed money.
• The investor is afraid of losing any of his initial investment, even a little. So even if he earns a lot at the end of investment period, if he has to go through losing any of his initial investment (seed money) during the investment, he would not like this investment plan.
• Which risk measure would be appropriate for this type of investor?

## Example

• Risk measures including variance, therefore, are not appropriate yardsticks for this type of investors. So I excluded variance, downside variance and sharpe ratios. ( I know sharpe ratio is a risk measure per se.)
• Maximum Drawdown seems to work, but whether seed money is being lost or not does not take into its account. As such, I am not sure if MDD is approrpriate.
• In the same sense, VaR and CVaR do not take into account the seed money
• I think you need some additional input here: if you are not willing to lose your initial investment, I understand that you do are comfy with any return above zero. Any return below zero is not acceptable to the investor. I think I got you wrong, though. – Kermittfrog Nov 7 '20 at 7:52
• Which additional information would be needed? – Eiffelbear Nov 7 '20 at 8:08
• Probably I do not fully understand the description, yet. If they are afraid of loosing their investment, any desirable return distribution must be strictly positive.else, they might be looking at the probability of a loss in excess of 0, no? So are they afraid of loosing “all” their initial investment or “some”? – Kermittfrog Nov 7 '20 at 8:18
• When you find an investment strategy with zero loss of losing my initial investment with unlimited potential upside, please let me know. – will Nov 7 '20 at 9:50
• Any trade you put on, in the moments after the trade, has an approximately 50% chance of being underwater. Probably the chances of a trade staying above 0 for every moment of the first day are very near to zero. So I think this investor just needs to hold cash in her domestic currency (maybe a risk free bank deposit if such a thing exists). – StackG Nov 7 '20 at 9:57

As many have suggested in the comments, it might be hard, if not impossible, to find an investment that gives positive returns with certainty. However, you might consider a metric such as

$$R=\underset{s\in [0,T]}{\min}r_{0,s}$$

where $$r_{0,s}$$ is the portfolio return between the initial investment point $$0$$ and $$s$$. $$R$$ gives the highest share of seed money lost between $$0$$ and $$T$$. Then you could try to find an investment that with high probability has a good $$R$$.

You can affect $$R$$ through dynamic trading. As in this paper, the optimal strategy seems to increase risk after positive returns. So you would start investing in the risk-free asset and gradually increase risk after that. This way it might in theory be possible to guarantee positive returns for initial investment, though not sure how well this works in practice.

• This is a good answer. Though you would probably have to use a very conservative quantile eg 0.01% otherwise the minimum among all possible returns will probably always eat into the initial investment. – Daneel Olivaw Nov 8 '20 at 9:04

Since you yourself said the investor is willing to take variance/risk as long as she doesn't lose her seed money, then her objective function is not entirely a function of any risk measure describing some dispersion from a central tendency, neither the lower tail distribution. Instead, her objective function is a function of her cumulative returns being below $$1$$ at any point in time.

A risk measure is pointless for controlling such preferences and searching for one is totally missing the point (although you yourself did already rule out every possible risk measure out there so why keep asking?). Formulating an objective function with cumulative returns is pointless as well since cumulative returns are non-stationary and multi-modal.

All the investor can use is a perpetual stop-loss limit order that signals divestiture when portfolio price falls below the initial investment ("seed") price.

• Formulating an objective function with cumulative returns is pointless as well since cumulative returns are non-stationary and multi-modal. Neither point is correct. First, people derive utility form wealth, not returns, and the initial investment + cumulative returns do yield wealth. Second, stationarity or multimodality of the distribution of the argument of a utility function have nothing to do with the validity or sensibility of the utility function. – Richard Hardy Dec 19 '20 at 18:33
• I never said anything about the stationarity and modality of a utility function. It is utility functions that are pointless since asset allocation can be carried out without making them the objective function – develarist Dec 19 '20 at 22:55
• Who said you did? While it is possible to work with asset allocation without an explicit utility function, it would usually yield nonsense results unless the implicit utility function is a sensible one. In any case, the sentence I cited above is incorrect. Nonstationarity and multimodality is not a problem for the objective of asset allocation (while it may or may not be a technical challenge in the implementation). – Richard Hardy Dec 20 '20 at 11:18
• so you're saying that results obtained, in the presence of non-stationarity and multimodality, are qualified since these two issues are only a numerical obstacle only? – develarist Dec 20 '20 at 13:12
• What I am actually saying in my previous comments is that nonstationarity and multimodality are not a problem in the situation that your answer discusses. No, it is not a numerical problem at all. It is a modeling problem in the sense that an appropriate model needs to be built and an appropriate estimator chosen to account for nonstationarity; multimodality is a non issue in any case. – Richard Hardy Dec 20 '20 at 13:55


text:
# Why there is a 'MACH' in this Compressibility Correction Chart?

I couldn't find what 'Mach 1.0' and 'Mach 0.8' are trying to indicate in the middle of the chart.

As I read it, the upper arch pointed to by Mach 1.0 represents the correction necessary at the different pressure altitudes when flying at Mach 1.0, while the lower arch pointed to by Mach 0.8 represent the correction necessary at those pressure altitudes when flying at Mach 0.8.


text:
Below is the ascii version of the abstract for 05-372. The html version should be ready soon.

E. Prodan
The analytic structure of Bloch functions for linear molecular chains
(830K, pdf)

ABSTRACT.  This paper deals with Hamiltonians of the form $H=-{\bf \nabla}^2+v(\rr)$, with $v(\rr)$ periodic along the $z$ direction, $v(x,y,z+b)=v(x,y,z)$. The wavefunctions of $H$ are the well known Bloch functions $\psi_{n,\lambda}(\rr)$, with the fundamental property $\psi_{n,\lambda}(x,y,z+b)=\lambda \psi_{n,\lambda}(x,y,z)$ and $\partial_z\psi_{n,\lambda}(x,y,z+b)=\lambda \partial_z\psi_{n,\lambda}(x,y,z)$. We give the generic analytic structure (i.e. the Riemann surface) of $\psi_{n,\lambda}(\rr)$ and their corresponding energy, $E_n(\lambda)$, as functions of $\lambda$. We show that $E_n(\lambda)$ and $\psi_{n,\lambda}(x,y,z)$ are different branches of two multi-valued analytic functions, $E(\lambda)$ and $\psi_\lambda(x,y,z)$, with an essential singularity at $\lambda=0$ and additional branch points, which are generically of order 1 and 3, respectively. We show where these branch points come from, how they move when we change the potential and how to estimate their location. Based on these results, we give two applications: a compact expression of the Green's function and a discussion of the asymptotic behavior of the density matrix for insulating molecular chains.


text:
# Temperature

This example shows how to use BaremetalPi.jl together with a AD converter MCP3008 and a 103 thermistor to read the ambient temperature. The connections between the components are shown in the following figure:

Warning

We are using the Raspberry Pi Zero W as an example. You must modify it according to your model.

The MCP3008 is a 8-channel 10-bit analog to digital converted that communicates using the SPI interface. Thus, the first thing is to make sure that SPI is enabled in your Linux distribution so that the devices /dev/spidevX.Y exists (X and Y are integers related to the SPI numbering). Please, check the manual of your distribution for more information.

After the connection, we need to start the SPI interface in BaremetalPi.jl. Here, we consider that the device in which the MCP3008 was connected is called /dev/spidev0.0.

using BaremetalPi

init_spi("/dev/spidev0.0", max_speed_hz = 1000)

Notice that we limit the sampling speed to 1000 Hz to improve the signal-to-noise ratio of the AD conversion as per MCP3008 datasheet.

To acquire a new measurement, we need to perform a full-duplex SPI transfer. Three bytes must be sent to the device: 0x01 0x80 0x00, which asks for a new measurement of the channel 0. For more information about how MCP3008 works, please, see the datasheet. This full-duplex transfer can be accomplished using BaremetalPi.jl as follows:

tx_buf = [0x01, 0x80, 0x00]
rx_buf = zeros(UInt8, 3)

ret = spi_transfer!(1, tx_buf, rx_buf)

ret must be 3 indicating that the device returned 3 bytes. The 10-bit AD measurement is then stored in rx_buf[2:3]. The conversion to voltage is performed as follows:

V = ( (UInt16(rx_buf[2] & 3) << 8) + rx_buf[3] )*3.3/1024

Now, we need to obtain the resistance of the thermistor using the equation of the voltage divider:

R_div = 10_000         # ............. Resistor value at the voltage divider [Ω]
th_R = R_div*(3.3/V - 1)

Finally, using the relationship between resistance and temperature for the selected thermistor, we can get a measurement of the ambient temperature:

th_β  = 3380           # ................ Beta coefficient of the thermistor [K]
th_R₀ = 10_000         # ............. Reference thermistor resistance at T₀ [Ω]
th_T₀ = 25             # ....... Reference temperature T₀ of the thermistor [°C]

T = 1/( log(th_R / th_R₀)/th_β + 1/(th_T₀ + 273.15) ) - 273.15

The following code prints the temperature every 5s:

using Dates
using Printf
using BaremetalPi

################################################################################
#                                Configuration
################################################################################

const R_div = 10_000   # ............. Resistor value at the voltage divider [Ω]
const th_β  = 3380     # ................ Beta coefficient of the thermistor [K]
const th_R₀ = 10_000   # ............. Reference thermistor resistance at T₀ [Ω]
const th_T₀ = 25       # ....... Reference temperature T₀ of the thermistor [°C]

################################################################################
#                                  Functions
################################################################################

function acquire_temperature()
# ==========================================================================

tx_buf = [0x01, 0x80, 0x00]
rx_buf = zeros(UInt8, 3)
V      = 0

ret = spi_transfer!(1, tx_buf, rx_buf)

if ret != 3
@warn("The data received from MCP3008 does not have 3 bytes.")
return NaN
end

V = ( (UInt16(rx_buf[2] & 3) << 8) + rx_buf[3] )*3.3/1024

if V == 0
@warn("The MCP3008 measured 0V.")
return NaN
end

if V > 3.25
@warn("The MCP3008 measured a too high voltage (> 3.25V).")
return NaN
end

# Convert the measurement to temperature
# ==========================================================================

th_R = R_div*(3.3/V - 1)
T    = 1/( log(th_R / th_R₀)/th_β + 1/(th_T₀ + 273.15) ) - 273.15

return T
end

function run()
# Let's sample at 1kHz to improve the accuracy of MCP3008.
init_spi("/dev/spidev0.0", max_speed_hz = 1000)

while true
T = acquire_temperature()

if !isnan(T)
@printf("%-30s %3.2f\n", string(now()), T)
else
println("[ERROR] Problem when acquiring AD measurement.")
end
sleep(5)
end
end

run()

Result:

2020-06-14T19:36:40.564        22.65
2020-06-14T19:36:46.235        22.65
2020-06-14T19:36:51.264        22.65
2020-06-14T19:36:56.286        22.65
2020-06-14T19:37:01.32         22.65
2020-06-14T19:37:06.354        22.65
2020-06-14T19:37:11.381        22.65
2020-06-14T19:37:16.415        22.65
2020-06-14T19:37:21.443        22.65
2020-06-14T19:37:26.465        23.57   -> I placed my finger on the thermistor.
2020-06-14T19:37:31.497        23.57
2020-06-14T19:37:36.525        23.16
2020-06-14T19:37:41.547        22.86
2020-06-14T19:37:46.581        22.76
Info

To improve the accuracy, measure the resistance of the voltage divider resistor and update the constant R_div. Furthermore, check the β of your thermistor and update the value in the constant th_β.


text:
# Problem with finding solutions to polynomial equation

What is the best method to find the solutions to this equation?

$$x^3(x^2+2)=-297$$

I have obtained the solution from the back of the book but have no idea as to the method to get the solution. Is it merely a matter of plugging in numbers?

• If you were to assume that $x$ is an integer, then you can look at the factorization of $297=3^3\cdot 11$ and use this to reach your conclusion that $x=-3$ is one solution. – JMoravitz Apr 20 at 21:09
• If you do not make the assumption that $x$ must be an integer, then you'll find there are potentially five different complex number solutions to the equation. In some special cases you could find them explicitly, however there is no guarantee you can for fifth degree or higher polynomials. – JMoravitz Apr 20 at 21:11
• Note also that because $x^5$ and $2x^3$ are strictly increasing functions of $x$ (for $x\in \mathbb R$) so is their sum, and it is easy to deduce from this that there will be precisely one real solution. $-4^5=-1024$ and the left-hand side is zero for $x=0$ so the solution will be between $-4$ and $0$. Because the equation has integer coefficients, and the leading coefficient is $1$, any rational solution will be an integer. Any integer solution will be odd. So it is easy to constrain the search space. – Mark Bennet Apr 20 at 21:56
• You might want to look at the rational root theorem if you don't know it already. – NickD Apr 21 at 0:36

Hint: $$x^3(x^2+2)=-297$$ or $$x^3(x^2+2)=(-3)^3(11)$$ $$x^3(x^2+2)=(-3)^3((-3)^2+2)$$
• you're right. That was the answer on the back of the book. But as for the method to find that, you just recognise that -297 can be factored by 11 and -27 or 11 and $-3^3$? Is this just a matter of experience with such problems that cradle to see the factors of large numbers and plug-in the solution accordingly? – esc1234 Apr 20 at 21:13


text:
# Ideal Spring Question [Units]

1. Oct 6, 2012

### Barrowlands

So, I'm trying to brush up on my undergrad physics, and I'm sure this is a bone-headed question, so please bear with me.

1. The problem statement, all variables and given/known data
A heavy object, when placed on a rubber pad that is to be used as a shock absorber, compresses the pad by 1cm. If the object is given a vertical tap, it will oscillate. Ignoring the damping, estimate the oscillation frequency. [The book i'm using actually gives the solution]

2. Relevant equations
x(t)=A*sin(sqrt(k/m)t+$\phi$)
ω=sqrt(k/m)
F=k|l-l0|

3. The attempt at a solution
We'll call x0 the equilibrium displacement, x0=1cm
k=spring constant of rubber
so
k(l-l0)=k*x0=mg (equilibrium)
gives us
k=(mg)/(x0)
then
ω=sqrt(k/m)
which eventually solves to
ω=sqrt(g/x0)

The book gives an answer of sqrt(980) rad/s. My question is given the units from ω=sqrt(g/x0) (meters, seconds, centimeters), how do they arrive at radians?

Edit: I just realized I put this in the wrong sub-forum, my apologies.

2. Oct 6, 2012

### voko

The unit of sqrt (g/x0) is s^-1. A "radian" is not really a unit, it is merely an indication that the corresponding dimensionless value is used as an angular measure.


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## beth12345 4 years ago how to find the value of 'X' when x^2-8x=8?

1. Mimi_x3

$x^2-8x-8 = 0$ Use the quadratic formula: $\frac{-(b)\pm\sqrt{(b)^{2}-4(a)(c)}}{2(a)}$ $a = 1 , b=-8 , c = -8$ And sub it in.

2. beth12345

ok thanx

3. anonymous

use the quadratic formula. $(-b \pm \sqrt{b ^{2} -4ac}) /2a$

4. anonymous

x^2-8x=8 x(x-8)=8 or x=2 and x-8=4 x=2, x=12

5. beth12345

thank you, i never thouht of it that way but i can see how it works :)

6. anonymous

but this solution is wrong

7. beth12345

?

8. anonymous

x=2, x=12 is a wrong solution

9. Mimi_x3

Completing the square would work as well. But using the quadratic formula is easier.

10. beth12345

i thought it would be like (2)[(12)-8]=8?

11. beth12345

oh ok, mimi, i did the formula and got 4+or-2root6, is that right?

12. anonymous

Ravi, in case you were wondering why it's wrong it's because you must factorise the equation equal to 0. 8 has an infinite number of non-integer factors, so you can't solve. 0, on the other hand, must have the value of a factor as 0.

13. beth12345

thanks samarilli, i see now why it's not the same answer as when i use the formula

14. Mimi_x3

Well, i got $\frac{-8\pm\sqrt{96}}{2}$

15. anonymous

thanks Simarilli


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## Discontinuity of the topological entropy for Lozi maps

I have shown that, like diffeomorphisms, piecewise affine surface homeomorphisms are approximated in entropy by horseshoes, away from their singularities. It follows in particular that their topological entropy is lower-semicontinuous: a small perturbation cannot cause a macroscopic drop in entropy.

The continuity of the entropy for such maps had been an open problem for some time. Rigorous numerical estimates by Duncan SANDS and Yutaka ISHII seemed to suggest some discontinuous drops, but investigation at a small scale suggested these drops to be steep yet continuous variations.

Izzet B. YILDIZ has solved this question by finding for Lozi maps $f_{a,b}(x,y)=(1-a|x|+by,x)$ on $\mathbb R^2$, small numbers $\epsilon_1,\epsilon_2>0$ such that, setting $(a,b)=(1.4+\epsilon_1,0.4+\epsilon_1)$, for all $0<\epsilon<\epsilon_2$:

• $h_{top}(f_{a,b})=0$;
• $h_{top}(f_{a+\epsilon,b})>\frac14\log\frac12(\sqrt{5}+1)$.

The verification turns out to be quite simple (once you know where to look!). The non-wandering set of $f_{a,b}$ is shown to be reduced to be reduced to the fixed points of its fourth iterates, yielding the zero entropy immediately. $f_{a+\epsilon,b}$ on the other hand is shown to admit 2 disjoint closed quadrilaterals $U,V$ such that $f^4(U)$ hyperbolically crosses both $U$ and $V$ and $f^4(V)$ hyperbolically crosses $U$. This means that the sides of $U$ and $V$ can be branded alternatively s and u with the following property. The image of a u side crosses each of $U,V$ it meets, intersecting both their s sides and none of their u sides. This again yields the entropy estimate.


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## Cryptology ePrint Archive: Report 2019/692

Synchronous Consensus with Optimal Asynchronous Fallback Guarantees

Erica Blum and Jonathan Katz and Julian Loss

Abstract: Typically, protocols for Byzantine agreement (BA) are designed to run in either a synchronous network (where all messages are guaranteed to be delivered within some known time $\Delta$ from when they are sent) or an asynchronous network (where messages may be arbitrarily delayed). Protocols designed for synchronous networks are generally insecure if the network in which they run does not ensure synchrony; protocols designed for asynchronous networks are (of course) secure in a synchronous setting as well, but in that case tolerate a lower fraction of faults than would have been possible if synchrony had been assumed from the start.

Fix some number of parties $n$, and $0 < t_a < n/3 \leq t_s < n/2$. We ask whether it is possible (given a public-key infrastructure) to design a BA protocol that (1) is resilient to $t_s$ corruptions when run in a synchronous network and (2) remains resilient to $t_a$ faults even if the network happens to be asynchronous. We show matching feasibility and infeasibility results demonstrating that this is possible if and only if $t_a + 2\cdot t_s < n$.

Category / Keywords: cryptographic protocols / Byzantine agreement

Date: received 11 Jun 2019, last revised 21 Jun 2019

Contact author: jkatz2 at gmail com

Available format(s): PDF | BibTeX Citation

Note: Added citation to related work

Short URL: ia.cr/2019/692

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