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*Scholarship

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*Industriousness

(1=Not at all industrious,10=Highly industrious)

*Ability to Handle Complex Litigation

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*Punctuality

(1=Chronically Late,10=Always on Time)

*General Ability to Handle Pre-Trial Matters

(1=Not all Able, 10=Extremely Able)

*General Ability as a Trial Judge

(1=Not all Able, 10=Extremely Able)

Flexibility In Scheduling

(1=Completely Inflexible,10=Very Flexible)

Criminal Rating Criteria (if applicable)

*Evenhandedness in Criminal Litigation

(1=Demonstrates Bias,10=Entirely Evenhanded)

General Inclination Regarding Bail

(1=Pro-Defense,10=Pro-Government)

Involvement in Plea Discussions

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General Inclination in Criminal Cases Pretrial Stage

(1=Pro-prosecution,10=Pro-defense)

General Inclination in Criminal Cases Trial Stage

(1=Pro-prosecution,10=Pro-defense)

General Inclination in Criminal Cases Sentencing Stage

(1=Pro-prosecution,10=Pro-defense)

Civil Rating Criteria (if applicable)

*Evenhandedness in Civil Litigation

(1=Not at all Evenhanded,10=Entirely Evenhanded)

Involvement in Settlement Discussions

(1=Not at all Involved,10=Very Involved)

General Inclination

(1=Pro-defendant, 10=Pro-plaintiff)

Comments

What others have said about
Hon. Michael Mattice

Comments

Other

Comment #: CA5834
Rating:1.0
Comments:
HIs ruling is unreasonable, because petition for stay order of "5 yards" won't do any justice. The petitioner lives within 5 feet and we share the same hallway and staircase leading down to the front door…

Also I contacted the petitioner's insurance company to see if it would cover for my auto accident but I did not mean to contact to his employee to interfere with his job.

If this judge could not see the motivation the petitioner brings, I'll h ave to file appeal to argue. Because the petitioner and his landlord have a malice in its intention, and I showed why I was objecting to grant his petition by showing the surveillance camera mounted pointing at my bedroom to bring any normal activity as something objectionable.

Civil Litigation - Govt.

Comment #: CA5313
Rating:9.5
Comments:
He is a solid judge he is very cautious and slow, which can be frustrating at times. However, for the most part all parties are better served due to his meticulous nature.

Other

Comment #: CA5227
Rating:Not Rated
Comments:
Judge Mattice ruled on a demurrer without leave to amend in a Securitized Mortgage Fraud. He did not know what an allonge is and did not understand about the Pooling and Servicing Agreement, which is instrumental if a Note is owned by a Trustee. Hereby he is assisting in perpetuating fraud and denying due process to present pertinent material. Shameful and a disgrace.

Other

Criminal Defense Lawyer

Comment #: CA2951
Rating:1.0
Comments:
This judge has no idea what has happened to the plaintiff, in fact doesn’t' know who the plaintiff is or why the complaint was brought!

It is my opinion he needs to retire because his memory and cognition is deficient.

As a plaintiff, I'm going to do everything I can to get him off my case as he is clearly incompetent due to a memory/cognition problem. My case has been in front of him for over a year, however he doesn't know who I am as a plaintiff! - J

Other

Comment #: CA2950
Rating:1.0
Comments:
This judge doesn't know who I am as referenced by public court documents. Even thought I am a valid, documented plantiff in his court for over a year, under his rule for over a year, he has had to ask my name from my attorney(confused by the defendants name, calling me her name) for more than 2 hearings! What a travesty!! I hope he starts reading my pleadings and figures out who I am to bring my Aunt justice!

Civil Litigation - Private

Comment #: CA560
Rating:2.1
Comments:
I saw Judge Mattice on the bench in Family Court. He was indulgent of the family law practioners he saw everyday regardless of their incompetence, and foul to everyone else, particularly pro per litigants. A disgrace.


Molecular heterogeneity in mitochondrial and chloroplast DNAs from normal and male sterile cytoplasms in sugar beets.
Mitochondrial (mt) and chloroplast (ct) DNAs were prepared from normal (N) and male sterile (S) cytoplasmic lines of sugar beet. The DNAs were cleaved with BamHI, EcoRI, HindIII and SalI enzymes, and the resultant fragments were fractionated by agarose gel electrophoresis. The results showed that N and S cytoplasms contained distinct mtDNA. Although most of the DNA fragments were common to these two cytoplasms, each cytoplasm was readily characterized by bands specific to that cytoplasm. In addition, these distinctive cleavage patterns were invariant in different nuclear backgrounds. In contrast to the marked variation in mtDNA, restriction fragment analyses of ctDNA demonstrated little difference between both cytoplasms. Only HindIII digestion showed one band missing in the S genome. The data presented here provides circumstantial evidence for mitochondrial involvement in the inheritance of cytoplasmic male sterility in sugar beet.


Q:

newgeometry makes content run to next page

Thank you all from the beginning for your help in this. I'm completely new to LaTeX as I'm writing my bachelor thesis, so I'm sure I've made lots of mistakes...
I'm using the geometry package because I need to modify the text width at a certain point in my thesis, and to do so I wanted to use \newgeometry inside a custom environment.
The problem is that the part of the text that should be influenced by the new geometry runs all the way down to the next page.
I have read this
Using \restoregeometry in environment, next page runs off the page bottom
question and, as you can see, I also used the \aftergroup comman, but still the issue persists.
My custom environment is:
\newenvironment{esempio}[1]%
{
    \vspace{1.5ex}
    \noindent
    \underline{#1}
    \nopagebreak
    \newgeometry{textwidth=\textwidth}
    %\leftskip=1cm
    %\rightskip\leftskip
}
{
    \par
    \aftergroup\restoregeometry
}

And i use it like:
\begin{esempio}{$k=2$}
    Il Poligono di Controllo \`e formato da $\{P_0, P_1, P_2\}$.
    La curva di Bezier \`e definita come $$P_0^2(t) = (1-t)P_0^1(t) + tP_1^1(t)$$
\end{esempio}

I get the " K=2 " underlined in the correct position, but the following text is all down to the next page.
My aim would be to have an environment that lets me format a body of text like this:
bla bla bla bla bla bla bla bla bla bla bla bla
K=2
   bla bla bla bla bla bla bla bla
   bla bla bla bla bla bla bla bla

As a sidenote, you can see I also tried with the \leftskip.
It gets the job done very well with the normal text, but most of the times I'll also include a picture on the side and I want the text to wrap around it, and with the \leftskip it leaves the space on the left of the image and so the text does not wrap nicely.

@ egreg: thank you for the warm welcome and the tips :)
@ Gonzalo: thanks, your code works like a charm for the text! However, as i said in the original question, a very common situation would be to insert a figure inside that text and i'd like the text to be wrapped around the picture.
However, this causes a couple of warnings and bad boxes in the compilation process and the figure gets moved down and placed not after the "esempio" box, but after even the next paragraph, and it also creates huge blank spaces... quite weird.
Below the MWE (rettaBezier is a picture 4cmx4cm square)
\documentclass[a4paper,11pt,italian]{book}

\usepackage[italian]{babel}
\usepackage{graphicx, wrapfig, subfig}
\usepackage{changepage}
\usepackage{calc}
\usepackage{amsmath, amsthm, amssymb, mathrsfs, setspace}
\usepackage{mycommands}
\usepackage{indentfirst}
\usepackage[format=hang,font=footnotesize,labelfont=bf]{caption}
\graphicspath{{./images/}{./matlab/}}
\usepackage{lipsum}

\begin{document}
\lipsum[2]
\begin{esempio}{$k=1$}
    \begin{wrapfigure}{o}[1cm]{0cm}
        \centering
        \includegraphics{rettaBezier}
        \caption{Retta ottenuta con $P_0 = [0, 0] , P_1 = [1, 1]$}
        \label{rettaBezier}
    \end{wrapfigure}
    \lipsum[2]
\end{esempio}

\lipsum[2]

\end{document}

A:

As egreg mentions in a comment, \newgeometry affects whole pages. To temporarily change the text area width, you can use the adjustwidth command from the changepage package:
\documentclass{article}
\usepackage{changepage}
\usepackage{lipsum}

\begin{document}

\lipsum[2]
\begin{adjustwidth}{-2cm}{-2cm}
\lipsum[2]
 \end{adjustwidth}
\lipsum[2]

\end{document}

Your esempio environment then could look like in the following example (change the lengths according to your needs):
\documentclass{article}
\usepackage{changepage}
\usepackage{soul}
\usepackage{lipsum}

\newenvironment{esempio}[1]%
  {\vspace{1.5ex}
  \begin{adjustwidth}{1cm}{1cm}
   \rlap{\ul{#1}}\par\nobreak
  }
  {\end{adjustwidth}}

\begin{document}

\begin{esempio}{$k=2$}
\lipsum[2]
\end{esempio}
\lipsum[2]

\end{document}

Without additional packages, you can use a list:
\documentclass{article}
\usepackage{soul}
\usepackage{lipsum}

\newenvironment{esempio}[1]%
  {\vspace{1.5ex}
  \list{}{\setlength\leftmargin{1cm}\setlength\rightmargin{1cm}}\item\relax
  \rlap{\ul{#1}}\nobreak
  }
  {\endlist}

\begin{document}

\lipsum[2]
\begin{esempio}{$k=2$}
\lipsum[2]
\end{esempio}
\lipsum[2]

\end{document}

To be able to use wrapfigure inside the esempio environment, one option is to use a minipage to enclose the figure and its wrapping text (of course, now the material inside the minipage won't admit page breaks):
\documentclass{article}
\usepackage{soul}
\usepackage{wrapfig}
\usepackage{lipsum}

\newenvironment{esempio}[1]%
  {\vspace{1.5ex}
  \list{}{\setlength\leftmargin{1cm}\setlength\rightmargin{1cm}}\item\relax
  \rlap{\ul{#1}}\nobreak
  }
  {\endlist}

\begin{document}

\lipsum[2]
\begin{esempio}{$k=2$}
\begin{minipage}[t]{\linewidth}
\begin{wrapfigure}{r}{5cm}
  \centering
  \rule{4cm}{3cm}
  \caption{a test figure}
  \label{fig:test}
\end{wrapfigure}
\lipsum[2]
\end{minipage}
\end{esempio}
\lipsum[2]

\end{document}

And the result:

A:

Thank you Gonzalo for your suggestion.
However, i hate the minipage and avoid any use of it as much as i can, therefore i came up with this solution
\newenvironment{esempio}[3]%
{
    \vspace{1.5ex}
    \rlap{\underline{#1}}
    \par
    \setlength{\parindent}{0cm}
    \nopagebreak
    \leftskip=#2cm
    \rightskip=#3cm
}
{
    \par
}

It might not be the most elegant code ever, but it gets the job done and fits well in my project expecially because it allows pagebreaks in the middle of the "esempio".


Exacerbation of collagen arthritis by noise stress.
We evaluated the effect of noise exposure on collagen arthritis in rats. Ninety-eight female Wistar rats were transported and subjected to a 90-decibel noise during a 1-h period for 7 days commencing 3 days prior to immunization with type II collagen. As a control, 99 rats received simultaneous immunizations but were otherwise left undisturbed. Noise exposure was associated with statistically significant increases in the prevalence of arthritis (p less than 0.05) and, early in its course, in the severity (p less than 0.001) of arthritis.


2010 NCAA Division II football rankings

The 2010 NCAA Division II football rankings are from the American Football Coaches Association (AFCA). This is for the 2010 season.

Legend

American Football Coaches Association poll

Notes

References

Rankings
Category:NCAA Division II football rankings


Q:

authorizeRequests() order makes a difference?

I am learning Spring and want to know why there is difference when I change order of these two authorizeRequests() methods:
This works fine:
security.authorizeRequests()
    .antMatchers("/css/**")
    .permitAll();

security.authorizeRequests()
    .anyRequest()
    .authenticated();

This does not:
security.authorizeRequests()
    .anyRequest()
    .authenticated();

security.authorizeRequests()
    .antMatchers("/css/**")
    .permitAll();

What I mean by "doesn't work" is that in my login page CSS is not applied while using second example. Why order of these two methods actually matters?

A:

When multiple children to the http.authorizeRequests() method each matcher is considered in the order they were declared. In your second example it define every request require authentication.


The present invention relates to an image processing system, and more particularly to a game computer system processing a variety of images such as a natural picture together with an animation picture.
In a conventional game computer system, image data are defined by color for each dot. The colors of the image data are managed by a color pallet formed in a memory, the color pallet storing many pallet codes (PLT) corresponding to color data.
In the conventional game computer system, image data are compressed (encoded) to be transmitted, and then the compressed data are extended (decoded) to be displayed. Each piece of image data is composed of the pallet code (PLT) and the number (CRL) thereof, which is called a pallet length. The compression method is called a "run-length" method.
When a single color mode is employed for each screen, image data may be fixed in length (bits); however, when plural color modes are used for one screen, the lengths of the image data are different depending on the color mode.
FIG. 1 shows the formats of image data according to the conventional game computer system, which employs 16, 32, 64 and 128 color modes. The pallet codes are defined by data of 4, 5, 6, and 7 bits for the 16, 32, 64 and 128 color modes, respectively. The length "L" of the pallet code in a color mode "m" is given by the following equation. EQU L=log.sub.2 m
For example, the length "L" of the pallet code in the 128 color mode becomes "7" as follows: EQU L=log.sub.2 128=log.sub.2 2.sup.7 =7
The data needs to have a width corresponding to a bus line to be transmitted thereon, because the widths of buses vary depending on the system.
When the image data are transmitted on a 8 bit bus, the data for the 16 color mode may be transmitted in entirely, as shown in FIG. 1; however, when the length of the image data to be transmitted is not a multiple of 8 bits, the data need to be divided, as shown in FIG. 2. For example, image data for the 32 color mode are compressed to 9 bits, the data are divided as 8 and 1 to be transmitted, and as a result, the left over one bit is transmitted with the following data.
In the conventional game computer system, when the screen is divided into plural areas of different colors, the color mode of the greatest number is selected, because each picture is displayed using only one color mode. For example, when an animation picture with 16 colors and a natural picture with 16M colors are synthesized on the screen, the synthesized picture is displayed in the 16M color mode. Such a processing method is not effective for using the memory.


In fact in Haskell inefficient implementations sometimes turn out to be wrong implementations.

In fact in Haskell inefficient implementations sometimes turn out to be wrong implementations.

A very simple examples is the function reverse . reverse which seems to be an inefficient implementation of id.

A very simple examples is the function reverse . reverse which seems to be an inefficient implementation of id.

−

But if you look closer it is not exactly equivalent to id, because for infinite input list, the function reverse . reverse is undefined, whereas id is defined.

+

In a language with [[strict semantics]], these two functions are the same.

+

But since the [[non-strict semantics]] of Haskell allows infinite data structures, there is subtle difference,

+

because for infinite input list, the function reverse . reverse is undefined, whereas id is defined.

Now lets consider a more complicated example.

Now lets consider a more complicated example.

Revision as of 16:55, 28 December 2008

In this article we want to demonstrate how to check efficiency of an implementation by checking for proper results for infinite input.
In general it is harder to reason about time and memory complexity of an implementation than on its correctness.
In fact in Haskell inefficient implementations sometimes turn out to be wrong implementations.
A very simple examples is the function reverse . reverse which seems to be an inefficient implementation of id.
In a language with strict semantics, these two functions are the same.
But since the non-strict semantics of Haskell allows infinite data structures, there is subtle difference,
because for infinite input list, the function reverse . reverse is undefined, whereas id is defined.

Now lets consider a more complicated example.
Say we want to program a function, that removes elements from the end of a list,
just like dropWhile removes elements from the beginning of a list.
We want to call it dropWhileRev.
(As a more concrete example imagine a function which removes trailing spaces.)
A simple implementation is

You will already guess, that it also does not work for infinite input lists.
And actually it is also inefficient, because reverse requires to compute all list nodes
(but it does not require to compute the values stored in the nodes).
Thus the full list skeleton must be hold in memory.
However it is possible to implement dropWhileRev in a way that works for more kinds of input.

Here, foldr formally inspects the list from right to left,
but actually it processes data from left to right.
Whenever a run of elements occurs, which matches the condition p,
then these elements are hold until the end of the list is encountered (then they are dropped),
or a non-matching list element is found (then they are emitted).
The crux is the part null xs, which requires to do recursive calls within foldr.
This works in many cases, but fails if the number of matching elements becomes too large.
The maximum memory consumption depends on the length of the runs of non-matching elements,
which is much more efficient than the naive implementation above.


Identifying juvenile and adult yellow perch females from males is no longer an obstacle for aquaculture producers of this high-value fish, thanks to U.S. Department of Agriculture (USDA) scientists. A new step-by-step procedure developed by the scientists makes it easier to separate fish by gender for growth performance, physiological studies and to manage broodstocks for reproduction and genetic selection.

Physiologist Brian Shepherd and his colleagues at the Agricultural Research Service (ARS) Dairy Forage and Aquaculture Research Unit in Milwaukee, Wisconsin, developed the systematic method to segregate yellow perch females from males during early growth stages. Because females tend to grow faster and larger than males, females could often be mistaken for males when being selected for genetic improvement prior to reproductive maturity. Previously, it was extremely difficult to identify gender until fish matured (up to two years). Read more …

Farmed salmon is rich in fat, but fillets of farmed salmon contain less marine omega-3 than previously, because a large fraction of the fish oil in the feed has been replaced by plant oil. Scientists have discovered a way of stimulating farmed salmon to convert plant oil in the feed to marine omega-3.

This means that farmed salmon may become a net producer of marine omega-3. Read more …

Demand for salmon is soaring and is driving expansion of aquaculture—fish farming. For years, environmentalists advised conscientious consumers to avoid farmed salmon, but that’s starting to change, thanks to an evolving industry.

Rich in protein and heart-healthy omega-3 fatty acids and low in saturated fats, salmon is increasingly being marketed as a healthy food. Demand for salmon has risen more than 20 percent in the last decade. Consumption is three times what it was in 1980. Read more …

WASHINGTON – The U.S. Department of Agriculture’s Animal and Plant Health Inspection Service (APHIS) is lifting the Viral Hemorrhagic Septicemia (VHS) Federal Order that was first issued in 2006 in response to an outbreak of the fish disease in the Great Lakes region.

After studying the disease, conducting surveillance and evaluating the latest science, APHIS has determined it can safely remove the Federal Order as long as states maintain existing VHS regulations and other practices to reduce risk.

By removing the Federal Order, which has become duplicative with state regulations, we can still protect the health of farmed and wild fish while also supporting the interstate movement needs of the aquaculture industry. Beginning June 2, APHIS will no longer prohibit or restrict the interstate movement of VHS-susceptible species of live fish from VHS-affected or at-risk states, including: Illinois, Indiana, Michigan, Minnesota, New York, Ohio, Pennsylvania and Wisconsin. In addition, APHIS will no longer restrict the importation of the same species of live fish from Ontario and Quebec, Canada into the United States. However, this action does not affect the U.S. Fish and Wildlife Service’s salmonid importation requirements as found in title 50 of the Code of Federal Regulations.

Although APHIS will no longer regulate VHS, the Agency’s Veterinary Services program will continue to work with states and industry to promote sound biosecurity practices and share scientific updates regarding the disease.

Please find attached (PDF) a Federal Register notice announcing a series of public meetings to obtain comments on a proposed rulethat would establish requirements for shippers, carriers by motor vehicle and rail vehicle, and receivers engaged in the transportation of food, including food for animals, to use sanitary transportation practices to help ensure the safety of the food or feed products they transport. This may have implications for the commercial aquaculture feed manufacturing and transportation industries.

The Food and Drug Administration (FDA or we) is announcing three public meetings to discuss the proposed rule that would establish requirements for shippers, carriers by motor vehicle and rail vehicle, and receivers engaged in the transportation of food, including food for animals, to use sanitary transportation practices to help ensure the safety of the food they transport. The proposed rule is part of our larger effort to focus on prevention of food safety problems throughout the food chain and is part of our implementation of the Sanitary Food Transportation Act of 2005 (2005 SFTA) and the FDA Food Safety Modernization Act (FSMA). The purpose of the public meetings is to inform the public of the provisions of the proposed rule and the rulemaking process (including how to submit comments, data, and other information to the rulemaking docket) as well as solicit oral stakeholder and public comments on the proposed rule and to respond to questions about the rule. usa online casino echeck

World Fishing & Aquaculture interviews Norwegian modern fish farming pioneer, Bjørn Myrseth to discover what he believes the future holds for the fast growing industry.

Fish that do not require fish protein in their feed will become important in tomorrow’s aquaculture, says Norwegian fish farming pioneer, Bjørn Myrseth. “I will continue to work with marine fish that can be grown in cages, but at the moment I am also interested in taking a look at the herbivore or omnivore fish that are sold at low prices such as tilapia and pangasius.”

This is far removed from the farming of rainbow trout and Atlantic salmon in Norway, which is where Mr Myrseth began his long career in aquaculture. This career has taken him all over the world and has involved the farming of many species. Read more …

A young Chilean researcher was awarded a prize by a prestigious US institution for her research aimed at developing technology to obtain antibiotics substitute for farmed salmon.

The new technological solution is environmentally friendly. Its goal is to improve productivity in salmon farming through the use of a food additive with an active compound derived from indigenous marine bacteria off the coast of Valparaíso Region.

Due to her innovative research, co-founder of the company Micro Marine Biotech and researcher at the University of Valparaiso, Claudia Ibacache, received an award from MIT Technology Review, a magazine from Massachusetts Institute of Technology (MIT), reported Valparaiso University. Read more …

Recirculating aquaculture systems (RAS) are increasingly used to rear aquatic animals – as this use increases so does the potential for loss from disease. Unfortunately, few drugs are presently approved in the U.S. for use in RAS to control disease. This survey<http://www.surveymonkey.com/s/Viterbo_RAS> seeks to develop information that will make it easier for safe and effective drugs to be approved for use in RAS.

As part of the drug approval process, the U.S. Food and Drug Administration assesses not only the efficacy of the drug for its intended purpose, but also its potential direct and indirect effects on the treated animals and the environment. There is little information currently available that summarizes how the various types of RAS are actually operated and the conditions under which a drug might be applied in those RAS. There is even less information available on how drugs might affect RAS biofilter operation and function; this information is important for determining the fate of drugs within RAS facilities, their potential effects on water quality, and for evaluating how much of the drugs might be released in RAS effluents. The lack of this broad baseline information on RAS and biofilters complicates the FDA’s assessments of potential risks to both the treated animal and the environment.

Viterbo University (La Crosse, Wisconsin) developed this survey<http://www.surveymonkey.com/s/Viterbo_RAS> to describe current RAS operations and procedures, which, when combined with other drug effects information, will help inform the decisions FDA makes in regards to the approval of drugs to control disease in animals reared in RAS.

* Most respondents will complete the survey in about 30 minutes.
What information will you be asked to provide?

* Information about your RAS (biofilter type/media/volume or size; rearing tank volumes; water flow/replacement/clarification/chemistry; species reared and feed protein level)
* Information about oral or waterborne drugs used or considered for use in your RAS including
* Drug impacts on biofilter function
* Annual number of treatments administered, treatment timing, number of tanks treated
* How treated water is handled within the RAS – i.e., can tank water bypass the biofilter and be discharged, etc.
* Pathogens/diseases that impact your facility
What if I don’t have time to complete the survey<http://www.surveymonkey.com/s/Viterbo_RAS>?

* We understand time is precious and you might not be able to complete the entire survey. If you can’t complete the entire survey, please complete Section 1 and the first two questions of Section 2.
How will the information be used?

* Viterbo University will summarize the information at national and regional scales; individual facility or state summaries will not be developed
* National summaries of RAS characteristics will be provided to FDA; regional summaries will be submitted only if needed. Individual facility or state information will not be submitted.
Who should complete the survey<http://www.surveymonkey.com/s/Viterbo_RAS>?

* All facilities that operate RAS, whether operated for profit, public fish stocking, education, or research, are encouraged to complete the survey.
How will my information be protected?

* Individual and facility identity and responses will be kept strictly confidential.
* Facilities that respond to the survey will be assigned a unique facility identification number.
* Individual facility information (name or location) will not be submitted to FDA; only nationally or regionally summarized data will be submitted to FDA.
* Viterbo University will retain completed and coded surveys on secure media accessible only to the survey coordinator
Why is Viterbo University completing this survey?

* Students and faculty from Viterbo have supported several research projects to enhance access to safe and effective drugs for use in aquaculture through collaborations with the U.S. Geological Survey Upper Midwest Environmental Sciences Center.

* Our involvement with this survey furthers the mission of the Biology Department at Viterbo to train the next generation of scientists by engaging students outside the classroom through involvement in real-world research problems to tackle science challenges
How can I get more information?

The National Aquaculture Association (NAA) is presenting a free webinar, “Fish Nutrition 101: What Fish Farmers Need to Know about Feeds and Feeding”, on November 19, 2013.

Feed represents the largest cost of production in aquaculture, and there are very few aspects of aquaculture that aren’t directly or indirectly influenced by feeds and feeding practices. Growth and efficiency, broodstock performance and gamete quality, product value, water quality and effluent management, budgets and business planning, environmental impacts, etc.—what you feed and how you feed it affects virtually everything from egg to plate.

In this webinar, we will discuss the basic nutritional requirements of fish and how these differ from terrestrial livestock, attributes of feeds and how to choose the best one for your operation, and feeding strategies to maximize efficiency. Special topics, including fish meal/fish oil sparing and omega-3 enriched products will also be discussed.

Please join us at this free National Aquaculture Association webinar to learn more about the practical aspects of fish nutrition, aquafeeds, and feeding practices.

Dr. Jesse Trushenski is an Associate Professor with the Center for Fisheries, Aquaculture, and Aquatic Sciences (CFAAS) at Southern Illinois University Carbondale where she heads a research team dedicated to aquaculture nutrition and fish physiology. Holding degrees from Western Washington University (B.S., 2002) and Southern Illinois University Carbondale (Ph.D., 2006), Dr. Trushenski is also President of the Fish Culture Section of the American Fisheries Society.

Linda ODierno has over 25 years of experience working with the fish and seafood industry and is currently the Outreach Specialist for the National Aquaculture Association. Prior to that, she served as Coordinator of Fish and Seafood Development for the New Jersey Department of Agriculture and was a Regional Seafood Specialist with New York Sea Grant.

Host:
This is the third in a series of webinars presented by the National Aquaculture Association. The NAA outreach program provides educational programming for the U.S. aquaculture industry, foodservice professionals, nutritionists and dieticians, retailers and wholesalers, and consumers. Webinar topics are selected with industry input to meet actual information needs. Recorded webinars are available on the NAA website:www.thenaa.net

Registration Link:

Here is the link for people to register for the https://naa.ilinc.com/perl/ilinc/lms/event.pl?int=1. To register for the event “Fish Nutrition 101: What Fish Farmers Need to Know about Feeds and Feeding” check the name to register and then click the “Register” button below the list of items.

Please see the information below on a new decision support tool for screening non-native freshwater fishes is now available on the Centre for Environment, Fisheries & Aquaculture (CEFAS) website. Thanks to Jeff Hill with the Tropical Aquaculture Laboratory at the University of Florida for providing this information.

This is the Fish Invasiveness Screening Kit (FISK) v2.03 developed in a collaboration between UF Fisheries and Aquatic Sciences (Hill and Larry Lawson), CEFAS (Gordon Copp), and the Florida Fish and Wildlife Conservation Commission (Scott Hardin) with assistance of Lorenzo Vilizzi (La Trobe University/Murray-Darling Freshwater Research Center).

This risk screening tool has been used in several international projects and published journal articles and is now freely available online (Excel spreadsheet tool and user guide pdf).


Robyn+Jeff // Cancun, Mexico // Married

I've always loved what Bob Goff says about extravagant love: "Love is never stationary. In the end, love doesn't just keep thinking about it or keep planning for it. Simply put: love does"

Robyn and Jeff met years ago at a wedding in Cancun, Mexico. Jeff wrote Robyn a letter once the week was over letting he know how much he loved getting to know her. And then years later, they got married on the same beach where they first met.

But what happened in-between those two weddings was what really mattered:

They continued to get to know each other, ran (literal) races together, challenged each other to train and PUSH themselves, Jeff came to South Carolina to help take care of Robyn's sister when she got sick and Robyn did the same when Jeff's sister was diagnosed with the same cancer. They DO. They are a couple that takes love as a verb and runs with it.

And even with me: I don't think I'll be able to express the gratitude I have for Robyn, Jeff and the Galvan clan for not only bringing me to Mexico to document their wedding but for bringing me there for their entire wedding week to simply relax, soak up the sun and simply do nothing at the amazing resort we all stayed at. Talk about extravagant love!

AND these two take marriage hardcore and finished an IRONMAN (!!!) competition yesterday. Crazy. Incredible.


1. Introduction {#sec1-cancers-10-00375}
===============

Mechanistic (formally mammalian) target of rapamycin (mTOR) is often referred to as a master regulator of cell growth \[[@B1-cancers-10-00375]\]. mTOR is a serine/threonine protein kinase that coordinates cell growth, metabolism, protein synthesis and autophagy as a protein kinase complex, termed mTOR complex 1 (mTORC1). mTORC1 is fundamentally involved in the build-up of cellular biomass, which is rate-limiting for hyper-proliferative cancer cells. As well as cell growth control, mTORC1 promotes metabolic transformation, neovascularization, cell survival and metastasis (reviewed in reference \[[@B2-cancers-10-00375]\]). Genetic mutations leading to mTORC1 hyperactivation often occur in sporadic cancer but also underlie several tumor predisposition syndromes, such as Tuberous Sclerosis Complex (TSC) \[[@B3-cancers-10-00375]\] and Cowden disease/PTEN hamartoma syndrome (reviewed in reference \[[@B4-cancers-10-00375]\]). mTORC1 hyperactivity has also been identified in numerous cancer types including lung (\>50% in both small cell and non-small cell lung cancer cases \[[@B5-cancers-10-00375],[@B6-cancers-10-00375]\]), ovarian (approximately 70% of ovarian cancers \[[@B7-cancers-10-00375]\]), renal cell carcinoma \[[@B8-cancers-10-00375]\] and head and neck cancers \[[@B9-cancers-10-00375]\]. Although uncommon, activating mutations within the kinase domain of mTOR occurs in cancer \[[@B10-cancers-10-00375],[@B11-cancers-10-00375]\]. More frequently, genes that function upstream of the mTORC1 signaling pathway are mutated in cancer, such as PI3K, PTEN and RAS, which then cause aberrant signal transduction through mTORC1 to drive cancer growth \[[@B12-cancers-10-00375]\].

TSC is an autosomal dominant condition caused by mutations in either TSC1 or TSC2 and is characterized by mTORC1 hyperactivity, tumor growth in multiple organs, neurocognitive problems and epilepsy \[[@B13-cancers-10-00375]\]. TSC1 and TSC2 form a tumor suppressor protein complex that functions as a GTPase-activating protein (GAP) towards the small G-protein, Ras homologue enriched in brain (Rheb) \[[@B14-cancers-10-00375],[@B15-cancers-10-00375]\]. When associated with TSC1, the GAP function of TSC2 switches Rheb from its active GTP-bound state to an inactive GDP-bound state to turn off mTORC1. Consequently, loss-of-function mutations in either TSC1 or TSC2 cause Rheb to become predominantly GTP-loaded, thereby constitutively turning on mTORC1. mTORC1 inhibitors, such as rapamycin analogues (rapalogues), are currently used as the first-line therapy to treat TSC and are effective at shrinking tumors and delaying disease progression. However, mTORC1 inhibitors are cytostatic meaning that TSC tumor cells regrow after treatment withdrawal (reviewed in reference \[[@B16-cancers-10-00375],[@B17-cancers-10-00375]\]). This cytostatic nature of mTOR inhibitors is a principal reason why rapalogues have had limited clinical success in the cancer setting, where the lack of cytotoxicity leads to acquired drug resistance (reviewed in reference \[[@B2-cancers-10-00375]\]).

To overcome the shortcomings of directly targeting mTORC1 in cancer, an alternative therapy might be to exploit the intrinsic vulnerabilities of cancer cells that have constitutive mTORC1 activation. In principle, you could take advantage of the cancer's inability to restore homeostatic balance during prolonged periods of drug induced cell stress to trigger a cytotoxic response. In contrast to cancer cells, non-cancerous cells would have greater flexibility in their homeostatic pathways, allowing them to better tolerate drug treatment. As loss of TSC1/2 and mTORC1 hyperactivity results in an increased burden of unfolded protein within the endoplasmic reticulum (ER), which causes membrane expansion of the ER and activation of the unfolded protein response (UPR) \[[@B18-cancers-10-00375]\], a possible therapy would be to employ ER stress inducers. To enhance the cytotoxic effect, you could also target cell survival pathways, such as autophagy. Previously, we examined the combined effect of chloroquine (an autophagy inhibitor) with nelfinavir (an ER stress inducer), which selectively enhanced cell death in mTORC1 hyperactive cells \[[@B19-cancers-10-00375]\]. Nelfinavir is a clinically approved HIV inhibitor that induces ER stress and shows promise as an anti-cancer agent \[[@B20-cancers-10-00375]\]. While these clinically viable drugs were previously shown to be selectively cytotoxic to mTORC1-active cells, combined chloroquine and nelfinavir treatment was not sufficient to kill all the cells, showing a heterogeneous drug effect and a significant level of drug resistance. In this current study, we wanted to refine our previous combinatory drug therapy to enhance their cytotoxic potency. To do this, we considered dual treatment with nelfinavir and mefloquine, which appeared to kill a higher percentage of *Tsc2*-deficient cells when compared to nelfinavir and chloroquine in our initial analysis \[[@B19-cancers-10-00375]\]. Mefloquine is a chloroquine derivative used in both the prevention and treatment of malaria and unlike chloroquine, more readily crosses the blood-brain barrier. Mefloquine has been reported to inhibit autophagy (via blocking the formation of autophagosomes \[[@B21-cancers-10-00375]\]), to induce reactive oxygen species (ROS) \[[@B22-cancers-10-00375]\] and to prevent PI3K/Akt/mTORC1 signaling \[[@B23-cancers-10-00375]\]. In this study, we demonstrate that nelfinavir and mefloquine show therapeutic promise as a drug combination that has high potency to kill TSC2-deficient cells and cancer cells with high mTORC1 activity.

2. Results {#sec2-cancers-10-00375}
==========

2.1. Nelfinavir and Mefloquine Synergize, and Selectively Kill Tsc2-Deficient Cell Lines {#sec2dot1-cancers-10-00375}
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To build on our previous study \[[@B19-cancers-10-00375]\] and to further explore the impact of nelfinavir and mefloquine dual therapy, we tested the ability of nelfinavir and mefloquine to synergize and induce cell death. Loss of cell viability was quantified with escalating concentrations of drug, either as single agents or in combination using flow cytometry with DRAQ7 staining. DRAQ7 can only penetrate membranes of cells that are compromised (i.e., are no longer viable). Loss of cell viability (determined by an increase of DRAQ7 fluorescence) with single drug treatments for nelfinavir and mefloquine is shown in [Figure 1](#cancers-10-00375-f001){ref-type="fig"}A,B, respectively. To ascertain how synergistic this combination was, a range of mefloquine concentrations were tested with a fixed 10 µM concentration of nelfinavir ([Figure 1](#cancers-10-00375-f001){ref-type="fig"}C). Quantification of cell death was processed in CompuSyn using a non-constant ratio approach to generate a Combination Index (CI) value, where it is considered that a score \<1 is synergistic, a score of 1 is additive, and a score \>1 is antagonistic. Results reveal that 10 µM mefloquine was synergistic with 10 µM nelfinavir to induce cell death in the *Tsc2*−/− mouse embryonic fibroblasts (MEFs) (CI value = 0.03). Due to this finding, further assays were carried out using 10 µM mefloquine and 10 µM nelfinavir. We observed selective cytotoxicity with the combinatory drug treatment in cells lacking *Tsc2*, i.e., (37.6% −/+ 11.8 standard deviation (St-Dev) in the *Tsc2*+/+ MEFs versus 96% −/+ 2 in the *Tsc2*−/− MEFs) ([Figure 1](#cancers-10-00375-f001){ref-type="fig"}D). Etoposide was employed as a positive control and induced cell death in both the *Tsc2*+/+ and *Tsc2*−/− MEFs.

To confirm that the genetic loss of *Tsc2* was responsible for selective cell death, mefloquine and nelfinavir were then tested in ELT3 (Eker rat leiomyoma-derived) cells ([Figure 1](#cancers-10-00375-f001){ref-type="fig"}E), another model of TSC \[[@B24-cancers-10-00375]\]. Results were comparable to that observed in the *Tsc2*−/− MEFs, where ELT3-V3 cells (lacking *Tsc2*) had a significant induction of cell death (76% −/+ 6.3) when compared to the rescued ELT3-T3 with *Tsc2* re-expressed (42% −/+ 5.4). To determine whether combined nelfinavir/mefloquine drug treatment could also target sporadic cancer cell lines, we tested three mTORC1-hyperactive cancer cells ([Figure 1](#cancers-10-00375-f001){ref-type="fig"}F). Results from flow cytometry showed that combined 10 µM nelfinavir with 10 µM mefloquine treatment caused a high degree of cytotoxicity in the MCF7 breast cancer cell line (67% −/+ 8.30), in the HCT116 colorectal cancer cell line (96% −/+ 1.95), and in the NCI-H460 lung cancer cell line (89% −/+ 2.5). As single drug treatments, nelfinavir and mefloquine were not cytotoxic to MCF7 cells. As a mono-agent, mefloquine was observed to potently kill the HCT116 and NCI-H460 cell lines (but to a lesser degree when compared to combination treatment with nelfinavir). Etoposide was used as a control, which was effective at killing the HCT116 and NCI-H460 cells, while the MCF-7 cells were notably resistant.

2.2. Combined Nelfinavir and Mefloquine Treatment Inhibits Tumor Formation, Induces Cytotoxicity in Tsc2−/− Spheroids, and Prevents Spheroid Outgrowth {#sec2dot2-cancers-10-00375}
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To determine whether nelfinavir and mefloquine combination treatment prevents the formation of tumors in vitro, *Tsc2*−/− MEFs were grown in soft agar in the continued presence or absence of drug over 14 days ([Figure 2](#cancers-10-00375-f002){ref-type="fig"}A). We observed that when combined, nelfinavir and mefloquine significantly reduced colony growth compared to single drug treatments, which as mono agents did not significantly reduce colony size. Average colony size was 49.1 µm +/− 19.27 in diameter after nelfinavir/mefloquine treatment compared to 90.7 µm +/− 22.12 observed in samples treated with dimethyl sulfoxide (DMSO). To examine whether drug treatments could induce cell death in established tumor spheroids, *Tsc2*−/− MEFs were grown as spheroids and once established were then incubated with either DMSO, nelfinavir or mefloquine (as single drug treatments or in combination) for 4 days ([Figure 2](#cancers-10-00375-f002){ref-type="fig"}B). To measure loss of cell viability, spheroids were DRAQ7-stained during drug incubation. Intensity of DRAQ7 fluorescence was higher after combined treatment with nelfinavir and mefloquine (1424 MFU (mean fluorescent units) −/+ 404), when compared to single drug treatment with nelfinavir (668 MFU −/+ 146), and DMSO (768 MFU −/+ 214). Elevated DRAQ7 fluorescence was also observed with single drug treatment with mefloquine (1216 MFU −/+ 328). Tumor spheroids were transferred to plastic tissue culture plates to assess recovery and outgrowth of cells after drug removal ([Figure 2](#cancers-10-00375-f002){ref-type="fig"}C). Cell outgrowth was measured over 72 h, and showed cell recovery from the tumor spheroid after single drug treatment with either nelfinavir or mefloquine, although cell outgrowth was much slower with mefloquine when compared to either DMSO or nelfinavir treatments. In contrast, cells were unable to recover after combined treatment with mefloquine and nelfinavir, as observed by no detectable outgrowth of cells over the 72 h period. These data reveal that the combination of mefloquine and nelfinavir is effective at killing the bulk tumor.

2.3. Homeostatic Balance is Lost in Tsc2−/− MEFs after Combination Treatment with Nelfinavir and Mefloquine {#sec2dot3-cancers-10-00375}
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We postulated that the most likely mechanism of cell death would be because of prolonged ER stress and an inability to recover. Therefore, we initially examined ER stress signaling. Western blots were carried out on a panel of ER stress markers (inositol-requiring and ER-to-nucleus signaling protein 1α (IRE1α), activating transcription factor 4 (ATF4), C/EBP homologous protein (CHOP), growth arrest and DNA damage-inducible protein (GADD34), and Sestrin 2 (SESN2)), with thapsigargin treatment as a positive control. We observed a large difference in expression of ER stress markers when comparing *Tsc2*−/− to their wildtype *Tsc2*+/+ controls ([Figure 3](#cancers-10-00375-f003){ref-type="fig"}A). For instance, the *Tsc2*−/− MEFs have a higher level of IRE1α, ATF4, CHOP and GADD34 protein expression after thapsigargin and nelfinavir as single drug treatments, while mefloquine weakly induced ER stress. The control cells exhibit a lower basal level of ER stress and show little ER stress induction with single or combination treatments. Analysis of p70 ribosomal protein S6 kinase 1 (S6K1) phosphorylation showed that *Tsc2*−/− MEFs had a high level of mTORC1 activity that was not altered with drug treatments. To confirm that ER stress was enhanced after treatment, X-box binding protein 1 (*Xbp1*) mRNA splicing was determined ([Figure 3](#cancers-10-00375-f003){ref-type="fig"}B), revealing a higher level of *Xbp1* mRNA splicing in *Tsc2*−/− cells after combination drug treatment when compared to the untreated *Tsc2*−/− and treated *Tsc2*+/+ controls. We also observed a high level of *Xbp1* mRNA splicing in untreated *Tsc2*−/− MEFs, showing that these cells have a higher basal level of ER stress.

To assess transcriptional changes, RNA sequencing was carried out on mRNA samples generated from *Tsc2*+/+ and *Tsc2*−/− MEFs treated with either DMSO or nelfinavir and mefloquine. We observed elevated expression of ER stress genes in both *Tsc2*+/+ and *Tsc2*−/− MEF cell lines after combined nelfinavir and mefloquine drug treatment, which was higher in the absence of *Tsc2* ([Figure 3](#cancers-10-00375-f003){ref-type="fig"}C,D and [Table S1](#app1-cancers-10-00375){ref-type="app"}). We also observed elevated expression of ER stress genes in untreated *Tsc2*−/− MEFs when compared to wildtype. Conversely, negative regulators of ER stress such as CREBRF and IMPACT were expressed at higher levels in the *Tsc2*+/+ MEFs when compared to the *Tsc2*−/− MEFs ([Figure 3](#cancers-10-00375-f003){ref-type="fig"}E and [Table S2](#app1-cancers-10-00375){ref-type="app"}). The volcano plot illustrates the differential expression of genes involved in ER stress between the *Tsc2*+/+ and *Tsc2*−/− MEFs after combined nelfinavir and mefloquine treatment ([Figure 3](#cancers-10-00375-f003){ref-type="fig"}E).

To determine the capacity of *Tsc2*+/+ and *Tsc2*−/− MEFs to effectively restore ER stress during treatment, we examined the expression of ER stress proteins after combined nelfinavir and mefloquine treatment at an early 6 h time point and at a longer 48 h time point. The 6 h time point reflects acute ER stress induction after treatment, while the 48 h time point reflects ER stress recovery during prolonged drug treatment ([Figure 3](#cancers-10-00375-f003){ref-type="fig"}F). In wildtype *Tsc2*+/+ MEFs, nelfinavir and mefloquine treatment increased the expression of CHOP and ATF4 protein at 6 h. However, by 48 h the expression of ATF4 and CHOP was restored back down to a level equivalent to untreated cells, indicating a complete recovery to the ER stress signaling pathway in the *Tsc2*+/+ MEFs. Similarly, the *Tsc2*−/− MEFs also recovered from ER stress at the 48 h time point. The ability to restore the ER stress signaling pathway in the *Tsc2*−/− MEFs implies that the ER stress pathway is unlikely the main trigger that instigates the observed cell death response.

2.4. Cytotoxicity of Tsc2-Deficient Cells with Nelfinavir and Mefloquine is Energy Dependent {#sec2dot4-cancers-10-00375}
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We next wanted to determine whether mTORC1-hyperactivity was important for the induction of cell death. We carried out flow cytometry with DRAQ7 staining in *Tsc2*−/− MEFs, HCT116, NCI-H460 and MCF7 cells treated with either DMSO, or combination with nelfinavir and mefloquine in the presence or absence of rapamycin for 48 h ([Figure 4](#cancers-10-00375-f004){ref-type="fig"}A). Rapamycin failed to rescue cell death in these cell lines, showing that the cytotoxic effect of this drug combination is not prevented when mTORC1 is inhibited. Control western blots showing that rapamycin treatment was sufficient to block ribosomal protein S6 (rpS6) phosphorylation in these cells are shown ([Figure 4](#cancers-10-00375-f004){ref-type="fig"}B). As mefloquine is classically known as an autophagy inhibitor, we next analyzed build-up of lipidated LC3 to measure autophagy inhibition after treatment with either mefloquine or chloroquine after 3 h of treatment ([Figure 4](#cancers-10-00375-f004){ref-type="fig"}C). While mefloquine by itself did not cause an increase of lipidated LC3, there was an increase in the LC3-II lipidated isoform when mefloquine was combined with nelfinavir. It should be noted that the level of lipidated LC3 was not enhanced as much as chloroquine and nelfinavir treated *Tsc2*−/− MEFs, indicating that mefloquine is not as potent as chloroquine at inhibiting autophagy in these cells. Given the weaker level of autophagy inhibition with mefloquine, we considered it unlikely that blockade of autophagy was triggering the cell death response.

To explore alternative mechanisms that might cause cell death, the RNA sequencing data was further analyzed to compare gene expression changes in the *Tsc2*+/+ and *Tsc2*−/− MEFs ([Figure 5](#cancers-10-00375-f005){ref-type="fig"}A−C). Key genes involved in the regulation of metabolism and energy homeostasis were markedly up-regulated in the *Tsc2*−/− MEFs during combined treatment with nelfinavir and mefloquine. Of interest, PPARGC1α (typically referred to as PGC1α (peroxisome proliferator-activated receptor gamma coactivator 1α)) was basally expressed at a higher level in *Tsc2*−/− MEFs when compared to the *Tsc2*+/+ controls, which was further enhanced upon combined treatment with nelfinavir and mefloquine. Transcriptionally regulated genes of PGC1α were also observed to be more highly expressed in the *Tsc2*−/− MEFs when compared to wildtype. These included genes involved in glucose and lipid metabolism, such as peroxisome proliferator-activated receptor delta (PPARδ) and gamma (PPARγ) where there is more than a 2-fold increase in PPARδ gene expression in *Tsc2*−/− MEFs compared to *Tsc2*+/+ MEFS and a nearly 17-fold difference in PPARγ expression. Expression of genes involved in glycolysis were also upregulated, which is suggestive of metabolic stress. These genes included pyruvate dehydrogenase kinase 1 (PDK1), pyruvate carboxylase (PCX), lactate dehydrogenase B (LDHB) and glycerol-3-phosphate dehydrogenase 1 (GPD1). AMP-dependent protein kinase (AMPK) is known to function upstream of PGC1α, and is involved in the gene-expression of PGC1α as well as its activity \[[@B25-cancers-10-00375]\]. AMPK-regulated genes involved in glucose metabolism/storage such as acetyl-CoA carboxylase 2 (ACC2, encoded by the ACACB gene) and glycogen synthase 1 (GYS1) were expressed at a much higher level in *Tsc2*−/− MEFs and were further increased upon drug treatment with nelfinavir and mefloquine. The overall increase of mRNA expression of key genes involved in energy metabolism indicates that the *Tsc2*−/− MEFs are likely energy stressed.

To determine whether energy stress could be a possible mechanism of cell death, flow cytometry with DRAQ7 labelling was carried out with samples treated for 48 h with either DMSO, or nelfinavir and mefloquine combination in the presence or absence of methyl pyruvate ([Figure 5](#cancers-10-00375-f005){ref-type="fig"}D). Methyl pyruvate can alleviate energy stress when supplemented to cells, as it is a direct substrate of the citric acid cycle to produce energy by oxidative phosphorylation \[[@B26-cancers-10-00375]\]. Methyl pyruvate partially rescued nelfinavir and mefloquine induced cell death when supplemented to the *Tsc2*−/− MEFs (causing a 40% rescue of cell death). We confirmed that methyl pyruvate partially restored energy homeostasis within cells, as observed by a reduction in phosphorylated 5'-AMP-activated protein kinase (AMPK) and acetyl-CoA carboxylase (ACC). Methyl pyruvate also reduced the protein levels of SESN2 at the 24 h time point ([Figure 5](#cancers-10-00375-f005){ref-type="fig"}E). To further assess the capacity of *Tsc2*−/− MEFs to effectively restore energy stress, we examined phosphorylation levels of ACC after combined nelfinavir and mefloquine treatment at 6 h and 24 h time points ([Figure 5](#cancers-10-00375-f005){ref-type="fig"}F). In the presence of methyl pyruvate, the *Tsc2*−/− MEFs behaved similarly to the *Tsc2*+/+ MEFs after treatment with nelfinavir and mefloquine, where ACC phosphorylation was increased at 6 h and was then restored to a level equivalent to untreated cells at 24 h. In contrast, in the absence of methyl pyruvate the *Tsc2*−/− MEFs had a delayed induction of ACC phosphorylation during nelfinavir and mefloquine treatment. For instance, phosphorylation of ACC was only marginally enhanced at 6 h, but was then markedly elevated at 24 h. Expression of the ER stress proteins, ATF4, CHOP and GADD34 were significantly reduced at 24 h, showing recovery of ER stress. Collectively, our data shows that the *Tsc2*−/− MEF cells become energy stressed during the longer time points of nelfinavir and mefloquine treatment while they recover from ER stress.

3. Discussion {#sec3-cancers-10-00375}
=============

The central hypothesis of this study was to exploit the homeostatic vulnerabilities within TSC2-deficient cells with mTORC1 hyperactivity. The volume of unfolded protein within the ER is intensified by mTORC1 hyperactivity, caused by heightened levels of de novo protein translation and reduced efficiency of autophagy to remove the unfolded protein. Consequently, cell lines lacking TSC2 become more sensitive to drug treatments that induce ER stress. In this study, we employed nelfinavir as the ER stress inducer, and mefloquine as a potential autophagy inhibitor. We revealed that nelfinavir and mefloquine synergize to selectively kill TSC2-deficient cells. The concentrations of both drugs used in this study are clinically viable. For instance, 10 µM falls within the concentration range of mefloquine found in patient serum \[[@B27-cancers-10-00375]\]. Regarding nelfinavir, the 10 µM concentration used in this study is higher than the manufacturer recommended trough concentration (1−3 µM). However, nelfinavir serum concentration has previously been reported in HIV patients at a similar concentration, ranging from 4.96 µM \[[@B28-cancers-10-00375]\] up to 18 µM \[[@B29-cancers-10-00375]\]. In fact, it has been reported that nelfinavir is well tolerated in cancer patients at doses 2.5 times the FDA-approved dose for HIV management \[[@B30-cancers-10-00375]\]. We observed that the combination of nelfinavir and mefloquine had a long-lasting effect on *Tsc2*−/− tumor spheroids, when compared to single drug treatments. For instance, with mefloquine, despite an increase in DRAQ7 staining, indicating a level of cell death, cell recovery and outgrowth from the bulk tumor after drug withdrawal was apparent. However, no cells recovered when nelfinavir was combined with mefloquine. As well as cells lacking *Tsc2*, the nelfinavir and mefloquine drug combination was effective at killing sporadic cancer cell lines with aberrant signaling through the TSC1/TSC2 signaling axis, with MCF7 cells being much more sensitive to the dual therapy when compared to single drug treatments.

Through transcriptional profiling of nelfinavir and mefloquine treated cells, we observed an ER stress signature at the early 6 h time-point of treatment, implying that ER stress might be involved in the observed cytotoxic response. However, when we examined the longer time points of combined nelfinavir and mefloquine treatment, we observed recovery of the ER stress ATF4-CHOP signaling pathway within the *Tsc2*−/− MEF cells. ER stress-mediated cell death occurs when the protein level of CHOP remains persistently elevated over a long time course of drug treatment. Given the temporary elevation of CHOP in the continued presence of drug in both the *Tsc2*+/+ and *Tsc2*−/− MEFs, our data indicates a good level of ER stress recovery and excludes the possibility that CHOP triggers cell death.

Further analysis of the RNA sequencing data in the *Tsc2*−/− MEFs implied that energy homeostasis pathways were elevated after co-treatment with nelfinavir and mefloquine. We rescued a significant degree of cell death with methyl pyruvate, indicating that cytotoxicity by nelfinavir and mefloquine was at least partially mediated through energy deficiency. Methyl pyruvate is a methyl ester of pyruvic acid that restores cellular energy production downstream of glycolysis by acting as a mitochondrial citric acid cycle substrate. While ER stress is unlikely the trigger of cell death, recovery from ER stress might contribute to the depletion of energy through the de novo protein synthesis of chaperone and heat shock proteins that are required for the unfolding and refolding of protein aggregates in the ER, processes that heavily consume ATP. A recent study showed that TSC2-knockdown leads to mitochondrial oxidative stress \[[@B31-cancers-10-00375]\]. This degree of energy stress is presumably why TSC2-deficent cells are vulnerable to conditions that induce energy starvation \[[@B32-cancers-10-00375]\].

Mefloquine has shown promise as an anti-cancer drug in several cancer model settings. Liu et al. showed that mefloquine (whether as a single agent or in combination with paclitaxel) induced apoptosis in gastric cancer cell lines and gastric cancer xenograft mouse models \[[@B23-cancers-10-00375]\]. PC3 cells, a prostate cancer cell line, were also sensitive to mefloquine at 10 µM after 24 h of treatment \[[@B22-cancers-10-00375]\]. However, no further toxicity was detected between 24 h to 72 h of mefloquine treatment, suggesting that mefloquine may not be fully effective as a monotherapy. Mefloquine was shown to inhibit autophagy, trigger ER stress and induced cell death in human breast cancer lines, and was more cytotoxic when compared to chloroquine \[[@B33-cancers-10-00375]\]. Our data is in line with these previous studies, except we observed weaker inhibition of autophagy with mefloquine treatment. We further enhanced the cytotoxic effectiveness of mefloquine with dual treatment with nelfinavir and determined that cell death was caused by an imbalance in energy homeostasis. In the cancer setting, mefloquine is currently in a phase I clinical factorial trial (NCT01430351) in combination with the DNA-damaging agent temozolomide for glioblastoma multiforme. It should be noted that mefloquine has advantages over chloroquine for the treatment of brain cancers, as mefloquine has better penetration through the blood brain barrier. In summary, we show that mefloquine synergizes with nelfinavir to induce a high degree of cell death through energy stress. There is clinical application for this drug combination in the cancer setting, where we can exploit the lack of flexibility that cancer cells have in restoring homeostatic balance.

4. Materials and Methods {#sec4-cancers-10-00375}
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4.1. Cell Culture and Drug Treatments {#sec4dot1-cancers-10-00375}
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Tsc2-null ELT3-V3 and control ELT3-T3 cells re-expressing TSC2 were kindly provided by Cheryl Walker (M.D. Anderson Cancer Centre, Houston, USA). *Tsc2*+/+ *p53*−/− and *Tsc2*−/− *p53*−/− MEFs were kindly provided by David J. Kwiatkowski (Harvard University, Boston, MA, USA). Human breast cancer (MCF7), human colorectal cancer (HCT116) and human lung carcinoma (NCI-H460) cell lines were purchased from ATCC. All cell lines were routinely tested using the Venor GeM Classic PCR kit from (CamBio) and were clear of mycoplasma. Cells were cultured in Dulbecco's Modified Eagle's Medium (DMEM), while the MCF7 cell line was incubated in Roswell Park Memorial Institute (RPMI) 1640. Cell media was supplemented with 10% (v/v) Fetal Bovine Serum (FBS) and 100 U/mL Penicillin and 100 μg/mL Streptomycin (Life Technologies Ltd., Paisley, UK). All cell lines were incubated at 37 °C, 5% (v/v) CO~2~ in a humidified incubator. Nelfinavir mesylate hydrate, chloroquine di-phosphate salt, mefloquine hydrochloride, rapamycin, etoposide and thapsigargin were purchased from Sigma Aldrich Ltd. (Gillingham, Dorset, UK). DMSO was used as a solvent to make stock solutions of etoposide (100 mM), mefloquine (50 mM), nelfinavir (30 mM), thapsigargin (10 mM) and rapamycin (100 µM). Chloroquine was made fresh in culture medium at a 100 mM stock and further diluted in culture medium to the required concentration before use. Drug(s) dissolved in DMSO or DMSO vehicle control were added to cells in culture media so that they were diluted to a final concentration that was below 0.8% (v/v) DMSO.

4.2. Flow Cytometry {#sec4dot2-cancers-10-00375}
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Treated cells were collected and incubated with 3 µM DRAQ7 (Biostatus, Leicestershire, UK) at room temperature for 10 min. Flow cytometry was performed using a FACS Calibur flow cytometer (Becton Dickinson (Cowley, UK)) with excitation set at 488 nm and detection of fluorescence in log mode at wavelengths greater than 695 nm. Cell Quest Pro software was used for signal acquisition. A total of 10,000 events per sample were collected.

4.3. Western Blotting {#sec4dot3-cancers-10-00375}
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S6K1, phospho-S6K1(Thr389), rpS6, phospho-rpS6(Ser235/236), IRE1α, CHOP, GADD34, ATF4, TSC2, SESN2, AMPK, phospho-AMPK(Thr172), ACC, phospho-ACC (Ser79) and β-actin antibodies were purchased from Cell Signaling Technology (Danvers, USA). LC3 antibodies were bought from Novus Ltd. (Cambridge, UK). Cells were washed in ice-cold phosphate buffered saline (PBS) and then lysed in cell lysis buffer (20 mM Tris (pH 7.5), 125 mM NaCl, 50 mM NaF, 5% (v/v) glycerol, 0.1% (v/v) Triton X-100, supplemented with 1 mM dithiothreitol (DTT), 1 µg/mL pepstatin, 20 µM leupeptin, 1 mM benzamidine, 2 µM antipain, 0.1 mM PMSF, 1 mM sodium orthovanadate and 1 nM okadaic acid prior to cell lysis). Cell lysates were sonicated using a diagenode bioruptor (Diagenode, Seraing, Belgium) and centrifuged at 13,000 rpm for 8 min. Protein concentrations were determined by a Bradford assay (Thermo Fisher Scientific, Paisley, UK). Samples were diluted in 4 × NuPAGE loading sample buffer (Life Technologies) with 25 mM DTT and boiled at 70 °C for 10 min. Western blot was performed as previously described \[[@B34-cancers-10-00375]\].

4.4. mRNA Extraction and Reverse Transcription {#sec4dot4-cancers-10-00375}
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Treated cells were washed in ice cold PBS then lysed using RNAprotect Cell Reagent (Qiagen, West Sussex, UK). RNA concentrations were determined by measuring the absorbance at 260 nm and 280 nm in a Nanodrop spectrophotometer. Total RNA from each sample (1 μg) was converted to copy DNA (cDNA) using the Quantitect reverse transcription kit (Qiagen) following the manufacturer's protocol.

4.5. XBP-1 Splicing {#sec4dot5-cancers-10-00375}
-------------------

Protocol for XBP-1 splicing is as described in reference \[[@B19-cancers-10-00375]\]. *Xbp1* primers (forward: 5′-AAA CAG AGT AGC AGC TCA GAC TGC-3′, reverse: 5′-TCC TTC TGG GTA GAC CTC TGG GA-3′, were synthesized by MWG Operon-Eurofin (Ebersberg, Germany)). β-actin primers were purchased from Qiagen (QT01136772). PCR was performed in an Applied Biosystems GeneAmp 9700 PCR system in the following conditions: Initial denaturation step (94 °C, 3 min); 31 cycles of denaturation (94 °C, 45 s); annealing step (60 °C, 30 s); extension step (72 °C, 1 min); final extension step (72 °C, 10 min). A 3% (w/v) agarose (Appleton, Birmingham, UK) 1 × Tris-acetate-EDTA (4.84 g Tris-base (pH 8.0), 0.372 g EDTA and 1.7 mL acetic acid in 1 L deionized water) was made with 0.005% (v/v) GelRed nucleic acid stain (Biotium, Fremont, CA, USA). DNA samples were loaded with Orange G loading buffer (15 mL 30% (v/v) glycerol, 100 mg Orange G powder, deionized water, total volume 50 mL) and resolved on the gel at 100 V. After 1 h, β-actin samples were analyzed. Samples were resolved for an additional 2 h for XBP-1 splicing. PCR products of *Xbp1* were 480 bp (unspliced) and 454 bp (spliced).

4.6. Tumor Formation Assay {#sec4dot6-cancers-10-00375}
--------------------------

A 1.2% (w/v) agar solution in PBS (Difco Agar Noble (BD, Oxford, UK)) was diluted with DMEM to a final concentration of 0.6% (w/v) then added to a 6 well plate and allowed to set. A total of 150,000 *Tsc2*−/− MEF cells were added to a 0.3% (w/v) agar-PBS:DMEM mixture and 3 mL was layered on top and left to set. Plates were incubated overnight with 2 mL of DMEM. The following day, the 2 mL of DMEM was replaced with DMEM containing either drugs or DMSO. Media and drugs were refreshed every 48--72 h for 14 days. Images were taken using an EVOS XL Core camera (Life Technologies). Tumor diameter was analyzed using Image J.

4.7. Tumor Outgrowth Assay {#sec4dot7-cancers-10-00375}
--------------------------

A total of 70 µl of 1.5% (w/v) agarose-PBS was added to each well of a 96 well plate and allowed to set. A total of 1000 *Tsc2*−/− MEF cells in 140 μL complete DMEM were added to each well. Spheroids were formed over 72 h. Following formation, spheroids were drug treated for 48 h. Cells were imaged and then treated for 48 h. Treatment was refreshed and a final concentration of 3 µM DRAQ7 was added to each well and incubated for an additional 48 h. Imaging and subsequent outgrowth was performed as previously described \[[@B35-cancers-10-00375]\].

4.8. RNA Sequencing {#sec4dot8-cancers-10-00375}
-------------------

Total RNA quality and quantity was assessed using Agilent 2100 Bioanalyser and an RNA Nano 6000 kit (Agilent Technologies, Cheshire, UK). A total of 100--900 ng of total RNA with a RIN value \>8 was used as the input and the sequencing libraries were prepared using the Illumina^®^ TruSeq^®^ RNA sample preparation v2. (Illumina Inc., Cambridge, UK). The steps included two rounds of purification of the polyA containing mRNA molecules using oligo-dT attached magnetic beads followed by RNA fragmentation, 1st strand cDNA synthesis, 2nd strand cDNA synthesis, adenylation of 3'-ends, adapter ligation, PCR amplification (15 cycles) and validation. The manufacturer's instructions were followed. The libraries were validated using the Agilent 2100 Bioanalyser and a high-sensitivity kit (Agilent Technologies) to ascertain the insert size, and the Qubit^®^ (Life Technologies) was used to perform the fluorometric quantitation. Following validation, the libraries were normalized to 4 nM and pooled together. The pool was then sequenced using a 75 base paired-end (2 × 75 bp PE) dual index read format on the Illumina^®^ HiSeq2500 in rapid mode according to the manufacturer's instructions. The pool was then sequenced using a 75 base paired-end (2 × 75 bp PE) dual index read format on the Illumina^®^ HiSeq2500 in high-output mode according to the manufacturer's instructions. Quality control checks of the resultant reads were performed using FastQC before mapping to the UCSC mouse mm10 reference genome using Tophat and Bowtie. Differentially expressed transcripts were identified using a DeSeq2 analysis \[[@B36-cancers-10-00375]\] on normalized count data with the design formula setup to analyze all pairwise comparisons in the dataset using contrasts. The resultant *p*-values were corrected for multiple testing and false discovery issues using the FDR method. Genes involved in cell survival were selected based on GO: 0008219 (cell death) from the complete list on AmiGo 2.

4.9. Statistical Analysis {#sec4dot9-cancers-10-00375}
-------------------------

All experiments were carried out with a minimum of three biological repeats. Where applicable, results are written as mean with a +/− St-Dev. Either a two-way ANOVA (with Bonferroni's multiple comparison post-hoc test) or one-way ANOVA (with Tukey's multiple comparison post-hoc test) was used to determine statistical significance. Significance was reported as a *p* value: \* *p* \< 0.05, \*\* *p* \< 0.01, \*\*\* *p* \< 0.001.

5. Conclusions {#sec5-cancers-10-00375}
==============

In conclusion, mefloquine synergizes with nelfinavir to induce a high degree of cell death through energy stress. There is potential to use mefloquine with nelfinavir in the cancer setting, to treat cancer cells that have a lack of flexibility in restoring homeostatic balance.

We would like to thank the Wales Gene Park for their contribution to this study.

The following are available online at <http://www.mdpi.com/2072-6694/10/10/375/s1>, Table S1: RNAseq raw data of mefloquine and nelfinavir treatment of *Tsc2* MEFs, Table S2: RNAseq of *Tsc2*−/− compared to *Tsc2*+/+ after mefloquine and nelfinavir treatment.

######

Click here for additional data file.

Conceptualization, D.M.D. and A.R.T.; Investigation, H.D.M. and C.E.J.; Resources, R.J.E. and A.R.T.; Data Curation, H.D.M. and E.A.D.; Methodology, R.J.E. and E.A.D.; Formal Analysis, H.D.M. and C.E.J.; Writing-Original Draft Preparation, A.R.T.; Writing-Review & Editing, H.D.M., E.A.D. and A.R.T.; Supervision, R.J.E., E.A.D., D.M.D. and A.R.T.; Project Administration, D.M.D. and A.R.T.; Funding Acquisition, R.J.E., D.M.D. and A.R.T.

This research was funded by the Tuberous Sclerosis Association (to H.D.M., E.A.D. and A.R.T.), grant numbers (2013-P05) and (2015-S01); Cancer Research Wales (to C.E.J., R.J.E. and A.R.T.), grant number (508502); the Tuberous Sclerosis Alliance (to A.R.T.), grant number (03-15); and the Hospital Saturday Fund (to A.R.T.). This work was also supported by Health and Care Research Wales (the Wales Cancer Research Centre) (to E.A.D. and A.R.T.).

The authors declare no conflict of interest. RE is non-executive director of Biostatus Ltd., the vendor of DRAQ7.The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.

![Mefloquine and nelfinavir synergize to kill *Tsc2*−/− Mouse embryonic fibroblasts (MEFs), ELT3-T3 and sporadic cancer cells. Dose response curves were performed in *Tsc2*+/+ and *Tsc2*−/− MEFs using flow cytometry to measure cell death following treatment with (**A**) nelfinavir (NFV); (**B**) mefloquine (MQ) and (**C**) combined mefloquine with a fixed concentration of 10 µM nelfinavir (MQ/NFV); (**D**) *Tsc2*+/+ and *Tsc2*−/− MEFs; (**E**) ELT3-T3 and ELT3-V3; (**F**) MCF7, HCT116 and NCI-H460 were treated with either DMSO, etoposide (ETO), 10 µM mefloquine (MQ), 10 µM nelfinavir (NFV) or mefloquine combined with nelfinavir (MQ/NFV) for 48 h. Cells were then tested by flow cytometry and cells were separated into viable and non-viable cell populations via DRAQ7 staining. Statistical significance is shown with combination treated *Tsc2*−/− MEFs or the ELT3-V3 cells to their wild-type controls, and comparing single drug treatment of mefloquine and combination with the MCF7, HCT116 and NCI-H460.](cancers-10-00375-g001){#cancers-10-00375-f001}

![Mefloquine and nelfinavir prevents colony formation and spheroid growth. (**A**) Colony formation was tested in *Tsc2*−/− MEFs seeded on soft agar that were treated for 14 days with Dimethyl Sulfoxide (DMSO), 10 µM mefloquine (MQ), 10 µM nelfinavir (NFV) or in combination. Tumor diameters were measured using Image J; scale bar is 200 μm. Significance was observed when comparing combined nelfinavir and mefloquine treatment to DMSO vehicle control. (**B**) *Tsc2*−/− MEF spheroids were treated under the same conditions as (**A**) for 96 h. DRAQ7 was supplemented for the final 36 h to monitor cell death before images were taken and DRAQ7 fluorescence quantified. (**C**) Spheroids treated in (**B**) were re-plated onto standard tissue culture plates and grown in drug-free media. Images were taken every 24 h and the area of outgrowth calculated using Image J, scale bar is 200 μm and outgrowth area is graphed.](cancers-10-00375-g002){#cancers-10-00375-f002}

![Mefloquine and nelfinavir drug combination causes increased ER stress in *Tsc2*−/− MEFs. (**A**) *Tsc2*+/+ and *Tsc2*−/− MEFs were treated with either DMSO, 1 µM thapsigargin (TPG), 10 µM mefloquine (MQ), 10 µM nelfinavir (NFV), or mefloquine and nelfinavir combination for 6 h, where indicated. Total protein levels of TSC2, IRE1α, ATF4, CHOP, GADD34, S6K1 and β-actin and S6K1 phosphorylated at Thr389 were detected by Western blot. (**B**) *Xbp1* mRNA splicing was determined from the same treatments as described in (**A**). PCR products were resolved on agarose gels (unspliced = 480 bp upper band, spliced = 454 bp lower band). (**C**--**E**) *Tsc2*+/+ and *Tsc2*−/− MEFs were treated with either DMSO or mefloquine and nelfinavir combination (MQ/NFV) for 6 h before being processed for RNA sequencing. A heat map for a panel of ER stress-linked genes is shown (**C**) and are graphed in (**D**). (**E**) Differences of mRNA expression between *Tsc2*+/+ and *Tsc2*−/− MEFs treated with mefloquine and nelfinavir is shown as a volcano plot and highlights ER stress genes. (**F**) *Tsc2*+/+ and *Tsc2*−/− MEFs were treated with DMSO or mefloquine (MQ) and nelfinavir (NFV) combination for 6 h and 48 h. Total protein levels of ATF4, IRE-1α, GADD34, CHOP and β-actin were determined by Western blot.](cancers-10-00375-g003){#cancers-10-00375-f003}

![Mefloquine and nelfinavir drug cytotoxicity is not associated with mTORC1 hyperactivity and causes minimal autophagy inhibition. (**A**) *Tsc2*−/− MEFs, NCI-H460, MCF7 and HCT116 cells were pre-treated with 50 nM rapamycin (RAP) for 1 h, where indicated, before being treated with 10 μM nelfinavir (NFV) and 10 µM mefloquine (MQ) for 48 h. Cells were stained with DRAQ7 and % cell death determined using flow cytometry. (**B**) Western blotting was carried out to determine rp-S6 phosphorylation at Ser235/236 in the cells treated in (**A**) after 48 h of treatment. (**C**) *Tsc2*+/+ and *Tsc2*−/− cells were treated with DMSO, 10 µM mefloquine (MQ), 20 µM chloroquine (CQ), 10 µM mefloquine or 20 µM chloroquine combined with 10 µM nelfinavir for 3 h. Accumulation of lipidated LC3-II were analyzed by Western blot. Total protein levels of β-actin were used as a loading control.](cancers-10-00375-g004){#cancers-10-00375-f004}

![Mefloquine and nelfinavir combined drug treatment induces cytotoxicity via energy stress in *Tsc2*−/− MEFs. (**A**) The RNA sequencing data used for [Figure 3](#cancers-10-00375-f003){ref-type="fig"}C−E was assessed for gene-expression of genes involved in energy homeostasis. A heatmap for a panel of energy stress-linked genes is shown. Differences of mRNA expression between *Tsc2*+/+ and *Tsc2*−/− MEFs treated with mefloquine and nelfinavir is shown as a volcano plot (**B**) and graphed (**C**). (**D**) *Tsc2*−/− cells were treated with DMSO, 10 μM mefloquine and 10 μM nelfinavir combination (MQ/NFV) or mefloquine/nelfinavir combination with the addition of 8 mM methyl pyruvate (MQ/NFV/MP) for 48 h. Cells were then stained with DRAQ7 and % cell death determined by flow cytometry. (**E**) *Tsc2*−/− were treated with either DMSO or 10 μM mefloquine and 10 μM nelfinavir combination in the presence or absence of 8 mM methyl pyruvate for 24 h and total and phosphorylated ACC and AMPK was determined by western blot. (**F**) *Tsc2*+/+ and *Tsc2*−/− cells were treated with either DMSO or 10 μM mefloquine and 10 μM nelfinavir combination in the presence or absence of 8 mM methyl pyruvate for 6 and 24 h, where indicated. Total protein levels of ACC, CHOP GADD34 and ATF4 as well as phosphorylated ACC were detected by Western blot.](cancers-10-00375-g005){#cancers-10-00375-f005}


Monday, May 21, 2018

Spectre Number 4, STEP RIGHT UP!

Updated based on IBM's documentation.

In the continuing saga of Meltdown and Spectre (tl;dr: G4/7400, G3 and likely earlier 60x PowerPCs don't seem vulnerable at all; G4/7450 and G5 are so far affected by Spectre while Meltdown has not been confirmed, but IBM documentation implies "big" POWER4 and up are vulnerable to both) is now Spectre variant 4. In this variant, the fundamental issue of getting the CPU to speculatively execute code it mistakenly predicts will be executed and observing the effects on cache timing is still present, but here the trick has to do with executing a downstream memory load operation speculatively before other store operations that the CPU (wrongly) believes the load does not depend on. The processor will faithfully revert the stores and the register load when the mispredict is discovered, but the loaded address will remain in the L1 cache and be observable through means similar to those in other Spectre-type attacks.

The G5, POWER4 and up are so aggressively out of order with memory accesses that they are almost certainly vulnerable. In an earlier version of this post, I didn't think the G3 and 7400 were vulnerable (as they don't appear to be to other Spectre variants), but after some poring over IBM's technical documentation I now believe with some careful coding it could be possible -- just not very probable. The details have to do with the G3 (and 7400)'s Load-Store Unit, or LSU, which is responsible for reading and writing memory. Unless a synchronizing instruction intervenes, up to one load instruction can execute ahead of a store, which makes the attack theoretically possible. However, the G3 and 7400 cannot reorder multiple stores in this fashion, and because only a maximum of two instructions may be dispatched to the LSU at any time (in practice less since those two instructions are spread across all of the processor's execution units), the victim load and the confounding store must be located immediately together or have no LSU-issued instructions between them. Even then, reliably ensuring that both instructions get dispatched in such a way that the CPU will reorder them in the (attacker-)desired order wouldn't be trivial.

The 7450, as with other Spectre variants, makes the attack a bit easier. It can dispatch up to four instructions to its execution units, which makes the attack more feasible because there is more theoretical flexibility on where the victim load can be located downstream (especially if all four instructions go to its LSU). However, it too can execute at most just one load instruction ahead of a store, and it cannot reorder stores either.

That said, as a practical matter, Spectre in any variant (including this one) is only a viable attack vector on Power Macs through native applications, which have far more effective methods of pwning your Power Mac at their disposal than an intermittently successful attempt to read memory. Although TenFourFox has a JavaScript JIT, no 7450 and probably not even the Quad is fast enough to obtain enough of a memory timing delta to make the attack functional (let alone reliable), and we disabled the high-resolution timers necessary for the exploit "way back" in FPR5 anyway. The new variant 4 is a bigger issue for Talos II owners like myself because such an attack is possible and feasible on the POWER9, but we can confidently expect that there will be patches from IBM and Raptor to address it soon.


Q:

Running a .NET 4.5 program on XP with .NET 2.0

We have a Windows Service written in C# 5.0 for .NET 4.5. My code is using some of the newer language features (async and await) and framework features (Task.Run, Task.Delay, IProgress, CancellationToken, etc).
Our product works fine on Windows 7 and 8 when .NET 4.5 is installed. Our problem is, we now need this to run on an XP machine. Even if .NET 4.5 could be installed on Windows XP, there's not enough space for it on this machine anyway.
Is there any way I can get a .NET 4.5 application to run on this machine? I've looked into compiling the program to native code using either Mono AOT Compiler or NGen.exe, but I haven't had any luck - I don't think either of them actually achieve what I'm trying to do.
Failing that - we've thought of creating a bootable preinstalled flash drive that runs a simple version of Windows (like WinPE?) with our application installed, but this is out of my depth and I wouldn't really know where to start. Is this a good idea? How could I approach this?
Another option we've had is to try and install .NET 4.0 and use the Async Targeting Pack.
Or does anyone have any other ideas? Is there any way I can build/compile/run my .NET 4.5 application (that makes use of .NET 4.5 features) on an XP machine running .NET 2.0?

A:

AsyncBridge is like the MS Async Targeting Pack, but also available for .NET 3.5.
However, it won't work with .NET 2.0; as the Task Parallel Library (which defines Task<T>) is only available for .NET 3.5 (and built into .NET 4.0).
As for Mono, to create a standalone application that includes the relevant portions of the framework, you need to create a bundle, but I'm not sure if those are supported on Windows. mono --aot and ngen just precompile an application for faster startup on the local machine, they don't remove the need for the framework to be installed.


A multilevel excess hazard model to estimate net survival on hierarchical data allowing for non-linear and non-proportional effects of covariates.
The excess hazard regression model is an approach developed for the analysis of cancer registry data to estimate net survival, that is, the survival of cancer patients that would be observed if cancer was the only cause of death. Cancer registry data typically possess a hierarchical structure: individuals from the same geographical unit share common characteristics such as proximity to a large hospital that may influence access to and quality of health care, so that their survival times might be correlated. As a consequence, correct statistical inference regarding the estimation of net survival and the effect of covariates should take this hierarchical structure into account. It becomes particularly important as many studies in cancer epidemiology aim at studying the effect on the excess mortality hazard of variables, such as deprivation indexes, often available only at the ecological level rather than at the individual level. We developed here an approach to fit a flexible excess hazard model including a random effect to describe the unobserved heterogeneity existing between different clusters of individuals, and with the possibility to estimate non-linear and time-dependent effects of covariates. We demonstrated the overall good performance of the proposed approach in a simulation study that assessed the impact on parameter estimates of the number of clusters, their size and their level of unbalance. We then used this multilevel model to describe the effect of a deprivation index defined at the geographical level on the excess mortality hazard of patients diagnosed with cancer of the oral cavity. Copyright © 2016 John Wiley & Sons, Ltd.