If you look at the news, you’ll see layoffs, buyouts, bankruptcy, going-out-of-business– there are a lot of companies in trouble right now

Ebay — laying off around 1500 workers.

Corbis — Cutting 170 Jobs, as its start-up rival Photoshelter closes its doors.

Washington Mutual in trouble

HP cutting 24,600 jobs

B of A – stocks tumble as the bank buys Merrill Lync

Fannie Mae and Freddie Mac taken over by the Feds a couple weeks back

I’ve read in many places that even though the economy is headed under, tech is still going strong. But with fewer jobs overall and less confidence, tech is sure to take a tumble as well. These are tough times that aren’t going to be solved by going out and buying buttons for your favorite political candidate.

Having a validated drug discovery platform is the first and most important criterion for defining a good platform company. The platform is typically comprised of a combination of technology, experienced personnel and intellectual property that can generate a stream of drug candidates. Most importantly, investing should be done only after a product of the platform demonstrates activity in clinical trials. Having a clinically validated product is not a guarantee for future success of the platform nor does it mean that the specific agent will reach the market, but it does imply that one or more of the platform’s products stand a reasonable chance of becoming a commercial drug. A validated platform may increase overall success rates, yet the odds of a particular drug candidate to make it all the way to approval are still low.

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Exelixis is active in the ever growing market of kinase inhibitors (KIs) for the treatment of cancer, that is, drugs that block the activity of kinases in cancer cells. Cancer cells are often described as cells that are out of control: They proliferate quickly, ignore death signals, invade nearby tissues and eventually metastasize to distant organs. These disease onset and advancement are associated with processes such as cell growth, motility and blood-vessel formation, which are governed by a complex network made of kinases. Thus, blocking these processes by inhibiting the relevant kinases has emerged as one of the most attractive approaches to fighting cancer.

Together with monoclonal antibodies, kinase inhibitors represent a paradigm shift in cancer treatment from cytotoxic agents to targeted therapies, a trend that is constantly growing. Like antibodies for cancer, kinase inhibitors target tumors while sparing healthy cells and consequently lead to better activity with fewer side effects. Kinase inhibitors, however, possess several advantages over antibodies. The most evident advantage is that KIs can hit targets inside the cell while antibodies can only bind targets presented on the cell surface, so internal targets are approachable only by KIs. Another advantage is the fact that KIs can be given orally, which is a major factor in terms of patient convenience, especially given the typical long treatment duration associated with targeted therapies. Another advantage, which will be later discussed in the article, is the ability to produce KIs that hit several targets at once.

1. I don't trust Google.
2. I don't trust the Internet.
3. The tools for creating private documents are so much better than the online ones.
4. I can get to my documents when I am offline.
5. The Internet is too slow.

• Security and stability
• Offline application mode
• Fast running and standards based application engine

It's become a big deal in IT security, too. Many corporate customers are demanding ROI models to demonstrate that a particular security investment pays off. And in response, vendors are providing ROI models that demonstrate how their particular security solution provides the best return on investment.

It's a good idea in theory, but it's mostly bunk in practice.

Before I get into the details, there's one point I have to make. "ROI" as used in a security context is inaccurate. Security is not an investment that provides a return, like a new factory or a financial instrument. It's an expense that, hopefully, pays for itself in cost savings. Security is about loss prevention, not about earnings. The term just doesn't make sense in this context.

But as anyone who has lived through a company's vicious end-of-year budget-slashing exercises knows, when you're trying to make your numbers, cutting costs is the same as increasing revenues. So while security can't produce ROI, loss prevention most certainly affects a company's bottom line.

And a company should implement only security countermeasures that affect its bottom line positively. It shouldn't spend more on a security problem than the problem is worth. Conversely, it shouldn't ignore problems that are costing it money when there are cheaper mitigation alternatives. A smart company needs to approach security as it would any other business decision: costs versus benefits.

The classic methodology is called annualized loss expectancy (ALE), and it's straightforward. Calculate the cost of a security incident in both tangibles like time and money, and intangibles like reputation and competitive advantage. Multiply that by the chance the incident will occur in a year. That tells you how much you should spend to mitigate the risk. So, for example, if your store has a 10 percent chance of getting robbed and the cost of being robbed is $10,000, then you should spend $1,000 a year on security. Spend more than that, and you're wasting money. Spend less than that, and you're also wasting money.

Of course, that $1,000 has to reduce the chance of being robbed to zero in order to be cost-effective. If a security measure cuts the chance of robbery by 40 percent -- to 6 percent a year -- then you should spend no more than $400 on it. If another security measure reduces it by 80 percent, it's worth $800. And if two security measures both reduce the chance of being robbed by 50 percent and one costs $300 and the other $700, the first one is worth it and the second isn't.

The Data Imperative

The key to making this work is good data; the term of art is "actuarial tail." If you're doing an ALE analysis of a security camera at a convenience store, you need to know the crime rate in the store's neighborhood and maybe have some idea of how much cameras improve the odds of convincing criminals to rob another store instead. You need to know how much a robbery costs: in merchandise, in time and annoyance, in lost sales due to spooked patrons, in employee morale. You need to know how much not having the cameras costs in terms of employee morale; maybe you're having trouble hiring salespeople to work the night shift. With all that data, you can figure out if the cost of the camera is cheaper than the loss of revenue if you close the store at night -- assuming that the closed store won't get robbed as well. And then you can decide whether to install one.

Cybersecurity is considerably harder, because there just isn't enough good data. There aren't good crime rates for cyberspace, and we have a lot less data about how individual security countermeasures -- or specific configurations of countermeasures -- mitigate those risks. We don't even have data on incident costs.

One problem is that the threat moves too quickly. The characteristics of the things we're trying to prevent change so quickly that we can't accumulate data fast enough. By the time we get some data, there's a new threat model for which we don't have enough data. So we can't create ALE models.

But there's another problem, and it's that the math quickly falls apart when it comes to rare and expensive events. Imagine you calculate the cost -- reputational costs, loss of customers, etc. -- of having your company's name in the newspaper after an embarrassing cybersecurity event to be $20 million. Also assume that the odds are 1 in 10,000 of that happening in any one year. ALE says you should spend no more than $2,000 mitigating that risk.

So far, so good. But maybe your CFO thinks an incident would cost only $10 million. You can't argue, since we're just estimating. But he just cut your security budget in half. A vendor trying to sell you a product finds a Web analysis claiming that the odds of this happening are actually 1 in 1,000. Accept this new number, and suddenly a product costing 10 times as much is still a good investment.

It gets worse when you deal with even more rare and expensive events. Imagine you're in charge of terrorism mitigation at a chlorine plant. What's the cost to your company, in money and reputation, of a large and very deadly explosion? $100 million? $1 billion? $10 billion? And the odds: 1 in a hundred thousand, 1 in a million, 1 in 10 million? Depending on how you answer those two questions -- and any answer is really just a guess -- you can justify spending anywhere from $10 to $100,000 annually to mitigate that risk.

Or take another example: airport security. Assume that all the new airport security measures increase the waiting time at airports by -- and I'm making this up -- 30 minutes per passenger. There were 760 million passenger boardings in the United States in 2007. This means that the extra waiting time at airports has cost us a collective 43,000 years of extra waiting time. Assume a 70-year life expectancy, and the increased waiting time has "killed" 620 people per year -- 930 if you calculate the numbers based on 16 hours of awake time per day. So the question is: If we did away with increased airport security, would the result be more people dead from terrorism or fewer?

Caveat Emptor

This kind of thing is why most ROI models you get from security vendors are nonsense. Of course their model demonstrates that their product or service makes financial sense: They've jiggered the numbers so that they do.

This doesn't mean that ALE is useless, but it does mean you should 1) mistrust any analyses that come from people with an agenda and 2) use any results as a general guideline only. So when you get an ROI model from your vendor, take its framework and plug in your own numbers. Don't even show the vendor your improvements; it won't consider any changes that make its product or service less cost-effective to be an "improvement." And use those results as a general guide, along with risk management and compliance analyses, when you're deciding what security products and services to buy.

This essay previously appeared in CSO Magazine.

At Consumer Reports, we have always believed that scientific testing is the best way to evaluate products. We also use a statistically-valid survey methodology to measure consumer experiences. In preparing our September security reports, we employed both methods as we have for many decades. Some additional notes on this column:

• The story was not, as you state, "filled with data sourced to eMarketer." That service provided just two pieces of data, namely the current number of Internet- and broadband-using U.S. Households
• Using a separate credit card for online transactions avoids having to cancel your main card should fraud occur.
• We test software against modified versions of actual malware because such threats are what security software will often be called upon to recognize on the job.

Finally, a note about your claim that Consumer Reports was invited to respond. Your e-mail to us requesting a comment was time-stamped on the same Saturday evening as your column is labeled as having posted. That left fewer than six hours to respond, on a weekend. It would have been helpful to have had more time.

The question whether biological viruses are forms of life has been debated, since they lack the respiratory and metabolic process of other accepted life forms. Naturally, different scientists have different reasons for opinions either way.

So how does the new virus-infecting-virus work?

With just 21 genes, Sputnik is tiny compared with its mama — but insidious. When the giant mamavirus infects an amoeba, it uses its large array of genes to build a ‘viral factory’, a hub where new viral particles are made. Sputnik infects this viral factory and seems to hijack its machinery in order to replicate. The team found that cells co-infected with Sputnik produce fewer and often deformed mamavirus particles, making the virus less infective. This suggests that Sputnik is effectively a viral parasite that sickens its host — seemingly the first such example.

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“It was the cause of great excitement in virology,” says Eugene Koonin at the National Center for Biotechnology Information in Bethesda, Maryland. “It crossed the imaginary boundary between viruses and cellular organisms.”

Science fiction, fantasy and the popular imagination have been fueled in recent decades by the concept of the cyborg, that fusion of machine and creature — but under the scientists’ new definition, even your laptop might be evidence of life…provided it’s infected by a computer virus.