But… Why care? How has this franchise managed to maintain such a prophetic status amongst gamers of all ages?

Well, for me, I remember fondly playing Duke Nukem 3D. It was one of the first games I played on my first computer, a 486 with Windows 3.1 installed (it eventually got Windows 95, what a revolution!) and my brother and I would take turns killing aliens and blowing up the theatre screen with an RPG, so we could go behind the screen and… I dunno, dance or something.

Having not been part of the Doom crowd, Duke Nukem 3D was my first real action shooter experience, but that wasn’t what made it stand out; at the time I was playing Quake, Shadow Warrior, Rise Of The Triad and probably a bunch of other FPS’s… But Duke was something different. He was some random bad-ass who decided to take on the invasion single-handedly, crass and proud of it, the kind of guy you hated to love so very very much. The whole game just LOVED not caring about what people might say and I think that bravery in design resonated with gamers. It tastefully crossed boundaries, not going so far that it would alienate people but far enough that we all chuckled along at the knuckle-head who in the middle of an alien invasion decided to tip the strippers he randomly happened upon. It was also very open, for an FPS game, with lots of little tunnel’ie areas to explore and Easter Eggs to find.

So without going on and on and on; I loved Duke Nukem 3D.

Step forward to the present; fourteen years after a sequel was promised, FINALLY it has been delivered. Hail To The King, Baby! After being the most infamous piece of vapourware for the majority of my life, Duke Nukem Forever is finally here!… But it unfortunately doesn’t live up to my expectations.

I’ve wracked my brain for reasons why. In a lot of ways it’s very similar. Most of the things I thought I loved about Duke Nukem 3D are in DNF… So what’s the problem? Well, there are several. Firstly, Duke Nukem 3D came from a different era of FPS gaming, where Doom was the trend-setter and all other FPS games were reviewed based at least in part on their “Doom-ness”. Duke Nukem 3D didn’t aim to copy Doom, but it shared some qualities… and here they are;

• Fast paced; moving felt like driving a race-car through narrow corridors
• A huge arsenal of weapons at your constant disposal
• Difficult (verging on impossible) fights that felt expansive
• This ever present feeling that at any moment a tidal wave of monsters could come crashing down on you and eat your insides before you even noticed the first teleport noise

Here’s the problem… Duke Nukem Forever has none of this. Most of the game is spent sort of wandering at what feels like a leisurely pace between fights, which occur in what are basically closed boxes that unlock when all of whatever you’re supposed to kill is dead. All the classic Duke weapons are there, but you can only carry two weapons at a time (YES, HELLO, WE KNOW HALO EXISTS, THANK-YOU DEVELOPERS) which typically have fairly restrictive ammo reserves… Despite this, not a single fight I’ve encountered has taken me more than two attempts to get through and running out of ammo basically means just switching to the gun of some enemy you’ve just dropped by whacking them with your scrotum. I’ve not once been intimidated by this game, if anything it’s bored me.

On top of this, even after hours of graphical settings tweaks I still can’t manage to make the game run without jerking around in bits, which is sad as the graphics, although acceptable, are not the mind-blowing level that I’d allow to jerk my PC around a bit. There are even sections in which the game just freezes up for anywhere from 5-30 seconds, starting up again as if nothing had happened, as if Duke is having an absent seizure that we get the pleasure of experiencing first hand.

Lastly, the sexual nature of the game… I don’t know, honestly, is about my best response. Being literally subjected to a digital striptease I suppose was intended to be sexy and entertaining but for me it came out creepy and alienating. “Should I be turned on by that?” is something I ended up asking myself a number of times as I awkwardly tried to get through another section of game where gelatinous ALMOST human breasts were bared upon the screen. The answer I always came away with was “… No, it is as creepy as it feels.” I don’t have a good reason for this, but there is something distinctly disturbing about something ALMOST human trying to illicit sexual excitement in me; I’m apparently not a robosexual.

HOWEVER; I played it. Through all these hassles, I played the whole damn thing. In bits it was fun. But Gearbox really needed to decide whether they wanted to make Duke Nukem Forever a comical Halo/COD: Modern Warfare or a “classical” FPS, realism be damned. They kind of landed somewhere in-between, poking fun at games like Halo while unashamedly utilizing its mechanics; bringing back the classical (read: stupid and weak) enemies but without the feeling of constant peril. It almost feels like they just said;

“F*** IT! It’s been 15 years, anyone got FPS development experience? Yes? GOOD! GET IT OUT!”

and forgot to playtest the result…

All that being said, I’m happier to have a not-perfect game now than no game after another 3 years of failed playtests and revisions. Yes, that is a backhanded compliment and yes, it is the perfect way to end this poor game’s review.

Over the past ten thousand years, humans have urbanised their surroundings. A link between urbanisation and increased disease occurrence can be seen using recent and historical records, however prehistorically it is difficult to assess whether this association remains.

Getting sick is nothing new. Humans (and our evolutionary ancestors) have succumbed to viral and bacterial infections for millions of years, immunological defences within the human body fighting back against these pathogens with varying degrees of effectiveness. This variation in effectiveness is attributable to genetic differences between individuals, exposed to the same conditions.

Even though proof of disease in prehistoric humans exists¹, there are significant problems with inferring disease occurrence information from prehistoric remains. Most infections do not cause skeletal lesions or remodelling¹, compounded by the fact that there are vastly fewer human fossils from prehistoric time available² as compared to more modern remains.

Due to evolution, both pathogens and the human immune system are caught in an “arms race” of development – each side adapting in successive generations of individuals, to more effectively infect hosts or destroy invading pathogens. Writing in Evolution, Barnes et al.³ postulates that if a positive correlation between urbanisation and disease occurrence exists, this genetic “arms race” would be expected to greater select for disease resistance in urbanised areas, as compared to areas not urbanised or urbanised for less time.

In order to measure disease resistance, a polymorphic gene known to be associated with susceptibility to Tuberculosis in humans (SLC11A1)⁴, was selected for analysis. One allele of this gene (1729 + 55del4) is protective against the disease.

Seventeen populations, with a range of urbanisation histories were selected and their polymorphic state for the Tuberculosis susceptibility gene (SLC11A1) measured. In order to determine the length of urbanisation for each of the selected populations, comprehensive archaeological and historical literature was searched to locate the first description of the region as a major town or city, or for evidence of high density living.

Using numerous statistical techniques⁵, it was determined that the length of time an environment had been urbanised for significantly impacted the polymorphic state for the Tuberculosis susceptibility gene (SLC11A1), as compared to the expected frequency of polymorphism in a non-urbanised environment. This difference in allelic diversity trended towards protecting against the disease in individuals who resided in areas of longest urbanisation (see Fig. 1).

Figure 1: Percentage of protective allele & total time urbanized



A number of possibly confounding issues were taken into account by the researchers³; shared demographic histories between populations, a number of issues relating to the method of determining urbanisation length in each population, the possibility that not urbanisation length, but domestication of cattle was the correlative factor and that only “Old World” populations were sampled. Each of these issues were either shown via statistical tests to have little to no effect on correlation, or it was determined that the bias they may have, if any, would serve to weaken, rather than strengthen, correlation.

Previous to this study by Barnes et al.³, the effects of urbanisation on disease occurrence were not well understood. Understanding this relationship not only sheds light on a previously unrecognised example of selection, but helps future researchers by creating a framework that utilises historical data to explain allele frequencies and selective pressures.

The results of this study are compelling; even after numerous statistical corrections for possible biases in the datasets, significant correlation between urbanisation and disease occurrence were shown.  However, there are a number of issues with the design of the study itself. That the urbanisation length was calculated for each population by means of a best estimate, found in archaeological and historical documents is of concern; as every statistical test used this data, it is possible these estimates “seeded” the entire study with inaccuracy. Also, Barnes et al.³ mentions that the Tuberculosis susceptibility gene (SLC11A1) has been proposed to associate with autoimmune diseases, creating the possibility of balancing selection affecting the polymorphism of the gene, yet this assertion is not statistically investigated.

One of the broader comments made in the abstract of this study is that a difficulty assessing the correlation between urban living and disease occurrence in prehistoric times exists, which is not directly answered. Indirectly however, the study does well in showing a correlation between the start of urbanisation and disease occurrence, hinting at the fact that prehistoric populations, due to their lack of urbanisation, had less disease occurrence. This is not directly proven (or even mentioned) and other studies have spoken to the difficulties that research faces when attempting to answer this question^{1, 2}, but the contrast drawn in this study acts as fantastic starting point for further prehistoric research.

Further study could also be conducted to determine if modern day urban and rural populations, within the same region, differ in allele frequency for the protective allele of the Tuberculosis susceptibility gene (SLC11A1) as the resolution of individuals in the seventeen regions sampled was not granular enough to determine this.

Although the topic of disease occurrence can be unclear, especially in prehistoric time, this study has built a foundation for the novel utilisation of historical data towards scientific endeavours, allowed a glimpse into prehistoric time by contrast with the modern-day and acts as a starting point for further research.

References:

1. Wood, J.W. et al. The Osteological Paradox (Current Anthropology, 1992)
2. Roberts, C.A & Cox, M. Health and disease in Britain : from prehistory to the present day. (Sutton Publishing, 2003)
3. Barnes, I. et al. Ancient Urbanisation Predicts Genetic (Evolution, 2010)
4. Bellamy, R.C. et al. Variations in the Nramp1 gene and susceptibility to tuberculosis in West Africans (New England Journal of Medicine, 1998)
5. Cook, R.D. Detection of influential observations in linear regression. (Technometrics, 1977)

This pattern describes how different species from the same phylum vary in similarity during development in a predictable way, such startlingly clear morphological similarities in such diverged species shocked early biologists.

Recognition of this pattern is nothing new, in 1828 a German biologist who had forgotten to label two of his remarked that;

“At present I am unable to determine the genus to which they belong. They may be lizards, small birds, or even mammals.”

Genomic Hourglass… Without genomic proof?

While the “Genomic Hourglass” morphological pattern has been recognised and academically discussed at length, research into gene-expression during development had not taken place. Gene-expression should logically be closely tied to developmental morphology, as it is the expression of the genes that allows biological processes to occur.

In order to measure gene-expression (the activiation and deactivation of genes – similar to opening a computer program to do work, then closing it when it’s done) during embryonic development, Kalinka et al. (2010) took six species of Drosophila (a commonly used research animal) and sampled 3000+ genes at 60 different points of their development. For each gene sampled, a gene-expression timeline was created, as shown via the example on the right.

Not every gene sampled had the same “variability” (likelihood to change). Genes that change rapidly are much more likely to be different between species, where genes that are stable are unlikely to have changed between the 6 different Drosophila species sampled from. This gene variability can be used to describe the “age” of the genes, as less variable genes are likely to be “older”, as they are less likely to change as compared to more variable and therefore “younger” genes.

The results showed a relationship between the percentage of similar genes activated at a particular point in development across the 6 species of Drosophila and the “age” of the genes. This revealed a “gene activation hourglass”, where newer genes were active at the beginning and end of development, with older genes active in the middle. This pattern correlates well with the ”morphological hourglass” pattern.

This research strongly suggests a link between gene age, gene activation and developmental morphological differentiation between similar species and that natural selection acts to convert patterns of gene expression during the middle of embryo development. However, further work on species with less similarity could give more strength to their findings.

Interestingly, researchers were able to determine gene functionality for each of the 3000+ gene’s sampled across the six species of Drosophila and noted that genes related to core developmental processes conformed strongly to the hourglass model, whereas genes involved with secondary metabolism, immunological process’ and responses to wounding stress conformed the least.

Kalinka, AT et al. (2010) Gene expression divergence recapitulates the developmental hourglass model, Nature 468, pages 811-814