Science
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What really causes a hangover? You’re not alone if you feel under the weather after a big drinking session. Here’s the science behind the morning after the night before. Phoebe Turner
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We all know the feeling of waking up with a pounding headache, nausea and the overwhelming urge to curl up in bed for the rest of the day. Many of us accept this fate as just one of the downsides of having a bit too much to drink, and often swear we’ll never drink again, which is often forgotten when someone fancies a trip to the pub! However, why is it that we get these horrible hangover symptoms? Well, let’s have a look at the science and figure this out once and for all. Shockingly, the amount we know about why we get hangovers is considerably less than you might think. The most commonly known theory is that of dehydration, and whilst the diuretic property (increase in urine production) of alcohol certainly supports this theory, simple dehydration is not the full story here. Ethanol, the chemical that makes up what we know as ‘alcohol’, is broken down in the liver, first to acetylaldehyde (AA), a toxic intermediary, then acetic acid, which is better known as the acidic component of vinegar. During this process, NAD+ — a compound found in every cell of the human body that is involved in many of the body’s essential functions — is converted to NADH. Without going into details, this causes an imbalance in NAD+/NADH levels. which is bad news. An imbalance can cause changes in the
production levels of hormones, electrolytes and fats. Many scientists conclude that this is the underlying factor behind a hangover, but this is still disputed due to the lack of evidence of a relationship between hangover severity and electrolyte levels.
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“Your choice of tipple can influence the severity of your hangover.”
Another theory behind the hangover lies with AA. This incredibly toxic intermediary product is produced in the breakdown of alcohol. Due to genetic variations, some bodies can only break down AA into acetic acid very slowly, which leads to a build-up of AA. This is known as the ‘alcohol flush reaction’. Studies have shown that people with a slow AA breakdown are more likely to experience a hangover more quickly and with greater severity, compelling many scientists to believe that AA build-up is the real culprit behind
Image: Peter Oumanski / TIME
a hangover. Therefore, when someone comes along and claims that they never get hangovers there may actually be some genetic truth to it. Furthermore, your choice of tipple can also influence the severity of your hangover, with drinks containing high levels of congeners more likely to produce a severe hangover. Congeners, such as methanol, are toxic byproducts formed in the process of making alcoholic drinks and are found in higher concentrations in darker-coloured drinks, such as red wine and rum. Contrary to popular belief, vodka may actually be your friend if you’re planning to avoid a bad hangover, due to it being a pale liquor with low congener levels — a win-win if you’re a fan of a vodka and coke! So now we know what causes a hangover! Or at least, we know as much as scientists currently know about the subject. Whether you’re a fresher about to experience your first taste of university life, or a seasoned university veteran going into your final year, science’s advice to help curb that hangover is to drink in moderation, learn to accept water as your new best friend and stick to pale drinks with lower congener levels. Good luck!
A-Levels 2020: Derailed by a mutant algorithm How did the government get A-Level results so wrong? Elizabeth Sarell An algorithm used to determine A-Level grades left thousands of students devastated after their predicted grades were marked down. Statistics showed that 39% of students had their results downgraded. Those downgraded were more likely to attend state schools - in contrast, not a single student from Eton had their grades reduced. The country erupted in protest, with some claiming that the algorithm was classist and others arguing that a computer is clearly smarter than a teacher and cannot be biased. When people hear about algorithms and Artificial Intelligence (AI), they often assume the computer has human-level intelligence capable of making decisions. But this is completely wrong - an algorithm is just a series of instructions for the computer to follow with no concept of morals. It simply does what you tell it!w Okay, but mustn’t it be intelligent? Well, it’s worth noting there are two types of AI: Weak and Strong. Chances are when you think of AI, you’re thinking of Strong AI, which is able to make decisions and learn in an attempt to mimic human intelligence. However,
most AI that is currently in use is Weak AI. Weak AI doesn’t resemble human intelligence, but rather it tends to notice patterns and then works out the next logical step, for instance, playing chess against a computer. The AI has been taught thousands of chess moves and searches through to determine the best possible next move based on what’s currently in play. It’s highly likely that weak AI was used in the A -Levels algorithm. So how do we end up with bias within algorithms? Well, the first kind of bias is caused by malicious intent. As previously mentioned, an algorithm will do what it’s told, so if you want to build an algorithm that discriminates against a group of people, the algorithm will do just that. The second kind of bias is the inability to recognise bias occurring within data. This is often harder to recognise, as people tend to just accept numbers, forgetting that numbers leave no room for real-world context. For example, you are tasked with building a system to direct police patrol cars around a city. The first thing you might look at is which areas have a higher arrest rate and then choose to distribute more officers there. However, what those numbers don’t reflect is the fact that areas with a higher percentage of people of colour historically have been, and still are, more
policed and therefore have been unfairly targeted. This results in a higher arrest rate. By not looking at why there are more arrests in certain areas, you could end up building a system which is systematically racist. It then becomes the responsibility of the algorithm developer to spot bias and identify prejudice within the data. They would need to adjust the algorithm to account for this. However, something as simple as a lack of diversity in a development team could mean that some forms of discrimination are overlooked. The A-Level results in August would appear to suggest that the algorithm used reinforced class prejudice against students from working-class backgrounds by consistently downgrading them. It is highly likely that this was a result of ignorance, but it does beg the question: who was ultimately responsible for overseeing the algorithm and highlighting this inbuilt prejudice? Had these issues been picked up at an earlier stage, the algorithm could have been a force for good - equalising results across private and state schools. However, what was ultimately developed just reaffirmed the classism that exists within the UK.
