Academic Reading Section 1
You are going to start IELTS Academic Reading The reading test consists of 3 sections. On this page you can find section 1. Read the text, answer all the questions READING PASSAGE 1
You should spend about 20 minutes on Questions 1-13, which are based on Reading Passage 1 below.
The animal that regrows its head
In a windowless lab at the University of Galway in Ireland, there’s a fish tank containing an extraordinary creature. Perched on blue cocktail sticks like lollipops, rows of seashells are coated in a strange “living hair”, buffeted by gently flowing seawater. This colony of tiny marine animals – known as “snail fur” – was harvested in Irish rockpools off the backs of hermit crabs, and is related to jellyfish, corals and sea anemones.
Each no bigger than a baby’s eyelash, they are called Hydractinia, and up close resemble a tree, each with a foot, a trunk and a tentacled head used for catching tasty passing detritus. They also have a superpower: when grazing fish frequently bite off those tentacle heads, they re-sprout to their former hirsute glory within a week.
It’s this talent that has captured the attention of Uri Frank and colleagues at Galway’s Regenerative Medicine Institute. Along with a growing number of researchers, he claims that the tissue regeneration seen in creatures like Hydractinia could be an ancient power possessed by most animals, including humans – it’s just dormant. So, how does this “snail fur” regrow itself? And could it hold the key to tissue regeneration in human beings too?
Many animals can regenerate body parts, from starfish to salamanders. But primitive snail fur is unusual, not least because its abilities are so extreme.
Marshalling stem cells
The key to Hydractinia’s regenerative talent is the fact that it retains its embryonic stem cells for life. This means that any wound healing process doesn’t just produce a scab and a scar but a whole new body part as it would in an embryo, even a head.
At a gathering of developmental biologists earlier this year, Frank showed a video of the creature’s head-budding process in action, embryonic stem cells that had been genetically altered to glow green rushing to the neck end of a headless Hydractinia. Attendees were agog. As one tweeted: “Uri Frank shows timelapse movie of Hydractinia stem cells physically moving across to head (wound site) – Wow!”
Since recording that video the Galway team have been working to understand how Hydractinia rebuilds its severed body and hope to publish their findings shortly in a scientific journal. While they’re keeping schtum about the details, the paper will focus on how the creature marshalls its stem cells to regrow its head – for example, how stem cells know the head’s missing – and where exactly the embryonic stem cells come from.
Studying Hydractinia has also led Frank and colleagues to ask a bigger question: why can only a few animals regenerate while most can’t? A salamander can regrow a lost tail but closely related frogs can’t regrow a lost limb. And if a tiny marine creature can regrow its own head, why can’t humans even regrow their adult teeth? After all, says Frank, it’s not as if human and Hydractinia stem cell systems are so very different.
Key stem cell processes are ancient and common to many animal species. For instance, the complex “Wnt” signalling system, which controls stem cells in developing embryos and, when uncontrolled, causes cancer, is very similar in all animals, including Hydractinia and people. It’s one of a handful of complex stem cell systems, each involving hundreds of elements, which have remained the same since Hydractinia branched off the evolutionary tree that eventually led to us around 600 million years ago.
Over the past decade or so, researchers have started to believe that stem cells first evolved in a creature even more ancient than Hydractinia, whose soft body has long since dissolved in ancient seabeds. In this as-yet-unknown creature, the power of regeneration may have first evolved, says Frank, endowing all later animals with a basic toolkit for regrowing lost body parts – one which mainly lies dormant in present-day life.
“It’s maybe not such a crazy idea. Stem cell systems are enormously complex and 600 million years may not be long enough to reinvent another system from scratch. So it’s more likely to believe that our stem cell system and Hydractinia’s stem cell system were actually inherited from a common ancestor,” says Frank. “And if you think about it, Hydractinia can grow a new head and, although we cannot as adults, we can do that as embryos when we make our own head. So it is possible that this ability to do so is switched off in human adults and in Hydractinia it’s not.”
This theory ties in with a study published last year in the journal Nature, about two varieties of an ancient form of flatworm, the planarian. This worm has been studied for over a century because of its amazing regenerative powers. Slice them up into tiny pieces and some planarian worms can regrow their bodies from even the tiniest tailpiece. Others need most of their body intact to regrow a head. Until now, that is.
Researchers at the Max Planck Institute tested the idea that all planarian flatworms have the same regenerative superpowers but that in some it’s switched off early in development. They were right. With a relatively simple tweak to the stem cell system of a developing embryo they turned a creature that in nature couldn’t regrow a head out of a tiny tailpiece, into one that could.
In Galway, Frank hopes his research will help to explain the apparently miraculous results from planarian experiments and unravel other mysteries, too. Why, for instance, do planarians easily grow new tails when Hydractinia struggles to regrow its foot? One idea is that body symmetry – front/back or left-right as in planarians and humans but not snail fur – may dictate where stem cells in the body can migrate to.
In theory, it’s possible that humans may harbour the same dormant regenerative superpowers as snail fur and flatworms, however far they seem from humans. At the most basic cellular level there are striking similarities. Studying them could teach us how to regrow damaged or lost body parts too. “While there’s no market for regrowing human heads,” says Frank, “wouldn’t it be great if we could repair spinal cords, damaged hearts, damaged kidneys, hands and any other organs we might lose?”
The flatworm studies imply this might not be quite as unthinkable as once thought. The Victorian father of regenerative science, Thomas Hunt Morgan carried out flatworm experiments showed their amazing powers to regrow a whole body from a stump in 1901. But he abandoned the study, writing: “We will never understand the phenomena of development and regeneration.”
Clearly, there are many mysteries of regeneration still to be revealed, yet now it seems that a tiny creature living in a fish tank in Galway and its ilk could help us unlock the bizarre process of regrowing body parts sooner than we thought.
Do the following statements agree with the information in the IELTS reading text?
In boxes 1-5 on your answer sheet, write
TRUE if the statement agrees with the information
FALSE if the statement contradicts the information
NOT GIVEN if there is no information on this
1. “Snail fur” is related to jellyfish, corals and sea anemones.
2. Judging by the picture, Hydractinia can regrow its head in a day.
3. Uri Frank thinks that even humans can possess regenerating powers.
4. Snail fur is similar to salamnders and starfish.
5. Healing in Hydractinia produces new body part.
Choose the correct letter, A, B, C or D.
Write the correct letter in boxes 6–8 on your answer sheet.
6. Which of the following DIDN’T happen at a gathering of developmental biologists?
Uri Frank showed a video of Hydractinia regenerating its head.
Some stem sells of the creature were glowing green.
Attendants were astonished by the show.
Research conference afterwards took place.
7. The Galway team will focus on what in their future paper?
How Hydractinia manages to regrow its head.
How stem cells know that the head is missing.
Where the stem cells come from.
All of the above.
8. According to Frank Uri and his team
human and Hydractinia stem cells are similar.
most organisms can regenerate themselves.
frogs can regrow lost limbs.
salamander and frogs are not closely related.
Complete the sentences below.
Write ONLY ONE WORD from the passage for each answer.
Write your answers in boxes 9-13 on your answer sheet.
9. “Wnt” signalling system can cause if uncontrolled.
10. Human and Hydractinia stem cells might actually be from a common .
11. The thing that dictates where stem cells in the body can migrate tomight be body .
12. Humans might possibly harbour the same regenerative superpowers as snail fur and flatworms.
13.Thomas Hunt Morgan said that we will never understand the of development and regeneration.
READING PASSAGE 2
You should spend about 20 minutes on Questions 14-27, which are based on Reading Passage 2 below.
All The Ways Women Are Still Pressured To Put Family Before Career
(A) There’s no denying that women around the world have made great strides toward equality in the past century. One hundred years ago, women in the United States still didn’t have the right to vote, and very few were allowed to pursue higher education or a meaningful career outside of their household duties. Fast forward to today, and more than 70 percent of women between the ages of 20 and 54 are active members of the national workforce. On top of this, 2015 marked the first year when women were, on average, more likely to have a bachelor’s degree than men, and this trend is on the rise.
(B) But despite all this newfound opportunity, the prevailing societal attitudes about what women are historically supposed to value still have a long way to go. That’s why we’ve partnered with SK-II to learn more about all of the ways women are still pressured to stick to outdated gender norms. “Women have won unprecedented rights thanks to the feminist movement, but as a society, we still expect women to prioritize family over career, or even over their own needs,” says Silvia Dutchevici, president and founder of the Critical Therapy Center in New York City. Dutchevici says many women feel pressure to “have it all,” meaning both a thriving career and the perfect family, but that can be very difficult to achieve.
(C) “Most women try to balance work and family,” Dutchevici says, “but that balance is seldom equal.” In fact, she says working mothers ― even those with partners ― often find themselves essentially working two full-time jobs: keeping their career together while doing the brunt of housework, cooking and child-rearing. This happens for a variety of reasons, but societal expectations about the roles of women and men at home are still very much to blame, says Tamra Lashchyk, a Wall Street executive, business coach and author of the book “Lose the Gum: A Survival Guide to Women on Wall Street.”
(D) “No matter how successful she is, the burden of running a household still falls on the woman’s shoulders,” Lashchyk says. “Men get more of a pass when it comes to these duties, especially those that involve children.” Lashchyk says much of this pressure on women to conform to a more domestic lifestyle comes from friends and family.
(E) “In many people’s minds, a woman’s career success pales in comparison to having a family,” she says. “Especially if the woman is single, no matter how great her professional achievements, almost every single one of her conversations with her family will include questions about her romantic life or lack thereof. I could literally tell my family I’d cured cancer and the conversation would still end with, ‘But are you dating anyone?’” While covert societal expectations might contribute to some of this inequality, workplace policies on maternity and paternity leave can hold a lot of the blame.
(F) “Unfortunately, many workplace policies regarding taking time off to care for family do not the changing times,” Dutchevici says. “Both men and women suffer in their careers when they prioritize family, but women carry far harsher punishments. Their choice to take time off and start a family can result in lower pay, and fewer promotions in the future. The right to family leave is not a woman’s issue, it is a society’s issue, a family’s issue.” Lashchyk agrees with this sentiment. “There should be more flexibility and benefits [in the workplace], like longer periods of time for paternity leave….If paternity leave was extended, men could share a greater responsibility in child care, and they could also spend more time bonding with their infant children, which is beneficial for the entire family.
(G) Another less visible way the modern workplace forces women to choose family over career has to do with the fact that women are pushing back pregnancy, says Jeni Mayorskaya, a fertility expert and CEO of Stork Club, an online community for women dedicated to fertility issues. “Compared to our parents, our generation is having children a decade later,” Mayorskaya says. “Unfortunately, when we hit our mid-30s and we’re finally ready for that managing position or that title of a partner at a firm we fought so hard for, we have to think about putting our career on pause and becoming a mom.”
(H) So what can women do to combat these societal pressures? Finding workplaces that offer flexible schedules, work-at-home opportunities and ample maternity and paternity leave is a good first step, but Dr. Neeta Bhushan, an emotional intelligence advocate and author, says women should also learn to put themselves first. “The first step is being mindful of your emotional health in your relationships with others and the relationship you have with yourself,“ Bhushan says. “When you put yourself first, you are able to make a bigger impact on your community. This is different than being selfish ― think beyond you. You want to make sure that you are being taken care of so that you can take care of others.”
Reading Passage 2 has eight paragraphs, A-H.
Which paragraph contains the following information?
Write the correct letter, A-H, in boxes 14-21 on your answer sheet.
14. Two “jobs” that women essentially do
15. Question about dating
16. Delaying pregnancy
17. The first year, when women are more likely to have bachelor’s a degree
18. The reasons to put yourself first
19. The source of conformation to domestic lifestyle
20. Our expectancy over women’s prioritization
21. Pros of extended paternity
Choose the correct letter, A, B, C or D.
Write the correct letter in boxes 22-27 on your answer sheet.
22. One hundred years ago, women in USA:
A . had no rights.
B were not allowed to pursue higher education.
C. couldn’t vote.
D. were members of the national workforce.
23. According to Silvia Dutchevici:
feminist movement has more disadvantages than advantages.
now we expect women to prioritize career over family.
now we expect women to prioritize their own needs over family.
women rarely achieve equal balance between family and work.
24. Tamra Lashchyk, a Wall Street executive, says that
most women are still responsible for the house duties.
men don’t really need to do any housework.
it’s more important for a women to have a career than a family.
both A and B.
25. Lashchyk agrees with Dutchevici on
women’s rights and feminism.
the fact that he right to family leave is a society’s issue.
the state of women’s rights in America.
the reason why women want to pursue their careers.
26. Jeni Mayorskaya says that
nowadays women give birth later than they used to.
now women don’t push pregnancy back.
when women are in their 30s, they have to think about putting career on pause to become a mother.
Both A and C.
27. According to the last paragraph, how can women deal with societal pressure?
They should be selfish.
They shouldn’t work at home.
They should put themselves first.
They should avoid marriage at all.
READING PASSAGE 3
You should spend about 20 minutes on Questions 28-40, which are based on Reading Passage 3 below.
The real risks of artificial intelligence
If you believe some AI-watchers, we are racing towards the Singularity – a point at which artificial intelligence outstrips our own and machines go on to improve themselves at an exponential rate. If that happens – and it’s a big if – what will become of us?
In the last few years, several high-profile voices, from Stephen Hawking to Elon Musk and Bill Gates have warned that we should be more concerned about possible dangerous outcomes of supersmart AI. And they’ve put their money where their mouth is: Musk is among several billionaire backers of OpenAI, an orgnisation dedicated to developing AI that will benefit humanity.
But for many, such fears are overblown. As Andrew Ng at Stanford University, who is also chief scientist at Chinese internet giant Baidu, puts it: fearing a rise of killer robots is like worrying about overpopulation on Mars.
That’s not to say our increasing reliance on AI does not carry real risks, however. In fact, those risks are already here. As smart systems become involved in ever more decisions in arenas ranging from healthcare to finance to criminal justice, there is a danger that important parts of our lives are being made without sufficient scrutiny. What’s more, AIs could have knock-on effects that we have not prepared for, such as changing our relationship with doctors to the way our neighbourhoods are policed.
What exactly is AI? Very simply, it’s machines doing things that are considered to require intelligence when humans do them: understanding natural language, recognising faces in photos, driving a car, or guessing what other books we might like based on what we have previously enjoyed reading. It’s the difference between a mechanical arm on a factory production line programmed to repeat the same basic task over and over again, and an arm that learns through trial and error how to handle different tasks by itself.
How is AI helping us? The leading approach to AI right now is machine learning, in which programs are trained to pick out and respond to patterns in large amounts of data, such as identifying a face in an image or choosing a winning move in the board game Go. This technique can be applied to all sorts of problems, such as getting computers to spot patterns in medical images, for example. Google’s artificial intelligence company DeepMind are collaborating with the UK’s National Health Service in a handful of projects, including ones in which their software is being taught to diagnose cancer and eye disease from patient scans. Others are using machine learning to catch early signs of conditions such as heart disease and Alzheimers.
Artificial intelligence is also being used to analyse vast amounts of molecular information looking for potential new drug candidates – a process that would take humans too long to be worth doing. Indeed, machine learning could soon be indispensable to healthcare.
Artificial intelligence can also help us manage highly complex systems such as global shipping networks. For example, the system at the heart of the Port Botany container terminal in Sydney manages the movement of thousands of shipping containers in and out of the port, controlling a fleet of automated, driverless straddle-carriers in a completely human-free zone. Similarly, in the mining industry, optimisation engines are increasingly being used to plan and coordinate the movement of a resource, such as iron ore, from initial transport on huge driverless mine trucks, to the freight trains that take the ore to port.
AIs are at work wherever you look, in industries from finance to transportation, monitoring the share market for suspicious trading activity or assisting with ground and air traffic control. They even help to keep spam out of your inbox. And this is just the beginning for artificial intelligence. As the technology advances, so too does the number of applications.
So what’s the problem? Rather than worrying about a future AI takeover, the real risk is that we can put too much trust in the smart systems we are building. Recall that machine learning works by training software to spot patterns in data. Once trained, it is then put to work analysing fresh, unseen data. But when the computer spits out an answer, we are typically unable to see how it got there.
There are obvious problems here. A system is only as good as the data it learns from. Take a system trained to learn which patients with pneumonia had a higher risk of death, so that they might be admitted to hospital. It inadvertently classified patients with asthma as being at lower risk. This was because in normal situations, people with pneumonia and a history of asthma go straight to intensive care and therefore get the kind of treatment that significantly reduces their risk of dying. The machine learning took this to mean that asthma + pneumonia = lower risk of death.
As AIs are rolled out to assess everything from your credit rating to suitability for a job you are applying for to criminals’ chance of reoffending, the risks that they will sometimes get it wrong – without us necessarily knowing – get worse.
Since so much of the data that we feed AIs is imperfect, we should not expect perfect answers all the time. Recognising that is the first step in managing the risk. Decision-making processes built on top of AIs need to be made more open to scrutiny. Since we are building artificial intelligence in our own image, it is likely to be both as brilliant and as flawed as we are.
Complete the sentences below.
Write NO MORE THAN TWO WORDS from the passage for each answer.
Write your answers in boxes 28-36 on your answer sheet.
28. Singularity is the point, where AI our own machines.
29. Many people, including Stephen Hawking, Elon Musk and Bill Gates warned us about possible of supersmart AI.
30. According to Andrew Ng, fearing a rise of is similar to worrying about overpopulation on Mars.
31. There is a danger that many important parts of our lives, like healthcar, finance and will be without sufficient scrutiny.
32. Simply put, AI is machines doing things that are considered to require when humans do them.
33. Nowadays, the main approach to AI is .
34. DeepMind in collaboration with the UK’s National Health Service works on many projects, including the one where software learns how to and eye disease.
35. In the nearest future machine learning could be to healthcare.
36. AI might also help in managing networks.
Do the following statements agree with the information given in Reading Passage 3?
In boxes 37–40 on your answer sheet, write
TRUE if the statement agrees with the information
FALSE if the statement contradicts the information
NOT GIVEN if there is no information on this
37. AI works in many different industries nowadays.
38. We shouldn’t put too much trust in AI in the future.
39. The quality of the data doesn’t affect the ability of AI to learn information correctly.
40. We can get perfect answers from AI all the time.