2019年12月12日朗阁雅思阅读考题题库

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Passage 1

The dugong: sea cow

Dugongs are herbivorous mammals that spend their entire lives in the sea. Their close relatives the manatees also venture into or live in fresh water. Together dugongs and manatees make up the order Sirenia (海牛目) or sea cows, so-named because dugongs and manatees are thought to have given rise to the myth of the mermaids or sirens (女巫) of the sea.

A

The dugong, which is a large marine mammal which, together with the manatees, looks rather like a cross between a rotund dolphin and a walrus. Its body, flippers and fluke resemble those of a dolphin but it has no dorsal fin. Its head looks somewhat like that of a walrus without the long tusks.

B

Dugongs, along with other Sirenians whose diet consists mainly of sea-grass; and the distribution of dugongs very closely follows that of these marine flowering plants. As seagrasses grow rooted in the sediment, they are limited by the availability of light. Consequently they are found predominantly in shallow coastal waters, and so too are dugongs. But, this is not the whole story. Dugongs do not eat all species of seagrass, preferring seagrass of higher nitrogen and lower fibre content.

C

Due to their poor eyesight, dugongs often use smell to locate edible plants. They also have a strong tactile sense, and feel their surroundings with their long sensitive bristles. They will dig up an entire plant and then shake it to remove the sand before eating it. They have been known to collect a pile of plants in one area before eating them. The flexible and muscular upper lip is used to dig out the plants. When eating they ingest the whole plant, including the roots, although when this is impossible they will feed on just the leaves. A wide variety of seagrass has been found in dugong stomach contents, and evidence exists they will eat algae when seagrass is scarce. Although almost completely herbivorous, they will occasionally eat invertebrates such as jellyfish, sea squirts, and shellfish.

D

A heavily grazed seagrass bed looks like a lawn mown by a drunk. Dugongs graze apparently at random within a seagrass bed, their trails meandering in all directions across the bottom. This is rather an inefficient means of removing seagrass that results in numerous small tufts remaining. And this is where the dugongs derive some advantage from their inefficiency. The species that recover most quickly from this disturbance, spreading out vegetatively from the remaining tufts, are those that dugongs like to eat. In addition, the new growth found in these areas tends to be exactly what hungry dugongs like.

E

Dugongs arc semi-nomadic, often travelling long distances in search of food, but staying within a certain range their entire life. Large numbers often move together from one area to another. It is thought that these movements are caused by changes in seagrass availability. Their memory allows them to return to specific points after long travels. Dugong movements mostly occur within a localised area of seagrass beds, and animals in the same region show individualistic patterns of movement.

F

Recorded numbers of dugongs are generally believed to be lower than actual numbers, due to a lack of accurate surveys. Despite this, the dugong population is thought to be shrinking, with a worldwide decline of 20 per cent in the last 90 years. They have disappeared from the waters of Hong Kong, Mauritius, and Taiwan, as well as parts of Cambodia, Japan, the Philippines and Vietnam. Further disappearances are likely. (In the late 1960s, herds of up to 500 dugongs were observed off the coast of East Africa and nearby islands. However, current populations in this area are extremely small, numbering 50 and below, and it is thought likely they will become extinct. The eastern side of the Red Sea is the home of large populations numbering in the hundreds, and similar populations are thought to exist on the western side. In the 1980s, it was estimated there could be as many as 4,000 dugongs in the Red Sea. The Persian Gulf has the second-largest dugong population in the world, inhabiting most of the southern coast, and the current population is believed to be around 7,500. Australia is home to the largest population, stretching from Shark Bay in Western Australia to Moreton Bay in Queensland. The population of Shark Bay is thought to be stable with over 10,000 dugongs.)

G

Experience from various parts of northern Australia suggests that Extreme weather such as cyclones and floods can destroy hundreds of square kilometres of seagrass meadows, as well as washing dugongs ashore. The recovery of seagrass meadows and the spread of seagrass into new areas, or areas where it has been destroyed, can take over a decade. For example, about 900 km2 of seagrass was lost in Hervey Bay in 1992, probably because of murky water from flooding of local rivers, and run-off turbulence from a cyclone three weeks later. Such events can cause extensive damage to seagrass communities through severe wave action, shifting sand and reduction in saltiness and light levels. Prior to the 1992 floods, the extensive seagrasses in Hervey Bay supported an estimated 1750 dugongs. Eight months after the floods the affected area was estimated to support only about 70 dugongs. Most animals presumably survived by moving to neighbouring areas. However, many died attempting to move to greener pastures, with emaciated carcasses washing up on beaches up to 900km away.

H

If dugongs do not get enough to eat they may calve later and produce fewer young. Food shortages can be caused by many factors, such as a loss of habitat, death and decline in quality of seagrass, and a disturbance of feeding caused by human activity. Sewage, detergents, heavy metal, hypersaline water, herbicides, and other waste products all negatively affect seagrass meadows. Human activity such as mining, trawling, dredging, land-reclamation, and boat propeller scarring also cause an increase in sedimentation which smothers seagrass and prevents light from reaching it. This is the most significant negative factor affecting seagrass. One of the dugong's preferred species of seagrass, Halophila ovalis, declines rapidly due to lack of light, dying completely after 30 days.

I

Despite being legally protected in many countries, the main causes of population decline remain anthropogenic and include hunting, habitat degradation, and fishing-related fatalities. Entanglement in fishing nets has caused many deaths, although there are no precise statistics. Most issues with industrial fishing occur in deeper waters where dugong populations are low, with local fishing being the main risk in shallower waters. As dugongs cannot stay.

Questions 1-4

Summary

Complete the following summary of the paragraphs of Reading Passage, using no more than two words from the Reading Passage for each answer. Write your answers in boxes 1-4 on your answer sheet.

Questions 5-9

Do the following statements agree with the information given in Reading Passage 1?

In boxes 5-9 on your answer sheet, write

TRUE   if the statement is true

FALSE   if the statement is false

NOT GIVEN  if the information is not given in the passage

5 The dugong will keep eating up the plant completely when they begin to feed.

6 It takes more than ten years for the re-growth of seagrass where it has been only grazed by Dugongs.

7 Even in facing food shortages, the strong individuals will not compete with weak small ones for food.

8 It is thought that the dugong rarely return to the old habitats when they finished plant.

9 Coastal industrial fishing poses the greatest danger to dugongs which are prone to be killed due to entanglement.

Questions 10-13

Answer the questions below.

Choose NO MORE THAN TWO WORDS AND/OR A NUMBER from the passage for each answer.

10 What is Dugong in resemblance to yet as people can easily tell them apart from the manatees by the fins in its back?

11 What is the major reason as Dugongs travelled long distances in herds from one place to another?

12 What number, has estimated to be, of dugong' population before the 1992  floods in Hervey Bay took place?

13 What is thought to be the lethal danger when dugongs were often trapped in?

Answer Key：

1 Nitrogen   2 sensitive bristles   3 trails

4 tufts    5 TRUE     6 FALSE

7 NOT GIVEN  8 FALSE     9 NOT GIVEN

10 Dolphin   11 Seagrass availability/ Food (shortage)/ Seagrass shortage

12 1750   13 Fishing net

Life code: unlocked!

A

On an airport shuttle bus to the Kavli Institute for Theoretical Physics in Santa Barbara, Calif., Chris Wiggins took a colleague's advice and opened a Microsoft Excel spreadsheet. It had nothing to do with the talk on biopolymer physics he was invited to give. Rather the columns and rows of numbers that stared back at him referred to the genetic activity of budding yeast. Specifically, the numbers represented the amount of messenger RNA (mRNA) expressed by all 6,200 genes of the yeast over the course of its reproductive cycle. “It was the first time I ever saw anything like this," Wiggins recalls of that spring day in 2002. "How to make sense of all these data?"

B

Instead of shirking from this question, the 36-year-old applied mathematician and physicist at Columbia University embraced it-and now six years later he thinks he has an answer. By foraying into fields outside his own, Wiggins has drudged up tools from a branch of artificial intelligence called machine learning to model the collective protein- making activity of genes from real-world biological data. Engineers originally designed these tools in the late 1950s to predict output from input. Wiggins and his colleagues have now brought machine learning to the natural sciences and tweaked it so that it can also tell a story-one not only about input and output but also about what happens inside a model of gene regulation, the black box in between.

C

The impetus for this work began in the late 1990s, when high-throughput techniques generated more mRNA expression profiles and DNA sequences than ever before, "opening up a completely different way of thinking about biological phenomena," Wiggins says. Key among these techniques were DNA microarrays, chips that provide a panoramic view of the activity of genes and their expression levels in any cell type, simultaneously and under myriad conditions. As noisy and incomplete as the data were, biologists could now query which genes turn on or off in different cells and determine the collection of proteins that give rise to a cell's characteristic features- healthy or diseased.

D

Yet predicting such gene activity requires uncovering the fundamental rules that govern it. "Over time, these rules have been locked in by cells," says theoretical physicist Harmen Bussemaker, now an associate professor of biology at Columbia. "Evolution has kept the good stuff." To find these rules, scientists needed statistics to infer the interaction between genes and the proteins that regulate them and to then mathematically describe this network's underlying structure-the dynamic pattern of gene and protein activity over time. But physicists who did not work with particles (or planets, for that matter) viewed statistics as nothing short of an anathema. "If your experiment requires statistics," British physicist Ernest Rutherford once said, "you ought to have done a better experiment."

E

But in working with microarrays, "the experiment has been done without you," Wiggins explains. "And biology doesn't hand you a model to make sense of the data." Even more challenging, the building blocks that make up DNA, RNA and proteins are assembled in myriad ways; moreover, subtly different rules of interaction govern their activity, making it difficult, if not impossible, to reduce their patterns of interaction to fundamental laws. Some genes and proteins are not even known. "You are trying to find something compelling about the natural world in a context where you don't know very much," says William Bialek, a biophysicist at Princeton University. "You're forced to be agnostic." Wiggins believes that many machine- learning algorithms perform well under precisely these conditions. When working with so many unknown variables, "machine learning lets the data decide what's worth looking at," he says.

F

At the Kavli Institute, Wiggins began building a model of a gene regulatory network in yeast—the set of rules by which genes and regulators collectively orchestrate how vigorously DNA is transcribed into mRNA. As he worked with different algorithms, he started to attend discussions on gene regulation led by Christina Leslie, who ran the computational biology group at Columbia at the time. Leslie suggested using a specific machine-learning tool called a classifier. Say the algorithm must discriminate between pictures that have bicycles in them and pictures that do not. A classifier sifts through labeled examples and measures everything it can about them, gradually learning the decision rules that govern the grouping. From these rules, the algorithm generates a model that can determine whether or not new pictures have bikes in them. In gene regulatory networks, the learning task becomes the problem of predicting whether genes increase or decrease their protein-making activity.

G

The algorithm that Wiggins and Leslie began building in the fall of 2002 was trained on the DNA sequences and mRNA levels of regulators expressed during a range of conditions in yeast-when the yeast was cold, hot, starved, and so on. Specifically, this algorithm-MEDUSA (for motif element discrimination using sequence agglomeration)-scans every possible pairing between a set of DNA promoter sequences, called motifs, and regulators. Then, much like a child might match a list of words with their definitions by drawing a line between the two, MEDUSA finds the pairing that best improves the fit between the model and the data it tries to emulate. (Wiggins refers to these pairings as edges.) Each time MEDUSA finds a pairing, it updates the model by adding a new rule to guide its search for the next pairing. It then determines the strength of each pairing by how well the rule improves the existing model. The hierarchy of numbers enables Wiggins and his colleagues to determine which pairings are more important than others and how they can collectively influence the activity of each of the yeast's 6,200 genes. By adding one pairing at a time, MEDUSA can predict which genes ratchet up their RNA production or clamp that production down, as well as reveal the collective mechanisms that orchestrate an organism's transcriptional logic.

Questions 1-6

The reading passage has seven paragraphs, A-G

Choose the correct heading for paragraphs A-G from the list below.

Write the correct number, i-x, in boxes 1-6 on your answer sheet.

1 Paragraph B

2 Paragraph C

3 Paragraph D

4 Paragraph E

5 Paragraph F

6 Paragraph C

Questions 7-9

Do the following statements agree with the information given in Reading Passage 1?

In boxes 7-9 on your answer sheet, write

TRUE   if the statement is true

FALSE   if the statement is false

NOT GIVEN  if the information is not given in the passage

7 Wiggins is the first man to use DNA microarrays for the research on genes.

8 There is almost no possibility for the effort to decrease the patterns of interaction between DNA, RNA and proteins.

9 Wiggins holds a very positive attitude on the future of genetic research.

Questions 10-13

Summary

Complete the following summary of the paragraphs of Reading Passage, using No More than Three words from the Reading Passage for each answer. Write your answers in boxes 10-13 on your answer sheet.

Answer Key：

1 ix      2 vi     3 x

4 vii      5 v     6 i

7 NOT GIVEN    8 TRUE    9 NOT GIVEN

10 biological phenomena  11 DNA microarrays

12 (myriad) conditions   13 their expression levels

Passage 2

What happiness is?

A

Economists accept that if people describe themselves as happy, then they are happy. However, psychologists differentiate between levels of happiness. The most immediate type involves a feeling; pleasure or joy. But sometimes happiness is a judgment that life is satisfying, and does not imply an emotional state. Esteemed psychologist Martin Seligman has spearheaded an effort to study the science of happiness. The bad news is that we're not wired to be happy. The good news is that we can do something about it. Since its origins in a Leipzig laboratory 130 years ago, psychology has had little to say about goodness and contentment. Mostly psychologists have concerned themselves with weakness and misery. There are libraries full of theories about why we get sad, worried, and angry. It hasn't been respectable science to study what happens when lives go well. Positive experiences, such as joy, kindness, altruism and heroism, have mainly been ignored. For every 100 psychology papers dealing with anxiety or depression, only one concerns a positive trait.

B

A few pioneers in experimental psychology bucked the trend. Professor Alice Isen of Cornell University and colleagues have demonstrated how positive emotions make people think faster and more creatively. Showing how easy it is to give people an intellectual boost, Isen divided doctors making a tricky diagnosis into three groups: one received candy, one read humanistic statements about medicine, one was a control group. The doctors who had candy displayed the most creative thinking and worked more efficiently. Inspired by Isen and others, Seligman got stuck in. He raised millions of dollars of research money and funded 50 research groups involving 150 scientists across the world. Four positive psychology centres opened, decorated in cheerful colours and furnished with sofas and baby-sitters. There were get-togethers on Mexican beaches where psychologists would snorkel and eat fajitas, then form "pods" to discuss subjects such as wonder and awe. A thousand therapists were coached in the new science.

C

But critics are demanding answers to big questions. What is the point of defining levels of happiness and classifying the virtues? Aren't these concepts vague and impossible to pin down? Can you justify spending funds to research positive states when there are problems such as famine, flood and epidemic depression to be solved? Seligman knows his work can be belittled alongside trite notions such as "the power of positive thinking". His plan to stop the new science floating "on the waves of self- improvement fashions" is to make sure it is anchored to positive philosophy above, and to positive biology below.

D

And this takes us back to our evolutionary past. Homo sapiens evolved during the Pleistocene era (1.8 m to 10,000 years ago), a time of hardship and turmoil. It was the Ice Age, and our ancestors endured long freezes as glaciers formed, then ferocious floods as the ice masses melted. We shared the planet with terrifying creatures such as mammoths, elephant-sized ground sloths and sabre-toothed cats. But by the end of the Pleistocene, all these animals were extinct. Humans, on the other hand, had evolved large brains and used their intelligence to make fire and sophisticated tools, to develop talk and social rituals. Survival in a time of adversity forged our brains into a persistent mould. Professor Seligman says: "Because our brain evolved during a time of ice, flood and famine, we have a catastrophic brain. The way the brain works is looking for what's wrong. The problem is, that worked in the Pleistocene era. It favoured you, but it doesn't work in the modern world."

E

Although most people rate themselves as happy, there is a wealth of evidence to show that negative thinking is deeply ingrained in the human psyche. Experiments show that we remember failures more vividly than s