영어 모의고사 (자동 편성)

Bacteria have been cutting up invading viral DNA for billions of years, long before any scientist ①thought to borrow the trick for use in a laboratory. The CRISPR-Cas9 system, as it is now known, relies on a short guide RNA that matches a specific DNA sequence, ②lead a scissor-like enzyme called Cas9 directly to that exact location in the genome. Once Cas9 arrives at the target site, it slices both strands of the DNA helix, and the cell's own repair machinery rushes in ③to mend the cut, often introducing a small change in the process. By supplying the cell with a template during this repair step, scientists can direct that machinery ④to insert a specific new sequence rather than merely disabling the original gene. What once required years of painstaking work to alter a single gene can now, with a properly designed guide RNA, ⑤be accomplished within days, a shift in speed that has reshaped nearly every corner of biological research.

Many people struggling to fall asleep in noisy environments turn to white noise machines, devices that ①emit a steady, uniform sound covering a broad range of frequencies. The appeal lies in a phenomenon called auditory masking, in which a constant background sound makes sudden noises ②less noticeable to the sleeping brain. Without this masking effect, an abrupt change in sound is far more likely ③to trigger a brief awakening, even if the sleeper does not consciously remember it the next morning. Studies comparing sleep in quiet rooms with sleep alongside white noise have found that participants using white noise ④experienced fewer of these brief awakenings. However, researchers caution that the benefit depends heavily on volume and consistency, since noise that is too loud or that ⑤varying unpredictably can itself become a source of disruption rather than a remedy.

Turning on automatic captions used to mean settling for garbled text full of obvious errors, but recent speech-recognition models have made same-language captions ①accurate enough that many viewers now leave them on by default, even when watching content in their native language. The technology listens to audio, breaks it into individual sounds, and matches those sounds against a massive library of words and phrases ②to predict what was actually said. For viewers who are deaf or hard of hearing, this improvement has quietly ③transformed everyday access to video content that once required a human transcriber. The system still stumbles over overlapping speakers, strong accents, and specialized vocabulary, ④occasionally producing captions that are unintentionally funny. Even with these flaws, most users agree that imperfect captions ⑤is far more useful than no captions at all.

Across many countries, high schools ①begins classes early in the morning, often before eight o'clock. This schedule assumes that teenagers can simply go to bed earlier to compensate, but biology tells a different story. During adolescence, the body's internal clock shifts later, ②delaying the release of melatonin until well after ten at night. As a result, most teenagers are not physiologically ready to fall asleep until much later than younger children or adults. When early start times force them ③to wake before their bodies have completed a full sleep cycle, the mismatch accumulates into a chronic sleep debt ④that affects concentration and mood throughout the school day. Researchers who study this mismatch argue that the underlying problem is not laziness or poor discipline among students, but a scheduling system ⑤built around adult rhythms rather than adolescent ones.

A smart thermostat that learns a family's daily rhythm can lower the temperature automatically before everyone wakes up cold, while smart lights that follow a resident's schedule no longer need to be switched on and off by hand. These small ①conveniences rest on a shared foundation: sensors collect data about temperature, motion, and light throughout the home, and an algorithm gradually learns which settings match each household's actual habits rather than a factory default. Over months of use, the system becomes noticeably ②better at anticipating needs, often adjusting settings moments before a resident would have reached for a switch. Yet convenience comes with a trade-off that many households ③overlook. Every one of these small adjustments depends on constant data collection, meaning that a detailed record of daily habits is quietly being ④stored somewhere ⑤beyond their immediate control, giving residents full and constant access to exactly how it is used.

Sleep is not a single, uniform state but a cycle that alternates between two very different modes: REM (rapid eye movement) sleep and non-REM sleep. During non-REM sleep, especially its deepest stage, the brain waves slow down dramatically, the body temperature drops, and tissue repair and growth hormone release reach their peak. This is the period most closely associated with physical restoration. REM sleep, by contrast, brings rapid eye movements, near-total muscle paralysis, and brain activity that closely resembles wakefulness. Most vivid dreaming occurs during this stage, and researchers believe _______________. A healthy night typically cycles through both stages four or five times, with non-REM dominating the early hours and REM periods lengthening toward morning. Disrupting either stage, through frequent awakenings or alcohol use, can leave a person feeling unrested even after a full night in bed.

Wearable devices that track sleep have become enormously popular, promising a detailed breakdown of light, deep, and REM sleep each night. These gadgets typically rely on movement sensors and heart rate monitors to estimate sleep stages, since directly measuring brain activity would require equipment found only in a sleep laboratory. This indirect approach, however, introduces significant uncertainty. Independent studies comparing wearable data with polysomnography, the gold-standard method used in clinical sleep labs, have found that _______________. Some trackers overestimate deep sleep, while others underestimate the amount of time spent in REM sleep, and the margin of error can vary widely between brands. Despite these limitations, many users have come to treat the nightly score generated by their device as an objective medical verdict, sometimes experiencing anxiety when the number falls below what they expect, a phenomenon researchers have begun calling orthosomnia.

The rise of big data has introduced ethical dilemmas that earlier generations of workers never had to consider. (A) Some believe that strict government regulations are necessary, even if such rules slow down innovation and raise costs for businesses. (B) Others argue that companies can regulate themselves effectively through clear ethical training, without needing heavy-handed government intervention. (C) A single careless decision, after all, can expose the private information of millions of people almost instantly.

Insomnia is rarely caused by a single factor; it usually emerges from an interaction between psychological stress and physiological arousal that keeps the body from settling into sleep. (A) At the same time, the body's stress response system can become chronically activated, releasing hormones such as cortisol that keep heart rate and body temperature elevated at night, both of which interfere with the natural drop in arousal needed to fall asleep. (B) Poor sleep habits, including irregular bedtimes and excessive screen use before bed, can further weaken the body's internal signals for sleep onset. Because these psychological and physiological factors reinforce one another, effective treatment for chronic insomnia addresses both the racing mind and the underlying pattern of physical tension, rather than relying on medication alone. (C) A common trigger is what researchers call cognitive hyperarousal, in which the mind continues to race with worries about work, relationships, or even the inability to sleep itself, creating a frustrating cycle where anxiety about insomnia becomes a cause of insomnia.

Unlike a static list of best-sellers, this assistant can adjust its suggestions on the spot, responding to follow-up questions the way a knowledgeable salesperson might in a physical store. Browsing an online store today often means chatting with an AI assistant that asks a few quick questions, such as budget or intended use, before suggesting a short list of products tailored to the answers. ( ① ) Retailers report that shoppers who use these chatbots tend to complete purchases more often and return fewer items, since the guided conversation helps buyers choose products that actually fit their needs. ( ② ) Some shoppers, however, remain wary of how personal the questions can feel. ( ③ ) They wonder how much of that conversation is quietly stored and reused to influence future purchases. ( ④ ) Consumer advocates argue that stores should clearly explain what happens to this data, rather than burying the explanation in a lengthy privacy policy that almost no one reads. ( ⑤ )