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TOEIC・英語 大学生・専門学校生・社会人

このプリントの穴埋めをして英文和英しなさいという問題です。助けてください

英語2A レポート課題(2026年前期) 以下の英文中の( 内に入れるのに適切と思われる1語を、 下の 入れなさい。 そのうえで全文を和訳しなさい。 の中から選んで ite of national diger Most funny stories are based on comic situations. In spite of national differences, certain funny situations have a ( 1 ) appeal. No matter ( 2 ) you live, you would find (3) difficult not to laugh at, say, Charlie Chaplin's early films. However, a new type of humor, called 'sick humor', has come into fashion. The following example of 'sick humor' will enable you to judge for yourself. A man ( 4 ) had broken his right leg was taken to a hospital a few days before Christmas. From the moment he arrived there, he kept on annoying his doctor to tell him ( 5 ) he would be able to go home. He felt afraid ( 6 ) having to spend Christmas in the hospital. On Christmas Day, the man still had his right leg in plaster. He spent a miserable day in bed thinking of all the ( 7 ) he was missing. The following day, however, the doctor consoled him by telling that his chances of being able to leave the hospital ( 8 ) time for New Year Celebrations were ( 9 ). The man took heart and, sure enough, on New Year's Eve he managed to walk along to a party. To ( 10 ) for his unpleasant experiences in the hospital, the man drank a little more than was good for him. He was still grumbling about hospitals at the end of the party when he slipped on a piece of ice and broke his left leg. blame compensate money yourself where of in at by with fun good whose who it when special universal

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TOEIC・英語 大学生・専門学校生・社会人

この長文問題の答えと解説をお願いします。

15 語数: 398 語 出題校 法政大 5 We are already aware that our every move online is tracked and analyzed. But you 2-53 couldn't have known how much Facebook can learn about you from the smallest of social interactions - a 'like'*. (1) Researchers from the University of Cambridge designed (2) a simple machine-learning 2-54 system to predict Facebook users' personal information based solely on which pages they had liked. E "We were completely surprised by the accuracy of the predictions," says Michael 2-55 Kosinski, lead researcher of the project. Kosinski and colleagues built the system by scanning likes for a sample of 58,000 volunteers, and matching them up with other 10 profile details such as age, gender, and relationship status. They also matched up those likes with the results of personality and intelligence tests the volunteers had taken. The team then used their model to make predictions about other volunteers, based solely on their likes. The system can distinguish between the profiles of black and white Facebook users, 15 getting it right 95 percent of the time. It was also 90 percent accurate in separating males and females, Democrats and Republicans. Personality traits like openness and intelligence were also estimated based on likes, and were as accurate in some areas as a standard personality test designed for the task. Mixing what a user likes with many kinds of other data from their real-life activities could improve these predictions even more. 20 Voting records, utility bills and marriage records are already being added to Facebook's database, where they are easier to analyze. Facebook recently partnered with offline data companies, which all collect this kind of information. This move will allow even deeper insights into the behavior of the web users. 25 30 (3) - Sarah Downey, a lawyer and analyst with a privacy technology company, foresees insurers using the information gained by Facebook to help them identify risky customers, and perhaps charge them with higher fees. But there are potential benefits for users, too. Kosinski suggests that Facebook could end up as an online locker for your personal information, releasing your profiles at your command to help you with career planning. Downey says the research is the first solid example of the kinds of insights that can be made through Facebook. "This study is a great example of how the little things you do online show so much about you,” she says. "You might not remember liking things, " but Facebook remembers and (4) it all adds up.", * a 'like': フェイスブック上で個人の好みを表示する機能。 日本語版のフェイスブックでは「いいね!」 と表記される。 2-56 2-57 2-58 36

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看護 大学生・専門学校生・社会人

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5) 細菌の染色所見で正しい組合せは 肺炎球菌 - ロ 髄膜炎菌 グラム染色陽性球菌 グラム染色陰性 らせん菌 グラム染色陽性・球菌 結核菌 チールネルゼン染色陰性桿菌 ボツリヌス菌 グラム染色陰性桿菌 - 大腸菌 チールネルゼン染色・ 陰性桿菌 インフルエンザ菌 グラム染色陽性・桿菌 マイコプラズマ グラム染色陰性球菌 - 6) 滅菌や消毒について正しいものは 乾熱滅菌と湿熱滅菌では、前者の方が滅菌力は強い 毒素を産生する細菌は、熱に対して強い 外膜を持つウイルスは消毒用アルコールに抵抗性である CIM ***INS OUT - 煮沸法では、すべての微生物を殺菌することが出来る - オートクレーブは高圧蒸気滅菌法とも呼ばれ、 2気圧・160℃ 30分の処理内容である - 生体に用いることの出来る消毒薬として、 消毒用アルコールやポビドンヨードがある 緑膿菌などバイオフィルムを形成する菌は、消毒薬に抵抗性を示す I 紫外線滅菌では、対象物の内部にまで深く紫外線が到達できるので、殺菌力が強い 7) 抗体について正しいものは 感染後、 最初に1g Aが産生され、次いで1g D, IgE IgG、IgM の順番に産生される * 感染症が治癒した後でも、IgG抗体は持続して高い値を示す 胎盤を介して母体から胎児へ移行する抗体は、IgA抗体である 抗体の役割は、ウイルスや毒素と結合して、それらを中和したり不活化する 不顕性感染では、 抗体が産生されることはない 予防接種の目的は、人為的に抗体を接種することである - 抗体は、特定の抗原に対してのみ反応する 新たな感染ではIgM抗体の測定が診断 (病原体の推測)に有用である 8) 髄膜炎について正しいものは - 細菌性髄膜炎では、髄液中の糖が増加する 天 ( ( 新生児期の髄膜炎の起因菌として、B群レンサ球菌やリステリア菌がある 髄膜炎の三主徴は 「発熱、頭痛、嘔吐」 であり、 それに意識障害などが加わりやすい 乳幼児では、髄膜刺激症状はあまりあきらかではない 髄膜炎を疑う症例では、速やかに抗菌薬の投与を始めることが重要である 頸部硬直が見られなければ、髄膜炎は否定してもよい 幼児では「首が後ろに垂れている、触ると泣く、弱々しい泣き声、 痙臓、哺乳不良」などでも疑う 腰椎穿刺による髄液採取が診断には不可欠であるが、脳ヘルニアを疑う所見が見られる場合は禁忌である

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