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Questões de Concurso Público Prefeitura de Ubatuba - SP 2026 para Professor da Educação Básica II - Inglês

Questões Discursivas

Foram encontradas 20 questões

Q4247243 Inglês
Read the text to answer question.


Using Machine Learning to Develop Personalized Vaccines for Cancer


Yale researchers have developed a machine learning model, called Immunostruct, that can help scientists create more personalized vaccines, including vaccines for cancer. They described the tool in Nature Machine Intelligence along with findings from applying it to cancer and immunology data.

    When a potential threat, such as a virus or tumor, arises in our body, our immune cells recognize peptides---essentially short proteins---on the surface of the invader and mount a defensive response. This small region that the immune system interacts with is known as an epitope. 

    Epitope-based vaccines are an emerging technology that contain specific peptides in order to trigger immune responses that precisely target particular diseases. Ongoing studies show that these vaccines are a promising potential immunotherapy for a range of cancers including melanomas, breast cancers, and glioblastomas. Researchers are also investigating whether these vaccines could more effectively combat new variants of infectious diseases.

    To develop these vaccines, scientists can use models that help them predict which peptides are most likely to trigger a strong immune response to a particular antigen. A limitation of many of these models, the researchers say, is that they treat peptides as a one-dimensional sequence of amino acids, not the three-dimensional, active structures that they are.

    Now, Yale researchers have created a model that also incorporates structural and biochemical properties of peptides. In the new study, they show that the multimodal model is more effective at identifying peptide candidates than its predecessors.

    "Cancer is extremely heterogeneous---which often makes it very hard to treat effectively," says Kevin B. Givechian, PhD, an MD-PhD student at Yale and co-first author on the study. “We have built a deep-learning model that integrates more information than had previously been combined to help us improve the identification of vaccine targets that stimulate people's immune system against their own tumor. Doing so would enable a more effective and less toxic method of treatment."


ВАCKMAN, Isabella. Using Machine Learning to Develop Personalized Vaccines for Cancer. Yale School of Medicine, 24 fev. 2026. Acesso em: 28 june. 2026.
According to the text, the main purpose of Immunostruct is to help scientists:
Alternativas
Q4247244 Não definido
Read the text to answer question.


Using Machine Learning to Develop Personalized Vaccines for Cancer


Yale researchers have developed a machine learning model, called Immunostruct, that can help scientists create more personalized vaccines, including vaccines for cancer. They described the tool in Nature Machine Intelligence along with findings from applying it to cancer and immunology data.

    When a potential threat, such as a virus or tumor, arises in our body, our immune cells recognize peptides---essentially short proteins---on the surface of the invader and mount a defensive response. This small region that the immune system interacts with is known as an epitope. 

    Epitope-based vaccines are an emerging technology that contain specific peptides in order to trigger immune responses that precisely target particular diseases. Ongoing studies show that these vaccines are a promising potential immunotherapy for a range of cancers including melanomas, breast cancers, and glioblastomas. Researchers are also investigating whether these vaccines could more effectively combat new variants of infectious diseases.

    To develop these vaccines, scientists can use models that help them predict which peptides are most likely to trigger a strong immune response to a particular antigen. A limitation of many of these models, the researchers say, is that they treat peptides as a one-dimensional sequence of amino acids, not the three-dimensional, active structures that they are.

    Now, Yale researchers have created a model that also incorporates structural and biochemical properties of peptides. In the new study, they show that the multimodal model is more effective at identifying peptide candidates than its predecessors.

    "Cancer is extremely heterogeneous---which often makes it very hard to treat effectively," says Kevin B. Givechian, PhD, an MD-PhD student at Yale and co-first author on the study. “We have built a deep-learning model that integrates more information than had previously been combined to help us improve the identification of vaccine targets that stimulate people's immune system against their own tumor. Doing so would enable a more effective and less toxic method of treatment."


ВАCKMAN, Isabella. Using Machine Learning to Develop Personalized Vaccines for Cancer. Yale School of Medicine, 24 fev. 2026. Acesso em: 28 june. 2026.
In the excerpt "our immune cells recognize peptides... and mount a defensive response," the word "mount" could be replaced, without changing its meaning, by:
Alternativas
Q4247245 Inglês
Read the text to answer question.


Using Machine Learning to Develop Personalized Vaccines for Cancer


Yale researchers have developed a machine learning model, called Immunostruct, that can help scientists create more personalized vaccines, including vaccines for cancer. They described the tool in Nature Machine Intelligence along with findings from applying it to cancer and immunology data.

    When a potential threat, such as a virus or tumor, arises in our body, our immune cells recognize peptides---essentially short proteins---on the surface of the invader and mount a defensive response. This small region that the immune system interacts with is known as an epitope. 

    Epitope-based vaccines are an emerging technology that contain specific peptides in order to trigger immune responses that precisely target particular diseases. Ongoing studies show that these vaccines are a promising potential immunotherapy for a range of cancers including melanomas, breast cancers, and glioblastomas. Researchers are also investigating whether these vaccines could more effectively combat new variants of infectious diseases.

    To develop these vaccines, scientists can use models that help them predict which peptides are most likely to trigger a strong immune response to a particular antigen. A limitation of many of these models, the researchers say, is that they treat peptides as a one-dimensional sequence of amino acids, not the three-dimensional, active structures that they are.

    Now, Yale researchers have created a model that also incorporates structural and biochemical properties of peptides. In the new study, they show that the multimodal model is more effective at identifying peptide candidates than its predecessors.

    "Cancer is extremely heterogeneous---which often makes it very hard to treat effectively," says Kevin B. Givechian, PhD, an MD-PhD student at Yale and co-first author on the study. “We have built a deep-learning model that integrates more information than had previously been combined to help us improve the identification of vaccine targets that stimulate people's immune system against their own tumor. Doing so would enable a more effective and less toxic method of treatment."


ВАCKMAN, Isabella. Using Machine Learning to Develop Personalized Vaccines for Cancer. Yale School of Medicine, 24 fev. 2026. Acesso em: 28 june. 2026.
In the sentence "Cancer is extremely heterogeneous," the adjective heterogeneous suggests that cancer is:
Alternativas
Q4247246 Não definido
Read the text to answer question.


Using Machine Learning to Develop Personalized Vaccines for Cancer


Yale researchers have developed a machine learning model, called Immunostruct, that can help scientists create more personalized vaccines, including vaccines for cancer. They described the tool in Nature Machine Intelligence along with findings from applying it to cancer and immunology data.

    When a potential threat, such as a virus or tumor, arises in our body, our immune cells recognize peptides---essentially short proteins---on the surface of the invader and mount a defensive response. This small region that the immune system interacts with is known as an epitope. 

    Epitope-based vaccines are an emerging technology that contain specific peptides in order to trigger immune responses that precisely target particular diseases. Ongoing studies show that these vaccines are a promising potential immunotherapy for a range of cancers including melanomas, breast cancers, and glioblastomas. Researchers are also investigating whether these vaccines could more effectively combat new variants of infectious diseases.

    To develop these vaccines, scientists can use models that help them predict which peptides are most likely to trigger a strong immune response to a particular antigen. A limitation of many of these models, the researchers say, is that they treat peptides as a one-dimensional sequence of amino acids, not the three-dimensional, active structures that they are.

    Now, Yale researchers have created a model that also incorporates structural and biochemical properties of peptides. In the new study, they show that the multimodal model is more effective at identifying peptide candidates than its predecessors.

    "Cancer is extremely heterogeneous---which often makes it very hard to treat effectively," says Kevin B. Givechian, PhD, an MD-PhD student at Yale and co-first author on the study. “We have built a deep-learning model that integrates more information than had previously been combined to help us improve the identification of vaccine targets that stimulate people's immune system against their own tumor. Doing so would enable a more effective and less toxic method of treatment."


ВАCKMAN, Isabella. Using Machine Learning to Develop Personalized Vaccines for Cancer. Yale School of Medicine, 24 fev. 2026. Acesso em: 28 june. 2026.
The verb tense in "Yale researchers have developed a machine learning model" is used to indicate that:
Alternativas
Q4247247 Inglês
Read the text to answer question.


Using Machine Learning to Develop Personalized Vaccines for Cancer


Yale researchers have developed a machine learning model, called Immunostruct, that can help scientists create more personalized vaccines, including vaccines for cancer. They described the tool in Nature Machine Intelligence along with findings from applying it to cancer and immunology data.

    When a potential threat, such as a virus or tumor, arises in our body, our immune cells recognize peptides---essentially short proteins---on the surface of the invader and mount a defensive response. This small region that the immune system interacts with is known as an epitope. 

    Epitope-based vaccines are an emerging technology that contain specific peptides in order to trigger immune responses that precisely target particular diseases. Ongoing studies show that these vaccines are a promising potential immunotherapy for a range of cancers including melanomas, breast cancers, and glioblastomas. Researchers are also investigating whether these vaccines could more effectively combat new variants of infectious diseases.

    To develop these vaccines, scientists can use models that help them predict which peptides are most likely to trigger a strong immune response to a particular antigen. A limitation of many of these models, the researchers say, is that they treat peptides as a one-dimensional sequence of amino acids, not the three-dimensional, active structures that they are.

    Now, Yale researchers have created a model that also incorporates structural and biochemical properties of peptides. In the new study, they show that the multimodal model is more effective at identifying peptide candidates than its predecessors.

    "Cancer is extremely heterogeneous---which often makes it very hard to treat effectively," says Kevin B. Givechian, PhD, an MD-PhD student at Yale and co-first author on the study. “We have built a deep-learning model that integrates more information than had previously been combined to help us improve the identification of vaccine targets that stimulate people's immune system against their own tumor. Doing so would enable a more effective and less toxic method of treatment."


ВАCKMAN, Isabella. Using Machine Learning to Develop Personalized Vaccines for Cancer. Yale School of Medicine, 24 fev. 2026. Acesso em: 28 june. 2026.
Read the excerpt: "these vaccines could more effectively combat new variants of infectious diseases."

The modal verb "could" in this context primarily expresses:
Alternativas
Q4247249 Inglês
Choose the option that completes the sentence with the correct particle to form the appropriate phrasal verb.

"Despite the initial tension between the two departments, the new employees managed to get ______ well with everyone during their first week."
Alternativas
Q4247250 Inglês
Choose the option that completes the sentence with the correct preposition of time.

"The annual shareholders' meeting is always held _______ the morning, so that international participants can join before their working day ends."
Alternativas
Q4247251 Não definido
Choose the option that completes the sentence with the correct article.

"During the selection process, the applicant proved to be _______ honest and competent professional."
Alternativas
Q4247255 Inglês
Choose the option that completes the sentence with the correct negative prefix.

"The candidate's argument was completely _________ relevant to the topic under discussion, so the committee decided to ignore it."
Alternativas
Q4247256 Inglês
Choose the option that correctly completes the sentence with the appropriate words, in the order they appear.

"Our department will (1) the additional equipment, (2) the finance team approves the budget and the supplier sends the full (3) by the end of the week." 
Alternativas
Q4247257 Inglês
Mark the alternative in which the conditional sentence is grammatically correct.
Alternativas
Q4247258 Inglês
In the passive voice, the focus shifts from who performs the action to what receives it. To transform an active sentence into a passive one, the object of the active sentence becomes the subject; the verb is formed with the appropriate tense of the verb "to be" + the past participle of the main verb; and the original subject (the agent) may be introduced by "by," when relevant. The verb tense of the active sentence must be preserved in the passive construction.

Consider the following sentence in the active voice:

"The manager approved the new project last week."

Mark the alternative that correctly transforms the sentence above into the passive voice.
Alternativas
Q4247259 Inglês
In a genre-based approach to English teaching, the key link established in the learning process is between language learning and:
Alternativas
Q4247260 Inglês
Match each sentence in Column I to its conditional type in Column II. Then choose the alternative with the correct sequence.

Column I
1 - If you don't water plants, they die.
2 - If I win the lottery, I will buy a house.
3 - If I had a car, I would drive to work.
4 - If we had booked earlier, we would have paid less. 

Column II
( ) First conditional
( ) Third conditional
( ) Zero conditional
( ) Second conditional
Alternativas
Q4247261 Inglês
Mark the alternative in which the highlighted word shows a pronunciation pattern different from the others regarding the sound of the vowel(s).
Alternativas
Q4247262 Inglês
Mark the alternative in which the "-ed" ending of the highlighted word is pronounced as /1d/.
Alternativas
Q4247264 Inglês
The sentence must report a completed action that occurred at a definite time in the past. Choose the correct alternative.
Alternativas
Q4247265 Inglês
Q48.png (541×144)
Available at: https://www.gocomics.com/garfield/2026/06/23
In the last panel, the cat says the man is funny, "but sadly, not on purpose." In this context, the expression "not on purpose" suggests that the man's comic effect is:
Alternativas
Q4247266 Inglês
Q49.png (422×273)
Observe the image, which shows the three tallest buildings in the world, and mark the alternative that uses comparatives and superlatives correctly, according to the information shown in the image.
Alternativas
Q4247267 Inglês

Q50.png (561×200)

Available at: https://www.gocomics.com/calvinandhobbes/2026/07/01


In the sentence "Think of something else," the word else means:

Alternativas
Respostas
1: D
2: B
3: B
4: E
5: E
6: B
7: C
8: B
9: D
10: A
11: C
12: E
13: A
14: A
15: D
16: C
17: C
18: C
19: D
20: C