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之前大家对揽瓜阁精读的反馈很好，就想着自己的时间开始把一些精读的文章根据JJ出题目～ 然后focus上线，IR需求 大家也大。就想着 把揽瓜阁的阅读 逻辑 IR 都放在这贴里打卡
每日的解析在揽瓜阁2024群更新
RC题源：揽瓜阁精读的文章+机经的题目
CR题源：本月中文JJ改编
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逻辑：写逻辑链分析
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【现在你的笔记越全，越能帮助你捋清思路，之后回顾总结。】
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1.CR
A local government conducted an online survey to gauge residents' opinions about a new art museum. All survey participants had visited the museum. The majority of respondents expressed dislike for the museum, leading to the conclusion that local art enthusiasts do not favor the museum.
Which of the following, if true, most weakens the argument that local art enthusiasts do not favor the museum?
A. People who regularly participate in online surveys tend to express negative opinions.
B. The online survey did not reach a significant portion of residents who are not active on the internet.
C. Art enthusiasts generally prefer traditional art over contemporary art displayed in the museum.
D. Most of the respondents were not familiar with the artists featured in the museum.
E. The survey was conducted shortly after the museum's highly publicized controversial exhibit.
A country implemented a policy to encourage companies to hire more employees. Under this policy, companies that reduce their weekly working hours without cutting employee salaries receive tax reductions. The goal is to enable companies to use the saved money to hire additional staff. However, the additional tax revenue generated by these new employees is significantly less than the reduction in taxes granted to the companies. Yet, it is argued that this policy will not result in a fiscal burden for the country. Which of the following, if true, best explains why the policy will not be a fiscal burden?
A. The new employees are likely to spend more, thus boosting the country's overall economic activity.
B. The reduction in working hours leads to increased productivity among existing employees.
C. The new employees were previously unemployed and claiming social welfare benefits, which they no longer receive.
D. The tax reductions are temporary and will be reassessed after a certain period.
E. The companies are likely to reinvest the tax savings into research and development, leading to future economic growth.
BC
2.DI
The exponential growth of artificial intelligence (AI) has catalyzed a paradigm shift across myriad industries, including healthcare, finance, and transportation. One of the most promising applications of AI lies in the realm of drug discovery, a traditionally time-consuming and capital-intensive endeavor that often spans years and necessitates billions of dollars in investment. The integration of AI algorithms, such as deep learning and natural language processing (NLP), has significantly augmented the efficiency and precision of the drug discovery process, heralding a new era of pharmaceutical innovation.
AI-powered drug discovery platforms harness vast repositories of biomedical data, encompassing genomic sequences, protein structures, and clinical trial results, to identify potential drug targets and predict their efficacy and safety profiles. By discerning patterns and correlations within these complex datasets, AI algorithms can generate novel hypotheses and prioritize the most promising drug candidates for further experimentation. This approach not only expedites the discovery timeline but also mitigates the risk of costly failures in later stages of development, thereby optimizing resource allocation and accelerating the delivery of life-saving therapies to patients.
Moreover, AI has unlocked the potential for drug repurposing, enabling the identification of new therapeutic indications for existing drugs. By leveraging sophisticated machine learning techniques, researchers can uncover previously unrecognized connections between drugs and diseases, opening up novel avenues for treatment. A recent study exemplifies this approach, wherein an AI-based platform was employed to screen a library of FDA-approved drugs against a specific type of cancer. The algorithm identified several compounds with potential anti-tumor activity, including a drug originally developed for the management of diabetes, underscoring the serendipitous nature of drug repurposing and the transformative power of AI in uncovering hidden drug-disease relationships.
However, the integration of AI in drug discovery is not without its challenges and ethical considerations. The quality and diversity of the training data used to develop AI models can profoundly impact their performance and introduce bias, necessitating rigorous data curation and validation processes. Ensuring the transparency and interpretability of AI-generated results is paramount for fostering trust among researchers and regulators, as the "black box" nature of some AI algorithms can hinder their acceptance and implementation. Furthermore, the ownership and privacy of patient data used in AI-driven drug discovery must be meticulously managed to safeguard individual rights and prevent misuse, as the sensitive nature of health information demands robust data governance frameworks.
Despite these challenges, the potential of AI to revolutionize drug discovery and accelerate the development of life-saving therapies cannot be understated. As AI technologies continue to mature and evolve, their impact on the pharmaceutical industry is poised to grow exponentially, transforming the landscape of drug development and ushering in a new era of precision medicine. Collaborations between AI experts, biomedical researchers, and pharmaceutical companies will be instrumental in harnessing the full potential of AI-powered drug discovery while navigating the complex ethical and regulatory landscape. By leveraging the synergies between human expertise and artificial intelligence, we can unlock previously unimaginable possibilities in drug discovery and development, ultimately benefiting millions of patients worldwide.
However, the path from AI-driven drug discovery to clinical application is fraught with significant hurdles. The regulatory frameworks governing drug development have not yet fully adapted to accommodate AI-driven approaches, necessitating the development of new guidelines and standards for evaluating and validating AI-generated drug candidates. Regulators must strike a delicate balance between fostering innovation and ensuring patient safety, as the novel nature of AI-based drug discovery raises unique concerns regarding the reproducibility and generalizability of results.
Furthermore, the integration of AI in drug discovery raises profound questions about the future of pharmaceutical research and the role of human scientists in the process. As AI algorithms become increasingly sophisticated and autonomous, there is a risk that they may supplant human expertise and intuition, leading to a potential erosion of the scientific workforce. It is crucial, therefore, to develop collaborative frameworks that leverage the strengths of both human and artificial intelligence, ensuring that the insights generated by AI are tempered by the contextual understanding and critical thinking skills of human researchers.
Moreover, the economic implications of AI-driven drug discovery cannot be overlooked. The development of AI algorithms and platforms requires significant upfront investments, which may exacerbate existing disparities in access to innovative therapies. There is a risk that AI-powered drug discovery may primarily benefit well -funded pharmaceutical companies and research institutions, potentially widening the gap between resource-rich and resource-poor settings. Ensuring equitable access to the fruits of AI-driven drug discovery will require concerted efforts from policymakers, industry leaders, and global health organizations to develop inclusive innovation models that prioritize public health outcomes over commercial interests.
Despite these challenges, the potential of AI to accelerate the discovery of life-saving therapies and democratize access to innovative treatments cannot be ignored. As we stand on the cusp of a new era in pharmaceutical research, it is imperative that we approach the integration of AI in drug discovery with a spirit of collaboration, transparency, and ethical responsibility. By fostering cross-disciplinary partnerships, investing in capacity building, and developing robust governance frameworks, we can harness the transformative power of AI to address the most pressing health challenges of our time and usher in a future of more precise, personalized, and accessible medicine for all.
In conclusion, the application of artificial intelligence in drug discovery represents a paradigm shift in pharmaceutical research, offering unprecedented opportunities to accelerate the development of life-saving therapies and revolutionize the way we approach disease treatment. However, realizing the full potential of AI-powered drug discovery will require a concerted effort to address the technical, ethical, and regulatory challenges that lie ahead. By fostering collaboration, transparency, and inclusivity, we can ensure that the benefits of AI-driven innovation are distributed equitably and that the insights generated by these powerful tools are leveraged for the greater good of humanity. As we embark on this transformative journey, let us remain committed to the core values of scientific inquiry, social responsibility, and patient-centered care, using the power of artificial intelligence to create a healthier, more resilient future for all.
Questions:
1. What is the primary advantage of using AI algorithms in the drug discovery process?
A. Reducing the cost of drug development
B. Eliminating the need for human researchers
C. Accelerating the discovery timeline and improving precision
D. Guaranteeing the safety and efficacy of drug candidates
2. How do AI-powered drug discovery platforms identify potential drug targets?
A. By conducting randomized clinical trials
B. By analyzing patterns and correlations within biomedical datasets
C. By relying solely on the intuition of human scientists
D. By testing drug candidates on animal models
3. What is the process of identifying new therapeutic indications for existing drugs called?
A. Drug repurposing
B. Drug screening
C. Drug validation
D. Drug optimization
4. According to the passage, what is a major challenge in integrating AI in drug discovery?
A. The lack of available biomedical data for training AI models
B. The high cost of developing AI algorithms and platforms
C. The potential bias and performance issues related to training data quality
D. The resistance from traditional pharmaceutical companies
5. What is crucial for fostering trust in AI-generated results among researchers and regulators?
A. Ensuring the profitability of AI-powered drug discovery
B. Obtaining approval from international regulatory bodies
C. Conducting extensive animal trials to validate the results
D. Ensuring the transparency and interpretability of the results
6. According to the passage, what must be meticulously managed to safeguard individual rights in AI-driven drug discovery?
A. The intellectual property rights of AI algorithms
B. The ownership and privacy of patient data
C. The distribution of profits from successful drug discoveries
D. The publication of research findings in peer-reviewed journals
7. What is a potential risk associated with the increasing sophistication and autonomy of AI algorithms in drug discovery?
A. The erosion of the scientific workforce and the replacement of human expertise
B. The increased likelihood of drug candidates failing in clinical trials
C. The exacerbation of existing health disparities between developed and developing countries
D. The diversion of research funding from basic science to AI-focused projects
8. According to the passage, what is necessary to ensure equitable access to the benefits of AI-driven drug discovery?
A. Prioritizing commercial interests over public health outcomes
B. Restricting the use of AI to well-funded pharmaceutical companies
C. Developing inclusive innovation models that prioritize public health outcomes
D. Limiting the sharing of AI-generated insights among research institutions
CBACDBAC
3.RC
I examined the effects of predation risk on the behaviour and population dynamics of snowshoe hares (Lepus americanus) during a cyclic peak and decline (1989-1993) near Kluane Lake, Yukon. Like most heavily preyed upon animals, snowshoe hares have to balance conflicting demands of obtaining food at a high rate and avoiding predators. The consequences of adopting predator avoidance behaviours under high risk of predation in winter may influence population dynamics of hares.
Changes in patterns of winter habitat use, survival, body mass, and female reproduction were compared on four experimental areas: (i) where predation risk was reduced by excluding-out terrestrial predators (FENCE), (ii) where food supply was supplemented with ad lib rabbit chow (FOOD), (iii) a combination of these two treatments (FENCE+FOOD), and (iv) an unmanipulated CONTROL. Three hypotheses were compared. The food hypothesis predicts that hares use habitats with the highest amounts of food: body mass remains high, but survival is reduced. The predator avoidance hypothesis predicts that hares use habitats with the lowest risk: survival is high, but body mass decreases. The predation-sensitive foraging (PSF) hypothesis predicts that both survival and body mass decline because a trade-off exists between predation risk and food availability. At peak densities hares used open habitats where food was readily available. However, as predation risk increased during the population decline, hares increased their use of safer, closed habitat and shifted their diet to include a greater proportion of poorer quality spruce twigs. This change in behaviour resulted in lower female body mass and reduced fecundity on the CONTROL area, even though sufficient winter forage was available. A similar decrease in body mass was observed on the FOOD treatment during the third year of the population decline. On FENCE+FOOD, female body mass and fecundity remained high during the decline. Similarly, body mass did not decline on the FENCE treatment. These results supported the PSF hypothesis where terrestrial predators were present (CONTROL and FOOD), and the food hypothesis where terrestrial predators were absent (FENCE and FENCE+FOOD). Hares appear to have a limited ability to reduce exposure to predators because they have no absolutely safe refuge from predators, and they have limited reserves of energy during winter. Preliminary evidence suggests that physiological stress associated with high risk and poor condition is elevated during the population decline. I suggest that deleterious maternal effects mediated by predation risk may introduce a lag of one generation into the 10- year population cycle of snowshoe hares.
1. The author's findings regarding the relationship between predation risk, habitat use, and diet selection in snowshoe hares most strongly support which of the following conclusions?
(A) The impact of predation risk on snowshoe hare behavior is modulated by the availability of alternative food sources and refuge habitats.
(B) The trade-off between predation risk and food availability in snowshoe hares is driven primarily by the energetic costs of predator avoidance behaviors.
(C) The population dynamics of snowshoe hares are more strongly influenced by top-down effects of predation than bottom-up effects of food availability.
(D) The ability of snowshoe hares to adapt to changes in predation risk is constrained by their physiological tolerance limits and evolutionary history.
(E) The cyclical nature of snowshoe hare population dynamics is a direct consequence of the time lag introduced by maternal effects.
2. Which of the following inferences about the role of physiological stress in the population dynamics of snowshoe hares is best supported by the passage?
(A) Physiological stress is a direct cause of the 10-year population cycle in snowshoe hares, independent of predation risk and food availability.
(B) Physiological stress mediates the relationship between predation risk and maternal effects, but has no direct influence on survival or reproduction.
(C) Physiological stress amplifies the negative effects of predation risk and poor body condition on survival and reproduction, particularly during the population decline.
(D) Physiological stress is a consequence, rather than a cause, of the changes in behavior and body condition associated with high predation risk.
(E) The passage provides insufficient evidence to determine the role of physiological stress in the population dynamics of snowshoe hares.
3. The results of the FENCE and FENCE+FOOD treatments, compared to the CONTROL and FOOD treatments, provide the strongest support for which of the following hypotheses?
(A) Predation risk has a stronger influence on snowshoe hare behavior and population dynamics than food availability.
(B) The effects of predation risk and food availability on snowshoe hare behavior and population dynamics are additive rather than interactive.
(C) The impact of predation risk on snowshoe hare behavior and population dynamics is contingent on the availability of safe refuges.
(D) The trade-off between predation risk and food availability in snowshoe hares is mediated by the energetic costs of predator avoidance behaviors.
(E) The cyclical nature of snowshoe hare population dynamics is driven by the alternating effects of predation risk and food availability.
4. The passage suggests that the limited ability of snowshoe hares to reduce exposure to predators is a consequence of which of the following?
(A) The lack of absolutely safe refuges from predators and the limited energy reserves of hares during winter
(B) The high energetic costs of predator avoidance behaviors and the low nutritional value of alternative food sources
(C) The evolutionary arms race between snowshoe hares and their predators, resulting in increasingly sophisticated predator detection and avoidance strategies
(D) The physiological stress response of snowshoe hares to high predation risk, which impairs their ability to assess and respond to predator cues
(E) The social structure of snowshoe hare populations, which limits the ability of individuals to coordinate their predator avoidance behaviors
5. Based on the information provided in the passage, which of the following predictions about the long-term population dynamics of snowshoe hares is most likely to be supported by further research?
(A) The 10-year population cycle will be dampened or eliminated if predation risk is consistently low across multiple generations.
(B) The amplitude of the 10-year population cycle will increase if food availability becomes more variable due to climate change.
(C) The period of the 10-year population cycle will shorten if the strength of maternal effects is reduced through genetic adaptation.
(D) The 10-year population cycle will be replaced by a longer-period cycle if the density of terrestrial predators decreases.
(E) The population dynamics of snowshoe hares will become less predictable if the trade-off between predation risk and food availability weakens.
6. The passage provides the most direct support for which of the following claims about the relationship between habitat use and predation risk in snowshoe hares?
(A) Snowshoe hares prefer closed habitats regardless of predation risk, but are forced into open habitats during the population peak due to high intraspecific competition.
(B) Snowshoe hares' use of open habitats decreases as predation risk increases, even if this results in reduced access to high-quality forage.
(C) Snowshoe hares' habitat use is determined solely by the availability of high-quality forage, with no influence of predation risk.
(D) Snowshoe hares' use of closed habitats increases as predation risk decreases, allowing them to exploit a wider range of food sources.
(E) Snowshoe hares' habitat use is random with respect to predation risk and food availability, as they lack the ability to assess and respond to these factors.
7. Which of the following statements best summarizes the main conclusion of the passage regarding the role of predation risk in the population dynamics of snowshoe hares?
(A) Predation risk has no significant impact on the population dynamics of snowshoe hares, which are primarily driven by food availability and other bottom-up factors.
(B) Predation risk directly determines the population dynamics of snowshoe hares, with high risk leading to population declines and low risk leading to population increases.
(C) Predation risk influences the population dynamics of snowshoe hares through its effects on behavior, body condition, and reproduction, particularly during the population decline.
(D) Predation risk and food availability have equal and opposite effects on the population dynamics of snowshoe hares, resulting in a stable equilibrium population size.
(E) The effects of predation risk on the population dynamics of snowshoe hares are unpredictable and vary significantly between different populations and regions.
8. The author's suggestion that "deleterious maternal effects mediated by predation risk may introduce a lag of one generation into the 10-year population cycle of snowshoe hares" is best interpreted as:
(A) A hypothesis that has been conclusively supported by the results of the study
(B) A speculative explanation for an observed pattern that requires further investigation
(C) A well-established theory that has been repeatedly confirmed by previous research
(D) An implausible suggestion that contradicts the main findings of the study
(E) A trivial detail that has no bearing on the overall conclusions of the passage
accaabcb
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发表于 2024-5-27 10:20:07
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