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関連する概念動画

Improving Translational Accuracy02:07

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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
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Language Development01:22

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Children master language quickly and with relative ease, supported by both biological predisposition and reinforcement. B. F. Skinner (1957) proposed that language is learned through reinforcement, while Noam Chomsky (1965) argued that language acquisition mechanisms are biologically determined.
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Scaling01:26

Scaling

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In designing and analyzing filters, resonant circuits, or circuit analysis at large, working with standard element values like 1 ohm, 1 henry, or 1 farad can be convenient before scaling these values to more realistic figures. This approach is widely utilized by not employing realistic element values in numerous examples and problems; it simplifies mastering circuit analysis through convenient component values. The complexity of calculations is thereby reduced, with the understanding that...
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Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.
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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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大規模な言語モデルを使用して,スケーラブルな科学的関心プロファイリング

Yilun Liang1,2, Gongbo Zhang1, Edward Sun3

  • 1Department of Biomedical Informatics, Columbia University, New York, NY, USA.

ArXiv
|September 2, 2025
PubMed
まとめ
この要約は機械生成です。

大型言語モデル (LLM) は科学的な興味のプロファイリングを自動化できます. メディカル・オブジェクト・ヘッディング (MeSH) 用語を使用して生成されたプロフィールは,人間で書かれた要約との違いにもかかわらず,抽象ベースのプロフィールよりも優れた読みやすさを示しました.

キーワード:
クールバック-ライブラー分岐大規模な言語モデル自然言語の生成研究者のプロフィール

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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
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科学分野:

  • バイオメディカル・インフォマティック
  • 研究における人工知能
  • 科学的コミュニケーション

背景:

  • 科学者の研究プロフィールは 人材発見と協働に不可欠ですが 時代遅れです
  • 自動化されたスケーラブルな方法が,現在の研究プロフィールを維持するために必要です.

研究 の 目的:

  • 科学的関心プロフィールを生成するための大型言語モデル (LLM) ベースの方法の設計と評価.
  • 機械で生成されたプロフィール (PubMedの摘要とMeSH用語から) と,研究者の自己要約した関心事とを比較する.

主な方法:

  • 595人の教員の研究興味をPubMedの概要とMeSHの用語に基づいてまとめました.
  • CUIMCの教員からの出版データ (タイトル,MeSH用語,概要) を収集した.
  • マシンで作成したプロフィールと,自分で作成したプロフィールを比較するために,手動と自動化された評価を行いました.

主要な成果:

  • 機械で生成されたプロフィールと自己記述されたプロフィール (低いROUGE-L,BLEU,METEORスコア) の間の語彙的重複は低かった.
  • BERTScore (F1: MeSHベースの0. 542,抽象ベースの0. 555) を用いて中程度の意味論的類似性が見つかりました.
  • 手動のレビューでは,読みやすさ (93.44%) と全体的な印象 (77.78% good/excellent) について,MeSHベースのプロファイル (67.86%) を好みました.

結論:

  • LLMは科学的なプロファイルの自動化のためのスケーラブルなソリューションを提供します.
  • MeSHから派生したプロフィールは,抽象的に派生したプロフィールと比較して優れた読みやすさとユーザー好みを示しています.
  • 機械で作成されたプロフィールは,人工プロフィールで新しいアイデアを生み出す可能性を強調し,概念の選択によって異なります.