Jove
Visualize
お問い合わせ
JoVE
x logofacebook logolinkedin logoyoutube logo
JoVEについて
概要リーダーシップブログJoVEヘルプセンター
著者向け
出版プロセス編集委員会範囲と方針査読よくある質問投稿
図書館員向け
推薦の声購読アクセスリソース図書館諮問委員会よくある質問
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experimentsアーカイブ
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教員リソースセンター教員サイト
利用規約
プライバシーポリシー
ポリシー

関連する概念動画

Performing a Simple Data Analysis using MS-Excel Function01:17

Performing a Simple Data Analysis using MS-Excel Function

1.1K
Microsoft Excel offers a suite of functions and tools ideal for statistical analysis, making it accessible to students and researchers. This article outlines fundamental Excel functions pivotal for data analysis.
SUM: This function calculates the total sum of a range of values. It's the foundation for aggregating data, essential for determining overall trends and totals in datasets.
AVERAGE: It computes the mean value of a given set of numbers, providing a quick insight into the central...
1.1K
Predicting Molecular Geometry02:27

Predicting Molecular Geometry

46.1K
VSEPR Theory for Determination of Electron Pair Geometries
46.1K
Machines01:19

Machines

581
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
A free-body diagram of the...
581
Machines: Problem Solving II01:30

Machines: Problem Solving II

677
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
677
Prediction Intervals01:03

Prediction Intervals

3.4K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
3.4K
Machines: Problem Solving I01:22

Machines: Problem Solving I

722
A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
The toggle clamp system is a machine structure consisting of movable, pin-connected multi-force members that form a stabilized system to transmit forces. The...
722

こちらも読む

関連記事

共著者、ジャーナル、引用グラフによってこの研究に関連する記事。

並び替え
Same author

"Doing Our Best:" A Qualitative Study of Researcher Challenges Administering Neuromodulation Across Different Phenotypes.

AJOB neuroscience·2026
Same author

Characteristics of Cardiovascular Disease Prediction Models Considering Mental Disorders: A Systematic Review.

Journal of the American Heart Association·2026
Same author

Risk factors for ethambutol-induced optic neuropathy: a systematic review and meta-analysis of comparative studies.

Canadian journal of ophthalmology. Journal canadien d'ophtalmologie·2026
Same author

A Multispecies, Modality-Agnostic Scalable In Vivo Mosaic Screening Platform for Therapeutic Target Discovery.

bioRxiv : the preprint server for biology·2026
Same author

Guidance for umbrella reviews of observational studies: A scoping review.

JCPP advances·2026
Same author

Pediatric SleepNet: A Deep Learning Network for Reliable Pediatric Sleep Staging Across Developmental Stages.

Sleep·2026
Same journal

Genetic Impacts on Variability of Body Fat Distribution Uncover Gene-Environment and Gene-Gene Interactions.

bioRxiv : the preprint server for biology·2026
Same journal

16S ribosomal RNA modification drives transcript-specific translation efficiency.

bioRxiv : the preprint server for biology·2026
Same journal

FlcE latches onto the FliL-stator complex to turbocharge flagellar motility in <i>Borrelia burgdorferi</i>.

bioRxiv : the preprint server for biology·2026
Same journal

Synaptic pruning, myelination and the emergence of psychiatric disorders in late adolescence.

bioRxiv : the preprint server for biology·2026
Same journal

Structural and functional insights into the Rcs phosphorelay.

bioRxiv : the preprint server for biology·2026
Same journal

The structural basis of RanGAP1 regulation and catalysis in nuclear transport.

bioRxiv : the preprint server for biology·2026
関連記事をすべて見る

関連する実験動画

Updated: Feb 13, 2026

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

762

マチモダルのデータから脳卒中後のアファシアのスピーチパフォーマンスを予測する説明可能な機械学習によるマルチモダルのデータ

Shreya Parchure, Arnav Gupta, Apoorva Kelkar

    bioRxiv : the preprint server for biology
    |February 12, 2026
    PubMed
    まとめ
    この要約は機械生成です。

    この研究は,アファシア (PWA) 患者における単語のスピーチの精度を予測する機械学習モデルを開発した. このモデルは,言語的困難と臨床データを用いて,アファシア治療をパーソナライズし,治療結果を改善します.

    さらに関連する動画

    Utilizing Repetitive Transcranial Magnetic Stimulation to Improve Language Function in Stroke Patients with Chronic Non-fluent Aphasia
    10:15

    Utilizing Repetitive Transcranial Magnetic Stimulation to Improve Language Function in Stroke Patients with Chronic Non-fluent Aphasia

    Published on: July 2, 2013

    18.4K
    Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
    04:09

    Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

    Published on: October 10, 2018

    8.9K

    関連する実験動画

    Last Updated: Feb 13, 2026

    Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
    07:13

    Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

    Published on: April 18, 2025

    762
    Utilizing Repetitive Transcranial Magnetic Stimulation to Improve Language Function in Stroke Patients with Chronic Non-fluent Aphasia
    10:15

    Utilizing Repetitive Transcranial Magnetic Stimulation to Improve Language Function in Stroke Patients with Chronic Non-fluent Aphasia

    Published on: July 2, 2013

    18.4K
    Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
    04:09

    Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

    Published on: October 10, 2018

    8.9K

    科学分野:

    • 神経科学は神経科学である.
    • コンピュータ言語学 コンピュータ言語学
    • スピーチ・ランゲージ・パソロジーの病理学

    背景:

    • アファシアは,脳卒中後の一般的な言語障害であり,しばしば慢性化します.
    • 現在,アファシア回復の予測方法は,精度が限られている.
    • アファシア治療の最適化には,個別化された予測が必要である.

    研究 の 目的:

    • 言語失語症 (PWA) の人の言語の単語の精度を予測する.
    • 予測の精度を向上させ,パーソナライズされたスピーチセラピーを可能にします.
    • アクセシブルなインプットと説明可能な特徴を使用して臨床的に適用可能なモデルを開発する.

    主な方法:

    • 組み合わせた多式入力:臨床スコア,構造MRI神経イメージング,単語の言語難易度メトリック (認知および発音の負担).
    • 言語の難易度を計算するために,自然主義的なcorpora (>10億語) を利用しました.
    • 4620件の試験で,ランダムな森林分類器を用いた従業員の遡及訓練,クロス検証,ブートストラップを実施した.

    主要な成果:

    • マルチモダルモデルは単一の入力モデル (AUROC 0.90 ± 0.04まで) を大幅に上回った.
    • 主な予測要因には,西アファシア バッテリースコア,意味論的要求,単語の長さ (音声,音節),脳の構造的整合性が含まれていました.
    • 簡素化され,臨床的に展開可能なモデル (AphasiaLENS) は,強い見通しの一般化 (AUROC 0.81-0.89) を示した.

    結論:

    • 言語の難しさ,臨床データ,神経イメージングを統合した機械学習モデルは,PWAのスピーチの精度を正確に予測することができます.
    • 簡素化され,説明可能なモデル (AphasiaLENS) は,個別化されたアファシア治療計画のための臨床的に有効なツールを提供します.
    • この発見は,アファシアにおける脳行動関係の理解を深め,将来の研究目標の指針となる.