23 papers · ranked by Valyu relevance
Chloe Crosschild, Colleen Varcoe, Helen J. Brown, Annette J. Browne
This article explores Indigenous Research Methodologies (IRM) in the context of Indigenous women’s health, integrating principles of Indigenous Data Sovereignty (IDS) and theoretical ideas from Critical Indigenous Feminism (CIF) and Red Intersectionality (RI). Building on ongoing critiques of dominant Eurocentric…
Anton Kolonin
Quantifying numerical data involves addressing two key challenges: first, determining whether the data can be naturally quantified, and second, identifying the numerical intervals or ranges of values that correspond to specific value classes, referred to as "quantums," which represent statistically meaningful states.…
Authors not listed
The foundational Henri-Michaelis-Menten equation, derived from measurements of initial reaction rates, describes enzyme kinetics using the Michaelis-Menten constant (KM) and maximum velocity (Vmax). While traditional methods focus on initial rates, progress curve analysis, which monitors the entire reaction process…
K. Tsolakidis, Artu Breuer, S. Bender, Stavroula Margaritaki + 3 more
Advances in fluorescence microscopy have dramatically expanded the range of biological questions that can be addressed, enabling quantitative observations of molecular interactions and cellular dynamics with unprecedented spatial and temporal resolution. However, the growing complexity of imaging data has outpaced our…
Daria K. Lorio-Barsten, Selena J. Layden
Quantitative methods remain the hallmark of research in applied behavior analysis. Yet, such methods frequently fail to capture the nuances of context where behavior analysis is practiced. Therefore, qualitative methods can provide complementary means to gain deeper insight into changes in socially significant…
Authors not listed
Quantitative analysis of small extracellular vesicles (sEVs) at single-particle resolution remains challenging due to their nanoscale dimensions and compositional heterogeneity. Existing methods often rely on specialized and costly instrumentation, limiting accessibility for many researchers and necessitates extensive…
Andrew M. Crawford, Julia Balough, Yu-Ying Chen, Qiaoling Jin + 6 more
X-ray fluorescence microscopy (XFM) continues to develop as a powerful quantitative technique for high resolution, label-free, elemental mapping of biological, environmental, and material samples. Methods for rigorously fitting spectra, increasing throughput, accounting for background signals, and deconvoluting…
Koji Kyoda, Hatsumi Okada, Shuichi Onami
Advances in live-cell imaging and image analysis have made it possible to quantitatively measure the spatiotemporal morphological dynamics of biological phenomena at scale. However, a general framework is still lacking for systematically extracting, from the resulting multivariate data, which relationships between…
Ellen Schmaljohn, Olalekan Usman, Christian Brommel, Kyle J. Kinney + 10 more
Chromosomal translocations are rare structural rearrangement outcomes of genome editing, requiring analytical frameworks that combine high quantitative accuracy with performant sensitivity and specificity. Amplicon sequencing offers a scalable means to detect rare rearrangements with ultra-deep targeted sequencing, but…
Jana Uher, Jan Ketil Arnulf, Barbara Hanfstingl
This Research Topic presents novel and revived perspectives on the fundamental problems underlying psychology's crises in replicability, validity, generalisability and thus, confidence in its findings. Our 15 articles present critical analyses of established theories and practices that are widely used in quantitative…
Ghaith Alfakhry, Danica Sims, Ariel Lindorff
Pragmatism isn't just a method-it's a philosophy that puts the research question first, bridges ontological divides, and empowers medical education scholars to transcend rigid paradigms. #MedEd
Mushan Li, Yanyuan Ma, Liqun Wang
We devise a novel estimator for a general quantile regression model with normal measurement errors in the covariates. The method is applicable to both linear and nonlinear quantile regressions and does not impose the quantile requirement on multiple quantile levels simultaneously. We circumvent the difficulties caused…
Authors not listed
We present a new method for fingerprint- ing atomic configurations relevant to ML-IAM training and application, utilizing the ChIMES descriptor. These fingerprints enable rigor- ous analysis of statistical distinguishability be- tween configurations. Sample applications in- clude assessing diversity within ML-IAP…
Authors not listed
Quantitative Structure-Activity Relationship (QSAR) modeling is a pillar of computational drug discovery. However, standard machine learning (ML) models are often confounded by the high-dimensional and intensely correlated nature of molecular descriptors. A model may identify a "bulk" property (e.g., molecular weight)…
Andrea L. Nevedal, Christine P. Kowalski, Erin P. Finley, Gemmae M. Fix + 2 more
Background Qualitative methods are central to implementation research. Qualitative research provides rich contextual insight into lived experiences of health and illness, healthcare systems and care delivery, and complex implementation processes. However, quantitative methods have historically been favored by editors…
Mutis, Muge, Beyaztas, Ufuk + 4 more
We present two innovative functional partial quantile regression algorithms designed to accurately and efficiently estimate the regression coefficient function within the function-on-function linear quantile regression model. Our algorithms utilize functional partial quantile regression decomposition to effectively…
Udara Kumaranathunga, Alysha De Livera, Luke A. Prendergast
In recent years, there has been much progress toward the development of methods for converting three- and five-number summary statistics (i.e. minimum, maximum, median, and quartiles) to means and standard deviations (SDs). This is commonly done in the meta-analysis setting, where some studies report means and SDs…
Authors not listed
We present an updated version of a priori computational intelligence, a methodology that integrates semi-empirical Quantum Mechanics calculations with supervised machine learning to predict optimal reaction conditions without prior extensive experimental work. First, the synergy between semi-empirical calculations and…
Carol Ting
It has long been a puzzle why, despite sustained reform efforts, many applied scientific fields remain dominated by Null Hypothesis Significance Testing (NHST), a framework that dichotomizes study results and privileges "statistically significant" findings. This paper examines that puzzle by situating the development…
Authors not listed
Chemical hardness is one of the fundamental concepts in chemical reactivity theory, rigorously defined within the framework of Conceptual Density Functional Theory (CDFT). The associated maximum hardness principle (MHP), which postulates that, a favorable direction of reaction is towards the state of maximum hardness…
Authors not listed
Computational methods for predictive modeling have been increasingly utilized in the early stages of drug discovery to supplement high-throughput screening. The advent of highly efficient and complex machine learning architectures necessitates new methods of collating the plethora of topological, geometrical, and…
Francesco G. Rinaldi, Eugenio Piasini
To make sense of a noisy world, living beings constantly face decisions between competing interpretations for ambiguous sensory data. This process parallels statistical model selection, where most frameworks, like the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC), are based on a…
Hao Wu, Chun Li, Bryan E. Shepherd
The causal inference literature has traditionally focused on estimating the mean of the potential outcome, whereas evaluating how a treatment affects the entire outcome distribution can provide additional information in biomedical research. Quantile treatment effect (QTE) captures such distributional differences…