22 papers · ranked by Valyu relevance
Junda Ying, Yuxuan Wang, He Xiao, Miao Huang + 2 more
Single-cell dynamics during cell state transitions are highly confined, ensuring precise control. This confinement can be described using low-dimensional manifolds. Leveraging information geometry, we developed a method to quantify cell state transition manifolds using single-cell RNA sequencing data. With this…
Nicolás Hardy, Dimitris Korobilis
We revisit macroeconomic time-varying parameter vector autoregressions (TVP-VARs), whose persistent coefficients may adapt too slowly to large, abrupt shifts such as those during major crises. We explore the performance of an adaptively-varying parameter (AVP) VAR that incorporates deterministic adjustments driven by…
Martin Ćalasan, Snežana Vujošević, Mihailo Micev, Shady H. E. Abdel Aleem + 1 more
Proton exchange membrane fuel cells (PEMFCs) have emerged as a promising technology due to their high efficiency, adaptability, and potential for integration into various applications, ranging from portable devices to large-scale power grids. A critical aspect of PEMFC research is the accurate modeling of its…
Marie Frederikke Garnæs, Rie Beck Olin, Pernille R. Jensen, Jan Henrik Ardenkjaer‐Larsen + 2 more
Hyperpolarized carbon-13 magnetic resonance has enabled the real-time observation of biochemical pathways in living cellular systems. Pharmacokinetic modeling of such experiments provides estimates of conversion rates between metabolites, which, in turn, can be used to distinguish between healthy and diseased tissues…
Authors not listed
We report a new charge model and a new general small molecule force field. Here, we address the development and benchmarking of both the Open Force Field (OpenFF) AshGC charge model, as well as the Sage 2.3.0 small molecule force field for drug-like molecules. AshGC is a graph neural network-based method for efficient…
Steven A. Frank, Antonio M. Scarfone
Diverse learning algorithms, optimization methods, and natural selection share a common mathematical structure despite their apparent differences. Here, I show that a simple notational partitioning of change by the Price equation reveals a universal force-metric-bias (FMB) law: $Δθ=(Mf+b+ξ)$. The force $f$ drives…
Authors not listed
Developing a transferable classical force field (FF) has historically been a lengthy, expert-informed process. In this work, we integrate optimization, machine learning, and data science techniques to accelerate the systematic design and parameterization of transferable FF models. As a demonstration, we create…
Jinling Chi, Chang Wang, Yangxue He, Chenxu Gou + 2 more
In gas turbine simulation, precise parameterization of components is essential for reliable performance prediction, yet manufacturers usually provide only limited operational data. To address this issue, this study proposes a modeling approach based on limited operational parameters and applies it to a 9FA heavy-duty…
Lillith Zijmers, Katie Abson, Jarrod Hadfield, Adam Eyre-Walker
A population’s ability to adapt is determined by its levels of additive genetic variance (V_A_), and while it is agreed that most organisms have genetic variation for most traits, the extent to which it varies between species is poorly characterised. Here we investigate this question by compiling 3209 and 1852…
Victor Dods
Originally motivated by creating first-person computer visualizations within Riemannian manifolds -- the author was led to study deformable-body mechanics, as rigid-body mechanics is not available in a generic Riemannian manifold due to its lack of nontrivial isometry group. Hyperelasticity is a particularly nice…
Xiaomi Liu, Kristopher Murdza, Yuxu Feng, Leila Lin + 2 more
Phenotypic variation within a single genotype under the same environment (intragenetic variation), the biologically meaningful part of V_error_, is frequently treated as a statistical nuisance rather than a biological reality, yet it represents an evolutionary driver of fitness that remains poorly integrated into…
N. L. Belyaev, R. Konoplich, Kirill Prokofiev
Precise modelling of a signal in processes with multiple observables, exhibiting a complex dependency on the underlying parameters, is often a difficult and challenging task. Predicting the results of experimental measurements in high-energy physics reactions serves a good example. The reaction rates and distributions…
Authors not listed
Bayesian optimization (BO) has become increasingly important for experimental optimization across scientific domains, yet implementing BO pipelines requires significant programming expertise and familiarity with specialized frameworks. This creates a barrier for domain experts who could benefit from BO but lack the…
Authors not listed
Background: Batch reactor process optimization has traditionally relied on Analysis of Variance (ANOVA) for factor effect quantification. However, Structural Equation Modeling (SEM) and machine learning (ML) offer complementary mechanistic and predictive capabilities that remain underexplored in chemical engineering…
Mounir Nisse
We introduce and study the maximal-variation locus in families and moduli spaces of projective curves, defined via conductor-level balancing of meromorphic differentials on the normalization. This notion captures precisely when the space of canonical differentials behaves with the expected dimension under degeneration.…
Yongqiang Tang
In linear mixed effects models (LMM), inference on fixed effects typically relies on either the generalized least squares (GLS) variance estimator obtained by assuming that the variance parameters are known, or on the Kenward-Roger method with a small-sample bias adjustment. A simulation study shows that both…
Tamara Ben-Ari, David Makowski
Over recent decades, numerous studies have compared agricultural productivity across different management systems and environments, with increasing attention to the stability of crop yields. Yield stability is a key component of agroecosystem resilience under global change, yet it remains unclear whether some cropping…
Gerda Claeskens, Nils Lid Hjort
The focused information criterion is used to make a choice among several statistical models, or among several variables to include in a model. Different from other such information criteria, the focused information criterion is constructed to select the best model for a given interest quantity, the focus of the…
Sudhagar Rajaprakasam, Sumaiya Sulthana Jafarullakhan, Vaishnavi Vijayakumar, Naaganoor Ananthan Saravanan + 5 more
Analysis of genotype-by-environment interactions (GEI) is critical for evaluating the yield and stability of genotypes in multi-environment experiments (METs). Either fixed models (AMMI and GGE biplots) or random effect models (Linear mixed models: LMM) are utilised to estimate GEI. From our preliminary METs using…
Burçin Yıldırım, Jennifer E. James
The distribution of fitness effects (DFE) — describing how harmful, neutral, or beneficial new mutations are — is central to understanding how populations evolve. Although the DFE varies across genomes and species, it remains unclear which aspects of genomic organization drive this variation. Here, we inferred…
Daihe Sui, Elizabeth Tipton
Standard random-effects meta-analysis relies heavily on the assumption that the underlying true effects are normally distributed. In the social sciences, where evidence synthesis increasingly involves large, highly heterogeneous datasets, this assumption is often restrictive and unjustified. Misspecification of the…
Authors not listed
Solvent-accessible surface area (SASA) is a central quantity in computational biochemistry, structural biology, and molecular modeling, with applications ranging from protein folding to ligand binding. Widely used approaches such as Shrake-Rupley, Lee-Richards, and LCPO estimate SASA through geometric sampling or…