Probability Distribution of the Shortest Path on the Percolation Cluster, its Backbone and Skeleton

We consider the mean distribution functions Phi(r|l), Phi(B)(r|l), and Phi(S)(r|l), giving the probability that two sites on the incipient percolation cluster, on its backbone and on its skeleton, respectively, connected by a shortest path of length l are separated by an Euclidean distance r. Following a scaling argument due to de Gennes for self-avoiding walks, we derive analytical expressions for the exponents g1=df+dmin-d and g1B=g1S-3dmin-d, which determine the scaling behavior of the distribution functions in the limit x=r/l^(nu) much less than 1, i.e., Phi(r|l) proportional to l^(-(nu)d)x^(g1), Phi(B)(r|l) proportional to l^(-(nu)d)x^(g1B), and Phi(S)(r|l) proportional to l^(-(nu)d)x^(g1S), with nu=1/dmin, where df and dmin are the fractal dimensions of the percolation cluster and the shortest path, respectively. The theoretical predictions for g1, g1B, and g1S are in very good agreement with our numerical results.Comment: 10 pages, 3 figure

Shear Viscosity of Clay-like Colloids in Computer Simulations and Experiments

Dense suspensions of small strongly interacting particles are complex systems, which are rarely understood on the microscopic level. We investigate properties of dense suspensions and sediments of small spherical Al_2O_3 particles in a shear cell by means of a combined Molecular Dynamics (MD) and Stochastic Rotation Dynamics (SRD) simulation. We study structuring effects and the dependence of the suspension's viscosity on the shear rate and shear thinning for systems of varying salt concentration and pH value. To show the agreement of our results to experimental data, the relation between bulk pH value and surface charge of spherical colloidal particles is modeled by Debye-Hueckel theory in conjunction with a 2pK charge regulation model.Comment: 15 pages, 8 figure

Multifractal behavior of linear polymers in disordered media

The scaling behavior of linear polymers in disordered media modelled by self-avoiding random walks (SAWs) on the backbone of two- and three-dimensional percolation clusters at their critical concentrations p_c is studied. All possible SAW configurations of N steps on a single backbone configuration are enumerated exactly. We find that the moments of order q of the total number of SAWs obtained by averaging over many backbone configurations display multifractal behavior, i.e. different moments are dominated by different subsets of the backbone. This leads to generalized coordination numbers \mu_q and enhancement exponents \gamma_q, which depend on q. Our numerical results suggest that the relation \mu_1 = p_ c \mu between the first moment \mu_1 and its regular lattice counterpart \mu is valid.Comment: 11 pages, 12 postscript figures, to be published in Phys. Rev.

Characterizing the Interpretability of Attention Maps in Digital Pathology

Interpreting machine learning model decisions is crucial for high-risk applications like healthcare. In digital pathology, large whole slide images (WSIs) are decomposed into smaller tiles and tile-derived features are processed by attention-based multiple instance learning (ABMIL) models to predict WSI-level labels. These networks generate tile-specific attention weights, which can be visualized as attention maps for interpretability. However, a standardized evaluation framework for these maps is lacking, questioning their reliability and ability to detect spurious correlations that can mislead models. We herein propose a framework to assess the ability of attention networks to attend to relevant features in digital pathology by creating artificial model confounders and using dedicated interpretability metrics. Models are trained and evaluated on data with tile modifications correlated with WSI labels, enabling the analysis of model sensitivity to artificial confounders and the accuracy of attention maps in highlighting them. Confounders are introduced either through synthetic tile modifications or through tile ablations based on their specific image-based features, with the latter being used to assess more clinically relevant scenarios. We also analyze the impact of varying confounder quantities at both the tile and WSI levels. Our results show that ABMIL models perform as desired within our framework. While attention maps generally highlight relevant regions, their robustness is affected by the type and number of confounders. Our versatile framework has the potential to be used in the evaluation of various methods and the exploration of image-based features driving model predictions, which could aid in biomarker discovery

Whole exome resequencing reveals recessive mutations in TRAP1 in individuals with CAKUT and VACTERL association

Congenital abnormalities of the kidney and urinary tract (CAKUT) account for approximately half of children with chronic kidney disease and they are the most frequent cause of end-stage renal disease in children in the US. However, its genetic etiology remains mostly elusive. VACTERL association is a rare disorder that involves congenital abnormalities in multiple organs including the kidney and urinary tract in up to 60% of the cases. By homozygosity mapping and whole exome resequencing combined with high-throughput mutation analysis by array-based multiplex PCR and next-generation sequencing, we identified recessive mutations in the gene TNF receptor-associated protein 1 (TRAP1) in two families with isolated CAKUT and three families with VACTERL association. TRAP1 is a heat shock protein 90-related mitochondrial chaperone possibly involved in antiapoptotic and endoplasmic reticulum-stress signaling. Trap1 is expressed in renal epithelia of developing mouse kidney E13.5 and in the kidney of adult rats, most prominently in proximal tubules and in thick medullary ascending limbs of Henle’s loop. Thus, we identified mutations in TRAP1 as highly likely causing CAKUT or CAKUT in VACTERL association

The Essential Role for Laboratory Studies in Atmospheric Chemistry

Laboratory studies of atmospheric chemistry characterize the nature of atmospherically relevant processes down to the molecular level, providing fundamental information used to assess how human activities drive environmental phenomena such as climate change, urban air pollution, ecosystem health, indoor air quality, and stratospheric ozone depletion. Laboratory studies have a central role in addressing the incomplete fundamental knowledge of atmospheric chemistry. This article highlights the evolving science needs for this community and emphasizes how our knowledge is far from complete, hindering our ability to predict the future state of our atmosphere and to respond to emerging global environmental change issues. Laboratory studies provide rich opportunities to expand our understanding of the atmosphere via collaborative research with the modeling and field measurement communities, and with neighboring disciplines

Structural Characterization of Minor Ampullate Spidroin Domains and Their Distinct Roles in Fibroin Solubility and Fiber Formation

10.1371/journal.pone.0056142PLoS ONE82

STE-QUEST - Test of the Universality of Free Fall Using Cold Atom Interferometry

In this paper, we report about the results of the phase A mission study of the atom interferometer instrument covering the description of the main payload elements, the atomic source concept, and the systematic error sources

The NCI Imaging Data Commons as a platform for reproducible research in computational pathology

Background and Objectives: Reproducibility is a major challenge in developing machine learning (ML)-based solutions in computational pathology (CompPath). The NCI Imaging Data Commons (IDC) provides >120 cancer image collections according to the FAIR principles and is designed to be used with cloud ML services. Here, we explore its potential to facilitate reproducibility in CompPath research. Methods: Using the IDC, we implemented two experiments in which a representative ML-based method for classifying lung tumor tissue was trained and/or evaluated on different datasets. To assess reproducibility, the experiments were run multiple times with separate but identically configured instances of common ML services. Results: The AUC values of different runs of the same experiment were generally consistent. However, we observed small variations in AUC values of up to 0.045, indicating a practical limit to reproducibility. Conclusions: We conclude that the IDC facilitates approaching the reproducibility limit of CompPath research (i) by enabling researchers to reuse exactly the same datasets and (ii) by integrating with cloud ML services so that experiments can be run in identically configured computing environments.Comment: 13 pages, 5 figures; improved manuscript, new experiments with P100 GP

\u3ci\u3eStaphylococcus aureus\u3c/i\u3e Metabolic Adaptations during the Transition from a Daptomycin Susceptibility Phenotype to a Daptomycin Nonsusceptibility Phenotype

Staphylococcus aureus is a major cause of nosocomial and community-acquired infections. The success of S. aureus as a pathogen is due in part to its many virulence determinants and resistance to antimicrobials. In particular, methicillin-resistant S. aureus has emerged as a major cause of infections and led to increased use of the antibiotics vancomycin and daptomycin, which has increased the isolation of vancomycin-intermediate S. aureus and daptomycin-nonsusceptible S. aureus strains. The most common mechanism by which S. aureus acquires intermediate resistance to antibiotics is by adapting its physiology and metabolism to permit growth in the presence of these antibiotics, a process known as adaptive resistance. To better understand the physiological and metabolic changes associated with adaptive resistance, six daptomycin-susceptible and -nonsusceptible isogenic strain pairs were examined for changes in growth, competitive fitness, and metabolic alterations. Interestingly, daptomycin nonsusceptibility coincides with a slightly delayed transition to the postexponential growth phase and alterations in metabolism. Specifically, daptomycin-nonsusceptible strains have decreased tricarboxylic acid cycle activity, which correlates with increased synthesis of pyrimidines and purines and increased carbon flow to pathways associated with wall teichoic acid and peptidoglycan biosynthesis. Importantly, these data provided an opportunity to alter the daptomycin nonsusceptibility phenotype by manipulating bacterial metabolism, a first step in developing compounds that target metabolic pathways that can be used in combination with daptomycin to reduce treatment failures