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

Cellular Automata Simulating Experimental Properties of Traffic Flows

A model for 1D traffic flow is developed, which is discrete in space and time. Like the cellular automaton model by Nagel and Schreckenberg [J. Phys. I France 2, 2221 (1992)], it is simple, fast, and can describe stop-and-go traffic. Due to its relation to the optimal velocity model by Bando et al. [Phys. Rev. E 51, 1035 (1995)], its instability mechanism is of deterministic nature. The model can be easily calibrated to empirical data and displays the experimental features of traffic data recently reported by Kerner and Rehborn [Phys. Rev. E 53, R1297 (1996)].Comment: For related work see http://www.theo2.physik.uni-stuttgart.de/helbing.html and http://traffic.comphys.uni-duisburg.de/member/home_schreck.htm

Highdicom: A Python library for standardized encoding of image annotations and machine learning model outputs in pathology and radiology

Machine learning is revolutionizing image-based diagnostics in pathology and radiology. ML models have shown promising results in research settings, but their lack of interoperability has been a major barrier for clinical integration and evaluation. The DICOM a standard specifies Information Object Definitions and Services for the representation and communication of digital images and related information, including image-derived annotations and analysis results. However, the complexity of the standard represents an obstacle for its adoption in the ML community and creates a need for software libraries and tools that simplify working with data sets in DICOM format. Here we present the highdicom library, which provides a high-level application programming interface for the Python programming language that abstracts low-level details of the standard and enables encoding and decoding of image-derived information in DICOM format in a few lines of Python code. The highdicom library ties into the extensive Python ecosystem for image processing and machine learning. Simultaneously, by simplifying creation and parsing of DICOM-compliant files, highdicom achieves interoperability with the medical imaging systems that hold the data used to train and run ML models, and ultimately communicate and store model outputs for clinical use. We demonstrate through experiments with slide microscopy and computed tomography imaging, that, by bridging these two ecosystems, highdicom enables developers to train and evaluate state-of-the-art ML models in pathology and radiology while remaining compliant with the DICOM standard and interoperable with clinical systems at all stages. To promote standardization of ML research and streamline the ML model development and deployment process, we made the library available free and open-source

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

Development of a PROTAC-Based Targeting Strategy Provides a Mechanistically Unique Mode of Anti-Cytomegalovirus Activity

Human cytomegalovirus (HCMV) is a major pathogenic herpesvirus that is prevalent worldwide and it is associated with a variety of clinical symptoms. Current antiviral therapy options do not fully satisfy the medical needs; thus, improved drug classes and drug-targeting strategies are required. In particular, host-directed antivirals, including pharmaceutical kinase inhibitors, might help improve the drug qualities. Here, we focused on utilizing PROteolysis TArgeting Chimeras (PROTACs), i.e., hetero-bifunctional molecules containing two elements, namely a target-binding molecule and a proteolysis-inducing element. Specifically, a PROTAC that was based on a cyclin-dependent kinase (CDK) inhibitor, i.e., CDK9-directed PROTAC THAL-SNS032, was analyzed and proved to possess strong anti-HCMV AD169-GFP activity, with values of EC50 of 0.030 µM and CC50 of 0.175 µM (SI of 5.8). Comparing the effect of THAL-SNS032 with its non-PROTAC counterpart SNS032, data indicated a 3.7-fold stronger anti-HCMV efficacy. This antiviral activity, as illustrated for further clinically relevant strains of human and murine CMVs, coincided with the mid-nanomolar concentration range necessary for a drug-induced degradation of the primary (CDK9) and secondary targets (CDK1, CDK2, CDK7). In addition, further antiviral activities were demonstrated, such as the inhibition of SARS-CoV-2 replication, whereas other investigated human viruses (i.e., varicella zoster virus, adenovirus type 2, and Zika virus) were found insensitive. Combined, the antiviral quality of this approach is seen in its (i) mechanistic uniqueness; (ii) future options of combinatorial drug treatment; (iii) potential broad-spectrum activity; and (iv) applicability in clinically relevant antiviral models. These novel data are discussed in light of the current achievements of anti-HCMV drug development

Managing changes initiated by industrial big data technologies : a technochange management model

With the adoption of Internet of Things and advanced data analytical technologies in manufacturing firms, the industrial sector has launched an evolutionary journey toward the 4th industrial revolution, or so called Industry 4.0. Industrial big data is a core component to realize the vision of Industry 4.0. However, the implementation and usage of industrial big data tools in manufacturing firms will not merely be a technical endeavor, but can also lead to a thorough management reform. By means of a comprehensive review of literature related to Industry 4.0, smart manufacturing, industrial big data, information systems (IS) and technochange management, this paper aims to analyze potential changes triggered by the application of industrial big data in manufacturing firms, from technological, individual and organizational perspectives. Furthermore, in order to drive these changes more effectively and eliminate potential resistance, a conceptual technochange management model was developed and proposed. Drawn upon theories reported in literature of IS technochange management, this model proposed four types of interventions that can be used to copy with changes initiated by industrial big data technologies, including human process intervention, techno-structural intervention, human resources management intervention and strategic intervention. This model will be of interests and value to practitioners and researchers concerned with business reforms triggered by Industry 4.0 in general and by industrial big data technologies in particular

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.