Bit flipping and time to recover

We call `bits' a sequence of devices indexed by positive integers, where every device can be in two states: $0$ (idle) and $1$ (active). Start from the `ground state' of the system when all bits are in $0$-state. In our first Binary Flipping (BF) model, the evolution of the system is the following: at each time step choose one bit from a given distribution $\mathcal{P}$ on the integers independently of anything else, then flip the state of this bit to the opposite. In our second Damaged Bits (DB) model a `damaged' state is added: each selected idling bit changes to active, but selecting an active bit changes its state to damaged in which it then stays forever. In both models we analyse the recurrence of the system's ground state when no bits are active. We present sufficient conditions for both BF and DB models to show recurrent or transient behaviour, depending on the properties of $\mathcal{P}$. We provide a bound for fractional moments of the return time to the ground state for the BF model, and prove a Central Limit Theorem for the number of active bits for both models

Stochastic systems with locally defined dynamics

This thesis considers two large classes of models related to the dynamical point processes. The first is the locally interactive sequential adsorption, or LISA, models. We provide the general LISA framework, show that a lot of well- understood models can be described within the framework, such as Polya urn schemes, fragmentation processes, cooperative sequential adsorption. We study several particular new examples of LISA processes which possess the feature of scalability. Our results describe the limiting behaviour of empirical measures of such processes. The second class is Bit Flipping models, where we study a behaviour of a sequence of independent bits, each flipping between several states at given intensity p_k. We investigate conditions on p_k at which the model switches from transient to recurrent behaviour, prove the central limit theorem for the transient case, and provide a bound for moments of the recurrence time in the recurrent case

Structure and properties of nanostructured ZrN coatings obtained by vacuum-arc evaporation using RF discharge

Nanostructured films of zirconium nitride have been synthesized using an ion plasma vacuum-arc deposition technique in combination with a high-frequency (RF) discharge on AISI 430 stainless steel at 150 °C. Structural examination using X-ray fluorescence (XRF), X-ray diffraction (XRD), scanning electron microscopy (SEM) with microanalysis (EDX), transmission electron microscopy (TEM), and nanoidentation was undertaken to reveal phase and chemical composition, surface morphology, microstructure and nanohardness of the coatings. The developed technology provided low-temperature film synthesis, minimized discharge breakdown decreasing formation of macroparticles (MPs) and allowed to deposit ZrN coatings with hardness variation 26.6–31.5 GPa and enhanced corrosion resistance characteristics. It was revealed that ZrN single-phase coatings of cubic modification with fine-crystalline grains of 20 nm in size were formed. The corrosion resistance of coatings has been tested in 0.9% quasiphysiological NaCl solution

Structure and properties of nanostructured ZrN coatings obtained by vacuum-arc evaporation using RF discharge

Nanostructured films of zirconium nitride have been synthesized using an ion plasma vacuum-arc deposition technique in combination with a high-frequency (RF) discharge on AISI 430 stainless steel at 150 °C. Structural examination using X-ray fluorescence (XRF), X-ray diffraction (XRD), scanning electron microscopy (SEM) with microanalysis (EDX), transmission electron microscopy (TEM), and nanoidentation was undertaken to reveal phase and chemical composition, surface morphology, microstructure and nanohardness of the coatings. The developed technology provided low-temperature film synthesis, minimized discharge breakdown decreasing formation of macroparticles (MPs) and allowed to deposit ZrN coatings with hardness variation 26.6–31.5 GPa and enhanced corrosion resistance characteristics. It was revealed that ZrN single-phase coatings of cubic modification with fine-crystalline grains of 20 nm in size were formed. The corrosion resistance of coatings has been tested in 0.9% quasiphysiological NaCl solution

Quantitative high-throughput phenotypic screening of pediatric cancer cell lines identifies multiple opportunities for drug repurposing

Drug repurposing approaches have the potential advantage of facilitating rapid and cost-effective development of new therapies. Particularly, the repurposing of drugs with known safety profiles in children could bypass or streamline toxicity studies. We employed a phenotypic screening paradigm on a panel of well-characterized cell lines derived from pediatric solid tumors against a collection of ∼3,800 compounds spanning approved drugs and investigational agents. Specifically, we employed titration-based screening where compounds were tested at multiple concentrations for their effect on cell viability. Molecular and cellular target enrichment analysis indicated that numerous agents across different therapeutic categories and modes of action had an antiproliferative effect, notably antiparasitic/protozoal drugs with non-classic antineoplastic activity. Focusing on active compounds with dosing and safety information in children according to the Children's Pharmacy Collaborative database, we identified compounds with therapeutic potential through further validation using 3D tumor spheroid models. Moreover, we show that antiparasitic agents induce cell death via apoptosis induction. This study demonstrates that our screening platform enables the identification of chemical agents with cytotoxic activity in pediatric cancer cell lines of which many have known safety/toxicity profiles in children. These agents constitute attractive candidates for efficacy studies in pre-clinical models of pediatric solid tumors

CATMoS: Collaborative Acute Toxicity Modeling Suite.

BACKGROUND: Humans are exposed to tens of thousands of chemical substances that need to be assessed for their potential toxicity. Acute systemic toxicity testing serves as the basis for regulatory hazard classification, labeling, and risk management. However, it is cost- and time-prohibitive to evaluate all new and existing chemicals using traditional rodent acute toxicity tests. In silico models built using existing data facilitate rapid acute toxicity predictions without using animals. OBJECTIVES: The U.S. Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM) Acute Toxicity Workgroup organized an international collaboration to develop in silico models for predicting acute oral toxicity based on five different end points: Lethal Dose 50 (LD50 value, U.S. Environmental Protection Agency hazard (four) categories, Globally Harmonized System for Classification and Labeling hazard (five) categories, very toxic chemicals [LD50 (LD50≤50mg/kg)], and nontoxic chemicals (LD50>2,000mg/kg). METHODS: An acute oral toxicity data inventory for 11,992 chemicals was compiled, split into training and evaluation sets, and made available to 35 participating international research groups that submitted a total of 139 predictive models. Predictions that fell within the applicability domains of the submitted models were evaluated using external validation sets. These were then combined into consensus models to leverage strengths of individual approaches. RESULTS: The resulting consensus predictions, which leverage the collective strengths of each individual model, form the Collaborative Acute Toxicity Modeling Suite (CATMoS). CATMoS demonstrated high performance in terms of accuracy and robustness when compared with in vivo results. DISCUSSION: CATMoS is being evaluated by regulatory agencies for its utility and applicability as a potential replacement for in vivo rat acute oral toxicity studies. CATMoS predictions for more than 800,000 chemicals have been made available via the National Toxicology Program's Integrated Chemical Environment tools and data sets (ice.ntp.niehs.nih.gov). The models are also implemented in a free, standalone, open-source tool, OPERA, which allows predictions of new and untested chemicals to be made. https://doi.org/10.1289/EHP8495

Stochastic systems with locally defined dynamics

We study three different classes of models of stochastic systems with locally defined dynamics. Our main points of interest are the limiting properties and convergence in these models.The first class is the locally interactive sequential adsorption, or LISA, models. We provide the general LISA framework, show that several classes of well-understood models fall within the framework, such as Polya urn schemes and fragmentation processes. We study several particular new examples of LISA processes having the feature of scalability. We provide the sufficient conditions for the existence of limiting empirical measures, and prove a bound for the speed of convergence.The second class is Bit Flipping models, where we study a behaviour of a sequence of independent bits, each flipping between several states at a given rate p_k. We define two particular models, Binary Flipping and Damaged Bits, and find the conditions on the rates {p_k} at which the models switch from the transient to the recurrent behaviour; as well as provide bounds for moments of the recurrence time under a certain set of conditions in the recurrent case, and prove the central limit theorem.The third class is Random Exchange Models where a countable collection of agents are trading independent random proportion of their masses with neighbours in a stepwise fashion. We find the stationary regimes for such models, and prove a limit theorem. As a corollary, we obtain a new invariance property of a stationary Poisson process on the real line with respect to a certain neighbour-dependent point shift

LISA: LOCALLY INTERACTING SEQUENTIAL ADSORPTION

We study a class of dynamically constructed point processes in which at every step a new point (particle) is added to the current configuration with a distribution depending on the local structure around a uniformly chosen particle. This class covers, in particular, generalized Polya urn scheme, Dubins-Freedman random measures, and cooperative sequential adsorption models studied previously. Specifically, we address models where the distribution of a newly added particle is determined by the distance to the closest particle from the chosen one. We address boundedness of the processes and convergence properties of the corresponding sample measure. We show that, in general, the limiting measure is random when it exists and that this is the case for a wide class of almost surely bounded processes