In-Silico Modeling of the Mitotic Spindle Assembly Checkpoint

The Mitotic Spindle Assembly Checkpoint ((M)SAC) is an evolutionary conserved mechanism that ensures the correct segregation of chromosomes by restraining cell cycle progression from entering anaphase until all chromosomes have made proper bipolar attachments to the mitotic spindle. Its malfunction can lead to cancer.We have constructed and validated for the human (M)SAC mechanism an in silico dynamical model, integrating 11 proteins and complexes. The model incorporates the perspectives of three central control pathways, namely Mad1/Mad2 induced Cdc20 sequestering based on the Template Model, MCC formation, and APC inhibition. Originating from the biochemical reactions for the underlying molecular processes, non-linear ordinary differential equations for the concentrations of 11 proteins and complexes of the (M)SAC are derived. Most of the kinetic constants are taken from literature, the remaining four unknown parameters are derived by an evolutionary optimization procedure for an objective function describing the dynamics of the APC:Cdc20 complex. MCC:APC dissociation is described by two alternatives, namely the "Dissociation" and the "Convey" model variants. The attachment of the kinetochore to microtubuli is simulated by a switching parameter silencing those reactions which are stopped by the attachment. For both, the Dissociation and the Convey variants, we compare two different scenarios concerning the microtubule attachment dependent control of the dissociation reaction. Our model is validated by simulation of ten perturbation experiments.Only in the controlled case, our models show (M)SAC behaviour at meta- to anaphase transition in agreement with experimental observations. Our simulations revealed that for (M)SAC activation, Cdc20 is not fully sequestered; instead APC is inhibited by MCC binding

IDENTIFYING A CUSTOMER CENTERED APPROACH FOR URBAN PLANNING: DEFINING A FRAMEWORK AND EVALUATING POTENTIAL IN A LIVABILITY CONTEXT

In transportation planning, public engagement is an essential requirement forinformed decision-making. This is especially true for assessing abstract concepts such aslivability, where it is challenging to define objective measures and to obtain input that canbe used to gauge performance of communities. This dissertation focuses on advancing adata-driven decision-making approach for the transportation planning domain in thecontext of livability. First, a conceptual model for a customer-centric framework fortransportation planning is designed integrating insight from multiple disciplines (chapter1), then a data-mining approach to extracting features important for defining customersatisfaction in a livability context is described (chapter 2), and finally an appraisal of thepotential of social media review mining for enhancing understanding of livability measuresand increasing engagement in the planning process is undertaken (chapter 3). The resultsof this work also include a sentiment analysis and visualization package for interpreting anautomated user-defined translation of qualitative measures of livability. The packageevaluates users satisfaction of neighborhoods through social media and enhances thetraditional approaches to defining livability planning measures. This approach has thepotential to capitalize on residents interests in social media outlets and to increase publicengagement in the planning process by encouraging users to participate in onlineneighborhood satisfaction reporting. The results inform future work for deploying acomprehensive approach to planning that draws the marketing structure of transportationnetwork products with residential nodes as the center of the structure

A geometric‐sensitivity‐difference based algorithm improves object depth‐localization for diffuse optical tomography in a circular‐array outward‐imaging geometry

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135030/1/mp1957.pd

Strategies for Solving High-Fidelity Aerodynamic Shape Optimization Problems

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140440/1/6.2014-2594.pd

HF-SCA: Hands-Free Strong Customer Authentication Based on a Memory-Guided Attention Mechanisms

Strong customer authentication (SCA) is a requirement of the European Union Revised Directive on Payment Services (PSD2) which ensures that electronic payments are performed with multifactor authentication. While increasing the security of electronic payments, the SCA impacted seriously on the shopping carts abandonment: an Italian bank computed that 22% of online purchases in the first semester of 2021 did not complete because of problems with the SCA. Luckily, the PSD2 allows the use of transaction risk analysis tool to exempt the SCA process. In this paper, we propose an unsupervised novel combination of existing machine learning techniques able to determine if a purchase is typical or not for a specific customer, so that in the case of a typical purchase the SCA could be exempted. We modified a well-known architecture (U-net) by replacing convolutional blocks with squeeze-and-excitation blocks. After that, a memory network was added in a latent space and an attention mechanism was introduced in the decoding side of the network. The proposed solution was able to detect nontypical purchases by creating temporal correlations between transactions. The network achieved 97.7% of AUC score over a well-known dataset retrieved online. By using this approach, we found that 98% of purchases could be executed by securely exempting the SCA, while shortening the customer’s journey and providing an elevated user experience. As an additional validation, we developed an Alexa skill for Amazon smart glasses which allows a user to shop and pay online by merely using vocal interaction, leaving the hands free to perform other activities, for example driving a car

Production process stability - Core assumption of INDUSTRY 4.0 concept

Today's industrial enterprises are confronted by implementation of INDUSTRY 4.0 concept with basic problem - stabilised manufacturing and supporting processes. Through this phenomenon of stabilisation, they will achieve positive digital management of both processes and continuously throughput. There is required structural stability of horizontal (business) and vertical (digitized) manufacturing processes, supported through digitalised technologies of INDUSTRY 4.0 concept. Results presented in this paper based on the research results and survey realised in more industrial companies. Following will described basic model for structural process stabilisation in manufacturing environment. © Published under licence by IOP Publishing Ltd

Statistical mechanics for biological applications: focusing on the immune system

The emergence in the last decades of a huge amount of data in many fields of biology triggered also an increase of the interest by quantitative disciplines for life sciences. Mathematics, physics and informatics have been providing quantitative models and advanced statistical tools in order to help the understanding of many biological problems. Statistical mechanics is a field that particularly contributed to quantitative biology because of its intrinsic predisposition in dealing with systems of many strongly interacting agents, noise, information processing and statistical inference. In this Thesis a collection of works at the interphase between statistical mechanics and biology is presented. In particular they are related to biological problems that can be mainly reconducted to the biology of the immune system. Beyond the unification key given by statistical mechanics of discrete systems and quantitative modeling and analysis of the immune system, the works presented here are quite diversified. The origin of this heterogeneity resides in the intent of using and learning many different techniques during the lapse of time needed for the preparation of the work reviewed in this Thesis. In fact the work presented in Chapter 3 mainly deals with statistical mechanics, networks theory and networks numerical simulations and analysis; Chapter 4 presents a mathematical physics oriented work; Chapter 5 and 6 deal with data analysis and in particular wth clinical data and amino acid sequences data sets, requiring the use of both analytical and numerical techniques. The Thesis is conceptually organized in two main parts. The first part (Chapters 1 and 2) is dedicated to the review of known results both in statistical mechanics and biology, while in the second part (Chapters 3, 4 and 6) the original works are presented together with briefs insights into the research fields in which they can be embedded. In particular, in Chapter 1 some of the most relevant models and techniques in statistical mechanics of mean field spin systems are reviewed, starting with the Ising model and then passing to the Sherrington-Kirkpatrik model for spin glasses and to the Hopfield model for attractors neural networks. The replica method is presented together with the stochastic stability method as a mathematically rigorous alternative to replicas. Chapter 2 is dedicated to a very schematic overview of the biology of the immune system. In Chapter 3, Section 3.1 is dedicated to the presentation of a mathematical phenomenological model for the study of the idiotypic network while Section 3.2 serves as a review of the statistical mechanics based models proposed by Elena 1 2 Introduction Agliari and Adriano Barra as toy models meant to underline the possible role of complex networks within the immune system. In Chapter 4 the mathematical model of an analogue neural network on a diluted graph is studied. It is shown how the problem can be mapped in a bipartite diluted spin glass. The model is rigorously solved at the replica symmetric level with the use of the stochastic stability technique and fluctuations analysis is used to study the spin glass transition of the system. A topological analysis of the network is also performed and different topological regimes are proven to emerge though the tuning of the model parameters. In Chapter 5 a model for the analysis of clinical records of testing sets of patients is presented. The model is based on a Markov chain over the space of clinical states. The machinery is applied to data concerning the insurgence of Tuberculosis and Non-Tuberculous Infections as side effects in patients treated with Tumor Necrosis Factor inhibitors. The analysis procedure is capable of capturing clinical details of the behaviors of different drugs. Lastly, Chapter 6 is dedicated to a statistical inference analysis on deep sequencing data of an antibodies repertoire with the purpose of studying the problem of antibodies affinity maturation. A partial antibodies repertoire from a HIV-1 infected donor presenting broadly neutralizing serum is used to infer a probability distribution in the space of sequences that is compared with neutralization power measurements and with the deposited crystallographic structure of a deeply matured antibody. The work is still in progress, but preliminary results are encouraging and are presented here

Statistical mechanics for biological applications: focusing on the immune system

The emergence in the last decades of a huge amount of data in many fields of biology triggered also an increase of the interest by quantitative disciplines for life sciences. Mathematics, physics and informatics have been providing quantitative models and advanced statistical tools in order to help the understanding of many biological problems. Statistical mechanics is a field that particularly contributed to quantitative biology because of its intrinsic predisposition in dealing with systems of many strongly interacting agents, noise, information processing and statistical inference. In this Thesis a collection of works at the interphase between statistical mechanics and biology is presented. In particular they are related to biological problems that can be mainly reconducted to the biology of the immune system. Beyond the unification key given by statistical mechanics of discrete systems and quantitative modeling and analysis of the immune system, the works presented here are quite diversified. The origin of this heterogeneity resides in the intent of using and learning many different techniques during the lapse of time needed for the preparation of the work reviewed in this Thesis. In fact the work presented in Chapter 3 mainly deals with statistical mechanics, networks theory and networks numerical simulations and analysis; Chapter 4 presents a mathematical physics oriented work; Chapter 5 and 6 deal with data analysis and in particular wth clinical data and amino acid sequences data sets, requiring the use of both analytical and numerical techniques. The Thesis is conceptually organized in two main parts. The first part (Chapters 1 and 2) is dedicated to the review of known results both in statistical mechanics and biology, while in the second part (Chapters 3, 4 and 6) the original works are presented together with briefs insights into the research fields in which they can be embedded. In particular, in Chapter 1 some of the most relevant models and techniques in statistical mechanics of mean field spin systems are reviewed, starting with the Ising model and then passing to the Sherrington-Kirkpatrik model for spin glasses and to the Hopfield model for attractors neural networks. The replica method is presented together with the stochastic stability method as a mathematically rigorous alternative to replicas. Chapter 2 is dedicated to a very schematic overview of the biology of the immune system. In Chapter 3, Section 3.1 is dedicated to the presentation of a mathematical phenomenological model for the study of the idiotypic network while Section 3.2 serves as a review of the statistical mechanics based models proposed by Elena 1 2 Introduction Agliari and Adriano Barra as toy models meant to underline the possible role of complex networks within the immune system. In Chapter 4 the mathematical model of an analogue neural network on a diluted graph is studied. It is shown how the problem can be mapped in a bipartite diluted spin glass. The model is rigorously solved at the replica symmetric level with the use of the stochastic stability technique and fluctuations analysis is used to study the spin glass transition of the system. A topological analysis of the network is also performed and different topological regimes are proven to emerge though the tuning of the model parameters. In Chapter 5 a model for the analysis of clinical records of testing sets of patients is presented. The model is based on a Markov chain over the space of clinical states. The machinery is applied to data concerning the insurgence of Tuberculosis and Non-Tuberculous Infections as side effects in patients treated with Tumor Necrosis Factor inhibitors. The analysis procedure is capable of capturing clinical details of the behaviors of different drugs. Lastly, Chapter 6 is dedicated to a statistical inference analysis on deep sequencing data of an antibodies repertoire with the purpose of studying the problem of antibodies affinity maturation. A partial antibodies repertoire from a HIV-1 infected donor presenting broadly neutralizing serum is used to infer a probability distribution in the space of sequences that is compared with neutralization power measurements and with the deposited crystallographic structure of a deeply matured antibody. The work is still in progress, but preliminary results are encouraging and are presented here