Quantifying short-term dynamics of Parkinson's disease using self-reported symptom data from an internet social network

Background: Parkinson’s disease (PD) is an incurable neurological disease with approximately 0.3% prevalence. The hallmark symptom is gradual movement deterioration. Current scientific consensus about disease progression holds that symptoms will worsen smoothly over time unless treated. Accurate information about symptom dynamics is of critical importance to patients, caregivers, and the scientific community for the design of new treatments, clinical decision making, and individual disease management. Long-term studies characterize the typical time course of the disease as an early linear progression gradually reaching a plateau in later stages. However, symptom dynamics over durations of days to weeks remains unquantified. Currently, there is a scarcity of objective clinical information about symptom dynamics at intervals shorter than 3 months stretching over several years, but Internet-based patient self-report platforms may change this. Objective: To assess the clinical value of online self-reported PD symptom data recorded by users of the health-focused Internet social research platform PatientsLikeMe (PLM), in which patients quantify their symptoms on a regular basis on a subset of the Unified Parkinson’s Disease Ratings Scale (UPDRS). By analyzing this data, we aim for a scientific window on the nature of symptom dynamics for assessment intervals shorter than 3 months over durations of several years. Methods: Online self-reported data was validated against the gold standard Parkinson’s Disease Data and Organizing Center (PD-DOC) database, containing clinical symptom data at intervals greater than 3 months. The data were compared visually using quantile-quantile plots, and numerically using the Kolmogorov-Smirnov test. By using a simple piecewise linear trend estimation algorithm, the PLM data was smoothed to separate random fluctuations from continuous symptom dynamics. Subtracting the trends from the original data revealed random fluctuations in symptom severity. The average magnitude of fluctuations versus time since diagnosis was modeled by using a gamma generalized linear model. Results: Distributions of ages at diagnosis and UPDRS in the PLM and PD-DOC databases were broadly consistent. The PLM patients were systematically younger than the PD-DOC patients and showed increased symptom severity in the PD off state. The average fluctuation in symptoms (UPDRS Parts I and II) was 2.6 points at the time of diagnosis, rising to 5.9 points 16 years after diagnosis. This fluctuation exceeds the estimated minimal and moderate clinically important differences, respectively. Not all patients conformed to the current clinical picture of gradual, smooth changes: many patients had regimes where symptom severity varied in an unpredictable manner, or underwent large rapid changes in an otherwise more stable progression. Conclusions: This information about short-term PD symptom dynamics contributes new scientific understanding about the disease progression, currently very costly to obtain without self-administered Internet-based reporting. This understanding should have implications for the optimization of clinical trials into new treatments and for the choice of treatment decision timescales.

Random walks in local dynamics of network losses

We suggest a model for data losses in a single node (memory buffer) of a packet-switched network (like the Internet) which reduces to one-dimensional discrete random walks with unusual boundary conditions. By construction, the model has critical behavior with a sharp transition from exponentially small to finite losses with increasing data arrival rate. We show that for a finite-capacity buffer at the critical point the loss rate exhibits strong fluctuations and non-Markovian power-law correlations in time, in spite of the Markovian character of the data arrival process.

Network evolution and the spatiotemporal dynamics of knowledge sourcing

Knowledge accessing from external organisations is important to firms, especially entrepreneurial ones which often cannot generate internally all the knowledge necessary for innovation. There is, however, a lack of evidence concerning the association between the evolution of firms and the evolution of their networks. The aim of this paper is to begin to fill this gap by undertaking an exploratory analysis of the relationship between the vintage of firms and their knowledge sourcing networks. Drawing on an analysis of firms in the UK, the paper finds some evidence of a U-shaped relationship existing between firm age and the frequency of accessing knowledge from certain sources. Emerging entrepreneurial firms tend to be highly active with regard to accessing knowledge for a range of sources and geographic locations, with the rate of networking dropping somewhat during the period of peak firm growth. For instance, it is found that firms tend to less frequently access knowledge sources such as universities and research institutes in their own region during a stage of peak turnover growth. Overall, the results suggest a complex relationship between the lifecycle of the firm and its networking patterns. It is concluded that policymakers need to become more aware that network formation and utilisation by firms is likely to vary dependent upon their lifecycle position.

Toward blueprints for network architecture, biophysical dynamics, and signal transduction

We review mathematical aspects of biophysical dynamics, signal transduction and network architecture that have been used to uncover functionally significant relations between the dynamics of single neurons and the networks they compose. We focus on examples that combine insights from these three areas to expand our understanding of systems neuroscience. These range from single neuron coding to models of decision making and electrosensory discrimination by networks and populations, as well as coincidence detection in pairs of dendrites and the dynamics of large networks of excitable dendritic spines. We conclude by describing some of the challenges that lie ahead as the applied mathematics community seeks to provide the tools that will ultimately underpin systems neuroscience.

Using complex network metrics to predict the persistence of metapopulations with asymmetric connectivity patterns

Almost all metapopulation modelling assumes that connectivity between patches is only a function of distance, and is therefore symmetric. However, connectivity will not depend only on the distance between the patches, as some paths are easy to traverse, while others are difficult. When colonising organisms interact with the heterogeneous landscape between patches, connectivity patterns will invariably be asymmetric. There have been few attempts to theoretically assess the effects of asymmetric connectivity patterns on the dynamics of metapopulations. In this paper, we use the framework of complex networks to investigate whether metapopulation dynamics can be determined by directly analysing the asymmetric connectivity patterns that link the patches. Our analyses focus on “patch occupancy” metapopulation models, which only consider whether a patch is occupied or not. We propose three easily calculated network metrics: the “asymmetry” and “average path strength” of the connectivity pattern, and the “centrality” of each patch. Together, these metrics can be used to predict the length of time a metapopulation is expected to persist, and the relative contribution of each patch to a metapopulation’s viability. Our results clearly demonstrate the negative effect that asymmetry has on metapopulation persistence. Complex network analyses represent a useful new tool for understanding the dynamics of species existing in fragmented landscapes, particularly those existing in large metapopulations.

User-representative feature selection for keystroke dynamics

Continuous user authentication with keystroke dynamics uses characters sequences as features. Since users can type characters in any order, it is imperative to find character sequences (n-graphs) that are representative of user typing behavior. The contemporary feature selection approaches do not guarantee selecting frequently-typed features which may cause less accurate statistical user-representation. Furthermore, the selected features do not inherently reflect user typing behavior. We propose four statistical based feature selection techniques that mitigate limitations of existing approaches. The first technique selects the most frequently occurring features. The other three consider different user typing behaviors by selecting: n-graphs that are typed quickly; n-graphs that are typed with consistent time; and n-graphs that have large time variance among users. We use Gunetti’s keystroke dataset and k-means clustering algorithm for our experiments. The results show that among the proposed techniques, the most-frequent feature selection technique can effectively find user representative features. We further substantiate our results by comparing the most-frequent feature selection technique with three existing approaches (popular Italian words, common n-graphs, and least frequent ngraphs). We find that it performs better than the existing approaches after selecting a certain number of most-frequent n-graphs.

Investigating the dynamics of the psychological contract : how and why individuals' contract beliefs change

The overall objective of this thesis is to explore how and why the content of individuals' psychological contracts changes over time. The contract is generally understood as "individual beliefs, shaped by the organisation, regarding the terms of an exchange agreement between individuals and their organisation" (Rousseau, 1995, p. 9). With an overall study sampling frame of 320 graduate organisational newcomers, a mixed method longitudinal research design comprised of three sequential, inter-related studies is employed in order to capture the change process. From the 15 semi-structured interviews conducted in Study 1, the key findings included identifying a relatively high degree of mutuality between employees' and their managers' reciprocal contract beliefs around the time of organisational entry. Also, at this time, individuals had developed specific components of their contract content through a mix of social network information (regarding broader employment expectations) and perceptions of various elements of their particular organisation's reputation (for more firm-specific expectations). Study 2 utilised a four-wave survey approach (available to the full sampling frame) over the 14 months following organisational entry to explore the 'shape' of individuals' contract change trajectories and the role of four theorised change predictors in driving these trajectories. The predictors represented an organisational-level informational cue (perceptions of corporate reputation), a dyadic-level informational cue (perceptions of manager-employee relationship quality) and two individual difference variables (affect and hardiness). Through the use of individual growth modelling, the findings showed differences in the general change patterns across contract content components of perceived employer (exhibiting generally quadratic change patterns) and employee (exhibiting generally no-change patterns) obligations. Further, individuals differentially used the predictor variables to construct beliefs about specific contract content. While both organisational- and dyadic-level cues were focused upon to construct employer obligation beliefs, organisational-level cues and individual difference variables were focused upon to construct employee obligation beliefs. Through undertaking 26 semi-structured interviews, Study 3 focused upon gaining a richer understanding of why participants' contracts changed, or otherwise, over the study period, with a particular focus upon the roles of breach and violation. Breach refers to an employee's perception that an employer obligation has not been met and violation refers to the negative and affective employee reactions which may ensue following a breach. The main contribution of these findings was identifying that subsequent to a breach or violation event a range of 'remediation effects' could be activated by employees which, depending upon their effectiveness, served to instigate either breach or contract repair or both. These effects mostly instigated broader contract repair and were generally cognitive strategies enacted by an individual to re-evaluate the breach situation and re-focus upon other positive aspects of the employment relationship. As such, the findings offered new evidence for a clear distinction between remedial effects which serve to only repair the breach (and thus the contract) and effects which only repair the contract more broadly; however, when effective, both resulted in individuals again viewing their employment relationships positively. Overall, in response to the overarching research question of this thesis, how and why individuals' psychological contract beliefs change, individuals do indeed draw upon various information sources, particularly at the organisational-level, as cues or guides in shaping their contract content. Further, the 'shapes' of the changes in beliefs about employer and employee obligations generally follow different, and not necessarily linear, trajectories over time. Finally, both breach and violation and also remedial actions, which address these occurrences either by remedying the breach itself (and thus the contract) or the contract only, play central roles in guiding individuals' contract changes to greater or lesser degrees. The findings from the thesis provide both academics and practitioners with greater insights into how employees construct their contract beliefs over time, the salient informational cues used to do this and how the effects of breach and violation can be mitigated through creating an environment which facilitates the use of effective remediation strategies.

Langevin dynamics modeling of the water diffusion tensor in partially aligned collagen networks

In this work, a Langevin dynamics model of the diffusion of water in articular cartilage was developed. Numerical simulations of the translational dynamics of water molecules and their interaction with collagen fibers were used to study the quantitative relationship between the organization of the collagen fiber network and the diffusion tensor of water in model cartilage. Langevin dynamics was used to simulate water diffusion in both ordered and partially disordered cartilage models. In addition, an analytical approach was developed to estimate the diffusion tensor for a network comprising a given distribution of fiber orientations. The key findings are that (1) an approximately linear relationship was observed between collagen volume fraction and the fractional anisotropy of the diffusion tensor in fiber networks of a given degree of alignment, (2) for any given fiber volume fraction, fractional anisotropy follows a fiber alignment dependency similar to the square of the second Legendre polynomial of cos(θ), with the minimum anisotropy occurring at approximately the magic angle (θMA), and (3) a decrease in the principal eigenvalue and an increase in the transverse eigenvalues is observed as the fiber orientation angle θ progresses from 0◦ to 90◦. The corresponding diffusion ellipsoids are prolate for θ < θMA, spherical for θ ≈ θMA, and oblate for θ > θMA. Expansion of the model to include discrimination between the combined effects of alignment disorder and collagen fiber volume fraction on the diffusion tensor is discussed.

Mapping the dynamics of the project management field : project management in action (part 6)

This is the sixth part of a Letter from the Editor series where the results are presented of an ongoing research undertaken in order to investigate the dynamic of the evolution of the field of project management and the key trends. Dynamics of networks is a key feature in strategic diagrams analysis. The radical change in the configuration of a network between two periods, or the change at subnetwork level reflects the dynamic of science. I present here an example of subnetwork comparison over the four periods of time considered in this study. I will develop and discuss an example of subnetwork transformation in future Letter from the Editor article..

From homepages to network profiles : balancing personal and social identity

The World Wide Web constitutes one of the most important inventions of the late 20th century: it has changed culture, society, business, communication, politics, and many other fields of human endeavour, not least also by providing a more user-friendly pathway of access to its major underlying technology, the Internet itself. Key phases in its development can be charted, especially by how it has been used to present and share information – and here, the personal or professional, private or official homepage stands in as a useful representation of wider Web trends overall. From hand-coded beginnings through several successive stages of experimentation and standardisation, to the shifting balance between personal sites and social networks, the homepage demonstrates how the Web itself, and its place in our lives, have changed.

A neural-network based flight controller for UASs

This paper presents a nonlinear gust-attenuation controller based on constrained neural-network (NN) theory. The controller aims to achieve sufficient stability and handling quality for a fixed-wing unmanned aerial system (UAS) in a gusty environment when control inputs are subjected to constraints. Constraints in inputs emulate situations where aircraft actuators fail requiring the aircraft to be operated with fail-safe capability. The proposed controller enables gust-attenuation property and stabilizes the aircraft dynamics in a gusty environment. The proposed flight controller is obtained by solving the Hamilton-Jacobi-Isaacs (HJI) equations based on an policy iteration (PI) approach. Performance of the controller is evaluated using a high-fidelity six degree-of-freedom Shadow UAS model. Simulations show that our controller demonstrates great performance improvement in a gusty environment, especially in angle-of-attack (AOA), pitch and pitch rate. Comparative studies are conducted with the proportional-integral-derivative (PID) controllers, justifying the efficiency of our controller and verifying its suitability for integration into the design of flight control systems for forced landing of UASs.

Measuring global economic interdependence : a hierarchical network approach

This paper investigates the business cycle co-movement across countries and regions since 1950 as a measure for quantifying the economic interdependence in the ongoing globalisation process. Our methodological approach is based on analysis of a correlation matrix and the networks it contains. Such an approach summarises the interaction and interdependence of all elements, and it represents a more accurate measure of the global interdependence involved in an economic system. Our results show (1) the dynamics of interdependence has been driven more by synchronisation in regional growth patterns than by the synchronisation of the world economy, and (2) world crisis periods dramatically increase the global co-movement in the world economy.

Visualization : A powerful tool for effective teaching of dynamics

Dynamics is an essential core engineering subject and it is considered as one of the hardest subjects in the engineering discipline. Many students acknowledged that Dynamics is very hard to understand and comprehend the abstract concepts through traditional teaching methods with normal tutorials and assignments. In this study, we conducted an investigation on the application of visualization technique to help students learning the unit with the fundamental theory displayed in the physical space. The research was conducted based on the following five basic steps of Action Learning Cycle including: Identifying problem, Planning action, Implementing, Evaluating, and Reporting. Through our studies, we have concluded that visualization technique can definitely help students in learning and comprehending the abstract theories and concepts of Dynamics.