Terms Of State Transitions Research Articles

ThreadStates: State-based Visual Analysis of Disease Progression

A growing number of longitudinal cohort studies are generating data with extensive patient observations across multiple timepoints. Such data offers promising opportunities to better understand the progression of diseases. However, these observations are usually treated as general events in existing visual analysis tools. As a result, their capabilities in modeling disease progression are not fully utilized. To fill this gap, we designed and implemented ThreadStates, an interactive visual analytics tool for the exploration of longitudinal patient cohort data. The focus of ThreadStates is to identify the states of disease progression by learning from observation data in a human-in-the-loop manner. We propose a novel Glyph Matrix design and combine it with a scatter plot to enable seamless identification, observation, and refinement of states. The disease progression patterns are then revealed in terms of state transitions using Sankey-based visualizations. We employ sequence clustering techniques to find patient groups with distinctive progression patterns, and to reveal the association between disease progression and patient-level features. The design and development were driven by a requirement analysis and iteratively refined based on feedback from domain experts over the course of a 10-month design study. Case studies and expert interviews demonstrate that ThreadStates can successively summarize disease states, reveal disease progression, and compare patient groups.

Temporal Logic for Programmable Logic Controllers

We address the formal verification of the control software of critical systems, i.e., ensuring the absence of design errors in a system with respect to requirements. Control systems are usually based on industrial controllers, also known as Programmable Logic Controllers (PLCs). A specific feature of a PLC is a scan cycle: 1) the inputs are read, 2) the PLC states change, and 3) the outputs are written. Therefore, in order to formally verify PLC, e.g., by model checking, it is necessary to describe the transition system taking into account this specificity and reason both in terms of state transitions within a cycle and in terms of larger state transitions according to the scan-cyclic semantics. We propose a formal PLC model as a hyperprocess transition system and temporal cycle-LTL logic based on LTL logic for formulating PLC property. A feature of the cycle-LTL logic is the possibility of viewing the scan cycle in two ways: as the effect of the environment (in particular, the control object) on the control system and as the effect of the control system on the environment. For both cases we introduce modified LTL temporal operators. We also define special modified LTL temporal operators to specify inside properties of scan cycles. We describe the translation of formulas of cycle-LTL into formulas of LTL, and prove its correctness. This implies the possibility ofmodel checking requirements expressed in logic cycle-LTL, by using well-known model checking tools with LTL as specification logic, e.g., Spin. We give the illustrative examples of requirements expressed in the cycle-LTL logic.

Towards the interpretation of complex visual hallucinations in terms of self-reorganization of neural networks

Patients suffering from dementia with Lewy body (DLB) often see complex visual hallucinations (CVH). Despite many pathological, clinical, and neuroimaging studies, the mechanism of CVH remains unknown. One possible scenario is that top-down information is being used to compensate for the lack of bottom-up information. To investigate this possibility and understand the underlying mathematical structure of the CVH mechanism, we propose a simple computational model of synaptic plasticity with particular focus on the effect of selective damage to the bottom-up network on self-reorganization. We show neurons that undergo a change in activity from a bottom-up to a top-down network framework during the reorganization process, which can be understood in terms of state transitions. Assuming that the pre-reorganization representation of this neuron remains after reorganization, it is possible to interpret neural response induced by top-down information as the sensation of bottom-up information. This situation might correspond to a hallucinatory situation in DLB patients. Our results agree with existing experimental evidence and provide new insights into data that have hitherto not been experimentally validated on patients with DLB.

REASONING ABOUT PROGRAMMABLE LOGIC CONTROLLERS

We address the formal verification of the control software of critical systems, i.e., ensuring the absence of design errors in a system with respect to requirements. Control systems are usually based on industrial controllers, also known as Programmable Logic Controllers (PLCs). A specific feature of a PLC is a scan cycle: 1) the inputs are read, 2) the PLC states change, and 3) the outputs are written. Therefore, in order to formally verify PLC, e.g., by model checking, it is necessary to reason both in terms of state transitions within a cycle and in terms of larger state transitions according to the scan-cyclic semantics. We develop a formalization of PLC as a hyperprocess transition system and an LTL-based temporal logic cycle-LTL for reasoning about PLC.

Latent resting-state network dynamics in boys and girls with attention-deficit/hyperactivity disorder.

Neuroimaging studies of subjects with ADHD typically show altered functional connectivity in prefrontal, striatal, and several temporal brain regions. While the majority of studies have focused on connectivity that is averaged over time, we investigated the temporal dynamics of brain network changes in resting-state fMRI. Using the ADHD-200 consortium, we characterized the time course of latent state changes using Hidden Markov Modeling, and compared state changes between boys and girls with ADHD along with typically developing controls. Sex differences were found in latent state switching, with boys dwelling longer in a given state than girls, and concurrently having fewer overall state transitions. These sex differences were found in children with ADHD and in typically developing controls. Children with ADHD were also found to be more variable in terms of state transitions than controls. These findings add to the growing literature on neural sex differences and may be related to the sex difference in focal versus diffuse attention.

State-Transition Diagrams for Biologists

It is clearly in the tradition of biologists to conceptualize the dynamical evolution of biological systems in terms of state-transitions of biological objects. This paper is mainly concerned with (but obviously not limited too) the immunological branch of biology and shows how the adoption of UML (Unified Modeling Language) state-transition diagrams can ease the modeling, the understanding, the coding, the manipulation or the documentation of population-based immune software model generally defined as a set of ordinary differential equations (ODE), describing the evolution in time of populations of various biological objects. Moreover, that same UML adoption naturally entails a far from negligible representational economy since one graphical item of the diagram might have to be repeated in various places of the mathematical model. First, the main graphical elements of the UML state-transition diagram and how they can be mapped onto a corresponding ODE mathematical model are presented. Then, two already published immune models of thymocyte behavior and time evolution in the thymus, the first one originally conceived as an ODE population-based model whereas the second one as an agent-based one, are refactored and expressed in a state-transition form so as to make them much easier to understand and their respective code easier to access, to modify and run. As an illustrative proof, for any immunologist, it should be possible to understand faithfully enough what the two software models are supposed to reproduce and how they execute with no need to plunge into the Java or Fortran lines.

Dynamics and state transitions during resuscitation in out-of-hospital cardiac arrest

The state or rhythm during resuscitation, i.e. ventricular fibrillation/tachycardia (VF/VT), asystole (ASY), pulseless electrical activity (PEA), or return of spontaneous circulation (ROSC) determines management. The state is unstable and will change either spontaneously (e.g. PEA-->ASY) or by intervention (e.g. VF-->ASY after DC shock); temporary ROSC may also occur. To gain insight into the dynamics of this process, we analyzed the state transitions over time using real-life data. Detailed recordings from 304 episodes of attempted resuscitation from out-of-hospital cardiac arrests of presumed cardiac etiology were obtained from modified Heartstart 4000 defibrillators. State transitions were visualized and described, and analyzed in terms of a Markov probability model. The median number of state transitions was 5 (range 1-39), and more transitions were observed with VF than PEA or asystole as the initial rhythm. Of 105 patients (35%) who regained ROSC at some point during CPR, only 65 (21%) achieved sustained ROSC; suggesting an unrealized survival potential. A 3-min transition probability matrix was estimated: for example, a patient early in VF has a probability of 31% to be in ASY, 32% of still being in VF, 5% to have temporary ROSC, and 2% to have sustained ROSC after 3 min. The dynamics of resuscitation can be described in terms of state transitions and a Markov probability model. This framework enables prediction of short-term clinical development, supports informed decisions during CPR, and suggests a novel area for research.

Inference of Boolean Networks Using Sensitivity Regularization

The inference of genetic regulatory networks from global measurements of gene expressions is an important problem in computational biology. Recent studies suggest that such dynamical molecular systems are poised at a critical phase transition between an ordered and a disordered phase, affording the ability to balance stability and adaptability while coordinating complex macroscopic behavior. We investigate whether incorporating this dynamical system-wide property as an assumption in the inference process is beneficial in terms of reducing the inference error of the designed network. Using Boolean networks, for which there are well-defined notions of ordered, critical, and chaotic dynamical regimes as well as well-studied inference procedures, we analyze the expected inference error relative to deviations in the networks' dynamical regimes from the assumption of criticality. We demonstrate that taking criticality into account via a penalty term in the inference procedure improves the accuracy of prediction both in terms of state transitions and network wiring, particularly for small sample sizes.

Modeling the Complex Living World

In the software development domain, models of software systems are typically used to understand and predict properties of the system or to produce implementations. A model is normally created before the software system is implemented and, in many cases, acts as a specification of behavioral and structural properties that must be present in implemented systems. In theory, implementations can be generated from formal models in a manner that ensures their correctness with respect to the models. In other non-engineering disciplines, models are used differently. For example, physicists use models primarily to understand and explain phenomena that occur in the world around them. They build models that are consistent with their observations of the phenomena, and they test the models to determine their fidelity and the circumstances under which the models make accurate predictions. Unlike software models, formal models of physical systems typically describe continuous behavior (with very few non-continuous disruptions) and therefore use concepts from continuous mathematics (e.g., differential equations). It may seem that scientists in non-engineering disciplines have very little use for software modeling techniques, but the complex problems that are currently tackled in the Life Sciences area indicate otherwise. Scientists in the Life Sciences tackle highly-complex problems that involve study of organs, cells, proteins and organic molecules that exhibit continuous as well as discrete, non-deterministic behavior that can be described in terms of state transitions. Bio-technological models describe state-based phenomena and are primarily used to understand the phenomena and to predict behavior in a variety of situations. Accurate models pave the way for the engineering of medicines and for the development of sophisticated “biological tools” to further improve our lives. Scientists in Life Sciences currently use software modeling techniques to describe discrete behaviors. What needs to be determined is whether current modeling techniques actually meet the needs of scientists in the Life Sciences. Specifically, answers to the following questions are needed: Are the concepts provided by current software modeling languages sufficient to describe the discrete aspects of phenomena in Life Sciences? Can scientists in the Life Sciences benefit from the use of domain-specific variants of general purpose software modeling languages? Furthermore, the issue of how to disseminate knowledge about software modeling techniques in the Life Sciences needs to be addressed. Is there a need for software modeling training and education programs that target scientists in the Life Sciences or is there a need for a program that integrates Life Sciences and Computer Science (as extension to bio-informatics)? We would like to see SoSyM become a vehicle for communicating high-quality work that involves analyzing the application of software modeling techniques in domains such as the Life Sciences. We strongly encourage authors working on novel applications of software modeling techniques in domains such as the Life Sciences, Economics and Social Sciences, to submit papers describing their results to SoSyM.

Determining a generalized Maxwell set

In elementary catastrophe theory certain polynomials are studied and algebraic conditions on the coefficients of these polynomials are derived. These conditions define curves, surfaces, etc. in the spaces of the coefficients. The condition that the polynomial has a degenerate critical point defines the standard bifurcation set and provides the well known cusp diagram in the coefficient space of the cusp catastrophe. In this paper we consider the condition that a polynomial has critical values that differ by a given amount. Using the discriminant of the discriminant of a polynomial and the algebraic theory of roots of a polynomial an algorithm is defined for deriving an algebraic expression corresponding to this condition. The consequences of the algorithm are given for the cusp catastrophe and diagrams in the coefficient space are drawn. The implications of the diagrams in terms of state transitions are derived, thus providing the starting point for a new range of applications of the cusp catastrophe.