Publications by authors named "Richard Darst"

Article Synopsis
  • The study examines how behavioral data from smartphones can be used to detect and monitor depression symptoms in patients.
  • Researchers collected smartphone data from 164 participants over a year, including both healthy individuals and patients with various depressive disorders.
  • The analysis revealed 32 key behavioral markers linked to depression, achieving an 82% accuracy in classifying depressed individuals and 75% accuracy in tracking changes in their depressive states.
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Background: Depression-related negative bias in emotional processing and memory may bias accuracy of recall of temporally distal symptoms. We tested the hypothesis that when responding to the Patient Health Questionnaire (PHQ-9) the responses reflect more accurately temporally proximal than distal mood states.

Methods: Currently, depressed psychiatric outpatients (N = 80) with depression confirmed in semi-structured interviews had the Aware application installed on their smartphones for ecological momentary assessment (EMA).

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Various public transport (PT) agencies publish their route and timetable information with the General Transit Feed Specification (GTFS) as the standard open format. Timetable data are commonly used for PT passenger routing. They can also be used for studying the structure and organization of PT networks, as well as the accessibility and the level of service these networks provide.

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Background: Mental and behavioral disorders are the main cause of disability worldwide. However, their diagnosis is challenging due to a lack of reliable biomarkers; current detection is based on structured clinical interviews which can be biased by the patient's recall ability, affective state, changing in temporal frames, etc. While digital platforms have been introduced as a possible solution to this complex problem, there is little evidence on the extent of usability and usefulness of these platforms.

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Most complex systems are intrinsically dynamic in nature. The evolution of a dynamic complex system is typically represented as a sequence of snapshots, where each snapshot describes the configuration of the system at a particular instant of time. This is often done by using constant intervals but a better approach would be to define dynamic intervals that match the evolution of the system's configuration.

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Detecting the time evolution of the community structure of networks is crucial to identify major changes in the internal organization of many complex systems, which may undergo important endogenous or exogenous events. This analysis can be done in two ways: considering each snapshot as an independent community detection problem or taking into account the whole evolution of the network. In the first case, one can apply static methods on the temporal snapshots, which correspond to configurations of the system in short time windows, and match afterward the communities across layers.

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Algorithms to find communities in networks rely just on structural information and search for cohesive subsets of nodes. On the other hand, most scholars implicitly or explicitly assume that structural communities represent groups of nodes with similar (nontopological) properties or functions. This hypothesis could not be verified, so far, because of the lack of network datasets with information on the classification of the nodes.

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Most of the complex social, technological, and biological networks have a significant community structure. Therefore the community structure of complex networks has to be considered as a universal property, together with the much explored small-world and scale-free properties of these networks. Despite the large interest in characterizing the community structures of real networks, not enough attention has been devoted to the detection of universal mechanisms able to spontaneously generate networks with communities.

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Most algorithms to detect communities in networks typically work without any information on the cluster structure to be found, as one has no a priori knowledge of it, in general. Not surprisingly, knowing some features of the unknown partition could help its identification, yielding an improvement of the performance of the method. Here we show that, if the number of clusters was known beforehand, standard methods, like modularity optimization, would considerably gain in accuracy, mitigating the severe resolution bias that undermines the reliability of the results of the original unconstrained version.

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In this paper, we consider in detail the properties of dynamical heterogeneity in lattice glass models (LGMs). LGMs are lattice models whose dynamical rules are based on thermodynamic, as opposed to purely kinetic, considerations. We devise a LGM that is not prone to crystallization and displays properties of a fragile glass-forming liquid.

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Globular proteins undergo structural transitions to denatured states when sufficient thermodynamic state or chemical perturbations are introduced to their native environment. Cold denaturation is a somewhat counterintuitive phenomenon whereby proteins lose their compact folded structure as a result of a temperature drop. The currently accepted explanation for cold denaturation is based on an associated favorable change in the contact free energy between water and nonpolar groups at colder temperatures which would weaken the hydrophobic interaction and is thought to eventually allow polymer entropy to disrupt protein tertiary structure.

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Recent reports have demonstrated that the correlation function of the fluorescence dichroism signal, measured as a probe of single molecule rotational dynamics, should not manifest a single exponential decay even for isotropic diffusion. This has called into question the attribution of observed nonexponential behavior in supercooled fluids and polymer systems to dynamical heterogeneity. We show here that, for the case of a high numerical aperture objective, the dichroism decay becomes indistinguishable from a single exponential.

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