1,782 research outputs found
Effects of Static Versus Dynamic Cupping on Ankle Dorsiflexion
PURPOSE: Cupping therapy involves lifting and separating fascial tissue to facilitate stretching and promote blood flow. Although cupping is a common treatment modality for pain, studies are inconsistent in regards to whether cupping improves other outcomes, like range of motion. Possessing a limited range of motion can lead to musculoskeletal injury. The aim of this research is to determine the acute effect of different methods of cupping therapy on ankle dorsiflexion. METHODS: A total of 35 healthy adults (age: 22.1±4.52 years) with an average ankle ROM of 34.68±4.22° at baseline were included in the study. Participants were randomly assigned to one of four cupping therapy groups: static cupping, dynamic cupping, static sham cupping, or dynamic sham cupping. Ankle ROM was measured pre- and immediately post-intervention. The minimal detectable change (MDC) for weight bearing ankle dorsiflexion was calculated based on the reliability of baseline measurements at 4.96º. A 2x4 mixed ANOVA was used to determine whether ankle ROM differed pre-to-post treatment. RESULTS: All groups showed improvement in ankle ROM post-intervention (38.41±4.95º), but no significant interaction effect between intervention and time (F(3,31)=1.31, p=.289). However, a significant main effect of time was observed(F(1,31)=33.69, p CONCLUSION: These findings support the potential of cupping as a modality for improving ankle ROM. Dynamic cupping may be more effective than static cupping for improving ankle ROM, so dynamic cupping can be considered as a potential intervention to address limited ankle ROM
The stability of a graph partition: A dynamics-based framework for community detection
Recent years have seen a surge of interest in the analysis of complex
networks, facilitated by the availability of relational data and the
increasingly powerful computational resources that can be employed for their
analysis. Naturally, the study of real-world systems leads to highly complex
networks and a current challenge is to extract intelligible, simplified
descriptions from the network in terms of relevant subgraphs, which can provide
insight into the structure and function of the overall system.
Sparked by seminal work by Newman and Girvan, an interesting line of research
has been devoted to investigating modular community structure in networks,
revitalising the classic problem of graph partitioning.
However, modular or community structure in networks has notoriously evaded
rigorous definition. The most accepted notion of community is perhaps that of a
group of elements which exhibit a stronger level of interaction within
themselves than with the elements outside the community. This concept has
resulted in a plethora of computational methods and heuristics for community
detection. Nevertheless a firm theoretical understanding of most of these
methods, in terms of how they operate and what they are supposed to detect, is
still lacking to date.
Here, we will develop a dynamical perspective towards community detection
enabling us to define a measure named the stability of a graph partition. It
will be shown that a number of previously ad-hoc defined heuristics for
community detection can be seen as particular cases of our method providing us
with a dynamic reinterpretation of those measures. Our dynamics-based approach
thus serves as a unifying framework to gain a deeper understanding of different
aspects and problems associated with community detection and allows us to
propose new dynamically-inspired criteria for community structure.Comment: 3 figures; published as book chapte
Emergence of slow-switching assemblies in structured neuronal networks
Unraveling the interplay between connectivity and spatio-temporal dynamics in
neuronal networks is a key step to advance our understanding of neuronal
information processing. Here we investigate how particular features of network
connectivity underpin the propensity of neural networks to generate
slow-switching assembly (SSA) dynamics, i.e., sustained epochs of increased
firing within assemblies of neurons which transition slowly between different
assemblies throughout the network. We show that the emergence of SSA activity
is linked to spectral properties of the asymmetric synaptic weight matrix. In
particular, the leading eigenvalues that dictate the slow dynamics exhibit a
gap with respect to the bulk of the spectrum, and the associated Schur vectors
exhibit a measure of block-localization on groups of neurons, thus resulting in
coherent dynamical activity on those groups. Through simple rate models, we
gain analytical understanding of the origin and importance of the spectral gap,
and use these insights to develop new network topologies with alternative
connectivity paradigms which also display SSA activity. Specifically, SSA
dynamics involving excitatory and inhibitory neurons can be achieved by
modifying the connectivity patterns between both types of neurons. We also show
that SSA activity can occur at multiple timescales reflecting a hierarchy in
the connectivity, and demonstrate the emergence of SSA in small-world like
networks. Our work provides a step towards understanding how network structure
(uncovered through advancements in neuroanatomy and connectomics) can impact on
spatio-temporal neural activity and constrain the resulting dynamics.Comment: The first two authors contributed equally -- 18 pages, including
supplementary material, 10 Figures + 2 SI Figure
Structure of complex networks: Quantifying edge-to-edge relations by failure-induced flow redistribution
The analysis of complex networks has so far revolved mainly around the role
of nodes and communities of nodes. However, the dynamics of interconnected
systems is commonly focalised on edge processes, and a dual edge-centric
perspective can often prove more natural. Here we present graph-theoretical
measures to quantify edge-to-edge relations inspired by the notion of flow
redistribution induced by edge failures. Our measures, which are related to the
pseudo-inverse of the Laplacian of the network, are global and reveal the
dynamical interplay between the edges of a network, including potentially
non-local interactions. Our framework also allows us to define the embeddedness
of an edge, a measure of how strongly an edge features in the weighted cuts of
the network. We showcase the general applicability of our edge-centric
framework through analyses of the Iberian Power grid, traffic flow in road
networks, and the C. elegans neuronal network.Comment: 24 pages, 6 figure
Comparison of Artificial Intelligence based approaches to cell function prediction
Predicting Retinal Pigment Epithelium (RPE) cell functions in stem cell implants using non-invasive bright field microscopy imaging is a critical task for clinical deployment of stem cell therapies. Such cell function predictions can be carried out using Artificial Intelligence (AI) based models. In this paper we used Traditional Machine Learning (TML) and Deep Learning (DL) based AI models for cell function prediction tasks. TML models depend on feature engineering and DL models perform feature engineering automatically but have higher modeling complexity. This work aims at exploring the tradeoffs between three approaches using TML and DL based models for RPE cell function prediction from microscopy images and at understanding the accuracy relationship between pixel-, cell feature-, and implant label-level accuracies of models. Among the three compared approaches to cell function prediction, the direct approach to cell function prediction from images is slightly more accurate in comparison to indirect approaches using intermediate segmentation and/or feature engineering steps. We also evaluated accuracy variations with respect to model selections (five TML models and two DL models) and model configurations (with and without transfer learning). Finally, we quantified the relationships between segmentation accuracy and the number of samples used for training a model, segmentation accuracy and cell feature error, and cell feature error and accuracy of implant labels. We concluded that for the RPE cell data set, there is a monotonic relationship between the number of training samples and image segmentation accuracy, and between segmentation accuracy and cell feature error, but there is no such a relationship between segmentation accuracy and accuracy of RPE implant labels
Drug Hypersensitivity Syndrome toCarbamazepine and Human Herpes Virus 6 Infection: Case Reportand Literature Review
Abstract.: We describe a patient with a drug-induced hypersensitivity syndrome to carbamazepine and a concomitant active infection with human herpes virus 6 (HHV-6). The potential role of HHV-6 regarding the drug-induced hypersensitivity syndrome is discussed and the main clinical features of this potentially fatal adverse drug reaction are highlighte
Evaluation Techniques and Systems for Answer Set Programming: a Survey
Answer set programming (ASP) is a prominent knowledge representation and reasoning paradigm that found both industrial and scientific applications. The success of ASP is due to the combination of two factors: a rich modeling language and the availability of efficient ASP implementations. In this paper we trace the history of ASP systems, describing the key evaluation techniques and their implementation in actual tools
- …