1,146 research outputs found
Effects of adenotonsillectomy on plasma inflammatory biomarkers in obese children with obstructive sleep apnea: A community-based study.
BackgroundObesity and obstructive sleep apnea syndrome (OSA) are highly prevalent and frequently overlapping conditions in children that lead to systemic inflammation, the latter being implicated in the various end-organ morbidities associated with these conditions.AimTo examine the effects of adenotonsillectomy (T&A) on plasma levels of inflammatory markers in obese children with polysomnographically diagnosed OSA who were prospectively recruited from the community.MethodsObese children prospectively diagnosed with OSA, underwent T&A and a second overnight polysomnogram (PSG) after surgery. Plasma fasting morning samples obtained after each of the two PSGs were assayed for multiple inflammatory and metabolic markers including interleukin (IL)-6, IL-18, plasminogen activator inhibitor-1 (PAI-1), monocyte chemoattractant protein-1 (MCP-1), matrix metalloproteinase-9 (MMP-9), adiponectin, apelin C, leptin and osteocrin.ResultsOut of 122 potential candidates, 100 obese children with OSA completed the study with only one-third exhibiting normalization of their PSG after T&A (that is, apnea-hypopnea index (AHI) ≤1/hour total sleep time). However, overall significant decreases in MCP-1, PAI-1, MMP-9, IL-18 and IL-6, and increases in adropin and osteocrin plasma concentrations occurred after T&A. Several of the T&A-responsive biomarkers exhibited excellent sensitivity and moderate specificity to predict residual OSA (that is, AHI⩾5/hTST).ConclusionsA defined subset of systemic inflammatory and metabolic biomarkers is reversibly altered in the context of OSA among community-based obese children, further reinforcing the concept on the interactive pro-inflammatory effects of sleep disorders such as OSA and obesity contributing to downstream end-organ morbidities
The Grail theorem prover: Type theory for syntax and semantics
As the name suggests, type-logical grammars are a grammar formalism based on
logic and type theory. From the prespective of grammar design, type-logical
grammars develop the syntactic and semantic aspects of linguistic phenomena
hand-in-hand, letting the desired semantics of an expression inform the
syntactic type and vice versa. Prototypical examples of the successful
application of type-logical grammars to the syntax-semantics interface include
coordination, quantifier scope and extraction.This chapter describes the Grail
theorem prover, a series of tools for designing and testing grammars in various
modern type-logical grammars which functions as a tool . All tools described in
this chapter are freely available
Estimation of the hydraulic parameters of unsaturated samples by electrical resistivity tomography
In situ and laboratory experiments have shown that electrical resistivity tomography (ERT) is an effective tool to image transient phenomena in soils. However, its application in quantifying soil hydraulic parameters has been limited. In this study, experiments of water inflow in unsaturated soil samples were conducted in an oedometer equipped to perform three-dimensional electrical measurements. Reconstructions of the electrical conductivity at different times confirmed the usefulness of ERT for monitoring the evolution of water content. The tomographic reconstructions were subsequently used in conjunction with a finite-element simulation to infer the water retention curve and the unsaturated hydraulic conductivity. The parameters estimated with ERT agree satisfactorily with those determined using established techniques, hence the proposed approach shows good potential for relatively fast characterisations. Similar experiments could be carried out on site to study the hydraulic behaviour of the entire soil deposi
Finite Automata for the Sub- and Superword Closure of CFLs: Descriptional and Computational Complexity
We answer two open questions by (Gruber, Holzer, Kutrib, 2009) on the
state-complexity of representing sub- or superword closures of context-free
grammars (CFGs): (1) We prove a (tight) upper bound of on
the size of nondeterministic finite automata (NFAs) representing the subword
closure of a CFG of size . (2) We present a family of CFGs for which the
minimal deterministic finite automata representing their subword closure
matches the upper-bound of following from (1).
Furthermore, we prove that the inequivalence problem for NFAs representing sub-
or superword-closed languages is only NP-complete as opposed to PSPACE-complete
for general NFAs. Finally, we extend our results into an approximation method
to attack inequivalence problems for CFGs
Graph-Based Shape Analysis Beyond Context-Freeness
We develop a shape analysis for reasoning about relational properties of data
structures. Both the concrete and the abstract domain are represented by
hypergraphs. The analysis is parameterized by user-supplied indexed graph
grammars to guide concretization and abstraction. This novel extension of
context-free graph grammars is powerful enough to model complex data structures
such as balanced binary trees with parent pointers, while preserving most
desirable properties of context-free graph grammars. One strength of our
analysis is that no artifacts apart from grammars are required from the user;
it thus offers a high degree of automation. We implemented our analysis and
successfully applied it to various programs manipulating AVL trees,
(doubly-linked) lists, and combinations of both
Distributed Edge Connectivity in Sublinear Time
We present the first sublinear-time algorithm for a distributed
message-passing network sto compute its edge connectivity exactly in
the CONGEST model, as long as there are no parallel edges. Our algorithm takes
time to compute and a
cut of cardinality with high probability, where and are the
number of nodes and the diameter of the network, respectively, and
hides polylogarithmic factors. This running time is sublinear in (i.e.
) whenever is. Previous sublinear-time
distributed algorithms can solve this problem either (i) exactly only when
[Thurimella PODC'95; Pritchard, Thurimella, ACM
Trans. Algorithms'11; Nanongkai, Su, DISC'14] or (ii) approximately [Ghaffari,
Kuhn, DISC'13; Nanongkai, Su, DISC'14].
To achieve this we develop and combine several new techniques. First, we
design the first distributed algorithm that can compute a -edge connectivity
certificate for any in time .
Second, we show that by combining the recent distributed expander decomposition
technique of [Chang, Pettie, Zhang, SODA'19] with techniques from the
sequential deterministic edge connectivity algorithm of [Kawarabayashi, Thorup,
STOC'15], we can decompose the network into a sublinear number of clusters with
small average diameter and without any mincut separating a cluster (except the
`trivial' ones). Finally, by extending the tree packing technique from [Karger
STOC'96], we can find the minimum cut in time proportional to the number of
components. As a byproduct of this technique, we obtain an -time
algorithm for computing exact minimum cut for weighted graphs.Comment: Accepted at 51st ACM Symposium on Theory of Computing (STOC 2019
EL-GAN: Embedding Loss Driven Generative Adversarial Networks for Lane Detection
Convolutional neural networks have been successfully applied to semantic
segmentation problems. However, there are many problems that are inherently not
pixel-wise classification problems but are nevertheless frequently formulated
as semantic segmentation. This ill-posed formulation consequently necessitates
hand-crafted scenario-specific and computationally expensive post-processing
methods to convert the per pixel probability maps to final desired outputs.
Generative adversarial networks (GANs) can be used to make the semantic
segmentation network output to be more realistic or better
structure-preserving, decreasing the dependency on potentially complex
post-processing. In this work, we propose EL-GAN: a GAN framework to mitigate
the discussed problem using an embedding loss. With EL-GAN, we discriminate
based on learned embeddings of both the labels and the prediction at the same
time. This results in more stable training due to having better discriminative
information, benefiting from seeing both `fake' and `real' predictions at the
same time. This substantially stabilizes the adversarial training process. We
use the TuSimple lane marking challenge to demonstrate that with our proposed
framework it is viable to overcome the inherent anomalies of posing it as a
semantic segmentation problem. Not only is the output considerably more similar
to the labels when compared to conventional methods, the subsequent
post-processing is also simpler and crosses the competitive 96% accuracy
threshold.Comment: 14 pages, 7 figure
SLUGBOT, an Aplysia-inspired Robotic Grasper for Studying Control
Living systems can use a single periphery to perform a variety of tasks and
adapt to a dynamic environment. This multifunctionality is achieved through the
use of neural circuitry that adaptively controls the reconfigurable
musculature. Current robotic systems struggle to flexibly adapt to unstructured
environments. Through mimicry of the neuromechanical coupling seen in living
organisms, robotic systems could potentially achieve greater autonomy. The
tractable neuromechanics of the sea slug
feeding apparatus, or buccal mass, make it an ideal candidate for applying
neuromechanical principles to the control of a soft robot. In this work, a
robotic grasper was designed to mimic specific morphology of the
feeding apparatus. These include the use of soft actuators
akin to biological muscle, a deformable grasping surface, and a similar
muscular architecture. A previously developed Boolean neural controller was
then adapted for the control of this soft robotic system. The robot was capable
of qualitatively replicating swallowing behavior by cyclically ingesting a
plastic tube. The robot's normalized translational and rotational kinematics of
the odontophore followed profiles observed despite
morphological differences. This brings -inspired control
one step closer to multifunctional neural control schema
and . Future additions may improve
SLUGBOT's viability as a neuromechanical research platform.Comment: Submitted and accepted to Living Machines 2022 conferenc
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