253 research outputs found
Identifying protein complexes from interaction networks based on clique percolation and distance restriction
Background: Identification of protein complexes in large interaction networks is crucial to understand principles of cellular organization and predict protein functions, which is one of the most important issues in the post-genomic era. Each protein might be subordinate multiple protein complexes in the real protein-protein interaction networks.Identifying overlapping protein complexes from protein-protein interaction networks is a considerable research topic.
Result: As an effective algorithm in identifying overlapping module structures, clique percolation method (CPM) has a wide range of application in social networks and biological networks. However, the recognition accuracy of algorithm CPM is lowly. Furthermore, algorithm CPM is unfit to identifying protein complexes with meso-scale when it applied in protein-protein interaction networks. In this paper, we propose a new topological model by extending the definition of k-clique community of algorithm CPM and introduced distance restriction, and develop a novel algorithm called CP-DR based on the new topological model for identifying protein complexes. In this new algorithm, the protein complex size is restricted by distance constraint to conquer the shortcomings of algorithm CPM. The algorithm CP-DR is applied to the protein interaction network of Sacchromyces cerevisiae and identifies many well known complexes.
Conclusion: The proposed algorithm CP-DR based on clique percolation and distance restriction makes it possible to identify dense subgraphs in protein interaction networks, a large number of which correspond to known protein complexes. Compared to algorithm CPM, algorithm CP-DR has more outstanding performance
The beauty in imperfection: how naturalness cues drive consumer preferences for ugly produce and reduce food waste
PurposeAn important reason for food waste is the rejection of ugly produce by consumers. Most previous research has examined the absolute negative impacts of ugly produce on consumersā preferences, no research has examined the conditions in which consumers prefer ugly (vs. typical) produce instead.This research investigates the circumstances under which these aesthetic imperfections become advantageous.MethodsWe conducted two between-subject design randomized experiments featuring two produce categories to examine when and why consumers prefer ugly produce.ResultsWe found that naturalness cues boost and even reverse consumersā preferences for ugly produce when combining ugly appearance with naturalness cues. The subtyping effect mediates the interaction of appearance (typical vs. ugly) of produce and naturalness cues (present vs. absent) on produceās evaluations.DiscussionOur findings provide more cost-effective strategies for retailers to reduce food waste. This paper fills in the research gaps on taping into the novel condition in which consumers prefer ugly (vs. typical) produce and the psychological mechanism behind this process. Based on schema incongruity theory, we argue that naturalness cues, as an enabler corresponding to the incongruous features of ugly produce, facilitate consumers to resolve the schema incongruity triggered by the ugly appearance and, in turn, boost consumersā preferences for ugly produce
LAW-Diffusion: Complex Scene Generation by Diffusion with Layouts
Thanks to the rapid development of diffusion models, unprecedented progress
has been witnessed in image synthesis. Prior works mostly rely on pre-trained
linguistic models, but a text is often too abstract to properly specify all the
spatial properties of an image, e.g., the layout configuration of a scene,
leading to the sub-optimal results of complex scene generation. In this paper,
we achieve accurate complex scene generation by proposing a semantically
controllable Layout-AWare diffusion model, termed LAW-Diffusion. Distinct from
the previous Layout-to-Image generation (L2I) methods that only explore
category-aware relationships, LAW-Diffusion introduces a spatial dependency
parser to encode the location-aware semantic coherence across objects as a
layout embedding and produces a scene with perceptually harmonious object
styles and contextual relations. To be specific, we delicately instantiate each
object's regional semantics as an object region map and leverage a
location-aware cross-object attention module to capture the spatial
dependencies among those disentangled representations. We further propose an
adaptive guidance schedule for our layout guidance to mitigate the trade-off
between the regional semantic alignment and the texture fidelity of generated
objects. Moreover, LAW-Diffusion allows for instance reconfiguration while
maintaining the other regions in a synthesized image by introducing a
layout-aware latent grafting mechanism to recompose its local regional
semantics. To better verify the plausibility of generated scenes, we propose a
new evaluation metric for the L2I task, dubbed Scene Relation Score (SRS) to
measure how the images preserve the rational and harmonious relations among
contextual objects. Comprehensive experiments demonstrate that our
LAW-Diffusion yields the state-of-the-art generative performance, especially
with coherent object relations
A comparison of the functional modules identified from time course and static PPI network data
<p>Abstract</p> <p>Background</p> <p>Cellular systems are highly dynamic and responsive to cues from the environment. Cellular function and response patterns to external stimuli are regulated by biological networks. A protein-protein interaction (PPI) network with static connectivity is dynamic in the sense that the nodes implement so-called functional activities that evolve in time. The shift from static to dynamic network analysis is essential for further understanding of molecular systems.</p> <p>Results</p> <p>In this paper, Time Course Protein Interaction Networks (TC-PINs) are reconstructed by incorporating time series gene expression into PPI networks. Then, a clustering algorithm is used to create functional modules from three kinds of networks: the TC-PINs, a static PPI network and a pseudorandom network. For the functional modules from the TC-PINs, repetitive modules and modules contained within bigger modules are removed. Finally, matching and GO enrichment analyses are performed to compare the functional modules detected from those networks.</p> <p>Conclusions</p> <p>The comparative analyses show that the functional modules from the TC-PINs have much more significant biological meaning than those from static PPI networks. Moreover, it implies that many studies on static PPI networks can be done on the TC-PINs and accordingly, the experimental results are much more satisfactory. The 36 PPI networks corresponding to 36 time points, identified as part of this study, and other materials are available at <url>http://bioinfo.csu.edu.cn/txw/TC-PINs.</url></p
A thermal bonding method for manufacturing Micromegas detectors
For manufacturing Micromegas detectors, the "bulk" method based on
photoetching, was successfully developed and widely used in nuclear and
particle physics experiments. However, the complexity of the method requires a
considerable number of advanced instruments and processing, limiting the
accessibility of this method for production of Micromegas detectors. In view of
these limitations with the bulk method, a new method based on thermal bonding
technique (TBM) has been developed to manufacture Micromegas detectors in a
much simplified and efficient way without etching. This paper describes the TBM
in detail and presents performance of the Micromegas detectors built with the
TBM. The effectiveness of this method was investigated by testing Micromegas
detector prototypes built with the method. Both X-rays and electron beams were
used to characterize the prototypes in a gas mixture of argon and CO2 (7%). A
typical energy resolution of ~16% (full width at half maximum, FWHM) and an
absolute gain greater than 10^4 were obtained with 5.9 keV X-rays. Detection
efficiency greater than 98% and a spatial resolution of ~65 {\mu}m were
achieved using a 5 GeV electron beam at the DESY test-beam facility. The gas
gain of a Micromegas detector could reach up to 10^5 with a uniformity of
better than 10% when the size of the avalanche gap was optimized thanks to the
flexibility of the TBM in defining the gap. Additionally, the TBM facilitates
the exploration of new detector structures based on Micromegas owing to the
much-simplified operation with the method.Comment: 15 pages, 17 figure
Plasma noise in TianQin time delay interferometry
TianQin is a proposed geocentric space-based gravitational wave observatory
mission, which requires time-delay interferometry (TDI) to cancel laser
frequency noise. With high demands for precision, solar-wind plasma environment
at km above the Earth may constitute a non-negligible noise source
to laser interferometric measurements between satellites, as charged particles
perturb the refractivity along light paths. In this paper, we first assess the
plasma noises along single links from space-weather models and numerical
orbits, and analyze the time and frequency domain characteristics.
Particularly, to capture the plasma noise in the entire measurement band of
Hz, we have performed additional space-weather
magnetohydrodynamic simulations in finer spatial and temporal resolutions and
utilized Kolmogorov spectra in high-frequency data generation. Then we evaluate
the residual plasma noises of the first- and second-generation TDI
combinations. Both analytical and numerical estimations have shown that under
normal solar conditions the plasma noise after TDI is less than the secondary
noise requirement. Moreover, TDI is shown to exhibit moderate suppression on
the plasma noise below Hz due to noise correlation between
different arms, when compared with the secondary noise before and after TDI.Comment: 12 pages, 15 figures, accepted by Phys. Rev.
One-shot Implicit Animatable Avatars with Model-based Priors
Existing neural rendering methods for creating human avatars typically either
require dense input signals such as video or multi-view images, or leverage a
learned prior from large-scale specific 3D human datasets such that
reconstruction can be performed with sparse-view inputs. Most of these methods
fail to achieve realistic reconstruction when only a single image is available.
To enable the data-efficient creation of realistic animatable 3D humans, we
propose ELICIT, a novel method for learning human-specific neural radiance
fields from a single image. Inspired by the fact that humans can effortlessly
estimate the body geometry and imagine full-body clothing from a single image,
we leverage two priors in ELICIT: 3D geometry prior and visual semantic prior.
Specifically, ELICIT utilizes the 3D body shape geometry prior from a skinned
vertex-based template model (i.e., SMPL) and implements the visual clothing
semantic prior with the CLIP-based pretrained models. Both priors are used to
jointly guide the optimization for creating plausible content in the invisible
areas. Taking advantage of the CLIP models, ELICIT can use text descriptions to
generate text-conditioned unseen regions. In order to further improve visual
details, we propose a segmentation-based sampling strategy that locally refines
different parts of the avatar. Comprehensive evaluations on multiple popular
benchmarks, including ZJU-MoCAP, Human3.6M, and DeepFashion, show that ELICIT
has outperformed strong baseline methods of avatar creation when only a single
image is available. The code is public for research purposes at
https://huangyangyi.github.io/ELICIT/.Comment: To appear at ICCV 2023. Project website:
https://huangyangyi.github.io/ELICIT
A Novel CRYGD Mutation (p.Trp43Arg) Causing Autosomal Dominant Congenital Cataract in a Chinese Family
To identify the genetic defect associated with autosomal dominant congenital nuclear cataract in a Chinese family, molecular genetic investigation via haplotype analysis and direct sequencing were performed Sequencing of the CRYGD gene revealed a c.127T>C transition, which resulted in a substitution of a highly conserved tryptophan with arginine at codon 43 (p.Trp43Arg). This mutation co-segregated with all affected individuals and was not observed in either unaffected family members or in 200 normal unrelated individuals. Biophysical studies indicated that the p.Trp43Arg mutation resulted in significant tertiary structural changes. The mutant protein was much less stable than the wild-type protein, and was more prone to aggregate when subjected to environmental stresses such as heat and UV irradiation. Ā© 2010 Wiley-Liss, Inc
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