353 research outputs found
Microbial community pattern detection in human body habitats via ensemble clustering framework
The human habitat is a host where microbial species evolve, function, and
continue to evolve. Elucidating how microbial communities respond to human
habitats is a fundamental and critical task, as establishing baselines of human
microbiome is essential in understanding its role in human disease and health.
However, current studies usually overlook a complex and interconnected
landscape of human microbiome and limit the ability in particular body habitats
with learning models of specific criterion. Therefore, these methods could not
capture the real-world underlying microbial patterns effectively. To obtain a
comprehensive view, we propose a novel ensemble clustering framework to mine
the structure of microbial community pattern on large-scale metagenomic data.
Particularly, we first build a microbial similarity network via integrating
1920 metagenomic samples from three body habitats of healthy adults. Then a
novel symmetric Nonnegative Matrix Factorization (NMF) based ensemble model is
proposed and applied onto the network to detect clustering pattern. Extensive
experiments are conducted to evaluate the effectiveness of our model on
deriving microbial community with respect to body habitat and host gender. From
clustering results, we observed that body habitat exhibits a strong bound but
non-unique microbial structural patterns. Meanwhile, human microbiome reveals
different degree of structural variations over body habitat and host gender. In
summary, our ensemble clustering framework could efficiently explore integrated
clustering results to accurately identify microbial communities, and provide a
comprehensive view for a set of microbial communities. Such trends depict an
integrated biography of microbial communities, which offer a new insight
towards uncovering pathogenic model of human microbiome.Comment: BMC Systems Biology 201
Printed texture guided color feature fusion for impressionism style rendering of oil paintings.
As a major branch of Non-Photorealistic Rendering (NPR), image stylization mainly uses computer algorithms to render a photo into an artistic painting. Recent work has shown that the ex-traction of style information such as stroke texture and color of the target style image is the key to image stylization. Given its stroke texture and color characteristics, a new stroke rendering method is proposed. By fully considering the tonal characteristics and the representative color of the original oil painting, it can fit the tone of the original oil painting image into a stylized image whilst keeping the artist's creative effect. The experiments have validated the efficacy of the proposed model in comparison to three state-of-the-arts. This method would be more suitable for the works of pointillism painters with a relatively uniform style, especially for natural scenes, otherwise, the results can be less satisfactory
A music cognition-guided framework for multi-pitch estimation.
As one of the most important subtasks of automatic music transcription (AMT), multi-pitch estimation (MPE) has been studied extensively for predicting the fundamental frequencies in the frames of audio recordings during the past decade. However, how to use music perception and cognition for MPE has not yet been thoroughly investigated. Motivated by this, this demonstrates how to effectively detect the fundamental frequency and the harmonic structure of polyphonic music using a cognitive framework. Inspired by cognitive neuroscience, an integration of the constant Q transform and a state-of-the-art matrix factorization method called shift-invariant probabilistic latent component analysis (SI-PLCA) are proposed to resolve the polyphonic short-time magnitude log-spectra for multiple pitch estimation and source-specific feature extraction. The cognitions of rhythm, harmonic periodicity and instrument timbre are used to guide the analysis of characterizing contiguous notes and the relationship between fundamental frequency and harmonic frequencies for detecting the pitches from the outcomes of SI-PLCA. In the experiment, we compare the performance of proposed MPE system to a number of existing state-of-the-art approaches (seven weak learning methods and four deep learning methods) on three widely used datasets (i.e. MAPS, BACH10 and TRIOS) in terms of F-measure (F1) values. The experimental results show that the proposed MPE method provides the best overall performance against other existing methods
Calcineurin Controls the Expression of Isoform 4CII of the Plasma Membrane Ca2+ Pump in Neurons *
Abstract The expression of the CII splice variant of the plasma membrane Ca2+ ATPase 4 (PMCA4) was down-regulated in granule neurons when they were cultured under conditions of partial membrane depolarization (25 mm KCl), which are required for long term in vitro survival of the neurons. These conditions, which cause a chronic increase of the resting free Ca2+ concentration in the neurons, have recently been shown to promote up-regulation of the PMCA2, 3, and 1CII isoforms. Whereas the chronic, i.e. >3 days, Ca2+ increase was necessary for the up-regulation of the PMCA1CII, 2, and 3, the down-regulation of the PMCA4CII mRNA was already evident 1–2 h after the start of culturing in 25 mm KCl. The immunosuppressant calcineurin inhibitor FK506 inhibited the down-regulation of the PMCA4CII at both the protein and the mRNA level but did not affect the changes of the other PMCA pumps. Direct evidence for the involvement of calcineurin in the down-regulation of the PMCA4CII was obtained by overexpressing a truncated, constitutively active, and Ca2+-independent form of calcineurin; under these conditions, depolarization was not required for the down-regulation of the PMCA4CII pump. De novosynthesis of (transcription) factors was required for the down-regulation of the PMCA4CII mRNA. Calcineurin, therefore, controls the neuronal transcription of PMCA4CII, a splice variant of the pump isoforms that is found almost exclusively in brain
Siamese residual neural network for musical shape evaluation in piano performance assessment.
Understanding and identifying musical shape plays an important role in music education and performance assessment. To simplify the otherwise time- and cost-intensive musical shape evaluation, in this paper we explore how artificial intelligence (AI) driven models can be applied. Considering musical shape evaluation as a classification problem, a light-weight Siamese residual neural network (S-ResNN) is proposed to automatically identify musical shapes. To assess the proposed approach in the context of piano musical shape evaluation, we have generated a new dataset, namely MSED-4k, containing 4116 music pieces derived by 147 piano preparatory exercises and performed in 28 categories of musical shapes. The experimental results show that the S-ResNN significantly outperforms a number of benchmark methods in terms of the precision, recall and F1 score
Surround-view Fisheye BEV-Perception for Valet Parking: Dataset, Baseline and Distortion-insensitive Multi-task Framework
Surround-view fisheye perception under valet parking scenes is fundamental
and crucial in autonomous driving. Environmental conditions in parking lots
perform differently from the common public datasets, such as imperfect light
and opacity, which substantially impacts on perception performance. Most
existing networks based on public datasets may generalize suboptimal results on
these valet parking scenes, also affected by the fisheye distortion. In this
article, we introduce a new large-scale fisheye dataset called Fisheye Parking
Dataset(FPD) to promote the research in dealing with diverse real-world
surround-view parking cases. Notably, our compiled FPD exhibits excellent
characteristics for different surround-view perception tasks. In addition, we
also propose our real-time distortion-insensitive multi-task framework Fisheye
Perception Network (FPNet), which improves the surround-view fisheye BEV
perception by enhancing the fisheye distortion operation and multi-task
lightweight designs. Extensive experiments validate the effectiveness of our
approach and the dataset's exceptional generalizability.Comment: 12 pages, 11 figure
ADU-Depth: Attention-based Distillation with Uncertainty Modeling for Depth Estimation
Monocular depth estimation is challenging due to its inherent ambiguity and
ill-posed nature, yet it is quite important to many applications. While recent
works achieve limited accuracy by designing increasingly complicated networks
to extract features with limited spatial geometric cues from a single RGB
image, we intend to introduce spatial cues by training a teacher network that
leverages left-right image pairs as inputs and transferring the learned 3D
geometry-aware knowledge to the monocular student network. Specifically, we
present a novel knowledge distillation framework, named ADU-Depth, with the
goal of leveraging the well-trained teacher network to guide the learning of
the student network, thus boosting the precise depth estimation with the help
of extra spatial scene information. To enable domain adaptation and ensure
effective and smooth knowledge transfer from teacher to student, we apply both
attention-adapted feature distillation and focal-depth-adapted response
distillation in the training stage. In addition, we explicitly model the
uncertainty of depth estimation to guide distillation in both feature space and
result space to better produce 3D-aware knowledge from monocular observations
and thus enhance the learning for hard-to-predict image regions. Our extensive
experiments on the real depth estimation datasets KITTI and DrivingStereo
demonstrate the effectiveness of the proposed method, which ranked 1st on the
challenging KITTI online benchmark.Comment: accepted by CoRL 202
A Microbiome-Based Index for Assessing Skin Health and Treatment Effects for Atopic Dermatitis in Children.
A quantitative and objective indicator for skin health via the microbiome is of great interest for personalized skin care, but differences among skin sites and across human populations can make this goal challenging. A three-city (two Chinese and one American) comparison of skin microbiota from atopic dermatitis (AD) and healthy pediatric cohorts revealed that, although city has the greatest effect size (the skin microbiome can predict the originated city with near 100% accuracy), a microbial index of skin health (MiSH) based on 25 bacterial genera can diagnose AD with 83 to ∼95% accuracy within each city and 86.4% accuracy across cities (area under the concentration-time curve [AUC], 0.90). Moreover, nonlesional skin sites across the bodies of AD-active children (which include shank, arm, popliteal fossa, elbow, antecubital fossa, knee, neck, and axilla) harbor a distinct but lesional state-like microbiome that features relative enrichment of Staphylococcus aureus over healthy individuals, confirming the extension of microbiome dysbiosis across body surface in AD patients. Intriguingly, pretreatment MiSH classifies children with identical AD clinical symptoms into two host types with distinct microbial diversity and treatment effects of corticosteroid therapy. These findings suggest that MiSH has the potential to diagnose AD, assess risk-prone state of skin, and predict treatment response in children across human populations.IMPORTANCE MiSH, which is based on the skin microbiome, can quantitatively assess pediatric skin health across cohorts from distinct countries over large geographic distances. Moreover, the index can identify a risk-prone skin state and compare treatment effect in children, suggesting applications in diagnosis and patient stratification
- …