66 research outputs found
Unsupervised Cross-Modality Domain Adaptation for Vestibular Schwannoma Segmentation and Koos Grade Prediction based on Semi-Supervised Contrastive Learning
Domain adaptation has been widely adopted to transfer styles across
multi-vendors and multi-centers, as well as to complement the missing
modalities. In this challenge, we proposed an unsupervised domain adaptation
framework for cross-modality vestibular schwannoma (VS) and cochlea
segmentation and Koos grade prediction. We learn the shared representation from
both ceT1 and hrT2 images and recover another modality from the latent
representation, and we also utilize proxy tasks of VS segmentation and brain
parcellation to restrict the consistency of image structures in domain
adaptation. After generating missing modalities, the nnU-Net model is utilized
for VS and cochlea segmentation, while a semi-supervised contrastive learning
pre-train approach is employed to improve the model performance for Koos grade
prediction. On CrossMoDA validation phase Leaderboard, our method received rank
4 in task1 with a mean Dice score of 0.8394 and rank 2 in task2 with
Macro-Average Mean Square Error of 0.3941. Our code is available at
https://github.com/fiy2W/cmda2022.superpolymerization
Solidification of High Organic Matter Content Sludge by Cement, Lime and Metakaolin
Based on orthogonal experimental design, the key solidification controlling technology of Solidified/Stabilized (S/S) sludge with high total organic content (TOC) by cement, lime and metakaolin was explored by macroscopic tests, chemical components measurements and microscopic analysis. The macroscopic tests show that, the permeability coefficient is mainly affected by initial water content and lime content, and the unconfined compression strength is mainly affected by cement content and lime content. The chemical components measurements show that, the solidification effect of S/S sludge with high TOC is controlled by organic matter consumption, and organic matter consumption is determined by the alkaline environment from the cement and lime hydration reactions, which is mainly affect by the initial water content and lime-metakaolin content ratio. The microscopic analysis results show that, lime consumes parts of organic matter while excess lime produces weak Ca(OH)2 crystal fluffy sheet structure, matakaolin produces pozzolanic reactions with cement and lime instead of soil particles, and consumes the weak Ca(OH)2 crystal fluffy sheet structure produced by superfluous lime. The research has confirmed key controlling points of S/S sludge in case of high TOC, which will provide theoretical guidance and technical support for S/S sludge promotion with high TOC
TransNets: Learning to Transform for Recommendation
Recently, deep learning methods have been shown to improve the performance of
recommender systems over traditional methods, especially when review text is
available. For example, a recent model, DeepCoNN, uses neural nets to learn one
latent representation for the text of all reviews written by a target user, and
a second latent representation for the text of all reviews for a target item,
and then combines these latent representations to obtain state-of-the-art
performance on recommendation tasks. We show that (unsurprisingly) much of the
predictive value of review text comes from reviews of the target user for the
target item. We then introduce a way in which this information can be used in
recommendation, even when the target user's review for the target item is not
available. Our model, called TransNets, extends the DeepCoNN model by
introducing an additional latent layer representing the target user-target item
pair. We then regularize this layer, at training time, to be similar to another
latent representation of the target user's review of the target item. We show
that TransNets and extensions of it improve substantially over the previous
state-of-the-art.Comment: Accepted for publication in the 11th ACM Conference on Recommender
Systems (RecSys 2017
User Diverse Preference Modeling by Multimodal Attentive Metric Learning
Most existing recommender systems represent a user's preference with a
feature vector, which is assumed to be fixed when predicting this user's
preferences for different items. However, the same vector cannot accurately
capture a user's varying preferences on all items, especially when considering
the diverse characteristics of various items. To tackle this problem, in this
paper, we propose a novel Multimodal Attentive Metric Learning (MAML) method to
model user diverse preferences for various items. In particular, for each
user-item pair, we propose an attention neural network, which exploits the
item's multimodal features to estimate the user's special attention to
different aspects of this item. The obtained attention is then integrated into
a metric-based learning method to predict the user preference on this item. The
advantage of metric learning is that it can naturally overcome the problem of
dot product similarity, which is adopted by matrix factorization (MF) based
recommendation models but does not satisfy the triangle inequality property. In
addition, it is worth mentioning that the attention mechanism cannot only help
model user's diverse preferences towards different items, but also overcome the
geometrically restrictive problem caused by collaborative metric learning.
Extensive experiments on large-scale real-world datasets show that our model
can substantially outperform the state-of-the-art baselines, demonstrating the
potential of modeling user diverse preference for recommendation.Comment: Accepted by ACM Multimedia 2019 as a full pape
Cortical morphological heterogeneity of schizophrenia and its relationship with glutamatergic receptor variations
Abstract
Background
Recent genetic evidence implicates glutamatergic-receptor variations in schizophrenia. Glutamatergic excess during early life in people with schizophrenia may cause excitotoxicity and produce structural deficits in the brain. Cortical thickness and gyrification are reduced in schizophrenia, but only a subgroup of patients exhibits such structural deficits. We delineate the structural variations among unaffected siblings and patients with schizophrenia and study the role of key glutamate-receptor polymorphisms on these variations.
Methods
Gaussian Mixture Model clustering was applied to the cortical thickness and gyrification data of 114 patients, 112 healthy controls, and 42 unaffected siblings to identify subgroups. The distribution of glutamate-receptor (GRM3, GRIN2A, and GRIA1) and voltage-gated calcium channel (CACNA1C) variations across the MRI-based subgroups was studied. The comparisons in clinical symptoms and cognition between patient subgroups were conducted.
Results
We observed a “hypogyric,” “impoverished-thickness,” and “supra-normal” subgroups of patients, with higher negative symptom burden and poorer verbal fluency in the hypogyric subgroup and notable functional deterioration in the impoverished-thickness subgroup. Compared to healthy subjects, the hypogyric subgroup had significant GRIN2A and GRM3 variations, the impoverished-thickness subgroup had CACNA1C variations while the supra-normal group had no differences.
Conclusions
Disrupted gyrification and thickness can be traced to the glutamatergic receptor and voltage-gated calcium channel dysfunction respectively in schizophrenia. This raises the question of whether MRI-based multimetric subtyping may be relevant for clinical trials of agents affecting the glutamatergic system
Prevotella genus and its related NOD-like receptor signaling pathway in young males with stage III periodontitis
BackgroundAs periodontitis progresses, the oral microbiota community changes dynamically. In this study, we evaluated the dominant bacteria and their roles in the potential pathway in young males with stage III periodontitis.Methods16S rRNA sequencing was performed to evaluate variations in the composition of oral bacteria between males with stage I and III periodontitis and identify the dominant bacteria of each group. Function prediction was obtained based on 16S rRNA sequencing data. The inhibitor of the predominant pathway for stage III periodontitis was used to investigate the role of the dominant bacteria in periodontitis in vivo and in vitro.ResultsChao1 index, Observed Species and Phylogenetic Diversity (PD) whole tree values were significantly higher in the stage III periodontitis group. β-diversity suggested that samples could be divided according to the stages of periodontitis. The dominant bacteria in stage III periodontitis were Prevotella, Prevotella_7, and Dialister, whereas that in stage I periodontitis was Cardiobacterium. KEGG analysis predicted that variations in the oral microbiome may be related to the NOD-like receptor signaling pathway. The inhibitor of this pathway, NOD-IN-1, decreased P. intermedia -induced Tnf-α mRNA expression and increased P. intermedia -induced Il-6 mRNA expression, consistent with the ELISA results. Immunohistochemistry confirmed the down-regulation of TNF-α and IL-6 expressions by NOD-IN-1 in P. intermedia–induced periodontitis.ConclusionThe composition of the oral bacteria in young males varied according to the stage of periodontitis. The species richness of oral microtia was greater in young males with stage III periodontitis than those with stage I periodontitis. Prevotella was the dominant bacteria in young males with stage III periodontitis, and inhibition of the NOD-like receptor signaling pathway can decrease the periodontal inflammation induced by P. intermedia
Cell transcriptomic atlas of the non-human primate Macaca fascicularis.
Studying tissue composition and function in non-human primates (NHPs) is crucial to understand the nature of our own species. Here we present a large-scale cell transcriptomic atlas that encompasses over 1 million cells from 45 tissues of the adult NHP Macaca fascicularis. This dataset provides a vast annotated resource to study a species phylogenetically close to humans. To demonstrate the utility of the atlas, we have reconstructed the cell-cell interaction networks that drive Wnt signalling across the body, mapped the distribution of receptors and co-receptors for viruses causing human infectious diseases, and intersected our data with human genetic disease orthologues to establish potential clinical associations. Our M. fascicularis cell atlas constitutes an essential reference for future studies in humans and NHPs.We thank W. Liu and L. Xu from the Huazhen Laboratory Animal Breeding
Centre for helping in the collection of monkey tissues, D. Zhu and H. Li from the Bioland
Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory) for
technical help, G. Guo and H. Sun from Zhejiang University for providing HCL and MCA gene
expression data matrices, G. Dong and C. Liu from BGI Research, and X. Zhang, P. Li and C. Qi
from the Guangzhou Institutes of Biomedicine and Health for experimental advice or providing
reagents. This work was supported by the Shenzhen Basic Research Project for Excellent
Young Scholars (RCYX20200714114644191), Shenzhen Key Laboratory of Single-Cell Omics
(ZDSYS20190902093613831), Shenzhen Bay Laboratory (SZBL2019062801012) and Guangdong Provincial Key Laboratory of Genome Read and Write (2017B030301011). In
addition, L.L. was supported by the National Natural Science Foundation of China (31900466),
Y. Hou was supported by the Natural Science Foundation of Guangdong Province
(2018A030313379) and M.A.E. was supported by a Changbai Mountain Scholar award
(419020201252), the Strategic Priority Research Program of the Chinese Academy of Sciences
(XDA16030502), a Chinese Academy of Sciences–Japan Society for the Promotion of Science
joint research project (GJHZ2093), the National Natural Science Foundation of China
(92068106, U20A2015) and the Guangdong Basic and Applied Basic Research Foundation
(2021B1515120075). M.L. was supported by the National Key Research and Development
Program of China (2021YFC2600200).S
Socializing One Health: an innovative strategy to investigate social and behavioral risks of emerging viral threats
In an effort to strengthen global capacity to prevent, detect, and control infectious diseases in animals and people, the United States Agency for International Development’s (USAID) Emerging Pandemic Threats (EPT) PREDICT project funded development of regional, national, and local One Health capacities for early disease detection, rapid response, disease control, and risk reduction. From the outset, the EPT approach was inclusive of social science research methods designed to understand the contexts and behaviors of communities living and working at human-animal-environment interfaces considered high-risk for virus emergence. Using qualitative and quantitative approaches, PREDICT behavioral research aimed to identify and assess a range of socio-cultural behaviors that could be influential in zoonotic disease emergence, amplification, and transmission. This broad approach to behavioral risk characterization enabled us to identify and characterize human activities that could be linked to the transmission dynamics of new and emerging viruses. This paper provides a discussion of implementation of a social science approach within a zoonotic surveillance framework. We conducted in-depth ethnographic interviews and focus groups to better understand the individual- and community-level knowledge, attitudes, and practices that potentially put participants at risk for zoonotic disease transmission from the animals they live and work with, across 6 interface domains. When we asked highly-exposed individuals (ie. bushmeat hunters, wildlife or guano farmers) about the risk they perceived in their occupational activities, most did not perceive it to be risky, whether because it was normalized by years (or generations) of doing such an activity, or due to lack of information about potential risks. Integrating the social sciences allows investigations of the specific human activities that are hypothesized to drive disease emergence, amplification, and transmission, in order to better substantiate behavioral disease drivers, along with the social dimensions of infection and transmission dynamics. Understanding these dynamics is critical to achieving health security--the protection from threats to health-- which requires investments in both collective and individual health security. Involving behavioral sciences into zoonotic disease surveillance allowed us to push toward fuller community integration and engagement and toward dialogue and implementation of recommendations for disease prevention and improved health security
Key factors influencing earthquake-induced liquefaction and their direct and mediation effects.
Many factors impact earthquake-induced liquefaction, and there are complex interactions between them. Therefore, rationally identifying the key factors and clarifying their direct and indirect effects on liquefaction help to reduce the complexity of the predictive model and improve its predictive performance. This information can also help researchers understand the liquefaction phenomenon more clearly. In this paper, based on a shear wave velocity (Vs) database, 12 key factors are quantitatively identified using a correlation analysis and the maximum information coefficient (MIC) method. Subsequently, the regression method combined with the MIC method is used to construct a multiple causal path model without any assumptions based on the key factors for clarifying their direct and mediation effects on liquefaction. The results show that earthquake parameters produce more important influences on the occurrence of liquefaction than soil properties and site conditions, whereas deposit type, soil type, and deposit age produce relatively small impacts on liquefaction. In the multiple causal path model, the influence path of each factor on liquefaction becomes very clear. Among the key factors, in addition to the duration of the earthquake and Vs, other factors possess multiple mediation paths that affect liquefaction; the thickness of the critical layer and thickness of the unsaturated zone between the groundwater table and capping layer are two indirect-only mediators, and the fines content and thickness of the impermeable capping layer induce suppressive effects on liquefaction. In addition, the constructed causal model can provide a logistic regression model and a structure of the Bayesian network for predicting liquefaction. Five-fold cross-validation is used to compare and verify their predictive performances
Deformation characteristics and stability evolution behavior of Woshaxi landslide during the initial impoundment period of the Three Gorges reservoir
The study area, Woshaxi landslide, is 400 m long and 700 m wide, with an average thickness of approximately 15 m and a volume of 4.2 × 106 m3. The Woshaxi landslide, which is located on the Qinggan River, a tributary of the Yangtze River in the Three Gorges reservoir area, is just 1.5 km from the Qianjiangping landslide. The Qianjiangping landslide following the Three Gorges reservoir impoundment was caused by the combined effects of rainfall and reservoir water-level fluctuation. In this study, the Woshaxi landslide’s deformation characteristics and mechanism are investigated based on deformation monitoring data and a geological survey during the initial impoundment period of the Three Gorges reservoir. Furthermore, based on the characteristics of the combined effects of reservoir water level fluctuation and rainfall in the Three Gorges reservoir area, the stability evolution behavior of the Woshaxi landslide during the initial impoundment period of the Three Gorges reservoir is investigated
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