74 research outputs found
PAM: Plaid Atoms Model for Bayesian Nonparametric Analysis of Grouped Data
We consider dependent clustering of observations in groups. The proposed
model, called the plaid atoms model (PAM), estimates a set of clusters for each
group and allows some clusters to be either shared with other groups or
uniquely possessed by the group. PAM is based on an extension to the well-known
stick-breaking process by adding zero as a possible value for the cluster
weights, resulting in a zero-augmented beta (ZAB) distribution in the model. As
a result, ZAB allows some cluster weights to be exactly zero in multiple
groups, thereby enabling shared and unique atoms across groups. We explore
theoretical properties of PAM and show its connection to known Bayesian
nonparametric models. We propose an efficient slice sampler for posterior
inference. Minor extensions of the proposed model for multivariate or count
data are presented. Simulation studies and applications using real-world
datasets illustrate the model's desirable performance
PAM-HC: A Bayesian Nonparametric Construction of Hybrid Control for Randomized Clinical Trials Using External Data
It is highly desirable to borrow information from external data to augment a
control arm in a randomized clinical trial, especially in settings where the
sample size for the control arm is limited. However, a main challenge in
borrowing information from external data is to accommodate potential
heterogeneous subpopulations across the external and trial data. We apply a
Bayesian nonparametric model called Plaid Atoms Model (PAM) to identify
overlapping and unique subpopulations across datasets, with which we restrict
the information borrowing to the common subpopulations. This forms a hybrid
control (HC) that leads to more precise estimation of treatment effects
Simulation studies demonstrate the robustness of the new method, and an
application to an Atopic Dermatitis dataset shows improved treatment effect
estimation
Construction of a dense genetic linkage map and mapping quantitative trait loci for economic traits of a doubled haploid population of Pyropia haitanensis (Bangiales, Rhodophyta)
The genotypes of 4550 LP markers that were mapped onto the genetic map. (XLSX 1645 kb
Identification of orange color-related gene, PhcpcC, in Pyropia haitanensis
Pigmentation-related mutations can be utilized to distinguish between differentially colored sectors of chimeric thalli, thereby facilitating the efficient breeding of economically valuable Pyropia/Porphyra seaweed species. However, the specific trait loci and alleles responsible for Pyropia/Porphyra coloration have yet to be identified, which limits the applicability of coloration mutants for breeding and genetic analyses. In this study, to preserve the genetic integrity of the population, only four-colored thalli were considered when constructing the doubled haploid (DH) Pyropia haitanensis population, which consisted of 480 homozygous offspring lines (representing the largest DH Pyropia/Porphyra population). The offspring lines in the DH population exhibited both wild-type colored and orange sectors, with a segregation ratio of approximately 1:1, indicating that the orange coloration was controlled by a single nuclear gene. Through BSA-seq analysis (99% confidence interval), a candidate region of 0.5 Mb was identified in the P. haitanensis genome. Additionally, a non-synonymous SNP [A/G] was detected at base-pair position 481 in the coding region of PhcpcC, which encodes a phycocyanin-associated rod linker protein. This SNP locus was verified in both DH and natural populations, with the wild-type colored lines having an A base and the orange lines having a G base at this locus. Therefore, PhcpcC may be the gene associated with the orange coloration of P. haitanensis. The molecular marker developed in this study can be employed to exploit pigmentation mutants for breeding and genetic analyses of Pyropia/Porphyra species
UPDP: A Unified Progressive Depth Pruner for CNN and Vision Transformer
Traditional channel-wise pruning methods by reducing network channels
struggle to effectively prune efficient CNN models with depth-wise
convolutional layers and certain efficient modules, such as popular inverted
residual blocks. Prior depth pruning methods by reducing network depths are not
suitable for pruning some efficient models due to the existence of some
normalization layers. Moreover, finetuning subnet by directly removing
activation layers would corrupt the original model weights, hindering the
pruned model from achieving high performance. To address these issues, we
propose a novel depth pruning method for efficient models. Our approach
proposes a novel block pruning strategy and progressive training method for the
subnet. Additionally, we extend our pruning method to vision transformer
models. Experimental results demonstrate that our method consistently
outperforms existing depth pruning methods across various pruning
configurations. We obtained three pruned ConvNeXtV1 models with our method
applying on ConvNeXtV1, which surpass most SOTA efficient models with
comparable inference performance. Our method also achieves state-of-the-art
pruning performance on the vision transformer model
Anti-Allergic Inflammatory Activity of Interleukin-37 Is Mediated by Novel Signaling Cascades in Human Eosinophils
IL-1 family regulatory cytokine IL-37b can suppress innate immunity and inflammatory activity in inflammatory diseases. In this study, IL-37b showed remarkable in vitro suppression of inflammatory tumor necrosis factor-α, IL-1β, IL-6, CCL2, and CXCL8 production in the coculture of human primary eosinophils and human bronchial epithelial BEAS-2B cells with the stimulation of bacterial toll-like receptor-2 ligand peptidoglycan, while antagonizing the activation of intracellular nuclear factor-κB, PI3K–Akt, extracellular signal-regulated kinase 1/2, and suppressing the gene transcription of allergic inflammation-related PYCARD, S100A9, and CAMP as demonstrated by flow cytometry, RNA-sequencing, and bioinformatics. Results therefore elucidated the novel anti-inflammation-related molecular mechanisms mediated by IL-37b. Using the house dust mite (HDM)-induced humanized asthmatic NOD/SCID mice for preclinical study, intravenous administration of IL-37b restored the normal plasma levels of eosinophil activators CCL11 and IL-5, suppressed the elevated concentrations of Th2 and asthma-related cytokines IL-4, IL-6, and IL-13 and inflammatory IL-17, CCL5, and CCL11 in lung homogenate of asthmatic mice. Histopathological results of lung tissue illustrated that IL-37b could mitigate the enhanced mucus, eosinophil infiltration, thickened airway wall, and goblet cells. Together with similar findings using the ovalbumin- and HDM-induced allergic asthmatic mice further validated the therapeutic potential of IL-37b in allergic asthma. The above results illustrate the novel IL-37-mediated regulation of intracellular inflammation mechanism linking bacterial infection and the activation of human eosinophils and confirm the in vivo anti-inflammatory activity of IL-37b on human allergic asthma
The Jiao Tong University Spectroscopic Telescope Project
The Jiao Tong University Spectroscopic Telescope (JUST) is a 4.4-meter f/6.0
segmentedmirror telescope dedicated to spectroscopic observations. The JUST
primary mirror is composed of 18 hexagonal segments, each with a diameter of
1.1 m. JUST provides two Nasmyth platforms for placing science instruments. One
Nasmyth focus fits a field of view of 10 arcmin and the other has an extended
field of view of 1.2 deg with correction optics. A tertiary mirror is used to
switch between the two Nasmyth foci. JUST will be installed at a site at Lenghu
in Qinghai Province, China, and will conduct spectroscopic observations with
three types of instruments to explore the dark universe, trace the dynamic
universe, and search for exoplanets: (1) a multi-fiber (2000 fibers)
medium-resolution spectrometer (R=4000-5000) to spectroscopically map galaxies
and large-scale structure; (2) an integral field unit (IFU) array of 500
optical fibers and/or a long-slit spectrograph dedicated to fast follow-ups of
transient sources for multimessenger astronomy; (3) a high-resolution
spectrometer (R~100000) designed to identify Jupiter analogs and Earth-like
planets, with the capability to characterize the atmospheres of hot exoplanets.Comment: 28 pages, 6 figure
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Prediction of chronic kidney disease progression using recurrent neural network and electronic health records
Chronic kidney disease (CKD) is a progressive loss in kidney function. Early detection of patients who will progress to late-stage CKD is of paramount importance for patient care. To address this, we develop a pipeline to process longitudinal electronic heath records (EHRs) and construct recurrent neural network (RNN) models to predict CKD progression from stages II/III to stages IV/V. The RNN model generates predictions based on time-series records of patients, including repeated lab tests and other clinical variables. Our investigation reveals that using a single variable, the recorded estimated glomerular filtration rate (eGFR) over time, the RNN model achieves an average area under the receiver operating characteristic curve (AUROC) of 0.957 for predicting future CKD progression. When additional clinical variables, such as demographics, vital information, lab test results, and health behaviors, are incorporated, the average AUROC increases to 0.967. In both scenarios, the standard deviation of the AUROC across cross-validation trials is less than 0.01, indicating a stable and high prediction accuracy. Our analysis results demonstrate the proposed RNN model outperforms existing standard approaches, including static and dynamic Cox proportional hazards models, random forest, and LightGBM. The utilization of the RNN model and the time-series data of previous eGFR measurements underscores its potential as a straightforward and effective tool for assessing the clinical risk of CKD patients concerning their disease progression
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