Fisher's linear discriminant analysis (LDA) is an easy-to-use supervised dimensionality reduction method. However, LDA may be ineffective against complicated class distributions. It is well-known ...that deep feedforward neural networks with rectified linear units as activation functions can map many input neighborhoods to similar outputs by a succession of space-folding operations. This short paper shows that the space-folding operation can reveal to LDA classification information in the subspace where LDA cannot find any. A composition of LDA with the space-folding operation can find classification information more than LDA can do. End-to-end fine-tuning can improve that composition further. Experimental results on artificial and open data sets have shown the feasibility of the proposed approach.
Trimethylamine N-oxide (TMAO) is a metabolite originated from bacterial metabolism of choline-rich foods. Evidence suggests an association between TMAO and atherosclerosis, but the relationship ...between TMAO and endothelial progenitor cells (EPCs) remains unclear. This study aimed to identify the relationship between TMAO concentrations, circulating EPCs, and endothelial function in patients with stable angina. Eighty-one stable angina subjects who underwent coronary angiography were enrolled. The circulating EPCs and flow-mediated vasodilation (FMD) were measured to evaluate endothelial function. Plasma TMAO and inflammatory markers, such as hsCRP and IL-1β, were determined. Furthermore, the effect of TMAO on EPCs was assessed in vitro. Patients with lower FMD had significantly decreased circulating EPCs, elevated TMAO, hsCRP, and IL-1β concentrations. Plasma TMAO levels were negatively correlated with circulating EPC numbers and the FMD, and positively correlated with hsCRP, IL-1β concentrations. In in vitro studies, incubation of TMAO in cultured EPCs promoted cellular inflammation, elevated oxidative stress, and suppressed EPC functions. Enhanced plasma TMAO levels were associated with reduced circulating EPCs numbers, endothelial dysfunction, and more adverse cardiovascular events. These findings provided evidence of TMAO's toxicity on EPCs, and delivered new insight into the mechanism of TMAO-mediated atherosclerosis, which could be derived from TMAO-downregulated EPC functions.
Monitoring the status of culture fish is an essential task for precision aquaculture using a smart underwater imaging device as a non-intrusive way of sensing to monitor freely swimming fish even in ...turbid or low-ambient-light waters. This paper developed a two-mode underwater surveillance camera system consisting of a sonar imaging device and a stereo camera. The sonar imaging device has two cloud-based Artificial Intelligence (AI) functions that estimate the quantity and the distribution of the length and weight of fish in a crowded fish school. Because sonar images can be noisy and fish instances of an overcrowded fish school are often overlapped, machine learning technologies, such as Mask R-CNN, Gaussian mixture models, convolutional neural networks, and semantic segmentation networks were employed to address the difficulty in the analysis of fish in sonar images. Furthermore, the sonar and stereo RGB images were aligned in the 3D space, offering an additional AI function for fish annotation based on RGB images. The proposed two-mode surveillance camera was tested to collect data from aquaculture tanks and off-shore net cages using a cloud-based AIoT system. The accuracy of the proposed AI functions based on human-annotated fish metric data sets were tested to verify the feasibility and suitability of the smart camera for the estimation of remote underwater fish metrics.
Tumor cells with diverse phenotypes and biological behaviors are influenced by stromal cells through secretory factors or direct cell-cell contact. Pancreatic ductal adenocarcinoma (PDAC) is ...characterized by extensive desmoplasia with fibroblasts as the major cell type. In the present study, we observe enrichment of myofibroblasts in a juxta-tumoral position with tumor cells undergoing epithelial-mesenchymal transition (EMT) that facilitates invasion and correlates with a worse clinical prognosis in PDAC patients. Direct cell-cell contacts forming heterocellular aggregates between fibroblasts and tumor cells are detected in primary pancreatic tumors and circulating tumor microemboli (CTM). Mechanistically, ATP1A1 overexpressed in tumor cells binds to and reorganizes ATP1A1 of fibroblasts that induces calcium oscillations, NF-κB activation, and activin A secretion. Silencing ATP1A1 expression or neutralizing activin A secretion suppress tumor invasion and colonization. Taken together, these results elucidate the direct interplay between tumor cells and bound fibroblasts in PDAC progression, thereby providing potential therapeutic opportunities for inhibiting metastasis by interfering with these cell-cell interactions.
Imaging sonar systems are widely used for monitoring fish behavior in turbid or low ambient light waters. For analyzing fish behavior in sonar images, fish segmentation is often required. In this ...paper, Mask R-CNN is adopted for segmenting fish in sonar images. Sonar images acquired from different shallow waters can be quite different in the contrast between fish and the background. That difference can make Mask R-CNN trained on examples collected from one fish farm ineffective to fish segmentation for the other fish farms. In this paper, a preprocessing convolutional neural network (PreCNN) is proposed to provide “standardized” feature maps for Mask R-CNN and to ease applying Mask R-CNN trained for one fish farm to the others. PreCNN aims at decoupling learning of fish instances from learning of fish-cultured environments. PreCNN is a semantic segmentation network and integrated with conditional random fields. PreCNN can utilize successive sonar images and can be trained by semi-supervised learning to make use of unlabeled information. Experimental results have shown that Mask R-CNN on the output of PreCNN is more accurate than Mask R-CNN directly on sonar images. Applying Mask R-CNN plus PreCNN trained for one fish farm to new fish farms is also more effective.
Abstract
Background
Gout is a highly hereditary disease, but not all those carrying well-known risk variants have developing gout attack even in hyperuricemia status. We performed a genome-wide ...association study (GWAS) and polygenic risk score (PRS) analysis to illustrate the new genetic architectures of gout and asymptomatic hyperuricemia (AH).
Methods
GWAS was performed to identify variants associated with gout/AH compared with normouricemia. The participants were males, enrolled from the Taiwan Biobank and China Medical University, and divided into discovery (
n
=39,594) and replication (
n
=891) cohorts for GWAS. For PRS analysis, the discovery cohort was grouped as base (
n
=21,814) and target (
n
=17,780) cohorts, and the score was estimated by grouping the polymorphisms into protective or not for the phenotypes in the base cohort.
Results
The genes
ABCG2
and
SLC2A9
were found as the major genetic factors governing gouty and AH, and even in those carrying the rs2231142 (
ABCG2
) wild-genotype. Surprisingly, variants on chromosome 1, such as rs7546668 (
DNAJC16
), rs10927807 (
AGMAT
), rs9286836 (
NUDT17
), rs4971100 (
TRIM46
), rs4072037 (
MUC1
), and rs2974935 (
MTX1
), showed significant associations with gout in both discovery and replication cohorts (all
p
-values < 1e−8). Concerning the PRS, the rates of gout and AH increased with increased quartile PRS in those SNPs having risk effects on the phenotypes; on the contrary, gout/AH rates decreased with increased quartile PRS in those protective SNPs.
Conclusions
We found new variants on chromosome 1 significantly relating to gout, and PRS predicts the risk of developing gout/AH more robustly based on the SNPs’ effect types on the trait.
Insulin resistance (IR) is a known risk factor for cardiovascular disease (CVD) in non-diabetic patients through the association of hyperglycemia or associated metabolic factors. The triglyceride ...glucose (TyG) index, which was defined by incorporating serum glucose and insulin concentrations, was developed as a surrogate marker of insulin resistance. We aimed to investigate the association between the TyG index and the early phase of subclinical atherosclerosis (SA) between the sexes.
The I-Lan Longitudinal Aging Study (ILAS) enrolled 1457 subjects aged 50-80 years. For each subject, demographic data and the TyG index {lnfasting triglyceride (mg/dL) × fasting plasma glucose (mg/dL)/
} were obtained. Patients were further stratified according to sex and the 50th percentile of the TyG index (≥ 8.55 or < 8.55). SA was defined as the mean carotid intima-media thickness (cIMT) at the 75th percentile of the entire cohort. Demographic characteristics and the presence of SA were compared between the groups. Logistic regression analysis was performed to assess the relationship between TyG index and SA.
Patients with a higher TyG index (≥ 8.55) had a higher body mass index (BMI), hypertension (HTN) and diabetes mellitus (DM). They had higher lipid profiles, including total cholesterol (T-Chol) and low-density lipoprotein (LDL), compared to those with a lower TyG index (< 8.55). Gender disparity was observed in non-diabetic women who had a significantly higher prevalence of SA in the high TyG index group than in the low TyG index group. In multivariate logistic regression analysis, a high TyG index was independently associated with SA in non-diabetic women after adjusting for traditional risk factors adjusted odds ratio (OR): 1.510, 95% CI 1.010-2.257, p = 0.045 but not in non-diabetic men. The TyG index was not associated with the presence of SA in diabetic patients, irrespective of sex.
A high TyG index was significantly associated with SA and gender disparity in non-diabetic patients. This result may highlight the need for a sex-specific risk management strategy to prevent atherosclerosis.
Background
Ruling out obstructive coronary artery disease (CAD) using coronary computed tomography angiography (CCTA) is time‐consuming and challenging. This study developed a deep learning (DL) ...model to assist in detecting obstructive CAD on CCTA to streamline workflows.
Methods
In total, 2929 DICOM files and 7945 labels were extracted from curved planar reformatted CCTA images. A modified Inception V3 model was adopted. To validate the artificial intelligence (AI) model, two cardiologists labelled and adjudicated the classification of coronary stenosis on CCTA. The model was trained to differentiate the coronary artery into binary stenosis classifications <50% and ≥50% stenosis. Using the quantitative coronary angiography (QCA) consensus results as a reference standard, the performance of the AI model and CCTA radiology readers was compared by calculating Cohen's kappa coefficients at patient and vessel levels. The net reclassification index was used to evaluate the net benefit of the DL model.
Results
The diagnostic accuracy of the AI model was 92.3% and 88.4% at the patient and vessel levels, respectively. Compared with CCTA radiology readers, the AI model had a better agreement for binary stenosis classification at both patient and vessel levels (Cohen kappa coefficient: .79 vs. .39 and .77 vs. .40, p < .0001). The AI model also exhibited significantly improved model discrimination and reclassification (Net reclassification index = .350; Z = 4.194; p < .001).
Conclusions
The developed AI model identified obstructive CAD, and the model results correlated well with QCA results. Incorporating the model into the reporting system of CCTA may improve workflows.
In this retrospective study, 2929 DICOM files and 7945 labels were extracted from curved planar reformatted coronary computed tomography angiography (CCTA) images for CCTA CAD classification artificial intelligence (AI) model development. A modified Inception V3 deep learning‐based stenosis classification AI model could identify obstructive CAD and correlate better with the quantitative coronary angiography consensus results than CCTA radiology readers at both patient and vessel levels (Cohen kappa coefficient .79 vs. .39 and .77 vs. .40, p < .0001).
Known to have pleiotropic functions, high-density lipoprotein (HDL) helps to regulate systemic inflammation during sepsis. As preserving HDL-C level is a promising therapeutic strategy for sepsis, ...the interaction between HDL and sepsis worth further investigation. This study aimed to determine the impact of sepsis on HDL's anti-inflammatory capacity and explore its correlations with disease severity and laboratory parameters.
We enrolled 80 septic subjects admitted to the intensive care unit and 50 controls admitted for scheduled coronary angiography in this cross-sectional study. We used apolipoprotein-B depleted (apoB-depleted) plasma to measure the anti-inflammatory capacity of HDL-C. ApoB-depleted plasma's anti-inflammatory capacity is defined as its ability to suppress tumor necrosis factor-α-induced vascular cell adhesion molecule-1 (VCAM-1) expression in human umbilical-vein endothelial cells. A subgroup analysis was conducted to investigate in septic subjects according to disease severity.
ApoB-depleted plasma's anti-inflammatory capacity was reduced in septic subjects relative to controls (VCAM-1 mRNA fold change: 50.1% vs. 35.5%; p < 0.0001). The impairment was more pronounced in septic subjects with than in those without septic shock (55.8% vs. 45.3%, p = 0.0022). Both associations were rendered non-significant with the adjustment for the HDL-C level. In sepsis patients, VCAM-1 mRNA fold change correlated with the SOFA score (Spearman's r = 0.231, p = 0.039), lactate level (r = 0.297, p = 0.0074), HDL-C level (r = -0.370, p = 0.0007), and inflammatory markers (C-reactive protein level: r = 0.441, p <0.0001; white blood cell: r = 0.353, p = 0.0013).
ApoB-depleted plasma's anti-inflammatory capacity is reduced in sepsis patients and this association depends of HDL-C concentration. In sepsis patients, this capacity correlates with disease severity and inflammatory markers. These findings explain the prognostic role of the HDL-C level in sepsis and indirectly support the rationale for targeting HDL-C as sepsis treatment.
Defects in multistage manufacturing processes (MMPs) decrease profitability and product quality. Therefore, MMP parameter optimization within a range is essential to prevent defects, achieve dynamic ...accuracy, and accommodate manufacturing tolerances. However, existing studies only focused on optimization in a single manufacturing stage of MMP, such as the weaving stage in fabric manufacturing. Furthermore, existing methods optimize for a single value rather than a range. Thus, we propose a novel approach called multistage parameter optimization for rule generation (MPORG) to prevent the occurrence of defects in MMPs. In the proposed approach, key parameters are identified and optimized to a range for each defect type. Subsequently, the optimized parameters for each defect type are merged. Our approach is novel because it optimizes parameters to a range rather than a single value, allowing engineers to select a value in this range according to their experience. It also provides results that are specific to a product type. Our approach outperformed the classification and regression tree (CART) algorithm and multiresponse CART method in experiments on an empirical fabric manufacturing dataset that we gathered. The experimental results demonstrated that the MPORG approach can prevent the occurrence of single-type or multiple-type defects by approximately 89%.
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