Biotransformation of ginsenosides Rb1, Rg3 and Rh2 in rat gastrointestinal tracts

<p>Abstract</p> <p>Background</p> <p>Ginsenosides such as Rb₁, Rg₃and Rh₂are major bioactive components of <it>Panax ginseng</it>. This <it>in vivo </it>study investigates the metabolic pathways of ginsenosides Rb₁, Rg₃and Rh₂orally administered to rats.</p> <p>Methods</p> <p>High performance liquid chromatography-mass spectrometry (LC-MS) and tandem mass spectrometry (MS-MS) techniques, particularly liquid chromatography electrospray ionization mass spectrometry (LC-ESI-MS), were used to identify the metabolites.</p> <p>Results</p> <p>Six metabolites of Rb₁, six metabolites of Rg₃and three metabolites of Rh₂were detected in the feces samples of the rats. Rh₂was a metabolite of Rb₁and Rg₃, whereas Rg₃was a metabolite of Rb₁. Some metabolites such as protopanaxadiol and monooxygenated protopanaxadiol are metabolites of all three ginsenosides.</p> <p>Conclusion</p> <p>Oxygenation and deglycosylation are two major metabolic pathways of the ginsenosides in rat gastrointestinal tracts.</p

NAD tagSeq reveals that NAD+-capped RNAs are mostly produced from a large number of protein-coding genes in Arabidopsis.

The 5' end of a eukaryotic mRNA transcript generally has a 7-methylguanosine (m7G) cap that protects mRNA from degradation and mediates almost all other aspects of gene expression. Some RNAs in Escherichia coli, yeast, and mammals were recently found to contain an NAD+ cap. Here, we report the development of the method NAD tagSeq for transcriptome-wide identification and quantification of NAD+-capped RNAs (NAD-RNAs). The method uses an enzymatic reaction and then a click chemistry reaction to label NAD-RNAs with a synthetic RNA tag. The tagged RNA molecules can be enriched and directly sequenced using the Oxford Nanopore sequencing technology. NAD tagSeq can allow more accurate identification and quantification of NAD-RNAs, as well as reveal the sequences of whole NAD-RNA transcripts using single-molecule RNA sequencing. Using NAD tagSeq, we found that NAD-RNAs in Arabidopsis were produced by at least several thousand genes, most of which are protein-coding genes, with the majority of these transcripts coming from &lt;200 genes. For some Arabidopsis genes, over 5% of their transcripts were NAD capped. Gene ontology terms overrepresented in the 2,000 genes that produced the highest numbers of NAD-RNAs are related to photosynthesis, protein synthesis, and responses to cytokinin and stresses. The NAD-RNAs in Arabidopsis generally have the same overall sequence structures as the canonical m7G-capped mRNAs, although most of them appear to have a shorter 5' untranslated region (5' UTR). The identification and quantification of NAD-RNAs and revelation of their sequence features can provide essential steps toward understanding the functions of NAD-RNAs

In-painting Radiography Images for Unsupervised Anomaly Detection

We propose space-aware memory queues for in-painting and detecting anomalies from radiography images (abbreviated as SQUID). Radiography imaging protocols focus on particular body regions, therefore producing images of great similarity and yielding recurrent anatomical structures across patients. To exploit this structured information, our SQUID consists of a new Memory Queue and a novel in-painting block in the feature space. We show that SQUID can taxonomize the ingrained anatomical structures into recurrent patterns; and in the inference, SQUID can identify anomalies (unseen/modified patterns) in the image. SQUID surpasses the state of the art in unsupervised anomaly detection by over 5 points on two chest X-ray benchmark datasets. Additionally, we have created a new dataset (DigitAnatomy), which synthesizes the spatial correlation and consistent shape in chest anatomy. We hope DigitAnatomy can prompt the development, evaluation, and interpretability of anomaly detection methods, particularly for radiography imaging.Comment: Main paper with appendi

PAH exposure is associated with enhanced risk for pediatric dyslipidemia through serum SOD reduction

Background: Exposure to polycyclic aromatic hydrocarbons (PAHs) is linked to abnormal lipid metabolism, but evidence regarding PAHs as risk factors for dyslipidemia is lacking. Objective: To investigate the respective role and interaction of PAH exposure and antioxidant consumption in the risk for pediatric dyslipidemia. Methods: We measured the concentrations of serum lipids, superoxide dismutase (SOD) and urinary hydroxylated PAHs (OH-PAHs) in 403 children, of which 203 were from an e-waste-exposed area (Guiyu) and 200 were from a reference area (Haojiang). Biological interactions were calculated by additive models. Results: Guiyu children had higher serum triglyceride concentration and dyslipidemia incidence, and lower serum concentration of high-density lipoprotein (HDL) than Haojiang children. Elevated OH-PAH concentration, and concomitant SOD reduction, were both associated with lower HDL concentration and higher hypo-HDL risk (S3OH-Phes: B for lgHDL = 0.048, P <0.01; OR for hypo-HDL = 3.708, 95% CI: 1.200, 11.453; SOD: BT3 for lgHDL = 0.061, P <0.01; ORT3 for hypo-HDL = 0.168, 95% CI: 0.030, 0.941; all were adjusted for confounders). Biological interaction between phenanthrol exposure and SOD reduction was linked to dyslipidemia risk (RERI = 2.783, AP = 0.498, S = 2.537). Children with both risk factors (higher S3OH-Phes and lower SOD) had 5.594times (95% CI: 1.119, 27.958) the dyslipidemia risk than children with neither risk factors (lower S3OH-Phes and higher SOD). Conclusion: High PAH exposure combined with SOD reduction is recommended for predicting elevated risk for pediatric dyslipidemia. Risk assessment of PAH-related dyslipidemia should take antioxidant concentration into consideration

Exploiting Structural Consistency of Chest Anatomy for Unsupervised Anomaly Detection in Radiography Images

Radiography imaging protocols focus on particular body regions, therefore producing images of great similarity and yielding recurrent anatomical structures across patients. Exploiting this structured information could potentially ease the detection of anomalies from radiography images. To this end, we propose a Simple Space-Aware Memory Matrix for In-painting and Detecting anomalies from radiography images (abbreviated as SimSID). We formulate anomaly detection as an image reconstruction task, consisting of a space-aware memory matrix and an in-painting block in the feature space. During the training, SimSID can taxonomize the ingrained anatomical structures into recurrent visual patterns, and in the inference, it can identify anomalies (unseen/modified visual patterns) from the test image. Our SimSID surpasses the state of the arts in unsupervised anomaly detection by +8.0%, +5.0%, and +9.9% AUC scores on ZhangLab, COVIDx, and CheXpert benchmark datasets, respectively. Code: https://github.com/MrGiovanni/SimSIDComment: IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI). arXiv admin note: substantial text overlap with arXiv:2111.1349

Non-targeted metabolomics and lipidomics LC-MS data from maternal plasma of 180 healthy pregnant women

BACKGROUND: Metabolomics has the potential to be a powerful and sensitive approach for investigating the low molecular weight metabolite profiles present in maternal fluids and their role in pregnancy. FINDINGS: In this Data Note, LC–MS metabolome, lipidome and carnitine profiling data were collected from 180 healthy pregnant women, representing six time points spanning all three trimesters, and providing sufficient coverage to model the progression of normal pregnancy. CONCLUSIONS: As a relatively large scale, real-world dataset with robust numbers of quality control samples, the data are expected to prove useful for algorithm optimization and development, with the potential to augment studies into abnormal pregnancy. All data and ISA-TAB format enriched metadata are available for download in the MetaboLights and GigaScience databases. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13742-015-0054-9) contains supplementary material, which is available to authorized users

A hybrid laser ablation and chemical etching process for manufacturing nature-inspired anisotropic superhydrophobic structures

The surface with anisotropic superhydrophobicity has great potential applications for drag reduction, drug delivery and microfluidic devices. Observation from natural biological surfaces proved that directional microstructures are indispensable to realize anisotropic superhydrophobicity. However, current lithography-based manufacturing approaches have limited capabilities to scale-up for real world industrial applications. This paper proposes a hybrid laser ablation and chemical etching process for manufacturing ratchet-like microstructures on 316L stainless steel for the first time. It harvests the advantages of both processes. The laser ablation will form specified recast layer and covered by oxide layer on the specimen, and these two layers can be easily removed in the chemical etching process hence to obtain the periodic ratchet-like microstructures. According to the experimental results, the direction of microstructures is same as with the laser beam feed direction. The width and depth of microstructures also can be well-controlled by laser power and pitch. The specimens with pith of 25 μm have contact angle larger than 150°. And the droplet easily rolls off along the laser beam feed direction but is pinned tightly in the opposite direction

Autonomous detection of damage to multiple steel surfaces from 360° panoramas using deep neural networks

Structural health assessments are essential for infrastructure. By using an autonomous panorama vision‐based inspection system, the limitations of the human cost and safety factors of previously time‐consuming tasks have been overcome. The main damage detection challenges to panorama images are (1) the lack of annotated panorama defect image data, (2) detection in high‐resolution images, and (3) the inherent distortion disturbance for panorama images. In this paper, a new PAnoramic surface damage DEtection Network (PADENet) is presented to solve the challenges by (a) using an unmanned aerial vehicle to capture panoramic images and a distorted panoramic augmentation method to expand the panoramic dataset, (b) employing the proposed multiple projection methods to process high‐resolution images, and (c) modifying the faster region‐based convolutional neural network and training via transfer learning on VGG‐16, which improves the precision for detecting multiple types of damage in distortion. The results show that the proposed method is optimal for surface damage detection

In Materia Neuron Spiking Plasticity for Sequential Event Processing Based on Dual-Mode Memristor

Artificial neurons are the fundamental elements in neuromorphic computing systems. Studies have revealed neuronal spike‐rate adaptation owing to intrinsic plasticity that neurons will adapt to the spiking patterns and store the events in the background spiking through clustered neuronal spiking. The event can be reactivated by specific retrieval clues instead of solely relying on synaptic plasticity. However, the neural adaptation, as well as the interactive adaptations of neuronal activity for information processing, have not been implemented. Herein, an artificial adaptive neuron via in materia modulation of the VO2/HfO2 based dual‐mode memristor is demonstrated. By changing the conductance of the HfO2 layer, the firing threshold can be modulated, thus the excitability and inhibition can be adjusted according to the previous stimuli without any complex peripherals, showing an adaptive firing rate even under the same stimuli. The artificial neuron clusters can emulate the concept of neuronal memory and neural adaptation, demonstrating spatiotemporal encoding capabilities via the correlated neural firing patterns. This conceptual work provides an alternative way to expand the computation power of spiking neural networks by exploiting the neural adaptation and could be enlightenment to maximize the synergy across both synapse and neuron in neuromorphic computing systems