The ketogenic diet (KD), which can induce changes in gut microbiota, has shown benefits for epilepsy and several neurodegenerative diseases. However, the effects of a KD on glucose and lipid ...metabolism remain inconclusive. Using two formulas of ketogenic diets (KDR with 89.5% fat and KDH with 91.3% fat), which are commonly used in mouse trials, we found that KDR but not KDH induced insulin resistance and damaged glucose homeostasis, while KDH induced more fat accumulation in mice. Further study showed that KD impacted glucose metabolism, which was related to the sources of fat, while both the sources and proportions of fat affected lipid metabolism. And the KD widely used in human studies still induced insulin resistance and fat accumulation in mice. Moreover, KDs changed the gut microbiota and metabolites in mice, and the sources and proportions of fat in the diets respectively changed the abundance of specific bacteria and metabolites which were correlated with parameters related to glucose intolerance and lipid accumulation. Overall, our study demonstrated that the metabolic disorders induced by KDs are closely related to the source and proportion of fat in the diet, which may be associated with the changes of the gut microbiota and metabolites.
The ketogenic diet with extremely high fat and very low carbohydrate levels is very popular in society today. Although it has beneficial effects on epilepsy and neurodegenerative diseases, how ketogenic diets impact host glucose and lipid metabolism and gut microbiota still needs further investigation. Here, we surveyed the effects of two ketogenic diets which are commonly used in mouse trials on metabolic phenotypes, gut microbiota, and metabolites in mice. We found that both ketogenic diets impaired glucose and lipid metabolism in mice, and this may be due to the sources and proportions of fat in the diets. This work highlights the potential risk of glucose and lipid metabolism disorders and the importance of evaluating the sources and proportions of fat in the diets, when using ketogenic diets for weight loss and the treatment of diseases.
Abstract
The incidence of metabolic syndrome is significantly higher in patients with irritable bowel syndrome (IBS), but the mechanisms involved remain unclear. Gut microbiota is causatively linked ...with the development of both metabolic dysfunctions and gastrointestinal disorders, thus gut dysbiosis in IBS may contribute to the development of metabolic syndrome. Here, we show that human gut bacterium
Ruminococcus gnavus
-derived tryptamine and phenethylamine play a pathogenic role in gut dysbiosis-induced insulin resistance in type 2 diabetes (T2D) and IBS. We show levels of
R. gnavus
, tryptamine, and phenethylamine are positively associated with insulin resistance in T2D patients and IBS patients. Monoassociation of
R. gnavus
impairs insulin sensitivity and glucose control in germ-free mice. Mechanistically, treatment of
R. gnavus
-derived metabolites tryptamine and phenethylamine directly impair insulin signaling in major metabolic tissues of healthy mice and monkeys and this effect is mediated by the trace amine-associated receptor 1 (TAAR1)-extracellular signal-regulated kinase (ERK) signaling axis. Our findings suggest a causal role for tryptamine/phenethylamine-producers in the development of insulin resistance, provide molecular mechanisms for the increased prevalence of metabolic syndrome in IBS, and highlight the TAAR1 signaling axis as a potential therapeutic target for the management of metabolic syndrome induced by gut dysbiosis.
In pathological image analysis, determination of gland morphology in histology images of the colon is essential to determine the grade of colon cancer. However, manual segmentation of glands is ...extremely challenging and there is a need to develop automatic methods for segmenting gland instances. Recently, due to the powerful noise-to-image denoising pipeline, the diffusion model has become one of the hot spots in computer vision research and has been explored in the field of image segmentation. In this paper, we propose an instance segmentation method based on the diffusion model that can perform automatic gland instance segmentation. Firstly, we model the instance segmentation process for colon histology images as a denoising process based on a diffusion model. Secondly, to recover details lost during denoising, we use Instance Aware Filters and multi-scale Mask Branch to construct global mask instead of predicting only local masks. Thirdly, to improve the distinction between the object and the background, we apply Conditional Encoding to enhance the intermediate features with the original image encoding. To objectively validate the proposed method, we compared several state-of-the-art deep learning models on the 2015 MICCAI Gland Segmentation challenge (GlaS) dataset (165 images), the Colorectal Adenocarcinoma Glands (CRAG) dataset (213 images) and the RINGS dataset (1500 images). Our proposed method obtains significantly improved results for CRAG (Object F1 0.853 ± 0.054, Object Dice 0.906 ± 0.043), GlaS Test A (Object F1 0.941 ± 0.039, Object Dice 0.939 ± 0.060), GlaS Test B (Object F1 0.893 ± 0.073, Object Dice 0.889 ± 0.069), and RINGS dataset (Precision 0.893 ± 0.096, Dice 0.904 ± 0.091). The experimental results show that our method significantly improves the segmentation accuracy, and the experiment results demonstrate the efficacy of the method.
•Our approach models gland instance segmentation in histology images as denoising with a diffusion model.•To improve segmentation, we use instance-aware methods to recover lost details post-denoising.•To improve object-background differentiation, we use conditional encoding to augment intermediate features with the original image encoding.
Submodular function has the property of diminishing marginal gain, and thus it has a wide range of applications in combinatorial optimization and in emerging disciplines such as machine learning and ...artificial intelligence. For any set
S
, most of previous works usually do not consider how to compute
f
(
S
) , but assume that there exists an oracle that will output
f
(
S
) directly. In reality, however, the process of computing the exact
f
is often inevitably inaccurate or costly. At this point, we adopt the easily available noise version
F
of
f
. In this paper, we investigate the problems of maximizing a non-negative monotone normalized submodular function minus a non-negative modular function under the
ε
-multiplicative noise in three situations, i.e., the cardinality constraint, the matroid constraint and the online unconstraint. For the above problems, we design three deterministic bicriteria approximation algorithms using greedy and threshold ideas and furthermore obtain good approximation guarantees.
We aim to explore the relationship between nocturnal sleep duration (NSD) and midday nap duration (MND) with body composition among Southwest Chinese adults.
Data on sleep duration of 3145 adults in ...Southwest China (59.4% women) were obtained between 2014 and 2015 through questionnaires. Height, weight, and waist circumference (WC) were measured to calculate body composition (body mass index (BMI), percentage of body fat (%BF), and fat mass index (FMI)). Linear regression models were used to assess gender-specific associations between NSD and body composition. The relationship between MND with the odds of overweight and central obesity has been evaluated by logistic regression models.
NSD has the inverse relation with males' BMI, WC, %BF and FMI after adjusting for all covariates (all P <0.0007), exclusive of females' (all P >0.4). After adjustment for potential confounders, compared to the subjects in the no midday nap group, the subjects who napped 0.1-1 hour were independently associated with a less prevalence of overweight in both women (OR: 0.72, 95%CI: 0.55-0.95) and men (OR: 0.71, 95%CI: 0.52-0.98). MND was not associated with central obesity.
Among Southwest Chinese adults, lower NSD might be related to higher BMI, WC, %BF and FMI among men. Additionally, MND is associated with overweight in adults.
Recently, deep convolutional neural networks (CNNs) have been widely adopted for ultrasound sequence tracking and shown to perform satisfactorily. However, existing trackers ignore the rich temporal ...contexts that exists between consecutive frames, making it difficult for these trackers to perceive information about the motion of the target.
In this paper, we propose a sophisticated method to fully utilize temporal contexts for ultrasound sequences tracking with information bottleneck. This method determines the temporal contexts between consecutive frames to perform both feature extraction and similarity graph refinement, and information bottleneck is integrated into the feature refinement process.
The proposed tracker combined three models. First, online temporal adaptive convolutional neural network (TAdaCNN) is proposed to focus on feature extraction and enhance spatial features using temporal information. Second, information bottleneck (IB) is incorporated to achieve more accurate target tracking by maximally limiting the amount of information in the network and discarding irrelevant information. Finally, we propose temporal adaptive transformer (TA-Trans) that efficiently encodes temporal knowledge by decoding it for similarity graph refinement. The tracker was trained on 2015 MICCAI Challenge on Liver Ultrasound Tracking (CLUST) dataset to evaluate the performance of the proposed method by calculating the tracking error (TE) between the predicted landmarks and the ground truth landmarks for each frame. The experimental results are compared with 13 state-of-the-art methods, and ablation studies are conducted.
On CLUST 2015 dataset, our proposed model achieves a mean TE of 0.81 ± 0.74 mm and a maximum TE of 1.93 mm for 85 point-landmarks across 39 ultrasound sequences in the 2D sequences. Tracking speed ranged from 41 to 63 frames per second (fps).
This study demonstrates a new integrated workflow for ultrasound sequences motion tracking. The results show that the model has excellent accuracy and robustness. Reliable and accurate motion estimation is provided for applications requiring real-time motion estimation in the context of ultrasound-guided radiation therapy.
Facing serious air pollution problems, the Chinese government has taken numerous measures to prevent and control air pollution. Understanding the sources of pollutants is crucial to the prevention of ...air pollution. Using numerical simulation method, this study analysed the contributions of the total local emissions and local emissions from different sectors (such as industrial, traffic, resident, agricultural, and power plant emissions) to PM2.5 concentration, backward trajectory, and potential source regions in Tangshan, a typical heavy industrial city in north China. The impact of multi-scale meteorological conditions on source apportionment was investigated. From October 2016 to March 2017, total local emissions accounted for 46.0% of the near-surface PM2.5 concentration. In terms of emissions from different sectors, local industrial emissions which accounted for 23.1% of the near-surface PM2.5 concentration in Tangshan, were the most important pollutant source. Agricultural emissions were the second most important source, accounting for 10.3% of the near-surface PM2.5 concentration. The contributions of emissions from power plants, traffic, residential sources were 2.0%, 3.0%, and 7.2%, respectively. The contributions of total local emissions and emissions from different sectors depended on multi-scale meteorological conditions, and static weather significantly enhanced the contribution of regional transport to the near-surface PM2.5 concentration. Eight cluster backward trajectories were identified for Tangshan. The PM2.5 concentration for the 8 cluster trajectories significantly differed. The near-surface PM2.5 in urban Tangshan (receptor point) was mainly from the local emissions, and another important potential source region was Tianjin. The results of the source apportionment suggested the importance of joint prevention and control of air pollution in some areas where cities or industrial regions are densely distributed.
Display omitted
•The contribution of local and different sectors emissions on PM2.5 concentration.•Backward trajectory and potential source regions of PM2.5.•The impact of multi-scale meteorological conditions on source apportionment.•Local emissions accounted for 46.0% of the near-surface PM2.5 concentration.•Static weather significantly enhanced the contribution of regional transport.
The contributions of local emissions depended on multi-scale meteorological conditions, and static weather significantly enhanced the contribution of regional transport in Tangshan.
As the electronic industry develops rapidly, nowadays, flexible dielectric materials with excellent integrated dielectric performances including high dielectric permittivity (
ɛ
) and breakdown ...strength (
E
b
) but low loss, are highly pursued. In this work, to concurrently improve the
ɛ
and
E
b
but restrain the loss of original Si/polyvinylidene fluoride (PVDF) composites, the core@shell structured Si@SiO
2
particles first were produced via high temperature oxidation process, and then incorporated into the PVDF to generate morphology-dependent composites with high-ɛ and
E
b
but low loss. The dielectric properties of the composites were investigated in terms of the filler types and concentrations, frequency, and theoretically fitted using the Havriliak-Negami equation to reveal the SiO
2
shell’ role in affecting the polarization mechanism. When compared to pure Si/PVDF at high filler loadings, remarkably inhibited dielectric loss and conductivity as well as enhanced
E
b
concurrently can be achieved in the Si@SiO
2
/PVDF composites still harvesting a high-ɛ. This is because the insulating SiO
2
shell not only effectively prevents the raw Si particles from direct physical contact, but also greatly impedes the long-range charge carrier migration via raising energy barrier subsequently leading to obviously enhanced
E
b
. Moreover, the dielectric loss and conductivity apparently decrease with increasing the SiO
2
shell thickness due to its pronounced suppression effect. The prepared Si@SiO
2
/PVDF with a high
E
b
and
ɛ
but low loss, show bright future uses in micro-electronic devices used for high-voltage purposes.
In the current research, we focus on uniparental inheritance of chloroplast genome of the living fossil plant, Ginkgo biloba L., one of the gymnosperms, using genomic data. Highlights are shown as ...the followings.•Our results provide strong genomic evidence to support plastid maternal inheritance mode of G. biloba, which is different from most other gymnosperms.•The combination of manually genetic crosses and genomic data is proved to be an efficient way to investigate the inheritance mode of chloroplasts genome in land plants.•The current research also provides a case study for further research of plastid inheritance in gymnosperms using genomic techniques, which will contribute to a better understanding of cytologically uniparental inheritance mode and evolutionary mechanism of plastids in both gymnosperms and angiosperms.
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