In recent years, obesity has become a global public health issue. It is closely associated with the occurrence of several chronic diseases, such as diabetes and cardiovascular diseases. Some edible ...and medicinal plants show anti-obesity activity, such as fruits, vegetables, spices, legumes, edible flowers, mushrooms, and medicinal plants. Numerous studies have indicated that these plants are potential candidates for the prevention and management of obesity. The major anti-obesity mechanisms of plants include suppressing appetite, reducing the absorption of lipids and carbohydrates, inhibiting adipogenesis and lipogenesis, regulating lipid metabolism, increasing energy expenditure, regulating gut microbiota, and improving obesity-related inflammation. In this review, the anti-obesity activity of edible and medicinal plants was summarized based on epidemiological, experimental, and clinical studies, with related mechanisms discussed, which provided the basis for the research and development of slimming products. Further studies should focus on the exploration of safer plants with anti-obesity activity and the identification of specific anti-obesity mechanisms.
Highlights • Veneer chipping was the prominent fracture mode in clinically retrieved zirconia–ceramic and metal–ceramic fixed dental prostheses. • All fracture initiated at the occlusal wear facets. ...• Zirconia–ceramic exhibited a higher rate of veneer fracture with larger chip sizes relative to their metal–ceramic counterparts.
Dark tea is a post-fermented tea with unique organoleptic characteristics. A unique microbial fermentation procedure is involved in dark tea processing that is considered a major factor to manage the ...quality of dark tea. Dark tea is gaining increasing attention recently because it contains various bioactive compounds with diverse biological functions.
This review summarizes the microbial fermentation process, main bioactive compounds, and important biological functions or health benefits of dark tea, emphasizing its protective effects against different diseases and relevant molecular mechanisms, and proposes the perspectives in research and application of dark tea.
Functional core microorganism-mediated bioconversion is crucial for the formation of dark tea's main quality components during the fermentation period. Dark tea contains a variety of bioactive compounds, including alkaloids, free amino acids, peptides, polyphenols, pigments, polysaccharides, and volatile compounds. Consumption of dark teas has been linked to multiple health benefits, such as antioxidant, anti-obesity, anti-diabetic, anti-cancer, cardiovascular protective, gastrointestinal protective, hepatoprotective, neuroprotective, and photoprotective activities. Future studies focusing on the health mechanisms of dark teas and the potential applications of dark tea in the food, pharmaceutical, and medical industries are guaranteed.
•Dark teas are rich in diverse bioactive compounds.•Several core microorganisms are involved in dark tea fermentation.•Dark teas possess plenty of biological functions, especially anti-obesity and cardiovascular protective effects.•It is recommended for the public to consume dark teas to prevent and manage certain chronic diseases.
As RNA-seq becomes the assay of choice for measuring gene expression levels, differential expression analysis has received extensive attentions of researchers. To date, for the evaluation of DE ...methods, most attention has been paid on validity. Yet another important aspect of DE methods, stability, is overlooked and has not been studied to the best of our knowledge.
In this study, we empirically show the need of assessing stability of DE methods and propose a stability metric, called Area Under the Correlation curve (AUCOR), that generates the perturbed datasets by a mixture distribution and combines the information of similarities between sets of selected features from these perturbed datasets and the original dataset.
Empirical results support that AUCOR can effectively rank the DE methods in terms of stability for given RNA-seq datasets. In addition, we explore how biological or technical factors from experiments and data analysis affect the stability of DE methods. AUCOR is implemented in the open-source R package AUCOR, with source code freely available at https://github.com/linbingqing/stableDE .
Diabetes mellitus has become a serious and growing public health concern. It has high morbidity and mortality because of its complications, such as diabetic nephropathy, diabetic cardiovascular ...complication, diabetic neuropathy, diabetic retinopathy, and diabetic hepatopathy. Epidemiological studies revealed that the consumption of tea was inversely associated with the risk of diabetes mellitus and its complications. Experimental studies demonstrated that tea had protective effects against diabetes mellitus and its complications via several possible mechanisms, including enhancing insulin action, ameliorating insulin resistance, activating insulin signaling pathway, protecting islet β-cells, scavenging free radicals, and decreasing inflammation. Moreover, clinical trials also confirmed that tea intervention is effective in patients with diabetes mellitus and its complications. Therefore, in order to highlight the importance of tea in the prevention and management of diabetes mellitus and its complications, this article summarizes and discusses the effects of tea against diabetes mellitus and its complications based on the findings from epidemiological, experimental, and clinical studies, with the special attention paid to the mechanisms of action.
StarCraft II (SC2) poses a grand challenge for reinforcement learning (RL), of which the main difficulties include huge state space, varying action space, and a long time horizon. In this work, we ...investigate a set of RL techniques for the full-length game of StarCraft II. We investigate a hierarchical RL approach, where the hierarchy involves two. One is the extracted macro-actions from experts’ demonstration trajectories to reduce the action space in an order of magnitude. The other is a hierarchical architecture of neural networks, which is modular and facilitates scale. We investigate a curriculum transfer training procedure that trains the agent from the simplest level to the hardest level. We train the agent on a single machine with 4 GPUs and 48 CPU threads. On a 64x64 map and using restrictive units, we achieve a win rate of 99% against the difficulty level-1 built-in AI. Through the curriculum transfer learning algorithm and a mixture of combat models, we achieve a 93% win rate against the most difficult non-cheating level built-in AI (level-7). In this extended version of the paper, we improve our architecture to train the agent against the most difficult cheating level AIs (level-8, level-9, and level-10). We also test our method on different maps to evaluate the extensibility of our approach. By a final 3-layer hierarchical architecture and applying significant tricks to train SC2 agents, we increase the win rate against the level-8, level-9, and level-10 to 96%, 97%, and 94%, respectively. Our codes and models are all open-sourced now at https://github.com/liuruoze/HierNet-SC2.
To provide a baseline referring the AlphaStar for our work as well as the research and open-source community, we reproduce a scaled-down version of it, mini-AlphaStar (mAS). The latest version of mAS is 1.07, which can be trained using supervised learning and reinforcement learning on the raw action space which has 564 actions. It is designed to run training on a single common machine, by making the hyper-parameters adjustable and some settings simplified. We then can compare our work with mAS using the same computing resources and training time. By experiment results, we show that our method is more effective when using limited resources. The inference and training codes of mini-AlphaStar are all open-sourced at https://github.com/liuruoze/mini-AlphaStar. We hope our study could shed some light on the future research of efficient reinforcement learning on SC2 and other large-scale games.
This letter investigates the maximum-likelihood transceiver design for a reconfigurable intelligent surface assisted multiple-input multiple-output system. The goal is to jointly optimize the ...precoder and the reflector such that the minimum distance between the received signals can be maximized. The design problem is challenging and we propose an efficient approach to overcome the difficulty. That is, the so-called 2-D precoder is first applied at the source, and then a gradient ascent algorithm is developed to optimize the reflector. It is demonstrated that the use of the 2-D precoder can significantly reduce the computational complexity of the proposed reflector design. Simulation results show that our transceiver effectively improves the symbol-vector error rate performance with an affordable computational cost.
With the popularity of IoT devices and cloud technology in the medical industry. Sharing EHRs (Electronic Health Records) among medical institutions improves the accuracy of medical diagnosis and ...promotes the development of public medical. However, it is difficult to share EHRs among hospitals, and patients typically don't know about the usage of their health records. In this paper, we propose a patient-controlled EHRs sharing scheme based on cloud computing collaborating blockchain technology. The medical abstract and the access strategy are stored in the blockchain to avoid being tampered with. To achieve the fine-grained access control, we propose the attribute-based encryption scheme and multi-keyword encryption scheme to encrypt EHRs. Moreover, we proposed a node-state-checkable Practical Byzantine Fault Tolerance consensus algorithm (sc-PBFT) to prevent the Byzantine nodes from sneaking into the consortium blockchain. First, we check the state of the elected master node to avoid the master node having any malicious records. Then, using pre-prepared, prepare, and commit processes to complete the consensus request submitted by the client. At last, the proposed consensus algorithm evaluates the state of the master node according to the completion of the three-stage process to reduce the impact of the malicious node on the whole consortium blockchain. By doing this, the malicious node will be marked and isolated into the isolation area. The experimental results show that the proposed sc-PBFT algorithm has better handling capability and lower consensus latency. Compared with the PBFT algorithm in the case of Byzantine nodes, sc-PBFT not only improves the robustness of the consortium blockchain network but also improves the handling capability.
Alzheimer’s an irreversible neurodegenerative disease with the most far-reaching impact, the most extensive, and the most difficult to cure in the world. It is also the most common disease of ...Alzheimer’s disease. With the rapid rise of data mining, machine learning and other fields, they have penetrated various disciplines. In particular, research in the field of AD is developing rapidly and has demonstrated strong vitality. In terms of data, Alzheimer’s Disease Neuroimaging Initiative (ADNI) researchers collect, verify and use a variety of data modalities as predictors of disease, including MRI and PET images, genetics, cognitive testing, cerebrospinal fluid and blood biomarkers, etc. Therefore, this paper uses a multi-task learning algorithm based on the ADNI data set to implement regression tasks and predict the cognitive scores of subjects in the next 3 years. This method can effectively assess the cognitive trends of patients in the future and aims to predict the progression of the disease. In addition, we used four different machine learning classification algorithms to conduct fusion research on AD multi-modal data, including MRI, PET, and cognitive scoring data. This method can determine the current patient’s cognitive stage, to achieve the effect of assisting doctors in diagnosis. Finally, we designed a multi-modal data platform technical architecture to standardize management and sharing of ADNI data and data obtained by offline medical institutions to improve the utilization and value of data. The design of the technical architecture proposed in this article is more easily scalable and compatible with other neurological diseases. Nowadays, the large amount of data being generated by AD can provide valuable solutions for the research of disease progression prediction and auxiliary diagnosis.
Objective
Adenosine deaminase (ADA) associated with cell-mediated immune responses is involved in many diseases. But little is known about the value of ADA activity in the progression of different ...diabetes types. The purpose of this study was to compare the ADA level between latent autoimmune diabetes in adults (LADA), type 2 diabetes (T2D) patients, and healthy controls (HC) and analyze its correlation with glycemic parameters and systemic cytokines.
Methods
This hospital-based study included 28 LADA patients, 52 T2D patients, and 50 HC. Serum ADA activity and concentrations of inflammatory cytokines were measured. Correlations of ADA level with different indicators were assessed by using spearman’s correlation method.
Results
Serum ADA activity was significantly higher in T2D patients compared with LADA (
p
= 0.008) and HC (
p
< 0.001). Correlation analysis of ADA with HbA1c% (
r
= 0.34,
p
= 0.003) and inflammatory cytokines (IL-6,
r
= 0.31,
p
= 0.007; IL-10,
r
= 0.22,
p
= 0.049) showed significant positive correlations.
Conclusions
Serum ADA activity may reflect the different immunopathogenesis between LADA and T2D patients.
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