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Red-emitting exo-AuNCs successfully stained the nucleus and were compatible with membrane-staining dyes. This is the first study to use exosomes to synthesize fluorescent nanomaterials for cellular imaging applications. As exosomes are naturally produced via secretion from almost all types of cell, the proposed method could serve as a strategy for low-cost production of versatile nanomaterials.
Trimethylamine N-oxide (TMAO) may play a key mediator role in the relationship between the diet, gut microbiota and cardiovascular diseases, particularly in people with kidney failure. The aim of this review is to evaluate which foods have a greater influence on blood or urinary trimethylamine N-oxide (TMAO) levels.

391 language articles were screened, and 27 were analysed and summarized for this review, using the keywords "TMAO" AND "egg" OR "meat" OR "fish" OR "dairy" OR "vegetables" OR "fruit" OR "food" in December 2020.

A strong correlation between TMAO and fish consumption, mainly saltwater fish and shellfish, but not freshwater fish, has been demonstrated. Associations of the consumption of eggs, dairy and meat with TMAO are less clear and may depend on other factors such as microbiota or cooking methods. Plant-based foods do not seem to influence TMAO but have been less investigated.

Consumption of saltwater fish, dark meat fish and shellfish seems to be associated with an increase in urine or plasma TMAO values. Further studies are needed to understand the relationship between increased risk of cardiovascular disease and plasma levels of TMAO due to fish consumption. Interventions coupled with long-term dietary patterns targeting the gut microbiota seem promising.
Consumption of saltwater fish, dark meat fish and shellfish seems to be associated with an increase in urine or plasma TMAO values. Further studies are needed to understand the relationship between increased risk of cardiovascular disease and plasma levels of TMAO due to fish consumption. Interventions coupled with long-term dietary patterns targeting the gut microbiota seem promising.Official statistics demonstrate the role of traffic accidents in the increasing number of fatalities, especially in emerging countries. In recent decades, the rate of deaths and injuries caused by traffic accidents in Iran, a rapidly growing economy in the Middle East, has risen significantly with respect to that of neighboring countries. The present study illustrates an exploratory spatial analysis' framework aimed at identifying and ranking hazardous locations for traffic accidents in Zanjan, one of the most populous and dense cities in Iran. This framework quantifies the spatiotemporal association among collisions, by comparing the results of different approaches (including Kernel Density Estimation (KDE), Natural Breaks Classification (NBC), and Knox test). Based on descriptive statistics, five distance classes (2-26, 27-57, 58-105, 106-192, and 193-364 meters) were tested when predicting location of the nearest collision within the same temporal unit. The empirical results of our work demonstrate that the largest roads and intersections in Zanjan had a significantly higher frequency of traffic accidents than the other locations. A comparative analysis of distance bandwidths indicates that the first (2-26 m) class concentrated the most intense level of spatiotemporal association among traffic accidents. Prevention (or reduction) of traffic accidents may benefit from automatic identification and classification of the most risky locations in urban areas. Thanks to the larger availability of open-access datasets reporting the location and characteristics of car accidents in both advanced countries and emerging economies, our study demonstrates the potential of an integrated analysis of the level of spatiotemporal association in traffic collisions over metropolitan regions.Extracts of the fungus Inonotus obliquus exhibit cytotoxic properties against different cancers; hence, this fungal species has been extensively studied. This study aimed to extract total triterpenoids from Inonotus obliquus using ionic liquids (ILs) and separate potential lactate dehydrogenase (LDH) inhibitors via ultrafiltration (UF)-high-speed countercurrent chromatography (HSCCC). Total triterpenoids from Inonotus obliquus were extracted by performing a single-factor experiment and employing a central composite design via ultrasonic-assisted extraction (UAE) and heat-assisted extraction (HAE). The extract was composed of 1-butyl-3-methylimidazolium bromide as the IL and methanol as the dispersant. Ultrafiltration-liquid chromatography (UF-LC) was used to rapidly scan the LDH inhibitors and betulin and lanosterol were identified as potential inhibitors. To obtain these target compounds, betulin and lanosterol with the purities of 95.9% and 97.8% were isolated from HSCCC within 120 min. Their structures were identified using several techniques, among which IL-HAE was fast and effective. This study reports the extraction of triterpenoids from Inonotus obliquus by IL for the first time. Collectively, the findings demonstrate that UF-LC is an effective tool for screening potential LDH inhibitors from crude extracts of I. obliquus and may help to identify bioactive substances against myocardial infarction, whereas high-purity compounds can be separated via UF-HSCCC.In this paper, we propose a new algorithm for distributed spectrum sensing and channel selection in cognitive radio networks based on consensus. The algorithm operates within a multi-agent reinforcement learning scheme. The proposed consensus strategy, implemented over a directed, typically sparse, time-varying low-bandwidth communication network, enforces collaboration between the agents in a completely decentralized and distributed way. The motivation for the proposed approach comes directly from typical cognitive radio networks' practical scenarios, where such a decentralized setting and distributed operation is of essential importance. Specifically, the proposed setting provides all the agents, in unknown environmental and application conditions, with viable network-wide information. Hence, a set of participating agents becomes capable of successful calculation of the optimal joint spectrum sensing and channel selection strategy even if the individual agents are not. AZD1775 The proposed algorithm is, by its nature, scalable and robust to node and link failures.
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