An insight into gut microbiota and metabolites in the mice with adenomyosis

BackgroundAdenomyosis (AM) is a benign uterine disease characterized pathologically by the invasion of endometrial tissue into the myometrium. The pathogenesis of AM is still far from clear. Although the gut microbiome and metabolomics are thought to contribute to a variety of diseases, the role of them in AM has not been revealed.ObjectiveTo investigate changes in the gut microbiota and derived metabolites in AM mice.MethodFemale ICR mice were randomly assigned to AM and control groups, and pituitary transplantation was employed to perform AM modeling. Then, the fecal samples were obtained for microbial (16S rRNA gene sequencing) and metabolomic (liquid chromatography mass spectrometry, LC-MS) analysis.ResultThe results of gut microbiota analysis showed that the intestinal microbiota composition of AM mice was altered. The ratio of Firmicutes/Bacteroidetes and the relative abundance of Lactobacillus in AM group increased compared with the control group. Sixty differential expressed metabolites were identified in intestinal metabolites, mainly involved in steroid hormone biosynthesis, cysteine and methionine metabolism, and alanine, aspartate, and glutamate metabolism. Further, correlation analysis verified that L-methionine and L-cystine were negatively correlated with Bacteroides and positively correlated with Desulfovibrio. The Pregnenolone, Androsterone glucuronide, and Testosterone glucuronide were negatively correlated with Unidentified_Ruminococcaceae and Alistipes, whereas they positively correlated with Bacteroides.ConclusionAM mice have a unique gut microbiome and intestinal metabolites

Projections of Ebola outbreak size and duration with and without vaccine use in Équateur, Democratic Republic of Congo, as of May 27, 2018.

As of May 27, 2018, 6 suspected, 13 probable and 35 confirmed cases of Ebola virus disease (EVD) had been reported in Équateur Province, Democratic Republic of Congo. We used reported case counts and time series from prior outbreaks to estimate the total outbreak size and duration with and without vaccine use. We modeled Ebola virus transmission using a stochastic branching process model that included reproduction numbers from past Ebola outbreaks and a particle filtering method to generate a probabilistic projection of the outbreak size and duration conditioned on its reported trajectory to date; modeled using high (62%), low (44%), and zero (0%) estimates of vaccination coverage (after deployment). Additionally, we used the time series for 18 prior Ebola outbreaks from 1976 to 2016 to parameterize the Thiel-Sen regression model predicting the outbreak size from the number of observed cases from April 4 to May 27. We used these techniques on probable and confirmed case counts with and without inclusion of suspected cases. Probabilistic projections were scored against the actual outbreak size of 54 EVD cases, using a log-likelihood score. With the stochastic model, using high, low, and zero estimates of vaccination coverage, the median outbreak sizes for probable and confirmed cases were 82 cases (95% prediction interval [PI]: 55, 156), 104 cases (95% PI: 58, 271), and 213 cases (95% PI: 64, 1450), respectively. With the Thiel-Sen regression model, the median outbreak size was estimated to be 65.0 probable and confirmed cases (95% PI: 48.8, 119.7). Among our three mathematical models, the stochastic model with suspected cases and high vaccine coverage predicted total outbreak sizes closest to the true outcome. Relatively simple mathematical models updated in real time may inform outbreak response teams with projections of total outbreak size and duration

Reliability Analysis of a Cold Standby System with Imperfect Repair and under Poisson Shocks

This paper considers the reliability analysis of a two-component cold standby system with a repairman who may have vacation. The system may fail due to intrinsic factors like aging or deteriorating, or external factors such as Poisson shocks. The arrival time of the shocks follows a Poisson process with the intensity λ>0. Whenever the magnitude of a shock is larger than the prespecified threshold of the operating component, the operating component will fail. The paper assumes that the intrinsic lifetime and the repair time on the component are an extended Poisson process, the magnitude of the shock and the threshold of the operating component are nonnegative random variables, and the vacation time of the repairman obeys the general continuous probability distribution. By using the vector Markov process theory, the supplementary variable method, Laplace transform, and Tauberian theory, the paper derives a number of reliability indices: system availability, system reliability, the rate of occurrence of the system failure, and the mean time to the first failure of the system. Finally, a numerical example is given to validate the derived indices

A Vehicle Trajectory Privacy Preservation Method Based on Caching and Dummy Locations in the Internet of Vehicles

In the internet of vehicles (IoVs), vehicle users should provide location information continuously when they want to acquire continuous location-based services (LBS), which may disclose the vehicle trajectory privacy. To solve the vehicle trajectory privacy leakage problem in the continuous LBS, we propose a vehicle trajectory privacy preservation method based on caching and dummy locations, abbreviated as TPPCD, in IoVs. In the proposed method, when a vehicle user wants to acquire a continuous LBS, the dummy locations-based location privacy preservation method under road constraint is used. Moreover, the cache is deployed at the roadside unit (RSU) to reduce the information interaction between vehicle users covered by the RSU and the LBS server. Two cache update mechanisms, the active cache update mechanism based on data popularity and the passive cache update mechanism based on dummy locations, are designed to protect location privacy and improve the cache hit rate. The performance analysis and simulation results show that the proposed vehicle trajectory privacy preservation method can resist the long-term statistical attack (LSA) and location correlation attack (LCA) from inferring the vehicle trajectory at the LBS server and protect vehicle trajectory privacy effectively. In addition, the proposed cache update mechanisms achieve a high cache hit rate

Ribonucleoprotein Complexes That Control Circadian Clocks

Circadian clocks are internal molecular time-keeping mechanisms that enable organisms to adjust their physiology and behavior to the daily surroundings. Misalignment of circadian clocks leads to both physiological and health impairment. Post-transcriptional regulation and translational regulation of circadian clocks have been extensively investigated. In addition, accumulating evidence has shed new light on the involvement of ribonucleoprotein complexes (RNPs) in the post-transcriptional regulation of circadian clocks. Numerous RNA-binding proteins (RBPs) and RNPs have been implicated in the post-transcriptional modification of circadian clock proteins in different model organisms. Herein, we summarize the advances in the current knowledge on the role of RNP complexes in circadian clock regulation

Improved Matching Algorithm with Anchor Argument for Rotate Target Detection

Convolutional neural networks (CNNs) have been widely used in the task of object detection in remote sensing. Remote sensing targets can have arbitrary angles, and many anchor-base methods use a lot of anchors with different angles which cause efficiency and precision problems. To solve the problem caused by too many anchors, this paper presents a novel matching algorithm in the matching stage of the rotating anchor and object, which determines a more accurate rotating region of interests (RRoIs) for target regression using the copies set for each oriented anchor. It makes use of the high recall rate brought by a large number of anchor boxes with different angles and avoids the computation brought by a large number of anchor boxes. We use the remote sensing datasets DOTA and HRSC2016 with rotation bounding boxes to evaluate our improved algorithm on Rotation RetinaNet and compare it with it. For the targets of high aspect ratios, such as large vehicles and ships, our method is superior to Rotation RetinaNet and achieves a better performance

Effects of various coolants on flow and heat transfer characteristics in a round-tip pin-finned internal channel

This paper performs a numerical study regarding the effects of different kinds of coolants on flow and heat transfer in a pin-finned internal channel. A new pin-fin structure detached from one endwall with a round-tip is proposed which aims to increase the heat transfer enhancement. As a contrast, the investigation of the typical fully-attached pin-fin and the flat-tip detached pin-fin is also considered. In order to evaluate the reliability of the numerical method, an experiment of an internal channel with round-tip pin-fin arrays is conducted to serve as the reference, and all cases are carried out with the shear stress transport (SST) turbulence model. The effects of coolants including air, mist/air, steam, and mist/steam are analyzed with the Reynolds number ranging from 15, 000 to 50, 000. The results reveal that the round-tip pin-fin outperforms in heat transfer enhancement and pressure loss deterioration among all three pin-fin structures. With respect to Reynolds numbers, the averaged Nusselt numbers increase gradually for all coolants, and cases of mist/steam achieve the highest values, followed by cases of steam, mist/air, and air, respectively, but the differences between the mist coolants and pure coolants are not obvious as the Reynolds number is lower than 30, 000. In conclusion, cases of mist/steam obtain the best thermal performances, and the internal channel with round-tip pin-fin arrays using mist/steam as coolant is most favorable

Underwater Image Restoration Based on a Parallel Convolutional Neural Network

Restoring degraded underwater images is a challenging ill-posed problem. The existing prior-based approaches have limited performance in many situations due to the reliance on handcrafted features. In this paper, we propose an effective convolutional neural network (CNN) for underwater image restoration. The proposed network consists of two paralleled branches: a transmission estimation network (T-network) and a global ambient light estimation network (A-network); in particular, the T-network employs cross-layer connection and multi-scale estimation to prevent halo artifacts and to preserve edge features. The estimates produced by these two branches are leveraged to restore the clear image according to the underwater optical imaging model. Moreover, we develop a new underwater image synthesizing method for building the training datasets, which can simulate images captured in various underwater environments. Experimental results based on synthetic and real images demonstrate that our restored underwater images exhibit more natural color correction and better visibility improvement against several state-of-the-art methods

Application of BiVO4–Microalgae Combined Treatment to Remove High Concentration Mixture of Sulfamethazine and Sulfadiazine

Sulfonamides (SAs) are the most common and bio-refractory antibiotics detected in surface water systems, which cause long-term toxic effects on aquatic organisms. This study used the combination of a BiVO4 photocatalyst and freshwater micro-green alga (Dictyosphaerium sp.) to remove sulfadiazine (SD) and sulfamethazine (SM2) at an initial concentration of 5 mg/L (1:1 v/v) for 7 days. We set up three gradient concentrations of BiVO4 (0.5, 1 and 2 g/L) combined with the same concentration (80 mg/L) of Dictyosphaerium sp. and then prepared corresponding concentrations of pure BiVO4 and pure microalgae as controls. We evaluated the ability of BiVO4 and Dictyosphaerium sp. combined technology to remove SAs by observing the removal efficiency of antibiotics and explained the degradation mechanism of antibiotics and the key role of microalgae by studying the changes of reactive oxygen species (ROS) and inorganic ions (nitrogen, sulfur). The results showed that the degradation rate of these two SAs in the 0.5 g/L BiVO4–algae group could reach >96% within 7 d, which was higher than that in the 2 g/L BiVO4 group (93%) and the algae group (28%). The increased degradation efficiency of SAs in BiVO4 and microalgae systems was mainly due to the increased amount of ROS. Meanwhile, more SAs were degraded to inorganic compounds such as NH4+-N, NO3−-N and SO42−-S under ROS stress. It was found that microalgae can absorb the degradation products of antibiotics such as NH4+-N for their own growth, thereby reducing the toxicity of antibiotic by-products. In addition, BiVO4 had no damaging effect on the autofluorescence intensity of the microalgae. Our study provides an efficient and eco-economic approach to remove antibiotics using visible-light irradiation in aquatic environments and provides new insights into the biological removal of other antibiotic contaminants in aquatic environments