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Elimination of intermuscular bones (IMBs) is vital to the aquaculture industry of cyprinids. In our previous study, we characterizedbmp6as essential in the development of IMBs in zebrafish. Knockout ofbmp6results in the absence of IMBs in zebrafish without affecting growth and reproduction. Therefore, we hypothesized thatbmp6could be used to generate new cyprinid strains without IMBs by gene editing. In this study, we established a gene editing strategy for knocking out the two orthologs ofbmp6in diploid crucian carp (Carassius auratus). We obtained an F3population with both orthologs knocked out, in which no IMBs were detected by bone staining and X-ray, indicating that the new strain without IMBs (named WUCI strain) was successfully generated. Furthermore, we extensively evaluated the performance of the new strain in growth, reproduction, nutrient components, muscle texture and structure, and metabolites in muscle. The results showed that the WUCI strain grew faster than the wild-type crucian carp at 4-month-age. The reproductive performance and flesh quality did not show significant differences between the WUCI strain and wild-type crucian carp. Moreover, the metabolomics analysis suggested that the muscle tissues of the WUCI strain significantly enriched some metabolites belonging to the Thiamine metabolism, Nicotinate and Nicotinamide Metabolism pathway, which plays beneficial effects in anti-aging, anti-oxidant, and anti-radiation damage. In conclusion, we established a strategy to eliminate IMBs in crucian carp and obtain a WUCI strain whose performance was expected compared to the wild-type crucian carp; meanwhile, the WUCI strain enriched some beneficial metabolites to human health in muscle tissue. This study is the first report that a farmed cyprinid strain without IMBs, which could be stably inherited, was obtained worldwide; it provided excellent germplasm for the cyprinids aquaculture industry and a useful molecular tool for eliminating IMBs in other cyprinids.

The vocal control system of songbirds has a significant laterality in song output control, and the adult zebra finch Taeniopygia guttata has a significant advantage in the right high vocal center (HVC) in song frequency domain and intensity control. We investigated the allometric relationship between song control nucleus and brain scale in zebra finches, investigated whether HVC and the robust nucleus of the archistriatum (RA) had similar differences in volume, cell density and cell number, and whether Testosterone (T) played a role in these differences. The results of Nissl staining and Immunohistochemistry show that although there is no significant difference between left and right HVC in nucleus volume, cell density and cell number (P > 0.05), the correlation parameters on the right side are always higher than those on the left side. On nucleus volume and cell number, both sides of the RA have no significant different (P > 0.05), but on the cell density has a significant (P 0.05). The growth rate of left and right HVC and RA is consistent with that of brain (K > 1). HVC and RA are well fitted to brain length, width and height, and the intercept is also very close. What’s more, the song nucleus always show negative growth relationship with the three-dimensional parameters of the brain. Therefore, while the living environment remains relatively constant, songbirds will continue to strictly follow the existing allometric growth pattern, and any individuals that deviate seriously from this development pattern will show more inadaptability.

The purpose of this study was to investigate the preventive effect and mechanism of Dendrobium alkaloids (DNLA) on oxidative stress-related death in neuronal cells. Our results demonstrated that DNLA has a direct neuroprotective effect through oxidative stress in N2A cells induced by hydrogen peroxide (H2O2). CCK8, lactate dehydrogenase (LDH), intracellular Ca2+, intracellular reactive oxygen species (ROS), and mitochondrial membrane potential (MMP) were used to evaluate the mechanism of DNLA neutralization by H2O2-induced injury. Results presented in the paper indicate that treatment with DNLA (35 ng/mL) significantly attenuated decreases in cell viability, release of LDH, and apoptosis after H2O2-induced neuronal injury. Furthermore, DNLA significantly reduced intracellular Ca2+ up-regulation, ROS production, and inhibited mitochondrial depolarization. Moreover, DNLA treatment significantly downregulated expressions of interleukin (IL)-1β, tumor necrosis factor (TNF)-α, IL-6, nitric oxide synthase, janus kinase-signal transducer and activators of transcription (JAK-STATs) signaling in N2A cells, all of which were H2O2-induced. Taken together, our findings suggested that DNLA may inhibit the expression of pro-inflammatory and pro-apoptotic factors by blocking JAK-STATs signaling after oxidative stress injury. This research provides a potential experimental basis for further application of DNLA to prevent various human nervous system diseases caused by oxidative stress.

Intermuscular bones (IBs) are small, hard-boned spicules located in the muscle tissue that mainly exist in the myosepta of lower teleosts, which hurt the edibleness and economic value of fish. The study of the development of IBs is very important for freshwater aquaculture fish, but the molecular mechanism of its formation and the key regulatory genes remain unclear. In this study, we first constructed two types of zebrafish mutants (the mutants losing IBs and the mutants with partial deletion of IBs) by knocking out

Empowered by today’s rich media generating devices and convenient Internet access, crowdsourced live streaming (CSLS) service has developed rapidly and become one of the most popular Internet services. Large crowdsourced live streaming providers (CSLSPs) are migrating their services to geo-distributed cloud platforms (GDCPs) for lower costs and higher availability. A CSLSP may rent compute and network resources from cloud providers for video transcoding, video delivering, user-requests handling, and other related tasks. However, due to dynamic requests by viewers and widely spread locations of broadcasters and viewers, it is still challenging for a CSLSP to serve demands of users with reasonable resources from the cloud-based geo-distributed data centers. To overcome this challenge cost-effectively, we propose an online algorithm to save operational costs for CSLSPs by jointly and dynamically choosing right data centers for broadcasters and viewers. Mathematical analysis is presented and proves that our proposed online algorithm can ensure operational costs to be within an upper bound above the optimal solution, while guaranteeing the QoE for viewers. We conduct extensive trace-driven illustrative studies and show that the proposed method can achieve suboptimal results and outperforms other alternative methods.

As a powerful and attractive method for detecting gene expression, qRT-PCR has been broadly used in aquacultural research. Understanding the biology of taimen (Hucho taimen) has attracted increased interest because of its ecological and economic values. Stable reference genes are required for the reliable quantification of gene expression, but such genes have not yet been optimised for taimen. In this study, the stability levels of 10 commonly used candidate reference genes were evaluated using four methods, geNorm, NormFinder, BestKeeper and RefFinder. The expression levels of the 10 genes were detected using samples from 48 experimental groups that consisted of six tissues (blood, heart, brain, gill, skin and liver) collected under five heat-stress conditions (18, 20, 22, 24 and 26°C) and four heat-stress processing times (4, 24, 48 and 96 h). RPS29 and RPL19 were the most stable genes among all the samples, whereas 28S rRNA, ARBPR and 18S rRNA were the least stable. These results were verified by an expression analysis of the heat-stress genes (hsp60 and hsp70) of taimen. In conclusion, RPS29 and RPL19 are the optimal reference genes for qRT-PCR analyses of taimen, irrespective of the tissue and experimental conditions, and these results allow the reliable study of gene expression in taimen.

Software defect prediction constructs a model from the previous version of a software project to predict defects in the current version, which can help software testers to focus on software modules with more defects in the current version. Most existing methods construct defect prediction models through minimizing the defect prediction error measures. Some researchers proposed model construction approaches that directly optimized the ranking performance in order to achieve an accurate order. In some situations, the model complexity is also considered. Therefore, defect prediction can be seen as a multi-objective optimization problem and should be solved by multi-objective approaches. And hence, in this paper, we employ an existing multi-objective evolutionary algorithm and propose a new multi-objective learning method based on it, to construct defect prediction models by simultaneously optimizing more than one goal. Experimental results over 30 sets of cross-version data show the effectiveness of the proposed multi-objective approaches.

Fermented vegetable–fruit beverages are a popular fermented food, with many potential health benefits. In this study, two commercial Lactobacillus plantarum strains were selected to ferment a beverage containing apples, pears, and carrots. The metabolites and antioxidant activities were examined during the fermentation process. Results showed that lactic acid and acetic acid accumulated gradually, whereas malic acid decreased. Glucose and fructose increased from 0.48 and 14.8 g/L to 7.7 and 20.8 g/L, respectively, while sucrose decreased slightly. Ascorbic acid also increased continuously during the fermentation to 90.74 mg/100 mL. DPPH and ABTS radical scavenging activity and FRAP reached their maximum value after 4–8 days. The accumulation of TPC, TFC, and SOD reached their maximum value on the 8th day of fermentation. Our study revealed that the L. plantarum-fermented vegetable–fruit beverage showed significant antioxidant activity, which is helpful in evaluating the fermentation end-point and developing a high-quality fermented beverage.

Dynamic requests of viewers from sparse and dispersed locations for crowdsourced-live-streaming (CSLS) service make current cloud service providers (CSPs) inadequate to provide sufficient quality of experience (QoE). To solve this issue, we propose a multi-CDN-assisted-CSLS (MCACLS) architecture, a novel cloud architecture complemented by multiple content delivery networks (Multi-CDNs). MCACLS architecture can enhance a CSP’s capacity of video distribution service and improve the quality of CSLS service for end-users while reducing the overall operational cost. MCACLS adaptively adjusts resources between a CSP and its leased CDN service in a fine granularity to deal with the volatility of user requests. However, scheduling resources cost-effectively in response to user requests from different regions is a critical issue that must be addressed. We formulate the above problem into a constrained stochastic optimization problem and propose an algorithm based on the Nash bargaining solution. Our proposed algorithm makes tradeoff between QoE of users and the overall operational cost for CSPs. Illustrative studies validate the advantages of MCACLS and show that it is more cost-effective, reducing the overall operational cost by up to 15% compared with other alternatives while achieving sufficient QoE for viewers.

The analysis and design of mechanical structures and systems has a long and in-depth research history, and has achieved fruitful results. In fact, in the traditional analysis and design of mechanical structures and systems, people have found that there are a lot of uncertainties in the disciplines of system science, transportation science, and management science. It is difficult for traditional deterministic design methods to effectively deal with this type of problems. Uncertainty issues, such as the uncertainty of the shape and size of the structure due to the influence of processing technology, processing equipment, environmental factors and human factors in the manufacturing process, these inaccurate and uncertain information mainly come from the processing technology, Processing equipment, environmental factors, and man-made factors, and evidence theory can effectively model the problem of cognitive uncertainty in engineering, using probability intervals composed of credibility and likelihood to describe the cognitive uncertainty brought about by Impact. This article introduces the basic concepts of evidence theory and the basic process of reliability analysis of conventional evidence theory, and optimizes it to a certain extent. Then, this paper uses the optimization method and conventional methods of this paper to deal with a practical engineering problem, and compares the results. The final research results show that the optimization method in this paper is more effective than conventional methods, and it is concluded that the theory of evidence is indeed applicable to mechanical engineering, and it can effectively analyze the reliability of machinery.

Mobile edge computing (MEC) is a promising paradigm to integrate computing and communication resources in mobile networks. MEC can improve mobile service quality and enhance Quality of Experience (QoE) by offloading computation tasks to MEC servers. However, a MEC server only can provide limited computational resources for users. In this paper, we consider a mobile edge computing system that provides three offloading policies that are: (i) executing tasks in local device, (ii) offloading tasks to servers in a local region, (iii)offloading tasks to servers in a nearby region. In the policy (iii), mobile user equipment can utilize computational resources of MEC servers in nearby regions to solve the problem of insufficient computational resources in local region servers. We formulate the computation offloading problem as a potential game and propose a Distributed Offloading strategy based on Jacobi algorithm (DOJ) for solving the computation offloading problem in a short period. The simulation results show that our proposed algorithm can reduce overall system costs and guarantee the QoE of users.

Software defect prediction can help to allocate testing resources efficiently through ranking software modules according to their defects. Existing software defect prediction models that are optimized to predict explicitly the number of defects in a software module might fail to give an accurate order because it is very difficult to predict the exact number of defects in a software module due to noisy data. This paper introduces a learning-to-rank approach to construct software defect prediction models by directly optimizing the ranking performance. In this paper, we build on our previous work, and further study whether the idea of directly optimizing the model performance measure can benefit software defect prediction model construction. The work includes two aspects: one is a novel application of the learning-to-rank approach to real-world data sets for software defect prediction, and the other is a comprehensive evaluation and comparison of the learning-to-rank method against other algorithms that have been used for predicting the order of software modules according to the predicted number of defects. Our empirical studies demonstrate the effectiveness of directly optimizing the model performance measure for the learning-to-rank approach to construct defect prediction models for the ranking task.

Software defect prediction employs attributes of software modules to identify defect-prone modules and thus improves software reliability by allocating testing resources more efficiently. Realizing that single-objective methods might be insufficient for solving defect prediction problems, some researchers have proposed multi-objective learning approaches, and proved better performance of multi-objective than single-objective methods. However, existing compared single-objective methods optimize a completely different goal from goals of multi-objective approaches, which might lead to bias. In this paper, we compare a multi-objective approach that optimizes two objectives and a single-objective approach that directly optimizes a trade-off of the two objectives, in order to further investigate the comparison of multi-objective and single-objective approaches. The conclusion will help to appropriately choose multi-objective or single-objective learning approaches for defect prediction.