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Table S1. The effect of IMB0901 on the weights of body, quadriceps, gastrocnemius, WAT, and BAT when administered in healthy mice. (DOCX 16 kb)

For decades, the edible mushroom Pleurotus eryngii (P. eryngii) has been cultivated as important raw materials for food and pharmaceutical industries in most of Asian countries, especially in China. Unfortunately, the generation and improvement of new cultivars are very difficult since there are many barriers which have not been solved thoroughly by gene editing tools, even though the CRISPR-Cas9 technique has been widely applied in other species. In this study, we identified the point-mutated variant of the endogenous sdhB gene (cbxr) as a more stable selection marker than hygromycin B resistance gene (hph) in P. eryngii. Furthermore, using a codon-optimized Cas9, a predicted native U6 promoter-guided sgRNA, as well as an optimized protoplast transformation system, a highly efficient pyrG gene editing system was established in P. eryngii, that incorporated varied insertions and deletions (indels) by non-homologous end joining (NHEJ) and homology-directed repair (HDR). Findings for a successful targeted gene editing strategy in the edible mushroom P. eryngii may open a new chapter for the improvement of edible mushroom cultivars.

Magnolol, an essential bioactive natural lignans isolated from Magnolia Officinalis, has various pharmacological activities, such as antibacterial, anti-inflammatory, antioxidation and anti-tumor. In this study, a practical strategy based on ultra-high-performance liquid chromatography coupled with linear ion trap-Orbitrap mass spectrometry (UHPLC-LTQ-Orbitrap MS) was established to reveal the metabolic fate of magnolol in rat plasma and urine. First of all, in the data-dependent scanning (DDS) acquisition mode, the ESI-MSn data sets of biological samples and reference standard were obtained by using a high-quality online data analysis method. Subsequently, off-line data-mining techniques including multiple mass defect filters (MMDFs) and high-resolution extracted ion chromatograms (HREICs) were applied to screen major-to-trace metabolites of magnolol at the full-scan ESI-MS1 level. Finally, a total of 177 metabolites (prototype compound included) were preliminarily observed and characterized according to accurate mass measurement, the characteristic fragmentation patterns, chromato-graphic behaviors, and corresponding Clog P values. The results showed that magnolol experienced a variety of biotransformation reactions in rats, including hydroxylation, methoxylation, dehydrogenation, carboxylation, sulfonation, glucuronide conjugation, glucose conjugation, N-acetylcysteine (NAC) conjugation, glutathione conjugation and their composite reactions. In conclusion, this study not only greatly expanded the understanding of the therapeutic material basis and pharmacological mechanism of magnolol, but also provided new ideas for its toxicity evaluation, safety monitoring and drug delivery forms design.

Time delay estimation is the key to acoustic source localization based on time delay, and the time difference caused by the difference of the same signal arriving at different microphone distances is estimated. The main algorithms now include generalized phase days method, adaptive least mean method, the cross power spectrum phase method and so on, all of them get the time delay essentially by seeking the maximum position of the cross-correlation function. This paper discusses several of the above algorithms, and compares their advantages and disadvantages.

Sound source localization technology is a kind of positioning technology that use acoustics and electronic devices to receive acoustic waves to determine the location of sound source. In recent years, it has been widely used, and this paper discusses the application status, development prospect and difficulties and challenges of sound source localization technology.

Based on time delay,Sound source localization method has a smaller amount of calculation compared with the sound source localization method of controllable beam synthesizer and high resolution spectral estimation, and can achieve a higher positioning accuracy after improved, so it is the most suitable method for real-time positioning, widely used in the field of passive acoustic localization. This paper tries to spread the factors that affect the positioning accuracy of time delay method, and provides the direction for improving the effect of time delay sound source localization.

With the rapid development of nanotechnology, nanomaterials have been used in numerous fields. However, these nanomaterials could also result in risk for human and environmental health. To make a comparison of the health effects of three different kinds of nanomaterials, 28 male BALB/c mice were randomly divided into four groups. Three experimental groups were exposed to different kinds of nanomaterials including graphene, graphene oxide and single wall carbon nanotubes (SWCNTs) by intraperitoneal injection while the control group received a saline injection. The exposure dose of experimental groups was 4mg/kg. After seven days, sections of mice kidney were taken, the organ coefficient of both kidney and brain was counted, and the reactive oxygen species (ROS) level, glutathione (GSH) and malondialdehyde (MDA) content was measured. Our results showed that in the experimental groups, the organ coefficient and GSH content in mice kidneys and brains decreased, whereas the ROS level and MDA content increased, when compared with the control. The graphene oxide group was statistically significant (p < 0.05), while the SWCNTs group had extremely significant difference (p < 0.01). Morphological changes in the kidney were also seen in the experimental groups. These results demonstrate that oxidative damage to mice kidneys and brains induced by SWCNTs and graphene oxide is more severe than graphene. The degree of damage caused by these three typical nanomaterials is different, probably due to several parameters including particle size, surface character, and shape.

It is one of the common tasks of machine vision defect detection to judge whether the resistance of PCB board is correct or not. The traditional detection method is to use the color industrial camera to take pictures, then transform the image from the RGB color model to the HSI / HSV color model, according to different colors in the new color space have different characteristics to split it out. However, this method can not meet the requirements of industrial which asked to detect a variety of defects in one workplace, and the segmentation threshold is difficult to set due to the resistance's mirror reflection. Therefore, this paper presents a new color ring recognition method which use Retinex algorithm and BP neural network classifier to distinguish color rings and use the image took by black and white industrial camera. Experiments show that the method has high accuracy and can meet the requirements of industrial defect detection.

In this paper, the air conditioning room temperature model is established and the temperature control system based on the model is designed. What's more, the fuzzy control system of air conditioning room temperature is obtained. Finally, the effect of PID controller and fuzzy controller is compared by simulation. The simulation results show that the fuzzy control system designed in this paper can realize the continuous control, reduce the indoor temperature fluctuation, and increase the comfort of air conditioning room.

Additional file 1. Additional information.

Predicting the goals of users can be extremely useful in e-commerce, online entertainment, information retrieval, and many other online services and applications. In this thesis, we study the task of user intent understanding, trying to bridge the gap between user expressions to online services and their goals behind it. As far as we know, most of the existing user intent studies are focusing on web search and social media domain. Studies on other areas are not enough. For example, as people more and more rely our daily life on cellphone, our information needs expressing to mobile devices and related services are increasing dramatically. Studies of user intent mining on mobile devices are not much. And the intentions of using mobile devices are different from the ones we use web search engine or social network. So we cannot directly apply the existing user intention to this area. Besides, user's intents are not stable but changing over time. And different interests will impact each other. Modeling such kind of dynamic user interests can help accurately understand and predict user's intent. But there're few existing works in this area. Moreover, user intent could be explicitly or implicitly expressed by users. The implicit intent expression is more close to human's natural language and also have great value to recognize and mine. To make further studies of these challenges, we first try to answer the question of "What is the user intent?" By referring amount of previous studies, we give our definition of user intent as "User intent is a task-specific, predefined or latent concept, topic or knowledge-base that is under an expression from a user who is trying to express his goal of information or service need." Then, we focus on the driving scenario when a user using cellphone and study the user intent in this domain. As far as we know, it is the first time of user intent analysis and categorization in this domain. And we also build a dataset of user input and related intent category and attributes by crowdsourcing and carefully handcraft. With the user intent taxonomy and dataset in hand, we conduct a user intent classification and user intent attribute recognition by supervised machine learning models. To classify the user intent for a user intent query, we use a convolutional neural network model to build a multi-class classifier. And then we use a sequential labeling method to recognize the intent attribute in the query. The experiment results show that our proposed method outperforms several baseline models in precision, recall, and F-score. In addition, we study the implicit user intent mining method through web search log data. By using a Restricted Boltzmann Machine, we make use of the correlation of query and click information to learn the latent intent behind a user web search. We propose a user intent prediction model on online discussion forum using Multivariate Hawkes Process. It dynamically models user intentions change and interact over time.The method models both of the internal and external factors of user's online forum response motivations, and also integrated the time decay fact of user's interests. We also present a data visualization method, using an enriched domain ontology to highlight the domain-specific words and entity relations within an article.