Deacylation of Calcium-Dependent Antibiotics from Streptomyces violaceoruber in Co-culture with Streptomyces sp. MG7-G1

When Streptomyces violaceoruber grows together with Streptomyces sp. MG7-G1, it reacts with strongly induced droplet production on its aerial mycelium. Initially the metabolite profile of droplets from S. violaceoruber in co-culture with Streptomyces sp. MG7-G1 was compared to samples from S. violaceoruber in single-culture by using high-performance liquid chromatography-mass spectrometry (HPLC-MS). Then, the exudate from agar plates of co-cultures and single cultures (after freezing and thawing) was also analysed. Several compounds were only observed when S. violaceoruber was grown in co-culture. Based on their high-resolution ESI mass spectra and their comparable retention times to the calcium-dependent antibiotics (CDAs) produced by S. violaceoruber, the new compounds were suspected to be deacylated calcium-dependent antibiotics (daCDAs), lacking the 2,3-epoxyhexanoyl residue of CDAs. This was verified by detailed analysis of the MS/MS spectra of the daCDAs in comparison to the CDAs. The major CDA compounds present in calcium ion-supplemented agar medium of co-cultures were daCDAs, thus suggesting that Streptomyces sp. MG7-G1 expresses a deacylase that degrades CDAs.     

复合流场上吸罩流场理论分析

提出在计算复合流场内存在两种不同流态的流动:势流和紊流。针对两种流动,分别建立了其数学模型,对于紊流流场,针对标准k-ε紊流模型的缺陷,采用了RNGk-ε紊流模型使紊流控制方程封闭。以及提出了边界条件的确定原则。为复合流场的数值计算提供了理论指导。     

基于深度学习的跨模态检索综述

由于多模态数据的快速增长,跨模态检索受到了研究者的广泛关注,其将一种模态的数据作为查询条件检索其他模态的数据,如用户可以用文本检索图像或/和视频。由于查询及其检索结果模态表征的差异,如何度量不同模态之间的相似性是跨模态检索的主要挑战。随着深度学习技术的推广及其在计算机视觉、自然语言处理等领域的显著成果,研究者提出了一系列以深度学习为基础的跨模态检索方法,极大缓解了不同模态间相似性度量的挑战,本文称之为深度跨模态检索。本文从以下角度综述有代表性的深度跨模态检索论文,基于所提供的跨模态信息将这些方法分为3类：基于跨模态数据间一一对应的、基于跨模态数据间相似度的以及基于跨模态数据语义标注的深度跨模态检索。一般来说,上述3类方法提供的跨模态信息呈现递增趋势,且提供学习的信息越多,跨模态检索性能越优。在上述不同类别下,涵盖了7类主流技术,即典型相关分析、一一对应关系保持、度量学习、似然分析、学习排序、语义预测以及对抗学习。不同类别下包含部分关键技术,本文将具体阐述其中有代表性的方法。同时对比提供不同跨模态数据信息下不同技术的区别,以阐述在提供了不同层次的跨模态数据信息下相关技术的关注点与使用异同。为评估不同的跨模态检索方法,总结了部分代表性的跨模态检索数据库。最后讨论了当前深度跨模态检索待解决的问题以及未来的研究方向。     

燃烧离子色谱法测定α-烯烃齐聚反应体系中催化剂BF3的含量

BF₃作为α-烯烃齐聚反应催化剂,其含量控制对催化反应的调控与产品质量的把控至关重要。通过建立样品前处理方法、优化仪器测试条件与标准溶液配制方法,建立利用燃烧离子色谱测定BF₃·醇催化的α-烯烃齐聚反应体系中F元素含量,进而计算出BF₃含量的方法,并考察了方法的重复性和准确性。结果表明：利用该方法可测定BF₃质量分数范围为2.68～53.50 g/kg的样品,前处理时适量的正丁醇和白油可有效稳定和稀释样品;测定稀释样品中F元素含量的标准曲线分为2段,线性范围分别为300～2000 mg/L和2000～3000 mg/L,线性相关系数R²分别为0.9992和0.9940;测定5次BF₃质量分数结果相对标准偏差均小于5%,方法加标回收率在95.0%～105.0%之间,方法检出限取决于前处理时的取样量,当取样量为5 g时,检出限为1 mg/kg。     

Support Vector machine and duration-aware conditional random field for identification of spatio-temporal activity patterns by combined indoor positioning and heart rate sensors

Tracking the spatio-temporal activity is highly relevant for domains like security, health, and quality management. Since animal welfare became a topic in politics and legislation locomotion patterns of livestock have received increasing interest. In contrast to the monitoring of pedestrians cattle activity tracking poses special challenges to both sensors and data analysis. Interesting states are not directly observable by a single sensor. In addition, sensors must be accepted by cattle and need to be robust enough to cope with a rough environment. In this article, we introduce the novel combination of heart rate and positioning sensors. Attached to neck and chest they are less interfering than accelerometers at the ankles. Exploiting the potential of such combined sensor system that records locomotion and non-spatial information from the heart rate sensor however is challenging. We introduce a novel two level method for the activity tracking focused on the duration and sequence of activity states. We combine Support Vector Machine (SVM) with Conditional Random Field (CRF) and extend Conditional Random fields by an explicit representation of duration. The SVM characterizes local activity states, whereas the CRF addresses sequences of local states to sequences incorporating spatial and non-spatial contextual knowledge. This combination provides a reliable and comprehensive identification of defined activity patterns, as well as their chronology and durations, suitable for the integration in an activity data base. This data base is used to extract physiological parameters and promises insights into internal states such as fitness, well-being and stress. Interestingly we were able to demonstrate a significant correlation between resting pulse rate and the day of pregnancy.     

Video archaeology: understanding video manipulation history

Facing the explosive growth of near-duplicate videos, video archaeology is quite desired to investigate the history of the manipulations on these videos. With the determination of derived videos according to the manipulations, a video migration map can be constructed with the pair-wise relationships in a set of near-duplicate videos. In this paper, we propose an improved video archaeology (I-VA) system by extending our previous work (Shen et al. 2010). The extensions include more comprehensive video manipulation detectors and improved techniques for these detectors. Specially, the detectors are used for two categories of manipulations, i.e., semantic-based manipulations and non-semantic-based manipulations. Moreover, the improved detecting algorithms are more stable. The key of I-VA is the construction of a video migration map, which represents the history of how near-duplicate videos have been manipulated. There are various applications based on the proposed I-VA system, such as better understanding of the meaning and context conveyed by the manipulated videos, improving current video search engines by better presentation based on the migration map, and better indexing scheme based on the annotation propagation. The system is tested on a collection of 12,790 videos and 3,481 duplicates. The experimental results show that I-VA can discover the manipulation relation among the near-duplicate videos effectively.     

Robustness of an aerobic metabolically vinyl chloride degrading bacterial enrichment culture

Degradation of the lower chlorinated ethenes is crucial to the application of natural attenuation or in situ bioremediation on chlorinated ethene contaminated sites. Recently, within mixtures of several chloroethenes as they can occur in contaminated groundwater inhibiting effects on aerobic chloroethene degradation have been shown. The current study demonstrated that metabolic vinyl chloride (VC) degradation by an enrichment culture originating from groundwater was not affected by an equimolar concentration (50 μM) of cis-1,2-dichloroethene (cDCE). Only cDCE concentrations at a ratio of 2.4:1 (initial cDCE to VC concentration) caused minor inhibition of VC degradation. Furthermore, the degradation of VC was not affected by the presence of trans-1,2-dichloroethene (tDCE), 1,1-dichloroethene (1,1-DCE), trichloroethene (TCE), and tetrachloroethene (PCE) in equimolar concentrations (50 μM). Only cDCE and tDCE were cometabolically degraded in small amounts. The VC-degrading culture demonstrated a broad pH tolerance from 5 to 9 with an optimum between 6 and 7. Results also showed that the culture could degrade VC concentrations up to 1,800 μM (110 mg/L).     

Squeezing More Past Knowledge for Online Class-Incremental Continual Learning

Continual learning (CL) studies the problem of learning to accumulate knowledge over time from a stream of data. A crucial challenge is that neural networks suffer from performance degradation on previously seen data, known as catastrophic forgetting, due to allowing parameter sharing. In this work, we consider a more practical online class-incremental CL setting, where the model learns new samples in an online manner and may continuously experience new classes. Moreover, prior knowledge is unavailable during training and evaluation. Existing works usually explore sample usages from a single dimension, which ignores a lot of valuable supervisory information. To better tackle the setting, we propose a novel replay-based CL method, which leverages multi-level representations produced by the intermediate process of training samples for replay and strengthens supervision to consolidate previous knowledge. Specifically, besides the previous raw samples, we store the corresponding logits and features in the memory. Furthermore, to imitate the prediction of the past model, we construct extra constraints by leveraging multi-level information stored in the memory. With the same number of samples for replay, our method can use more past knowledge to prevent interference. We conduct extensive evaluations on several popular CL datasets, and experiments show that our method consistently outperforms state-of-the-art methods with various sizes of episodic memory. We further provide a detailed analysis of these results and demonstrate that our method is more viable in practical scenarios.      

Exploring cognitive style and task-specific preferences for process representations

Process models describe someone’s understanding of processes. Processes can be described using unstructured, semi-formal or diagrammatic representation forms. These representations are used in a variety of task settings, ranging from understanding processes to executing or improving processes, with the implicit assumption that the chosen representation form will be appropriate for all task settings. We explore the validity of this assumption by examining empirically the preference for different process representation forms depending on the task setting and cognitive style of the user. Based on data collected from 120 business school students, we show that preferences for process representation formats vary dependent on application purpose and cognitive styles of the participants. However, users consistently prefer diagrams over other representation formats. Our research informs a broader research agenda on task-specific applications of process modeling. We offer several recommendations for further research in this area.