Occurrence and diversity of Listeria spp. in seafood processing plant environments

Listeria contamination in processing plant environments is a major issue for the seafood industry worldwide; faster and more reliable results are therefore desired for early detection and monitoring of environmental Listeria spp. This study aimed to gain a better understanding of the prevalence and diversity of Listeria spp., and to evaluate a rapid detection method, the 3M Molecular Detection Assay (MDA) Listeria, for its ability to detect Listeria spp. in environmental samples from seafood processing plants. Duplicate environmental sponge samples (n = 444) were collected from 152 different sites within three seafood processing plants, and analyzed for Listeria spp. by the MDA method (after 26 and 48 h of enrichment) and the U.S. Food and Drug Administration Bacteriological Analytical Manual method. Overall, detection of Listeria spp. by the two methods did not differ significantly (p > 0.05); 11 (4.9%) and 13 (5.9%) samples were positive for Listeria spp. by the MDA and FDA-BAM method, respectively. The sensitivity of the MDS was 87.0% (95% CI: 77.4–96.6%), specificity was 97.6% (95% CI: 95.5–99.7%), accuracy was 95.3%, and the positive predictive value was 89.4% (95% CI: 80.5–98.2%). Classification of 19 Listeria isolates by partial SigB sequencing analysis identified three allelic types. Twelve of these isolates were ATs 58 and 60 which were classified as Listeria monocytogenes lineage I and serotypes 1/2b, 3b, 4b, 4d, 4e, by multiplex-PCR serotyping. Six Listeria isolates were classified as Listeria innocua (AT31). Our data show that the 3M Molecular Detection Assay Listeria provides rapid and reliable results for detection and monitoring of Listeria spp., which are important for seafood processing plants. Effective Listeria monitoring programs will allow for improved development of Listeria control measures in order to minimize cross-contamination in finished products.     

基于特征融合的中文简历解析方法研究

针对基于规则和统计的传统中文简历解析方法效率低、成本高、泛化能力差的缺点，提出一种基于特征融合的中文简历解析方法，即级联Word2Vec生成的词向量和用BLSTM（Bidirectional Long Short-Term Memory）建模字序列生成的词向量，然后再结合BLSTM和CRF（Conditional Random Fields）对中文简历进行解析（BLSTM-CRF）。为了提高中文简历解析的效率，级联包含字序列信息的词向量和用Word2Vec生成的词向量，融合成一个新的词向量表示；再由BLSTM强大的学习能力融合词的上下文信息，输出所有可能标签序列的分值给CRF层；再由CRF引入标签之间约束关系求解最优序列。利用梯度下降算法训练神经网络，使用预先训练的词向量和Dropout优化神经网络，最终完成对中文简历的解析工作。实验结果表明，所提的特征融合方法优于传统的简历解析方法。     

On Cost Aware Cloudlet Placement for Mobile Edge Computing

As accessing computing resources from the remote cloud inherently incurs high end-to-end (E2E) delay for mobile users, cloudlets, which are deployed at the edge of a network, can potentially mitigate this problem. Although some research works focus on allocating workloads among cloudlets, the cloudlet placement aiming to minimize the deployment cost (i.e., consisting of both the cloudlet cost and average E2E delay cost) has not been addressed effectively so far. The locations and number of cloudlets have a crucial impact on both the cloudlet cost in the network and average E2E delay of users. Therefore, in this paper, we propose the Cost Aware cloudlet PlAcement in moBiLe Edge computing (CAPABLE) strategy, where both the cloudlet cost and average E2E delay are considered in the cloudlet placement. To solve this problem, a Lagrangian heuristic algorithm is developed to achieve the suboptimal solution. After cloudlets are placed in the network, we also design a workload allocation scheme to minimize the E2E delay between users and their cloudlets by considering the user mobility. The performance of CAPABLE has been validated by extensive simulations.      

Preparation of molecularly imprinted fluorescence sensor based on carbon quantum dots via precipitation polymerization for fluorescence detection of tetracycline

A facile and effective method was proposed to prepare the molecularly imprinted fluorescence sensor with carbon quantum dots, which were modified vinyl groups by acrylic acid on the surface. The obtained fluorescence composite material was investigated by transmission electron microscope and Fourier transform infrared spectra. After the experimental conditions were optimized, a linear range of 1.0–60 μmol L⁻¹ was obtained and the detection limit was 0.17 μmol L⁻¹. The novel fluorescence sensor can be successfully used to detect tetracycline in real samples. This study provides a convenient strategy for selective recognition and rapid detection of tetracycline in the complex environment.     

Social licence: power imbalances and levels of consciousness – two case studies

ABSTRACT

Social licence to operate (SLO) is a term that is finding increasing acceptance in a number of industries. Like all new terms, its precise meaning and implications are still being investigated. Using data from previous studies, this paper offers an analysis of the SLO of two case studies with each study being viewed separately through the grid of a distinct theoretical framework. Case study 1 looks at the development of differential social licence negotiated in the Hamlet of Cambridge Bay, Nunavut, as a result of an impact and benefit agreement negotiated between Tahera Diamond Corp. and the Kitikmeot Inuit Organisation in 2004. The study demonstrates that general acceptance of resource development can be uneven and reflect an unequal distribution of decision-making power. In case study 2, stakeholders of a failed mineral development project were queried across time about the specifics of the proposed mine development and were queried about resource development across different levels of consciousness. Perhaps, SLO is variable across different levels of consciousness. The paper concludes with observations about the variable nature of SLO acceptance across populations and across levels of consciousness within individuals. Perhaps, the concept of SLO is, in fact, complex, difficult to define and measure and, at this point, of limited utility as a measure of resource development acceptance.     

Detection of correlation characteristics between financial time series based on multi-resolution analysis

Interactions between financial time series are complex and changeable in both time and frequency domains. To reveal the evolution characteristics of the time-varying relations between bivariate time series from a multi-resolution perspective, this study introduces an approach combining wavelet analysis and complex networks. In addition, to reduce the influence the phase lag between the time series has on the correlations, we propose dynamic time-warping (DTW) correlation coefficients to reflect the correlation degree between bivariate time series. Unlike previous studies that symbolized the time series only based on the correlation strength, the second-level symbol is set according to the correlation length during the coarse-graining process. This study presents a novel method to analyze bivariate time series and provides more information for investors and decision makers when investing in the stock market. We choose the closing prices of two stocks in China’s market as the sample and explore the evolutionary behavior of correlation modes from different resolutions. Furthermore, we perform experiments to discover the critical correlation modes between the bull market and the bear market on the high-resolution scale, the clustering effect during the financial crisis on the middle-resolution scale, and the potential pseudo period on the low-resolution scale. The experimental results exactly match reality, which provides powerful evidence to prove that our method is effective in financial time series analysis.     

Novel strategy for encapsulation and targeted delivery of poorly water-soluble active substances

Carriers for targeted delivery and controlled release of poorly water-soluble active substances (PWSAS) are facing three challenges: (a) the encapsulation issues, (b) limitations of PWSAS water solubility, and (c) burst drug release which can be pharmacologically dangerous and economically inefficient. The present study brings a novel strategy for encapsulation and controlled release of PWSAS—caffeine in concentrations which are higher than its maximal water solubility without the possibility of burst effect. The modification of hydrophilic carrier based on poly(methacylic acid) was done using casein and liposomes. To further increase the maximal caffeine loading inside the carrier nicotinamide was used. The release study of the encapsulated PWSAS was elaborated with respect to morphology of the carriers and interactions that could be established between its structural components. The carriers swelling and the release of caffeine and nicotinamide were also investigated depending on caffeine concentration, the presence of different liposomal formulations and the volume ratio of liposomal formulation, in three media with different pH simulating the path of the carrier through the human gastrointestinal tract. The synthesized carriers are promising candidates for encapsulation of PWSAS in concentrations which are higher than its maximal water solubility and for the targeted delivery of those dosages.     

Sustainable supply chain management in Indian organisations: an empirical investigation

The present work is an attempt to investigate the adoption of sustainable supply chain management (SSCM) practices amongst manufacturing and process based organisations in India and its impact on organisational performance encompassing all three dimensions of sustainability. SSCM practices conceived in the present study include environmental management practices (EMP), socially inclusive practices for employees (SPE), socially inclusive practices for community (SPC), operations practices (OP) and supply chain integration (SCI) which were treated as exogenous variables. Organisational performance considered in this study includes five dimensions, namely environmental performance (EPR), employee-centred social performance (ESP), community-centred social performance (CSP), operations performance (OPR) and competitiveness, which were regarded as endogenous variables. The analysis was carried out with the help of structural equation modelling considering natural logarithm of manpower as a control variable. Few major findings are mentioned. EMP does not have any significant association with OPR, nor does it result in competitiveness. However, when jointly mediated through both EPR and OPR, EMP leads to competitiveness. SPC has significant negative association with competitiveness, when only direct relationship is considered. However, indirect relationship between SPC and competitiveness shows significant positive association when mediated through CSP. The resultant total effect between SPC and competitiveness turns out to be insignificant. Further, OPR fully mediates the relationship between OP and competitiveness. Managerial implications of the findings are discussed.     

Proton-Detected Solid-State NMR of the Cell-Free Synthesized α-Helical Transmembrane Protein NS4B from Hepatitis C Virus

Proton-detected 100 kHz magic-angle-spinning (MAS) solid-state NMR is an emerging analysis method for proteins with only hundreds of microgram quantities, and thus allows structural investigation of eukaryotic membrane proteins. This is the case for the cell-free synthesized hepatitis C virus (HCV) nonstructural membrane protein 4B (NS4B). We demonstrate NS4B sample optimization using fast reconstitution schemes that enable lipid-environment screening directly by NMR. 2D spectra and relaxation properties guide the choice of the best sample preparation to record 2D ¹H-detected ¹H,¹⁵N and 3D ¹H,¹³C,¹⁵N correlation experiments with linewidths and sensitivity suitable to initiate sequential assignments. Amino-acid-selectively labeled NS4B can be readily obtained using cell-free synthesis, opening the door to combinatorial labeling approaches which should enable structural studies.     

基于注意力机制与文本信息的用户关系抽取

随着社交媒体的发展,用户之间的关系网络对于社交媒体的分析有很大的帮助。因此,该文主要研究用户好友关系检测。以往的关于用户好友关系抽取的研究主要基于社交媒体上的结构化信息,比如其他好友关系,用户的不同属性等。但是,很多时候用户本身并没有大量的好友信息存在,同时也不一定有很多确定的属性。因此,我们希望基于用户发表的文本信息来对用户关系进行预测。不同于以往的潜在好友推荐算法,该文提出了一种基于注意力机制以及长短时记忆网络(long short-term memory,LSTM)的好友关系预测模型,将好友之间的评论分开处理,通过分析用户之间的评论来判断是否具备一定的好友关系。该模型将好友双方信息拼接后的结果作为输入,并将注意力机制应用于LSTM的输出。实验表明,用户之间的评论对于好友关系预测确实有较大的实际意义,该文提出的模型较之于多个基准系统的效果,取得了明显的提升。在不加入任何其它非文本特征的情况下,实验结果的准确率达到了77%。