Labor Migration, Self-reliance, and Neoliberal Government Policy: Paint Factory Workers in Shenzhen and Zhuhai, China

This paper examines the effects that changes in the Chinese government’s methods of labor control have had on rural migrant workers in the Guangdong province. The situation of rural migrant workers in China resembles that of foreign migrant workers in other countries, because institutional barriers such as the hukou (household registration) system prevent rural migrants from becoming permanent residents of cities and reduce them to second class citizens with limited benefits. By employing ethnographic data from anthropological field research among migrant workers in paint factories in the cities of Shenzhen and Zhuhai, this paper discusses how neoliberal labor control has created self-reliant migrant workers. Neoliberal labor control is represented by the gradual relaxation of rural-to-urban migration restrictions and propaganda movements that promote neoliberal discourses. Drawing from the Foucauldian concept of governmentality, the paper argues that neoliberal labor policies facilitate exploitation because they allow the government to reduce state welfare for rural migrant workers living in cities while at the same time mitigating the potential for unrest

Run-time risk management in adaptive ICT systems

We will present results of the SERSCIS project related to risk management and mitigation strategies in adaptive multi-stakeholder ICT systems. The SERSCIS approach involves using semantic threat models to support automated design-time threat identification and mitigation analysis. The focus of this paper is the use of these models at run-time for automated threat detection and diagnosis. This is based on a combination of semantic reasoning and Bayesian inference applied to run-time system monitoring data. The resulting dynamic risk management approach is compared to a conventional ISO 27000 type approach, and validation test results presented from an Airport Collaborative Decision Making (A-CDM) scenario involving data exchange between multiple airport service providers

An integrative analysis of cancer gene expression studies using Bayesian latent factor modeling

We present an applied study in cancer genomics for integrating data and inferences from laboratory experiments on cancer cell lines with observational data obtained from human breast cancer studies. The biological focus is on improving understanding of transcriptional responses of tumors to changes in the pH level of the cellular microenvironment. The statistical focus is on connecting experimentally defined biomarkers of such responses to clinical outcome in observational studies of breast cancer patients. Our analysis exemplifies a general strategy for accomplishing this kind of integration across contexts. The statistical methodologies employed here draw heavily on Bayesian sparse factor models for identifying, modularizing and correlating with clinical outcome these signatures of aggregate changes in gene expression. By projecting patterns of biological response linked to specific experimental interventions into observational studies where such responses may be evidenced via variation in gene expression across samples, we are able to define biomarkers of clinically relevant physiological states and outcomes that are rooted in the biology of the original experiment. Through this approach we identify microenvironment-related prognostic factors capable of predicting long term survival in two independent breast cancer datasets. These results suggest possible directions for future laboratory studies, as well as indicate the potential for therapeutic advances though targeted disruption of specific pathway components.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS261 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Chronic leptomeningitis and spinal intradural mass secondary to Alternaria infection in a patient with ventriculoperitoneal shunt

Fungal infection following placement of ventriculostomy or ventriculoperitoneal (VP) shunt is uncommon. We report the first case of Alternaria related central nervous system (CNS) shunt infection in a patient with CNS ependymoma manifesting as leptomeningitis and a spinal intradural mass. This case illustrates the diagnostic and management challenges

Scalable Bayesian modeling, monitoring and analysis of dynamic network flow data

Traffic flow count data in networks arise in many applications, such as automobile or aviation transportation, certain directed social network contexts, and Internet studies. Using an example of Internet browser traffic flow through site-segments of an international news website, we present Bayesian analyses of two linked classes of models which, in tandem, allow fast, scalable and interpretable Bayesian inference. We first develop flexible state-space models for streaming count data, able to adaptively characterize and quantify network dynamics efficiently in real-time. We then use these models as emulators of more structured, time-varying gravity models that allow formal dissection of network dynamics. This yields interpretable inferences on traffic flow characteristics, and on dynamics in interactions among network nodes. Bayesian monitoring theory defines a strategy for sequential model assessment and adaptation in cases when network flow data deviates from model-based predictions. Exploratory and sequential monitoring analyses of evolving traffic on a network of web site-segments in e-commerce demonstrate the utility of this coupled Bayesian emulation approach to analysis of streaming network count data.Comment: 29 pages, 16 figure

A network visualisation approach and global stock market integration

This paper applies a visualized network approach to explain the tendency of integration or co-movement among global equity markets. We utilize daily prices of stock market indices of 57 countries from January 1997 to August 2012 to establish both the Minimum Spanning Tree Network (MSTN) and Graphic Network (GN). We study network features including connectivity and centrality through robust indicators. Our results clearly show that there has been a tendency over time for markets to become more integrated globally even during periods of market stress. The centrality results suggest the US and Hong Kong markets have been the most dominant markets in their geographic region. For Europe, we find three dominant centres, the UK, France and Germany in contrast to previous literature suggesting that the UK was the dominant country. We further identify that Japan and Australia, instead of acting as dominant countries in their region, serve as bridging countries between the region and the rest of the world. Finally, we find that Africa does not form a cluster and individual African countries tend to connect to other developed markets in a scattered manner

In Vivo Evolution of Butane Oxidation by Terminal Alkane Hydroxylases AlkB and CYP153A6

Enzymes of the AlkB and CYP153 families catalyze the first step in the catabolism of medium-chain-length alkanes, selective oxidation of the alkane to the 1-alkanol, and enable their host organisms to utilize alkanes as carbon sources. Small, gaseous alkanes, however, are converted to alkanols by evolutionarily unrelated methane monooxygenases. Propane and butane can be oxidized by CYP enzymes engineered in the laboratory, but these produce predominantly the 2-alkanols. Here we report the in vivo-directed evolution of two medium-chain-length terminal alkane hydroxylases, the integral membrane di-iron enzyme AlkB from Pseudomonas putida GPo1 and the class II-type soluble CYP153A6 from Mycobacterium sp. strain HXN-1500, to enhance their activity on small alkanes. We established a P. putida evolution system that enables selection for terminal alkane hydroxylase activity and used it to select propane- and butane-oxidizing enzymes based on enhanced growth complementation of an adapted P. putida GPo12(pGEc47{Delta}B) strain. The resulting enzymes exhibited higher rates of 1-butanol production from butane and maintained their preference for terminal hydroxylation. This in vivo evolution system could be useful for directed evolution of enzymes that function efficiently to hydroxylate small alkanes in engineered hosts