C-H…O hydrogen bond mediated chain reversal in a peptide containing g-amino acid residue, determined directly from powder X-ray diffraction data

The finding that peptides containing -amino acid residues give rise to folding patterns hitherto unobserved in -amino acid peptides[1] has stimulated considerable interest in the conformational properties of peptides built from , and residues,[2] as the introduction of additional methylene (CH2) units into peptide chains provides further degrees of conformational freedom.

Operationalizing Supply Chain vs. Supply Chain Competion

Competition is an immensely important area of study in economic theory, business and strategy. It is known to be vital in meeting consumers’ growing expectations, stimulating increase in the size of the market, pushing innovation, reducing cost and consequently generating better value for end users, among other things. Having said that, it is important to recognize that supply chains, as we know it, has changed the way companies deal with each other both in confrontational or conciliatory terms. As such, with the rise of global markets and outsourcing destinations, increased technological development in transportation, communication and telecommunications has meant that geographical barriers of distance with regards to competition are a thing of the past in an increasingly flat world. Even though the dominant articulation of competition within management and business literature rests mostly within economic competition theory, this thesis draws attention to the implicit shift in the recognition of other forms of competition in today’s business environment, especially those involving supply chain structures. Thus, there is popular agreement within a broad business arena that competition between companies is set to take place along their supply chains. Hence, management’s attention has been focused on how supply chains could become more aggressive making each firm in its supply chain more efficient. However, there is much disagreement on the mechanism through which such competition pitching supply chain against supply chain will take place. The purpose of this thesis therefore, is to develop and conceptualize the notion of supply chain vs. supply chain competition, within the discipline of supply chain management. The thesis proposes that competition between supply chains may be carried forward via the use of competition theories that emphasize interaction and dimensionality, hence, encountering friction from a number of sources in their search for critical resources and services. The thesis demonstrates how supply chain vs. supply chain competition may be carried out theoretically, using generated data for illustration, and practically using logistics centers as a way to provide a link between theory and corresponding practice of this evolving competition mode. The thesis concludes that supply chain vs. supply chain competition, no matter the conceptualization taken, is complex, novel and can be very easily distorted and abused. It therefore calls for the joint development of regulatory measures by practitioners and policymakers alike, to guide this developing mode of competition.

The Value of Research Data

Scientific research revolves around the production, analysis, storage, management, and re-use of data. Data sharing offers important benefits for scientific progress and advancement of knowledge. However, several limitations and barriers in the general adoption of data sharing are still in place. Probably the most important challenge is that data sharing is not yet very common among scholars and is not yet seen as a regular activity among scientists, although important efforts are being invested in promoting data sharing. In addition, there is a relatively low commitment of scholars to cite data. The most important problems and challenges regarding data metrics are closely tied to the more general problems related to data sharing. The development of data metrics is dependent on the growth of data sharing practices, after all it is nothing more than the registration of researchers’ behaviour. At the same time, the availability of proper metrics can help researchers to make their data work more visible. This may subsequently act as an incentive for more data sharing and in this way a virtuous circle may be set in motion. This report seeks to further explore the possibilities of metrics for datasets (i.e. the creation of reliable data metrics) and an effective reward system that aligns the main interests of the main stakeholders involved in the process. The report reviews the current literature on data sharing and data metrics. It presents interviews with the main stakeholders on data sharing and data metrics. It also analyses the existing repositories and tools in the field of data sharing that have special relevance for the promotion and development of data metrics. On the basis of these three pillars, the report presents a number of solutions and necessary developments, as well as a set of recommendations regarding data metrics. The most important recommendations include the general adoption of data sharing and data publication among scholars; the development of a reward system for scientists that includes data metrics; reducing the costs of data publication; reducing existing negative cultural perceptions of researchers regarding data publication; developing standards for preservation, publication, identification and citation of datasets; more coordination of data repository initiatives; and further development of interoperability protocols across different actors.

A Data Dropout Compensation Algorithm Based on the Iterative Learning Control Methodology for Discrete-Time Systems

This paper deals with the convergence of a remote iterative learning control system subject to data dropouts. The system is composed by a set of discrete-time multiple input-multiple output linear models, each one with its corresponding actuator device and its sensor. Each actuator applies the input signals vector to its corresponding model at the sampling instants and the sensor measures the output signals vector. The iterative learning law is processed in a controller located far away of the models so the control signals vector has to be transmitted from the controller to the actuators through transmission channels. Such a law uses the measurements of each model to generate the input vector to be applied to its subsequent model so the measurements of the models have to be transmitted from the sensors to the controller. All transmissions are subject to failures which are described as a binary sequence taking value 1 or 0. A compensation dropout technique is used to replace the lost data in the transmission processes. The convergence to zero of the errors between the output signals vector and a reference one is achieved as the number of models tends to infinity.

Marketing of value-added products from silver carp and involvement of rural people in the production and marketing chain

Cost-profit analysis and market testing of some value-added products from silver carp such as fish mince block, fish sausage, fish ball, fish stick and fish burger were analyzed during April 2001 to March 2002. The study also explored the possibility to involve rural low-income people in the production and marketing of such products. The production of silver carp was higher in greater Jessore and Mymensingh districts but the price remained low during the peak-harvesting season in October to November. The price varied with size of the fish, season, market characteristics and effective demand of the buyers. Price of about 500 g size fish was found to be Tk. 20-25/kg in the rural markets. The average size of fish in the rural markets was 3S0-550 g while that in the urban markets it was 700-1,200 g. The cost of production of the value added products and profit margin were assessed on the basis of market price of the raw material as well as that of the finished products, transportation, storage and marketing costs. The profit margins of 34%, 39%, 81% and 31% of their sales price were obtained for fish sausage, fish ball, fish stick and fish burger, respectively. Actual production cost could be minimized if the fish is purchased directly from the farmers. Consumer's acceptance and marketability tests showed that both rural and urban people preferred fish ball than fish sausage. However, response towards the taste, flavor and color of fish ball and fish sausage was found to vary with occupations and age of the consumers. A correlation was observed between age group and acceptance of new products. Fish ball, fish stick and fish burger were found to be the most preferable items to the farmers because of easy formulation process with common utensils. Good marketing linkage and requirement of capital had been identified as the prerequisites for operating small-scale business on value-added fish products.

Exploring geographical dispersion in Thailand-based food supply chain (FSC)

Purpose: The purpose of this paper is to explore the key influential factors and their implications on food supply chain (FSC) location decisions from a Thailand-based manufacturer's view. Design/methodology/approach: In total, 21 case studies were conducted with eight Thailand-based food manufacturers. In each case, key influential factors were observed along with their implications on upstream and downstream FSC location decisions. Data were collected through semi-structured interviews and documentations. Data reduction and data display in tables were used to help data analysis of the case studies. Findings: This exploratory research found that, in the food industry, FSC geographical dispersion pattern could be determined by four factors: perishability, value density, economic-political forces, and technological forces. Technological forces were found as an enabler for FSC geographical dispersion whereas the other three factors could be both barriers and enablers. The implications of these four influential factors drive FSC towards four key patterns of FSC geographical dispersion: local supply chain (SC), supply-proximity SC, market-proximity SC, and international SC. Additionally, the strategy of the firm was found to also be an influential factor in determining FSC geographical dispersion. Research limitations/implications: Despite conducting 21 cases, the findings in this research are based on a relatively small sample, given the large size of the industry. More case evidence from a broader range of food product market and supply items, particularly ones that have significantly different patterns of FSC geographical dispersions would have been insightful. The consideration of additional influential factors such as labour movement between developing countries, currency fluctuations and labour costs, would also enrich the framework as well as improve the quality and validity of the research findings. The different strategies employed by the case companies and their implications on FSC location decisions should also be further investigated along with cases outside Thailand, to provide a more comprehensive view of FSC geographical location decisions. Practical implications: This paper provides insights how FSC is geographically located in both supply-side and demand-side from a manufacturing firm's view. The findings can also provide SC managers and researchers a better understanding of their FSCs. Originality/value: This research bridges the existing gap in the literature, explaining the geographical dispersion of SC particularly in the food industry where the characteristics are very specific, by exploring the internationalization ability of Thailand-based FSC and generalizing the key influential factors - perishability (lead time), value density, economic-political forces, market opportunities, and technological advancements. Four key patterns of FSC internationalization emerged from the case studies. © Emerald Group Publishing Limited.

The intelligent product driven supply chain

Establishing connectivity of products with real-time information about themselves can at one level provide accurate data, and at another, allow products to assess and influence their own destiny. In this way, the specification for an intelligent product is being built - one whose information content is permanently bound to its material content. This paper explores the impact of such development on supply chains, contrasting between simple and complex product supply chains. The Auto-ID project is on track to enable such connectivity between products and information using a single, open-standard, data repository for storage and retrieval of product information. The potential impact on the design and management of supply chains is immense. This paper provides an introduction to of some of these changes, demonstrating that by enabling intelligent products, Auto ID systems will be instrumental in driving future supply chains. The paper also identifies specific application areas for this technology in the product supply chain.

Expressive RFID data access policies for the Pharmaceuticals supply chain

The Pharma(ceuticals) industry is at a cross-roads. There are growing concerns that illegitimate products are penetrating the supply chain. There are proposals in many countries to apply RFID and other traceability technologies to solve this problem. However there are several trade-offs and one of the most crucial is between data visibility and confidentiality. In this paper, we use the TrakChain assessment framework tools to study the US Pharma supply chain and to compare candidate solutions to achieve traceability data security: Point-of-Dispense Authentication, Network-based electronic Pedigree, and Document-based electronic Pedigree. We also propose extensions to a supply chain authorization language that is able to capture expressive data sharing conditions considered necessary by the industry's trading partners. © 2013 IEEE.

Computational Inference of Genome-Wide Protein-DNA Interactions Using High-Throughput Genomic Data

Transcriptional regulation has been studied intensively in recent decades. One important aspect of this regulation is the interaction between regulatory proteins, such as transcription factors (TF) and nucleosomes, and the genome. Different high-throughput techniques have been invented to map these interactions genome-wide, including ChIP-based methods (ChIP-chip, ChIP-seq, etc.), nuclease digestion methods (DNase-seq, MNase-seq, etc.), and others. However, a single experimental technique often only provides partial and noisy information about the whole picture of protein-DNA interactions. Therefore, the overarching goal of this dissertation is to provide computational developments for jointly modeling different experimental datasets to achieve a holistic inference on the protein-DNA interaction landscape.

We first present a computational framework that can incorporate the protein binding information in MNase-seq data into a thermodynamic model of protein-DNA interaction. We use a correlation-based objective function to model the MNase-seq data and a Markov chain Monte Carlo method to maximize the function. Our results show that the inferred protein-DNA interaction landscape is concordant with the MNase-seq data and provides a mechanistic explanation for the experimentally collected MNase-seq fragments. Our framework is flexible and can easily incorporate other data sources. To demonstrate this flexibility, we use prior distributions to integrate experimentally measured protein concentrations.

We also study the ability of DNase-seq data to position nucleosomes. Traditionally, DNase-seq has only been widely used to identify DNase hypersensitive sites, which tend to be open chromatin regulatory regions devoid of nucleosomes. We reveal for the first time that DNase-seq datasets also contain substantial information about nucleosome translational positioning, and that existing DNase-seq data can be used to infer nucleosome positions with high accuracy. We develop a Bayes-factor-based nucleosome scoring method to position nucleosomes using DNase-seq data. Our approach utilizes several effective strategies to extract nucleosome positioning signals from the noisy DNase-seq data, including jointly modeling data points across the nucleosome body and explicitly modeling the quadratic and oscillatory DNase I digestion pattern on nucleosomes. We show that our DNase-seq-based nucleosome map is highly consistent with previous high-resolution maps. We also show that the oscillatory DNase I digestion pattern is useful in revealing the nucleosome rotational context around TF binding sites.

Finally, we present a state-space model (SSM) for jointly modeling different kinds of genomic data to provide an accurate view of the protein-DNA interaction landscape. We also provide an efficient expectation-maximization algorithm to learn model parameters from data. We first show in simulation studies that the SSM can effectively recover underlying true protein binding configurations. We then apply the SSM to model real genomic data (both DNase-seq and MNase-seq data). Through incrementally increasing the types of genomic data in the SSM, we show that different data types can contribute complementary information for the inference of protein binding landscape and that the most accurate inference comes from modeling all available datasets.

This dissertation provides a foundation for future research by taking a step toward the genome-wide inference of protein-DNA interaction landscape through data integration.

NAFTA and the apparel commodity chain: Corporate strategies, interfirm networks, and industrial upgrading

The apparel industry is one of the oldest and largest export industries in the world, with global trade and production networks that connect firms and workers in countries at all levels of economic development. This chapter examines the impact of the North American Free Trade Agreement (NAFTA) as one of the most recent and significant developments to affect patterns of international trade and production in the apparel and textile industries. Tr ade policies are changing the institutional environment in which firms in this industry operate, and companies are responding to these changes with new strategies designed to increase their profitability and strengthen their control over the apparel commodity chain. Our hypothesis is that lead firms are establishing qualitatively different kinds of regional production networks in North America from those that existed prior to NAFTA, and that these networks have important consequences for industrial upgrading in the Mexican textile and apparel industries. Post-NAFTA crossborder production arrangements include full-package networks that link lead firms in the United States with apparel and textile manufacturers, contractors, and suppliers in Mexico. Full-package production is increasing the local value added provided by the apparel commodity chain in Mexico and creating new opportunities for Mexican firms and workers. The chapter is divided into four main sections. The first section uses trade and production data to analyze shifts in global apparel flows, highlighting the emergence and consolidation of a regional trade bloc in North America. The second section discusses the process of industrial upgrading in the apparel industry and introduces a distinction between assembly and full-package production networks. The third section includes case studies based on published industry sources and strategic interviews with several lead companies whose strategies are largely responsible for the shifting trade patterns and NAFTA-inspired cross-border production networks discussed in the previous section. The fourth section considers the implications of these changes for employment in the North American apparel industry. © 2009 by Temple University Press. All rights reserved.