Investigating Stops in Alaska: Can Coleman Survive a Multifactored Balance

Globalization and centralization have resulted in prolonged transportation time between producer and consumer, and thus put more demand on the perseveration of a product for longer duration and protect it from oxidation. The presence of oxygen in packages severely foreshortens the storage life as it yield losses of nutrients and allow microbial growth, which can cause changes in smell, taste as well as discoloration. Earlier food and beverage containers were made in inorganic materials e.g. metal and glass, however lately more and more focus have been on synthetic organic materials as these show several advantages, e.g. weight. However, still today most of the commercial packaging materials, organic or inorganic, are not considered to be environmental friendly. Thus, efforts have to be made today in order to invent alternative materials that can make the society of tomorrow more sustainable. Cellulose is the most abundant biopolymer in the world, hence making it desirable to use in “green” packaging applications. Furthermore, cellulose has proven being able to form films with great gas barrier potential under specific conditions. However, cellulose based materials are sensitive to moisture with severely increased oxygen transmission with increased relative humidity as a result; hence it is desired to make cellulose less hygroscopic by chemical modification. First, nanofibrillated cellulose (NFC) with 720 mmol carboxylic groups/g fiber was produced by oxidation of dissolving pulp before homogenization. Thereafter a polymer was synthesized utilizing Initiator A as an initiator at T1 and T2. The polymer synthesized at T1 yielded a polymer with a viscosity average molecular weight of 5770 g/mol.  The polymer was then grafted on the oxidized NFC through a coupling reaction performed in Buffer C using Coupling agent A. The grafting procedure was performed in Buffer C at ambient conditions giving rise to a material composed of 33 wt% synthetic polymer and 67 wt% NFC. The coupling was conducted several times in order to investigate how the final product can be affected by varying reactant feed and dispersion method. Finally, films of NFC and NFC-g-Polymer were manufactured by vacuum filtration from a 0.05 wt% Solvent A dispersion and were evaluated with field emission scanning electron microscopy

The Saccharomyces cerevisiae SEC14 Gene Encodes a Cytosolic Factor That Is Required for Transport of Secretory Proteins from the Yeast Golgi Complex

We have obtained and characterized a genomic clone of SEC14, a Saccharomyces cerevisiae gene whose product is required for export of yeast secretory proteins from the Golgi complex. Gene disruption experiments indicated that SEC14 is an essential gene for yeast vegetative growth. Nucleotide sequence analysis revealed the presence of an intron within the SEC14 structural gene, and predicted the synthesis of a hydrophilic polypeptide of 35 kD in molecular mass. In confirmation, immunoprecipitation experiments demonstrated SEC14p to be an unglycosylated polypeptide, with an apparent molecular mass of some 37 kD, that behaved predominantly as a cytosolic protein in subcellular fractionation experiments. These data were consistent with the notion that SEC14p is a cytosolic factor that promotes protein export from yeast Golgi. Additional radiolabeling experiments also revealed the presence of SEC14p-related polypeptides in extracts prepared from the yeasts Kluyveromyces lactis and Schizosaccharomyces pombe. Furthermore, the K. lactis SEC14p was able to functionally complement S. cerevisiae sec14ts defects. These data suggested a degree of conservation of SEC14p structure and function in these yeasts species

Regulation of Neuronal Survival and Death by E2F-Dependent Gene Repression and Derepression

AbstractNeuronal death induced by a variety of means requires participation of the E2F family of transcription factors. Here, we show that E2F acts as a gene silencer in neurons and that repression of E2F-responsive genes is required for neuronal survival. Moreover, neuronal death evoked by DNA damaging agents or trophic factor withdrawal is characterized by derepression of E2F-responsive genes. Such derepression, rather than direct E2F-promoted gene activation, is required for death. Among the genes that are derepressed in neurons subjected to DNA damage or trophic factor withdrawal are the transcription factors B- and C-myb. Overexpression of B- and C-myb is sufficient to evoke neuronal death. These findings support a model in which E2F-dependent gene repression and derepression play pivotal roles in neuronal survival and death, respectively

Ordered choices and heterogeneity in attribute processing

A growing number of empirical studies involve the assessment of influences on a choice amongst ordered discrete alternatives. Ordered logit and probit models are well known, including extensions to accommodate random parameters and heteroscedasticity in unobserved variance. This paper extends the ordered choice random parameter model to permit random parameterization of thresholds and decomposition to establish observed sources of systematic variation in the threshold parameter distribution. We illustrate the empirical gains of this model over the traditional ordered choice model in the context of identifying candidate influences on the role that specific attributes play, in the sense of being ignored or not, in an individual’s choice amongst unlabelled attribute packages of alternative tolled and non-tolled routes for the commuting trip. The empirical ordering represents the number of attributes attended to from the full fixed set. The evidence suggests that there is significant heterogeneity associated with the thresholds, that can be connected to systematic sources associated with the respondent (i.e., gender) and the choice experiment, and hence the generalized extension of the ordered choice model is an improvement, behaviourally, over the simpler model

Non-attendance and dual processing of common-metric attributes in choice analysis: A latent class specification

There is a growing literature that promotes the presence of process heterogeneity in the way that individuals evaluate packages of attributes in real or hypothetical markets and make choices. A centerpiece of current research is the identification of rules that individuals invoke when processing information in stated choice experiments. These rules may be heuristics used in everyday choice making as well as manifestations of ways of coping with the amount of information shown in choice experiment scenarios. In this paper, using the latent class framework, we define classes based on rules that recognise the non-attendance of one or more attributes, as well as on the addition and the parameter transfer of common-metric attributes. These processing strategies are postulated to be used in real markets as a form of cognitive rationalization. We use a stated choice data set, where car driving individuals choose between tolled and non-tolled routes, to translate this new evidence into a willingness to pay (WTP) for travel time savings, and contrast it with the results from a model specification in which all attributes are assumed to be attended to and are not added up with parameter preservation. We find that the WTP is significantly higher, on average, than the estimate obtained from the commonly used full relevance and attribute preservation specification

A Latent Class Model for Discrete Choice Analysis: Contrasts with Mixed Logit

The multinomial logit model (MNL) has for many years provided the fundamental platform for the analysis of discrete choice. The basic model’s several shortcomings, most notably its inherent assumption of independence from irrelevant alternatives (IIA) have motivated researchers to develop a variety of alternative formulations. The mixed logit model stands as one of the most significant of these extensions. This paper proposes a semi-parametric extension of the MNL, based on the latent class formulation, which resembles the mixed logit model but which relaxes its requirement that the analyst makes specific assumptions about the distributions of parameters across individuals. An application of the model to the choice of long distance travel by three road types (2-lane, 4-lane without a median and 4-lane with a median) by car in New Zealand is used to compare the MNL latent class model with mixed logit

The Mixed Logit Model: The State of Practice

The mixed logit model is considered to be the most promising state of the art discrete choice model currently available. Increasingly researchers and practitioners are estimating mixed logit models of various degrees of sophistication with mixtures of revealed preference and stated choice data. It is timely to review progress in model estimation since the learning curve is steep and the unwary are likely to fall into a chasm if not careful. These chasms are very deep indeed given the complexity of the mixed logit model. Although the theory is relatively clear, estimation and data issues are far from clear. Indeed there is a great deal of potential mis-inference consequent on trying to extract increased behavioural realism from data that are often not able to comply with the demands of mixed logit models. Possibly for the first time we now have an estimation method that requires extremely high quality data if the analyst wishes to take advantage of the extended behavioural capabilities of such models. This paper focuses on the new opportunities offered by mixed logit models and some issues to be aware of to avoid misuse of such advanced discrete choice methods by the practitioner

Ordered choices and heterogeneity in attribute processing

A growing number of empirical studies involve the assessment of influences on a choice amongst ordered discrete alternatives. Ordered logit and probit models are well known, including extensions to accommodate random parameters and heteroscedasticity in unobserved variance. This paper extends the ordered choice random parameter model to permit random parameterization of thresholds and decomposition to establish observed sources of systematic variation in the threshold parameter distribution. We illustrate the empirical gains of this model in the context of an individual’s choice amongst unlabelled attribute packages of alternative tolled and non-tolled routes for the commuting trip, and the role that each attribute plays, in the sense of being ignored or not. The ordering represents the number of attributes attended to from the full fixed set. The evidence suggests that there is significant heterogeneity associated with the thresholds that can be connected to systematic sources associated with the respondent (i.e., gender) and the choice experiment (i.e., aggregation treatment of components of travel time)