Cookbooks, Memories and Family Recipes: Greek Cypriot immigrants' cultural maintenance and adaptation in Melbourne

This paper draws from a larger oral history project on the domestic food cultures of a group of Greek Cypriots residing in Melbourne between 1947 and 2003. It explores the ways in which food, and specifically recipes, reveal immigrants’ processes of cultural maintenance and change. By analysing these immigrants’ accounts I show how memories and practical knowledge from mothers, families and friends were important for immigrants’ attempts to maintain their heritage and culture, not only in the food they ate, but also their relationships and personal identities. In line with this, I further argue that cookbooks and other popular media also provided important sources for innovation and cultural transmission. Sharing recipes amongst friends and family in Melbourne was a means for Greek Cypriot immigrants to communicate and negotiate relationships with others; in doing so they also reinforced and contributed to new knowledge about Cypriot identity

Reducing Spectral Analyte Prediction Error with Penalties on Interferents

A goal of chemometric multivariate calibration (modeling) is to predict analyte concentration in a sample using spectral data. Multiple types of modeling methods have been used to predict analyte concentration. However, the samples contain interferents that influence the model and if not fully corrected by the model, analyte concentration prediction errors occur. To reduce the prediction errors caused by interferent species in the system, two new methods were designed to incorporate interferent information. One of the methods uses interferent spectra to require the model to be orthoganol to the interferents. The other method uses interferent spectra to form an orthogonal or oblique model to the interferents. The methods are compared to ridge regression and partial least squares using a near infrared data set. Sum of ranking is used to select models. The new methods have better analyte prediction errors and robustness, but more data sets need to be tested to confirm that both new methods are more effective

Classification using Sum of Ranking Differences of Outlier Measures

A useful application in analytical chemistry is classifying unknown samples into classes. Single-class classification is a type of classification approach where only one well-defined class is of interest. Outlier detection is useful for defining class membership for unknown samples, since outlier detection removes samples that are not represented by the sample class space. When using outlier detection, there are two problems: which outlier measure to use and the tuning parameter value for the chosen outlier measure. The proposed technique for single-class classification using outlier measures eliminates these two problems. To avoid selecting any one particular outlier measure, multiple measures are evaluated by using sum of ranking differences (SRD). The method of SRD is used to evaluate multiple outlier measures to obtain a consensus in classifying a sample. In regards to tuning parameters, a parameter window is used to avoid doing more work, such as having a training set of samples to select a tuning parameter. Wavelength selection and fusing spectra from different instrument is used in conjunction with SRD to provide a robust characterization of the class of interest. Presented are results for the new classification approach on spectral food data sets

Multivariate Calibration Domain Adaptation with Unlabeled Data

Multivariate calibration is about modeling the relationship between a substance\u27s chemical profile and its spectrum (here, near-infrared) in order to predict the concentration of new samples with known spectra. However, these new samples are often measured under different conditions than the primary conditions; different instruments, instrument drift, and temperature all affect the measurement conditions. Domain adaptation (DA) methods force the model to ignore these differences in order to generate an accurate model for the new domain (secondary conditions). There are two fundamental DA processes that individual methods can be classified under. One augments a few samples from the secondary domain with chemical reference values (labels) to the primary data and the other augments only secondary spectra (unlabeled data). In this work, we compare two existing labeled DA methods and two existing unlabeled DA methods to two novel labeled methods and a novel unlabeled approach. Since DA methods require selection of hyperparameters, a model selection framework based on model diversity and prediction similarity (MDPS) is applied to the DA methods. Regardless of the DA method, the MDPS process is shown to select models more accurate than the first quartile of all models generated by the DA process in three near-infrared datasets

Glutamate Release in the Nucleus Accumbens Core Is Necessary for Heroin Seeking

Long-term changes in glutamate transmission in the nucleus accumbens core (NAcore) contribute to the reinstatement of drug seeking after extinction of cocaine self-administration. Whether similar adaptations in glutamate transmission occur during heroin and cue-induced reinstatement of heroin seeking is unknown. After 2 weeks of heroin self-administration and 2 weeks of subsequent extinction training, heroin seeking was induced by a noncontingent injection of heroin or by presentation of light/tone cues previously paired with heroin infusions. Microdialysis was conducted in the NAcore during reinstatement of heroin seeking in animals extinguished from heroin self-administration or in subjects receiving parallel (yoked) noncontingent saline or heroin. Reinstatement by either heroin or cue increased extracellular glutamate in the NAcore in the self-administration group, but no increase was elicited during heroin-induced reinstatement in the yoked control groups. The increase in glutamate during heroin-induced drug seeking was abolished by inhibiting synaptic transmission in the NAcore with tetrodotoxin or by inhibiting glutamatergic afferents to the NAcore from the prelimbic cortex. Supporting critical involvement of glutamate release, heroin seeking induced by cue or heroin was blocked by inhibiting AMPA/kainite glutamate receptors in the NAcore. Interestingly, although a heroin-priming injection increased dopamine equally in animals trained to self-administer heroin and in yoked-saline subjects, inhibition of dopamine receptors in the NAcore also blocked heroin- and cue-induced drug seeking. Together, these findings show that recruitment of the glutamatergic projection from the prelimbic cortex to NAcore is necessary to initiate the reinstatement of heroin seeking

Sum of ranking differences (SRD) to ensemble multivariate calibration model merits for tuning parameter selection and comparing calibration methods

Most multivariate calibration methods require selection of tuning parameters, such as partial least squares (PLS) or the Tikhonov regularization variant ridge regression (RR). Tuning parameter values determine the direction and magnitude of respective model vectors thereby setting the resultant predication abilities of the model vectors. Simultaneously, tuning parameter values establish the corresponding bias/variance and the underlying selectivity/sensitivity tradeoffs. Selection of the final tuning parameter is often accomplished through some form of cross-validation and the resultant root mean square error of cross-validation (RMSECV) values are evaluated. However, selection of a "good" tuning parameter with this one model evaluation merit is almost impossible. Including additional model merits assists tuning parameter selection to provide better balanced models as well as allowing for a reasonable comparison between calibration methods. Using multiple merits requires decisions to be made on how to combine and weight the merits into an information criterion. An abundance of options are possible. Presented in this paper is the sum of ranking differences (SRD) to ensemble a collection of model evaluation merits varying across tuning parameters. It is shown that the SRD consensus ranking of model tuning parameters allows automatic selection of the final model, or a collection of models if so desired. Essentially, the user's preference for the degree of balance between bias and variance ultimately decides the merits used in SRD and hence, the tuning parameter values ranked lowest by SRD for automatic selection. The SRD process is also shown to allow simultaneous comparison of different calibration methods for a particular data set in conjunction with tuning parameter selection. Because SRD evaluates consistency across multiple merits, decisions on how to combine and weight merits are avoided. To demonstrate the utility of SRD, a near infrared spectral data set and a quantitative structure activity relationship (QSAR) data set are evaluated using PLS and RR

Leveraging Multiple Linear Regression for Wavelength Selection

In multivariate calibration, wavelengths selection is often used to lower prediction errors of sample properties. As a result, many methods have been created to select wavelengths. Several of the wavelength selection methods involve many tuning parameters that are typically complex or difficult to work with. The purpose of this poster is to show an easy way to select wavelengths while using few simple tuning parameters. The proposed method uses multiple linear regression (MLR) as an indicator to which wavelengths should be used to create a model. From a collection of random MLR models, those models with an acceptable bias/variance balance are evaluated to determine the wavelengths most frequently used. Portions of the most frequently selected wavelengths are chosen as the final MLR selected wavelengths. These MLR selected wavelengths are used to produce a calibration model by the method of partial least squares (PLS). This proposed wavelength selection method is compared to PLS models containing all wavelengths using several near infrared data sets. The PLS models with the selected wavelengths show an improvement in prediction error, suggesting this method as a simple way to select wavelengths

Long-Term Neuroadaptations Produced by Withdrawal from Repeated Cocaine Treatment: Role of Dopaminergic Receptors in Modulating Cortical Excitability

Dopamine (DA) modulates neuronal activity in the prefrontal cortex (PFC) and is necessary for optimal cognitive function. Dopamine transmission in the PFC is also important for the behavioral adaptations produced by repeated exposure to cocaine. Therefore, we investigated the effects of repeated cocaine treatment followed by withdrawal (2– 4 weeks) on the responsivity of cortical cells to electrical stimulation of the ventral tegmental area (VTA) and to systemic administration of DA D1 or D2 receptor antagonists. Cortical cells in cocaine- and saline-treated animals exhibited a similar decrease in excitability after the administration of D1 receptor antagonists. In contrast, cortical neurons from cocaine-treated rats exhibited a lack of D2-mediated regulation relative to saline rats. Furthermore, in contrast to saline-treated animals, VTA stimulation did not increase cortical excitability in the cocaine group. These data suggest that withdrawal from repeated cocaine administration elicits some long-term neuroadaptations in the PFC, including (1) reduced D2-mediated regulation of cortical excitability, (2) reduced responsivity of cortical cells to phasic increases in DA, and (3) a trend toward an overall decrease in excitability of PFC neurons