Consumer Perceptions of Labels and the Willingness to Pay for “Second Generation” Genetically Modified Products

Environmental and consumer groups have called for mandatory labeling of genetically modified (GM) food products in the United States, stating that consumers have the “right to know.” But evidence exists suggesting that consumers often cannot correctly interpret the meaning of scientific labels. Herein we use a nonhypothetical field experiment to examine how well consumers interpret GM labels, focusing on the solitary secondgeneration GM product currently on the U.S. market—GM cigarettes. Our results suggest that while consumers pay less for GM cigarettes when they are labeled as GM, these labels seem to be misinforming consumers. This evidence implies that consumers could be better off without mandatory GM labeling

”Nu ska vi leka Nordensallad!”:att undervisa svenska som första främmande språk för elever i första klassen

Abstrakt. Syftet med denna biämnesavhandling var att studera tidig språkinlärning och dess speciella drag samt skapa ett fungerande materialpaket för förstaklassister i svenska. Temat är aktuellt, eftersom studier i det första främmande språket (A1) redan börjar på första klass i Finland sedan januari 2020. Eftersom språkundervisning i Finland traditionellt har baserat sig i stort sett på skriven text, ställer reformen nya krav mot språklärare. Med denna avhandling ville vi lyfta fram särskilda drag man borde beakta när man undervisar ett främmande språk för barn och skapa ett sådant material som utgår från barnens förutsättningar att lära sig ett främmande språk. Vi fördjupade oss i den finska läroplanen av tvåspråkig undervisning och A1-svenska på första klass, i främmandespråksinlärning ur hjärnforskningens perspektiv samt i arbetssätt som är mest passande för barn för att lära sig ett främmande språk. De viktigaste verk som vi baserade våra studier på är Huotilainens Näin aivot oppivat, Camerons Teaching Languages to Young Learners och Abrahamssons Andraspråksinlärning. Vi diskuterade också tidig främmandespråkundervisningens för- och nackdelar. Sammanfattningsvis kan konstateras, att forskare inte är helt enade om fördelarna med tidig främmandespråksinlärning. Uttal lär man sig bäst som barn, men när man blir äldre är man också kognitivt mer beredd för att lära sig ett nytt språk. Grundläggande vid tidig främmandespråkundervisning är att fästa vikt vid sättet på vilket språket undervisas för att kunna utnyttja den sensitivitet som barn har för språk. Enligt forskarna ska språk undervisas med likadana arbetssätt som används för att främja utveckling av barnens förstaspråk. Musik har en central roll i språkundervisning och dessutom ingår det ramsor, lek, spel och olika motionsbaserade aktiviteter i de bästa sätten att undervisa språk för barn. Mångsidiga arbetssätt bidrar till glädje i lärandet och genom lek och spel vågar också de blyga eleverna använda ett främmande språk. Ytterligare är inflöde på den riktiga nivån en väsentlig del av språkundervisningen, för det låter eleverna förstå det främmande språket med hjälp av kunskaper som de redan har och ger utrymme för vidare språkutveckling. På grund av våra studier om tidig språkinlärning och -undervisning skapade vi ett materialpaket med elva olika temalektioner. Vårt materialpaket innehåller sånger, lek, spel och olika motionsbaserade arbetssätt där eleverna kan träna svenska muntligt. Vi tycker att vi lyckades skapa ett sådant material som vi själva och också andra svensklärare kommer att ha nytta av.Tiivistelmä. Tämän sivuainetutkielman tarkoituksena oli tutkia varhaista vieraankielenoppimista, sen erityispiirteitä ja lapsille sopivia opetusmenetelmiä, sekä luoda materiaalipaketti varhennettuun ruotsin opetukseen edellä mainitun tutkimuksen pohjalta. Aihe on ajankohtainen, sillä ensimmäisen vieraan kielen opetus (A1) siirtyi alkamaan ensimmäiseltä luokalta vuoden 2020 tammikuusta lähtien. Kieltenopettajat ovat varhennuksen myötä uuden haasteen edessä, koska vieraan kielen opetus on aiemmin alkanut Suomessa myöhemmin. Opettajat ovat opettaneet lukemaan jo oppineita oppilaita ja opetus on perinteisesti pohjautunut pitkälti kirjallisiin teksteihin. Tutkielmassa syvennyttiin kaksikielisen- sekä varhennetun kieltenopetuksen opetussuunnitelmiin, vieraan kielen oppimiseen aivotutkimuksen näkökulmasta ja lapsille parhaiten soveltuviin työskentelytapoihin vieraan kielen opetuksessa. Keskeisimmät lähdeteokset olivat Huotilaisen Näin aivot oppivat, Cameronin Teaching Languages to Young Learners ja Abrahamssonin Andraspråksinlärning. Tutkimuksessa pohdittiin myös varhennetun kielten opetuksen mahdollisia hyötyjä ja varjopuolia. Tiivistäen voidaan todeta, etteivät tutkijoiden näkemykset varhennetun vieraan kielen opetuksen hyödyistä ole aivan yhtenäiset. Lapset oppivat usein ääntämistä aikuisia paremmin, kun taas isommat oppijat oppivat esimerkiksi kielen rakennetta huomattavasti nopeammin kognitiivisten kykyjensä ansiosta. Oleellista varhennetussa kieltenopetuksessa onkin kiinnittää huomiota lapsille soveltuviin opetusmenetelmiin. Tutkijat esittävät, että lasten vieraan kielen opetuksessa kannattaa suosia samankaltaisia metodeja, joilla edistetään oppijan ensimmäisen kielen kehitystä. Musiikilla on keskeinen rooli kielenopetuksessa. Samoin erilaiset lorut, leikit, pelit ja erilaiset toiminnallisuuteen perustuvat menetelmät ovat otollisia tapoja harjoitella vierasta kieltä, sillä leikin varjolla ujommatkin oppijat uskaltautuvat käyttämään vierasta kieltä ja oppiminen tapahtuu ilon kautta. Edellä mainittujen lisäksi opettajan käyttämä luokkakieli on omiaan viemään oppijoiden vieraan kielen kehitystä eteenpäin, sillä samat fraasit toistuvat usein ja käytetty kieli on sopivalla tasolla kielenoppijoiden tasoon nähden. Yllä esitellyn tutkimuksen pohjalta loimme materiaalipaketin varhennettuun A1-ruotsiin. Materiaalipakettimme sisältää 11 erilaista teematuntia, jotka sisältävät lauluja, leikkejä, pelejä sekä erilaisia toiminnallisuuteen perustuvia tapoja harjoitella vierasta kieltä suullisesti. Onnistuimme mielestämme hyvin materiaalipaketin laadinnassa ja toivomme siitä oleva hyötyä paitsi itsellemme, myös muille ruotsinopettajille

Probabilistic modeling and machine learning in structural and systems biology

This supplement contains extended versions of a selected subset of papers presented at the workshop PMSB 2007, Probabilistic Modeling and Machine Learning in Structural and Systems Biology, Tuusula, Finland, from June 17 to 18, 2006

The Impact of Free Trial Acceptance on Demand for Alternative Nicotine Products: Evidence from Experimental Auctions

Objectives: This study explored the relationship between product trials and consumer demand for alternative nicotine products (ANP). Methods: An experimental auction was conducted with 258 adult smokers, wherein participants were randomly assigned to one of four experimental conditions. The participants received the opportunity to try, but did not have to accept, one of three relatively novel ST products (i.e., snus, dissolvable tobacco, or medicinal nicotine), or they were placed into a control group (i.e., no trial). All the participants then bid on all three of these products, as well as on cigarettes. We assessed interest in using ANP based on both trial of the product and bids placed for the products in the experimental auction. Results: Fewer smokers were willing to try snus (44 %) than dissolvable tobacco (64 %) or medicine nicotine (68 %). For snus, we find modest evidence suggesting that willingness to try is associated with greater demand for the product. For dissolvable tobacco or medicinal nicotine, we find no evidence that those who accept the product trial have higher demand for the product. Conclusions: Free trials of a novel ANP were not strongly associated with product demand, as assessed by willingness to pay. Given the debate over the potential for ANP to reduce the harm from smoking, these results are important in understanding the impact of free trial offers on adoption of ST product as a strategy to reduce harm from tobacco use

An analytic and systematic framework for estimating metabolic flux ratios from 13C tracer experiments

<p>Abstract</p> <p>Background</p> <p>Metabolic fluxes provide invaluable insight on the integrated response of a cell to environmental stimuli or genetic modifications. Current computational methods for estimating the metabolic fluxes from ¹³<it>C </it>isotopomer measurement data rely either on manual derivation of analytic equations constraining the fluxes or on the numerical solution of a highly nonlinear system of isotopomer balance equations. In the first approach, analytic equations have to be tediously derived for each organism, substrate or labelling pattern, while in the second approach, the global nature of an optimum solution is difficult to prove and comprehensive measurements of external fluxes to augment the ¹³<it>C </it>isotopomer data are typically needed.</p> <p>Results</p> <p>We present a novel analytic framework for estimating metabolic flux ratios in the cell from ¹³<it>C </it>isotopomer measurement data. In the presented framework, equation systems constraining the fluxes are derived automatically from the model of the metabolism of an organism. The framework is designed to be applicable with all metabolic network topologies, ¹³<it>C </it>isotopomer measurement techniques, substrates and substrate labelling patterns.</p> <p>By analyzing nuclear magnetic resonance (NMR) and mass spectrometry (MS) measurement data obtained from the experiments on glucose with the model micro-organisms <it>Bacillus subtilis </it>and <it>Saccharomyces cerevisiae </it>we show that our framework is able to automatically produce the flux ratios discovered so far by the domain experts with tedious manual analysis. Furthermore, we show by <it>in silico </it>calculability analysis that our framework can rapidly produce flux ratio equations – as well as predict when the flux ratios are unobtainable by linear means – also for substrates not related to glucose.</p> <p>Conclusion</p> <p>The core of ¹³<it>C </it>metabolic flux analysis framework introduced in this article constitutes of flow and independence analysis of metabolic fragments and techniques for manipulating isotopomer measurements with vector space techniques. These methods facilitate efficient, analytic computation of the ratios between the fluxes of pathways that converge to a common junction metabolite. The framework can been seen as a generalization and formalization of existing tradition for computing metabolic flux ratios where equations constraining flux ratios are manually derived, usually without explicitly showing the formal proofs of the validity of the equations.</p

Learning with multiple pairwise kernels for drug bioactivity prediction

Motivation: Many inference problems in bioinformatics, including drug bioactivity prediction, can be formulated as pairwise learning problems, in which one is interested in making predictions for pairs of objects, e.g. drugs and their targets. Kernel-based approaches have emerged as powerful tools for solving problems of that kind, and especially multiple kernel learning (MKL) offers promising benefits as it enables integrating various types of complex biomedical information sources in the form of kernels, along with learning their importance for the prediction task. However, the immense size of pairwise kernel spaces remains a major bottleneck, making the existing MKL algorithms computationally infeasible even for small number of input pairs.Results: We introduce pairwiseMKL, the first method for time- and memory-efficient learning with multiple pairwise kernels. pairwiseMKL first determines the mixture weights of the input pairwise kernels, and then learns the pairwise prediction function. Both steps are performed efficiently without explicit computation of the massive pairwise matrices, therefore making the method applicable to solving large pairwise learning problems. We demonstrate the performance of pairwiseMKL in two related tasks of quantitative drug bioactivity prediction using up to 167 995 bioactivity measurements and 3120 pairwise kernels: (i) prediction of anticancer efficacy of drug compounds across a large panel of cancer cell lines; and (ii) prediction of target profiles of anticancer compounds across their kinome-wide target spaces. We show that pairwiseMKL provides accurate predictions using sparse solutions in terms of selected kernels, and therefore it automatically identifies also data sources relevant for the prediction problem

Multi-Target Prediction: A Unifying View on Problems and Methods

Multi-target prediction (MTP) is concerned with the simultaneous prediction of multiple target variables of diverse type. Due to its enormous application potential, it has developed into an active and rapidly expanding research field that combines several subfields of machine learning, including multivariate regression, multi-label classification, multi-task learning, dyadic prediction, zero-shot learning, network inference, and matrix completion. In this paper, we present a unifying view on MTP problems and methods. First, we formally discuss commonalities and differences between existing MTP problems. To this end, we introduce a general framework that covers the above subfields as special cases. As a second contribution, we provide a structured overview of MTP methods. This is accomplished by identifying a number of key properties, which distinguish such methods and determine their suitability for different types of problems. Finally, we also discuss a few challenges for future research

Building multiclass classifiers for remote homology detection and fold recognition

BACKGROUND: Protein remote homology detection and fold recognition are central problems in computational biology. Supervised learning algorithms based on support vector machines are currently one of the most effective methods for solving these problems. These methods are primarily used to solve binary classification problems and they have not been extensively used to solve the more general multiclass remote homology prediction and fold recognition problems. RESULTS: We present a comprehensive evaluation of a number of methods for building SVM-based multiclass classification schemes in the context of the SCOP protein classification. These methods include schemes that directly build an SVM-based multiclass model, schemes that employ a second-level learning approach to combine the predictions generated by a set of binary SVM-based classifiers, and schemes that build and combine binary classifiers for various levels of the SCOP hierarchy beyond those defining the target classes. CONCLUSION: Analyzing the performance achieved by the different approaches on four different datasets we show that most of the proposed multiclass SVM-based classification approaches are quite effective in solving the remote homology prediction and fold recognition problems and that the schemes that use predictions from binary models constructed for ancestral categories within the SCOP hierarchy tend to not only lead to lower error rates but also reduce the number of errors in which a superfamily is assigned to an entirely different fold and a fold is predicted as being from a different SCOP class. Our results also show that the limited size of the training data makes it hard to learn complex second-level models, and that models of moderate complexity lead to consistently better results