The Ethnic 'Other' in Ukrainian History Textbooks: The Case of Russia and the Russians

This paper examines portrayals of Russia and the Russians in two generations of Ukrainian history textbooks. It observes that the textbooks are highly condemning of Ukraine's main ethnic other in the guise of foreign ruler: the tsarist authorities and the Soviet regime are always attributed dubious and malicious intentions even if there is appreciation for some of their policies. By contrast, the books, certainly those of the second generation, refrain from presenting highly biased accounts of the ethnic other as a national group (i.e. Russians). Instances where negative judgements do fall onto Russians are counterbalanced by excerpts criticizing ethnic Ukrainians or highlighting conflicting interests within the Ukrainian ethnic group. The negative appraisal of the ethnic other as foreign ruler is clearly instrumental for the nation-building project as it sustains a discourse legitimating the existence of Ukraine as independent state. However, recent trends in history education, the paper concludes, suggest that the importance of nurturing patriotism as a national policy objective is diminishing

The SOCS-1 gene methylation in chronic myeloid leukemia patients

SOCS-1, an important protein in the JAK/STAT pathway, has a role in the down stream of BCR-ABL protein kinase. We investigated 56 CML patients and 16 controls for the methylation status of SOCS-1 gene promoter and Exon 2 regions. Exon 2 was found to be methylated in 58.9% of the patients and 93.8% of the controls [P = 0.020, OR = 0.121(0.015-0.957)%95CI]. The promoter region was found unmethylated in all patient samples and controls. Although previous studies revealed a relation between SOCS1 gene Exon-2 hypermethylation and CML development or progression, the results of this study showed no such correlation. On the contrary, our results might be indicating hypomethylation in CML patients, this hypothesis need to be studied in larger study population. © 2007 Wiley-Liss, Inc

A conceptual framework for crop-based agri-food supply chain characterization under uncertainty

[EN] Crop-based Agri-food Supply Chains (AFSCs) are complex systems that face multiple sources of uncertainty that can cause a significant imbalance between supply and demand in terms of product varieties, quantities, qualities, customer requirements, times and prices, all of which greatly complicate their management. Poor management of these sources of uncertainty in these AFSCs can have negative impact on quality, safety, and sustainability by reducing the logistic efficiency and increasing the waste. Therefore, it becomes crucial to develop models in order to deal with the key sources of uncertainty. For this purpose, it is necessary to precisely understand and define the problem under study. Even, the characterisation process of this domains is also a difficult and time-consuming task, especially when the right directions and standards are not in place. In this chapter, a Conceptual Framework is proposed that systematically collects those aspects that are relevant for an adequate crop-based AFSC management under uncertainty.Authors of this publication acknowledge the contribution of the Project 691249, RUC-APS "Enhancing and implementing Knowledge based ICT solutions within high Risk and Uncertain Conditions for Agriculture Production Systems" (www.ruc-aps.eu), funded by the European Union under their funding scheme H2020-MSCA-RISE-2015Alemany Díaz, MDM.; Esteso, A.; Ortiz Bas, Á.; Hernández Hormazabal, JE.; Fernández, A.; Garrido, A.; Martin, J.... (2021). A conceptual framework for crop-based agri-food supply chain characterization under uncertainty. Studies in Systems, Decision and Control. 280:19-33. https://doi.org/10.1007/978-3-030-51047-3_2S1933280Taylor, D.H., Fearne, A.: Towards a framework for improvement in the management of demand in agri-food supply chains. 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Dyn. 3, 136–155 (2012

Hybrid multicriteria fuzzy classification of network traffic patterns, anomalies, and protocols

© 2017, Springer-Verlag London Ltd., part of Springer Nature. Traffic classification in computer networks has very significant roles in network operation, management, and security. Examples include controlling the flow of information, allocating resources effectively, provisioning quality of service, detecting intrusions, and blocking malicious and unauthorized access. This problem has attracted a growing attention over years and a number of techniques have been proposed ranging from traditional port-based and payload inspection of TCP/IP packets to supervised, unsupervised, and semi-supervised machine learning paradigms. With the increasing complexity of network environments and support for emerging mobility services and applications, more robust and accurate techniques need to be investigated. In this paper, we propose a new supervised hybrid machine-learning approach for ubiquitous traffic classification based on multicriteria fuzzy decision trees with attribute selection. Moreover, our approach can handle well the imbalanced datasets and zero-day applications (i.e., those without previously known traffic patterns). Evaluating the proposed methodology on several benchmark real-world traffic datasets of different nature demonstrated its capability to effectively discriminate a variety of traffic patterns, anomalies, and protocols for unencrypted and encrypted traffic flows. Comparing with other methods, the performance of the proposed methodology showed remarkably better classification accuracy

Building inclusive nations in the age of migration

This is an Accepted Manuscript of an article published by Taylor & Francis in Identities: Global Studies in Culture and Power on 06 Apr 2016, available online: http://dx.doi.org/10.1080/1070289X.2016.1148607Nation and diversity are often casted in oppositional terms. The present joint-intervention explores the limits and possibilities of what we call ‘inclusive nation’, i.e. a nation which embraces rather than expunging diversity. To reflect on this idea, the Loughborough University Nationalism Network (LUNN) organized a symposium, bringing together both academics and relevant stakeholders, to explore both theoretically and practically the feasibility of the inclusive nation. For reason of space, here we present only the theoretical views of academics. While Billig and Yuval-Davis highlight the inherent exclusive thrust of nationalism, Kaufmann and Hearn suggest two distinct ways to move away the traditional understanding of nationalism as a site of singularity, oppression and exclusion. A final rejoinder by Nyhagen pushes the debate further interrogating the boundaries of national belonging

Priming with recombinant auxotrophic BCG expressing HIV-1 Gag, RT and Gp120 and boosting with recombinant MVA induces a robust T cell response in mice

In previous studies we have shown that a pantothenate auxotroph of Myocbacterium bovis BCG (BCGΔ panCD ) expressing HIV-1 subtype C Gag induced Gag-specific immune responses in mice and Chacma baboons after prime-boost immunization in combination with matched rMVA and VLP vaccines respectively. In this study recombinant BCG (rBCG) expressing HIV-1 subtype C reverse transcriptase and a truncated envelope were constructed using both the wild type BCG Pasteur strain as a vector and the pantothenate auxotroph. Mice were primed with rBCG expressing Gag and RT and boosted with a recombinant MVA, expressing a polyprotein of Gag, RT, Tat and Nef (SAAVI MVA-C). Priming with rBCGΔ panCD expressing Gag or RT rather than the wild type rBCG expressing Gag or RT resulted in higher frequencies of total HIV-specific CD8 + T cells and increased numbers of T cells specific to the subdominant Gag and RT epitopes. Increasing the dose of rBCG from 10 5 cfu to 10 7 cfu also led to an increase in the frequency of responses to subdominant HIV epitopes. A mix of the individual rBCGΔ panCD vaccines expressing either Gag, RT or the truncated Env primed the immune system for a boost with SAAVI MVA-C and generated five-fold higher numbers of HIV-specific IFN-γ-spot forming cells than mice primed with rBCGΔ panCD containing an empty vector control. Priming with the individual rBCGΔ panCD vaccines or the mix and boosting with SAAVI MVA-C also resulted in the generation of HIV-specific CD4 + and CD8 + T cells producing IFN-γ and TNF-α and CD4 + cells producing IL-2. The rBCG vaccines tested in this study were able to prime the immune system for a boost with rMVA expressing matching antigens, inducing robust, HIV-specific T cell responses to both dominant and subdominant epitopes in the individual proteins when used as individual vaccines or in a mix

Longitudinal machine learning modeling of MS patient trajectories improves predictions of disability progression

Background and Objectives: Research in Multiple Sclerosis (MS) has recently focused on extracting knowledge from real-world clinical data sources. This type of data is more abundant than data produced during clinical trials and potentially more informative about real-world clinical practice. However, this comes at the cost of less curated and controlled data sets. In this work we aim to predict disability progression by optimally extracting information from longitudinal patient data in the real-world setting, with a special focus on the sporadic sampling problem. Methods: We use machine learning methods suited for patient trajectories modeling, such as recurrent neural networks and tensor factorization. A subset of 6682 patients from the MSBase registry is used. Results: We can predict disability progression of patients in a two-year horizon with an ROC-AUC of 0.85, which represents a 32% decrease in the ranking pair error (1-AUC) compared to reference methods using static clinical features. Conclusions: Compared to the models available in the literature, this work uses the most complete patient history for MS disease progression prediction and represents a step forward towards AI-assisted precision medicine in MS

Jewish Immigrants in Israel: Disintegration Within Integration?

In her chapter, ‘Disintegration within integration’, Amandine Desille examines more recent transformations of Israel’s Law of Return – the Israeli immigration policy which provides the (imagined) repatriation of Diaspora Jews to Israel – in a context of liberalisation of the Israeli economy and the devolution of power to local authorities. Today, new immigrants follow two paths of ‘integration’: ‘direct absorp-tion’, where immigrants are granted benefits while being free to settle wherever they find fit; and ‘community absorption’, where immigrants are placed in ‘absorption centres’ and see their entitlements conditioned by residence, religious observance and more. Those two paths are ‘ethnicised’ in the sense that they depend on country of origin – Western immigrants, considered as economically useful, benefit from direct absorption and a more pluralist attitude of local governments, while immi-grants from Africa and Asia are the objects of an assimilationist policy. This situa-tion of ‘(dis)integration’ within what is supposed to be an inclusive immigrant policy for all Jews, shows the extent to which new criteria of perceived economic performance limit the integration of specific segments of newcomers. The rescaling of immigration and immigrant policies to subnational governments, although it has introduced a more multicultural approach, antagonist to the assimilationist ideology at work in Israel, has not enabled an alternative policy framework which is more accommodating to all.info:eu-repo/semantics/publishedVersio

Authoritative subspecies diagnosis tool for European honey bees based on ancestryinformative SNPs

Background With numerous endemic subspecies representing four of its five evolutionary lineages, Europe holds a large fraction of Apis mellifera genetic diversity. This diversity and the natural distribution range have been altered by anthropogenic factors. The conservation of this natural heritage relies on the availability of accurate tools for subspecies diagnosis. Based on pool-sequence data from 2145 worker bees representing 22 populations sampled across Europe, we employed two highly discriminative approaches (PCA and F-ST) to select the most informative SNPs for ancestry inference. Results Using a supervised machine learning (ML) approach and a set of 3896 genotyped individuals, we could show that the 4094 selected single nucleotide polymorphisms (SNPs) provide an accurate prediction of ancestry inference in European honey bees. The best ML model was Linear Support Vector Classifier (Linear SVC) which correctly assigned most individuals to one of the 14 subspecies or different genetic origins with a mean accuracy of 96.2% +/- 0.8 SD. A total of 3.8% of test individuals were misclassified, most probably due to limited differentiation between the subspecies caused by close geographical proximity, or human interference of genetic integrity of reference subspecies, or a combination thereof. Conclusions The diagnostic tool presented here will contribute to a sustainable conservation and support breeding activities in order to preserve the genetic heritage of European honey bees.The SmartBees project was funded by the European Commission under its FP7 KBBE programme (2013.1.3-02, SmartBees Grant Agreement number 613960) https://ec.europa.eu/research/fp7.MP was supported by a Basque Government grant (IT1233-19). The funders provided the financial support to the research, but had no role in the design of the study, analysis, interpretations of data and in writing the manuscript