PANACEA (Platform for Automatic, Normalised Annotation and Cost-Effective Acquisition of Language Resources for Human Language Technologies)

The objective of PANACEA is to build a factory of LRs that automates the stages involved in the acquisition, production, updating and maintenance of LRs required by MT systems and by other applications based on language technologies, and simplifies eventual issues regarding intellectual property rights. This automation will cut down the cost, time and human effort significantly. These reductions of costs and time are the only way to guarantee the continuous supply of LRs that MT and other language technologies will be demanding in the multilingual Europe.

Towards the automatic merging of language resources

Language Resources are a critical component for Natural Language Processing applications. Throughout the years many resources were manually created for the same task, but with different granularity and coverage information. To create richer resources for a broad range of potential reuses, nformation from all resources has to be joined into one. The hight cost of comparing and merging different resources by hand has been a bottleneck for merging existing resources. With the objective of reducing human intervention, we present a new method for automating merging resources. We have addressed the merging of two verbs subcategorization frame (SCF) lexica for Spanish. The results achieved, a new lexicon with enriched information and conflicting information signalled, reinforce our idea that this approach can be applied for other task of NLP.

Towards the Fully Automatic Merging of Lexical Resources: A Step Forward

This article reports on the results of the research done towards the fully automatically merging of lexical resources. Our main goal is to show the generality of the proposed approach, which have been previously applied to merge Spanish Subcategorization Frames lexica. In this work we extend and apply the same technique to perform the merging of morphosyntactic lexica encoded in LMF. The experiments showed that the technique is general enough to obtain good results in these two different tasks which is an important step towards performing the merging of lexical resources fully automatically.

Automatic Lexical Semantic Classification of Nouns

The work we present here addresses cue-based noun classification in English and Spanish. Its main objective is to automatically acquire lexical semantic information by classifying nouns into previously known noun lexical classes. This is achieved by using particular aspects of linguistic contexts as cues that identify a specific lexical class. Here we concentrate on the task of identifying such cues and the theoretical background that allows for an assessment of the complexity of the task. The results show that, despite of the a-priori complexity of the task, cue-based classification is a useful tool in the automatic acquisition of lexical semantic classes.

Towards the Automatic Merging of Lexical Resources: Automatic Mapping

Lexical Resources are a critical component for Natural Language Processing applications. However, the high cost of comparing and merging different resources has been a bottleneck to have richer resources with a broad range of potential uses for a significant number of languages.With the objective of reducing cost byeliminating human intervention, we present a new method for automating the merging of resources,with special emphasis in what we call the mapping step. This mapping step, which converts the resources into a common format that allows latter the merging, is usually performed with huge manual effort and thus makes the whole process very costly. Thus, we propose a method to perform this mapping fully automatically. To test our method, we have addressed the merging of two verb subcategorization frame lexica for Spanish, The resultsachieved, that almost replicate human work, demonstrate the feasibility of the approach.

Automatic Detection of Non-deverbal Event Nouns for Quick Lexicon Production

In this work we present the results of experimental work on the development of lexical class-based lexica by automatic means. Our purpose is to assess the use of linguistic lexical-class based information as a feature selection methodology for the use of classifiers in quick lexical development. The results show that the approach can help reduce the human effort required in the development of language resources significantly.

A Method Towards the Fully Automatic Merging of Lexical Resources

Lexical Resources are a critical component for Natural Language Processing applications. However, the high cost of comparing and merging different resources has been a bottleneck to obtain richer resources and a broader range of potential uses for a significant number of languages. With the objective of reducing cost by eliminating human intervention, we present a new method towards the automatic merging of resources. This method includes both, the automatic mapping of resources involved to a common format and merging them, once in this format. This paper presents how we have addressed the merging of two verb subcategorization frame lexica for Spanish, but our method will be extended to cover other types of Lexical Resources. The achieved results, that almost replicate human work, demonstrate the feasibility of the approach.

Identification of cancer stem cells in Ewing's sarcoma.

Cancer stem cells that display tumor-initiating properties have recently been identified in several distinct types of malignancies, holding promise for more effective therapeutic strategies. However, evidence of such cells in sarcomas, which include some of the most aggressive and therapy-resistant tumors, has not been shown to date. Here, we identify and characterize cancer stem cells in Ewing's sarcoma family tumors (ESFT), a highly aggressive pediatric malignancy believed to be of mesenchymal stem cell (MSC) origin. Using magnetic bead cell separation of primary ESFT, we have isolated a subpopulation of CD133+ tumor cells that display the capacity to initiate and sustain tumor growth through serial transplantation in nonobese diabetic/severe combined immunodeficiency mice, re-establishing at each in vivo passage the parental tumor phenotype and hierarchical cell organization. Consistent with the plasticity of MSCs, in vitro differentiation assays showed that the CD133+ cell population retained the ability to differentiate along adipogenic, osteogenic, and chondrogenic lineages. Quantitative real-time PCR analysis of genes implicated in stem cell maintenance revealed that CD133+ ESFT cells express significantly higher levels of OCT4 and NANOG than their CD133- counterparts. Taken together, our observations provide the first identification of ESFT cancer stem cells and demonstration of their MSC properties, a critical step towards a better biological understanding and rational therapeutic targeting of these tumors.

Forensic identification of urine samples: a comparison between nuclear and mitochondrial DNA markers.

Urine samples from 20 male volunteers of European Caucasian origin were stored at 4 degrees C over a 4-month period in order to compare the identification potential of nuclear DNA (nDNA) and mitochondrial DNA (mtDNA) markers. The amount of nDNA recovered from urines dramatically declined over time. Consequently, nDNA likelihood ratios (LRs) greater than 1,000 were obtained for 100, 70 and 55% of the urines analysed after 6, 60 and 120 days, respectively. For the mtDNA, HVI and HVII sequences were obtained for all samples tested, whatever the period considered. Nevertheless, the highest mtDNA LR of 435 was relatively low compared to its nDNA equivalent. Indeed, LRs obtained with only three nDNA loci could easily exceed this value and are quite easier to obtain. Overall, the joint use of nDNA and mtDNA markers enabled the 20 urine samples to be identified, even after the 4-month period.

Identification of a new murine tumor necrosis factor receptor locus that contains two novel murine receptors for tumor necrosis factor-related apoptosis-inducing ligand (TRAIL).

Tumor necrosis factor (TNF) ligand and receptor superfamily members play critical roles in diverse developmental and pathological settings. In search for novel TNF superfamily members, we identified a murine chromosomal locus that contains three new TNF receptor-related genes. Sequence alignments suggest that the ligand binding regions of these murine TNF receptor homologues, mTNFRH1, -2 and -3, are most homologous to those of the tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) receptors. By using a number of in vitro ligand-receptor binding assays, we demonstrate that mTNFRH1 and -2, but not mTNFRH3, bind murine TRAIL, suggesting that they are indeed TRAIL receptors. This notion is further supported by our demonstration that both mTNFRH1:Fc and mTNFRH2:Fc fusion proteins inhibited mTRAIL-induced apoptosis of Jurkat cells. Unlike the only other known murine TRAIL receptor mTRAILR2, however, neither mTNFRH2 nor mTNFRH3 has a cytoplasmic region containing the well characterized death domain motif. Coupled with our observation that overexpression of mTNFRH1 and -2 in 293T cells neither induces apoptosis nor triggers NFkappaB activation, we propose that the mTnfrh1 and mTnfrh2 genes encode the first described murine decoy receptors for TRAIL, and we renamed them mDcTrailr1 and -r2, respectively. Interestingly, the overall sequence structures of mDcTRAILR1 and -R2 are quite distinct from those of the known human decoy TRAIL receptors, suggesting that the presence of TRAIL decoy receptors represents a more recent evolutionary event.

Predictors of political orientation among US-born Mexican Americans: cultural identification, acculturation attitudes and socioeconomic status

With each passing election, U.S. political campaigns have renewed their efforts in courting the “Latino vote,” yet the Latino population is not a culturally homogenous voting bloc. This study examined how cultural identifications and acculturation attitudes in U.S. born Mexican Americans interacted with socioeconomic status (SES) to predict political orientation. Individuals who held stronger Mexican identity and supported biculturalism as an acculturation strategy had a more liberal orientation, while belonging to a higher SES group and holding stronger assimilation attitudes predicted a less liberal orientation. Mexican cultural identification interacted with SES such that those who held a weaker Mexican identity, but came from a higher social class were less liberal and more moderate in their political orientation. Weak Mexican identification and higher SES also predicted weaker endorsement of bicultural acculturation attitudes, which in turn, mediated the differences in political orientation. The acceptance of one’s ethnic identity and endorsement of bicultural attitudes predicted a more liberal political orientation. In light of these findings, political candidates should be cautious in how they pander to Latino constituents—referencing the groups’ ethnic culture or customs may distance constituents who are not strongly identified with their ethnic culture.