Measuring and analizing child labor: methodological issues

Current statistics on child labor are generally based on economically active children. This paper will argue that these figures are not a workable proxy for data on child labor, generating numbers of child laborers, and their gender composition that do not represent the group described by the international definition of child labor. This raises the question of reliable alternatives ways of measuring children's activities, with the aim of analyzing the incidence of child labor. The paper addresses this, and proposes a child labor module that can be linked to surveys of labor force, or living conditions. It also proposes some ideas for how to analyze data on children's activities, and child labor.StreetChildren,Youth and Governance,Children and Youth,Labor Standards,Health Monitoring&Evaluation

Measuring and analyzing child labor : methodological issues

The paper argues that the current statistics on child labor (generally based on economically active children), are not a workable proxy for data on child labor, generating numbers of child laborers, and their gender composition, that do not represent the groups described by the international definition of child labor. This raises the question of reliable alternative ways of measuring children's activities, with the aim of analyzing the incidence of child labor. The paper addresses this, and proposes a child labor module that can be linked to surveys of labor force, or living conditions. It also proposes some ideas on how to analyze data on children's activities, and child labor.Street Children,Youth and Governance,Children and Youth,Labor Standards,Health Monitoring&Evaluation

All-paths graph kernel for protein-protein interaction extraction with evaluation of cross-corpus learning

Background Automated extraction of protein-protein interactions (PPI) is an important and widely studied task in biomedical text mining. We propose a graph kernel based approach for this task. In contrast to earlier approaches to PPI extraction, the introduced all-paths graph kernel has the capability to make use of full, general dependency graphs representing the sentence structure. Results We evaluate the proposed method on five publicly available PPI corpora, providing the most comprehensive evaluation done for a machine learning based PPI-extraction system. We additionally perform a detailed evaluation of the effects of training and testing on different resources, providing insight into the challenges involved in applying a system beyond the data it was trained on. Our method is shown to achieve state-of-the-art performance with respect to comparable evaluations, with 56.4 F-score and 84.8 AUC on the AImed corpus. Conclusion We show that the graph kernel approach performs on state-of-the-art level in PPI extraction, and note the possible extension to the task of extracting complex interactions. Cross-corpus results provide further insight into how the learning generalizes beyond individual corpora. Further, we identify several pitfalls that can make evaluations of PPI-extraction systems incomparable, or even invalid. These include incorrect cross-validation strategies and problems related to comparing F-score results achieved on different evaluation resources. Recommendations for avoiding these pitfalls are provided. </div

Large-scale event extraction from literature with multi-level gene normalization

Text mining for the life sciences aims to aid database curation, knowledge summarization and information retrieval through the automated processing of biomedical texts. To provide comprehensive coverage and enable full integration with existing biomolecular database records, it is crucial that text mining tools scale up to millions of articles and that their analyses can be unambiguously linked to information recorded in resources such as UniProt, KEGG, BioGRID and NCBI databases. In this study, we investigate how fully automated text mining of complex biomolecular events can be augmented with a normalization strategy that identifies biological concepts in text, mapping them to identifiers at varying levels of granularity, ranging from canonicalized symbols to unique gene and proteins and broad gene families. To this end, we have combined two state-of-the-art text mining components, previously evaluated on two community-wide challenges, and have extended and improved upon these methods by exploiting their complementary nature. Using these systems, we perform normalization and event extraction to create a large-scale resource that is publicly available, unique in semantic scope, and covers all 21.9 million PubMed abstracts and 460 thousand PubMed Central open access full-text articles. This dataset contains 40 million biomolecular events involving 76 million gene/protein mentions, linked to 122 thousand distinct genes from 5032 species across the full taxonomic tree. Detailed evaluations and analyses reveal promising results for application of this data in database and pathway curation efforts. The main software components used in this study are released under an open-source license. Further, the resulting dataset is freely accessible through a novel API, providing programmatic and customized access (http://www.evexdb.org/api/v001/). Finally, to allow for large-scale bioinformatic analyses, the entire resource is available for bulk download from http://evexdb.org/download/, under the Creative Commons -Attribution - Share Alike (CC BY-SA) license

Proceedings of the 4th BioNLP Shared Task Workshop

We present the TurkuNLP entry to the BioNLP Shared Task 2016 Bacteria Biotopes event extraction (BB3-event) subtask. We propose a deep learning-based approach to event extraction using a combination of several Long Short-Term Memory (LSTM) networks over syntactic dependency graphs. Features for the proposed neural network are generated based on the shortest path connecting the two candidate entities in the dependency graph. We further detail how this network can be efficiently trained to have good generalization performance even when only a very limited number of training examples are available and part-of-speech (POS) and dependency type feature representations must be learned from scratch. Our method ranked second among the entries to the shared task, achieving an F-score of 52.1% with 62.3% precision and 44.8% recall</p

PESCADOR, a web-based tool to assist text-mining of biointeractions extracted from PubMed queries

BACKGROUND: Biological function is greatly dependent on the interactions of proteins with other proteins and genes. Abstracts from the biomedical literature stored in the NCBI's PubMed database can be used for the derivation of interactions between genes and proteins by identifying the co-occurrences of their terms. Often, the amount of interactions obtained through such an approach is large and may mix processes occurring in different contexts. Current tools do not allow studying these data with a focus on concepts of relevance to a user, for example, interactions related to a disease or to a biological mechanism such as protein aggregation. RESULTS: To help the concept-oriented exploration of such data we developed PESCADOR, a web tool that extracts a network of interactions from a set of PubMed abstracts given by a user, and allows filtering the interaction network according to user-defined concepts. We illustrate its use in exploring protein aggregation in neurodegenerative disease and in the expansion of pathways associated to colon cancer. CONCLUSIONS: PESCADOR is a platform independent web resource available at: http://cbdm.mdc-berlin.de/tools/pescador

Pathophysiology, diagnosis and treatment of somatosensory tinnitus: a scoping review

Somatosensory tinnitus is a generally agreed subtype of tinnitus that is associated with activation of the somatosensory, somatomotor, and visual-motor systems. A key characteristic of somatosensory tinnitus is that is modulated by physical contact or movement. Although it seems common, its pathophysiology, assessment and treatment are not well defined. We present a scoping review on the pathophysiology, diagnosis, and treatment of somatosensory tinnitus, and identify priority directions for further research. Methods: Literature searches were conducted in Google Scholar, PubMed, and EMBASE databases. Additional broad hand searches were conducted with the additional terms etiology, diagnose, treatment. Results: Most evidence on the pathophysiology of somatosensory tinnitus suggests that somatic modulations are the result of altered or cross-modal synaptic activity within the dorsal cochlear nucleus or between the auditory nervous system and other sensory subsystems of central nervous system (e.g., visual or tactile). Presentations of somatosensory tinnitus are varied and evidence for the various approaches to treatment promising but limited. Discussion and Conclusions: Despite the apparent prevalence of somatosensory tinnitus its underlying neural processes are still not well understood. Necessary involvement of multidisciplinary teams in its diagnosis and treatment has led to a large heterogeneity of approaches whereby tinnitus improvement is often only a secondary effect. Hence there are no evidence-based clinical guidelines, and patient care is empirical rather than research-evidence-based. Somatic testing should receive further attention considering the breath of evidence on the ability of patients to modulate their tinnitus through manouvers. Specific questions for further research and review are indicated