Moussab/ORKG-training-evaluation-set huggingface dataset:
Research field: Artificial Intelligence
Title: Personalized PageRank Based Multi-document Summarization
Abstract: This paper presents a novel multi-document summarization approach based on personalized pagerank (PPRSum). In this algorithm, we uniformly integrate various kinds of information in the corpus. At first, we train a salience model of sentence global features based on Naive Bayes Model. Secondly, we generate a relevance model for each corpus utilizing the query of it. Then, we compute the personalized prior probability for each sentence in the corpus utilizing the salience model and the relevance model both. With the help of personalized prior probability, a Personalized PageRank ranking process is performed depending on the relationships among all sentences in the corpus. Additionally, the redundancy penalty is imposed on each sentence. The summary is produced by choosing the sentences with both high query-focused information richness and high information novelty. Experiments on DUC2007 are performed and the ROUGE evaluation results show that PPRSum ranks between the 1st and the 2nd systems on DUC2007 main task.
Extracted statement — implementation: PPRSum
Research field: Artificial Intelligence
Title: QAKiS: an open domain QA system based on relational patterns.
Abstract: We present QAKiS, a system for open domain Question Answering over linked data. It addresses the problem of question interpretation as a relation-based match, where fragments of the question are matched to binary relations of the triple store, using relational textual patterns automatically collected. For the demo, the relational patterns are automatically extracted from Wikipedia, while DBpedia is the RDF data set to be queried using a natural language interface.
Extracted statement — implementation: QAKiS
Research field: Artificial Intelligence
Title: QAnswer-enhanced entity matching for question answering over linked data
Abstract: QAnswer is a question answering system that uses DBpedia as a knowledge base and converts natural language questions into a SPARQL query. In order to improve the match between entities and relations and natural language text, we make use of Wikipedia to extract lexicalizations of the DBpedia entities and then match them with the question. These entities are validated on the ontology, while missing ones can be inferred. The proposed system was tested in the QALD-5 challenge and it obtained a F1 score of 0.30, which placed QAnswer in the second position in the challenge, despite the fact that the system used only a small subset of the properties in DBpedia, due to the long extraction process.
Extracted statement — implementation: QAnswer
Research field: Artificial Intelligence
Title: The SOFC-Exp Corpus and Neural Approaches to Information Extraction in the Materials Science Domain
Abstract: This paper presents a new challenging information extraction task in the domain of materials science. We develop an annotation scheme for marking information on experiments related to solid oxide fuel cells in scientific publications, such as involved materials and measurement conditions. With this paper, we publish our annotation guidelines, as well as our SOFC-Exp corpus consisting of 45 open-access scholarly articles annotated by domain experts. A corpus and an inter-annotator agreement study demonstrate the complexity of the suggested named entity recognition and slot filling tasks as well as high annotation quality. We also present strong neural-network based models for a variety of tasks that can be addressed on the basis of our new data set. On all tasks, using BERT embeddings leads to large performance gains, but with increasing task complexity, adding a recurrent neural network on top seems beneficial. Our models will serve as competitive baselines in future work, and analysis of their performance highlights difficult cases when modeling the data and suggests promising research directions.
Extracted statement — dataset: SOFC-Exp
Research field: Artificial Intelligence
Title: YNU-HPCC at SemEval-2021 Task 11: Using a BERT Model to Extract Contributions from NLP Scholarly Articles
Abstract: This paper describes the system we built as the YNU-HPCC team in the SemEval-2021 Task 11: NLPContributionGraph. This task involves first identifying sentences in the given natural language processing (NLP) scholarly articles that reflect research contributions through binary classification; then identifying the core scientific terms and their relation phrases from these contribution sentences by sequence labeling; and finally, these scientific terms and relation phrases are categorized, identified, and organized into subject-predicate-object triples to form a knowledge graph with the help of multiclass classification and multi-label classification. We developed a system for this task using a pre-trained language representation model called BERT that stands for Bidirectional Encoder Representations from Transformers, and achieved good results. The average F1-score for Evaluation Phase 2, Part 1 was 0.4562 and ranked 7th, and the average F1-score for Evaluation Phase 2, Part 2 was 0.6541, and also ranked 7th.
Extracted statement — Team Name: YNU-HPCC
Research field: Artificial Intelligence
Title: Template-based Question Answering using Recursive Neural Networks
Abstract: Most question answering (QA) systems over Linked Data, i.e. Knowledge Graphs, approach the question answering task as a conversion from a natural language question to its corresponding SPARQL query. A common approach is to use query templates to generate SPARQL queries with slots that need to be filled. Using templates instead of running an extensive NLP pipeline or end-to-end model shifts the QA problem into a classification task, where the system needs to match the input question to the appropriate template. This paper presents an approach to automatically learn and classify natural language questions into corresponding templates using recursive neural networks. Our model was trained on 5000 questions and their respective SPARQL queries from the preexisting LC-QuAD dataset grounded in DBpedia, spanning 5042 entities and 615 predicates. The resulting model was evaluated using the FAIR GERBIL QA framework resulting in 0.419 macro f-measure on LC-QuAD and 0.417 macro f-measure on QALD-7.
Extracted statement — has research problem: DBPedia
Research field: Atomic, Molecular and Optical Physics
Title: Absolute vacuum ultraviolet flux in inductively coupled plasmas and chemical modifications of 193 nm photoresist
Abstract: Vacuum ultraviolet (VUV) photons in plasma processing systems are known to alter surface chemistry and may damage gate dielectrics and photoresist. We characterize absolute VUV fluxes to surfaces exposed in an inductively coupled argon plasma, 1–50 mTorr, 25–400 W, using a calibrated VUV spectrometer. We also demonstrate an alternative method to estimate VUV fluence in an inductively coupled plasma (ICP) reactor using a chemical dosimeter-type monitor. We illustrate the technique with argon ICP and xenon lamp exposure experiments, comparing direct VUV measurements with measured chemical changes in 193 nm photoresist-covered Si wafers following VUV exposure.
Extracted statement — Plasma_discharge: ICP
Research field: Atomic, Molecular and Optical Physics
Title: Quantification of the VUV radiation in low pressure hydrogen and nitrogen plasmas
Abstract: Hydrogen and nitrogen containing discharges emit intense radiation in a broad wavelength region in the VUV. The measured radiant power of individual molecular transitions and atomic lines between 117 nm and 280 nm are compared to those obtained in the visible spectral range and moreover to the RF power supplied to the ICP discharge. In hydrogen plasmas driven at 540 W of RF power up to 110 W are radiated in the VUV, whereas less than 2 W is emitted in the VIS. In nitrogen plasmas the power level of about 25 W is emitted both in the VUV and in the VIS. In hydrogen–nitrogen mixtures, the NH radiation increases the VUV amount. The analysis of molecular and atomic hydrogen emission supported by a collisional radiative model allowed determining plasma parameters and particle densities and thus particle fluxes. A comparison of the fluxes showed that the photon fluxes determined from the measured emission are similar to the ion fluxes, whereas the atomic hydrogen fluxes are by far dominant. Photon fluxes up to 5 × 1020 m−2 s−1 are obtained, demonstrating that the VUV radiation should not be neglected in surface modifications processes, whereas the radiant power converted to VUV photons is to be considered in power balances. Varying the admixture of nitrogen to hydrogen offers a possibility to tune photon fluxes in the respective wavelength intervals.
Extracted statement — Plasma_discharge: ICP
Research field: Atomic, Molecular and Optical Physics
Title: HBr Plasma Treatment Versus VUV Light Treatment to Improve 193 nm Photoresist Pattern Linewidth Roughness
Abstract: We have studied the impact of HBr plasma treatment and the role of the VUV light emitted by this plasma on the chemical modifications and resulting roughness of both blanket and patterned photoresists. The experimental results show that both treatments lead to similar resist bulk chemical modifications that result in a decrease of the resist glass transition temperature (Tg). This drop in Tg allows polymer chain rearrangement that favors surface roughness smoothening. The smoothening effect is mainly attributed to main chain scission induced by plasma VUV light. For increased VUV light exposure time, the crosslinking mechanism dominates over main chain scission and limits surface roughness smoothening. In the case of the HBr plasma treatment, the synergy between Bromine radicals and VUV light leads to the formation of dense graphitized layers on top and sidewalls surfaces of the resist pattern. The presence of a dense layer on the HBr cured resist sidewalls prevents from resist pattern reflowing but on the counter side leads to increased surface roughness and linewidth roughness compared to VUV light treatment.
Extracted statement — Feed_gases: HBr
Research field: Biochemistry
Title: Dynamic Impacts of the Inhibition of the Molecular Chaperone Hsp90 on the T-Cell Proteome Have Implications for Anti-Cancer Therapy
Abstract: The molecular chaperone Hsp90-dependent proteome represents a complex protein network of critical biological and medical relevance. Known to associate with proteins with a broad variety of functions termed clients, Hsp90 maintains key essential and oncogenic signalling pathways. Consequently, Hsp90 inhibitors are being tested as anti-cancer drugs. Using an integrated systematic approach to analyse the effects of Hsp90 inhibition in T-cells, we quantified differential changes in the Hsp90-dependent proteome, Hsp90 interactome, and a selection of the transcriptome. Kinetic behaviours in the Hsp90-dependent proteome were assessed using a novel pulse-chase strategy (Fierro-Monti et al., accompanying article), detecting effects on both protein stability and synthesis. Global and specific dynamic impacts, including proteostatic responses, are due to direct inhibition of Hsp90 as well as indirect effects. As a result, a decrease was detected in most proteins that changed their levels, including known Hsp90 clients. Most likely, consequences of the role of Hsp90 in gene expression determined a global reduction in net de novo protein synthesis. This decrease appeared to be greater in magnitude than a concomitantly observed global increase in protein decay rates. Several novel putative Hsp90 clients were validated, and interestingly, protein families with critical functions, particularly the Hsp90 family and cofactors themselves as well as protein kinases, displayed strongly increased decay rates due to Hsp90 inhibitor treatment. Remarkably, an upsurge in survival pathways, involving molecular chaperones and several oncoproteins, and decreased levels of some tumour suppressors, have implications for anti-cancer therapy with Hsp90 inhibitors. The diversity of global effects may represent a paradigm of mechanisms that are operating to shield cells from proteotoxic stress, by promoting pro-survival and anti-proliferative functions. Data are available via ProteomeXchange with identifier PXD000537.
Extracted statement — Material: T-cells
Research field: Bioinformatics
Title: ACME: Automated Cell Morphology Extractor for Comprehensive Reconstruction of Cell Membranes
Abstract: The quantification of cell shape, cell migration, and cell rearrangements is important for addressing classical questions in developmental biology such as patterning and tissue morphogenesis. Time-lapse microscopic imaging of transgenic embryos expressing fluorescent reporters is the method of choice for tracking morphogenetic changes and establishing cell lineages and fate maps in vivo. However, the manual steps involved in curating thousands of putative cell segmentations have been a major bottleneck in the application of these technologies especially for cell membranes. Segmentation of cell membranes while more difficult than nuclear segmentation is necessary for quantifying the relations between changes in cell morphology and morphogenesis. We present a novel and fully automated method to first reconstruct membrane signals and then segment out cells from 3D membrane images even in dense tissues. The approach has three stages: 1) detection of local membrane planes, 2) voting to fill structural gaps, and 3) region segmentation. We demonstrate the superior performance of the algorithms quantitatively on time-lapse confocal and two-photon images of zebrafish neuroectoderm and paraxial mesoderm by comparing its results with those derived from human inspection. We also compared with synthetic microscopic images generated by simulating the process of imaging with fluorescent reporters under varying conditions of noise. Both the over-segmentation and under-segmentation percentages of our method are around 5%. The volume overlap of individual cells, compared to expert manual segmentation, is consistently over 84%. By using our software (ACME) to study somite formation, we were able to segment touching cells with high accuracy and reliably quantify changes in morphogenetic parameters such as cell shape and size, and the arrangement of epithelial and mesenchymal cells. Our software has been developed and tested on Windows, Mac, and Linux platforms and is available publicly under an open source BSD license (https://github.com/krm15/ACME).
Extracted statement — creates: ACME
Research field: Bioinformatics
Title: ASPASIA: A toolkit for evaluating the effects of biological interventions on SBML model behaviour
Abstract: A calibrated computational model reflects behaviours that are expected or observed in a complex system, providing a baseline upon which sensitivity analysis techniques can be used to analyse pathways that may impact model responses. However, calibration of a model where a behaviour depends on an intervention introduced after a defined time point is difficult, as model responses may be dependent on the conditions at the time the intervention is applied. We present ASPASIA (Automated Simulation Parameter Alteration and SensItivity Analysis), a cross-platform, open-source Java toolkit that addresses a key deficiency in software tools for understanding the impact an intervention has on system behaviour for models specified in Systems Biology Markup Language (SBML). ASPASIA can generate and modify models using SBML solver output as an initial parameter set, allowing interventions to be applied once a steady state has been reached. Additionally, multiple SBML models can be generated where a subset of parameter values are perturbed using local and global sensitivity analysis techniques, revealing the model’s sensitivity to the intervention. To illustrate the capabilities of ASPASIA, we demonstrate how this tool has generated novel hypotheses regarding the mechanisms by which Th17-cell plasticity may be controlled in vivo. By using ASPASIA in conjunction with an SBML model of Th17-cell polarisation, we predict that promotion of the Th1-associated transcription factor T-bet, rather than inhibition of the Th17-associated transcription factor RORγt, is sufficient to drive switching of Th17 cells towards an IFN-γ-producing phenotype. Our approach can be applied to all SBML-encoded models to predict the effect that intervention strategies have on system behaviour. ASPASIA, released under the Artistic License (2.0), can be downloaded from http://www.york.ac.uk/ycil/software.
Extracted statement — creates: ASPASIA
Research field: Bioinformatics
Title: ASPASIA: A toolkit for evaluating the effects of biological interventions on SBML model behaviour
Abstract: A calibrated computational model reflects behaviours that are expected or observed in a complex system, providing a baseline upon which sensitivity analysis techniques can be used to analyse pathways that may impact model responses. However, calibration of a model where a behaviour depends on an intervention introduced after a defined time point is difficult, as model responses may be dependent on the conditions at the time the intervention is applied. We present ASPASIA (Automated Simulation Parameter Alteration and SensItivity Analysis), a cross-platform, open-source Java toolkit that addresses a key deficiency in software tools for understanding the impact an intervention has on system behaviour for models specified in Systems Biology Markup Language (SBML). ASPASIA can generate and modify models using SBML solver output as an initial parameter set, allowing interventions to be applied once a steady state has been reached. Additionally, multiple SBML models can be generated where a subset of parameter values are perturbed using local and global sensitivity analysis techniques, revealing the model’s sensitivity to the intervention. To illustrate the capabilities of ASPASIA, we demonstrate how this tool has generated novel hypotheses regarding the mechanisms by which Th17-cell plasticity may be controlled in vivo. By using ASPASIA in conjunction with an SBML model of Th17-cell polarisation, we predict that promotion of the Th1-associated transcription factor T-bet, rather than inhibition of the Th17-associated transcription factor RORγt, is sufficient to drive switching of Th17 cells towards an IFN-γ-producing phenotype. Our approach can be applied to all SBML-encoded models to predict the effect that intervention strategies have on system behaviour. ASPASIA, released under the Artistic License (2.0), can be downloaded from http://www.york.ac.uk/ycil/software.
Extracted statement — deposits: ASPASIA
Research field: Bioinformatics
Title: BEAST 2: A Software Platform for Bayesian Evolutionary Analysis
Abstract: We present a new open source, extensible and flexible software platform for Bayesian evolutionary analysis called BEAST 2. This software platform is a re-design of the popular BEAST 1 platform to correct structural deficiencies that became evident as the BEAST 1 software evolved. Key among those deficiencies was the lack of post-deployment extensibility. BEAST 2 now has a fully developed package management system that allows third party developers to write additional functionality that can be directly installed to the BEAST 2 analysis platform via a package manager without requiring a new software release of the platform. This package architecture is showcased with a number of recently published new models encompassing birth-death-sampling tree priors, phylodynamics and model averaging for substitution models and site partitioning. A second major improvement is the ability to read/write the entire state of the MCMC chain to/from disk allowing it to be easily shared between multiple instances of the BEAST software. This facilitates checkpointing and better support for multi-processor and high-end computing extensions. Finally, the functionality in new packages can be easily added to the user interface (BEAUti 2) by a simple XML template-based mechanism because BEAST 2 has been re-designed to provide greater integration between the analysis engine and the user interface so that, for example BEAST and BEAUti use exactly the same XML file format.
Extracted statement — deposits: BEAST
Research field: Bioinformatics
Title: Preliminary Observations of Population Genetics and Relatedness of the Broadnose Sevengill Shark, Notorynchus cepedianus, in Two Northeast Pacific Estuaries
Abstract: The broadnose sevengill shark, Notorynchus cepedianus, a common coastal species in the eastern North Pacific, was sampled during routine capture and tagging operations conducted from 2005–2012. One hundred and thirty three biopsy samples were taken during these research operations in Willapa Bay, Washington and in San Francisco Bay, California. Genotypic data from seven polymorphic microsatellites (derived from the related sixgill shark, Hexanchus griseus) were used to describe N. cepedianus genetic diversity, population structure and relatedness. Diversity within N. cepedianus was found to be low to moderate with an average observed heterozygosity of 0.41, expected heterozygosity of 0.53, and an average of 5.1 alleles per microsatellite locus. There was no evidence of a recent population bottleneck based on genetic data. Analyses of genetic differences between the two sampled estuaries suggest two distinct populations with some genetic mixing of sharks sampled during 2005–2006. Relatedness within sampled populations was high, with percent relatedness among sharks caught in the same area indicating 42.30% first-order relative relationships (full or half siblings). Estuary-specific familial relationships suggest that management of N. cepedianus on the U.S. West Coast should incorporate stock-specific management goals to conserve this ecologically important predator.
Extracted statement — uses: BOTTLENECK
Research field: Bioinformatics
Title: Identification of Leukemia Subtypes from Microscopic Images Using Convolutional Neural Network
Abstract: Leukemia is a fatal cancer and has two main types: Acute and chronic. Each type has two more subtypes: Lymphoid and myeloid. Hence, in total, there are four subtypes of leukemia. This study proposes a new approach for diagnosis of all subtypes of leukemia from microscopic blood cell images using convolutional neural networks (CNN), which requires a large training data set. Therefore, we also investigated the effects of data augmentation for an increasing number of training samples synthetically. We used two publicly available leukemia data sources: ALL-IDB and ASH Image Bank. Next, we applied seven different image transformation techniques as data augmentation. We designed a CNN architecture capable of recognizing all subtypes of leukemia. Besides, we also explored other well-known machine learning algorithms such as naive Bayes, support vector machine, k-nearest neighbor, and decision tree. To evaluate our approach, we set up a set of experiments and used 5-fold cross-validation. The results we obtained from experiments showed that our CNN model performance has 88.25% and 81.74% accuracy, in leukemia versus healthy and multi-class classification of all subtypes, respectively. Finally, we also showed that the CNN model has a better performance than other well-known machine learning algorithms.
Extracted statement — Used models: CNN