Moussab/ORKG-training-evaluation-set huggingface dataset:
Research field: Information Science
Title: CCOHA: Clean Corpus of Historical American English
Abstract: Modelling language change is an increasingly important area of interest within the fields of sociolinguistics and historical linguistics. In recent years, there has been a growing number of publications whose main concern is studying changes that have occurred within the past centuries. The Corpus of Historical American English (COHA) is one of the most commonly used large corpora in diachronic studies in English. This paper describes methods applied to the downloadable version of the COHA corpus in order to overcome its main limitations, such as inconsistent lemmas and malformed tokens, without compromising its qualitative and distributional properties. The resulting corpus CCOHA contains a larger number of cleaned word tokens which can offer better insights into language change and allow for a larger variety of tasks to be performed.
Extracted statement — description: The Corpus of Historical American English (COHA) is one of the most commonly used large corpora in diachronic studies in English
Research field: Information Systems, Process and Knowledge Management
Title: Smart Cities in Europe
Abstract: Urban performance currently depends not only on a city's endowment of hard infrastructure (physical capital), but also, and increasingly so, on the availability and quality of knowledge communication and social infrastructure (human and social capital). The latter form of capital is decisive for urban competitiveness. Against this background, the concept of the “smart city” has recently been introduced as a strategic device to encompass modern urban production factors in a common framework and, in particular, to highlight the importance of Information and Communication Technologies (ICTs) in the last 20 years for enhancing the competitive profile of a city. The present paper aims to shed light on the often elusive definition of the concept of the “smart city.” We provide a focused and operational definition of this construct and present consistent evidence on the geography of smart cities in the EU27. Our statistical and graphical analyses exploit in depth, for the first time to our knowledge, the most recent version of the Urban Audit data set in order to analyze the factors determining the performance of smart cities. We find that the presence of a creative class, the quality of and dedicated attention to the urban environment, the level of education, and the accessibility to and use of ICTs for public administration are all positively correlated with urban wealth. This result prompts the formulation of a new strategic agenda for European cities that will allow them to achieve sustainable urban development and a better urban landscape.
Extracted statement — has research domain: definition of the concept of the “smart city.”
Research field: Information Systems, Process and Knowledge Management
Title: Interdisciplinary Online Hackathons as an Approach to Combat the COVID-19 Pandemic: Case Study
Abstract: Background The COVID-19 outbreak has affected the lives of millions of people by causing a dramatic impact on many health care systems and the global economy. This devastating pandemic has brought together communities across the globe to work on this issue in an unprecedented manner. Objective This case study describes the steps and methods employed in the conduction of a remote online health hackathon centered on challenges posed by the COVID-19 pandemic. It aims to deliver a clear implementation road map for other organizations to follow. Methods This 4-day hackathon was conducted in April 2020, based on six COVID-19–related challenges defined by frontline clinicians and researchers from various disciplines. An online survey was structured to assess: (1) individual experience satisfaction, (2) level of interprofessional skills exchange, (3) maturity of the projects realized, and (4) overall quality of the event. At the end of the event, participants were invited to take part in an online survey with 17 (+5 optional) items, including multiple-choice and open-ended questions that assessed their experience regarding the remote nature of the event and their individual project, interprofessional skills exchange, and their confidence in working on a digital health project before and after the hackathon. Mentors, who guided the participants through the event, also provided feedback to the organizers through an online survey. Results A total of 48 participants and 52 mentors based in 8 different countries participated and developed 14 projects. A total of 75 mentorship video sessions were held. Participants reported increased confidence in starting a digital health venture or a research project after successfully participating in the hackathon, and stated that they were likely to continue working on their projects. Of the participants who provided feedback, 60% (n=18) would not have started their project without this particular hackathon and indicated that the hackathon encouraged and enabled them to progress faster, for example, by building interdisciplinary teams, gaining new insights and feedback provided by their mentors, and creating a functional prototype. Conclusions This study provides insights into how online hackathons can contribute to solving the challenges and effects of a pandemic in several regions of the world. The online format fosters team diversity, increases cross-regional collaboration, and can be executed much faster and at lower costs compared to in-person events. Results on preparation, organization, and evaluation of this online hackathon are useful for other institutions and initiatives that are willing to introduce similar event formats in the fight against COVID-19.
Extracted statement — Has finding: hackathon encouraged and enabled them to progress faster, for example, by building interdisciplinary teams, gaining new insights and feedback provided by their mentors, and creating a functional prototype
Research field: Information Systems, Process and Knowledge Management
Title: Open data hackathons: an innovative strategy to enhance entrepreneurial intention
Abstract: <jats:sec>
<jats:title content-type="abstract-subheading">Purpose</jats:title>
<jats:p>In terms of entrepreneurship, open data benefits include economic growth, innovation, empowerment and new or improved products and services. Hackathons encourage the development of new applications using open data and the creation of startups based on these applications. Researchers focus on factors that affect nascent entrepreneurs’ decision to create a startup but researches in the field of open data hackathons have not been fully investigated yet. This paper aims to suggest a model that incorporates factors that affect the decision of establishing a startup by developers who have participated in open data hackathons.</jats:p>
</jats:sec>
<jats:sec>
<jats:title content-type="abstract-subheading">Design/methodology/approach</jats:title>
<jats:p>In total, 70 papers were examined and analyzed using a three-phased literature review methodology, which was suggested by Webster and Watson (2002). These surveys investigated several factors that affect a nascent entrepreneur to create a startup.</jats:p>
</jats:sec>
<jats:sec>
<jats:title content-type="abstract-subheading">Findings</jats:title>
<jats:p>Eventually, by identifying the motivations for developers to participate in a hackathon, and understanding the benefits of the use of open data, researchers will be able to elaborate the proposed model and evaluate if the contest has contributed to the decision of establish a startup and what factors affect the decision to establish a startup apply to open data developers, and if the participants of the contest agree with these factors.</jats:p>
</jats:sec>
<jats:sec>
<jats:title content-type="abstract-subheading">Originality/value</jats:title>
<jats:p>The paper expands the scope of open data research on entrepreneurship field, stating the need for more research to be conducted regarding the open data in entrepreneurship through hackathons.</jats:p>
</jats:sec>
Extracted statement — Has finding: need for more research to be conducted regarding the open data in entrepreneurship through hackathons
Research field: Information Systems, Process and Knowledge Management
Title: Smart Cities in Europe
Abstract: Urban performance currently depends not only on a city's endowment of hard infrastructure (physical capital), but also, and increasingly so, on the availability and quality of knowledge communication and social infrastructure (human and social capital). The latter form of capital is decisive for urban competitiveness. Against this background, the concept of the “smart city” has recently been introduced as a strategic device to encompass modern urban production factors in a common framework and, in particular, to highlight the importance of Information and Communication Technologies (ICTs) in the last 20 years for enhancing the competitive profile of a city. The present paper aims to shed light on the often elusive definition of the concept of the “smart city.” We provide a focused and operational definition of this construct and present consistent evidence on the geography of smart cities in the EU27. Our statistical and graphical analyses exploit in depth, for the first time to our knowledge, the most recent version of the Urban Audit data set in order to analyze the factors determining the performance of smart cities. We find that the presence of a creative class, the quality of and dedicated attention to the urban environment, the level of education, and the accessibility to and use of ICTs for public administration are all positively correlated with urban wealth. This result prompts the formulation of a new strategic agenda for European cities that will allow them to achieve sustainable urban development and a better urban landscape.
Extracted statement — Has output: operational definition of this construct and present consistent evidence on the geography of smart cities in the EU27
Research field: Machine Learning
Title: DEHB: Evolutionary Hyperband for Scalable, Robust and Efficient Hyperparameter Optimization
Abstract: Modern machine learning algorithms crucially rely on several design decisions to achieve strong performance, making the problem of Hyperparameter Optimization (HPO) more important than ever. Here, we combine the advantages of the popular bandit-based HPO method Hyperband (HB) and the evolutionary search approach of Differential Evolution (DE) to yield a new HPO method which we call DEHB. Comprehensive results on a very broad range of HPO problems, as well as a wide range of tabular benchmarks from neural architecture search, demonstrate that DEHB achieves strong performance far more robustly than all previous HPO methods we are aware of, especially for high-dimensional problems with discrete input dimensions. For example, DEHB is up to 1000x faster than random search. It is also efficient in computational time, conceptually simple and easy to implement, positioning it well to become a new default HPO method.
Extracted statement — keywords: evolutionary search
Research field: Machine Learning
Title: NLTK: The Natural Language Toolkit
Abstract: NLTK, the Natural Language Toolkit, is a suite of open source program modules, tutorials and problem sets, providing ready-to-use computational linguistics courseware. NLTK covers symbolic and statistical natural language processing, and is interfaced to annotated corpora. Students augment and replace existing components, learn structured programming by example, and manipulate sophisticated models from the outset.
Extracted statement — description: NLTK, the Natural Language Toolkit, is a suite of open source program modules, tutorials and problem sets, providing ready-to-use computational linguistics courseware. NLTK covers symbolic and statistical natural language processing, and is interfaced to annotated corpora.
Research field: Machine Learning
Title: Sample-Efficient Automated Deep Reinforcement Learning
Abstract: Despite significant progress in challenging problems across various domains, applying state-of-the-art deep reinforcement learning (RL) algorithms remains challenging due to their sensitivity to the choice of hyperparameters. This sensitivity can partly be attributed to the non-stationarity of the RL problem, potentially requiring different hyperparameter settings at various stages of the learning process. Additionally, in the RL setting, hyperparameter optimization (HPO) requires a large number of environment interactions, hindering the transfer of the successes in RL to real-world applications. In this work, we tackle the issues of sample-efficient and dynamic HPO in RL. We propose a population-based automated RL (AutoRL) framework to meta-optimize arbitrary off-policy RL algorithms. In this framework, we optimize the hyperparameters and also the neural architecture while simultaneously training the agent. By sharing the collected experience across the population, we substantially increase the sample efficiency of the meta-optimization. We demonstrate the capabilities of our sample-efficient AutoRL approach in a case study with the popular TD3 algorithm in the MuJoCo benchmark suite, where we reduce the number of environment interactions needed for meta-optimization by up to an order of magnitude compared to population-based training.
Extracted statement — keywords: Off-Policy RL
Research field: Machine Learning
Title: Active Learning Yields Better Training Data for Scientific Named Entity Recognition
Abstract: Despite significant progress in natural language processing, machine learning models require substantial expertannotated training data to perform well in tasks such as named entity recognition (NER) and entity relations extraction. Furthermore, NER is often more complicated when working with scientific text. For example, in polymer science, chemical structure may be encoded using nonstandard naming conventions, the same concept can be expressed using many different terms (synonymy), and authors may refer to polymers with ad-hoc labels. These challenges, which are not unique to polymer science, make it difficult to generate training data, as specialized skills are needed to label text correctly. We have previously designed polyNER, a semi-automated system for efficient identification of scientific entities in text. PolyNER applies word embedding models to generate entity-rich corpora for productive expert labeling, and then uses the resulting labeled data to bootstrap a context-based classifier. PolyNER facilitates a labeling process that is otherwise tedious and expensive. Here, we use active learning to efficiently obtain more annotations from experts and improve performance. Our approach requires just five hours of expert time to achieve discrimination capacity comparable to that of a state-of-the-art chemical NER toolkit.
Extracted statement — description: We have previously designed polyNER, a semi-automated system for efficient identification of scientific entities in text. PolyNER applies word embedding models to generate entity-rich corpora for productive expert labeling, and then uses the resulting labeled data to bootstrap a context-based classifier. PolyNER facilitates a labeling process that is otherwise tedious and expensive. Here, we use active learning to efficiently obtain more annotations from experts and improve performance.
Research field: Medicinal Chemistry and Pharmaceutics
Title: Resveratrol loaded polymeric micelles for theranostic targeting of breast cancer cells
Abstract: Treatment of breast cancer underwent extensive progress in recent years with molecularly targeted therapies. However, non-specific pharmaceutical approaches (chemotherapy) persist, inducing severe side-effects. Phytochemicals provide a promising alternative for breast cancer prevention and treatment. Specifically, resveratrol (res) is a plant-derived polyphenolic phytoalexin with potent biological activity but displays poor water solubility, limiting its clinical use. Here we have developed a strategy for delivering res using a newly synthesized nano-carrier with the potential for both diagnosis and treatment. Methods: Res-loaded nanoparticles were synthesized by the emulsion method using Pluronic F127 block copolymer and Vitamin E-TPGS. Nanoparticle characterization was performed by SEM and tunable resistive pulse sensing. Encapsulation Efficiency (EE%) and Drug Loading (DL%) content were determined by analysis of the supernatant during synthesis. Nanoparticle uptake kinetics in breast cancer cell lines MCF-7 and MDA-MB-231 as well as in MCF-10A breast epithelial cells were evaluated by flow cytometry and the effects of res on cell viability via MTT assay. Results: Res-loaded nanoparticles with spherical shape and a dominant size of 179±22 nm were produced. Res was loaded with high EE of 73±0.9% and DL content of 6.2±0.1%. Flow cytometry revealed higher uptake efficiency in breast cancer cells compared to the control. An MTT assay showed that res-loaded nanoparticles reduced the viability of breast cancer cells with no effect on the control cells. Conclusions: These results demonstrate that the newly synthesized nanoparticle is a good model for the encapsulation of hydrophobic drugs. Additionally, the nanoparticle delivers a natural compound and is highly effective and selective against breast cancer cells rendering this type of nanoparticle an excellent candidate for diagnosis and therapy of difficult to treat mammary malignancies.
Extracted statement — Highlights: An MTT assay showed that res-loaded nanoparticles reduced the viability of breast cancer cells with no effect on the control cells.
Research field: Medicinal Chemistry and Pharmaceutics
Title: Application of camelid heavy-chain variable domains (VHHs) in prevention and treatment of bacterial and viral infections
Abstract: ABSTRACT Camelid heavy-chain variable domains (VHHs) are the smallest, intact, antigen-binding units to occur in nature. VHHs possess high degrees of solubility and robustness enabling generation of multivalent constructs with increased avidity – characteristics that mark their superiority to other antibody fragments and monoclonal antibodies. Capable of effectively binding to molecular targets inaccessible to classical immunotherapeutic agents and easily produced in microbial culture, VHHs are considered promising tools for pharmaceutical biotechnology. With the aim to demonstrate the perspective and potential of VHHs for the development of prophylactic and therapeutic drugs to target diseases caused by bacterial and viral infections, this review article will initially describe the structural features that underlie the unique properties of VHHs and explain the methods currently used for the selection and recombinant production of pathogen-specific VHHs, and then thoroughly summarize the experimental findings of five distinct studies that employed VHHs as inhibitors of host–pathogen interactions or neutralizers of infectious agents. Past and recent studies suggest the potential of camelid heavy-chain variable domains as a novel modality of immunotherapeutic drugs and a promising alternative to monoclonal antibodies. VHHs demonstrate the ability to interfere with bacterial pathogenesis by preventing adhesion to host tissue and sequestering disease-causing bacterial toxins. To protect from viral infections, VHHs may be employed as inhibitors of viral entry by binding to viral coat proteins or blocking interactions with cell-surface receptors. The implementation of VHHs as immunotherapeutic agents for infectious diseases is of considerable potential and set to contribute to public health in the near future.
Extracted statement — Mechanism of Antiviral Action: Binding to viral coat proteins or blocking interactions with cell-surface receptors
Research field: Medicinal Chemistry and Pharmaceutics
Title: PLGA nanoparticles modified with a BBB-penetrating peptide co-delivering Aβ generation inhibitor and curcumin attenuate memory deficits and neuropathology in Alzheimer's disease mice
Abstract: Alzheimer's disease (AD) is the most common form of dementia, characterized by the formation of extracellular senile plaques and neuronal loss caused by amyloid β (Aβ) aggregates in the brains of AD patients. Conventional strategies failed to treat AD in clinical trials, partly due to the poor solubility, low bioavailability and ineffectiveness of the tested drugs to cross the blood-brain barrier (BBB). Moreover, AD is a complex, multifactorial neurodegenerative disease; one-target strategies may be insufficient to prevent the processes of AD. Here, we designed novel kind of poly(lactide-co-glycolic acid) (PLGA) nanoparticles by loading with Aβ generation inhibitor S1 (PQVGHL peptide) and curcumin to target the detrimental factors in AD development and by conjugating with brain targeting peptide CRT (cyclic CRTIGPSVC peptide), an iron-mimic peptide that targets transferrin receptor (TfR), to improve BBB penetration. The average particle size of drug-loaded PLGA nanoparticles and CRT-conjugated PLGA nanoparticles were 128.6 nm and 139.8 nm, respectively. The results of Y-maze and new object recognition test demonstrated that our PLGA nanoparticles significantly improved the spatial memory and recognition in transgenic AD mice. Moreover, PLGA nanoparticles remarkably decreased the level of Aβ, reactive oxygen species (ROS), TNF-α and IL-6, and enhanced the activities of super oxide dismutase (SOD) and synapse numbers in the AD mouse brains. Compared with other PLGA nanoparticles, CRT peptide modified-PLGA nanoparticles co-delivering S1 and curcumin exhibited most beneficial effect on the treatment of AD mice, suggesting that conjugated CRT peptide, and encapsulated S1 and curcumin exerted their corresponding functions for the treatment.
Extracted statement — Surface functionalized with: Brain targeting peptide CRT (cyclic CRTIGPSVC peptide)
Research field: Medicinal Chemistry and Pharmaceutics
Title: Resveratrol loaded polymeric micelles for theranostic targeting of breast cancer cells
Abstract: Treatment of breast cancer underwent extensive progress in recent years with molecularly targeted therapies. However, non-specific pharmaceutical approaches (chemotherapy) persist, inducing severe side-effects. Phytochemicals provide a promising alternative for breast cancer prevention and treatment. Specifically, resveratrol (res) is a plant-derived polyphenolic phytoalexin with potent biological activity but displays poor water solubility, limiting its clinical use. Here we have developed a strategy for delivering res using a newly synthesized nano-carrier with the potential for both diagnosis and treatment. Methods: Res-loaded nanoparticles were synthesized by the emulsion method using Pluronic F127 block copolymer and Vitamin E-TPGS. Nanoparticle characterization was performed by SEM and tunable resistive pulse sensing. Encapsulation Efficiency (EE%) and Drug Loading (DL%) content were determined by analysis of the supernatant during synthesis. Nanoparticle uptake kinetics in breast cancer cell lines MCF-7 and MDA-MB-231 as well as in MCF-10A breast epithelial cells were evaluated by flow cytometry and the effects of res on cell viability via MTT assay. Results: Res-loaded nanoparticles with spherical shape and a dominant size of 179±22 nm were produced. Res was loaded with high EE of 73±0.9% and DL content of 6.2±0.1%. Flow cytometry revealed higher uptake efficiency in breast cancer cells compared to the control. An MTT assay showed that res-loaded nanoparticles reduced the viability of breast cancer cells with no effect on the control cells. Conclusions: These results demonstrate that the newly synthesized nanoparticle is a good model for the encapsulation of hydrophobic drugs. Additionally, the nanoparticle delivers a natural compound and is highly effective and selective against breast cancer cells rendering this type of nanoparticle an excellent candidate for diagnosis and therapy of difficult to treat mammary malignancies.
Extracted statement — Highlights: Flow cytometry revealed higher uptake efficiency in breast cancer cells compared to the control.