Prophylactic Dose of Dalteparin in Pregnant Women With History of Venous Thromboembolisms and/or Thrombophilia: Real-World Data.

Women with a history of venous thromboembolisms (VTEs) and/or thrombophilia are at increased risk of VTE during pregnancy. We analysed our cohort of such women who were treated with a prophylactic doses of dalteparin. 152 pregnant women with 179 pregnancies were classified into 3 groups: (1) previous VTE without thrombophilia (122 pregnancies); (2) previous VTE with thrombophilia (26 pregnancies) and (3) thrombophilia only (31 pregnancies). They were treated with prophylactic dalteparin in the prepartum and postpartum periods or only in the postpartum period. Occurrences of symptomatic VTE and bleeding episodes were followed, as well as dalteparin discontinuation and anti-Xa activity. Symptomatic deep vein thrombosis occurred in 4 women (2.2%) with 2 episodes in group 1 (in the postpartum period) and 2 episodes in group 2 (one in the prepartum and another in the postpartum period). Seven episodes (3.9%) of minor bleeding occurred. Dalteparin was not stopped in any women. Anti-Xa levels were within the prophylactic range. Our real-world data show a low incidence of thrombosis and minor bleeding in pregnant women treated with prophylactic dalteparin. The incidence of recurrent VTE was lower than that reported in women with similar risk, but without prophylactic anticoagulation.

Retweet communities reveal the main sources of hate speech.

We address a challenging problem of identifying main sources of hate speech on Twitter. On one hand, we carefully annotate a large set of tweets for hate speech, and deploy advanced deep learning to produce high quality hate speech classification models. On the other hand, we create retweet networks, detect communities and monitor their evolution through time. This combined approach is applied to three years of Slovenian Twitter data. We report a number of interesting results. Hate speech is dominated by offensive tweets, related to political and ideological issues. The share of unacceptable tweets is moderately increasing with time, from the initial 20% to 30% by the end of 2020. Unacceptable tweets are retweeted significantly more often than acceptable tweets. About 60% of unacceptable tweets are produced by a single right-wing community of only moderate size. Institutional Twitter accounts and media accounts post significantly less unacceptable tweets than individual accounts. In fact, the main sources of unacceptable tweets are anonymous accounts, and accounts that were suspended or closed during the years 2018-2020.

Evolution of topics and hate speech in retweet network communities.

Twitter data exhibits several dimensions worth exploring: a network dimension in the form of links between the users, textual content of the tweets posted, and a temporal dimension as the time-stamped sequence of tweets and their retweets. In the paper, we combine analyses along all three dimensions: temporal evolution of retweet networks and communities, contents in terms of hate speech, and discussion topics. We apply the methods to a comprehensive set of all Slovenian tweets collected in the years 2018-2020. We find that politics and ideology are the prevailing topics despite the emergence of the Covid-19 pandemic. These two topics also attract the highest proportion of unacceptable tweets. Through time, the membership of retweet communities changes, but their topic distribution remains remarkably stable. Some retweet communities are strongly linked by external retweet influence and form super-communities. The super-community membership closely corresponds to the topic distribution: communities from the same super-community are very similar by the topic distribution, and communities from different super-communities are quite different in terms of discussion topics. However, we also find that even communities from the same super-community differ considerably in the proportion of unacceptable tweets they post.

Dynamics of online hate and misinformation.

Online debates are often characterised by extreme polarisation and heated discussions among users. The presence of hate speech online is becoming increasingly problematic, making necessary the development of appropriate countermeasures. In this work, we perform hate speech detection on a corpus of more than one million comments on YouTube videos through a machine learning model, trained and fine-tuned on a large set of hand-annotated data. Our analysis shows that there is no evidence of the presence of "pure haters", meant as active users posting exclusively hateful comments. Moreover, coherently with the echo chamber hypothesis, we find that users skewed towards one of the two categories of video channels (questionable, reliable) are more prone to use inappropriate, violent, or hateful language within their opponents' community. Interestingly, users loyal to reliable sources use on average a more toxic language than their counterpart. Finally, we find that the overall toxicity of the discussion increases with its length, measured both in terms of the number of comments and time. Our results show that, coherently with Godwin's law, online debates tend to degenerate towards increasingly toxic exchanges of views.

Community evolution in retweet networks.

Communities in social networks often reflect close social ties between their members and their evolution through time. We propose an approach that tracks two aspects of community evolution in retweet networks: flow of the members in, out and between the communities, and their influence. We start with high resolution time windows, and then select several timepoints which exhibit large differences between the communities. For community detection, we propose a two-stage approach. In the first stage, we apply an enhanced Louvain algorithm, called Ensemble Louvain, to find stable communities. In the second stage, we form influence links between these communities, and identify linked super-communities. For the detected communities, we compute internal and external influence, and for individual users, the retweet h-index influence. We apply the proposed approach to three years of Twitter data of all Slovenian tweets. The analysis shows that the Slovenian tweetosphere is dominated by politics, that the left-leaning communities are larger, but that the right-leaning communities and users exhibit significantly higher impact. An interesting observation is that retweet networks change relatively gradually, despite such events as the emergence of the Covid-19 pandemic or the change of government.

Impact investing market on Twitter: influential users and communities.

The 2008 financial crisis unveiled the intrinsic failures of the financial system as we know it. As a consequence, impact investing started to receive increasing attention, as evidenced by the high market growth rates. The goal of impact investment is to generate social and environmental impact alongside a financial return. In this paper we identify the main players in the sector and how they interact and communicate with each other. We use Twitter as a proxy of the impact investing market, and analyze relevant tweets posted over a period of ten months. We apply network, contents and sentiment analysis on the acquired dataset. Our study shows that Twitter users exhibit favourable leaning (predominantly neutral or positive) towards impact investing. Retweet communities are decentralised and include users from a variety of sectors. Despite some basic common vocabulary used by all retweet communities identified, the vocabulary and the topics discussed by each community vary largely. We note that an additional effort should be made in raising awareness about the sector, especially by policymakers and media outlets. The role of investors and the academia is also discussed, as well as the emergence of hybrid business models within the sector and its connections to the tech industry. This paper extends our previous study, one of the first analyses of Twitter activities in the impact investing market.

Profiling the EU lobby organizations in Banking and Finance.

Creating a map of actors and their leanings is important for policy makers and stakeholders in the European Commission's 'Better Regulation Agenda'. We explore publicly available information about the European lobby organizations from the Transparency Register, and from the open public consultations in the area of Banking and Finance. We consider three complementary types of information about lobbying organizations: (i) their formal categorization in the Transparency Register, (ii) their responses to the public consultations, and (iii) their self-declared goals and activities. We consider responses to the consultations as the most relevant indicator of the actual leaning of an individual lobbyist. We partition and cluster the organizations according to their demonstrated interests and the similarities among their responses. Thus each lobby organization is assigned a profile which shows its prevailing interest in consultations' topics, similar organizations in interests and responses, and a prototypical question and answer. We combine methods from network analysis, clustering, and text mining to obtain these profiles. Due to the non-homogeneous consultations, we find that it is crucial to first construct a response network based on interests in consultations topics, and only then proceed with more detailed analysis of the actual answers to consultations. The results provide a first step in the understanding of how lobby organizations engage in the policy making process.

Stance and influence of Twitter users regarding the Brexit referendum.

Social media are an important source of information about the political issues, reflecting, as well as influencing, public mood. We present an analysis of Twitter data, collected over 6 weeks before the Brexit referendum, held in the UK in June 2016. We address two questions: what is the relation between the Twitter mood and the referendum outcome, and who were the most influential Twitter users in the pro- and contra-Brexit camps? First, we construct a stance classification model by machine learning methods, and are then able to predict the stance of about one million UK-based Twitter users. The demography of Twitter users is, however, very different from the demography of the voters. By applying a simple age-adjusted mapping to the overall Twitter stance, the results show the prevalence of the pro-Brexit voters, something unexpected by most of the opinion polls. Second, we apply the Hirsch index to estimate the influence, and rank the Twitter users from both camps. We find that the most productive Twitter users are not the most influential, that the pro-Brexit camp was four times more influential, and had considerably larger impact on the campaign than the opponents. Third, we find that the top pro-Brexit communities are considerably more polarized than the contra-Brexit camp. These results show that social media provide a rich resource of data to be exploited, but accumulated knowledge and lessons learned from the opinion polls have to be adapted to the new data sources.

Development of an analytical method for the determination of polybrominated diphenyl ethers in mussels and fish by gas chromatography-Inductively coupled plasma mass spectrometry.

Six congeners of polybrominated diphenyl ethers (PBDEs): BDE 28, BDE 47, BDE 99, BDE 100, BDE 153 and BDE 154, were determined by a reliable and sensitive analytical method based on gas chromatography coupled to inductively coupled plasma mass spectrometry (GC-ICP-MS) in mussel and fish tissue samples. For their extraction, 30min of ultrasound-assisted extraction with a 25% aqueous solution of tetramethylammonium hydroxide (TMAH) and an additional 2h of mechanical shaking with tris(hydroxymethyl)aminomethane (Tris)-citrate buffer and iso-octane were applied. An effective cleaning, with minor solvent consumption, was achieved by passing the extract through a column filled with Florisil. PBDEs in the organic phase were quantified by GC-ICP-MS. Accuracy checks were performed by analyzing reference materials NIST SRM 2974a (freeze-dried mussel tissue) and SRM 1946 (fresh fish tissue homogenate) samples with a standard addition calibration method and by comparative analysis with species-specific isotope-dilution GC-ICP-MS. Good agreement of results between the determined and certified values were obtained (recoveries lied between 94 and 105%). Limits of detection (LODs) expressed on wet weight (ww) basis were 0.003ngg-1 for BDE 28, 0.006ngg-1 for BDE 47, 0.008ngg-1 for BDE 99, 0.004ngg-1 for BDE100, 0.005ngg-1 for BDE 153 and 0.009ngg-1 for BDE 154. The analytical method was applied for the determination of PBDEs in marine mussels and fish samples from the northern Adriatic Sea and fish samples from the Sava River. Among the six PBDEs congeners determined, BDE 47, BDE 100 and BDE 99 were commonly detected in the samples analysed.

Development of an analytical method for the determination of polybrominated diphenyl ethers in sewage sludge by the use of gas chromatography coupled to inductively coupled plasma mass spectrometry.

Polybrominated diphenyl ethers (PBDEs) are flame retardants. As a consequence of their widespread use, they have been released into the environment. PBDEs are lipophilic organic contaminants that enter wastewater treatment plants (WWTPs) from urban, agricultural and industrial discharges. Because of their low aqueous solubility and resistance to biodegradation, up to 90% of the PBDEs are accumulated in the sewage sludge during the wastewater treatment. To assess the possibilities for sludge re-use, a reliable determination of the concentrations of these PBDEs is of crucial importance. Six PBDE congeners (BDE 28, BDE 47, BDE 99, BDE 100, BDE 153 and BDE 154) are listed as priority substances under the EU Water Framework Directive. In the present work a simple analytical method with minimal sample-preparation steps was developed for a sensitive and reliable determination of the six PBDEs in sewage sludge by the use of gas chromatography coupled to inductively coupled plasma mass spectrometry (GC-ICP-MS). For this purpose an extraction procedure was optimised. Different extracting agents (methanol (MeOH), acetic acid (AcOH)/MeOH mixture (3:1) and 0.1 mol L(-1) hydrochloric acid (HCl) in MeOH) followed by the addition of a Tris-citrate buffer (co-extracting agent) and iso-octane were applied under different modes of extraction (mechanical shaking, microwave- and ultrasound-assisted extraction). Mechanical shaking or the microwave-assisted extraction of sewage sludge with 0.1 mol L(-1) HCl in MeOH and the subsequent addition of the Tris-citrate buffer and the iso-octane extracted the PBDEs from the complex sludge matrix most effectively. However, due to easier sample manipulation during the extraction step, mechanical shaking was used. The PBDEs in the organic phase were quantified with GC-ICP-MS by applying a standard addition calibration method. The spike recovery test (recoveries between 95 and 104%) and comparative analyses with the species-specific isotope-dilution (ID) GC-ICP-MS confirmed the accuracy of the developed analytical procedure. The procedure is sensitive (limits of detection (LODs) for PBDEs congeners between 0.2 and 0.3 ng g(-1)), repeatable and reproducible (RSDs 2.2-5.7%) and was applied for the determination of PBDEs in sewage sludge samples collected three times at the municipal WWTP over a period of 16 years.

Sentiment of Emojis.

There is a new generation of emoticons, called emojis, that is increasingly being used in mobile communications and social media. In the past two years, over ten billion emojis were used on Twitter. Emojis are Unicode graphic symbols, used as a shorthand to express concepts and ideas. In contrast to the small number of well-known emoticons that carry clear emotional contents, there are hundreds of emojis. But what are their emotional contents? We provide the first emoji sentiment lexicon, called the Emoji Sentiment Ranking, and draw a sentiment map of the 751 most frequently used emojis. The sentiment of the emojis is computed from the sentiment of the tweets in which they occur. We engaged 83 human annotators to label over 1.6 million tweets in 13 European languages by the sentiment polarity (negative, neutral, or positive). About 4% of the annotated tweets contain emojis. The sentiment analysis of the emojis allows us to draw several interesting conclusions. It turns out that most of the emojis are positive, especially the most popular ones. The sentiment distribution of the tweets with and without emojis is significantly different. The inter-annotator agreement on the tweets with emojis is higher. Emojis tend to occur at the end of the tweets, and their sentiment polarity increases with the distance. We observe no significant differences in the emoji rankings between the 13 languages and the Emoji Sentiment Ranking. Consequently, we propose our Emoji Sentiment Ranking as a European language-independent resource for automated sentiment analysis. Finally, the paper provides a formalization of sentiment and a novel visualization in the form of a sentiment bar.

Emotional Dynamics in the Age of Misinformation.

According to the World Economic Forum, the diffusion of unsubstantiated rumors on online social media is one of the main threats for our society. The disintermediated paradigm of content production and consumption on online social media might foster the formation of homogeneous communities (echo-chambers) around specific worldviews. Such a scenario has been shown to be a vivid environment for the diffusion of false claim. Not rarely, viral phenomena trigger naive (and funny) social responses-e.g., the recent case of Jade Helm 15 where a simple military exercise turned out to be perceived as the beginning of the civil war in the US. In this work, we address the emotional dynamics of collective debates around distinct kinds of information-i.e., science and conspiracy news-and inside and across their respective polarized communities. We find that for both kinds of content the longer the discussion the more the negativity of the sentiment. We show that comments on conspiracy posts tend to be more negative than on science posts. However, the more the engagement of users, the more they tend to negative commenting (both on science and conspiracy). Finally, zooming in at the interaction among polarized communities, we find a general negative pattern. As the number of comments increases-i.e., the discussion becomes longer-the sentiment of the post is more and more negative.

Extraction of temporal networks from term co-occurrences in online textual sources.

A stream of unstructured news can be a valuable source of hidden relations between different entities, such as financial institutions, countries, or persons. We present an approach to continuously collect online news, recognize relevant entities in them, and extract time-varying networks. The nodes of the network are the entities, and the links are their co-occurrences. We present a method to estimate the significance of co-occurrences, and a benchmark model against which their robustness is evaluated. The approach is applied to a large set of financial news, collected over a period of two years. The entities we consider are 50 countries which issue sovereign bonds, and which are insured by Credit Default Swaps (CDS) in turn. We compare the country co-occurrence networks to the CDS networks constructed from the correlations between the CDS. The results show relatively small, but significant overlap between the networks extracted from the news and those from the CDS correlations.

GMOseek: a user friendly tool for optimized GMO testing.

BACKGROUND: With the increasing pace of new Genetically Modified Organisms (GMOs) authorized or in pipeline for commercialization worldwide, the task of the laboratories in charge to test the compliance of food, feed or seed samples with their relevant regulations became difficult and costly. Many of them have already adopted the so called "matrix approach" to rationalize the resources and efforts used to increase their efficiency within a limited budget. Most of the time, the "matrix approach" is implemented using limited information and some proprietary (if any) computational tool to efficiently use the available data. RESULTS: The developed GMOseek software is designed to support decision making in all the phases of routine GMO laboratory testing, including the interpretation of wet-lab results. The tool makes use of a tabulated matrix of GM events and their genetic elements, of the laboratory analysis history and the available information about the sample at hand. The tool uses an optimization approach to suggest the most suited screening assays for the given sample. The practical GMOseek user interface allows the user to customize the search for a cost-efficient combination of screening assays to be employed on a given sample. It further guides the user to select appropriate analyses to determine the presence of individual GM events in the analyzed sample, and it helps taking a final decision regarding the GMO composition in the sample. GMOseek can also be used to evaluate new, previously unused GMO screening targets and to estimate the profitability of developing new GMO screening methods. CONCLUSION: The presented freely available software tool offers the GMO testing laboratories the possibility to select combinations of assays (e.g. quantitative real-time PCR tests) needed for their task, by allowing the expert to express his/her preferences in terms of multiplexing and cost. The utility of GMOseek is exemplified by analyzing selected food, feed and seed samples from a national reference laboratory for GMO testing and by comparing its performance to existing tools which use the matrix approach. GMOseek proves superior when tested on real samples in terms of GMO coverage and cost efficiency of its screening strategies, including its capacity of simple interpretation of the testing results.

Cohesiveness in financial news and its relation to market volatility.

Motivated by recent financial crises, significant research efforts have been put into studying contagion effects and herding behaviour in financial markets. Much less has been said regarding the influence of financial news on financial markets. We propose a novel measure of collective behaviour based on financial news on the Web, the News Cohesiveness Index (NCI), and we demonstrate that the index can be used as a financial market volatility indicator. We evaluate the NCI using financial documents from large Web news sources on a daily basis from October 2011 to July 2013 and analyse the interplay between financial markets and finance-related news. We hypothesise that strong cohesion in financial news reflects movements in the financial markets. Our results indicate that cohesiveness in financial news is highly correlated with and driven by volatility in financial markets.

Development of an analytical procedure for the determination of polybrominated diphenyl ethers in environmental water samples by GC-ICP-MS.

Polybrominated diphenyl ethers (PBDEs) are flame retardants, which due to their widespread use are frequently present as pollutants in the environment. In the EU Water Framework Directive (WFD) six PBDE congeners (BDE 28, BDE47, BDE 99, BDE 100, BDE 153 and BDE 154) are listed as priority substances. The uncertainty of the analytical method used for their determination in water samples at environmental quality standard (EQS) level (0.5 ng L(-1) for the ΣPBDEs) should be equal or less than 50% and the limit of quantification (LOQ) for ΣPBDEs below 0.15 ng L(-1). To meet these requirements, an analytical procedure for the determination of these six PBDEs in environmental water samples by gas chromatography-inductively coupled plasma mass spectrometry (GC-ICP-MS) was developed. The acidification of water samples to pH 2 maintained the stability of PBDEs for at least 20 days. The use of Tris-citrate buffer enabled efficient desorption of PBDEs from suspended particulate matter (SPM) and humic acids (HA), and their further quantitative solvent extraction into 2 mL of iso-octane. When 300 mL of water sample was used for analysis and the organic phase concentrated to 25 µL, the expanded uncertainty for determination of PBDEs at EQS level was found to be around 40% (a coverage factor for a confidence level of 95%, k=2), and the LOQ for the ΣPBDEs 0.109 ng L(-1). Finally, to demonstrate the applicability of the newly developed GC-ICP-MS procedure, PBDEs were determined in river and sea water samples.

SegMine workflows for semantic microarray data analysis in Orange4WS.

BACKGROUND: In experimental data analysis, bioinformatics researchers increasingly rely on tools that enable the composition and reuse of scientific workflows. The utility of current bioinformatics workflow environments can be significantly increased by offering advanced data mining services as workflow components. Such services can support, for instance, knowledge discovery from diverse distributed data and knowledge sources (such as GO, KEGG, PubMed, and experimental databases). Specifically, cutting-edge data analysis approaches, such as semantic data mining, link discovery, and visualization, have not yet been made available to researchers investigating complex biological datasets. RESULTS: We present a new methodology, SegMine, for semantic analysis of microarray data by exploiting general biological knowledge, and a new workflow environment, Orange4WS, with integrated support for web services in which the SegMine methodology is implemented. The SegMine methodology consists of two main steps. First, the semantic subgroup discovery algorithm is used to construct elaborate rules that identify enriched gene sets. Then, a link discovery service is used for the creation and visualization of new biological hypotheses. The utility of SegMine, implemented as a set of workflows in Orange4WS, is demonstrated in two microarray data analysis applications. In the analysis of senescence in human stem cells, the use of SegMine resulted in three novel research hypotheses that could improve understanding of the underlying mechanisms of senescence and identification of candidate marker genes. CONCLUSIONS: Compared to the available data analysis systems, SegMine offers improved hypothesis generation and data interpretation for bioinformatics in an easy-to-use integrated workflow environment.

Gene expression data analysis using closed item set mining for labeled data.

This article presents an approach to microarray data analysis using discretised expression values in combination with a methodology of closed item set mining for class labeled data (RelSets). A statistical 2 x 2 factorial design analysis was run in parallel. The approach was validated on two independent sets of two-color microarray experiments using potato plants. Our results demonstrate that the two different analytical procedures, applied on the same data, are adequate for solving two different biological questions being asked. Statistical analysis is appropriate if an overview of the consequences of treatments and their interaction terms on the studied system is needed. If, on the other hand, a list of genes whose expression (upregulation or downregulation) differentiates between classes of data is required, the use of the RelSets algorithm is preferred. The used algorithms are freely available upon request to the authors.

Semantic subgroup discovery: using ontologies in microarray data analysis.

A major challenge for next generation data mining systems is creative knowledge discovery from highly diverse and distributed data and knowledge sources. This paper presents an approach to information fusion and creative knowledge discovery from semantically annotated knowledge sources: by using ontology information as background knowledge for semantic subgroup discovery, rules are constructed that allow the expert to recognize gene groups that are differentially expressed in different types of tissues. The paper presents also current directions in creative knowledge discovery through bisociative data analysis, illustrated on a systems biology case study.

CSM-SD: methodology for contrast set mining through subgroup discovery.

This paper addresses a data analysis task, known as contrast set mining, whose goal is to find differences between contrasting groups. As a methodological novelty, it is shown that this task can be effectively solved by transforming it to a more common and well-understood subgroup discovery task. The transformation is studied in two learning settings, a one-versus-all and a pairwise contrast set mining setting, uncovering the conditions for each of the two choices. Moreover, the paper shows that the explanatory potential of discovered contrast sets can be improved by offering additional contrast set descriptors, called the supporting factors. The proposed methodology has been applied to uncover distinguishing characteristics of two groups of brain stroke patients, both with rapidly developing loss of brain function due to ischemia:those with ischemia caused by thrombosis and by embolism, respectively.