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CO2 hydrate offers some substantial applications for Carbon Capture and Storage (CCS). While CO2 hydrate chemistry and CO2 capture are reviewed in part 1 of this review, CO2 transportation and storage are discussed in this part. Basically, CO2 transportation is required between CO2 capture plants and CO2 sequestration sites. It is imperative to acknowledge that most strategies for achieving deep decarbonization are linked to the expansion of the current transport infrastructure. When dealing with substantial distances between CO2 capture plants and CO2 sequestration sites, the expenses associated with CO2 transportation can surpass the capture process itself. Therefore, despite the benefits of CO2 hydrates in CCS, challenges, such as flow assurance issues, may arise. For example, CO2 hydrate formation can lead to pipeline blockages, emphasizing the need for CO2 gas hydrate flow assurance study as discussed in this part. Additionally, site selection for CO2 storage requires careful consideration. Geological storage, whether in hydrate form or through the injection of CO2 or high-CO2 content mixtures, offers potential advantages, such as long-term storage and self-sealing capabilities. However, there are some challenges like CO2 hydrate processes in porous media, injectivity, flow behaviour in hydrate reservoirs, mechanical behaviour, etc., which are discussed in this review.

Gas hydrates are solid crystalline compounds formed by water and gas molecules through molecular interactions, typically at low temperatures and high pressures. While gas hydrates are generally known as flow assurance challenges for the oil and gas industries (e.g., pipeline blockages), numerous studies have shown the potential application of gas hydrate in carbon capture and storage (CCS). Due to the more thermodynamic stability of CO2 hydrate compared to other industrial emission gas components like nitrogen, CO2 hydrates have emerged as a viable mechanism for CO2 capture. Moreover, a large volume of CO2 can be stored securely in the stable structure of gas hydrates, providing an additional benefit for CO2 storage in geological formations. Thus, gas hydrates can be suggested as a technology for mitigating CO2 emissions. Notwithstanding the CO2 hydrate advantages in CCS, they may also present some challenges, particularly in terms of flow assurance. For example, CO2 hydrate formation during CO2 transportation can cause a serious pipeline blockage. Therefore, the fundamental understanding of gas hydrates is crucial for CCS. In the first part of this review, the principle on gas hydrates (especially CO2 hydrates) and CO2 hydrate-based carbon capture are discussed.

Propellers are important sources of ship vibrations. Experimental model tests in tow-tank facilities are established methods to assess the design of propellers and ship hulls. However, during these tests, the ship model structural dynamics may affect the results when measuring the propeller-induced hull pressure pulses. This paper presents the results of towing tests on a scaled model of a bulk carrier at the atmospheric towing tank at NRC-OCRE combined with Operational Deflection Shape Analysis (ODS) of the model’s structures. The model was equipped with a four-blade fixed-pitch propeller. Twelve pressure sensors and two sets of eight accelerometers measured propeller-induced pressures and vibrations during the tests. Further, an Experimental Modal Analysis (EMA) identified the model’s natural frequencies and mode shapes. The towing tests were performed at different model hull speeds (Vm) and propeller speeds. The signals acquired were post-processed in MATLAB® to find the pressure amplitudes, perform an ODS, assess the structural dynamics, and check possible resonances. The results indicate that although the local plating does not resonate with the exciting pressure pulsations, the model structure has global modes in the frequency range of interest.

The status-quo of misinformation moderation is a central authority, usually social platforms, deciding what content constitutes misinformation and how it should be handled. However, to preserve users' autonomy, researchers have explored democratized misinformation moderation. One proposition is to enable users to assess content accuracy and specify whose assessments they trust. We explore how these affordances can be provided on the web, without cooperation from the platforms where users consume content. We present a browser extension that empowers users to assess the accuracy of any content on the web and shows the user assessments from their trusted sources in-situ. Through a two-week user study, we report on how users perceive such a tool, the kind of content users want to assess, and the rationales they use in their assessments. We identify implications for designing tools that enable users to moderate content for themselves with the help of those they trust.

A new approach to online discussion, which situates student discussions in the margins of the course content, can enhance student engagement with course materials. However, in high-enrollment classes, the large number of comments can overwhelm and intimidate students. Some become frustrated by the volume of potential online interactions and by a perceived lack of immediate relevance to their studies. Likewise, instructors are disappointed when outstanding discussions, that they deem valuable for all to see, get lost in the clutter. To address these challenges, we propose visual spotlighting mechanisms for increasing the saliency of selected comments. We piloted and deployed multiple designs in two high-enrollment biology courses at a large public university in the United States. Interviews, surveys, and a controlled experiment show that spotlighting relevant comments in heavily annotated texts positively affects students' engagement, measured in terms of their attention to comments, and their reported sense of validation and pride. Students also reported their preferences for certain spotlighting designs.

Maintenance and Rehabilitation (M&R) scheduling is one of the vital aspects of a pavement management system (PMS). This study aims to establish accurate M&R plans for a large-scale pavement network. To this intent, parameters affecting pavement deterioration were identified from the literature, then Random Forest Regression was employed to determine the effective features for pavement deterioration modelling. An accurate pavement deterioration function was generated by applying significant features. The most robust metaheuristic and evolutionary algorithms were selected and adjusted to solve the M&R scheduling optimisation problem, including the Water Cycle Algorithm (WCA), Arithmetic Optimisation Algorithm (AOA), Differential Evolutionary (DE), Ant Colony Optimisation (ACO), Particle Swarm Optimisation (PSO), and Genetic Algorithm (GA). The performance of the mentioned algorithms was compared to help researchers and decision-makers to select the appropriate algorithm for M&R scheduling optimisation. WCA and AOA showed to have the best performance among the adapted algorithms. Compared to AOA, DE, ACO, PSO, and GA, WCA's objective function was calculated to be 45%, 74%, 74%, 77% and 83% less, while its M&R cost was cheaper by 13%, 16%, 27%, 19%, and 18%, respectively.

In this study, sustainable mixture designs of three concrete types, including fly ash concrete, silica fume concrete, and ground granulated blast furnace slag concrete, were investigated. To this end, the compressive strength formulas of each concrete type made with supplementary cementitious materials were obtained by introducing a new machine learning algorithm, called coyote optimization programming. The accuracy of this algorithm proved to be greater than that of conventional and recently developed machine learning methods. An optimization problem is modeled, in which the compressive strengths, price, and environmental impact of the sustainable concrete mixture designs were estimated using global warming potential, energy consumption, and material consumption as the sustainability parameters. Results reveal that increasing the compressive strength reduces the sustainability of concrete, and thus, manufacturing concrete with a higher compressive strength than the one obtained from the design process contradicts the concrete’s performance. Moreover, the 30-MPa sustainable fly ash concrete was proven to be the most sustainable mix with a gray relational grade of 1. This optimal mixture designed in this study can decrease the unit cost, global warming potential, energy consumption, and material consumption by 36.6%, 51%, 43%, and 11%, respectively. Research highlights: A novel machine learning method called coyote optimization programming was introduced in this study.Applying optimization techniques to design concrete mixture proportions can reduce the unit cost by 36.6%.The introduced approach can decrease the global warming potential, energy consumption, and material consumption by 51%, 43%, and 11%, respectively.The application of supplementary cementitious materials in the concrete mixtures significantly enhances sustainability.

While digital assistants are increasingly used to help with various productivity tasks, less attention has been paid to employing them in the domain of business documents. To build an agent that can handle users' information needs in this domain, we must first understand the types of assistance that users desire when working on their documents. In this work, we present results from two user studies that characterize the information needs and queries of authors, reviewers, and readers of business documents. In the first study, we used experience sampling to collect users' questions in-situ as they were working with their documents, and in the second, we built a human-in-the-loop document Q&A system which rendered assistance with a variety of users' questions. Our results have implications for the design of document assistants that complement AI with human intelligence including whether particular skillsets or roles within the document are needed from human respondents, as well as the challenges around such systems.

Neurodegenerative diseases such as Parkinson’s and Alzheimer’s affect millions of people and are uniformly untreatable. A significant unmet need exists for faster and higher throughput approaches to modeling neurodegenerative diseases and identifying candidate small molecule therapies. Herein, I present a new chemical biology approach to identifying small molecules candidates for modeling and treating neurodegenerative diseases, using pontocerebellar hypoplasia type 1B and amyotrophic lateral sclerosis as test cases. The approach combines in silico rational design, in vitro biophysical testing, and in vivo assays in zebrafish to yield a powerful discovery platform for small molecule models and therapies for neurodegenerative disease.

Personality detection is an old topic in psychology and Automatic Personality Prediction (or Perception) (APP) is the automated (computationally) forecasting of the personality on different types of human generated/exchanged contents (such as text, speech, image, video). The principal objective of this study is to offer a shallow (overall) review of natural language processing approaches on APP since 2010. With the advent of deep learning and following it transfer-learning and pre-trained model in NLP, APP research area has been a hot topic, so in this review, methods are categorized into three; pre-trained independent, pre-trained model based, multimodal approaches. Also, to achieve a comprehensive comparison, reported results are informed by datasets., Comment: This is a preprint of an article published in "Journal of Computational Social Science". The final authenticated version is available online at: https://doi.org/10.1007/s42001-022-00178-4

© 2020 ACM. Personalized document recommendation systems aim to provide users with a quick shortcut to the documents they may want to access next, usually with an explanation about why the document is recommended. Previous work explored various methods for better recommendations and better explanations in different domains. However, there are few efforts that closely study how users react to the recommended items in a document recommendation scenario. We conducted a large-scale log study of users' interaction behavior with the explainable recommendation on one of the largest cloud document platforms office.com. Our analysis reveals a number of factors, including display position, file type, authorship, recency of last access, and most importantly, the recommendation explanations, that are associated with whether users will recognize or open the recommended documents. Moreover, we specifically focus on explanations and conduct an online experiment to investigate the influence of different explanations on user behavior. Our analysis indicates that the recommendations help users access their documents significantly faster, but sometimes users miss a recommendation and resort to other more complicated methods to open the documents. Our results suggest opportunities to improve explanations and more generally the design of systems that provide and explain recommendations for documents.

The fertility influence of spousal intimate relationships is unknown. Drawing on the Giddens’s theory of transformation of intimacy, this study proposed a hypothesis that couples supporting egalitarian intimate relationships, with a greater risk profile attached to the relationship, and having less attachments to the external normative pressures shaping marital relations, are more likely to have low-fertility intentions and preferences. Using data from a self-administered pilot survey (n = 375 prospective grooms and brides) designed by the authors, and employing multivariate regression models, we found that the lower attachment to external social forces in mate selection was associated with the lower ideal number of children, and those with a greater spousal relational egalitarianism and a higher risk profile attached to their relationships preferred lower number of children and were less likely to intend to have children after marriage. The study sheds new light on the determinants of low fertility., Social Sciences and Humanities Research Council of Canadahttps://doi.org/10.13039/501100000155, Nipissing Universityhttps://doi.org/10.13039/100009369, Peer Reviewed

Separating the effects of human activities/climate change on lotic ecosystems is one of the important components of environmental management as well as water resources maintenance. A Mann–Kendall analysis of hydro-climatic parameters and vegetation cover (VC), calculated using normalized difference vegetation index (NDVI), during the period 1985–2014 suggested a significant decrease and increase of river flow and temperature at p < 0.01, as well as an insignificant decline and increment of precipitation and VC, respectively within the arid and semi-arid region, that is, Zilbier River basin in north-western Iran. A separation of human activities/climate change effects on the reduction of river flow was carried out using three alternative approaches: a simple eco-hydrological method (coupled water-energy budget (ECH)), elasticity-based analysis (Budyko framework (EBA)), and a process-based watershed model based on the Soil and Water Assessment Tool (SWAT). The efficiency of these approaches was assessed over the periods 1985–1994, 1995–2014, and under five potential near-future human activities/climate change scenarios (S1–S5) by 2030. The results indicated that the climate change impacts on river flow was more severe than those of human activities. Climate change contributed to an average of 83.6% and 77.0% reduction in river flow in the past and the realistic future scenarios (i.e., S4 and S5), respectively, while human activities accounted for 16.4% and 30%. According to our findings, despite the fact that ECH results are more in line with the SWAT model, in case of physical characterization inaccessibility, ECH and EBA (as simple descriptive and quantitative models, respectively) can be used to separate, simulate and project the impacts of human activities and climate changes on river flow.

© 2020 ACM. Personalized document recommendation systems aim to provide users with a quick shortcut to the documents they may want to access next, usually with an explanation about why the document is recommended. Previous work explored various methods for better recommendations and better explanations in different domains. However, there are few efforts that closely study how users react to the recommended items in a document recommendation scenario. We conducted a large-scale log study of users' interaction behavior with the explainable recommendation on one of the largest cloud document platforms office.com. Our analysis reveals a number of factors, including display position, file type, authorship, recency of last access, and most importantly, the recommendation explanations, that are associated with whether users will recognize or open the recommended documents. Moreover, we specifically focus on explanations and conduct an online experiment to investigate the influence of different explanations on user behavior. Our analysis indicates that the recommendations help users access their documents significantly faster, but sometimes users miss a recommendation and resort to other more complicated methods to open the documents. Our results suggest opportunities to improve explanations and more generally the design of systems that provide and explain recommendations for documents.

When users on social media share content without considering its veracity, they may unwittingly be spreading misinformation. In this work, we investigate the design of lightweight interventions that nudge users to assess the accuracy of information as they share it. Such assessment may deter users from posting misinformation in the first place, and their assessments may also provide useful guidance to friends aiming to assess those posts themselves. In support of lightweight assessment, we first develop a taxonomy of the reasons why people believe a news claim is or is not true; this taxonomy yields a checklist that can be used at posting time. We conduct evaluations to demonstrate that the checklist is an accurate and comprehensive encapsulation of people's free-response rationales. In a second experiment, we study the effects of three behavioral nudges---1) checkboxes indicating whether headings are accurate, 2) tagging reasons (from our taxonomy) that a post is accurate via a checklist and 3) providing free-text rationales for why a headline is or is not accurate---on people's intention of sharing the headline on social media. From an experiment with 1668 participants, we find that both providing accuracy assessment and rationale reduce the sharing of false content. They also reduce the sharing of true content, but to a lesser degree that yields an overall decrease in the fraction of shared content that is false. Our findings have implications for designing social media and news sharing platforms that draw from richer signals of content credibility contributed by users. In addition, our validated taxonomy can be used by platforms and researchers as a way to gather rationales in an easier fashion than free-response.

Background: Keyword extraction is a popular research topic in the field of natural language processing. Keywords are terms that describe the most relevant information in a document. The main problem that researchers are facing is how to efficiently and accurately extract the core keywords from a document. However, previous keyword extraction approaches have utilized the text and graph features, there is the lack of models that can properly learn and combine these features in a best way. Methods: In this paper, we develop a multimodal Key-phrase extraction approach, namely Phraseformer, using transformer and graph embedding techniques. In Phraseformer, each keyword candidate is presented by a vector which is the concatenation of the text and structure learning representations. Phraseformer takes the advantages of recent researches such as BERT and ExEm to preserve both representations. Also, the Phraseformer treats the key-phrase extraction task as a sequence labeling problem solved using classification task. Results: We analyze the performance of Phraseformer on three datasets including Inspec, SemEval2010 and SemEval 2017 by F1-score. Also, we investigate the performance of different classifiers on Phraseformer method over Inspec dataset. Experimental results demonstrate the effectiveness of Phraseformer method over the three datasets used. Additionally, the Random Forest classifier gain the highest F1-score among all classifiers. Conclusions: Due to the fact that the combination of BERT and ExEm is more meaningful and can better represent the semantic of words. Hence, Phraseformer significantly outperforms single-modality methods.

When users on social media share content without considering its veracity, they may unwittingly be spreading misinformation. In this work, we investigate the design of lightweight interventions that nudge users to assess the accuracy of information as they share it. Such assessment may deter users from posting misinformation in the first place, and their assessments may also provide useful guidance to friends aiming to assess those posts themselves. In support of lightweight assessment, we first develop a taxonomy of the reasons why people believe a news claim is or is not true; this taxonomy yields a checklist that can be used at posting time. We conduct evaluations to demonstrate that the checklist is an accurate and comprehensive encapsulation of people's free-response rationales. In a second experiment, we study the effects of three behavioral nudges -- 1) checkboxes indicating whether headings are accurate, 2) tagging reasons (from our taxonomy) that a post is accurate via a checklist and 3) providing free-text rationales for why a headline is or is not accurate -- on people's intention of sharing the headline on social media. From an experiment with 1668 participants, we find that both providing accuracy assessment and rationale reduce the sharing of false content. They also reduce the sharing of true content, but to a lesser degree that yields an overall decrease in the fraction of shared content that is false. Our findings have implications for designing social media and news sharing platforms that draw from richer signals of content credibility contributed by users. In addition, our validated taxonomy can be used by platforms and researchers as a way to gather rationales in an easier fashion than free-response., Comment: In CSCW'21

Human personality is significantly represented by those words which he/she uses in his/her speech or writing. As a consequence of spreading the information infrastructures (specifically the Internet and social media), human communications have reformed notably from face to face communication. Generally, Automatic Personality Prediction (or Perception) (APP) is the automated forecasting of the personality on different types of human generated/exchanged contents (like text, speech, image, video, etc.). The major objective of this study is to enhance the accuracy of APP from the text. To this end, we suggest five new APP methods including term frequency vector-based, ontology-based, enriched ontology-based, latent semantic analysis (LSA)-based, and deep learning-based (BiLSTM) methods. These methods as the base ones, contribute to each other to enhance the APP accuracy through ensemble modeling (stacking) based on a hierarchical attention network (HAN) as the meta-model. The results show that ensemble modeling enhances the accuracy of APP., Comment: This is a preprint of an article published in "Neural Computing and Applications"

With technologies that have democratized the production and reproduction of information, a significant portion of daily interacted posts in social media has been infected by rumors. Despite the extensive research on rumor detection and verification, so far, the problem of calculating the spread power of rumors has not been considered. To address this research gap, the present study seeks a model to calculate the Spread Power of Rumor (SPR) as the function of content-based features in two categories: False Rumor (FR) and True Rumor (TR). For this purpose, the theory of Allport and Postman will be adopted, which it claims that importance and ambiguity are the key variables in rumor-mongering and the power of rumor. Totally 42 content features in two categories "importance" (28 features) and "ambiguity" (14 features) are introduced to compute SPR. The proposed model is evaluated on two datasets, Twitter and Telegram. The results showed that (i) the spread power of False Rumor documents is rarely more than True Rumors. (ii) there is a significant difference between the SPR means of two groups False Rumor and True Rumor. (iii) SPR as a criterion can have a positive impact on distinguishing False Rumors and True Rumors., Comment: This is a preprint of an article published in "Journal of Ambient Intelligence and Humanized Computing"

Over the last two decades, we have seen extensive industrial research in the area of computational advertising. In this paper, our goal is to study the performance of various online learning algorithms to identify and display the best ads/offers with the highest conversion rates to web users. We formulate our ad-selection problem as a Multi-Armed Bandit problem which is a classical paradigm in Machine Learning. We have been applying machine learning, data mining, probability, and statistics to analyze big data in the ad-tech space and devise efficient ad selection strategies. This article highlights some of our findings in the area of computational advertising from 2011 to 2015.

Seyed Mostafa Mir,1– 3 Sadra Samavarchi Tehrani,4,5 Golnaz Goodarzi,4,5 Zahra Jamalpoor,1 Jahanbakhsh Asadi,6 Nafiseh Khelghati,7 Durdi Qujeq,2,3 Mahmood Maniati8 1Trauma Research Center, AJA University of Medical Sciences, Tehran, Iran; 2Student Research Committee, Babol University of Medical Sciences, Babol, Iran; 3Cellular and Molecular Biology Research Center, Health Research Institute, Babol University of Medical Sciences, Babol, Iran; 4Department of Clinical Biochemistry, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran; 5Student Scientific Research Center, Tehran University of Medical Sciences, Tehran, Iran; 6Metabolic Disorders Research Center, Golestan University of Medical Sciences, Gorgan, Iran; 7Department of Clinical Biochemistry, School of Medicine, Urmia University of Medical Sciences, Urmia, Iran; 8School of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, IranCorrespondence: Zahra JamalpoorTrauma Research Center, AJA University of Medical Sciences, Tehran, IranTel +989126011941Email z_jamalpoor2000@yahoo.comAbstract: Different factors influence the development‎ and control of ageing. It is well known that progressive telomere shorting is one of the molecular mechanisms underlying ageing. The shelterin complex consists of six telomere-specific proteins which are involved in the protection of chromosome ends. More particularly, this vital complex protects the telomeres from degradation, prevents from activation of unwanted repair systems, regulates the activity of telomerase, and has a crucial role in cellular senescent and ageing-related pathologies. This review explores the organization and function of telomeric DNA along with the mechanism of telomeres during ageing, followed by a discussion of the critical role of shelterin components and their changes during ageing.Keywords: ageing, sheltering complex, telomeres

Recent years have seen the extensive use of phylogeographic approaches to unveil the dispersal history of virus epidemics. Spatially explicit reconstructions of viral spread represent valuable sources of lineage movement data that can be exploited to investigate the impact of underlying environmental layers on the dispersal of pathogens. Here, we performed phylogeographic inference and applied different post hoc approaches to analyse a new and comprehensive data set of viral genomes to elucidate the dispersal history and dynamics of rabies virus (RABV) in Iran, which have remained largely unknown. We first analysed the association between environmental factors and variations in dispersal velocity among lineages. Second, we present, test and apply a new approach to study the link between environmental conditions and the dispersal direction of lineages. The statistical performance (power of detection, false-positive rate) of this new method was assessed using simulations. We performed phylogeographic analyses of RABV genomes, allowing us to describe the large diversity of RABV in Iran and to confirm the cocirculation of several clades in the country. Overall, we estimate a relatively high lineage dispersal velocity, similar to previous estimates for dog rabies virus spread in northern Africa. Finally, we highlight a tendency for RABV lineages to spread in accessible areas associated with high human population density. Our analytical workflow illustrates how phylogeographic approaches can be used to investigate the impact of environmental factors on several aspects of viral dispersal dynamics., SCOPUS: ar.j, info:eu-repo/semantics/published

Speech Acts (SAs) are one of the important areas of pragmatics, which give us a better understanding of the state of mind of the people and convey an intended language function. Knowledge of the SA of a text can be helpful in analyzing that text in natural language processing applications. This study presents a dictionary-based statistical technique for Persian SA recognition. The proposed technique classifies a text into seven classes of SA based on four criteria: lexical, syntactic, semantic, and surface features. WordNet as the tool for extracting synonym and enriching features dictionary is utilized. To evaluate the proposed technique, we utilized four classification methods including Random Forest (RF), Support Vector Machine (SVM), Naive Bayes (NB), and K-Nearest Neighbors (KNN). The experimental results demonstrate that the proposed method using RF and SVM as the best classifiers achieved a state-of-the-art performance with an accuracy of 0.95 for classification of Persian SAs. Our original vision of this work is introducing an application of SA recognition on social media content, especially the common SA in rumors. Therefore, the proposed system utilized to determine the common SAs in rumors. The results showed that Persian rumors are often expressed in three SA classes including narrative, question, and threat, and in some cases with the request SA., Comment: Published Link: http://jscit.nit.ac.ir/article_103557.html

Trypanosoma brucei is the causative agent of sleeping sickness, a major threat to economic stability and public health across sub-Saharan Africa. This protozoan parasite features a complex digenetic life cycle that alternates between the tsetse fly (Glossina spp.) vector and the mammalian host. Infections in both the fly and vector are characterized by intimate contact with tissue surfaces. Recent studies have shown that when cultivated on semisolid agarose surfaces, T. brucei engages in social behavior, termed social motility, which manifests as parasites assembling into groups that move collectively to form symmetrical arrays of radial projections. Herein we describe two novel modulators of social motility in T. brucei, the zinc finger protein ZC3H34 and the prolyl isomerase cyclophilin A (CyPA). Using RNA interference, we show that knockdown of either ZC3H34 or CyPA delays social motility. The delay in social motility observed with CyPA knockdown is not due to decreased propulsive motility, although ZC3H34-depleted parasites do show reduced propulsive motility. Our study provides new insights into the biochemistry of trypanosome social behavior and identifies potential targets for chemical inhibition of vector-to-host parasite transmission.

Mutations of EXOSC3 have been linked to the rare neurological disorder known as Pontocerebellar Hypoplasia type 1B (PCH1B). EXOSC3 is one of three putative RNA-binding structural cap proteins that guide RNA into the RNA exosome, the cellular machinery that degrades RNA. Using RNAcompete, we identified a G-rich RNA motif binding to EXOSC3. Surface plasmon resonance (SPR) and microscale thermophoresis (MST) indicated an affinity in the low micromolar range of EXOSC3 for long and short G-rich RNA sequences. Although several PCH1B-causing mutations in EXOSC3 did not engage a specific RNA motif as shown by RNAcompete, they exhibited lower binding affinity to G-rich RNA as demonstrated by MST. To test the hypothesis that modification of the RNA-protein interface in EXOSC3 mutants may be phenocopied by small molecules, we performed an in-silico screen of 50 000 small molecules and used enzyme-linked immunosorbant assays (ELISAs) and MST to assess the ability of the molecules to inhibit RNA-binding by EXOSC3. We identified a small molecule, EXOSC3-RNA disrupting (ERD) compound 3 (ERD03), which ( i) bound specifically to EXOSC3 in saturation transfer difference nuclear magnetic resonance (STD-NMR), ( ii) disrupted the EXOSC3-RNA interaction in a concentration-dependent manner, and ( iii) produced a PCH1B-like phenotype with a 50% reduction in the cerebellum and an abnormally curved spine in zebrafish embryos. This compound also induced modification of zebrafish RNA expression levels similar to that observed with a morpholino against EXOSC3. To our knowledge, this is the first example of a small molecule obtained by rational design that models the abnormal developmental effects of a neurodegenerative disease in a whole organism.

The aim of this study was to investigate the evolution with time of ceftiofur-resistant Escherichia coli clinical isolates from pigs in Quebec, Canada, between 1997 and 2012 with respect to pathotypes, clones and antimicrobial resistance. Eighty-five ceftiofur-resistant E. coli isolates were obtained from the OIE (World Organisation for Animal Health) Reference Laboratory for Escherichia coli. The most prevalent pathovirotypes were enterotoxigenic E. coli (ETEC): F4 (40%), extraintestinal pathogenic E. coli (ExPEC) (16.5%) and Shiga toxin-producing E. coli (STEC): F18 (8.2%). Susceptibility testing to 15 antimicrobial agents revealed a high prevalence of resistance to 13 antimicrobials, with all isolates being multidrug-resistant. bla(CMY-2) (96.5%) was the most frequently detected beta-lactamase gene, followed by bla(TEM) (49.4%) and bla(CTX-M) (3.5%). Pulsed-field gel electrophoresis (PFGE) applied to 45 representative E. coli isolates revealed that resistance to ceftiofur is spread both horizontally and clonally. In addition, the emergence of extended-spectrum beta-lactamase-producing E. coli isolates carrying bla(CTX-M) was observed in 2011 and 2012 in distinct clones. The most predominant plasmid incompatibility (Inc) groups were IncFIB, IncI1, IncA/C and IncFIC. Resistance to gentamicin, kanamycin and chloramphenicol as well as the frequency of bla(TEM) and IncA/C significantly decreased over the study period, whereas the frequency of IncI1 and multidrug resistance to seven antimicrobial categories significantly increased. These findings reveal that extended-spectrum cephalosporin-resistant porcine E. coli isolates in Quebec belong to several different clones with diverse antimicrobial resistance patterns and plasmids. Furthermore, bla(CMY-2) was the major beta-lactamase gene in these isolates. From 2011, we report the emergence of bla(CTX-M) in distinct clones.

This project was designed to help Hi3G Access AB’s IT department to get a better overview of the testing state in the different cross functional teams. Furthermore it was designed to identify teams and system where regression tests had the potential and the need of being automated. This information was gathered by conducting a survey designed for all the software testers at the company, followed by performing semi-structured interviews with one tester from each team. For the most suitable system and test case, an automation solution prototype was created with an automation tool using an agile test automation process model. The automation tool was evaluated regarding complexity, userfriendliness, available documentation and performance. The team and the system decided to be most prioritized and suitable was the team that worked with the Enterprise resource planning (ERP) system. The automation tool used for the prototype was Oracle applications testing suite (OATS). The project revealed that many teams at Hi3G Access AB were quite far in the automation process. The tool evaluated in the project presented itself to be straightforward and easy to learn/use. However the tool was missing a widespread discussion board and also lacked free support., Projektet var utformat för att hjälpa Hi3G Access ABs IT-avdelning få en bättre överblick gällande de olika korsfunktionella gruppers testsituation. Vidare var målet att identifiera team och system där regressionstester hade potential och behov av automatisering. Denna information samlades in genom att utföra en enkät avsedd för alla mjukvarutestare på företaget, följt av att utföra semistrukturerade intervjuer med en testare från varje team.För det mest lämpliga system och testfall skapades en automatiserad prototyplösning genom en agil testautomatiseringsmodell. Automatiseringsverktyget utvärderas gällande komplexitet, användarvänlighet, tillgänglig dokumentation och prestanda.Teamet och systemet som valdes som mest prioriterat och lämpligt var teamet som arbetade med Enterprise resource planning (ERP) systemet. Automatiseringsverktyget som användes för prototypen var Oracle Applications Testing Suite (OATS).Projektet visade att många team på Hi3G Access AB hade kommit ganska långt i automatiseringsprocessen. Verktyget som utvärderades visade sig vara okomplicerat och lätt att både lära sig och att använda. Däremot så var avsaknaden av ett utbrett diskussionsforum ett faktum samt att produkten saknade fri support.

Twitter as a new form of social media potentially contains useful information that opens new opportunities for content analysis on tweets. This paper examines the predictive power of Twitter regarding the US presidential election of 2012. For this study, we analyzed 32 million tweets regarding the US presidential election by employing a combination of machine learning techniques. We devised an advanced classifier for sentiment analysis in order to increase the accuracy of Twitter content analysis. We carried out our analysis by comparing Twitter results with traditional opinion polls. In addition, we used the Latent Dirichlet Allocation model to extract the underlying topical structure from the selected tweets. Our results show that we can determine the popularity of candidates by running sentiment analysis. We can also uncover candidates popularities in the US states by running the sentiment analysis algorithm on geo-tagged tweets. To the best of our knowledge, no previous work in the field has presented a systematic analysis of a considerable number of tweets employing a combination of analysis techniques by which we conducted this study. Thus, our results aptly suggest that Twitter as a well-known social medium is a valid source in predicting future events such as elections. This implies that understanding public opinions and trends via social media in turn allows us to propose a cost- and time-effective way not only for spreading and sharing information, but also for predicting future events.

A heavy duty vehicle looses approximately 30-40 % of the energy in the fuel as waste heat through the exhaust system. Recovering this waste heat would make the vehicle meet the legislative and market demands of emissions and fuel consumption easier. This recovery is possible by transforming the waste heat to electric power using a thermoelectric generator. However, the thermoelectric generator electric characteristics makes direct usage of it unprotable, thus an electric power conditioner is necessary. First a study of dierent DC-DC converters is presented, based on that the most suitable converter for thermoelectric application is determined. In order to maximize the harvested power, maximum power point tracking algorithms have been studied and analyzed. After the investigation, the single ended primary inductor converter was simulated and implemented with a perturb and observe algorithm, and the incremental conductance algorithm. The converter was tested with a 20 W thermoelectric generator, and evaluated.The results show that the incremental conductance is more robust and stable compared to the perturb and observe algorithm. Further on, the incremental conductance also has a higher average eciency during real implementation.

In this paper, we demonstrate the possibility of predicting people's hometowns by using their geotagged photos posted on Flickr website. We employ Kruskal's algorithm to cluster photos taken by a user and predict the user's hometown. Our results prove that using social profiles of photographers allows researchers to predict the locations of their taken photos with higher accuracies. This in return can improve the previous methods which were purely based on visual features of photos \cite{Hays:im2gps}.

Osteoid osteoma is a benign bone tumor of undetermined etiology, composed of a central zone named nidus which is an atypical bone completely enclosed within a wellvascularized stroma and a peripheral sclerotic reaction zone. There are three types of radiographic features: cortical, medullary and subperiosteal. Forty-four patients with osteoid osteoma were studied retrospectively. In plain films, 35 patients presented as the cortical type, six cases were located in the medullary zone and three had subperiosteal osteoid osteoma. In all the cases, the nidus was visualized on computed tomography (CT) scan. The nidus was visible in four out of five patients who had also undergone magnetic resonance imaging (MRI). Double-density sign, seen on radionuclide bone scans was positive in all patients. MRI is more sensitive in the diagnosis of bone marrow and soft tissue abnormalities adjacent to the lesion, and in the nidus that is located closer to the medullary zone. On the other hand, CT is more specific when it comes to detecting the lesion’s nidus.

The investigation of the dissociation equilibrium of fluorescein in aqueous micellar solution was determined spectrophotometrically at 25 °C and at ionic strength of 0.1 mol dm−3. For this purpose, the effect of nonionic (TX100), cationic (CTAB) and anionic (SDS) surfactants on the absorption spectra of fluorescein at different pH values were studied. To evaluate the pH-absorbance data, a resolution based on the combination of soft- and hard-modeling is applied. The acidity constants of all related equilibria are estimated using the whole spectral fitting of the collected data to an established factor analysis model. DATAN program was applied for determination of acidity constants. Results show that the pKa values of fluorescein are influenced as the percentages of an anionic and a cationic surfactant such as SDS and CTAB, respectively, are added to the solution of this reagent. Also, neutral surfactant such as TX100 only affects pKa1. Effects of surfactant on acidity constants and pure spectrum of each component are also discussed.

The main goal of routing protocol is to efficiency delivers data from source to destination. All routing protocols are the same in this goal, but the way they adopt to achieve it is different, so routing strategy has an egregious role on the performance of an ad hoc network. Most of routing protocols proposed for ad hoc networks have a flat structure. These protocols expand the control overhead packets to discover or maintain a route. On the other hand a number of hierarchical-based routing protocols have been developed, mostly are based on layered design. These protocols improve network performances especially when the network size grows up since details about remote portion of network can be handled in an aggregate manner. Although, there is another approach to design a protocol called cross-layer design. Using this approach information can exchange between different layer of protocol stack, result in optimizing network performances. In this paper, we intend to exert cross-layer design to optimize Cluster Based Routing Protocol (Cross-CBRP). Using NS-2 network simulator we evaluate rate of cluster head changes, throughput and packet delivery ratio. Comparisons denote that Cross-CBRP has better performances with respect to the original CBRP.