Miracle-dz/UniFAR-FTUs huggingface dataset:
Loneliness and hypervigilance to social threats in adults

A current theoretical model (Cacioppo & Hawkley, 2009) proposes that lonely people are hypervigilant (i.e. on high alert) to social threats in the social environment. This leads to attention, memory, and confirmatory biases, which undermine the opportunity to develop positive social relationships. This thesis outlines a series of six studies that systematically examine the hypervigilance to social threat hypothesis in loneliness using adult samples. The studies described in this thesis make an original contribution to the loneliness literature and uses different experimental paradigms to examine whether lonely adults are hypervigilant to social threats that are visually presented. Studies 1 and 5 bridge the gap in the current knowledge to examine the visual attention processing of lonely adults to social threat depicted as social rejection stimuli using eye-tracker methodology. Study 2 investigates whether loneliness is associated to eye-gaze and emotion processing utilising a cognitive paradigm. Studies 3 and 4 extend the literature on visual attention processing of lonely adults to investigate the processing of emotional information depicted as facial expressions using eye-tracker methodology. Specifically, study 3 uses a paradigm of four different emotional expressions (i.e. anger, afraid, happy and neutral), and study 4 utilises a face in a crowd paradigm for which different ratios of happy to angry faces were presented. Study 6 extends the work on hypervigilance to social threats depicted as social rejection stimuli to examine how these stimuli are processed by lonely adults in the brain using EEG methodology. Findings from study 1 and 5 suggest that lonely adults show visual attentional biases to social threat stimuli linked to social rejection. Specifically, study 1 findings indicate that lonely adults show a hypervigilance-avoidance pattern of processing towards social rejection stimuli, whilst study 5 findings indicate that lonely adults show disengagement difficulties when processing social rejection stimuli. Study 2 indicates that loneliness is not associated to eye-gaze and emotion processing. Study 3 and 4 provide support that lonely adults are more attentive to angry facial expressions presented as static images. Findings from study 6 indicate that lonely adults detect and process social threats quickly compared to non-social threats in the brain. As outlined in Cacioppo and Hawkley’s theoretical model, the findings of this thesis support the idea that loneliness is related to initial cognitive processes. Specifically, lonely adults are hypervigilant to social threats depicted as angry facial expressions and social rejection stimuli. Thus, the thesis examines an important process within the model. The findings of the thesis can be used to inform ideas for future academic and intervention work in the loneliness field.
Pivot , Box and Trilingual : Lexicon Extraction for Low-Resource Language Pairs with Extended Topic Models

Data-driven approaches to natural language processing have been shown to be greatly effective, and the case of bilingual lexicon extraction is no exception. While training data is readily available for many language pairs, many existing approaches fail for languages for which there simply does not exist parallel data. While there have been many studies on bilingual lexicon extraction, there has been little focus on the important problem of accommodating low-resource language pairs. We present a variety of solutions to this problem, demonstrating their application to a practical scenario, and compare their effectiveness to mainstream approaches. In this paper we develop pivot-based approaches for bilingual lexicon extraction using the framework of topic modelling [1]. Topic modelling has been a popular approach for bilingual lexicon extraction, however its use as a pivot model has yet to be explored.
Anatomy of methodologies for measuring consumer behavior in neuromarketing research

Over the last decade, research methods aimed to explain and anticipate how effective marketing campaigns are. However, most of the conventional techniques have failed. Since emotions are mediators of how consumers process messages, understanding and modeling of cognitive responses to advertisements have always been a challenge in methodology. In this context, starting the analysis from the gaps in traditional marketing research, the study reviews and discusses the advantages and limitations of using these relatively new alternative techniques such as neuroimaging or bio-signal analysis in neuromarketing research, providing a brief analysis on when they are used and what do they measure for each tool. Neuromarketing is the branch of neuroscience research that aims to better understand the consumer through his unconscious processes and has application in marketing, explaining consumer's preferences, motivations and expectations, predicting his behavior and explaining successes or failures of advertising messages. Literature results indicate that neuroimaging tools have the potential to provide valuable consumer insights and can develop marketing research. The study closes by explaining the implications of neuromarketing studies and anticipate the needs for the evolution of this field.
Mutational Analysis of the Flagellar Protein FliG: Sites of Interaction with FliM and Implications for Organization of the Switch Complex

ABSTRACT The switch complex at the base of the bacterial flagellum is essential for flagellar assembly, rotation, and switching. In Escherichia coli and Salmonella, the complex contains about 26 copies of FliG, 34 copies of FliM, and more then 100 copies of FliN, together forming the basal body C ring. FliG is involved most directly in motor rotation and is located in the upper (membrane-proximal) part of the C ring. A crystal structure of the middle and C-terminal parts of FliG shows two globular domains connected by an α-helix and a short extended segment. The middle domain of FliG has a conserved surface patch formed by the residues EHPQ125-128 and R160 (the EHPQR motif), and the C-terminal domain has a conserved surface hydrophobic patch. To examine the functional importance of these and other surface features of FliG, we made mutations in residues distributed over the protein surface and measured the effects on flagellar assembly and function. Mutations preventing flagellar assembly occurred mainly in the vicinity of the EHPQR motif and the hydrophobic patch. Mutations causing aberrant clockwise or counterclockwise motor bias occurred in these same regions and in the waist between the upper and lower parts of the C-terminal domain. Pull-down assays with glutathione S-transferase-FliM showed that FliG interacts with FliM through both the EHPQR motif and the hydrophobic patch. We propose a model for the organization of FliG and FliM subunits that accounts for the FliG-FliM interactions identified here and for the different copy numbers of FliG and FliM in the flagellum.
The fiscally moderate Italian populist voter: Evidence from a survey experiment

Do economic policy preferences distinguish populist voters from mainstream ones? We compare the preference profile of the voters of the Five Star Movement (M5S), one of the most successful southern European populist parties, with the profile of voters of other parties at both the 2013 national and the 2014 European Parliament elections by means of a conjoint analysis experiment on economic policy programs. Despite economic insecurity and recent recessions being key drivers of populist voting, we provide evidence that M5S supporters are fiscally moderate: they are happy with the current size of government and oppose more spending. Their Euroscepticism, shared with right-wing voters and representing a new domestic divide, takes the form of a lukewarm support for the euro, which they would readily ditch if it were to improve economic performance.
Hamiltonian approach to hybrid plasma models

The Hamiltonian structures of several hybrid kinetic-fluid models are identified explicitly, upon considering collisionless Vlasov dynamics for the hot particles interacting with a bulk fluid. After presenting different pressure-coupling schemes for an ordinary fluid interacting with a hot gas, the paper extends the treatment to account for a fluid plasma interacting with an energetic ion species. Both current-coupling and pressure-coupling MHD schemes are treated extensively. In particular, pressure-coupling schemes are shown to require a transport-like term in the Vlasov kinetic equation, in order for the Hamiltonian structure to be preserved. The last part of the paper is devoted to studying the more general case of an energetic ion species interacting with a neutralizing electron background (hybrid Hall-MHD). Circulation laws and Casimir functionals are presented explicitly in each case.
Novel perspective: exercise training stimulus triggers the expression of the oncoprotein human double minute-2 in human skeletal muscle

High expression levels of human double minute‐2 (Hdm2) are often associated with increased risk of cancer. Hdm2 is well established as an oncoprotein exerting various tumorigenic effects. Conversely, the physiological functions of Hdm2 in nontumor cells and healthy tissues remain largely unknown. We previously demonstrated that exercise training stimulates expression of murine double minute‐2 (Mdm2), the murine analog of Hdm2, in rodent skeletal muscle and Mdm2 was required for exercise‐induced muscle angiogenesis. Here we showed that exercise training stimulated the expression of Hdm2 protein in human skeletal muscle from +38% to +81%. This robust physiological response was observed in 60–70% of the subjects tested, in both young and senior populations. Similarly, exercise training stimulated the expression of platelet endothelial cell adhesion molecule‐1, an indicator of the level of muscle capillarization. Interestingly, a concomitant decrease in the tumor suppressor forkhead box O‐1 (FoxO1) transcription factor levels did not occur with training although Mdm2/Hdm2 is known to inhibit FoxO1 expression in diseased skeletal muscle. This could suggest that Hdm2 has different targets when stimulated in a physiological context and that exercise training could be considered therapeutically in the context of cancer in combination with anti‐Hdm2 drug therapies in order to preserve Hdm2 physiological functions in healthy tissues.
Bits of Falling Sky and Global Pandemics: Moral Panic and Severe Acute Respiratory Syndrome (SARS)

Whether it's a story about crime, the weather, politics, Hollywood celebrities, or public health, sensationalistic and exploitative coverage is a media staple. The mass media's coverage of the outbreak of Severe Acute Respiratory Syndrome (SARS) in the spring of 2003 was no exception. The media's construction of the source, virulence, and transmissibility of this disease, a previously unknown cousin of the common cold, diverged considerably from its medical realities and contributed to a widespread though short-lived moral panic. Drawing on work in the areas of the sociology of health and critical criminology, this article explores the claims-making activities behind the SARS “epidemic. ” Specifically, it addresses how threats to the public well-being are manufactured by the media and how these threats draw upon past and present cultural myths of dangerous “others” and contribute to unwarranted public fear, intolerance, and distrust.
Indices, Graphs and Null Models: Analyzing Bipartite Ecological Networks

Many analyses of ecological networks in recent years have introduced new indices to describe network properties. As a consequence, tens of indices are available to address similar questions, differing in specific detail, sensitivity in detecting the property in question, and robustness with respect to network size and sampling intensity. Furthermore, some indices merely reflect the number of species participating in a network, but not their interrelationship, requiring a null model approach. Here we introduce a new, free software calculating a large spectrum of network indices, visualizing bipartite networks and generating null models. We use this tool to explore the sensitivity of 26 network indices to network dimensions, sampling intensity and singleton observations. Based on observed data, we investigate the interrelationship of these indices, and show that they are highly correlated, and heavily influenced by network dimensions and connectance. Finally, we re-evaluate five common hypotheses about network properties, comparing 19 pollination networks with three differently complex null models: 1. The number of links per species ("degree") follow (truncated) power law distributions. 2. Generalist pollinators interact with specialist plants, and vice versa (dependence asymmetry). 3. Ecological networks are nested. 4. Pollinators display complementarity, owing to specialization within the network. 5. Plant-pollinator networks are more robust to extinction than random networks. Our results indicate that while some hypotheses hold up against our null models, others are to a large extent understandable on the basis of network size, rather than ecological interrelationships. In particular, null model pattern of dependence asymmetry and robustness to extinction are opposite to what current network paradigms suggest. Our analysis, and the tools we provide, enables ecologists to readily contrast their findings with null model expectations for many different questions, thus separating statistical inevitability from ecological process.
Acetate Kinase Isozymes Confer Robustness in Acetate Metabolism

Acetate kinase (ACK) (EC no: 2.7.2.1) interconverts acetyl-phosphate and acetate to either catabolize or synthesize acetyl-CoA dependent on the metabolic requirement. Among all ACK entries available in UniProt, we found that around 45% are multiple ACKs in some organisms including more than 300 species but surprisingly, little work has been done to clarify whether this has any significance. In an attempt to gain further insight we have studied the two ACKs (AckA1, AckA2) encoded by two neighboring genes conserved in Lactococcus lactis (L. lactis) by analyzing protein sequences, characterizing transcription structure, determining enzyme characteristics and effect on growth physiology. The results show that the two ACKs are most likely individually transcribed. AckA1 has a much higher turnover number and AckA2 has a much higher affinity for acetate in vitro. Consistently, growth experiments of mutant strains reveal that AckA1 has a higher capacity for acetate production which allows faster growth in an environment with high acetate concentration. Meanwhile, AckA2 is important for fast acetate-dependent growth at low concentration of acetate. The results demonstrate that the two ACKs have complementary physiological roles in L. lactis to maintain a robust acetate metabolism for fast growth at different extracellular acetate concentrations. The existence of ACK isozymes may reflect a common evolutionary strategy in bacteria in an environment with varying concentrations of acetate.
Predictive Uncertainty Estimation of Hydrological Multi-Model Ensembles Using Pair-Copula Construction

Predictive uncertainty (PU) is defined as the probability of occurrence of an observed variable of interest, conditional on all available information. In this context, hydrological model predictions and forecasts are considered to be accessible but yet uncertain information. To estimate the PU of hydrological multi-model ensembles, we apply a method based on the use of copulas which enables modelling the dependency structures between variates independently of their marginal distributions. Given that the option to employ copula functions imposes certain limitations in the multivariate case, we model the multivariate distribution as a cascade of bivariate copulas by using the pair-copula construction. We apply a mixture of probability distributions to estimate the marginal densities and distributions of daily flow rates for various meteorological and hydrological situations. The proposed method is applied to a multi-model ensemble involving two hydrological and one statistical flow models at two gauge stations in the Moselle river basin. Verification and inter-comparison with other PU assessment methods show that copulas are well-suited for this scope and constitute a valid approach for predictive uncertainty estimation of hydrological multi-model predictions.
Selection and characterization of naturally occurring high acidification rate Streptococcus thermophilus strains

Among Streptococcus thermophilus cultures, the principle component of yoghurt and cheese starters, a minority of strains forms the group of ‘H’-strains which show an unusually high acidification rate, grow faster and coagulate milk 3–5 hours earlier than the typical S. thermophilus cultures. A large-scale screening study was performed to select ‘H’-strains of S. thermophilus from more than 100 samples of home-made yoghurt, industrial yoghurt starters and single cultures, maintained in the LBB culture collection. Only four strains – LBB.TN1, LBB.M23, LBB.M34 and LBB.M60 – were isolated/selected due to their ability to form large yellowish colonies on milk agar, supplemented with beta-glycerophosphate and bromocresol purple. While in general S. thermophilus is described as a species with limited proteolytic capacity and in contrast to all other tested S. thermophilus cultures, the four selected strains invariably gave positive amplification product with the polymerase chain reaction when primers, specific for the membrane proteinase-coding gene prtS were used. The macrorestriction profiles of the genomic DNA of the four strains confirmed that they are non-isogenic and not related to each other. When grown in milk and compared to the control industrial strain LBB.A, the four strains showed a dramatically faster acidification, coagulating milk within four hours. The application of strain TN1 or M23 as adjunct culture to industrial yoghurt starter LBB.BY5-12 resulted in shortening the fermentation time with more than 30 min.
Stereoscopic tracking velocimetry based on neural networks for particle tracking

Stereoscopic tracking velocimetry (STV) can be a very efficient diagnostics tool for detecting three-dimensional three-component flows with great experimental freedom and computational processing speed but for a restricted region. To achieve the goal of near-real-time measurement with reasonable measurement accuracy, a particle tracking algorithm has been developed, which is an essential part of STV. The developed particle tracking is based on an optimization approach, hence it is a good candidate to be solved by applying computational neural networks. In this paper, we present the new tracking algorithm and its measurement applications to the material processing involving directional solidification as well as to a pulsating free-jet flow. Preliminary comparison of experimental and numerical results is also presented. We believe that by utilizing the massive parallel-processing power of neural networks for optimization, reliable solutions in the STV application can be obtained for near-real-time data extraction and display.
Gate leakage and its reduction in deep submicron SRAM

In this work the impact of gate leakage on SRAM is described and two approaches for reducing gate leakage currents are examined in detail. In one approach, the supply voltage is reduced while in the other the potential of the ground node is raised. In both the approaches the effective voltage across SRAM cell is reduced in inactive mode using a dynamic self-controllable switch. Simulation results based on BPTM (Berkeley Predictive Technology Model) for 45nm channel length device show that the scheme in which supply voltage level is reduced is more efficient in reducing gate leakage than the one in which ground node potential is raised. Results obtained show that 96% reduction in the leakage currents of SRAM can be achieved.
Evaluation of urban development proposals An ANP approach

� Abstract— In this paper a new approach to prioritize urban planning projects in an efficient and reliable way is presented. It is based on environmental pressure indices and multicriteria decision methods. The paper introduces a rigorous method with acceptable complexity of rank ordering urban development proposals according to their environmental pressure. The technique combines the use of Environmental Pressure Indicators, the aggregation of indicators in an Environmental Pressure Index by means of the Analytic Network Process method and interpreting the information obtained from the experts during the decision-making process. The ANP method allows the aggregation of the experts' judgments on each of the indicators into one Environmental Pressure Index. In addition, ANP is based on utility ratio functions which are the most appropriate for the analysis of uncertain data, like experts' estimations. Finally, unlike the other multicriteria techniques, ANP allows the decision problem to be modelled using the relationships among dependent criteria. The method has been applied to the proposal for urban development of La Carlota airport in Caracas (Venezuela). The Venezuelan Government would like to see a recreational project develop on the abandoned area and mean a significant improvement for the capital. There are currently three options on their table which are currently under evaluation. They include a Health Club, a Residential area and a Theme Park. The participating experts coincided in the appreciation that the method proposed in this paper is useful and an improvement from traditional techniques such as environmental impact studies, life- cycle analysis, etc. They find the results obtained coherent, the process seems sufficiently rigorous and precise, and the use of resources is significantly less than in other methods.
A model for motivation assessment in intelligent tutoring systems

Motivation has an undeniable role in the effectiveness of intelligent tutoring systems. In this research, a model is proposed to integrate students' motivation in intelligent tutoring systems. This model is based on the ARCS Model of Motivational Design and log file analysis to estimate students' motivation. Through expert analysis, it was determined that seven attributes (task time, grade, task difficulty, student's interest in the subject, accordance between content presentation and student's learning style, student's skill level and previous motivational state) affect motivation directly and must be included in the model. In order to determine how accurately these attributes can assess the motivational state of students, a reading comprehension test environment was created using Moodle. Fourteen users participated in the study. Random Forest algorithm was used to classify the collected data into “motivated” and “unmotivated” classes. The correct classification rate was 61%. Although the data set is not big enough, however, this preliminary result show that the model is promising and can be further tested and improved.
Random walk based biased sampling for data collection on communication networks

Sampling via random walks is the first choice for collecting random samples of online-social networks, peer-to-peer networks, and the World Wide Web. This paper proposes an algorithm for random-walk sampling, which allows us to collect a biased (non-random) sample, depending on which nodes are to be investigated in detail. Since the stationary distribution of a random walker under the proposed algorithm can be analytically derived, the bias involved in a collected sample can be removed using the notion of change of measure in probability theory, which is also presented in this paper. The effectiveness of the proposals is verified using simulation experiments based on the data of real networks.