Miracle-dz/UniFAR-FTUs huggingface dataset:
Bronchoalveolar Lavage Results Are Independent of Season, Age, Gender and Collection Site

Background Clinical interpretation of bronchoalveolar lavage fluid results is dependent on the availability of reference values for healthy individuals. Only a few studies have published such reference values and the applicability of results is restricted by small sample sizes and the limited representativeness of the study population. We aim to investigate the influence of age, gender, collection site and season on bronchoalveolar lavage fluid results and to establish reference values for use in clinical practice. Methodology/Principal Findings Bronchoalveolar lavage fluid data from 295 healthy never-smoking volunteers, investigated during 1990–2009, were analyzed retrospectively. 47 volunteers had 2–5 repeat lavages during the course of several years. Fluid recovery, total number of cells, cell concentration, and differential cell counts on cytospin prepared slides were recorded. Reference values, as represented by the 5th to the 95th percentile, were 72–96% for macrophages, 2–26% for lymphocytes, 0–4% for neutrophils and 0–1% for eosinophils. Basophils and mast cells were rare. When repeat lavages were performed, there was a relatively large intra-individual variability, mainly for macrophages and lymphocytes. An age dependent decrease of lavage fluid return was present, but there was no age dependent correlation with any of the other BALF parameters. The BALF cell parameters were independent of gender, season and site (lingula vs. middle lobe). Conclusions/Significance Our data show that bronchoalveolar lavage fluid cell differential count is independent of age, gender, season and collection site (RML or lingua). It therefore seems acceptable to use the same reference values for all never-smoking individuals.
Putting Actions in Context: Visual Action Adaptation Aftereffects Are Modulated by Social Contexts

The social context in which an action is embedded provides important information for the interpretation of an action. Is this social context integrated during the visual recognition of an action? We used a behavioural visual adaptation paradigm to address this question and measured participants’ perceptual bias of a test action after they were adapted to one of two adaptors (adaptation after-effect). The action adaptation after-effect was measured for the same set of adaptors in two different social contexts. Our results indicate that the size of the adaptation effect varied with social context (social context modulation) although the physical appearance of the adaptors remained unchanged. Three additional experiments provided evidence that the observed social context modulation of the adaptation effect are owed to the adaptation of visual action recognition processes. We found that adaptation is critical for the social context modulation (experiment 2). Moreover, the effect is not mediated by emotional content of the action alone (experiment 3) and visual information about the action seems to be critical for the emergence of action adaptation effects (experiment 4). Taken together these results suggest that processes underlying visual action recognition are sensitive to the social context of an action.
Scenario and Incentives of Foreign Direct Investment (FDI) in Bangladesh

FDI is considered as an important tool for economic development of developing country. The term foreign direct investment is considered as one of the important contributors for the economic development of a country in particular and the world in general with the concept of globalization. Foreign direct investment has great impact on the development of developing country like Bangladesh. Foreign Direct Investment increases trade and investment, labor mobility, capital inflow, technology and know-how to the recipient country. The secondary data has been analyzed using tabular and graphical tools. Governments have now changed their policies from restricting towards promoting foreign investment. Though the government of Bangladesh provides fiscal, financial and other incentives, the actual flow of FDI is below the desired level.
Combining Perception, Action, Intention and Value: A Control Theoretic Approach to Driving Performance

Driving is a prototypical example of a closed-loop control problem. This chapter presents a tutorial introduction to the logic of closed-loop systems and the implications for theory and research on driver performance. The human-vehicle system is parsed into three coupled components: (1) the problem constraints; (2) the observer constraints; and (3) the control constraints. This paper hopes to illustrate the importance of each component to a full appreciation of the closed-loop dynamic and to consider how the coupling across these components will determine the emergent stability of human-vehicle systems. This paper hopes that this tutorial will help to provide common ground between behavioral scientists and engineers, so that each can better appreciate how the human factor, the vehicle, and the driving ecology interact to shape system performance. This paper also hopes that this will provide a theoretical context for interdisciplinary driver research using simulators. There is a growing appreciation among those who study human performance that context matters. Thus, increasingly it becomes important to be able to evaluate performance under conditions that are representative of natural life experiences. Driving simulators provide a unique bridge between the complexity of natural environments and the demands for controlled observation in order to test hypotheses about human performance. This is equally important for addressing practical issues associated with training and design, as well as for basic issues associated with understanding adaptive cognitive systems.
Molecular mechanisms of genetic polymorphisms of drug metabolism.

One of the major causes of interindividual variation of drug effects is genetic variation of drug metabolism. Genetic polymorphisms of drug-metabolizing enzymes give rise to distinct subgroups in the population that differ in their ability to perform certain drug biotransformation reactions. Polymorphisms are generated by mutations in the genes for these enzymes, which cause decreased, increased, or absent enzyme expression or activity by multiple molecular mechanisms. Moreover, the variant alleles exist in the population at relatively high frequency. Genetic polymorphisms have been described for most drug metabolizing enzymes. The molecular mechanisms of three polymorphisms are reviewed here. The acetylation polymorphism concerns the metabolism of a variety of arylamine and hydrazine drugs, as well as carcinogens by the cytosolic N-acetyltransferase NAT2. Seven mutations of the NAT2 gene that occur singly or in combination define numerous alleles associated with decreased function. The debrisoquine-sparteine polymorphism of drug oxidation affects the metabolism of more than 40 drugs. The poor metabolizer phenotype is caused by several "loss of function" alleles of the cytochrome P450 CYP2D6 gene. On the other hand, "ultrarapid" metabolizers are caused by duplication or amplification of an active CYP2D6 gene. Intermediate metabolizers are often heterozygotes or carry alleles with mutations that decrease enzyme activity only moderately. The mephenytoin polymorphism affects the metabolism of mephenytoin and several other drugs. Two mutant alleles of CYP2C19 have so far been identified to cause this polymorphism. These polymorphisms show recessive transmission of the poor or slow metabolizer phenotype, i.e. two mutant alleles define the genotype in these individuals. Simple DNA tests based on the primary mutations have been developed to predict the phenotype. Analysis of allele frequencies in different populations revealed major differences, thereby tracing the molecular history and evolution of these polymorphisms.
W-tree

World Wide Web applications need to use, constantly update, and maintain large webgraphs for executing several tasks, such as calculating the web impact factor, finding hubs and authorities, performing link analysis by webometrics tools, and ranking webpages by web search engines. Such webgraphs need to use a large amount of main memory, and, frequently, they do not completely fit in, even if compressed. Therefore, applications require the use of external memory. This article presents a new compact representation for webgraphs, called w-tree, which is designed specifically for external memory. It supports the execution of basic queries (e.g., full read, random read, and batch random read), set-oriented queries (e.g., superset, subset, equality, overlap, range, inlink, and co-inlink), and some advanced queries, such as edge reciprocal and hub and authority. Furthermore, a new layout tree designed specifically for webgraphs is also proposed, reducing the overall storage cost and allowing the random read query to be performed with an asymptotically faster runtime in the worst case. To validate the advantages of the w-tree, a series of experiments are performed to assess an implementation of the w-tree comparing it to a compact main memory representation. The results obtained show that w-tree is competitive in compression time and rate and in query time, which may execute several orders of magnitude faster for set-oriented queries than its competitors. The results provide empirical evidence that it is feasible to use a compact external memory representation for webgraphs in real applications, contradicting the previous assumptions made by several researchers.
Speeding up MAP with Column Generation and Block Regularization

In this paper, we show how the connections between max-product message passing and linear programming relaxations for MAP allow for a more efficient exact algorithm than standard dynamic programming. Our proposed algorithm uses column generation to pass messages only on a small subset of the possible assignments to each variable, while guaranteeing to find the exact solution. This algorithm is more than two times faster than Viterbi decoding for part-of-speech tagging on WSJ data and equivalently fast as beam search with a beam of size two, while being exact. The empirical performance of column generation depends on how quickly we can rule out entire sets of assignments to the edges of the chain, which is done by bounding the contribution of the pairwise factors to the score of the solution. This provides an opportunity at the intersection of inference and learning: at training time, we can regularize the model in a way that makes inference faster without changing its structure.
Spam Filtering: an Active Learning Approach using Incremental Clustering

This paper introduces a method that deals with unwanted mail messages by combining active learning with incremental clustering. The proposed approach is motivated by the fact that the user cannot provide the correct category for all received messages. The email messages are divided into chronological batches (e.g. one per day). The user is asked to give the correct categories (labels) for the messages of the first batch and from then on the proposed algorithm decides when to ask for a new label, based on a clustering of the messages that is incrementally updated. We test different variants of the algorithm on a number of different datasets and show that it achieves very good results with only 2% of all email messages labelled by the user.
Chromosome segregation in Eubacteria Kit Pogliano

It is now clear that bacterial chromosomes rapidly separate in a manner independent of cell elongation, suggesting the existence of a mitotic apparatus in bacteria. Recent studies of bacterial cells reveal filamentous structures similar to the eukaryotic cytoskeleton, proteins that mediate polar chromosome anchoring during Bacillus subtilis sporulation, and SMC interacting proteins that are involved in chromosome condensation. A picture is thereby developing of how bacterial chromosomes are organized within the cell, how they are separated following duplication, and how these processes are coordinated with the cell cycle.
Development of a Generic Anti-PEG Antibody Assay Using BioScale’s Acoustic Membrane MicroParticle Technology

Immunogenicity testing for PEGylated biotherapeutics should include methods to detect both anti-protein and anti-PEG antibodies (anti-PEG). Although some methods have been published for the detection of anti-PEG antibodies, the information is incomplete and, in some cases, reagents used (such as Tween-20) are known to interfere with detection. This rapid communication describes the use of BioScale’s Acoustic Membrane MicroParticle (AMMP®) technology using the ViBE® Workstation to measure anti-PEG antibodies in human serum samples. Briefly, a sample spiked with monoclonal human IgG anti-PEG antibody is diluted in buffer and incubated with paramagnetic beads coated with linear chain mPEG to capture anti-PEG antibodies. The complex is then captured on an acoustic membrane coated with Protein A. The change in mass on the membrane caused by the binding of the complex to the membrane results in a signal proportional to the mass of anti-PEG antibodies. The data indicate that an assay with a sensitivity of less than 1000 ng/mL for IgG is achievable. This level of sensitivity is better than current published reports on IgG anti-PEG antibody detection.
Lower error floor of LDPC codes based on trapping sets elimination

LDPC codes with excellent performance have great potential in applications of wireless mobile communications. However, the error floor phenomenon existing in LDPC codes limits its further application in disk storage and deep-space communications which require very low bit error rate (BER) system. The researchers find that trapping sets are the major reason why the LDPC codes have error floor. In this paper, we proposed an elimination algorithm to reduce small trapping sets. It was applying node rearrangement method to optimize the neighborhood of target nodes. In this way, cycles will be broken and the small size of trapping sets can be eliminated. The experimental results show that it can lower the error floor to improve the performance of LDPC codes. Besides, we also can keep the degree distribution, code rates and girth features of the original code with this method.
Portfolio investment and oil sensitivity: the case of the Romanian stock market

In this article we investigate the impact of oil price changes and gold price changes on a set of stock returns of four major Romanian oil companies and on an equally weighted portfolio comprised of these stocks. Employing two different data frequencies and pair wise Granger causality tests we find that multifactor models which include unexpected oil price changes and unexpected gold price changes as macroeconomic factors are suitable in the decision-making process of an investor that wants to invest in the Romanian oil industry. Keywords: Oil company stock returns, oil prices, gold prices, portfolio investment;
Improved Prediction of Non-functional Properties in Software Product Lines with Domain Context

Software Product Lines (SPLs) enable software reuse by systematically managing commonalities and variability. Usually, commonalities and variability are expressed by features. Functional requirements of a software product are met by selecting appropriate features. However, selecting features also influences non-functional properties. To satisfy non-functional requirements of a software product, as well, the effect of a feature selection on non-functional properties has to be known. Often an SPL allows a vast number of valid products, which renders a test of non-functional properties on the basis of all valid products impractical. Recent research offers a solution to this problem: the effect of features on non-functional properties of software products is predicted by measuring in advance. A sample of feature configurations is generated, executed with a predefined benchmark, and then non-functional properties are measured. Based on these data a model is created that allows to predict non-functional properties of a software product before actually building it. However, in some domains contextual influences, such as input data, can heavily affect nonfunctional properties. We argue that the measurement of the effect of features on non-functional properties can be drastically improved by considering contextual influences of a domain. We study this assumption on input data as an example for a contextual influence and using an artificial but intuitive case study from the domain of compression algorithms. Our study shows that prediction accuracy of non-functional properties can be significantly improved.
Modeling of a steerable catheter based on beam theory

Catheter-based cardiac ablation is an interventional treatment for heart arrhythmias. Pull-wire steerable catheters are guided to the heart chambers through the vasculature in order to deliver energy to destroy faulty electrical pathways in the heart. The effectiveness of this treatment is dependent on the accuracy of positioning the catheter tip at the target location and also on maintaining contact with the target while the heart is beating. Therefore, it is desirable to perform hybrid force/position control of the catheter tip. We have studied the problem of modeling the distal part of a steerable catheter using beam theory and have developed and validated a static force-deflection model through extensive experiments. It is shown that the model can estimate the shape of the bending section of a catheter using force information and without requiring extensive knowledge of the catheter's internal structure.
Revisiting the dynamic effect of foreign direct investment on economic growth in LDCs

The relationship between foreign direct investment (FDI) and gross domestic product (GDP) has long been considered as an unresolved debate in growth literature. Scores of studies have highlighted ambiguous conclusion regarding the exact effect of FDI on economic growth among the recipient countries. Though transfers of technologies, development of financial sectors across the world including in the LDCs have promised a positive role of FDI in the LDCs, however, lack of transparency, accountability, widespread corruption including the motivation of the MNCs have always clouded the outcome. Using most robust methodologies for panel data, this study concludes that LDCs thrive towards growth appetite can very little be fulfilled with FDI, rather domestic fixed capital formulation, efficient use of the existing abundant labour force can foster the economic growth much aggressively even in the long run.
Detecting Communities from Bipartite Networks Based on Bipartite Modularities

Discovering communities from networks is one of the important and challenging research topics of social network analysis. Although Newman's modularity is often used for evaluating division of unipartite networks, it is not suitable for evaluating division of bipartite networks that are composed of two types of vertices. To compensate for the situation, Guimera and Barber propose bipartite modularities. This paper discusses the characteristics of these bipartite modularities and proposes another bipartite modularity. Experimental results show that our new bipartite modularity allows one-to-many correspondence between communities of different vertex types.
Matching surfaces with characteristic points

We study approximation algorithms for a matching problem that is motivated by medical applications. Given a small set of points P ⊂ R 3 and a surface S, the optimal matching of P with S is represented by a rigid transformation which maps P as ‘close as possible’ to S. Previous solutions either require polynomial runtime of high degree [2] or they make use of heuristic techniques which could be trapped in some local minimum. We propose a modification of the problem setting by introducing subsets of characteristic points Pc ⊆ P and Sc ⊆ S, and assuming that points from Pc must be matched with points from Sc. We will show that especially in the case |Pc |≥ 2 this restriction results in new fast and reliable algorithms for the matching problem.
Facilitating youth entrepreneurship. An analysis of awareness and promotion programmes in formal and non-formal education

Having reduced youth unemployment to half by 2015 is one of the goals that Heads of State of all member countries of the United Nations adopted in the Millenium Declaration. The ILO is exploring ways to increase youth employment which will help end the vicious cycle of poverty and social exclusion. The present Working Paper addresses education for entrepreneurship as one of the strategies to reduce youth unemployment. It takes up one of the recommendations of the High Level Panel, set-up by the UN Secretary General (working group on Entrepreneurship), stipulating that 'the education system must recognize the need for developing the skills and attitudes that make up an entrepreneurial mindset such as lateral thinking, questioning, independence and self-reliance. This education should continue through vocational training, business incubation and the start-up phase for young entrepreneurs'. The review presented in Part I analyses awareness and promotion programmes in formal and non-formal education from around the world. Part II contains an extensive directory with short descriptions of 166 current and recently completed programmes for entrepreneurship education. Together, they constitute a rich source of information for use by planners and decision-makers on youth employment and education policies. In addition, the wealth of details and references on the implementation of such policies may inspire and guide the many practitioners of youth training and job creation programmes.