Miracle-dz/UniFAR-FTUs huggingface dataset:
Influence of Lactoferrin on the Function of Human Polymorphonuclear Leukocytes and Monocytes

Polymorphonuclear leukocytes (PMN) exposed to highly purified human lactoferrin (from colostrum) exhibit an increased random motility (at least 2.5‐fold) and are primed to produce more superoxide [12.1 ± 1.2 nmol O2 ‐/min/106 PMN preincubated with lactoferrin (0.5 mg/ml) against 6.4 ± 2.3 with cells without lactoferrin after FMLP stimulation]. The action of lactoferrin seemed to be specific, because it could be abolished by simultaneous addition of antilactoferrin antibody. Addition of transferrin and iron salts to PMN was without effect. Between iron‐poor and iron‐saturated lactoferrin there was no difference in influence on PMN function except for a higher FMLP stimulated superoxide production by iron‐saturated lactoferrin. Aggregation, degranulation (β‐glucuronidase, lysozyme), and bacterial killing were not influenced by lactoferrin. Incubation of monocytes and monocyte‐derived macrophages with lactoferrin did not alter their motility or their superoxide production rates. Our findings indicate that PMN become more effective after exposure to lactoferrin by having a greater motility and producing superoxide at a faster rate.
Using multiple vector quantization and semicontinuous hidden Markov models for speech recognition

Although the continuous HMM (CHMM) technique seems to be the most flexible and complete tool for speech modeling, it is not always used for the implementation of speech recognition systems due to several problems related to training and computational complexity. Besides, it is not clear the superiority of continuous models over other well-known types of HMMs, such as discrete (DHMM) or semicontinuous (SCHMM) models, or multiple vector quantization (MVQ) models, a new type of HMM modeling. The authors propose a new variant of HMM models, the SCMVQ, HMM models (semicontinuous multiple vector quantization HMM), that uses one VQ codebook per recognition unit and several quantization candidates, Formally, SCMVQ modeling is the closest one to CHMM, although requiring less computation than SCHMMs. Besides, the authors show that SCMVQs can obtain better recognition results than DHMMs, SCHMMs or MVQs. <<ETX>>
Joint Effects of Smoking and Sedentary Lifestyle on Lung Function in African Americans: The Jackson Heart Study Cohort

This study examined: (a) differences in lung function between current and non current smokers who had sedentary lifestyles and non sedentary lifestyles and (b) the mediating effect of sedentary lifestyle on the association between smoking and lung function in African Americans. Sedentary lifestyle was defined as the lowest quartile of the total physical activity score. The results of linear and logistic regression analyses revealed that non smokers with non sedentary lifestyles had the highest level of lung function, and smokers with sedentary lifestyles had the lowest level. The female non-smokers with sedentary lifestyles had a significantly higher FEV1% predicted and FVC% predicted than smokers with non sedentary lifestyles (93.3% vs. 88.6%; p = 0.0102 and 92.1% vs. 86.9%; p = 0.0055 respectively). FEV1/FVC ratio for men was higher in non smokers with sedentary lifestyles than in smokers with non sedentary lifestyles (80.9 vs. 78.1; p = 0.0048). Though smoking is inversely associated with lung function, it seems to have a more deleterious effect than sedentary lifestyle on lung function. Physically active smokers had higher lung function than their non physically active counterparts.
Development of the DINA-CH Full Tokamak Simulator

CEA-Cadarache, 13108 Saint-Paul-Lez-Durance, France Introduction A wide variety of tokamak plasma simulator classes has been exploited, roughly grouped as: a) prescribed transport, prescribed bou ndary for the simplest cases, b) fixed or prescribed boundary transport simulators, c) free-b oundary evolution with prescribed transport simulators and finally d) free-boundary w ith transport self-consistent with the boundary evolution. In the mid-1990s, at the time the basic ITER designs were being developed, transport modelling was less advanced th an today and intriguingly, the most challenging class of self-consistent free boundary codes was the most popular. It was only later, with advanced understanding of transport tha t the present class of prescribed boundary transport codes developed into todays popular tool s. In the context of the mid-1990s the candidate self-consistent simulation codes were res tricted to TSC, the most advanced, DINA [1], relatively new and CORSICA, more primitive at the time with a restricted current diffusion model. The ITER expert group encouraged a benchmarking of these codes and a programme of model validation was launched on the T CV tokamak, as a continuation of a then existing validation programme of linear contro l modelling [2]. The choice of the code to be benchmarked fell upon the DINA code and this pap er summarises the development phases. Development Initial benchmarking took the existing linear mode lling benchmarking experiments on TCV [2] and repeated them on the DIN A code [3]. The benchmarking was considered a success but the work had to be carried out by the DINA team due to the complexity and mono-bloc nature of the DINA code. A second benchmarking exercise was then performed in the same environment to validate the dynamics of VDEs on which the vertical stabilisation control modelling depends in timately. The results [4] were very encouraging and demonstrated, in the specific condi tions of the highly elongated TCV vacuum vessel, non-exponential growth as the locati on of the plasma current moved downwards towards the base plate, creating an S-sha pe VDE. This second benchmarking
On Multiterminal Secrecy Capacities

Shannon-theoretic secret key generation by several parties is considered for source models in which the distinct components of a multiple source observed separately by multiple terminals, and for channel models in which a secure noisy channel with one input terminal and multiple output terminals, and, additionally in both cases, a public noiseless channel of unlimited capacity, are available for accomplishing this goal. The secret key is generated for a set A of terminals, with the remaining terminals (if any) cooperating in this task through their public communication. We show that for source models in which secrecy is required from an eavesdropper that observes only the public communication and perhaps also a set of terminals disjoint from A, secrecy capacity can be achieved with noninteractive communication, the key being generated by any chosen terminal in the secret key-seeking set A of terminals obliviously of the public communication. For models in which the eavesdropper also possesses side information that is not available to any of the terminals cooperating in secrecy generation, an upper bound for the secrecy capacity and a sufficient condition for its tightness are given. The latter partially fills a gap in the authors' previous work [6].
Comparison of Performance of Structural Models: Evidence from China

In order to further empirical study of the performance of structural models in China, this paper empirically tests four structural models of corporate bonds pricing: those of Merton (1974), modified Merton with exogenous recovery rate, Black and Cox (1976) and Leland and Toft (1996). The article implement these models by using a sample of 45 Chinese firms issuing short-term financing bonds in 2006. Empirical results show that the predicted spreads in four models are lower than actual spreads on average, particularly those of Merton model. Comparing the different models we find the spreads from Black and Cox (1976) and Leland and Toft (1996) are higher than the basic structural models, but accuracy is a problem. The modified Merton model can further improve Merton (1974)'s underestimation. The basic structural models including Merton (1974) and modified Merton can more comparatively catch the trend of movement of credit spreads than so-called extended models. The performance of recently developed structural models is not superior to the original Merton models. More accurate structural models must not only avoid underestimation but also improve the accuracy.
Ancient DNA of the Extinct Lava Shearwater (Puffinus olsoni) from the Canary Islands Reveals Incipient Differentiation within the P. puffinus Complex

Background The loss of species during the Holocene was, dramatically more important on islands than on continents. Seabirds from islands are very vulnerable to human-induced alterations such as habitat destruction, hunting and exotic predators. For example, in the genus Puffinus (family Procellariidae) the extinction of at least five species has been recorded during the Holocene, two of them coming from the Canary Islands. Methodology/Principal Findings We used bones of the two extinct Canary shearwaters (P. olsoni and P. holeae) to obtain genetic data, for use in providing insights into the differentiation process within the genus Puffinus. Although mitochondrial DNA (mtDNA) cytochrome b sequences were successfully retrieved from four Holocene specimens of the extinct Lava shearwater (P. olsoni) from Fuerteventura (Canary Islands), the P. holeae specimens yielded no DNA. Only one haplotype was detected in P. olsoni, suggesting a low genetic diversity within this species. Conclusions The phylogenetic analyses based on the DNA data reveal that: (i) the “Puffinus puffinus complex”, an assemblage of species defined using osteological characteristics (P. puffinus, P. olsoni, P. mauretanicus, P. yelkouan and probably P. holeae), shows unresolved phylogenetic relationships; (ii) despite the differences in body size and proportions, P. olsoni and the extant P. puffinus are sister species. Several hypotheses can be considered to explain the incipient differentiation between P. olsoni and P. puffinus.
GreenHDFS: towards an energy-conserving, storage-efficient, hybrid Hadoop compute cluster

Hadoop Distributed File System (HDFS) presents unique challenges to the existing energy-conservation techniques and makes it hard to scale-down servers. We propose an energy-conserving, hybrid, logical multi-zoned variant of HDFS for managing data-processing intensive, commodity Hadoop cluster. Green HDFS's data-classification-driven data placement allows scale-down by guaranteeing substantially long periods (several days) of idleness in a subset of servers in the datacenter designated as the Cold Zone. These servers are then transitioned to high-energy-saving, inactive power modes. This is done without impacting the performance of the Hot zone as studies have shown that the servers in the data-intensive compute clusters are under-utilized and, hence, opportunities exist for better consolidation of the workload on the Hot Zone. Analysis of the traces of a Yahoo! Hadoop cluster showed significant heterogeneity in the data's access patterns which can be used to guide energy-aware data placement policies. The trace-driven simulation results with three-month-long real-life HDFS traces from a Hadoop cluster at Yahoo! show a 26% energy consumption reduction by doing only Cold zone power management. Analytical cost model projects savings of $14.6 million in 3-year total cost of ownership (TCO) and simulation results extrapolate savings of $2.4 million annually when Green-HDFS technique is applied across all Hadoop clusters (amounting to 38000 servers) at Yahoo.
Ab initio theory of phase transitions and thermoelasticity of minerals

Accurate quantum-mechanical simulations have significantly extended the current picture of the Earth and hold a great promise for the future of the Earth and planetary sciences. Studies of phase transitions, equations of state, elasticity and thermoelastic properties of the Earth-forming minerals are essential to geophysics. This chapter gives a basic background of the physics of the deep Earth and outlines the theory of phase transitions, equations of state, elasticity and thermoelastic properties. A particular emphasis is put on the principles of quantum-mechanical simulations and some recent results relevant to geophysics. The importance of quantum-mechanical simulations is reflected by the award of the 1998 Nobel Prize in Chemistry to W. Kohn and J. Pople, who were among the pioneers of this field. Areas of application of such simulations are extremely diverse and include studies of the electronic structure, reactivity, catalysis, bulk and surface structure, prediction of materials structures and properties, especially at extreme conditions, calculation of phase diagrams and studies of phase transitions etc. One of the most exciting areas of application of such simulations is the study of the Earth(and planet-) forming minerals at the extreme conditions of the Earth’s interior. One can accurately predict the structures, properties, and behaviour of minerals. This often reveals new aspects of mineral crystal chemistry and allows one to explain geophysical measurements and understand better how the Earth works as a planet. This chapter consists of five major parts – Part I: Brief geophysical introduction, Part II: Thermoelastic properties, Part III: Phase transitions, Part IV: Simulation methods and Part V: Examples from recent studies. Each part is supplied with a number of references for further reading.
Task and Participant Scheduling of Trading Platforms in Vehicular Participatory Sensing Networks

The vehicular participatory sensing network (VPSN) is now becoming more and more prevalent, and additionally has shown its great potential in various applications. A general VPSN consists of many tasks from task, publishers, trading platforms and a crowd of participants. Some literature treats publishers and the trading platform as a whole, which is impractical since they are two independent economic entities with respective purposes. For a trading platform in markets, its purpose is to maximize the profit by selecting tasks and recruiting participants who satisfy the requirements of accepted tasks, rather than to improve the quality of each task. This scheduling problem for a trading platform consists of two parts: which tasks should be selected and which participants to be recruited? In this paper, we investigate the scheduling problem in vehicular participatory sensing with the predictable mobility of each vehicle. A genetic-based trading scheduling algorithm (GTSA) is proposed to solve the scheduling problem. Experiments with a realistic dataset of taxi trajectories demonstrate that GTSA algorithm is efficient for trading platforms to gain considerable profit in VPSN.
The preparedness of elementary music teachers to include students with challenging behavior in their classrooms

Students with disabilities, some with emotional and behavior disorders, are included in almost all elementary music classes. Students with emotional behavior disorders are one of the greatest challenges for teachers. To be effective, teachers must develop strategies and inclusive practices specifically geared towards intervention. With the quantity of students served and only limited class time with students, the music teacher is often unaware of the unique needs of specific special learners. Music teacher preparation has been inadequate in training teachers for inclusion. Elementary music educators rarely have outside support to deal with classroom challenges as they serve students with disabilities and at-risk students. Music teacher training is focused on content, not behavior management. This study examined the perceptions of randomly selected elementary music educators who were members of MENC: The National Association for Music Education regarding their preparedness to effectively manage five areas of severe behavior often exhibited by students with emotional behavior disorders: withdrawal, impulsivity, argumentative behavior, aggression towards peers, and aggression towards the teacher. The researcher devised a 39 item online survey instrument based on supporting literature. The survey was given to randomly selected participants. Two hundred sixty-nine elementary music educators from across the United States completed the survey providing information on incidence frequency, preparedness, training in behavior management, and the amount of behavior support available.
Application of Neural Networks in the Semantic Parsing Re-Ranking Problem

Automatic program generation allows end-users to benefit from greatly from increased productivity. However, general Natural Language Programming tools fail to provide the benefits of the ambiguity and expressivity of English. We reduce program generation into a semantic parsing problem. Given a command and input, we procedurally generate a large set of candidate programs, and then align the command to a program using deep learning. We then use neural network models, specifically recurrent neural networks, to predict the correct program. By restricting the domain to string operations, we show how we can perform reasonably well.
Secret Sharing for Health Data in Multi-provider Clouds

The accelerated adoption of cloud computing among enterprises is due to the multiple benefits the technology provides, one of them the simplification of inter-organizational information sharing, which is of utmost importance in healthcare. Nevertheless, moving sensitive health records to the cloud still implies severe security and privacy risks. With this background, we present a novel secure architecture for sharing electronic health records in a cloud environment. We first conducted a systematic literature review and interviews with different experts from the German healthcare industry that allowed us to derive real-world processes and corresponding security and privacy requirements. Based on these results, we designed our multi-provider cloud architecture that satisfies many of the requirements by providing increased availability, confidentiality and integrity of the medical records stored in the cloud. This architecture features secret sharing as an important measure to distribute health records as fragments to different cloud services, which can provide higher redundancy and additional security and privacy protection in the case of key compromise, broken encryption algorithms or their insecure implementation. Finally, we evaluate and select a secret-sharing algorithm for our multi-cloud architecture. We implemented both Shamir's secret-sharing scheme and Rabin's information dispersal algorithm and performed several experiments measuring the execution time. Our results indicate that an adoption of Rabin's algorithm would create a low overhead, giving strong indicators to the feasibility of our approach.
Two-Stage Power Conversion Architecture Suitable for Wide Range Input Voltage

This paper presents a merged-two-stage circuit topology suitable for either wide-range dc input voltage or ac line voltage at low-to-moderate power levels (e.g., up to 30 W). This two-stage topology is based on a soft-charged switched-capacitor preregulator/transformation stage and a high-frequency magnetic regulator stage. Soft charging of the switched capacitor circuit, zero voltage switching of the high-frequency regulator circuit, and time-based indirect current control are used to maintain high efficiency, high power density, and high power factor. The proposed architecture is applied to an LED driver circuit, and two implementations are demonstrated: a wide input voltage range dc-dc converter and a line interfaced ac-dc converter. The dc-dc converter shows 88%-96% efficiency at 30-W power across 25-200-V input voltage range, and the ac-dc converter achieves 88% efficiency with 0.93 power factor at 8.4-W average power. Contributions of this paper include: 1) demonstrating the value of a merged two-stage architecture to provide substantial design benefits in high-input voltage, low-power step down conversion applications, including both wide-range-input dc-dc and line-input ac-dc systems; 2) introduction of a multimode soft-charged SC stage for the merged architecture that enables compression of an 8:1 input voltage range into a 2:1 intermediate range, along with its implementation, loss considerations, and driving methods; and 3) merging of this topology with an resonant transition discontinuous-mode inverted buck stage and pseudocurrent control to enable step-down power conversion (e.g., for LED lighting) operating at greatly increased frequencies and reduced magnetics size than with more conventional approaches.
The C-terminal Nuclear Localization Signal of the Sex-determining Region Y (SRY) High Mobility Group Domain Mediates Nuclear Import through Importin β1*

The sex-determining factor SRY is a DNA-binding protein that diverts primordial gonads from the ovarian pathway toward male differentiation to form testes. It gains access to the nucleus through two distinct nuclear localization signals (NLSs) that flank the high mobility group (HMG) DNA-binding domain, but the mechanisms through which these NLSs operate have not been studied. In this study, we reconstitute the nuclear import of SRY in vitro, demonstrating a lack of requirement for exogenous factors for nuclear accumulation and a significant reduction in nuclear transport in the presence of antibodies to importin β but not importin α. Using a range of quantitative binding assays including enzyme-linked immunosorbent assay, fluorescence polarization, and native gel mobility electrophoresis, we assess the binding of importins to SRY, demonstrating a high affinity recognition (in the low nmrange) by Impβ independent of Impα. In assessing the contribution of each NLS, we found that the N-terminal NLS was recognized poorly by importins, whereas the C-terminal NLS was bound by importin β with similar affinity to SRY. We also found that RanGTP, but not RanGDP, could dissociate the SRY-importin β complex in solution using FP. We describe a novel double-fluorescent label DNA binding assay to demonstrate mutual exclusivity between importin β recognition and DNA binding on the part of SRY, which may represent an alternative release mechanism upon nuclear entry. This study represents the first characterization of the nuclear import pathway for a HMG domain-containing protein. Importantly, it demonstrates for the first time that recognition of SRY by Impβ is of comparable affinity to that with which Impα/β recognizes conventional NLS-containing substrates.