Miracle-dz/UniFAR-FTUs huggingface dataset:
and metabolic glucose-sweetened with meals in obese men and women: influence of insulin resistance on plasma triglyceride responses.

Context: Compared with glucose-sweetened beverages, consumption of fructose-sweetened beverages with meals elevates postprandial plasma triglycerides and lowers 24-h insulin and leptin profiles in normal-weight women. The effects of fructose, compared with glucose, ingestion on metabolic profiles in obese subjects has not been studied. Objective: The objective of the study was to compare the effects of fructose- and glucose-sweet-ened beverages consumed with meals on hormones and metabolic substrates in obese subjects. Design and Setting: The study had a within-subject design conducted in the clinical and translational research center. Interventions: Subjects were studied under two conditions involving ingestion of mixed nutrient
Eﬀect of a booster dose of serogroup B meningococcal vaccine on antibody response to Neisseria meningitidis in mice vaccinated with diﬀerent immunization schedules

The generation and maintenance of memory antibody response by diﬀerent primary immunization schedules with the Cuban-produced outer membrane protein based vaccine was investigated in a murine model. We analyzed the duration of the antibody response (IgG-ELISA and bactericidal titer) and the eﬀect of a booster dose on the antibody response. The IgG avidity index was determined in an attempt to ﬁnd a marker for memory development. This study also included an analysis of IgG subclasses induced by primary and booster immunization. The speciﬁcity of bactericidal antibodies was investigated using local strains of the same serotype/serosubtype (4,7:P1.19,15) as the vaccine strain and mutant strains lacking major outer membrane proteins. A signiﬁcant recall response was induced by a booster dose given 7 months after a primary series of 2, 3 or 4 doses of vaccine. The primary antibody response showed a positive dose-eﬀect. In contrast, a negative dose-eﬀect was found on the booster bactericidal antibody response. There was a signiﬁcant increase in IgG1 levels after the fourth and booster doses. Three doses of vaccine were required to induce a signiﬁcant increase in IgG avidity. Two injections of vaccine induced a signiﬁcant antibody response to PorA protein, while 4 injections induced a larger range of speciﬁcities.
Improving Student Retention and Soft Skills: Faculty Experiences on Transi-tioning to Active Learning Approaches on First-Year Engineering Programs at Universidad Panamericana

Carmen Villa is the director for the Center for Innovation in Education at Universidad Panamericana. She teaches courses at the College of Engineering and at the School of Pedagogy. She received a B.Sc. degree in computer science engineering from Tec de Monterrey in Mexico City; a D.E. A. in computer science form the INPG in Grenoble, France; and a Phd.D. in educational administration and human resource development from Texas A&M University. Her research interests include underrepresented populations in higher education, cultural practices and teaching and their impact on education for Hispanic students, women and minorities in engineering.
Decomposition for Judgmental Forecasting and Estimation

Forecasters often need to estimate uncertain quantities, but with limited time and resources. Decomposition is a method for dealing with such problems by breaking down (decomposing) the estimation task down into a set of components that can be more readily estimated, and then combining the component estimates to produce a target estimate. Estimators can effectively apply decomposition to either multiplicative or segmented forecasts, though multiplicative decomposition is especially sensitive to correlated errors in component values. Decomposition is most used for highly uncertain estimates, such as ones having a large numerical value (e.g., millions or more) or quantities in an unfamiliar metric. When possible, multiple estimations should be used and the results aggregated. In addition, multiple decompositions can be applied to the same estimation problem and the results resolved into a single estimate. Decomposition should be used only when the estimation can make component estimates more accurately or more confidently than the target estimate.
Pollination biology of the Galápagos endemic, Tournefortia rufo-sericea (Boraginaceae)

Tournefortia rufo-sericea is an endemic member of the Galápagos angiosperm ﬂora. Although not uncommon within the archipelago, its status is presently listed as vulnerable and, as such, a complete knowledge of its pollination biology may prove useful in preventing its decline. Pollination experiments, ﬂower-visitor observations, nectar and ﬂu-orescence studies, as well as pollen : ovule ratio and pollen size studies were included in this investigation. The small, white ﬂowers of this species set fruit via open pollination (81%), autonomous self-pollination (80%), diurnal pollination (80%), and nocturnal pollination (85%). Ants are the most common visitor to the ﬂowers of this species, primarily during the day, whereas beetles and moths make visits at night. A small amount of nectar is presumably produced, as moths are often seen probing the corollas. Experiments with ﬂuorescent dust did not support inter-ﬂower pollen movement. The pollen : ovule ratio was 4972 : 1, which suggests xenogamy, and the mean pollen size was 21 µ m. Despite the pollen : ovule ratio, it appears that this species exhibits a breeding strategy of facultative autogamy, in which the majority of ﬂowers set fruit via autonomous self-pollination, but limited outcrossing may occur when pollinators are available. Conservation efforts for T. rufo-sericea should focus on the control or eradication of exotic species. © 2007 The Linnean Society of London, Botanical Journal of the Linnean Society , 2007, 153 , 21–31.
Data Visualization and Improving Accuracy of Attrition Using Stacked Classifier

—In any organization, managing Human Resources is an important task. Loss of employees lowers the overall productivity of the team and is also financially costly. Attrition of employees leaves behind a void that is costly to fill [2]. Machine Learning can be utilized for predicting an employee’s attrition. This paper evaluates the algorithms which can be used to predict the employee attrition on the IBM HR Analytics Employee Attrition & Performance dataset [1] taken from Kaggle with 35 attributes like Job Satisfaction, Percentage Salary Hike, Work Life Balance, etc. taking into consideration all aspects right from distance from home to the number of working hours. In order to predict Attrition, which is the dependent variable, classification algorithms under supervised learning are used. This paper provides the most optimal solution with the Stacked Classifier, an ensemble model which in this case averages Adaptive Boosting, Decision Tree Classifier and Support Vector Machine algorithms ultimately giving a high accuracy of 90.65.
Microbial Ecology Biological soil crusts from different soil substrates harbor distinct bacterial groups with the potential to produce exopolysaccharides and lipopolysaccharides

35 Biological soil crusts (BSCs) play an important role in improving soil stability and resistance to erosion by 36 promoting aggregation of soil particles. During initial development, BSCs are dominated by bacteria. Some 37 bacterial members of the crusts can contribute to the formation of soil aggregates by producing 38 exopolysaccharides and lipopolysaccharides that act as “glue” for soil particles. However, little is known 39 about the dynamics of “soil glue” producers during the initial development of BSCs. We hypothesized that 40 different types of initial BSCs harbor distinct producers of adhesive polysaccharides. To investigate this, 41 we performed a microcosm experiment, cultivating BSCs on two soil substrates. High-throughput shotgun 42 sequencing was used to obtain metagenomic information on microbiomes of bulk soils from the beginning 43 of the experiment, and BSCs sampled after four and ten months of incubation. We discovered that the 44 relative abundance of genes involved in the biosynthesis of exopolysaccharides and lipopolysaccharides 45 increased in BSCs compared to bulk soils. At the same time, communities of potential “soil glue” producers 46 that were highly similar in bulk soils underwent differentiation once BSCs started to develop. In the bulk 47 soils, the investigated genes were harbored mainly by Betaproteobacteria, whereas in the BSCs, the major 48 potential producers of adhesive polysaccharides were, aside from Alphaproteobacteria, either 49 Cyanobacteria or Chloroflexi and Acidobacteria. Overall, our results indicate that the potential to form 50 exopolysaccharides and lipopolysaccharides is an important bacterial trait for initial BSCs, and is 51 maintained despite the shifts in bacterial community composition during BSC development. 52
Mothers’ perceptions of the UK physical activity and sedentary behaviour guidelines for the early years (Start Active, Stay Active): a study.

Objectives: Higher levels of physical activity (PA) during early childhood have been associated with improved health outcomes, whereas sedentary behaviour (SB) has been associated with poorer health outcomes in children. In 2011, the UK produced guidelines for PA and SB in children under 5 years. Mothers have been identified as key influences in young children ’ s PA and SB. The aim of this study was to use in-depth interviews with mothers of preschool children to examine attitudes to the guidance. Design: Qualitative study using one-to-one, semistructured interviews; Data were analysed thematically using a framework approach. Setting: Mothers were recruited from preschools, nurseries, and mother and toddler groups located in four areas of varying socioeconomic status within Bristol, UK. Participants: 24 mothers who were considered the main or joint carer for a preschool child who was at least 2 years of age but had not yet started formal schooling. Results: Mothers are not aware of the UK PA and SB guidelines for the early years. They believe that their
Software Aging Forecasting Using Time Series Model

With the emergence of virtualization and cloud computing technologies, several services are housed on virtualization platform. Virtualization is the technology that many cloud service providers rely on for efficient management and coordination of the resource pool. As essential services are also housed on cloud platform, it is necessary to ensure continuous availability by implementing all necessary measures. Windows Active Directory is one such service that Microsoft developed for Windows domain networks. It is included in Windows Server operating systems as a set of processes and services for authentication and authorization of users and computers in a Windows domain type network. The service is required to run continuously without downtime. As a result, there are chances of accumulation of errors or garbage leading to software aging which in turn may lead to system failure and associated consequences. This results in software aging. In this work, software aging patterns of Windows active directory service is studied. Software aging of active directory needs to be predicted properly so that rejuvenation can be triggered to ensure continuous service delivery. In order to predict the accurate time, a model that uses time series forecasting technique is built.
Ambient Agent Model for Supporting Volunteers during Stress

Stress during natural disaster has been reported everywhere. Stress is a common psychological stimulus that occurs in a person caused by stressors. Natural disaster is a specific form of stressor that can carry wide area of implications within the population. It is the most common factor which contributes to high level of stress and other psychological problems of individuals. This article proposed an ambient agent model to support volunteers who experienced stress during flooding event. Set of properties and variables are identified through past literatures and series of interview to construct the model. A number of well-known relations between events and the courses of stress are summarized and it is shown that the model exhibits their patterns. Differential equations have been used in formalizing the model. Set of equations reflecting relations involved in the proposed model are presented. From the developed models, a variety of simulation experiments was conducted. The proposed model is essential and can be encapsulated within an intelligent agent or robots that can be used to support volunteers who experienced stress during flood.
Stability diagrams for continuous wide-range control of two mutually delay-coupled semiconductor lasers

The dynamics of two mutually delay-coupled semiconductor lasers has been frequently studied experimentally, numerically, and analytically either for weak or strong detuning between the lasers. Here, we present a systematic numerical investigation spanning all detuning ranges. We report high-resolution stability diagrams for wide ranges of the main control parameters of the laser, as described by the Lang–Kobayashi model. In particular, we detail the parameter influence on dynamical performance and map the distribution of chaotic pulsations and self-generated periodic spiking with arbitrary periodicity. Special attention is given to the unfolding of regular pulse packages for both symmetric and non-symmetric configurations with respect to detuning. The influence of the delay –time on the self-organization of periodic and chaotic laser phases as a function of the coupling and detuning is also described in detail.
Amitriptyline and nortriptyline inhibit interleukin-1 b and tumour necrosis factor- a release by rat mixed glial and microglial cell cultures

Pro-inﬂammatory cytokines, such as interleukin (IL)-1 b and tumour necrosis factor (TNF)- a have been suggested to be involved in the pathophysiology of depression and in the mechanism of action of antidepressant drugs. Until now the eﬀect of antidepressants on cytokines has been examined only in plasma, blood mononuclear cells and spleen, which reﬂect the activity of peripheral cytokine network. The aim of this study was to evaluate the eﬀect of amitriptyline and its metabolite nortriptyline on the release of IL-1 b and TNF- a by lipopolysaccharide (LPS)-activated rat mixed glial and microglial cell cultures. LPS stimulated the release of both cytokines. The exposure of mixed glial culture to amitriptyline and nortriptyline led to a decrease in both IL-1 b and TNF- a release. Moreover, amitriptyline reduced LPS-stimulated IL-1 b release by microglial cultures. Although amitriptyline reduced secretion of both cytokines, the drug did not aﬀect IL-1 b and TNF- a mRNAs in mixed cell cultures. Our study has shown for the ﬁrst time that amitriptyline and nortriptyline administered at concentrations which may be achieved in plasma and brain structures during treatment, inhibit the secretion of IL-1 b and TNF- a in rat mixed glial and microglial cell cultures. The obtained results support the previous observations that antidepressants are able to reduce peripheral release of pro-inﬂammatory cytokines and suggest that the cytokine network may be involved in the central mechanism of action of amitriptyline and nortriptyline.
Information Security in Smart Cities Using OpenID, SAML and OAuth to increase security in urban environment

— As the population living in cities and metropolis grows, the need for the transformation of a city into a Smart City grows, too. The Smart City concept refers to a broad range of definitions and technologies; among those it is possible to identify topics such as Internet of Things, ubiquity, empowering citizens, interoperability of services/systems, green systems, and open data. Whatever is the explored concept, all of them try to reach a single goal, namely, turn the city into a better place to live through the use of Information Technology. In this context, more specifically, interoperable environments play an important role as it provides means to connect different services by creating new systems that are able to provide a bigger variety of information to its users. In this matter, a new range of challenge rises; among those challenges, information security is one of the most important to be discussed. This paper presents a group of security issues that raises a dangerous concern in Smart City solutions, discusses how identity standards, such as OpenID, SAML and OAuth, impact on those issues.
BigDataOcean Project: Early anomaly detection from big maritime vessel traffic data

This paper discusses the concept and results of the BigDataOcean project, and specifically the anomaly detection pilot. While in the past, surveillance had suffered from a lack of data, current tracking technologies have transformed the problem into one of an overabundance of information, with needs which go well beyond the capabilities of traditional processing and algorithmic approaches. The major challenge faced today is developing the capacity to identify patterns emerging within huge amounts of data, fused from various sources and detecting outliers in a timely fashion, to act proactively and minimise the impact of possible threats. Within this context we first define an “anomaly”, before proceeding to present the BigDataOcean anomaly detection service; a service for the classification and early detection of anomalous vessel patterns. The service makes use of state-of-the-art big data technologies and novel algorithms which form the basis for a service capable of real time anomaly detection.
Why sin?

This article focuses on the theory put forward by Donald Capps in his The Depleted Self ( Sin in a narcissistic age . Minneapolis: Fortress Press, 1993) that there is a particular link between some forms of narcissism and the commitment of particular deadly sins. The data in the research reported in this paper robustly validate the idea that the psychological consequences of the bureaucratic consumer society, understood as a lack of individual well-being leading to a narcissistic defense, show the theological relevance of the deadly sins in understanding those conditions in society which on a global scale appear to threaten the individuality of the single person. Narcissism, both in its covert and overt mode, seems to be the most important variable in explaining the two factors of sin: resentment and desire. In addition to the effects already accounted for by well-being and narcissism, the possible contribution of religion and biography is explored.
Identification and characterization of differentially expressed exosomal microRNAs in bovine milk infected with Staphylococcus aureus CURRENT

Background: MicroRNAs (miRNA) in milk-derived exosomes may reflect pathophysiological changes caused by mastitis. This study profiled miRNAs in exosomes from both normal milk and mastitic milk infected by Staphylococcus aureus (S. aureus). The potential targets for differentially expressed (DE) miRNAs were predicted and the target genes for bta-miR-378 and bta-miR-185 were also validated. Results: Total RNA from milk exosomes was collected from healthy cows (n=3, the control group) and S. aureus infected cows (n=6, the SA group). Two hundred ninety miRNAs (221 known and 69 novel ones) were identified. Among them, 22 known and 15 novel miRNAs were differentially expressed. Target genes of DE miRNAs were significantly enriched in intracellular protein transport, endoplasmic reticulum and identical protein binding. Two miRNAs (bta-miR-378 and bta-miR-185) with high read counts and log 2 fold changes (>3.5) were significantly higher in mastitic milk infected with S. aureus. One target gene (VAT1L) of bta-miR-378 and five target genes (DYRK1B, MLLT3, HP1BP3, NPR2 and PGM1) of bta-miR-185 were validated. Conclusion: DE miRNAs in exosomes from normal and S. aureus infected milk were identified. The predicted targets for two DE miRNAs (bta-miR-378 and bta-miR-185) were further validated. The linkage between the validated target genes and diseases suggested that we should pay particular attention to exosome miRNAs from mastitic milk in terms of milk safety. Kyoto Genomes;
Chapter 2 The evolution of HPSG

HPSG was developed to express insights from theoretical linguistics in a precise formalism that was computationally tractable. It drew ideas from a wide variety of traditions in linguistics, logic, and computer science. Its chief architects were Carl Pollard and Ivan Sag, and its most direct precursors were Generalized Phrase Structure Grammar and Head Grammar. The theory has been applied in the construction of computational systems for the analysis of a variety of languages; a few of these systems have been used in practical applications. This chapter sketches the history of the development and application of the theory.
GE‐RM: Efficient global estimation and refined model for salient object detection via elaborate receptive fields

Recently, a deep learning technique has been introduced to saliency detection and has achieved promising results, but most of them are based on superpixel algorithms. Consequently, their performances and efficiencies depend largely on the results of a segmentation algorithm. Instead of classifying superpixels, we treat salient object detection as a dense prediction task. Fully convolutional networks show strong potential in dense prediction tasks, but the resolution and the quality of output maps need improving due to the loss of location information. In order to achieve high‐quality saliency maps, we propose a very efficient method using two Fully Convolutional Networks (FCNs) to extract global and local information respectively via different receptive fields. The global model produces accurate but coarse saliency maps, while the refined model produces full‐sized, fine results. We evaluate our method on eight public datasets and find that our method outperforms the other state‐of‐the‐art methods. Besides, our method runs much faster than the existing deep learning methods.
Behaviourally Cloning River Raid Agents

We investigate the feasibility and difﬁculties of using behavioural cloning to obtain player models using the 1982 video game River Raid. We attempt to clone both virtual game-playing agents (a ﬁxed (non-improving) reinforcement learning agent and a random agent sampling actions uniformly) as well as an actual human agent. The behavioural clones’ performance is evaluated on the micro-level through comparison of the state-conditioned and unconditional action distributions, and on the macro-level by comparing the (cloned) agents’ survival time and score per episode. Using our methodology, cloning virtual agents seems feasible to varying extents, even with somewhat limited amounts of data. However, our method fails to create reliable behavioural clones of human players. We conclude with a discussion of some of the more important reasons that might cause this: a lack of training data, the problem of covariate shift, and improving and inconsistent play-style over time.