Miracle-dz/UniFAR-FTUs huggingface dataset:
Yersinia-induced Apoptosis In Vivo Aids in the Establishment of a Systemic Infection of Mice

Pathogenic Yersinia cause a systemic infection in mice that is dependent on the presence of a large plasmid encoding a number of secreted virulence proteins called Yops. We previously demonstrated that a plasmid-encoded Yop, YopJ, was essential for inducing apoptosis in cultured macrophages. Here we report that YopJ is a virulence factor in mice and is important for the establishment of a systemic infection. The oral LD50 for a yopJ mutant Yersinia pseudotuberculosis increases 64-fold compared with wild-type. Although the yopJ mutant strain is able to reach the spleen of infected mice, the mutant strain seldom reaches the same high bacterial load that is seen with wild-type Yersinia strain and begins to be cleared from infected spleens on day 4 after infection. Furthermore, when in competition with wild-type Yersinia in a mixed infection, the yopJ mutant strain is deficient for spread from the Peyer's patches to other lymphoid tissue. We also show that wild-type Yersinia induces apoptosis in vivo of Mac-1+ cells from infected mesenteric lymph nodes or spleens, as measured by quantitative flow cytometry of TUNEL (Tdt-mediated dUTP–biotin nick-end labeling)-positive cells. The levels of Mac-1+, TUNEL+ cells from tissue infected with the yopJ mutant strain were equivalent to the levels detected in cells from uninfected tissue. YopJ is necessary for the suppression of TNF-α production seen in macrophages infected with wild-type Yersinia, based on previous in vitro studies (Palmer, L.E., S. Hobbie, J.E. Galan, and J.B. Bliska. 1998. Mol. Microbiol. 27:953–965). We conclude here that YopJ plays a role in the establishment of a systemic infection by inducing apoptosis and that this is consistent with the ability to suppress the production of the proinflammatory cytokine tumor necrosis factor α.
Collective-Intelligence Recommender Systems: Advancing Computer Tailoring for Health Behavior Change Into the 21st Century

Background What is the next frontier for computer-tailored health communication (CTHC) research? In current CTHC systems, study designers who have expertise in behavioral theory and mapping theory into CTHC systems select the variables and develop the rules that specify how the content should be tailored, based on their knowledge of the targeted population, the literature, and health behavior theories. In collective-intelligence recommender systems (hereafter recommender systems) used by Web 2.0 companies (eg, Netflix and Amazon), machine learning algorithms combine user profiles and continuous feedback ratings of content (from themselves and other users) to empirically tailor content. Augmenting current theory-based CTHC with empirical recommender systems could be evaluated as the next frontier for CTHC. Objective The objective of our study was to uncover barriers and challenges to using recommender systems in health promotion. Methods We conducted a focused literature review, interviewed subject experts (n=8), and synthesized the results. Results We describe (1) limitations of current CTHC systems, (2) advantages of incorporating recommender systems to move CTHC forward, and (3) challenges to incorporating recommender systems into CTHC. Based on the evidence presented, we propose a future research agenda for CTHC systems. Conclusions We promote discussion of ways to move CTHC into the 21st century by incorporation of recommender systems.
FlowMax: A Computational Tool for Maximum Likelihood Deconvolution of CFSE Time Courses

The immune response is a concerted dynamic multi-cellular process. Upon infection, the dynamics of lymphocyte populations are an aggregate of molecular processes that determine the activation, division, and longevity of individual cells. The timing of these single-cell processes is remarkably widely distributed with some cells undergoing their third division while others undergo their first. High cell-to-cell variability and technical noise pose challenges for interpreting popular dye-dilution experiments objectively. It remains an unresolved challenge to avoid under- or over-interpretation of such data when phenotyping gene-targeted mouse models or patient samples. Here we develop and characterize a computational methodology to parameterize a cell population model in the context of noisy dye-dilution data. To enable objective interpretation of model fits, our method estimates fit sensitivity and redundancy by stochastically sampling the solution landscape, calculating parameter sensitivities, and clustering to determine the maximum-likelihood solution ranges. Our methodology accounts for both technical and biological variability by using a cell fluorescence model as an adaptor during population model fitting, resulting in improved fit accuracy without the need for ad hoc objective functions. We have incorporated our methodology into an integrated phenotyping tool, FlowMax, and used it to analyze B cells from two NFκB knockout mice with distinct phenotypes; we not only confirm previously published findings at a fraction of the expended effort and cost, but reveal a novel phenotype of nfkb1/p105/50 in limiting the proliferative capacity of B cells following B-cell receptor stimulation. In addition to complementing experimental work, FlowMax is suitable for high throughput analysis of dye dilution studies within clinical and pharmacological screens with objective and quantitative conclusions.
COMPARISON OF SINGLE-DIMENSIONAL AND MULTI-DIMENSIONAL OPTIMIZATION APPROACHES IN ADAPTIVE MODEL PREDICTIVE CONTROL FOR AIR-FUEL RATIO OF SI ENGINES

With development of fast modern computers, it has become possible to extend model predictive control (MPC) method to automotive engine control systems, which is traditionally applied to plants with dynamics slow enough to allow computations between samples. In this paper MPC based on an adaptive neural network model is attempted for air fuel ratio (AFR), in which the model is adapted on-line to cope with nonlinear dynamics and parameter uncertainties. A radial basis function (RBF) network is employed and the recursive least squares (RLS) algorithm is used for weight updating. Based on the adaptive model, a MPC strategy for controlling air-fuel ratio is realized to a nonlinear simulation of the engines. Finally, both single-variable and multi-variable optimizations algorithms are used to find the optimal solution of MPC problems and are compared in term of their control performance and time consumption.
Auditory fMRI correlates of loudness perception for monaural and diotic stimulation

Loudness, the perceptual correlate of sound intensity, is formed along the auditory pathway from cochlea to cortex. While the loudness of a sound is largely determined by its level, there are more acoustical factors like bandwidth, duration, and also personal factors like the individual hearing status that may affect loudness. Binaural loudness summation refers to the finding that a binaural sound is perceived as louder than the same sound presented monaurally at the same level. Some hearing-impaired listeners show an increased binaural loudness summation. The background for this effect is unclear. We report an fMRI study with normal-hearing and hearing-impaired listeners for monaural and diotic stimuli. All listeners completed a loudness scaling procedure, to allow for data analysis with respect to sound intensity as well as individual loudness. Previous results indicated increasing brain activation with level and loudness at all stages of the auditory pathway, although with systematic differences across.
Efficient Shadows from Sampled Environment Maps

This paper addresses the problem of efficiently calculating shadows from environment maps. Since accurate rendering of shadows from environment maps requires hundreds of lights, the expensive computation is determining visibility from each pixel to each light direction, such as by ray-tracing. We show that coherence in both spatial and angular domains can be used to reduce the number of shadow rays that need to be traced. Specifically, we use a coarse-to-fine evaluation of the image, predicting visibility by reusing visibility calculations from four nearby pixels that have already been evaluated. This simple method allows us to explicitly mark regions of uncertainty in the prediction. By only tracing rays in these and neighboring directions, we are able to reduce the number of shadow rays traced by up to a factor of 20 while maintaining error rates below 0.01%. For many scenes, our algorithm can add shadowing from hundreds of lights at twice the cost of rendering without shadows.
Applications of NMR in Dairy Research

NMR is a robust analytical technique that has been employed to investigate the properties of many substances of agricultural relevance. NMR was first used to investigate the properties of milk in the 1950s and has since been employed in a wide range of studies; including properties analysis of specific milk proteins to metabolomics techniques used to monitor the health of dairy cows. In this brief review, we highlight the different uses of NMR in the dairy industry.
Design and Analysis of a New Feature-Distributed Malware

In this paper, we propose a new advanced malware that distributes its features to multiple software components in order to bypass various security policies such as application white listing and security tools like anti-virus. A tool that automatically generates such malware has been developed, and malware instances generated by this tool have been evaluated, showing the risks of the proposed malware. The new threat proposed in this paper is particularly important in modern computing platforms since they have progressed to more secure environments with various defensive techniques such as application-based permission and application white listing. In addition, anti-virus solutions are improving their detection techniques, especially based on behavioural properties. Our offensive technique is designed to overcome these hurdles so that appropriate defensive mitigations can be explored before the adversary develops such offensive technique as they always have done.
Effect of Green Tea on Plasma Adiponectin Levels: A Systematic Review and Meta-analysis of Randomized Controlled Clinical Trials

ABSTRACT Objective: Our objective was to perform a systematic review and meta-analysis on randomized controlled trials (RCTs) assessing the effect of green tea on serum adiponectin concentration. Method: We searched PubMed, ISI Web of Science, Scopus, and the Google Scholar databases up to November 2016. RCTs conducted among human adults studied the effects of green tea and green tea extract on serum adiponectin concentrations as an outcome variable was included. The weighted mean differences and standard deviations (SD) of change in serum adiponectin levels were calculated. The random effects model was used for deriving a summary of mean estimates with their corresponding SDs. The protocol was registered with PROSPERO (No. CRD42017057716). Result: Fourteen RCTs were eligible to be included in the systematic review and the meta-analysis. Our analysis showed that green tea did not significantly affect adiponectin concentrations in comparison with placebo (weighted mean difference = −0.02 µg/ml, 95% confidence interval [CI], −0.41, 0.38; p = 0.936). There was a substantial heterogeneity between studies (I2 = 91.7%; p < 0.0001). Subgroup analyses based on sex, type of intervention, continent, and body mass index (BMI) could not explain the sources of heterogeneity. Metaregression analyses revealed that the dose and duration of green tea ingestion did not have any effect on adiponectin concentrations. Conclusion: Green tea could not change the circulatory adiponectin levels. The dose and duration of green tea could not change the result. RCTs with longer follow-up periods and higher doses are needed to replicate our results.
Seborrheic Dermatitis and Dandruff: A Comprehensive Review

Seborrheic Dermatitis (SD) and dandruff are of a continuous spectrum of the same disease that affects the seborrheic areas of the body. Dandruff is restricted to the scalp, and involves itchy, flaking skin without visible inflammation. SD can affect the scalp as well as other seborrheic areas, and involves itchy and flaking or scaling skin, inflammation and pruritus. Various intrinsic and environmental factors, such as sebaceous secretions, skin surface fungal colonization, individual susceptibility, and interactions between these factors, all contribute to the pathogenesis of SD and dandruff. In this review, we summarize the current knowledge on SD and dandruff, including epidemiology, burden of disease, clinical presentations and diagnosis, treatment, genetic studies in humans and animal models, and predisposing factors. Genetic and biochemical studies and investigations in animal models provide further insight on the pathophysiology and strategies for better treatment.
The Concept of the ‘Situation” in Psychology

In the previous chapter, Dekker and Lutzhoft introduced the dichotomy of mind and matter that is typically suggested by discussions of Situation Awareness (SA). In some respects this chapter provides another tactic for escaping this dichotomy to address the issue of “what matters. ” That is, we propose an approach to situations that reflects the relations associated with satisfying functional goals in work ecologies. This approach is based on Rasmussen’s (1986) Abstraction Hierarchy and is consistent with Simon’s concept of problem space and Gibson’s concept of ecology. Note that the concepts of problem space and ecology do not reflect environments independent of observers. Rather, they describe situation constraints that are intimately related to observers in terms of intentions and intrinsic constraints on perception and action. Similarly, these constraints reflect the environments in terms of consequences and external constraints on perception and action. The abstraction hierarchy describes the confluence of internal and external constraints as a nested hierarchy of means-ends relations. In his famous parable of the ant traveling across a cluttered beach, Herbert Simon (1969; see also Vicente, 1999) offered a hypothesis that should be a challenge to all who are interested in SA. He wrote,
Plant Growth-Promoting Bacteria: Mechanisms and Applications

The worldwide increases in both environmental damage and human population pressure have the unfortunate consequence that global food production may soon become insufficient to feed all of the world's people. It is therefore essential that agricultural productivity be significantly increased within the next few decades. To this end, agricultural practice is moving toward a more sustainable and environmentally friendly approach. This includes both the increasing use of transgenic plants and plant growth-promoting bacteria as a part of mainstream agricultural practice. Here, a number of the mechanisms utilized by plant growth-promoting bacteria are discussed and considered. It is envisioned that in the not too distant future, plant growth-promoting bacteria (PGPB) will begin to replace the use of chemicals in agriculture, horticulture, silviculture, and environmental cleanup strategies. While there may not be one simple strategy that can effectively promote the growth of all plants under all conditions, some of the strategies that are discussed already show great promise.
A three-dimensional Galactic extinction model

A large-scale three-dimensional model of Galactic extinction is presented based on the Galactic dust distribution model of Drimmel & Spergel (2001). The extinction A V to any point within the Galactic disk can be quickly deduced using a set of three-dimensional Cartesian grids. Extinctions from the model are compared to empirical extinction measures, including lines-of-sight in and near the Galactic plane using optical and NIR extinction measures: in particular we show how extinction can be derived from NIR color-magnitude diagrams in the Galactic plane to a distance of 8 kiloparsec.
Formation control and obstacle avoidance for nonholonomic robots using decentralized MPC

In this paper, a decentralized model predictive control is proposed to solve the problem of formation stabilization and tracking for a group of nonholonomic mobile robots. Decentralized constrained control laws are developed by minimizing some objective functions. In the stabilization problem, the robots start from an arbitrary initial condition and reach a certain formation i.e. desired relative position and orientation. In the tracking problem under consideration, a desired trajectory is given to the leader of the formation. The followers keep a specified distance and orientation while following their leaders to keep a certain formation. Virtual force method is also used to avoid obstacles. All the agents in the formation avoid obstacles by producing a local virtual force with respect to their distance from the obstacle. Simulations for nonholonomic mobile robots formation stabilization and tracking with obstacle avoidance are performed to test the effectiveness of the proposed decentralized predictive control.
References for Data Stream Algorithms

Many scenarios, such as network analysis, utility monitoring, and financial applications, generate massive streams of data. These streams consist of millions or billions of simple updates every hour, and must be processed to extract the information described in tiny pieces. These notes provide an introduction to (and set of references for) data stream algorithms, and some of the techniques that have been developed over recent years to help mine the data while avoiding drowning in these massive flows of information.
Cost estimation in agile development projects

One of the key measures of the resilience of a project is its ability to reach completion on time and on budget, regardless of the turbulent and uncertain environment it may operate within. Cost estimation and tracking are therefore paramount when developing a system. Cost estimation has long been a difficult task in systems development, and although much research has focused on traditional methods, little is known about estimation in the agile method arena. This is ironic given that the reduction of cost and development time is the driving force behind the emergence of the agile method paradigm. This study investigates the applicability of current estimation techniques to more agile development approaches by focusing on four case studies of agile method use across different organisations. The study revealed that estimation inaccuracy was a less frequent occurrence for these companies. The frequency with which estimates are required on agile projects, typically at the beginning of each iteration, meant that the companies found estimation easier than when traditional approaches were used. The main estimation techniques used were expert knowledge and analogy to past projects. A number of recommendations can be drawn from the research: estimation models are not a necessary component of the process; fixed price budgets can prove beneficial for both developers and customers; and experience and past project data should be documented and used to aid the estimation of subsequent projects.
Asymptotic Analysis of Multi-Antenna Cognitive Radio Systems Using Extreme Value Theory

We consider a spectrum-sharing cognitive radio system with antenna selection applied at the secondary transmitter (ST). Based on the extreme value theory, we deduce a simple and accurate expression for the asymptotic distribution of the signal to interference plus noise ratio at the secondary receiver. Using this result, the asymptotic mean capacity and the outage capacity for the secondary user (SU) are derived. The obtained asymptotic capacities approach the exact results as the number of transmit antennas $N$ increases. Results indicate that the rate of the SU scales as $\log(N)$ when the transmit power of the ST is limited by the maximum allowable interference level, while rate scales as $\log(\log(N))$ if ST is limited by the maximum transmit power.
Effect of hyperthermia on calbindin-D 28k immunoreactivity in the hippocampal formation following transient global cerebral ischemia in gerbils

Calbindin D-28K (CB), a Ca2+-binding protein, maintains Ca2+ homeostasis and protects neurons against various insults. Hyperthermia can exacerbate brain damage produced by ischemic insults. However, little is reported about the role of CB in the brain under hyperthermic condition during ischemic insults. We investigated the effects of transient global cerebral ischemia on CB immunoreactivity as well as neuronal damage in the hippocampal formation under hyperthermic condition using immunohistochemistry for neuronal nuclei (NeuN) and CB, and Fluoro-Jade B histofluorescence staining in gerbils. Hyperthermia (39.5 ± 0.2°C) was induced for 30 minutes before and during transient ischemia. Hyperthermic ischemia resulted in neuronal damage/death in the pyramidal layer of CA1–3 area and in the polymorphic layer of the dentate gyrus at 1, 2, 5 days after ischemia. In addition, hyperthermic ischemia significantly decreaced CB immunoreactivity in damaged or dying neurons at 1, 2, 5 days after ischemia. In brief, hyperthermic condition produced more extensive and severer neuronal damage/death, and reduced CB immunoreactivity in the hippocampus following transient global cerebral ischemia. Present findings indicate that the degree of reduced CB immunoreactivity might be related with various neuronal damage/death overtime and corresponding areas after ischemic insults.
The Temporal Elements of Psychological Resilience: An Integrative Framework for the Study of Individuals, Families, and Communities

Psychological resilience has become a popular concept. Owing to that popularity, the word resilience has taken on myriad and often overlapping meanings. To be a useful framework for psychological research and theory, the authors argue, the study of resilience must explicitly reference each of four constituent temporal elements: (a) baseline or preadversity functioning, (b) the actual aversive circumstances, (c) postadversity resilient outcomes, and (d) predictors of resilient outcomes. Using this framework to review the existing literature, the most complete body of evidence is available on individual psychological resilience in children and adults. By contrast, the research on psychological resilience in families and communities is far more limited and lags well behind the rich theoretical perspective available from those literatures. The vast majority of research on resilience in families and communities has focused primarily on only one temporal element, possible predictors of resilient outcomes. Surprisingly, however, almost no scientific evidence is actually available for community or family resilient outcomes. We close by suggesting that there is room for optimism and that existing methods and measures could be relatively easily adapted to help fill these gaps. To that end, we propose a series of steps to guide future research.