Interactions of allergens and irritants in susceptible populations in producing lung dysfunction: implications for future research.

Environmental agents, when applied in combination or sequentially, can induce a wide variety of adverse health effects in humans. To determine the effects of sequential allergen challenge and acid exposure on human bronchial epithelial cell function, we subjected normal, nonallergic control and ragweed-allergic individuals to bronchoscopic segmental ragweed challenge in vivo. We harvested bronchial epithelial cells by brush biopsy both before challenge and 24 hr after challenge and exposed cells to an acid stress in vitro (pH 5 for 3 hr), followed by a 1-hr recovery period at normal pH. In normal, nonallergic subjects, segmental allergen challenge produced no effects on ciliary activity; pH 5 exposure produced reduced ciliary activity (a decrease in the percent of the initially active area), with significant recovery after cells were returned to a normal pH. Ciliary activity from allergic subjects was also inhibited by pH 5 exposure; however, activity was not recovered when cells were placed in medium of normal pH. Ciliary activity in allergics who developed a stress response postantigen challenge, as determined by an induction of the 27 kDa stress (heat shock) protein, displayed no ciliary dysfunction when exposed to a pH 5 stress. In this case, a stress sufficient to provoke a heat shock (stress) protein (HSP) response (but not one that produced more severe lung injury and did not provoke an HSP response) protected cells from a subsequent acid stress. Because of our observations and recent findings reported in the literature, we suggest that in order to define the wide variety of health effects of environmental agents, control as well as at-risk populations should be studied and the ability to define potentially beneficial as well as detrimental effects should be built into the experimental design. Inclusion of different and novel end points also should be considered

Improving estimation efficiency for regression with MNAR covariates

For regression with covariates missing not at random where the missingness depends on the missing covariate values, complete‐case (CC) analysis leads to consistent estimation when the missingness is independent of the response given all covariates, but it may not have the desired level of efficiency. We propose a general empirical likelihood framework to improve estimation efficiency over the CC analysis. We expand on methods in Bartlett et al. (2014, Biostatistics 15, 719–730) and Xie and Zhang (2017, Int J Biostat 13, 1–20) that improve efficiency by modeling the missingness probability conditional on the response and fully observed covariates by allowing the possibility of modeling other data distribution‐related quantities. We also give guidelines on what quantities to model and demonstrate that our proposal has the potential to yield smaller biases than existing methods when the missingness probability model is incorrect. Simulation studies are presented, as well as an application to data collected from the US National Health and Nutrition Examination Survey.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154274/1/biom13131-sup-0002-web_supp.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154274/2/biom13131_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154274/3/biom13131.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154274/4/biom13131-sup-0003-supmat.pd

Time-varying Learning and Content Analytics via Sparse Factor Analysis

We propose SPARFA-Trace, a new machine learning-based framework for time-varying learning and content analytics for education applications. We develop a novel message passing-based, blind, approximate Kalman filter for sparse factor analysis (SPARFA), that jointly (i) traces learner concept knowledge over time, (ii) analyzes learner concept knowledge state transitions (induced by interacting with learning resources, such as textbook sections, lecture videos, etc, or the forgetting effect), and (iii) estimates the content organization and intrinsic difficulty of the assessment questions. These quantities are estimated solely from binary-valued (correct/incorrect) graded learner response data and a summary of the specific actions each learner performs (e.g., answering a question or studying a learning resource) at each time instance. Experimental results on two online course datasets demonstrate that SPARFA-Trace is capable of tracing each learner's concept knowledge evolution over time, as well as analyzing the quality and content organization of learning resources, the question-concept associations, and the question intrinsic difficulties. Moreover, we show that SPARFA-Trace achieves comparable or better performance in predicting unobserved learner responses than existing collaborative filtering and knowledge tracing approaches for personalized education

Distill-and-Compare: Auditing Black-Box Models Using Transparent Model Distillation

Black-box risk scoring models permeate our lives, yet are typically proprietary or opaque. We propose Distill-and-Compare, a model distillation and comparison approach to audit such models. To gain insight into black-box models, we treat them as teachers, training transparent student models to mimic the risk scores assigned by black-box models. We compare the student model trained with distillation to a second un-distilled transparent model trained on ground-truth outcomes, and use differences between the two models to gain insight into the black-box model. Our approach can be applied in a realistic setting, without probing the black-box model API. We demonstrate the approach on four public data sets: COMPAS, Stop-and-Frisk, Chicago Police, and Lending Club. We also propose a statistical test to determine if a data set is missing key features used to train the black-box model. Our test finds that the ProPublica data is likely missing key feature(s) used in COMPAS.Comment: Camera-ready version for AAAI/ACM AIES 2018. Data and pseudocode at https://github.com/shftan/auditblackbox. Previously titled "Detecting Bias in Black-Box Models Using Transparent Model Distillation". A short version was presented at NIPS 2017 Symposium on Interpretable Machine Learnin

In vitro fermentation of different ratios of alfalfa and starch or inulin incubated with an equine faecal inoculum

The aim of this work was to assess the impact of substituting starch (S) or inulin (I) with high-temperature dried alfalfa (HTDA) as substrates for in vitro fermentation with an equine faecal inoculum. A series of experiments were conducted to assess the fermentation kinetics of HTDA (chopped [CA] or ground [GA]) and either S or I mixed in the following ratios; 100:0, 80:20, 60:40, 40:60 and 20:80S/I: CA/GA, respectively. For each experiment, a further set of bottles containing identical ratios of S/I: CA/GA were also prepared, with the exception that the alfalfa received a simulated foregut digestion treatment (SFD) as prior to incubation. Total gas production increased (P<0.05) as the ratio of S/I to alfalfa increased. Total gas production was lower in bottles containing SFD-treated alfalfa (P<0.001). Dry matter loss decreased proportionately with increasing level of alfalfa substitution of S/I (P<0.001). Values for pH were lower in bottles containing S or I, with pH values in bottles containing S alone falling to almost 6 and those with I dropping to pH 5 and under. However, the substitution of S or I with 40% alfalfa produced pH values above 6.7, which is within physiological levels encountered in the large intestine of the horse. Consequently, there appears to be considerable potential to buffer the deleterious effects of high-starch/fructan diets with the substitution of these substrates with high-temperature dried alfalfa

Alterations in vasodilator-stimulated phosphoprotein (VASP) phosphorylation: associations with asthmatic phenotype, airway inflammation and β\u3csub\u3e2\u3c/sub\u3e-agonist use

Background Vasodilator-stimulated phosphoprotein (VASP) mediates focal adhesion, actin filament binding and polymerization in a variety of cells, thereby inhibiting cell movement. Phosphorylation of VASP via cAMP and cGMP dependent protein kinases releases this brake on cell motility. Thus, phosphorylation of VASP may be necessary for epithelial cell repair of damage from allergen-induced inflammation. Two hypotheses were examined: (1) injury from segmental allergen challenge increases VASP phosphorylation in airway epithelium in asthmatic but not nonasthmatic normal subjects, (2) regular in vivo β2-agonist use increases VASP phosphorylation in asthmatic epithelium, altering cell adhesion. Methods Bronchial epithelium was obtained from asthmatic and non-asthmatic normal subjects before and after segmental allergen challenge, and after regularly inhaled albuterol, in three separate protocols. VASP phosphorylation was examined in Western blots of epithelial samples. DNA was obtained for β2-adrenergic receptor haplotype determination. Results Although VASP phosphorylation increased, it was not significantly greater after allergen challenge in asthmatics or normals. However, VASP phosphorylation in epithelium of nonasthmatic normal subjects was double that observed in asthmatic subjects, both at baseline and after challenge. Regularly inhaled albuterol significantly increased VASP phosphorylation in asthmatic subjects in both unchallenged and antigen challenged lung segment epithelium. There was also a significant increase in epithelial cells in the bronchoalveolar lavage of the unchallenged lung segment after regular inhalation of albuterol but not of placebo. The haplotypes of the β2-adrenergic receptor did not appear to associate with increased or decreased phosphorylation of VASP. Conclusion Decreased VASP phosphorylation was observed in epithelial cells of asthmatics compared to nonasthmatic normals, despite response to β-agonist. The decreased phosphorylation does not appear to be associated with a particular β2-adrenergic receptor haplotype. The observed decrease in VASP phosphorylation suggests greater inhibition of actin reorganization which is necessary for altering attachment and migration required during epithelial repair

Challenges in Collaborative HRI for Remote Robot Teams

Collaboration between human supervisors and remote teams of robots is highly challenging, particularly in high-stakes, distant, hazardous locations, such as off-shore energy platforms. In order for these teams of robots to truly be beneficial, they need to be trusted to operate autonomously, performing tasks such as inspection and emergency response, thus reducing the number of personnel placed in harm's way. As remote robots are generally trusted less than robots in close-proximity, we present a solution to instil trust in the operator through a `mediator robot' that can exhibit social skills, alongside sophisticated visualisation techniques. In this position paper, we present general challenges and then take a closer look at one challenge in particular, discussing an initial study, which investigates the relationship between the level of control the supervisor hands over to the mediator robot and how this affects their trust. We show that the supervisor is more likely to have higher trust overall if their initial experience involves handing over control of the emergency situation to the robotic assistant. We discuss this result, here, as well as other challenges and interaction techniques for human-robot collaboration.Comment: 9 pages. Peer reviewed position paper accepted in the CHI 2019 Workshop: The Challenges of Working on Social Robots that Collaborate with People (SIRCHI2019), ACM CHI Conference on Human Factors in Computing Systems, May 2019, Glasgow, U

Detection of trend changes in time series using Bayesian inference

Change points in time series are perceived as isolated singularities where two regular trends of a given signal do not match. The detection of such transitions is of fundamental interest for the understanding of the system's internal dynamics. In practice observational noise makes it difficult to detect such change points in time series. In this work we elaborate a Bayesian method to estimate the location of the singularities and to produce some confidence intervals. We validate the ability and sensitivity of our inference method by estimating change points of synthetic data sets. As an application we use our algorithm to analyze the annual flow volume of the Nile River at Aswan from 1871 to 1970, where we confirm a well-established significant transition point within the time series.Comment: 9 pages, 12 figures, submitte

Large-scale Nonlinear Variable Selection via Kernel Random Features

We propose a new method for input variable selection in nonlinear regression. The method is embedded into a kernel regression machine that can model general nonlinear functions, not being a priori limited to additive models. This is the first kernel-based variable selection method applicable to large datasets. It sidesteps the typical poor scaling properties of kernel methods by mapping the inputs into a relatively low-dimensional space of random features. The algorithm discovers the variables relevant for the regression task together with learning the prediction model through learning the appropriate nonlinear random feature maps. We demonstrate the outstanding performance of our method on a set of large-scale synthetic and real datasets.Comment: Final version for proceedings of ECML/PKDD 201

Reliable estimation of prediction uncertainty for physico-chemical property models

The predictions of parameteric property models and their uncertainties are sensitive to systematic errors such as inconsistent reference data, parametric model assumptions, or inadequate computational methods. Here, we discuss the calibration of property models in the light of bootstrapping, a sampling method akin to Bayesian inference that can be employed for identifying systematic errors and for reliable estimation of the prediction uncertainty. We apply bootstrapping to assess a linear property model linking the 57Fe Moessbauer isomer shift to the contact electron density at the iron nucleus for a diverse set of 44 molecular iron compounds. The contact electron density is calculated with twelve density functionals across Jacob's ladder (PWLDA, BP86, BLYP, PW91, PBE, M06-L, TPSS, B3LYP, B3PW91, PBE0, M06, TPSSh). We provide systematic-error diagnostics and reliable, locally resolved uncertainties for isomer-shift predictions. Pure and hybrid density functionals yield average prediction uncertainties of 0.06-0.08 mm/s and 0.04-0.05 mm/s, respectively, the latter being close to the average experimental uncertainty of 0.02 mm/s. Furthermore, we show that both model parameters and prediction uncertainty depend significantly on the composition and number of reference data points. Accordingly, we suggest that rankings of density functionals based on performance measures (e.g., the coefficient of correlation, r2, or the root-mean-square error, RMSE) should not be inferred from a single data set. This study presents the first statistically rigorous calibration analysis for theoretical Moessbauer spectroscopy, which is of general applicability for physico-chemical property models and not restricted to isomer-shift predictions. We provide the statistically meaningful reference data set MIS39 and a new calibration of the isomer shift based on the PBE0 functional.Comment: 49 pages, 9 figures, 7 table