The comparison of immunomodulatory effects of peripheral mononuclear cells against proliferation in U937 in junior elderly habitual morning swimming in Taiwan cohort

The present investigation was carried out to evaluate the effect of greater immunomodulatory effects of human peripheral blood mononuclear cells against proliferation in human leukemia cells. To achieve this, cells U937 in junior elderly (with cool environmental physical activities) subjects with habitual morning swimming and sedentary lifestyle were recruited in relatively cool season in Taiwan; the isolated human peripheral blood mononuclear cells were stimulated by phytohemagglutinin to obtain the conditioned medium which contains various cytokines. However, the differential effects of the conditioned medium on growth inhibition in U937 leukemia cells were observed. The cytokines, including interferon-gamma, tumor necrosis factors-alpha and interleukine-2 secreted into conditioned medium were higher in the morning-swimming subjects than in the sedentary-lifestyle ones. Similarly, serum white blood cell, creatine phosphokinase, immunoglobulin G and immunoglobulin A in the morning-swimming and sedentary-lifestyle groups indicated that no further inflammatory status existed in the morning-swimming group. In summary, greater immunomodulatory effects of human peripheral blood mononuclear cells against proliferation in human leukemia cells U937 in junior elderly subjects came from the effects of regular moderate exercise in cool temperature rather than of inflammatory effects.Keywords: Immunomodulatory, junior elderly, morning swimming, leukemia, U937, human peripheral blood mononuclear cell

Introducing a framework to assess newly created questions with Natural Language Processing

Statistical models such as those derived from Item Response Theory (IRT) enable the assessment of students on a specific subject, which can be useful for several purposes (e.g., learning path customization, drop-out prediction). However, the questions have to be assessed as well and, although it is possible to estimate with IRT the characteristics of questions that have already been answered by several students, this technique cannot be used on newly generated questions. In this paper, we propose a framework to train and evaluate models for estimating the difficulty and discrimination of newly created Multiple Choice Questions by extracting meaningful features from the text of the question and of the possible choices. We implement one model using this framework and test it on a real-world dataset provided by CloudAcademy, showing that it outperforms previously proposed models, reducing by 6.7% the RMSE for difficulty estimation and by 10.8% the RMSE for discrimination estimation. We also present the results of an ablation study performed to support our features choice and to show the effects of different characteristics of the questions' text on difficulty and discrimination.Comment: Accepted at the International Conference of Artificial Intelligence in Educatio

Integrating knowledge tracing and item response theory: A tale of two frameworks

Traditionally, the assessment and learning science commu-nities rely on different paradigms to model student performance. The assessment community uses Item Response Theory which allows modeling different student abilities and problem difficulties, while the learning science community uses Knowledge Tracing, which captures skill acquisition. These two paradigms are complementary - IRT cannot be used to model student learning, while Knowledge Tracing assumes all students and problems are the same. Recently, two highly related models based on a principled synthesis of IRT and Knowledge Tracing were introduced. However, these two models were evaluated on different data sets, using different evaluation metrics and with different ways of splitting the data into training and testing sets. In this paper we reconcile the models' results by presenting a unified view of the two models, and by evaluating the models under a common evaluation metric. We find that both models are equivalent and only differ in their training procedure. Our results show that the combined IRT and Knowledge Tracing models offer the best of assessment and learning sciences - high prediction accuracy like the IRT model, and the ability to model student learning like Knowledge Tracing

Intraindividual association between shift work and risk of drinking problems: Data from the Finnish Public Sector Cohort

OBJECTIVES: Studies concerning the association between shift work and drinking problems showed inconsistent results. We used data from a large occupational cohort to examine the association between shift work and different types of drinking behaviour. METHODS: A total of 93 121 non-abstinent workers from the Finnish Public Sector Study were enrolled in the study. Six waves of survey data were collected between 2000 and 2017. Work schedules were categorised as regular day, non-night shift and night shift work, and shift intensities were calculated from registered working hour data. Two indicators of adverse drinking behaviour were measured: at-risk drinking (>7 and >14 drinks per week in women and men, respectively) and high-intensity drinking (measured as pass-out experience). Intraindividual analysis was conducted using fixed-effects regression to examine the association between shift work and drinking behaviours. RESULTS: Compared with regular day work, night shift work was associated with an increased risk of high-intensity drinking (OR 1.28, 95% CI 1.07 to 1.52) but a lower risk of at-risk drinking (OR 0.85, 95% CI 0.74 to 0.99). Shift workers who worked long shifts had a lower risk of at-risk drinking compared with those who rarely worked long shifts (OR 0.58, 95% CI 0.37 to 0.93). CONCLUSIONS: Associations between shift work and alcohol use vary according to drinking patterns. Workers engaged in high-intensity drinking more often during night shift schedules compared with day work, but did not drink averagely higher volume

Probabilistic Guarantees for Safe Deep Reinforcement Learning

Deep reinforcement learning has been successfully applied to many control tasks, but the application of such agents in safety-critical scenarios has been limited due to safety concerns. Rigorous testing of these controllers is challenging, particularly when they operate in probabilistic environments due to, for example, hardware faults or noisy sensors. We propose MOSAIC, an algorithm for measuring the safety of deep reinforcement learning agents in stochastic settings. Our approach is based on the iterative construction of a formal abstraction of a controller's execution in an environment, and leverages probabilistic model checking of Markov decision processes to produce probabilistic guarantees on safe behaviour over a finite time horizon. It produces bounds on the probability of safe operation of the controller for different initial configurations and identifies regions where correct behaviour can be guaranteed. We implement and evaluate our approach on agents trained for several benchmark control problems

Exploiting Polyhedral Symmetries in Social Choice

A large amount of literature in social choice theory deals with quantifying the probability of certain election outcomes. One way of computing the probability of a specific voting situation under the Impartial Anonymous Culture assumption is via counting integral points in polyhedra. Here, Ehrhart theory can help, but unfortunately the dimension and complexity of the involved polyhedra grows rapidly with the number of candidates. However, if we exploit available polyhedral symmetries, some computations become possible that previously were infeasible. We show this in three well known examples: Condorcet's paradox, Condorcet efficiency of plurality voting and in Plurality voting vs Plurality Runoff.Comment: 14 pages; with minor improvements; to be published in Social Choice and Welfar

Mellin-Barnes Representation for the Genus-g Finite Temperature String Theory

The Mellin-Barnes representation for the free energy of the genus-$g$ string is constructed. It is shown that the interactions of the open bosonic string do not modify the critical (Hagedorn) temperature. However,for the sectors having a spinor structure, the critical temperature exists also for all $g$ and depends on the windings. The appearance of a periodic structure is briefly discussed.Comment: 9 pages, report UTF 294 (1993

Model construction of telephone intervention transitional care among early discharged colostomy patients

2011-2012 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Loss of Dendritic HCN1 Subunits Enhances Cortical Excitability and Epileptogenesis

Hyperpolarization-activated cation nonselective 1 (HCN1) plasticity in entorhinal cortical (EC) and hippocampal pyramidal cell dendrites is a salient feature of temporal lobe epilepsy. However, the significance remains undetermined. We demonstrate that adult HCN1 null mice are more susceptible to kainic acid-induced seizures. After termination of these with an anticonvulsant, the mice also developed spontaneous behavioral seizures at a significantly more rapid rate than their wild-type littermates. This greater seizure susceptibility was accompanied by increased spontaneous activity in HCN1(-/-) EC layer III neurons. Dendritic I-h in these neurons was ablated, too. Consequentially, HCN1(-/-) dendrites were more excitable, despite having significantly more hyperpolarized resting membrane potentials (RMPs). In addition, the integration of EPSPs was enhanced considerably such that, at normal RMP, a 50 Hz train of EPSPs produced action potentials in HCN1(-/-) neurons. As a result of this enhanced pyramidal cell excitability, spontaneous EPSC frequency onto HCN1(-/-) neurons was considerably greater than that onto wild types, causing an imbalance between normal excitatory and inhibitory synaptic activity. These results suggest that dendritic HCN channels are likely to play a critical role in regulating cortical pyramidal cell excitability. Furthermore, these findings suggest that the reduction in dendritic HCN1 subunit expression during epileptogenesis is likely to facilitate the disorder