Dynamic reasoning in a knowledge-based system

Any space based system, whether it is a robot arm assembling parts in space or an onboard system monitoring the space station, has to react to changes which cannot be foreseen. As a result, apart from having domain-specific knowledge as in current expert systems, a space based AI system should also have general principles of change. This paper presents a modal logic which can not only represent change but also reason with it. Three primitive operations, expansion, contraction and revision are introduced and axioms which specify how the knowledge base should change when the external world changes are also specified. Accordingly the notion of dynamic reasoning is introduced, which unlike the existing forms of reasoning, provide general principles of change. Dynamic reasoning is based on two main principles, namely minimize change and maximize coherence. A possible-world semantics which incorporates the above two principles is also discussed. The paper concludes by discussing how the dynamic reasoning system can be used to specify actions and hence form an integral part of an autonomous reasoning and planning system

The role of urea in neuronal degeneration and sensitization: an in vitro model of uremic neuropathy

Background: Uremic neuropathy commonly affects patients with chronic kidney disease (CKD), with painful sensations in the feet, followed by numbness and weakness in the legs and hands. The symptoms usually resolve following kidney transplantation, but the mechanisms of uremic neuropathy and associated pain symptoms remain unknown. As blood urea levels are elevated inpatients with CKD, we examined the morphological and functional effects of clinically observed levels of urea on sensory neurons. Methods: Rat DRG neurons were treated with 10or50 mMol/L urea for 48 hours, fixed and immunostained for PGP9.5 and βIII tubulin immunofluorescence, ,. Neurons were also immunostained for TRPV1, TRPM8 and Gap43 expression, and the capsaic insensitivity of urea or vehicle treated neurons was determined.Results: Urea treated neurons had degenerating neurites with diminished PGP9.5 immunofluorescence,and swollen, retracted growth cones. βIII tubulin appeared clumped after urea treatment. Neurite lengths were significantly reduced to 60 ± 2.6%(10 mMol/L, **P<0.01), and to 56.2± 3.3 %, (50 mMol/L, **P<0.01),urea treatmentfor 48 hours, compared with control neurons. Fewer neurons survived urea treatment,with 70.08 ± 13.3% remaining after 10 mMol/L (*P<0.05), and 61.49 ± 7.4 % after 50 mMol/L ureatreatment (**P<0.01), compared with controls. The proportion of neurons expressing TRPV1 wasreduced after urea treatment, but not TRPM8 expressing neurons. In functional studies, treatment with urea resulted in dose-dependent neuronal sensitization.Capsaicinresponses were significantly increased to 115.29 ± 3.4%(10 mMol/L, **P<0.01) and 125.3 ± 4.2%(50 mMol/L,**P<0.01), compared with controls. Sensitization due to urea was eliminated in the presence of the TRPV1 inhibitor SB705498, the MEKinhibitor PD98059,the PI3 kinase inhibitor LY294002, and the TRPM8 inhibitor AMTB. ConclusionNeurite degenerationandsensitization are consistent with uremic neuropathy,, and provide a disease-relevant model to test new treatments

SEISMIC: A Self-Exciting Point Process Model for Predicting Tweet Popularity

Social networking websites allow users to create and share content. Big information cascades of post resharing can form as users of these sites reshare others' posts with their friends and followers. One of the central challenges in understanding such cascading behaviors is in forecasting information outbreaks, where a single post becomes widely popular by being reshared by many users. In this paper, we focus on predicting the final number of reshares of a given post. We build on the theory of self-exciting point processes to develop a statistical model that allows us to make accurate predictions. Our model requires no training or expensive feature engineering. It results in a simple and efficiently computable formula that allows us to answer questions, in real-time, such as: Given a post's resharing history so far, what is our current estimate of its final number of reshares? Is the post resharing cascade past the initial stage of explosive growth? And, which posts will be the most reshared in the future? We validate our model using one month of complete Twitter data and demonstrate a strong improvement in predictive accuracy over existing approaches. Our model gives only 15% relative error in predicting final size of an average information cascade after observing it for just one hour.Comment: 10 pages, published in KDD 201

Physical Properties of Metallic Antiferromagnetic CaCo{1.86}As2 Single Crystals

We report studies of CaCo{1.86}As2 single crystals. The electronic structure is probed by angle-resolved photoemission spectroscopy (ARPES) measurements of CaCo{1.86}As2 and by full-potential linearized augmented-plane-wave calculations for the supercell Ca8Co15As16 (CaCo{1.88}As2). Our XRD crystal structure refinement is consistent with the previous combined refinement of x-ray and neutron powder diffraction data showing a collapsed-tetragonal ThCr2Si2-type structure with 7(1)% vacancies on the Co sites corresponding to the composition CaCo{1.86}As2 [D. G. Quirinale et al., Phys. Rev. B 88, 174420 (2013)]. The anisotropic magnetic susceptibility chi(T) data are consistent with the magnetic neutron diffraction data of Quirianale et al. that demonstrate the presence of A-type collinear antiferromagnetic order below the Neel temperature TN = 52(1) K with the easy axis being the tetragonal c axis. However, no clear evidence from the resistivity rho(T) and heat capacity Cp(T) data for a magnetic transition at TN is observed. A metallic ground state is demonstrated from band calculations and the rho(T), Cp(T) and ARPES data, and spin-polarized calculations indicate a competition between the A-type AFM and FM ground states. The Cp(T) data exhibit a large Sommerfield electronic coefficient reflecting a large density of states at the Fermi energy D(EF), consistent with the band structure calculations which also indicate a large D(EF) arising from Co 3d bands. At 1.8 K the M(H) data for H|| c exhibit a well-defined first-order spin-flop transition at an applied field of 3.5 T. The small ordered moment of 0.3 muB/Co obtained from the M(H) data at low T, the large exchange enhancement of chi and the lack of a self-consistent interpretation of the chi(T) and M(H,T) data in terms of a local moment Heisenberg model together indicate that the magnetism of CaCo{1.86}As2 is itinerant.Comment: 18 pages, 15 figures, 4 tables, 61 references; v2: extended the fits of experimental data by additional electronic structure calculations; published versio

DESIGN AND OPTIMIZATION OF DOXORUBICIN HCL PRONIOSOMES BY-DESIGN OF EXPERIMENT

Objective: The present research work was designed to formulate and optimize doxorubicin HCl proniosomes by design of experiment (DoE). Methods: A 4-factor, 3-level Box-Behnken design was used to explain multiple linear regression analysis and contour 3D plot responses. The independent variables selected were tween 20, cholesterol, hydration volume and sonication time; dependent variables percentage entrapment efficiency (PEE), mean vesicle size (MVS). Based on the Box-Behnken design 29 trial runs were studied and optimized for PEE and MVS. Further "Model F-Value" was calculated to confirm the omission of insignificant terms from the full-model equation to derive a multiple linear regression analysis to predict the PEE and MVS of niosomes derived from proniosomes. 3D plots were constructed to show the influence of independent variables on dependent variables. Results: PEE of doxorubicin HCl proniosomes was found to be in the range of 40.21-87.5%. The polynomial equation for PEE exhibited a good correlation coefficient (0.5524) and the "Model F-Value" of 7.41 implies the model is significant. P-values less than 0.0500 indicate model terms are significant. The MVS of doxorubicin HCl proniosomes was found to be in the range of 325.2 nm to 420.25 nm. The mathematical model generated for MVS (R2) was found to be significant with model F-value of 54.22. There is only a 0.01% chance that a "Model F-Value" this large could occur due to noise (P&lt;0.0500) and R2 value of 0.9004. Conclusion: The DoE of Box-Behnken design demonstrated the role of the derived equation, 3D plot in predicting the values of dependent variables for the preparation and optimization of doxorubicin HCl proniosomes. The results suggest that doxorubicin HCl proniosomes can act as a promising carrier