BACKGROUND AND PURPOSEAMG 181 is a human anti-a4b7 antibody currently in phase 1 and 2 trials in subjects with inflammatory bowel diseases. AMG 181 specifically targets the a4b7 integrin heterodimer, blocking its interaction with mucosal addressin cell adhesion molecule-1 (MAdCAM-1), the principal ligand that mediates a4b7 T cell gut-homing. EXPERIMENTAL APPROACHWe studied the in vitro pharmacology of AMG 181, and the pharmacokinetics and pharmacodynamics of AMG 181 after single or weekly i.v. or s.c. administration in cynomolgus monkeys for up to 13 weeks. KEY RESULTSAMG 181 bound to a4b7, but not a4b1 or aEb7, and potently inhibited a4b7 binding to MAdCAM-1 (but not vascular cell adhesion molecule-1) and thus inhibited T cell adhesion. Following single i.v. administration, AMG 181 Cmax was dose proportional from 0.01 to 80 mg·kg -1 , while AUC increased more than dose proportionally. Following s.c. administration, dose-proportional exposure was observed with single dose ranging from 5 to 80 mg·kg -1 and after 13 weekly doses at levels between 20 and 80 mg·kg -1 . AMG 181 accumulated two-to threefold after 13 weekly 80 mg·kg -1 i.v. or s.c. doses. AMG 181 had an s.c. bioavailability of 80%. The linear elimination half-life was 12 days, with a volume of distribution close to the intravascular plasma space. The mean trend for the magnitude and duration of AMG 181 exposure, immunogenicity, a4b7 receptor occupancy and elevation in gut-homing CD4+ central memory T cell count displayed apparent correlations. CONCLUSIONS AND IMPLICATIONSAMG 181 has in vitro pharmacology, and pharmacokinetic/pharmacodynamic and safety characteristics in cynomolgus monkeys that are suitable for further investigation in humans.

The modern 'machine-learning models' are a section of artificially intelligent machines used to implement complex models, which can learn and improve from experience with respect to certain class of jobs, without being specifically programmed. In the present analysis, a comparative study is made of the popular machine-learning techniques regarding the prediction of auroral activity as reflected by the auroral electrojet index (AE index) during geomagnetically disturbed periods. The study also explores the suitability of the online sequential version of the best machine-learning algorithm, which has the potential for real-time forecast of the AE index from short-time input datasets with extremely fast convergence than batch-training methods. The study discusses the need for the correct choice of the input dataset that can be used for predicting the AE index from several combinations of input datasets which include coupling functions, geomagnetic indices and solar wind parameters. The study reveals that extreme learning machine and its online sequential version are promising models which could predict the AE index extremely fast with a high degree of accuracy even during disturbance periods. The study also shows that the choice of the polar cap index (PC index) as an input parameter is extremely important for an accurate prediction of the AE index.

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This article investigates the effects of different steering geometries on the steering response, system stability and frequency response of bicycles. A computer model was developed using Simulink TM . The model simulates different bicycle designs allowing several different steering geometries to be quantified in terms of performance. It was validated by data available in literature and from an experimental investigation conducted with a bicycle fitted with steering and roll sensors. Three key variables were examined in detail: the head tube angle, front fork rake and bicycle speed. Their actual importance was determined by systematically changing each key variable one at a time while keeping all other terms constant. Large variations in roll and yaw responses show how sensitive bicycles are to small changes in head tube angles and rake dimensions. At higher speeds, the observed steering responses support the common observation that bicycles are more stable and easier to ride at higher speeds. These simulations show the importance of correctly designing a bicycle's steering geometry in order to optimise steering performance and the sensitivity of bicycles to small changes in geometry.

The first super storm of solar cycle 24 occurred on "St. Patrick's Day" (17 March 2015), with a minimum Dst level of − 223 nT. Five major substorms in this super storm were selected, with minimum values of local electrojet index (IL) ranging from − 1662 to − 673 nT. The selected substorms are all in the 22:00 MLT-06:00 MLT sector of the auroral oval region showing associated Pi2s and negative bays in the H-component of magnetograms, derived from the IMAGE magnetometer longitudinal (Fennoscandia) chain. The solar wind energy input is estimated as time integral of Akasofu's epsilon parameter, determined from the SuperMAG magnetometer. The local ionospheric Joule heating (local JH) rate, in the midnight or post-midnight sectors, is estimated using a modified form of Ahn's empirical conversion. The Global ionospheric Joule heating rate in the northern hemisphere (global JH) is taken from OpenGGCM model. For the substorm in the main phase of the superstorm, the local JH consumes only 9% (8%, if the IL is replaced by AL index in the empirical conversion relation) of the global JH. However, 40-86% (39-48%, if the IL is replaced by AL index in the empirical conversion relation) of global JH is consumed as local JH for the remaining substorms.