Long-term changes and variability of monthly extreme temperatures in India

Extreme temperatures are changing worldwide together with changes in the mean temperatures. This study investigates the long-term trends and variations of the monthly maximum and minimum temperatures and their effects on seasonal fluctuations in various climatological regions in India. The magnitude of the trends and their statistical significance were determined by parametric ordinary least square regression techniques and the variations were determined by the respective coefficient of variations. The results showed that the monthly maximum temperature increased, though unevenly, over the last century. Minimum temperature changes were more variable than maximum temperature changes, both temporally and spatially, with results of lesser significance. The results of this study are good indicators of Indian climate variability and its changes over the last century. © Springer-Verlag 2009.

Modelling of long-term ground response to tunnelling under St James's Park, London

Following a tunnel excavation in low-permeability soil, it is commonly observed that the ground surface continues to settle and ground loading on the tunnel lining changes, as the pore pressures in the ground approach a new equilibrium condition. The monitored ground response following the tunnelling under St James's Park, London, shows that the mechanism of subsurface deformation is composed of three different zones: swelling, consolidation and rigid body movement. The swelling took place in a confined zone above the tunnel crown, extending vertically to approximately 5 m above it. On the sides of the tunnel, the consolidation of the soil occurred in the zone primarily within the tunnel horizon, from the shoulder to just beneath the invert, and extending laterally to a large offset from the tunnel centreline. Above these swelling and consolidation zones the soil moved downward as a rigid body. In this study, soil-fluid coupled three-dimensional finite element analyses were performed to simulate the mechanism of long-term ground response monitored at St James's Park. An advanced critical state soil model, which can simulate the behaviour of London Clay in both drained and undrained conditions, was adopted for the analyses. The analysis results are discussed and compared with the field monitoring data. It is found that the observed mechanism of long-term subsurface ground and tunnel lining response at St James's Park can be simulated accurately only when stiffness anisotropy, the variation of permeability between different units within the London Clay and non-uniform drainage conditions for the tunnel lining are considered. This has important implications for future prediction of the long-term behaviour of tunnels in clays.

Controlled adhesion and growth of long term glial and neuronal cultures on Parylene-c

This paper explores the long term development of networks of glia and neurons on patterns of Parylene-C on a SiO 2 substrate. We harvested glia and neurons from the Sprague-Dawley (P1-P7) rat hippocampus and utilized an established cell patterning technique in order to investigate cellular migration, over the course of 3 weeks. This work demonstrates that uncontrolled glial mitosis gradually disrupts cellular patterns that are established early during culture. This effect is not attributed to a loss of protein from the Parylene-C surface, as nitrogen levels on the substrate remain stable over 3 weeks. The inclusion of the anti-mitotic cytarabine (Ara-C) in the culture medium moderates glial division and thus, adequately preserves initial glial and neuronal conformity to underlying patterns. Neuronal apoptosis, often associated with the use of Ara-C, is mitigated by the addition of brain derived neurotrophic factor (BDNF). We believe that with the right combination of glial inhibitors and neuronal promoters, the Parylene-C based cell patterning method can generate structured, active neural networks that can be sustained and investigated over extended periods of time. To our knowledge this is the first report on the concurrent application of Ara-C and BDNF on patterned cell cultures. © 2011 Delivopoulos, Murray.

Short- and long-term changes in joint co-contraction associated with motor learning as revealed from surface EMG.

In the field of motor control, two hypotheses have been controversial: whether the brain acquires internal models that generate accurate motor commands, or whether the brain avoids this by using the viscoelasticity of musculoskeletal system. Recent observations on relatively low stiffness during trained movements support the existence of internal models. However, no study has revealed the decrease in viscoelasticity associated with learning that would imply improvement of internal models as well as synergy between the two hypothetical mechanisms. Previously observed decreases in electromyogram (EMG) might have other explanations, such as trajectory modifications that reduce joint torques. To circumvent such complications, we required strict trajectory control and examined only successful trials having identical trajectory and torque profiles. Subjects were asked to perform a hand movement in unison with a target moving along a specified and unusual trajectory, with shoulder and elbow in the horizontal plane at the shoulder level. To evaluate joint viscoelasticity during the learning of this movement, we proposed an index of muscle co-contraction around the joint (IMCJ). The IMCJ was defined as the summation of the absolute values of antagonistic muscle torques around the joint and computed from the linear relation between surface EMG and joint torque. The IMCJ during isometric contraction, as well as during movements, was confirmed to correlate well with joint stiffness estimated using the conventional method, i.e., applying mechanical perturbations. Accordingly, the IMCJ during the learning of the movement was computed for each joint of each trial using estimated EMG-torque relationship. At the same time, the performance error for each trial was specified as the root mean square of the distance between the target and hand at each time step over the entire trajectory. The time-series data of IMCJ and performance error were decomposed into long-term components that showed decreases in IMCJ in accordance with learning with little change in the trajectory and short-term interactions between the IMCJ and performance error. A cross-correlation analysis and impulse responses both suggested that higher IMCJs follow poor performances, and lower IMCJs follow good performances within a few successive trials. Our results support the hypothesis that viscoelasticity contributes more when internal models are inaccurate, while internal models contribute more after the completion of learning. It is demonstrated that the CNS regulates viscoelasticity on a short- and long-term basis depending on performance error and finally acquires smooth and accurate movements while maintaining stability during the entire learning process.