Effects of increasing neuromuscular electrical stimulation current intensity on cortical sensorimotor network activation: A time domain fNIRS study

Neuroimaging studies have shown neuromuscular electrical stimulation (NMES)-evoked movements activate regions of the cortical sensorimotor network, including the primary sensorimotor cortex (SMC), premotor cortex (PMC), supplementary motor area (SMA), and secondary somatosensory area (S2), as well as regions of the prefrontal cortex (PFC) known to be involved in pain processing. The aim of this study, on nine healthy subjects, was to compare the cortical network activation profile and pain ratings during NMES of the right forearm wrist extensor muscles at increasing current intensities up to and slightly over the individual maximal tolerated intensity (MTI), and with reference to voluntary (VOL) wrist extension movements. By exploiting the capability of the multi-channel time domain functional near-infrared spectroscopy technique to relate depth information to the photon time-of-flight, the cortical and superficial oxygenated (O₂Hb) and deoxygenated (HHb) hemoglobin concentrations were estimated. The O₂Hb and HHb maps obtained using the General Linear Model (NIRS-SPM) analysis method, showed that the VOL and NMES-evoked movements significantly increased activation (i.e., increase in O₂Hb and corresponding decrease in HHb) in the cortical layer of the contralateral sensorimotor network (SMC, PMC/SMA, and S2). However, the level and area of contralateral sensorimotor network (including PFC) activation was significantly greater for NMES than VOL. Furthermore, there was greater bilateral sensorimotor network activation with the high NMES current intensities which corresponded with increased pain ratings. In conclusion, our findings suggest that greater bilateral sensorimotor network activation profile with high NMES current intensities could be in part attributable to increased attentional/pain processing and to increased bilateral sensorimotor integration in these cortical regions.

Comparison of decision tree, support vector machines, and Bayesian network approaches for classification of falls in Parkinson’s disease

Being able to accurately predict the risk of falling is crucial in patients with Parkinson’s dis- ease (PD). This is due to the unfavorable effect of falls, which can lower the quality of life as well as directly impact on survival. Three methods considered for predicting falls are decision trees (DT), Bayesian networks (BN), and support vector machines (SVM). Data on a 1-year prospective study conducted at IHBI, Australia, for 51 people with PD are used. Data processing are conducted using rpart and e1071 packages in R for DT and SVM, con- secutively; and Bayes Server 5.5 for the BN. The results show that BN and SVM produce consistently higher accuracy over the 12 months evaluation time points (average sensitivity and specificity > 92%) than DT (average sensitivity 88%, average specificity 72%). DT is prone to imbalanced data so needs to adjust for the misclassification cost. However, DT provides a straightforward, interpretable result and thus is appealing for helping to identify important items related to falls and to generate fallers’ profiles.

Salan: A microprocessor-based medium speed experimental local area network

In this paper the implementation and application of a microprocessor-based medium speed experimental local area network using a coaxial cable transmission medium are dealt with. A separate unidirectional control wire has been used in order to provide a collision-free and fair medium access arbitration. As an application of the network, the design of a packet voice communication system is discussed.

Micro-CT based characterisation of changes to the vascular network following closed soft tissue trauma and cryotherapy

This project has investigated how the architecture of the blood vessels supplying nutrients to skeletal muscles is affected by muscle contusion injuries, and how it changes during healing with or without initial treatment of the injury by icing. In order to do this, we used contrast agents to visualise blood vessels in 3D with micro-computed tomography imaging. This research significantly contributes to the fields of orthopaedics, traumatology and sports medicine, as it improves our understanding of muscle contusion injuries. Furthermore, the methods developed in this thesis may help to improve the diagnosis and monitoring of these injuries.

Cellular and network mechanisms generating spontaneous population events in the immature rat hippocampus

Distinct endogenous network events, generated independently of sensory input, are a general feature of various structures of the immature central nervous system. In the immature hippocampus, these type of events are seen as "giant depolarizing potentials" (GDPs) in intracellular recordings in vitro. GABA, the major inhibitory neurotransmitter of the adult brain, has a depolarizing action in immature neurons, and GDPs have been proposed to be driven by GABAergic transmission. Moreover, GDPs have been thought to reflect an early pattern that disappears during development in parallel with the maturation of hyperpolarizing GABAergic inhibition. However, the adult hippocampus in vivo also generates endogenous network events known as sharp (positive) waves (SPWs), which reflect synchronous discharges of CA3 pyramidal neurons and are thought to be involved in cognitive functions. In this thesis, mechanisms of GDP generation were studied with intra- and extracellular recordings in the neonatal rat hippocampus in vitro and in vivo. Immature CA3 pyramidal neurons were found to generate intrinsic bursts of spikes and to act as cellular pacemakers for GDP activity whereas depolarizing GABAergic signalling was found to have a temporally non-patterned facilitatory role in the generation of the network events. Furthermore, the data indicate that the intrinsic bursts of neonatal CA3 pyramidal neurons and, consequently, GDPs are driven by a persistent Na+ current and terminated by a slow Ca2+-dependent K+ current. Gramicidin-perforated patch recordings showed that the depolarizing driving force for GABAA receptor-mediated actions is provided by Cl- uptake via the Na-K-C1 cotransporter, NKCC1, in the immature CA3 pyramids. A specific blocker of NKCC1, bumetanide, inhibited SPWs and GDPs in the neonatal rat hippocampus in vivo and in vitro, respectively. Finally, pharmacological blockade of the GABA transporter-1 prolonged the decay of the large GDP-associated GABA transients but not of single postsynaptic GABAA receptor-mediated currents. As a whole the data in this thesis indicate that the mechanism of GDP generation, based on the interconnected network of bursting CA3 pyramidal neurons, is similar to that involved in adult SPW activity. Hence, GDPs do not reflect a network pattern that disappears during development but they are the in vitro counterpart of neonatal SPWs.

Dynamic Bayesian network inferencing for non-homogeneous complex systems

Dynamic Bayesian Networks (DBNs) provide a versatile platform for predicting and analysing the behaviour of complex systems. As such, they are well suited to the prediction of complex ecosystem population trajectories under anthropogenic disturbances such as the dredging of marine seagrass ecosystems. However, DBNs assume a homogeneous Markov chain whereas a key characteristics of complex ecosystems is the presence of feedback loops, path dependencies and regime changes whereby the behaviour of the system can vary based on past states. This paper develops a method based on the small world structure of complex systems networks to modularise a non-homogeneous DBN and enable the computation of posterior marginal probabilities given evidence in forwards inference. It also provides an approach for an approximate solution for backwards inference as convergence is not guaranteed for a path dependent system. When applied to the seagrass dredging problem, the incorporation of path dependency can implement conditional absorption and allows release from the zero state in line with environmental and ecological observations. As dredging has a marked global impact on seagrass and other marine ecosystems of high environmental and economic value, using such a complex systems model to develop practical ways to meet the needs of conservation and industry through enhancing resistance and/or recovery is of paramount importance.

VEGFR-3 and Tie pathways in vascular network formation

The blood and lymphatic vascular systems are essential for life, but they may become harnessed for sinister purposes in pathological conditions. For example, tumors learn to grow a network of blood vessels (angiogenesis), securing a source of oxygen and nutrients for sustained growth. On the other hand, damage to the lymph nodes and the collecting lymphatic vessels may lead to lymphedema, a debilitating condition characterized by peripheral edema and susceptibility to infections. Promoting the growth of new lymphatic vessels (lymphangiogenesis) is an attractive approach to treat lymphedema patients. Angiopoietin-1 (Ang1), a ligand for the endothelial receptor tyrosine kinases Tie1 and Tie2. The Ang1/Tie2 pathway has previously been implicated in promoting endothelial stability and integrity of EC monolayers. The studies presented here elucidate a novel function for Ang1 as a lymphangiogenic factor. Ang1 is known to decrease the permeability of blood vessels, and could thus act as a more global antagonist of plasma leakage and tissue edema by promoting growth of lymphatic vessels and thereby facilitating removal of excess fluid and other plasma components from the interstitium. These findings reinforce the idea that Ang1 may have therapeutic value in conditions of tissue edema. VEGFR-3 is present on all endothelia during development, but in the adult its expression becomes restricted to the lymphatic endothelium. VEGF-C and VEGF-D are ligands for VEGFR-3, and potently promote lymphangiogenesis in adult tissues, with direct and remarkably specific effects on the lymphatic endothelium in adult tissues. The data presented here show that VEGF-C and VEGF-D therapy can restore collecting lymphatic vessels in a novel orthotopic model of breast cancer-related lymphedema. Furthermore, the study introduces a novel approach to improve VEGF-C/VEGF-D therapy by using engineered heparin-binding forms of VEGF-C, which induced the rapid formation of organized lymphatic vessels. Importantly, VEGF-C therapy also greatly improved the survival and integration of lymph node transplants. The combination of lymph node transplantation and VEGF-C therapy provides a basis for future therapy of lymphedema. In adults, VEGFR-3 expression is restricted to the lymphatic endothelium and the fenestrated endothelia of certain endocrine organs. These results show that VEGFR-3 is induced at the onset of angiogenesis in the tip cells that lead the formation of new vessel sprouts, providing a tumor-specific vascular target. VEGFR-3 acts downstream of VEGF/VEGFR-2 signals, but, once induced, can sustain angiogenesis when VEGFR-2 signaling is inhibited. The data presented here implicate VEGFR-3 as a novel regulator of sprouting angiogenesis along with its role in regulating lymphatic vessel growth. Targeting VEGFR-3 may provide added efficacy to currently available anti-angiogenic therapeutics, which typically target the VEGF/VEGFR-2 pathway.

WinBUGS code to run a network meta-analysis

WinBUGS code and data to reproduce our network meta-analysis from "Control strategies to prevent total hip replacement-related infections: a systematic review and mixed treatment comparison" published in BMJ Open.

Quasi-2D XY Magnetic Properties and Slow Relaxation in a Body Centered Metal Organic Network of [Co-4] Clusters

Octahedral Co2+ centers have been connected by mu(3)-OH and mu(2)-OH2 units forming [Co-4] clusters which are linked by pyrazine forming a two-dimensional network. The two-dimensional layers are bridged by oxybisbenzoate (OBA) ligands giving rise to a three-dimensional structure. The [Co-4] clusters bond with the pyrazine and the OBA results in a body-centered arrangement of the clusters, which has been observed for the first time. Magnetic studies reveal a noncollinear frustrated spin structure of the bitriangular cluster, resulting in a net magnetic moment of 1.4 mu B per cluster. For T > 32 K, the correlation length of the cluster moments shows a stretched-exponential temperature dependence typical of a Berezinskii-Kosterlitz-Thouless model, which points to a quasi-2D XY behavior. At lower temperature and down to 14 K, the compound behaves as a soft ferromagnet and a slow relaxation is observed, with an energy barrier of ca. 500 K. Then, on further cooling, a hysteretic behavior takes place with a coercive field that reaches 5 Tat 4 K. The slow relaxation is assigned to the creation/annihilation of vortex-antivortex pairs, which are the elementary excitations of a 2D XY spin system.

Risk-based surveillance for avian influenza control along poultry market chains in South China: The value of social network analysis.

Over the past two decades, the poultry sector in China went through a phase of tremendous growth as well as rapid intensification and concentration. Highly pathogenic avian influenza virus (HPAIV) subtype H5N1 was first detected in 1996 in Guangdong province, South China and started spreading throughout Asia in early 2004. Since then, control of the disease in China has relied heavily on wide-scale preventive vaccination combined with movement control, quarantine and stamping out. This strategy has been successful in drastically reducing the number of outbreaks during the past 5 years. However, HPAIV H5N1 is still circulating and is regularly isolated in traditional live bird markets (LBMs) where viral infection can persist, which represent a public health hazard for people visiting them. The use of social network analysis in combination with epidemiological surveillance in South China has identified areas where the success of current strategies for HPAI control in the poultry production sector may benefit from better knowledge of poultry trading patterns and the LBM network configuration as well as their capacity for maintaining HPAIV H5N1 infection. We produced a set of LBM network maps and estimated the associated risk of HPAIV H5N1 within LBMs and along poultry market chains, providing new insights into how live poultry trade and infection are intertwined. More specifically, our study provides evidence that several biosecurity factors such as daily cage cleaning, daily cage disinfection or manure processing contribute to a reduction in HPAIV H5N1 presence in LBMs. Of significant importance is that the results of our study also show the association between social network indicators and the presence of HPAIV H5N1 in specific network configurations such as the one represented by the counties of origin of the birds traded in LBMs. This new information could be used to develop more targeted and effective control interventions.

Ranking of Prospective New Generation Location for a Power Network in a Deregulated System

In developing countries high rate of growth in demand of electric energy is felt, and so the addition of new generating units becomes necessary. In deregulated power systems private generating stations are encouraged to add new generations. Finding the appropriate location of new generator to be installed can be obtained by running repeated power flows, carrying system studies like analyzing the voltage profile, voltage stability, loss analysis etc. In this paper a new methodology is proposed which will mainly consider the existing network topology into account. A concept of T-index is introduced in this paper, which considers the electrical distances between generator and load nodes.This index is used for ranking significant new generation expansion locations and also indicates the amount of permissible generations that can be installed at these new locations. This concept facilitates for the medium and long term planning of power generation expansions within the available transmission corridors. Studies carried out on a sample 7-bus system, EHV equivalent 24-bus system and IEEE 39 bus system are presented for illustration purpose.

Automatic Lameness Detection in a Milking Robot : Instrumentation, measurement software, algorithms for data analysis and a neural network model

The aim of this thesis is to develop a fully automatic lameness detection system that operates in a milking robot. The instrumentation, measurement software, algorithms for data analysis and a neural network model for lameness detection were developed. Automatic milking has become a common practice in dairy husbandry, and in the year 2006 about 4000 farms worldwide used over 6000 milking robots. There is a worldwide movement with the objective of fully automating every process from feeding to milking. Increase in automation is a consequence of increasing farm sizes, the demand for more efficient production and the growth of labour costs. As the level of automation increases, the time that the cattle keeper uses for monitoring animals often decreases. This has created a need for systems for automatically monitoring the health of farm animals. The popularity of milking robots also offers a new and unique possibility to monitor animals in a single confined space up to four times daily. Lameness is a crucial welfare issue in the modern dairy industry. Limb disorders cause serious welfare, health and economic problems especially in loose housing of cattle. Lameness causes losses in milk production and leads to early culling of animals. These costs could be reduced with early identification and treatment. At present, only a few methods for automatically detecting lameness have been developed, and the most common methods used for lameness detection and assessment are various visual locomotion scoring systems. The problem with locomotion scoring is that it needs experience to be conducted properly, it is labour intensive as an on-farm method and the results are subjective. A four balance system for measuring the leg load distribution of dairy cows during milking in order to detect lameness was developed and set up in the University of Helsinki Research farm Suitia. The leg weights of 73 cows were successfully recorded during almost 10,000 robotic milkings over a period of 5 months. The cows were locomotion scored weekly, and the lame cows were inspected clinically for hoof lesions. Unsuccessful measurements, caused by cows standing outside the balances, were removed from the data with a special algorithm, and the mean leg loads and the number of kicks during milking was calculated. In order to develop an expert system to automatically detect lameness cases, a model was needed. A probabilistic neural network (PNN) classifier model was chosen for the task. The data was divided in two parts and 5,074 measurements from 37 cows were used to train the model. The operation of the model was evaluated for its ability to detect lameness in the validating dataset, which had 4,868 measurements from 36 cows. The model was able to classify 96% of the measurements correctly as sound or lame cows, and 100% of the lameness cases in the validation data were identified. The number of measurements causing false alarms was 1.1%. The developed model has the potential to be used for on-farm decision support and can be used in a real-time lameness monitoring system.