Thoracolumbar dorsolateral laminectomy with osteotomy of the spinous process in fourteen dogs

OBJECTIVE: To describe outcome after an alternative unilateral approach to the thoracolumbar spine for dorsal laminectomy. STUDY DESIGN: Retrospective clinical study. ANIMALS: Dogs (n=14) with thoracolumbar spinal cord compression. METHODS: Thoracolumbar spinal cord compression was lateral (6 dogs), dorsal (4), and dorsolateral (4) caused by subarachnoid (7) and synovial cysts (2) and intradural-extramedullary neoplasia (5). All dogs were treated by dorsal laminectomy with osteotomy of the spinous process using a unilateral paramedian approach. The contralateral paraspinal muscles were not stripped from the spinous process and the osteoligamentous complexes were preserved. Retraction of the spinous process and muscles to the contralateral side resulted in complete visualization of the dorsal vertebral arch thereby allowing dorsal laminectomy to be performed. RESULTS: No technique complications occurred. Approximately 75% exposure of the spinal cord (dorsal and lateral compartments) was achieved providing adequate visualization and treatment of the lesions. Transient deterioration of neurologic state occurred in 5 dogs because of extensive spinal cord manipulation. At long-term follow-up, 6 dogs were normal, 6 had clinical improvement, and 2 were unchanged. CONCLUSION: Dorsal laminectomy after osteotomy and retraction of the spinous process may be considered in canine patients with dorsal, dorsolateral, or lateral compression to facilitate adequate decompression of the spinal cord. CLINICAL SIGNIFICANCE: This surgical technique offers an alternative approach to the thoracolumbar spine and spinal cord by a modified dorsal laminectomy that preserves the paraspinal muscle integrity on the contralateral side.

A Nonstationary Space-Time Gaussian Process Model for Partially Converged Simulations

In the context of expensive numerical experiments, a promising solution for alleviating the computational costs consists of using partially converged simulations instead of exact solutions. The gain in computational time is at the price of precision in the response. This work addresses the issue of fitting a Gaussian process model to partially converged simulation data for further use in prediction. The main challenge consists of the adequate approximation of the error due to partial convergence, which is correlated in both design variables and time directions. Here, we propose fitting a Gaussian process in the joint space of design parameters and computational time. The model is constructed by building a nonstationary covariance kernel that reflects accurately the actual structure of the error. Practical solutions are proposed for solving parameter estimation issues associated with the proposed model. The method is applied to a computational fluid dynamics test case and shows significant improvement in prediction compared to a classical kriging model.

Mindfulness-based cognitive therapie for depression (MBCT) in clinical practice - baseline patient characteristics, process and outcome

Introduction: Mindfulness based cognitive therapy for depression (MBCT) has shown to be effective for the reduction of depressive relapse. However, additional information regarding baseline patient characteristics and process features related to positive response could be helpful both for the provision of MBCT in clinical practice, as well as for its further development. Method: Baseline characteristics, process data, and immediate outcome (symptom change, change in attitudes and trait mindfulness) of 108 patients receiving MBCT in routine care were recorded. A newly developed self-report measure (Daily Mindfulness Scale, DMS) was applied daily during the MBCT program. Additionally, patients filed daily reports on their mindfulness practice. There was no control group available. Results: Patients with more severe initial symptoms indicated greater amounts of symptom improvement, but did not show great rates of dropout from the MBCT intervention. Younger age was related to higher rates of dropout. Contradictory to some previous data, patients with lower levels of initial trait mindfulness showed greater improvement in symptoms, even after controlling for initial levels of symptoms. Adherence to daily mindfulness practice was high. Consistent with this result, the duration of daily mindfulness practice was not related to immediate outcome. Process studies using multivariate time series analysis revealed a specific role of daily mindfulness in reducing subsequent negative mood. Conclusions: Within the range of patient present in this study and the given study design, results support the use of MBCT in more heterogeneous groups. This demanding intervention was well tolerated by patients with higher levels of symptoms, and resulted in significant improvements regarding residual symptoms. Process-outcome analyses of initial trait mindfulness and daily mindfulness both support the crucial role of changes in mindfulness for the effects of MBCT.

Foreign Direct Investment in Agriculture: Land Grab or Food Security Improvement?

“Large-scale acquisition of land by foreign investors” is the correct term for a process where the verdict of guilt is often quicker than the examination. But is there something really new about land grab except in its extent? In comparison with colonial and post-colonial plantation operations, should foreign investors today behave differently? We generally accept coffee and banana exports as pro-growth and pro-development, just as for cars, beef and insurance. What then is wrong with an investment contract allowing the holder to buy a farm and to export wheat to Saudi Arabia, or soybeans and maize as cattle feed to Korea, or to plant and process sugar cane and palm oil into ethanol for Europe and China? Assuming their land acquisition was legal, should foreigners respect more than investment contracts and national legislation? And why would they not take advantage of the legal protection offered by international investment law and treaties, not to speak of concessional finance, infrastructure and technical cooperation by a development bank, or the tax holidays offered by the host state? Remember Milton Friedman’s often-quoted quip: “The business of business is business!” And why would the governments signing those contracts not know whether and which foreign investment projects are best for their country, and how to attract them? This chapter tries to show that land grab, where it occurs, is not only yet another symptom of regulatory failures at the national level and a lack of corporate social responsibility by certain private actors. National governance is clearly the most important factor. Nonetheless, I submit that there is an international dimension involving investor home states in various capacities. The implication is that land grab is not solely a question whether a particular investment contract is legal or not. This chapter deals with legal issues which seem to have largely escaped the attention of both human rights lawyers and, especially, of investment lawyers. I address this fragmentation between different legal disciplines, rules, and policies, by asking two basic questions: (i) Do governments and parliaments in investor home countries have any responsibility in respect of the behaviour of their investors abroad? (ii) What should they and international regulators do, if anything?

Quantifying uncertainty on Pareto fronts with Gaussian process conditional simulations

Multi-objective optimization algorithms aim at finding Pareto-optimal solutions. Recovering Pareto fronts or Pareto sets from a limited number of function evaluations are challenging problems. A popular approach in the case of expensive-to-evaluate functions is to appeal to metamodels. Kriging has been shown efficient as a base for sequential multi-objective optimization, notably through infill sampling criteria balancing exploitation and exploration such as the Expected Hypervolume Improvement. Here we consider Kriging metamodels not only for selecting new points, but as a tool for estimating the whole Pareto front and quantifying how much uncertainty remains on it at any stage of Kriging-based multi-objective optimization algorithms. Our approach relies on the Gaussian process interpretation of Kriging, and bases upon conditional simulations. Using concepts from random set theory, we propose to adapt the Vorob’ev expectation and deviation to capture the variability of the set of non-dominated points. Numerical experiments illustrate the potential of the proposed workflow, and it is shown on examples how Gaussian process simulations and the estimated Vorob’ev deviation can be used to monitor the ability of Kriging-based multi-objective optimization algorithms to accurately learn the Pareto front.

Global Optimization with Sparse and Local Gaussian Process Models

We present a novel surrogate model-based global optimization framework allowing a large number of function evaluations. The method, called SpLEGO, is based on a multi-scale expected improvement (EI) framework relying on both sparse and local Gaussian process (GP) models. First, a bi-objective approach relying on a global sparse GP model is used to determine potential next sampling regions. Local GP models are then constructed within each selected region. The method subsequently employs the standard expected improvement criterion to deal with the exploration-exploitation trade-off within selected local models, leading to a decision on where to perform the next function evaluation(s). The potential of our approach is demonstrated using the so-called Sparse Pseudo-input GP as a global model. The algorithm is tested on four benchmark problems, whose number of starting points ranges from 102 to 104. Our results show that SpLEGO is effective and capable of solving problems with large number of starting points, and it even provides significant advantages when compared with state-of-the-art EI algorithms.

Differentiating the Multipoint Expected Improvement for Optimal Batch Design

This work deals with parallel optimization of expensive objective functions which are modelled as sample realizations of Gaussian processes. The study is formalized as a Bayesian optimization problem, or continuous multi-armed bandit problem, where a batch of q > 0 arms is pulled in parallel at each iteration. Several algorithms have been developed for choosing batches by trading off exploitation and exploration. As of today, the maximum Expected Improvement (EI) and Upper Confidence Bound (UCB) selection rules appear as the most prominent approaches for batch selection. Here, we build upon recent work on the multipoint Expected Improvement criterion, for which an analytic expansion relying on Tallis’ formula was recently established. The computational burden of this selection rule being still an issue in application, we derive a closed-form expression for the gradient of the multipoint Expected Improvement, which aims at facilitating its maximization using gradient-based ascent algorithms. Substantial computational savings are shown in application. In addition, our algorithms are tested numerically and compared to state-of-the-art UCB-based batchsequential algorithms. Combining starting designs relying on UCB with gradient-based EI local optimization finally appears as a sound option for batch design in distributed Gaussian Process optimization.

The Arduous Process of Climate Change Negotiations: How Science Can Facilitate the Desired Outcome

Effective interaction between climate science and policy is important for moving climate negotiations forward to reach an ambitious global climate change deal. Lack of progress in the United Nations Framework Convention on Climate Change (UNFCCC) negotiations during recent years is a good reason for taking a closer look at the process of climate science–policy interaction to identify and eliminate existing shortcomings hindering climate policymaking. This paper examines the current state of climate science–policy interaction and suggests ways to integrate scientific input into the UNFCCC process more effectively. Suggestions relate to improvement in institutional structures, processes and procedures of the UNFCCC and the Intergovernmental Panel on Climate Change (IPCC), quality of scientific input, credibility of scientific message and public awareness of climate change.