Reduction of Opioid Medication Use in Chronic Pain Patients by Adding Memantine: A Pilot Study

An investigational clinical research pilot study is being conducted at a nursing facility and an academic primary care office to evaluate memantine as an adjunct to opioid therapy for treatment of chronic pain. Memantine can be beneficial in reducing pain because it is an N-methyl-D-aspartate receptor (NMDAr) antagonist with a pivotal action in the hippocampus: it initiates long-term potentiation in the anterior cingulate cortex and forebrain. These areas of action have a high probability for reducing the affective quality of pain. This open-label, non-randomized pilot study is designed to observe any effects which may occur during addition of memantine to the therapeutic regimen of nursing home patients and office patients who take daily opioids (oxycodone, oxycodone/acetaminophen combination or hydromorphone) on an as-needed basis (prn) for chronic pain. The objective is to gauge, preliminarily, whether patients benefit from using memantine as an adjunct to their daily oxycodone/acetaminophen or hydromorphone treatment by increased analgesia, a reduction of opioid used, and increased bowel movements. Memantine efficacy was assessed using pain diaries where patients recorded on a daily basis the amount of opioid used, pain scores (from \u270\u27 [no pain] to \u2710\u27 [worst pain ever]), and number of bowel movements. Data are collected for six weeks; initially a two-week, no-memantine observation period, followed by a four-week treatment phase. Collected data are then analyzed. With the first patient to complete the study, a trend of decreased pain scores over the six-week study was observed. There was also an indication of decreased opioid use but may be due to inconsistencies; bowel movements fluctuated and did not show a trend. The second patient showed trends of a decreased pain score with a decrease in opioid dosage over the course of the study and slightly lower bowel movements per week. This pilot study presented insight to the plausible use of memantine as an adjunct to treat chronic pain patients. Although the number of patients that participated in this pilot was small, the trends observed may help to launch this type of study into a larger scale. Thus, these initial data present insight to the plausible use of memantine as an adjunct to treat chronic non-malignant pain patients who take an opioid daily

Reducing parental anxiety using a family based intervention for youth mental health : a randomized controlled trial

This paper presents findings on parent anxiety and attachment relationship style from the Deakin Family Options (DFO) pilot study, a randomized controlled pilot study comparing a family-based treatment (BEST Plus), versus a youth only treatment (CBT) versus a group who received both of these treatments (COMBINED). Eligible participants were families with a young person (aged 12 - 25 years) with a high prevalence mental health problem. Youth from participating families scored in the clinical or subclinical range for depression, anxiety and/or substance misuse symptoms on standardized measures during the initial assessment. The collected sample was drawn from regional and urban centers in Victoria, Australia and allocated to treatment condition using a simple randomization procedure (parallel design). It was hypothesized that families receiving the BEST Plus would experience greater reductions in youth and parent mental health symptoms, and improved parent-child relationships, compared with those in the CBT condition. This paper describes and discusses changes in parent anxiety and parent attachment, according to whether the parent participated in a treatment (BEST Plus) or did not (NONBEST Plus). Participants were blind to the study hypotheses. In total 71 parent participants returned pre data and were allocated to a treatment group. In this paper, data from parent participants who completed pre and post measures (n = 48) and pre, post, and 6-month follow-up measures (n = 28) on anxiety and attachment were analyzed by group (BEST Plus versus NONBEST Plus). The results of this study suggest that parent anxiety decreased significantly more following parent involvement in a group treatment, than for parents that did not receive treatment. Unexpectedly, avoidant attachment increased in the no treatment group, but remained relatively stable following the BEST Plus group. There were no significant findings in relation to compulsive traits and anxious attachment. These findings are discussed in light of the study limitations.<br /

An eddy resolving tidal-driven model of the South China Sea assimilating along-track SLA data using the EnOI

The upper ocean circulation in the South China Sea (SCS) is driven by the Asian monsoon, the Kuroshio intrusion through the Luzon Strait, strong tidal currents, and a complex topography. Here, we demonstrate the benefit of assimilating along-track altimeter data into a nested configuration of the HYbrid Coordinate Ocean Model that includes tides. Including tides in models is important because they interact with the main circulation. However, assimilation of altimetry data into a model including tides is challenging because tides and mesoscale features contribute to the elevation of ocean surface at different time scales and require different corrections. To address this issue, tides are filtered out of the model output and only the mesoscale variability is corrected with a computationally cheap data assimilation method: the Ensemble Optimal Interpolation (EnOI). This method uses a running selection of members to handle the seasonal variability and assimilates the track data asynchronously. The data assimilative system is tested for the period 1994–1995, during which time a large number of validation data are available. Data assimilation reduces the Root Mean Square Error of Sea Level Anomalies from 9.3 to 6.9 cm and improves the representation of the mesoscale features. With respect to the vertical temperature profiles, the data assimilation scheme reduces the errors quantitatively with an improvement at intermediate depth and deterioration at deeper depth. The comparison to surface drifters shows an improvement of surface current by approximately −9% in the Northern SCS and east of Vietnam. Results are improved compared to an assimilative system that does not include tides and a system that does not consider asynchronous assimilation

On the difficult tradeoff between security and privacy: Challenges for the management of digital identities

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-19713-5_39The deployment of security measures can lead in many occasions to an infringement of users’ privacy. Indeed, nowadays we have many examples about surveillance programs or personal data breaches in online service providers. In order to avoid the latter problem, we need to establish security measures that do not involve a violation of privacy rights. In this communication we discuss the main challenges when conciliating information security and users’ privacy.This work was supported by Comunidad de Madrid (Spain) under the project S2013/ICE-3095-CM (CIBERDINE)

Data assimilation as a learning tool to infer ordinary differential equation representations of dynamical models

Recent progress in machine learning has shown how to forecast and, to some extent, learn the dynamics of a model from its output, resorting in particular to neural networks and deep learning techniques. We will show how the same goal can be directly achieved using data assimilation techniques without leveraging on machine learning software libraries, with a view to high-dimensional models. The dynamics of a model are learned from its observation and an ordinary differential equation (ODE) representation of this model is inferred using a recursive nonlinear regression. Because the method is embedded in a Bayesian data assimilation framework, it can learn from partial and noisy observations of a state trajectory of the physical model. Moreover, a space-wise local representation of the ODE system is introduced and is key to coping with high-dimensional models. It has recently been suggested that neural network architectures could be interpreted as dynamical systems. Reciprocally, we show that our ODE representations are reminiscent of deep learning architectures. Furthermore, numerical analysis considerations of stability shed light on the assets and limitations of the method. The method is illustrated on several chaotic discrete and continuous models of various dimensions, with or without noisy observations, with the goal of identifying or improving the model dynamics, building a surrogate or reduced model, or producing forecasts solely from observations of the physical model

Flexible Resolution of Authorisation Conflicts in Distributed Systems

Flexible Resolution of Authorisation Conflicts in Distributed System

FastLAS: scalable inductive logic programming incorporating domain-specific optimisation criteria

Inductive Logic Programming (ILP) systems aim to find a setof logical rules, called a hypothesis, that explain a set of ex-amples. In cases where many such hypotheses exist, ILP sys-tems often bias towards shorter solutions, leading to highlygeneral rules being learned. In some application domains likesecurity and access control policies, this bias may not be de-sirable, as when data is sparse more specific rules that guaran-tee tighter security should be preferred. This paper presents anew general notion of ascoring functionover hypotheses thatallows a user to express domain-specific optimisation criteria.This is incorporated into a new ILP system, calledFastLAS,that takes as input a learning task and a customised scoringfunction, and computes an optimal solution with respect tothe given scoring function. We evaluate the accuracy of Fast-LAS over real-world datasets for access control policies andshow that varying the scoring function allows a user to tar-get domain-specific performance metrics. We also compareFastLAS to state-of-the-art ILP systems, using the standardILP bias for shorter solutions, and demonstrate that FastLASis significantly faster and more scalable

Polynomial Kernels and User Reductions for the Workflow Satisfiability Problem

The Workflow Satisfiability Problem (WSP) is a problem of practical interest that arises whenever tasks need to be performed by authorized users, subject to constraints defined by business rules. We are required to decide whether there exists a plan -- an assignment of tasks to authorized users -- such that all constraints are satisfied. The WSP is, in fact, the conservative Constraint Satisfaction Problem (i.e., for each variable, here called task, we have a unary authorization constraint) and is, thus, NP-complete. It was observed by Wang and Li (2010) that the number k of tasks is often quite small and so can be used as a parameter, and several subsequent works have studied the parameterized complexity of WSP regarding parameter k. We take a more detailed look at the kernelization complexity of WSP(\Gamma) when \Gamma\ denotes a finite or infinite set of allowed constraints. Our main result is a dichotomy for the case that all constraints in \Gamma\ are regular: (1) We are able to reduce the number n of users to n' <= k. This entails a kernelization to size poly(k) for finite \Gamma, and, under mild technical conditions, to size poly(k+m) for infinite \Gamma, where m denotes the number of constraints. (2) Already WSP(R) for some R \in \Gamma\ allows no polynomial kernelization in k+m unless the polynomial hierarchy collapses.Comment: An extended abstract appears in the proceedings of IPEC 201

Impact of assimilating a merged sea-ice thickness from CryoSat-2 and SMOS in the Arctic reanalysis

Accurately forecasting the sea-ice thickness (SIT) in the Arctic is a major challenge. The new SIT product (referred to as CS2SMOS) merges measurements from the CryoSat-2 and SMOS satellites on a weekly basis during the winter. The impact of assimilating CS2SMOS data is tested for the TOPAZ4 system – the Arctic component of the Copernicus Marine Environment Monitoring Services (CMEMS). TOPAZ4 currently assimilates a large set of ocean and sea-ice observations with the Deterministic Ensemble Kalman Filter (DEnKF). Two parallel reanalyses are conducted without (Official run) and with (Test run) assimilation of CS2SMOS data from 19 March 2014 to 31 March 2015. Since only mapping errors were provided in the CS2SMOS observation, an arbitrary term was added to compensate for the missing errors, but was found a posteriori too large. The SIT bias (too thin) is reduced from 16 to 5&thinsp;cm and the standard errors decrease from 53 to 38&thinsp;cm (by 28&thinsp;%) when compared to the assimilated SIT. When compared to independent SIT observations, the error reduction is 24&thinsp;% against the ice mass balance (IMB) buoy 2013F and by 12.5&thinsp;% against SIT data from the IceBridge campaigns. The improvement of sea-ice volume persists through the summer months in the absence of CS2SMOS data. Comparisons to sea-ice drift from the satellites show that dynamical adjustments reduce the drift errors around the North Pole by about 8&thinsp;%–9&thinsp;% in December 2014 and February 2015. Finally, using the degrees of freedom for signal (DFS), we find that CS2SMOS makes the prime source of information in the central Arctic and in the Kara Sea. We therefore recommend the assimilation of C2SMOS for Arctic reanalyses in order to improve the ice thickness and the ice drift.</p