Executive function and postural instability in people with Parkinson’s disease

The specific aspects of cognition contributing to balance and gait have not been clarified in people with Parkinson’s disease (PD). Twenty PD participants and twenty age- and gender-matched healthy controls were assessed on cognition and clinical mobility tests. General cognition was assessed with the Mini Mental State Exam and the Addenbrooke’s Cognitive Exam. Executive function was evaluated using the Trail Making Tests (TMT-A and TMT-B) and a computerized cognitive battery which included a series of choice reaction time (CRT) tests. Clinical gait and balance measures included the Tinetti, Timed Up & Go, Berg Balance and Functional Reach tests. PD participants performed significantly worse than the controls on the tests of cognitive and executive function, balance and gait. PD participants took longer on Trail Making Tests, CRT-Location and CRT-Colour (inhibition response). Furthermore, executive function, particularly longer times on CRT-Distracter and greater errors on the TMT-B were associated with worse balance and gait performance in the PD group. Measures of general cognition were not associated with balance and gait measures in either group. For PD participants, attention and executive function were impaired. Components of executive function, particularly those involving inhibition response and distracters, were associated with poorer balance and gait performance in PD.

Fast rates for estimation error and oracle inequalities for model section

We consider complexity penalization methods for model selection. These methods aim to choose a model to optimally trade off estimation and approximation errors by minimizing the sum of an empirical risk term and a complexity penalty. It is well known that if we use a bound on the maximal deviation between empirical and true risks as a complexity penalty, then the risk of our choice is no more than the approximation error plus twice the complexity penalty. There are many cases, however, where complexity penalties like this give loose upper bounds on the estimation error. In particular, if we choose a function from a suitably simple convex function class with a strictly convex loss function, then the estimation error (the difference between the risk of the empirical risk minimizer and the minimal risk in the class) approaches zero at a faster rate than the maximal deviation between empirical and true risks. In this paper, we address the question of whether it is possible to design a complexity penalized model selection method for these situations. We show that, provided the sequence of models is ordered by inclusion, in these cases we can use tight upper bounds on estimation error as a complexity penalty. Surprisingly, this is the case even in situations when the difference between the empirical risk and true risk (and indeed the error of any estimate of the approximation error) decreases much more slowly than the complexity penalty. We give an oracle inequality showing that the resulting model selection method chooses a function with risk no more than the approximation error plus a constant times the complexity penalty.

Local Rademacher complexities

We propose new bounds on the error of learning algorithms in terms of a data-dependent notion of complexity. The estimates we establish give optimal rates and are based on a local and empirical version of Rademacher averages, in the sense that the Rademacher averages are computed from the data, on a subset of functions with small empirical error. We present some applications to classification and prediction with convex function classes, and with kernel classes in particular.

Exponentiated gradient algorithms for conditional random fields and max-margin Markov Networks

Log-linear and maximum-margin models are two commonly-used methods in supervised machine learning, and are frequently used in structured prediction problems. Efficient learning of parameters in these models is therefore an important problem, and becomes a key factor when learning from very large data sets. This paper describes exponentiated gradient (EG) algorithms for training such models, where EG updates are applied to the convex dual of either the log-linear or max-margin objective function; the dual in both the log-linear and max-margin cases corresponds to minimizing a convex function with simplex constraints. We study both batch and online variants of the algorithm, and provide rates of convergence for both cases. In the max-margin case, O(1/ε) EG updates are required to reach a given accuracy ε in the dual; in contrast, for log-linear models only O(log(1/ε)) updates are required. For both the max-margin and log-linear cases, our bounds suggest that the online EG algorithm requires a factor of n less computation to reach a desired accuracy than the batch EG algorithm, where n is the number of training examples. Our experiments confirm that the online algorithms are much faster than the batch algorithms in practice. We describe how the EG updates factor in a convenient way for structured prediction problems, allowing the algorithms to be efficiently applied to problems such as sequence learning or natural language parsing. We perform extensive evaluation of the algorithms, comparing them to L-BFGS and stochastic gradient descent for log-linear models, and to SVM-Struct for max-margin models. The algorithms are applied to a multi-class problem as well as to a more complex large-scale parsing task. In all these settings, the EG algorithms presented here outperform the other methods.

Improved design rules for hollow flange sections subject to lateral distortional buckling

The LiteSteel Beam (LSB) is a new hollow flange section with a unique geometry consisting of torsionally rigid rectangular hollow flanges and a relatively slender web. It is subjected to lateral distortional buckling when used as flexural members, which reduces its member moment capacity. An investigation into the flexural behaviour of LSBs using experiments and numerical analyses led to the development of new design rules for LSBs subject to lateral distortional buckling. However, the comparison of moment capacity results with the new design rules showed that they were conservative for some LSB sections while slightly unconservative for others due to the effects of section geometry. It is also unknown whether these design rules are applicable to other hollow flange sections such as hollow flange beams (HFB). This paper presents the details of a study into the lateral distortional buckling behaviour of hollow flange sections such as LSBs, HFBs and their variations. A geometrical parameter defined as the ratio of flange torsional rigidity to the major axis flexural rigidity of the web (GJf/EIxweb) was found to be a critical parameter in evaluating the lateral distortional buckling behaviour and moment capacities of hollow flange sections. New design rules were therefore developed by using a member slenderness parameter modified by K, where K is a function of GJf/EIxweb. The new design rules based on the modified slenderness parameter were found to be accurate in calculating the moment capacities of not only LSBs and HFBs, but also other types of hollow flange sections.

A Bayesian regression approach to assess uncertainty in pollutant wash-off modelling

Due to knowledge gaps in relation to urban stormwater quality processes, an in-depth understanding of model uncertainty can enhance decision making. Uncertainty in stormwater quality models can originate from a range of sources such as the complexity of urban rainfall-runoff-stormwater pollutant processes and the paucity of observed data. Unfortunately, studies relating to epistemic uncertainty, which arises from the simplification of reality are limited and often deemed mostly unquantifiable. This paper presents a statistical modelling framework for ascertaining epistemic uncertainty associated with pollutant wash-off under a regression modelling paradigm using Ordinary Least Squares Regression (OLSR) and Weighted Least Squares Regression (WLSR) methods with a Bayesian/Gibbs sampling statistical approach. The study results confirmed that WLSR assuming probability distributed data provides more realistic uncertainty estimates of the observed and predicted wash-off values compared to OLSR modelling. It was also noted that the Bayesian/Gibbs sampling approach is superior compared to the most commonly adopted classical statistical and deterministic approaches commonly used in water quality modelling. The study outcomes confirmed that the predication error associated with wash-off replication is relatively higher due to limited data availability. The uncertainty analysis also highlighted the variability of the wash-off modelling coefficient k as a function of complex physical processes, which is primarily influenced by surface characteristics and rainfall intensity.

Molecular architecture of the human sinus node: insights into the function of the cardiac pacemaker.

BACKGROUND: Although we know much about the molecular makeup of the sinus node (SN) in small mammals, little is known about it in humans. The aims of the present study were to investigate the expression of ion channels in the human SN and to use the data to predict electrical activity. METHODS AND RESULTS: Quantitative polymerase chain reaction, in situ hybridization, and immunofluorescence were used to analyze 6 human tissue samples. Messenger RNA (mRNA) for 120 ion channels (and some related proteins) was measured in the SN, a novel paranodal area, and the right atrium (RA). The results showed, for example, that in the SN compared with the RA, there was a lower expression of Na(v)1.5, K(v)4.3, K(v)1.5, ERG, K(ir)2.1, K(ir)6.2, RyR2, SERCA2a, Cx40, and Cx43 mRNAs but a higher expression of Ca(v)1.3, Ca(v)3.1, HCN1, and HCN4 mRNAs. The expression pattern of many ion channels in the paranodal area was intermediate between that of the SN and RA; however, compared with the SN and RA, the paranodal area showed greater expression of K(v)4.2, K(ir)6.1, TASK1, SK2, and MiRP2. Expression of ion channel proteins was in agreement with expression of the corresponding mRNAs. The levels of mRNA in the SN, as a percentage of those in the RA, were used to estimate conductances of key ionic currents as a percentage of those in a mathematical model of human atrial action potential. The resulting SN model successfully produced pacemaking. CONCLUSIONS: Ion channels show a complex and heterogeneous pattern of expression in the SN, paranodal area, and RA in humans, and the expression pattern is appropriate to explain pacemaking.

Application of K-tree to document clustering

Digital collections are growing exponentially in size as the information age takes a firm grip on all aspects of society. As a result Information Retrieval (IR) has become an increasingly important area of research. It promises to provide new and more effective ways for users to find information relevant to their search intentions. Document clustering is one of the many tools in the IR toolbox and is far from being perfected. It groups documents that share common features. This grouping allows a user to quickly identify relevant information. If these groups are misleading then valuable information can accidentally be ignored. There- fore, the study and analysis of the quality of document clustering is important. With more and more digital information available, the performance of these algorithms is also of interest. An algorithm with a time complexity of O(n2) can quickly become impractical when clustering a corpus containing millions of documents. Therefore, the investigation of algorithms and data structures to perform clustering in an efficient manner is vital to its success as an IR tool. Document classification is another tool frequently used in the IR field. It predicts categories of new documents based on an existing database of (doc- ument, category) pairs. Support Vector Machines (SVM) have been found to be effective when classifying text documents. As the algorithms for classifica- tion are both efficient and of high quality, the largest gains can be made from improvements to representation. Document representations are vital for both clustering and classification. Representations exploit the content and structure of documents. Dimensionality reduction can improve the effectiveness of existing representations in terms of quality and run-time performance. Research into these areas is another way to improve the efficiency and quality of clustering and classification results. Evaluating document clustering is a difficult task. Intrinsic measures of quality such as distortion only indicate how well an algorithm minimised a sim- ilarity function in a particular vector space. Intrinsic comparisons are inherently limited by the given representation and are not comparable between different representations. Extrinsic measures of quality compare a clustering solution to a “ground truth” solution. This allows comparison between different approaches. As the “ground truth” is created by humans it can suffer from the fact that not every human interprets a topic in the same manner. Whether a document belongs to a particular topic or not can be subjective.

Caveat Anicula! Beware of the Quiet Little Old Ladies : Demographics, Pharmacotherapy, Survival and Readmissions in a 10 Year Cohort of Heart Failure Patients with Preserved Systolic Function

Objective--To determine whether heart failure with preserved systolic function (HFPSF) has different natural history from left ventricular systolic dysfunction (LVSD). Design and setting--A retrospective analysis of 10 years of data (for patients admitted between 1 July 1994 and 30 June 2004, and with a study census date of 30 June 2005) routinely collected as part of clinical practice in a large tertiary referral hospital.Main outcome measures-- Sociodemographic characteristics, diagnostic features, comorbid conditions, pharmacotherapies, readmission rates and survival.Results--Of the 2961 patients admitted with chronic heart failure, 753 had echocardiograms available for this analysis. Of these, 189 (25%) had normal left ventricular size and systolic function. In comparison to patients with LVSD, those with HFPSF were more often female (62.4% v 38.5%; P = 0.001), had less social support, and were more likely to live in nursing homes (17.9% v 7.6%; P < 0.001), and had a greater prevalence of renal impairment (86.7% v 6.2%; P = 0.004), anaemia (34.3% v 6.3%; P = 0.013) and atrial fibrillation (51.3% v 47.1%; P = 0.008), but significantly less ischaemic heart disease (53.4% v 81.2%; P = 0.001). Patients with HFPSF were less likely to be prescribed an angiotensin-converting enzyme inhibitor (61.9% v 72.5%; P = 0.008); carvedilol was used more frequently in LVSD (1.5% v 8.8%; P < 0.001). Readmission rates were higher in the HFPSF group (median, 2 v 1.5 admissions; P = 0.032), particularly for malignancy (4.2% v 1.8%; P < 0.001) and anaemia (3.9% v 2.3%; P < 0.001). Both groups had the same poor survival rate (P = 0.912). Conclusions--Patients with HFPSF were predominantly older women with less social support and higher readmission rates for associated comorbid illnesses. We therefore propose that reduced survival in HFPSF may relate more to comorbid conditions than suboptimal cardiac management.

Optimal strategies and minimax lower bounds for online convex games

A number of learning problems can be cast as an Online Convex Game: on each round, a learner makes a prediction x from a convex set, the environment plays a loss function f, and the learner’s long-term goal is to minimize regret. Algorithms have been proposed by Zinkevich, when f is assumed to be convex, and Hazan et al., when f is assumed to be strongly convex, that have provably low regret. We consider these two settings and analyze such games from a minimax perspective, proving minimax strategies and lower bounds in each case. These results prove that the existing algorithms are essentially optimal.

The undirected feedback vertex set problem has a Poly(k) Kernel

Resolving a noted open problem, we show that the Undirected Feedback Vertex Set problem, parameterized by the size of the solution set of vertices, is in the parameterized complexity class Poly(k), that is, polynomial-time pre-processing is sufficient to reduce an initial problem instance (G, k) to a decision-equivalent simplified instance (G', k') where k' � k, and the number of vertices of G' is bounded by a polynomial function of k. Our main result shows an O(k11) kernelization bound.

Delta opioid-receptor function in the dorsal striatum plays a role in high levels of ethanol consumption in rats

Binge-like patterns of excessive drinking during young adulthood increase the propensity for alcohol use disorders (AUDs) later in adult life; however, the mechanisms that drive this are not completely understood. Previous studies showed that the δ-opioid peptide receptor (DOP-R) is dynamically regulated by exposure to ethanol and that the DOP-R plays a role in ethanol-mediated behaviors. The aim of this study was to determine the role of the DOP-R in high ethanol consumption from young adulthood through to late adulthood by measuring DOP-R-mediated [(35)S]GTPγS binding in brain membranes and DOP-R-mediated analgesia using a rat model of high ethanol consumption in Long Evans rats. We show that DOP-R activity in the dorsal striatum and DOP-R-mediated analgesia changes during development, being highest during early adulthood and reduced in late adulthood. Intermittent access to ethanol but not continuous ethanol or water from young adulthood leads to an increase in DOP-R activity in the dorsal striatum and DOP-R-mediated analgesia into late adulthood. Multiple microinfusions of naltrindole into the dorsal striatum or multiple systemic administration of naltrindole reduces ethanol consumption, and following termination of treatment, DOP-R activity in the dorsal striatum is attenuated. These findings suggest that DOP-R activity in the dorsal striatum plays a role in high levels of ethanol consumption and suggest that targeting the DOP-R is an alternative strategy for the treatment of AUDs.

In situ SANS study of the surface and pore filling adsorption of subcritical and supercritical CO2 in porous silica as a function of pore size

We applied small-angle neutron scattering (SANS) and ultra small-angle neutron scattering (USANS) to monitor evolution of the CO2 adsorption in porous silica as a function of CO2 pressure and temperature in pores of different sizes. The range of pressures (0 < P < 345 bar) and temperatures (T=18 OC, 35 OC and 60 OC) corresponded to subcritical, near critical and supercritical conditions of bulk fluid. We observed that the adsorption behavior of CO2 is fundamentally different in large and small pores with the sizes D > 100 Å and D < 30 Å, respectively. Scattering data from large pores indicate formation of a dense adsorbed film of CO2 on pore walls with the liquid-like density (ρCO2)ads≈0.8 g/cm3. The adsorbed film coexists with unadsorbed fluid in the inner pore volume. The density of unadsorbed fluid in large pores is temperature and pressure dependent: it is initially lower than (ρCO2)ads and gradually approaches it with pressure. In small pores compressed CO2 gas completely fills the pore volume. At the lowest pressures of the order of 10 bar and T=18 OC, the fluid density in smallest pores available in the matrix with D ~ 10 Å exceeds bulk fluid density by a factor of ~ 8. As pressure increases, progressively larger pores become filled with the condensed CO2. Fluid densification is only observed in pores with sizes less than ~ 25 – 30 Å. As the density of the invading fluid reaches (ρCO2)bulk~ 0.8 g/cm3, pores of all sizes become uniformly filled with CO2 and the confinement effects disappear. At higher densities the fluid in small pores appears to follow the equation of state of bulk CO2 although there is an indication that the fluid density in the inner volume of large pores may exceed the density of the adsorbed layer. The equivalent internal pressure (Pint) in the smallest pores exceeds the external pressure (Pext) by a factor of ~ 5 for both sub- and supercritical CO2. Pint gradually approaches Pext as D → 25 – 30 Å and is independent of temperature in the studied range of 18 OC ≤ T ≤ 60 OC. The obtained results demonstrate certain similarity as well as differences between adsorption of subcritical and supercritical CO2 in disordered porous silica. High pressure small angle scattering experiments open new opportunities for in situ studies of the fluid adsorption in porous media of interest to CO2 sequestration, energy storage, and heterogeneous catalysis.