Recurrent Rhombencephalitis Associated With Anti-GAD65 Antibody.

A 66-year-old man developed diplopia, ataxia, and right-hand dexterity loss. Brain MRI revealed T2-hyperintensities in the right cerebellar peduncles, pons, medulla, and cerebellum (Figure 1, A-D).

Multisystemic RFC1-Related Disorder: Expanding the Phenotype Beyond Cerebellar Ataxia, Neuropathy, and Vestibular Areflexia Syndrome.

Background and Objectives: The RFC1 spectrum has become considerably expanded as multisystemic features beyond the triad of cerebellar ataxia, neuropathy, and vestibular areflexia syndrome (CANVAS) have started to be unveiled, although many still require clinical replication. Here, we aimed to clinically characterize a cohort of RFC1-positive patients by addressing both classic and multisystemic features. In a second part of this study, we prospectively assessed small nerve fibers (SNF) and autonomic function in a subset of these RFC1-related patients. Methods: We retrospectively enrolled 67 RFC1-positive patients from multiple neurologic centers in Portugal. All patients underwent full neurologic and vestibular evaluation, as well as neuroimaging and neurophysiologic studies. For SNF and autonomic testing (n = 15), we performed skin biopsies, quantitative sensory testing, sudoscan, sympathetic skin response, heart rate deep breathing, and tilt test. Results: Multisystemic features beyond CANVAS were present in 82% of the patients, mainly chronic cough (66%) and dysautonomia (43%). Other features included motor neuron (MN) affection and motor neuropathy (18%), hyperkinetic movement disorders (16%), sleep apnea (6%), REM and non-REM sleep disorders (5%), and cranial neuropathy (5%). Ten patients reported an inverse association between cough and ataxia severity. A very severe epidermal denervation was found in skin biopsies of all patients. Autonomic dysfunction comprised cardiovascular (67%), cardiovagal (54%), and/or sudomotor (50%) systems. Discussion: The presence of MN involvement, motor neuropathy, small fiber neuropathy, or extrapyramidal signs should not preclude RFC1 testing in cases of sensory neuronopathy. Indeed, the RFC1 spectrum can overlap not only with multiple system atrophy but also with hereditary motor and sensory neuropathy, hereditary sensory and autonomic neuropathy, and feeding dystonia phenotypes. Some clinical-paraclinical dissociations can pose diagnostic challenges, namely large and small fiber neuropathy and sudomotor dysfunction which are usually subclinical.

Identification and automatic segmentation of multiphasic cell growth using a linear hybrid model.

This article considers a new mathematical model for the description of multiphasic cell growth. A linear hybrid model is proposed and it is shown that the two-parameter logistic model with switching parameters can be represented by a Switched affine AutoRegressive model with eXogenous inputs (SARX). The growth phases are modeled as continuous processes, while the switches between the phases are considered to be discrete events triggering a change in growth parameters. This framework provides an easily interpretable model, because the intrinsic behavior is the same along all the phases but with a different parameterization. Another advantage of the hybrid model is that it offers a simpler alternative to recent more complex nonlinear models. The growth phases and parameters from datasets of different microorganisms exhibiting multiphasic growth behavior such as Lactococcus lactis, Streptococcus pneumoniae, and Saccharomyces cerevisiae, were inferred. The segments and parameters obtained from the growth data are close to the ones determined by the experts. The fact that the model could explain the data from three different microorganisms and experiments demonstrates the strength of this modeling approach for multiphasic growth, and presumably other processes consisting of multiple phases.

Modeling multiple experiments using regularized optimization: A case study on bacterial glucose utilization dynamics.

The aim of inverse modeling is to capture the systems׳ dynamics through a set of parameterized Ordinary Differential Equations (ODEs). Parameters are often required to fit multiple repeated measurements or different experimental conditions. This typically leads to a multi-objective optimization problem that can be formulated as a non-convex optimization problem. Modeling of glucose utilization of Lactococcus lactis bacteria is considered using in vivo Nuclear Magnetic Resonance (NMR) measurements in perturbation experiments. We propose an ODE model based on a modified time-varying exponential decay that is flexible enough to model several different experimental conditions. The starting point is an over-parameterized non-linear model that will be further simplified through an optimization procedure with regularization penalties. For the parameter estimation, a stochastic global optimization method, particle swarm optimization (PSO) is used. A regularization is introduced to the identification, imposing that parameters should be the same across several experiments in order to identify a general model. On the remaining parameter that varies across the experiments a function is fit in order to be able to predict new experiments for any initial condition. The method is cross-validated by fitting the model to two experiments and validating the third one. Finally, the proposed model is integrated with existing models of glycolysis in order to reconstruct the remaining metabolites. The method was found useful as a general procedure to reduce the number of parameters of unidentifiable and over-parameterized models, thus supporting feature selection methods for parametric models.

Information geometric algorithm for estimating switching probabilities in space-varying HMM.

This paper proposes an iterative natural gradient algorithm to perform the optimization of switching probabilities in a space-varying hidden Markov model, in the context of human activity recognition in long-range surveillance. The proposed method is a version of the gradient method, developed under an information geometric viewpoint, where the usual Euclidean metric is replaced by a Riemannian metric on the space of transition probabilities. It is shown that the change in metric provides advantages over more traditional approaches, namely: 1) it turns the original constrained optimization into an unconstrained optimization problem; 2) the optimization behaves asymptotically as a Newton method and yields faster convergence than other methods for the same computational complexity; and 3) the natural gradient vector is an actual contravariant vector on the space of probability distributions for which an interpretation as the steepest descent direction is formally correct. Experiments on synthetic and real-world problems, focused on human activity recognition in long-range surveillance settings, show that the proposed methodology compares favorably with the state-of-the-art algorithms developed for the same purpose.

Manifold learning for object tracking with multiple nonlinear models.

This paper presents a novel manifold learning algorithm for high-dimensional data sets. The scope of the application focuses on the problem of motion tracking in video sequences. The framework presented is twofold. First, it is assumed that the samples are time ordered, providing valuable information that is not presented in the current methodologies. Second, the manifold topology comprises multiple charts, which contrasts to the most current methods that assume one single chart, being overly restrictive. The proposed algorithm, Gaussian process multiple local models (GP-MLM), can deal with arbitrary manifold topology by decomposing the manifold into multiple local models that are probabilistic combined using Gaussian process regression. In addition, the paper presents a multiple filter architecture where standard filtering techniques are integrated within the GP-MLM. The proposed approach exhibits comparable performance of state-of-the-art trackers, namely multiple model data association and deep belief networks, and compares favorably with Gaussian process latent variable models. Extensive experiments are presented using real video data, including a publicly available database of lip sequences and left ventricle ultrasound images, in which the GP-MLM achieves state of the art results.

Modeling and classifying human activities from trajectories using a class of space-varying parametric motion fields.

Many approaches to trajectory analysis, such as clustering or classification, use probabilistic generative models, thus not requiring trajectory alignment/registration. Switched linear dynamical models (e.g., HMMs) have been used in this context, due to their ability to describe different motion regimes. However, these models are not suitable for handling space-dependent dynamics that are more naturally captured by nonlinear models. As is well known, these are more difficult to identify. In this paper, we propose a new way of modeling trajectories, based on a mixture of parametric motion vector fields that depend on a small number of parameters. Switching among these fields follows a probabilistic mechanism, characterized by a field of stochastic matrices. This approach allows representing a wide variety of trajectories and modeling space-dependent behaviors without using global nonlinear dynamical models. Experimental evaluation is conducted in both synthetic and real scenarios. The latter concerning with human trajectory modeling for activity classification, a central task in video surveillance.

Optimization strategies for metabolic networks.

BACKGROUND: The increasing availability of models and data for metabolic networks poses new challenges in what concerns optimization for biological systems. Due to the high level of complexity and uncertainty associated to these networks the suggested models often lack detail and liability, required to determine the proper optimization strategies. A possible approach to overcome this limitation is the combination of both kinetic and stoichiometric models. In this paper three control optimization methods, with different levels of complexity and assuming various degrees of process information, are presented and their results compared using a prototype network. RESULTS: The results obtained show that Bi-Level optimization lead to a good approximation of the optimum attainable with the full information on the original network. Furthermore, using Pontryagin's Maximum Principle it is shown that the optimal control for the network in question, can only assume values on the extremes of the interval of its possible values. CONCLUSIONS: It is shown that, for a class of networks in which the product that favors cell growth competes with the desired product yield, the optimal control that explores this trade-off assumes only extreme values. The proposed Bi-Level optimization led to a good approximation of the original network, allowing to overcome the limitation on the available information, often present in metabolic network models. Although the prototype network considered, it is stressed that the results obtained concern methods, and provide guidelines that are valid in a wider context.

Control of depth of anesthesia using MUSMAR--exploring electromyography and the analgesic dose as accessible disturbances.

The problem of controlling the level of depth of anesthesia measured by the Bispectral Index (BIS) of the electroencephalogram of patients under general anesthesia, is considered. It is assumed that the manipulated variable is the infusion rate of the hypnotic drug propofol, while the drug remifentanil is also administered for analgesia. Since these two drugs interact, the administration rate of remifentanil is considered as an accessible disturbance in combination with the level of electromyography (EMG) that also interferes with the BIS signal. In order to tackle the high uncertainty present on the system, the predictive adaptive controller MUSMAR is used. The performance of the controller is illustrated by means of simulation with 45 patient individual adjusted models, which incorporate the effect of the drugs interaction on BIS. This controller structure proved to be robust to the EMG and remifentanil disturbances, patient variability, changing reference values and noise.

Observer design in switching control of neuromuscular blockade: clinical cases.

This paper concerns the application of multiple model switched methods to the control of neuromuscular blockade of patients undergoing anaesthesia. Since the model representing the neuromuscular blockade process is subject to a high level of uncertainty due both to inter-patient variability and time variations, switched methods provide the adaptation capability needed to achieve the desired performance. The paper contributions are twofold: first, it is shown that, for the type of process control problem considered, the design of the associated observer must be carefully performed. Guidelines are provided for adequate selection of the characteristic polynomial defining the observer error dynamics. Second, clinical results using atracurium as blocking agent are reported in order to illustrate the use of the proposed control structure in actual clinical practice.

Control of neuromuscular blockade in the presence of sensor faults.

The problem of embedding sensor fault tolerance in feedback control of neuromuscular blockade is considered. For tackling interruptions of feedback measurements, a structure based upon Bayesian inference as well as a predictive filter is proposed. This algorithm is general and can be applied to different situations. Here, it is incorporated in an adaptive automatic system for feedback control of neuromuscular blockade using continuous infusion of muscle relaxants. A significant contribution consists in the experimental clinical testing of the algorithm in patients undergoing surgery.