The Design and Development of Instrumented Toys for the Assessment of Infant Cognitive Flexibility.

The first years of an infant's life represent a sensitive period for neurodevelopment where one can see the emergence of nascent forms of executive function (EF), which are required to support complex cognition. Few tests exist for measuring EF during infancy, and the available tests require painstaking manual coding of infant behaviour. In modern clinical and research practice, human coders collect data on EF performance by manually labelling video recordings of infant behaviour during toy or social interaction. Besides being extremely time-consuming, video annotation is known to be rater-dependent and subjective. To address these issues, starting from existing cognitive flexibility research protocols, we developed a set of instrumented toys to serve as a new type of task instrumentation and data collection tool suitable for infant use. A commercially available device comprising a barometer and an inertial measurement unit (IMU) embedded in a 3D-printed lattice structure was used to detect when and how the infant interacts with the toy. The data collected using the instrumented toys provided a rich dataset that described the sequence of toy interaction and individual toy interaction patterns, from which EF-relevant aspects of infant cognition can be inferred. Such a tool could provide an objective, reliable, and scalable method of collecting early developmental data in socially interactive contexts.

Three simple steps to improve the interpretability of EEG-SVM studies.

Machine-learning systems that classify electroencephalography (EEG) data offer important perspectives for the diagnosis and prognosis of a wide variety of neurological and psychiatric conditions, but their clinical adoption remains low. We propose here that much of the difficulties translating EEG-machine-learning research to the clinic result from consistent inaccuracies in their technical reporting, which severely impair the interpretability of their often-high claims of performance. Taking example from a major class of machine-learning algorithms used in EEG research, the support-vector machine (SVM), we highlight three important aspects of model development (normalization, hyperparameter optimization, and cross-validation) and show that, while these three aspects can make or break the performance of the system, they are left entirely undocumented in a shockingly vast majority of the research literature. Providing a more systematic description of these aspects of model development constitute three simple steps to improve the interpretability of EEG-SVM research and, in fine, its clinical adoption.

Validation of the Simplified Criteria for the Diagnosis of Autoimmune Hepatitis in Chilean-Hispanic Patients.

INTRODUCTION AND AIM: In 2008 the International autoimmune hepatitis (AIH) Group proposed the simplified diagnostic criteria for this disease. The original cohort study was performed in 11 international centers, but validation studies are scarce in Latin-America. The aim of this study is validate these criteria in Hispanic patients. MATERIAL AND METHODS: A retrospective cohort of patients undergoing percutaneous liver biopsy and follow-up of at least 12 months was recruited from a Chilean University hospital. Patients with previous immunosuppressive therapy and liver transplant recipients were excluded. The diagnostic accuracy was analyzed using as gold standard the clinical course during long-term follow-up. Sensitivity, specificity, positive and negative predictive values (PPV and NPV) and area under the ROC curve (AUROC) were calculated. RESULTS: Four hundred eighty one patients were evaluated, 294 were included. 218 (74.15%) were female, mean age 48.5 (± 12.3) years, mean follow-up 34 (± 18) months. 66 patients had AIH or overlap syndrome (22.45%), 96 (32.65%) non-alcoholic steatohepatitis, 40 (13.61%) primary biliary cholangitis, 31 (10.54%) hepatitis C, 8 (2.72%) hepatitis B, 53 (18.02%) other etiologies. The AUROC for AIH simplified criteria was 0.976. Using a cutoff ≥ 6 and ≥ 7 points, the sensitivity was 86.4% and 54.6%; specificity, 98.7% and 99.6%; PPV, 95% and 97.3%; and NPV, 96.2% and 88.6%, respectively. CONCLUSION: Simplified criteria for the diagnosis of AIH have a high accuracy in our Chilean-Hispanic cohort. The female gender is strongly associated to AIH and could help in difficult cases. Further studies with a prospective design are necessary to confirm these observations.

Nonlinear Memory Capacity of Parallel Time-Delay Reservoir Computers in the Processing of Multidimensional Signals.

This letter addresses the reservoir design problem in the context of delay-based reservoir computers for multidimensional input signals, parallel architectures, and real-time multitasking. First, an approximating reservoir model is presented in those frameworks that provides an explicit functional link between the reservoir architecture and its performance in the execution of a specific task. Second, the inference properties of the ridge regression estimator in the multivariate context are used to assess the impact of finite sample training on the decrease of the reservoir capacity. Finally, an empirical study is conducted that shows the adequacy of the theoretical results with the empirical performances exhibited by various reservoir architectures in the execution of several nonlinear tasks with multidimensional inputs. Our results confirm the robustness properties of the parallel reservoir architecture with respect to task misspecification and parameter choice already documented in the literature.

Performance of small-scale dairy farms in the highlands of central Mexico during the dry season under traditional feeding strategies.

In Mexico, small-scale dairy systems (SSDS) represent over 78 % of dairy farms and contribute with 37 % of national milk production; however, they face high feeding costs. The objective of this study was to assess the performance of SSDS during the dry season in terms of milk yields, milk composition and feeding costs under traditional feeding strategies, to identify areas of opportunity for improving their profitability. The information was collected from 22 SSDS every month during dry season. Feeds were classified in quality forages (QF), supplements (SU) and straws (ST). Two factors were identified: factor 1-a positive relationship among QF, SU, milk yield and ration cost and factor 2-represented straw usage. Four feeding strategies were identified: (1) low-cost feeding strategy; (2) home-grown feeding strategy; (3) high-cost feeding strategy; and (4) straw-based feeding strategy. There were differences (P < 0.001) among feeding strategies for QF, SU, ST, total dry matter offered (TDMO), ration cost (RC), external inputs, home-grown inputs and milk yield. There were no significant (P > 0.05) differences among feeding strategies for fat and protein contents in milk. It is concluded that to improve performance and profitability and enhance sustainability in SSDS, farmers should base feeding strategies on home-grown quality forages, as it was the case in group 2 which showed lower feeding cost and better milk yield. It is also recommended to increase the inclusion of quality forages like grazing pastures and maize silages during the dry season and to avoid the inclusion of straws.

Infinite-dimensional reservoir computing.

Reservoir computing approximation and generalization bounds are proved for a new concept class of input/output systems that extends the so-called generalized Barron functionals to a dynamic context. This new class is characterized by the readouts with a certain integral representation built on infinite-dimensional state-space systems. It is shown that this class is very rich and possesses useful features and universal approximation properties. The reservoir architectures used for the approximation and estimation of elements in the new class are randomly generated echo state networks with either linear or ReLU activation functions. Their readouts are built using randomly generated neural networks in which only the output layer is trained (extreme learning machines or random feature neural networks). The results in the paper yield a recurrent neural network-based learning algorithm with provable convergence guarantees that do not suffer from the curse of dimensionality when learning input/output systems in the class of generalized Barron functionals and measuring the error in a mean-squared sense.

Tracing curves in the plane: Geometric-invariant learning from human demonstrations.

The empirical laws governing human-curvilinear movements have been studied using various relationships, including minimum jerk, the 2/3 power law, and the piecewise power law. These laws quantify the speed-curvature relationships of human movements during curve tracing using critical speed and curvature as regressors. In this work, we provide a reservoir computing-based framework that can learn and reproduce human-like movements. Specifically, the geometric invariance of the observations, i.e., lateral distance from the closest point on the curve, instantaneous velocity, and curvature, when viewed from the moving frame of reference, are exploited to train the reservoir system. The artificially produced movements are evaluated using the power law to assess whether they are indistinguishable from their human counterparts. The generalisation capabilities of the trained reservoir to curves that have not been used during training are also shown.

Quantum reservoir computing in finite dimensions.

Most existing results in the analysis of quantum reservoir computing (QRC) systems with classical inputs have been obtained using the density matrix formalism. This paper shows that alternative representations can provide better insight when dealing with design and assessment questions. More explicitly, system isomorphisms are established that unify the density matrix approach to QRC with the representation in the space of observables using Bloch vectors associated with Gell-Mann bases. It is shown that these vector representations yield state-affine systems previously introduced in the classical reservoir computing literature and for which numerous theoretical results have been established. This connection is used to show that various statements in relation to the fading memory property (FMP) and the echo state property (ESP) are independent of the representation and also to shed some light on fundamental questions in QRC theory in finite dimensions. In particular, a necessary and sufficient condition for the ESP and FMP to hold is formulated using standard hypotheses, and contractive quantum channels that have exclusively trivial semi-infinite solutions are characterized in terms of the existence of input-independent fixed points.

Discrete-Time Signatures and Randomness in Reservoir Computing.

A new explanation of the geometric nature of the reservoir computing (RC) phenomenon is presented. RC is understood in the literature as the possibility of approximating input-output systems with randomly chosen recurrent neural systems and a trained linear readout layer. Light is shed on this phenomenon by constructing what is called strongly universal reservoir systems as random projections of a family of state-space systems that generate Volterra series expansions. This procedure yields a state-affine reservoir system with randomly generated coefficients in a dimension that is logarithmically reduced with respect to the original system. This reservoir system is able to approximate any element in the fading memory filters class just by training a different linear readout for each different filter. Explicit expressions for the probability distributions needed in the generation of the projected reservoir system are stated, and bounds for the committed approximation error are provided.

Fading memory echo state networks are universal.

Echo state networks (ESNs) have been recently proved to be universal approximants for input/output systems with respect to various Lp-type criteria. When 1≤p<∞, only p-integrability hypotheses need to be imposed, while in the case p=∞ a uniform boundedness hypotheses on the inputs is required. This note shows that, in the last case, a universal family of ESNs can be constructed that contains exclusively elements that have the echo state and the fading memory properties. This conclusion could not be drawn with the results and methods available so far in the literature.

Chaos on compact manifolds: Differentiable synchronizations beyond the Takens theorem.

This paper shows that a large class of fading memory state-space systems driven by discrete-time observations of dynamical systems defined on compact manifolds always yields continuously differentiable synchronizations. This general result provides a powerful tool for the representation, reconstruction, and forecasting of chaotic attractors. It also improves previous statements in the literature for differentiable generalized synchronizations, whose existence was so far guaranteed for a restricted family of systems and was detected using Hölder exponent-based criteria.

Reservoir Computing Universality With Stochastic Inputs.

The universal approximation properties with respect to L p -type criteria of three important families of reservoir computers with stochastic discrete-time semi-infinite inputs are shown. First, it is proven that linear reservoir systems with either polynomial or neural network readout maps are universal. More importantly, it is proven that the same property holds for two families with linear readouts, namely, trigonometric state-affine systems and echo state networks, which are the most widely used reservoir systems in applications. The linearity in the readouts is a key feature in supervised machine learning applications. It guarantees that these systems can be used in high-dimensional situations and in the presence of large data sets. The L p criteria used in this paper allow the formulation of universality results that do not necessarily impose almost sure uniform boundedness in the inputs or the fading memory property in the filter that needs to be approximated.

Association of serum vitamin D levels with chronic disease and mortality.

INTRODUCTION: Introduction: whether hypovitaminosis D is an overarching cause of increased mortality or a prognostic marker of poor health has not been well elucidated. Objectives: we sought to determine the association of serum 25-hydroxyvitamin D [25-(OH)-D3] levels with the clinical biochemical parameters and mortality risk in chronic diseases. Methods: we reviewed the clinical charts and collected the clinical biochemical parameters of patients diagnosed with chronic conditions who had at least one 25-(OH)-D3 determination, with or without calcium and vitamin D supplementation, and who were selected using a cluster random sampling design (n = 1,705). The analysis was focused on metabolic disorders (type-2 diabetes mellitus [T2DM] and obesity), autoimmune disorders, and mortality. Multivariate logistic regression analyses were performed. Results: low 25-(OH)-D3 levels were reported in 1,433 (84.0%) patients, of which 774 (45.4%) had insufficiency (20-29 ng/mL) and 659 (38.6%) patients had deficiency (< 20 ng/mL). Lower 25-(OH)-D3 levels in T2DM patients were associated with higher glycosylated hemoglobin levels (p < 0.001). Patients with 25-(OH)-D3 levels < 12.5 ng/mL had a higher mortality risk than those with levels ≥ 12.5 ng/mL (HR: 3.339; 95% CI: 1.342-8.308). We observed lower 25-(OH)-D3 levels in patients with grade-III obesity (p = 0.01). We found a higher risk of 25-(OH)-D3 deficiency in rheumatoid arthritis, type-1 diabetes, and systemic lupus erythematosus (p = 0.032, p = 0.002, p = 0.049, respectively). Conclusions: we found a significant relationship between 25-(OH)-D3 levels and glycemic control, body mass index, autoimmune disease, and mortality risk. Nevertheless, whether hypovitaminosis D plays a causal role or is a consequence of chronic disease remains controversial.

INTRODUCCIÓN: Introducción: si la hipovitaminosis D constituye una causa general de mayor mortalidad o un marcador de mal pronóstico para la salud no se ha dilucidado por completo. Objetivos: determinar la asociación de los niveles séricos de 25-hidroxivitamina D [25-(OH)-D3] con los parámetros clínico-bioquímicos y el riesgo de mortalidad en la enfermedad crónica. Métodos: se revisaron los expedientes clínicos y recopilamos los parámetros clínico-bioquímicos de pacientes diagnosticados de enfermedades crónicas que tenían al menos una determinación de 25-(OH)-D3, con o sin suplemento de calcio y vitamina D, y que se seleccionaron mediante muestreo aleatorio por grupos (n = 1705). El análisis se centró en los trastornos metabólicos (diabetes mellitus de tipo 2 [DM2] y obesidad), los trastornos autoinmunes y la mortalidad. Se realizaron análisis multivariados de regresión logística. Resultados: se encontraron niveles bajos de 25-(OH)-D3 en 1433 (84,0%) pacientes, de los cuales 774 (45,4%) tenían insuficiencia (20-29 ng/mL) y 659 (38,6%) tenían deficiencia (< 20 ng/mL) de esta vitamina. Los niveles más bajos de 25-(OH)-D3 en los pacientes con DM2 se asociaron a niveles más altos de hemoglobina glucosilada (p < 0,001). Los pacientes con niveles de 25-(OH)-D3 < 12,5 ng/mL tenían mayor riesgo de mortalidad que aquellos con niveles ≥ 12,5 ng/mL (HR: 3,339; IC del 95%: 1,342-8,308). Apreciamos niveles más bajos de 25-(OH)-D3 en los pacientes con obesidad de grado III (p = 0,01). Se encontró un mayor riesgo de deficiencia de 25-(OH)-D3 en la artritis reumatoide, la diabetes de tipo 1 y el lupus eritematoso sistémico (p = 0,032, p = 0,002, p = 0,049, respectivamente). Conclusiones: apreciamos una relación significativa entre los niveles de 25-(OH)-D3 y el control glucémico, el índice de masa corporal, la enfermedad autoinmune y el riesgo de mortalidad. Sin embargo, sigue siendo controvertido si la hipovitaminosis D desempeña un papel causal o constituye una consecuencia de las enfermedades crónicas.

Echo state networks are universal.

This paper shows that echo state networks are universal uniform approximants in the context of discrete-time fading memory filters with uniformly bounded inputs defined on negative infinite times. This result guarantees that any fading memory input/output system in discrete time can be realized as a simple finite-dimensional neural network-type state-space model with a static linear readout map. This approximation is valid for infinite time intervals. The proof of this statement is based on fundamental results, also presented in this work, about the topological nature of the fading memory property and about reservoir computing systems generated by continuous reservoir maps.

Bedside Evaluation of the Functional Organization of the Auditory Cortex in Patients with Disorders of Consciousness.

To measure the level of residual cognitive function in patients with disorders of consciousness, the use of electrophysiological and neuroimaging protocols of increasing complexity is recommended. This work presents an EEG-based method capable of assessing at an individual level the integrity of the auditory cortex at the bedside of patients and can be seen as the first cortical stage of this hierarchical approach. The method is based on two features: first, the possibility of automatically detecting the presence of a N100 wave and second, in showing evidence of frequency processing in the auditory cortex with a machine learning based classification of the EEG signals associated with different frequencies and auditory stimulation modalities. In the control group of twelve healthy volunteers, cortical frequency processing was clearly demonstrated. EEG recordings from two patients with disorders of consciousness showed evidence of partially preserved cortical processing in the first patient and none in the second patient. From these results, it appears that the classification method presented here reliably detects signal differences in the encoding of frequencies and is a useful tool in the evaluation of the integrity of the auditory cortex. Even though the classification method presented in this work was designed for patients with disorders of consciousness, it can also be applied to other pathological populations.

Optimal nonlinear information processing capacity in delay-based reservoir computers.

Reservoir computing is a recently introduced brain-inspired machine learning paradigm capable of excellent performances in the processing of empirical data. We focus in a particular kind of time-delay based reservoir computers that have been physically implemented using optical and electronic systems and have shown unprecedented data processing rates. Reservoir computing is well-known for the ease of the associated training scheme but also for the problematic sensitivity of its performance to architecture parameters. This article addresses the reservoir design problem, which remains the biggest challenge in the applicability of this information processing scheme. More specifically, we use the information available regarding the optimal reservoir working regimes to construct a functional link between the reservoir parameters and its performance. This function is used to explore various properties of the device and to choose the optimal reservoir architecture, thus replacing the tedious and time consuming parameter scannings used so far in the literature.

Stochastic nonlinear time series forecasting using time-delay reservoir computers: performance and universality.

Reservoir computing is a recently introduced machine learning paradigm that has already shown excellent performances in the processing of empirical data. We study a particular kind of reservoir computers called time-delay reservoirs that are constructed out of the sampling of the solution of a time-delay differential equation and show their good performance in the forecasting of the conditional covariances associated to multivariate discrete-time nonlinear stochastic processes of VEC-GARCH type as well as in the prediction of factual daily market realized volatilities computed with intraday quotes, using as training input daily log-return series of moderate size. We tackle some problems associated to the lack of task-universality for individually operating reservoirs and propose a solution based on the use of parallel arrays of time-delay reservoirs.

[Helicobacter pylori infection in symptomatic patients with benign gastroduodenal diseases: analysis of 5.664 cases]. / Infección por Helicobacter pylori en pacientes sintomáticos con patología gastroduodenal benigna: análisis de 5.664 pacientes.

UNLABELLED: Infection with Helicobacter pylori (H. pylori) is highly prevalent in Chile, but there are no systematic studies in patients with upper gastrointestinal symptoms. AIM: To determine the prevalence of H. pylori infection, according to age, gender and endoscopic pathology in a large sample of patients. METHODS: We studied 7,893 symptomatic patients submitted to upper gastrointestinal endoscopy between July 1996 and December 2003 in the context of a screening program of gastric cancer in a high risk population. H. pylori infection was determined by rapid urease test (RUT) in antral mucosa. We excluded 158 patients with gastric cancer (2%) and 2,071 patients without RUT. RESULTS: We included 5,664 patients, mean age 50.7 +/- 13.9 years, women 72.1%. Endoscopic diagnoses were normal in 59.3%, erosive esophagitis in 20%, gastric ulcer (GU) in 8.1%, duodenal ulcer (DU) in 6.4%, and erosive gastropathy in 6.2%. RUT was positive in 78% of patients. After adjusting for age and sex and with respect to patients with normal endoscopy, frequency of H. pylori infection was 86.6% in DU (OR 2.1, 95% CI 1.5-2.8, p < 0.001); 81.4% in GU (OR 1.8, 95% CI 1.4-2.4; p < 0.001 ); 79.9% in erosive gastropathy (OR 1.4, 95% CI 1.03-1.8; p = 0.03) and 77.4% in erosive esophagitis (OR 1.1, 95% CI: 0.9-1.3; p = NS). The probability of H. pylori infection decreased significantly with age, more markedly in men with normal endoscopy. CONCLUSIONS: Prevalence of H. pylori infection is very high in symptomatic Chilean patients and even higher in those with gastroduodenal ulcer or erosions, while in patients with erosive esophagitis is similar to those with normal endoscopy. The frequency of infection decreases with age, probably as a consequence of rising frequency of gastric mucosal atrophy.