Efecto de la suplementación con ácidos grasos de cadena media sobre la evolución de peso en lechones con poco peso al nacimiento

La evolución de peso en los períodos de lactación y transición de 583 lechones fue estudiada mediante un análisis estadístico, evaluando el efecto de la suplementación con ácidos grasos de cadena media (AGCM) en lechones con poco peso al nacimiento. 188 de los 375 lechones que nacieron con un peso al nacimiento (PN) &1250 g recibieron 3mL de AGCM cada 24 h durante los primeros 3 días de vida; su peso medio al destete (día 28) fue inferior respecto al grupo control (lechones no suplementados) (-114,17 g). No obstante, 106 de los 180 lechones nacidos con un PN &1000 g fueron suplementados, y su peso medio al destete y a finales de transición (día 63) fue superior respecto al grupo control (destete: +315,16 g; día 63: +775,47 g). Finalmente, los lechones suplementados con PN&800 g tuvieron los peores resultados: su diferencia de peso medio al destete fue de -177,58 g respecto al grupo control. Por lo tanto, en esta prueba fueron estudiados los lechones con un PN entre 800 y 999 g porque el grupo suplementado al destete tuvo una diferencia de peso medio considerable respecto al grupo control:+511,58 g. Asimismo, considerando una probabilidad de error inferior a 0,05, no hubieron diferencias significativas en las diferentes categorías de PN analizadas. De todas maneras, es importante destacar el alto grado de significación en la suplementación con AGCM en lechones con PN entre 800 y 999g (P=0,059). Por otra parte, el PN del grupo suplementado con PN&1000 g fue inferior que el del grupo no suplementado con PN&1000 g; esta diferencia de PN fue significativa (P=0,004) y como consecuencia el grado de significación en la suplementación con AGCM en lechones con PN entre 800 y 999 g fue inferior al esperado. Además, en esta prueba se incluyeron algunos resultados generales y también un análisis simple de supervivencia, aunque no era el objetivo principal

Using graphical probability analysis (Bayes Nets) to evaluate a conditional DNA inclusion

This paper discusses the analysis of cases in which the inclusion or exclusion of a particular suspect, as a possible contributor to a DNA mixture, depends on the value of a variable (the number of contributors) that cannot be determined with certainty. It offers alternative ways to deal with such cases, including sensitivity analysis and object-oriented Bayesian networks, that separate uncertainty about the inclusion of the suspect from uncertainty about other variables. The paper presents a case study in which the value of DNA evidence varies radically depending on the number of contributors to a DNA mixture: if there are two contributors, the suspect is excluded; if there are three or more, the suspect is included; but the number of contributors cannot be determined with certainty. It shows how an object-oriented Bayesian network can accommodate and integrate varying perspectives on the unknown variable and how it can reduce the potential for bias by directing attention to relevant considerations and distinguishing different sources of uncertainty. It also discusses the challenge of presenting such evidence to lay audiences.

Spatio-temporal Brain Dynamics Mediating Post-error Behavioral Adjustments.

Optimal behavior relies on flexible adaptation to environmental requirements, notably based on the detection of errors. The impact of error detection on subsequent behavior typically manifests as a slowing down of RTs following errors. Precisely how errors impact the processing of subsequent stimuli and in turn shape behavior remains unresolved. To address these questions, we used an auditory spatial go/no-go task where continual feedback informed participants of whether they were too slow. We contrasted auditory-evoked potentials to left-lateralized go and right no-go stimuli as a function of performance on the preceding go stimuli, generating a 2 × 2 design with "preceding performance" (fast hit [FH], slow hit [SH]) and stimulus type (go, no-go) as within-subject factors. SH trials yielded SH trials on the following trials more often than did FHs, supporting our assumption that SHs engaged effects similar to errors. Electrophysiologically, auditory-evoked potentials modulated topographically as a function of preceding performance 80-110 msec poststimulus onset and then as a function of stimulus type at 110-140 msec, indicative of changes in the underlying brain networks. Source estimations revealed a stronger activity of prefrontal regions to stimuli after successful than error trials, followed by a stronger response of parietal areas to the no-go than go stimuli. We interpret these results in terms of a shift from a fast automatic to a slow controlled form of inhibitory control induced by the detection of errors, manifesting during low-level integration of task-relevant features of subsequent stimuli, which in turn influences response speed.

Decreased Probability of Initial Pain Cessation in Classic Trigeminal Neuralgia Treated With Gamma Knife Surgery in Case of Previous Microvascular Decompression: A Prospective Series of 45 Patients With >1 Year of Follow-up.

BACKGROUND: Microvascular decompression (MVD) is the reference technique for pharmacoresistant trigeminal neuralgia (TN). OBJECTIVE: To establish whether the safety and efficacy of Gamma Knife surgery for recurrent TN are influenced by prior MVD. METHODS: Between July 1992 and November 2010, 54 of 737 patients (45 of 497 with >1 year of follow-up) had a history of MVD (approximately half also with previous ablative procedure) and were operated on with Gamma Knife surgery for TN in the Timone University Hospital. A single 4-mm isocenter was positioned in the cisternal portion of the trigeminal nerve at a median distance of 7.6 mm (range, 3.9-11.9 mm) anterior to the emergence of the nerve. A median maximum dose of 85 Gy (range, 70-90 Gy) was delivered. RESULTS: The median follow-up time was 39.5 months (range, 14.1-144.6 months). Thirty-five patients (77.8%) were initially pain free in a median time of 14 days (range, 0-180 days), much lower compared with our global population of classic TN (P = .01). Their actuarial probabilities of remaining pain-free without medication at 3, 5, 7, and 10 years were 66.5%, 59.1%, 59.1%, and 44.3%. The hypoesthesia actuarial rate at 1 year was 9.1% and remained stable until 12 years (median, 8 months). CONCLUSION: Patients with previous MVD showed a significantly lower probability of initial pain cessation compared with our global population with classic TN (P = .01). The toxicity was low (only 9.1% hypoesthesia); furthermore, no patient reported bothersome hypoesthesia. However, the probability of maintaining pain relief without medication was 44.3% at 10 years, similar to our global series of classic TN (P = .85). ABBREVIATIONS: BNI, Barrow Neurological InstituteCI, confidence intervalCTN, classic trigeminal neuralgiaGKS, Gamma Knife surgeryHR, hazard ratioMVD, microvascular decompressionTN, trigeminal neuralgia.

A new estimator to minimize the error due to breathing in the measurement of respiratory impedance

This paper presents a new respiratory impedance estimator to minimize the error due to breathing. Its practical reliability was evaluated in a simulation using realistic signals. These signals were generated by superposing pressure and flow records obtained in two conditions: 1) when applying forced oscillation to a resistance- inertance- elastance (RIE) mechanical model; 2) when healthy subjects breathed through the unexcited forced oscillation generator. Impedances computed (4-32 Hz) from the simulated signals with the new estimator resulted in a mean value which was scarcely biased by the added breathing (errors less than 1 percent in the mean R, I , and E ) and had a small variability (coefficients of variation of R, I, and E of 1.3, 3.5, and 9.6 percent, respectively). Our results suggest that the proposed estimator reduces the error in measurement of respiratory impedance without appreciable extracomputational cost.

MAR elements regulate the probability of epigenetic switching between active and inactive gene expression.

Gene expression often cycles between active and inactive states in eukaryotes, yielding variable or noisy gene expression in the short-term, while slow epigenetic changes may lead to silencing or variegated expression. Understanding how cells control these effects will be of paramount importance to construct biological systems with predictable behaviours. Here we find that a human matrix attachment region (MAR) genetic element controls the stability and heritability of gene expression in cell populations. Mathematical modeling indicated that the MAR controls the probability of long-term transitions between active and inactive expression, thus reducing silencing effects and increasing the reactivation of silent genes. Single-cell short-terms assays revealed persistent expression and reduced expression noise in MAR-driven genes, while stochastic burst of expression occurred without this genetic element. The MAR thus confers a more deterministic behavior to an otherwise stochastic process, providing a means towards more reliable expression of engineered genetic systems.

Local and global error models to improve uncertainty quantification

In groundwater applications, Monte Carlo methods are employed to model the uncertainty on geological parameters. However, their brute-force application becomes computationally prohibitive for highly detailed geological descriptions, complex physical processes, and a large number of realizations. The Distance Kernel Method (DKM) overcomes this issue by clustering the realizations in a multidimensional space based on the flow responses obtained by means of an approximate (computationally cheaper) model; then, the uncertainty is estimated from the exact responses that are computed only for one representative realization per cluster (the medoid). Usually, DKM is employed to decrease the size of the sample of realizations that are considered to estimate the uncertainty. We propose to use the information from the approximate responses for uncertainty quantification. The subset of exact solutions provided by DKM is then employed to construct an error model and correct the potential bias of the approximate model. Two error models are devised that both employ the difference between approximate and exact medoid solutions, but differ in the way medoid errors are interpolated to correct the whole set of realizations. The Local Error Model rests upon the clustering defined by DKM and can be seen as a natural way to account for intra-cluster variability; the Global Error Model employs a linear interpolation of all medoid errors regardless of the cluster to which the single realization belongs. These error models are evaluated for an idealized pollution problem in which the uncertainty of the breakthrough curve needs to be estimated. For this numerical test case, we demonstrate that the error models improve the uncertainty quantification provided by the DKM algorithm and are effective in correcting the bias of the estimate computed solely from the MsFV results. The framework presented here is not specific to the methods considered and can be applied to other combinations of approximate models and techniques to select a subset of realizations

Processes models, environmental analyses, and cognitive architectures: Quo vadis quantum probability theory? (Commentary on Pothos and Busemeyer)

A lot of research in cognition and decision making suffers from a lack of formalism. The quantum probability program could help to improve this situation, but we wonder whether it would provide even more added value if its presumed focus on outcome models were complemented by process models that are, ideally, informed by ecological analyses and integrated into cognitive architectures.

Accurate and efficient simulation of multiphase flow in a heterogeneous reservoir by using error estimate and control in the multiscale finite-volume framework

The multiscale finite-volume (MSFV) method is designed to reduce the computational cost of elliptic and parabolic problems with highly heterogeneous anisotropic coefficients. The reduction is achieved by splitting the original global problem into a set of local problems (with approximate local boundary conditions) coupled by a coarse global problem. It has been shown recently that the numerical errors in MSFV results can be reduced systematically with an iterative procedure that provides a conservative velocity field after any iteration step. The iterative MSFV (i-MSFV) method can be obtained with an improved (smoothed) multiscale solution to enhance the localization conditions, with a Krylov subspace method [e.g., the generalized-minimal-residual (GMRES) algorithm] preconditioned by the MSFV system, or with a combination of both. In a multiphase-flow system, a balance between accuracy and computational efficiency should be achieved by finding a minimum number of i-MSFV iterations (on pressure), which is necessary to achieve the desired accuracy in the saturation solution. In this work, we extend the i-MSFV method to sequential implicit simulation of time-dependent problems. To control the error of the coupled saturation/pressure system, we analyze the transport error caused by an approximate velocity field. We then propose an error-control strategy on the basis of the residual of the pressure equation. At the beginning of simulation, the pressure solution is iterated until a specified accuracy is achieved. To minimize the number of iterations in a multiphase-flow problem, the solution at the previous timestep is used to improve the localization assumption at the current timestep. Additional iterations are used only when the residual becomes larger than a specified threshold value. Numerical results show that only a few iterations on average are necessary to improve the MSFV results significantly, even for very challenging problems. Therefore, the proposed adaptive strategy yields efficient and accurate simulation of multiphase flow in heterogeneous porous media.

Homeodomain binding motifs modulate the probability of odorant receptor gene choice in transgenic mice.

Odorant receptor (OR) genes constitute with 1200 members the largest gene family in the mouse genome. A mature olfactory sensory neuron (OSN) is thought to express just one OR gene, and from one allele. The cell bodies of OSNs that express a given OR gene display a mosaic pattern within a particular region of the main olfactory epithelium. The mechanisms and cis-acting DNA elements that regulate the expression of one OR gene per OSN - OR gene choice - remain poorly understood. Here, we describe a reporter assay to identify minimal promoters for OR genes in transgenic mice, which are produced by the conventional method of pronuclear injection of DNA. The promoter transgenes are devoid of an OR coding sequence, and instead drive expression of the axonal marker tau-β-galactosidase. For four mouse OR genes (M71, M72, MOR23, and P3) and one human OR gene (hM72), a mosaic, OSN-specific pattern of reporter expression can be obtained in transgenic mice with contiguous DNA segments of only ~300 bp that are centered around the transcription start site (TSS). The ~150bp region upstream of the TSS contains three conserved sequence motifs, including homeodomain (HD) binding sites. Such HD binding sites are also present in the H and P elements, DNA sequences that are known to strongly influence OR gene expression. When a 19mer encompassing a HD binding site from the P element is multimerized nine times and added upstream of a MOR23 minigene that contains the MOR23 coding region, we observe a dramatic increase in the number of transgene-expressing founders and lines and in the number of labeled OSNs. By contrast, a nine times multimerized 19mer with a mutant HD binding site does not have these effects. We hypothesize that HD binding sites in the H and P elements and in OR promoters modulate the probability of OR gene choice.

Free inertial processes driven by Gaussian noise: Probability distributions, anomalous diffusion and fractal behavior

We study the motion of an unbound particle under the influence of a random force modeled as Gaussian colored noise with an arbitrary correlation function. We derive exact equations for the joint and marginal probability density functions and find the associated solutions. We analyze in detail anomalous diffusion behaviors along with the fractal structure of the trajectories of the particle and explore possible connections between dynamical exponents of the variance and the fractal dimension of the trajectories.

Generalized Langevin equations: Anomalous diffusion and probability distributions

We study the motion of a particle governed by a generalized Langevin equation. We show that, when no fluctuation-dissipation relation holds, the long-time behavior of the particle may be from stationary to superdiffusive, along with subdiffusive and diffusive. When the random force is Gaussian, we derive the exact equations for the joint and marginal probability density functions for the position and velocity of the particle and find their solutions.

Discriminant ECOC: a heuristic method for application dependent design of error correcting output codes

We present a heuristic method for learning error correcting output codes matrices based on a hierarchical partition of the class space that maximizes a discriminative criterion. To achieve this goal, the optimal codeword separation is sacrificed in favor of a maximum class discrimination in the partitions. The creation of the hierarchical partition set is performed using a binary tree. As a result, a compact matrix with high discrimination power is obtained. Our method is validated using the UCI database and applied to a real problem, the classification of traffic sign images.