1,721,343 research outputs found
Influence of mRNA self-structure on hybridization: Computational tools for antisense sequence selection.
No full textAntisense targeting, an innovative technology based on preventing biosynthesis through sequence-specific hybridization of mRNA to synthetic oligodeoxynucleotides (ODNs), is used to selectively and transiently downregulate the expression of any gene product. Its potential applications are both investigative (neurobiology and related disciplines) and therapeutic (oncology, virology, genetic diseases), and several antisense-based drugs are currently undergoing clinical trials. However, the reported efficiencies vary and there is still a lack of clarity in the underlying mechanisms of action. A critical factor of anti-sense efficiency is the issue of target site selection, as both mRNA and ODN molecules exhibit a significant amount of highly heterogeneous self-structure and the region selected for targeting may well be sterically or energetically inaccessible. Because of the prohibitively large chain length, mRNA structural information is not accessible by X-ray crystallography or NMR, making a modeling approach indispensable. I outline how the latest molecular modeling techniques can be employed to establish the secondary (2D) and tertiary (3D) structures into which a given mRNA folds during and after transcription. The aim should be to integrate 2D prediction results achieved by (a) free-energy minimization, (b) kinetic folding simulations, (c) iterative population breeding by genetic algorithms, and (d) phylogenetic comparison techniques. These results can form the input of a 3D structure prediction paradigm based on constraint-satisfying programming, governed by experimental molecular mechanical constraints, and refined by molecular dynamics simulations. Finally, the automated docking (by simulated annealing) of ODN molecules to the mRNA structure can provide information about the accessibility of target mRNA regions for hybridization. To date, the great majority of studies that employ antisense as a tool select their target sequences more or less randomly. Although the wealth of molecular interactions that take place within a cell makes complete predictability unrealistic, the kind of information that can be extracted from such techniques is of relevance to every application of antisense technology, both investigative and therapeutic. © 2000 Academic Press
On the use of Trace-weighted images in body diffusional kurtosis imaging
No full textDiffusional kurtosis imaging (DKI) has proven to be a promising diffusion-MRI technique whose first and most established applications are in neuroimaging. Recently, a number of preliminary studies have assessed the feasibility and potential usefulness of DKI in extra-cranial regions such as prostate, liver, kidney, bladder and breast. The stringent time constraints in most routine body MRI exams frequently mandate the acquisition of diffusion-weighted images (DWIs) with (only) three diffusion weighting directions (i.e. the main orthogonal directions). The aim of this study was to evaluate the potential error introduced in the estimation of the average of the three directional diffusional kurtosis values (K) by using, for each b-value, the geometric mean (trace-weighted image) of acquired DWIs (as is common practice in most diffusion-MRI studies of the body) instead of fitting the DKI model to DWIs acquired along each direction prior to averaging. By solving the DKI model analytically while imposing three orthogonal diffusion weighting directions and two non-null b-values (800 and 2000s/mm(2)), extensive simulations were performed for different K values (0-3) and a wide range of diffusion anisotropy values. The error in the estimates of K induced by geometrical averaging of DWIs was assessed and compared to the uncertainty in K caused by DWIs noise for low (20), medium (40) and high (80) signal-to-noise ratio (SNR) values. The simulations showed that geometrical averaging of the DWIs introduces a noticeable error in estimated K. While the error in K varies non-monotonically with K and with the degree of diffusion anisotropy, there is a trend of increasing absolute error with both increasing K and increasing degree of diffusion anisotropy. In particular, for values of K close to 1 and low/moderate (0-0.4) diffusion anisotropy degrees (typical of various body tissues), the absolute error in K can range up to 60% of K. In this case, at all SNR values (20, 40, 80), the absolute error in K can be greater than the uncertainty introduced by noise. In clinical body applications of DKI, the widespread and growing practice of using trace-weighted images to estimate K can introduce a substantial error, hence hampering interpretation of results as well as multi-center comparisons, and should therefore be avoided
Echo state network models for nonlinear Granger causality
No full textWhile Granger causality (GC) has been often employed in network neuroscience, most GC applications are based on linear multivariate autoregressive (MVAR) models. However, real-life systems like biological networks exhibit notable nonlinear behaviour, hence undermining the validity of MVAR-based GC (MVAR-GC). Most nonlinear GC estimators only cater for additive nonlinearities or, alternatively, are based on recurrent neural networks or long short-term memory networks, which present considerable training difficulties and tailoring needs. We reformulate the GC framework in terms of echo-state networks-based models for arbitrarily complex networks, and characterize its ability to capture nonlinear causal relations in a network of noisy Duffing oscillators, showing a net advantage of echo state GC (ES-GC) in detecting nonlinear, causal links. We then explore the structure of ES-GC networks in the human brain employing functional MRI data from 1003 healthy subjects drawn from the human connectome project, demonstrating the existence of previously unknown directed within-brain interactions. In addition, we examine joint brain-heart signals in 15 subjects where we explore directed interaction between brain networks and central vagal cardiac control in order to investigate the so-called central autonomic network in a causal manner. This article is part of the theme issue 'Advanced computation in cardiovascular physiology: new challenges and opportunities'
Sleep quality relates to emotional reactivity via intracortical myelination
Full text linka good quality and amount of sleep are fundamental to preserve cognition and affect. New evidence also indicates that poor sleep is detrimental to brain myelination. In this study, we test the hypothesis that sleep quality and/or quantity relate to variability in cognitive and emotional function via the mediating effect of interindividual differences in proxy neuroimaging measures of white matter integrity and intracortical myelination. by employing a demographically and neuropsychologically well-characterized sample of healthy people drawn from the human connectome project (n = 974), we found that quality and amount of sleep were only marginally linked to cognitive performance. In contrast, poor quality and short sleep increased negative affect (i.e. anger, fear, and perceived stress) and reduced life satisfaction and positive emotionality. at the brain level, poorer sleep quality and shorter sleep duration related to lower intracortical myelin in the mid-posterior cingulate cortex (p = 0.038), middle temporal cortex (p = 0.024), and anterior orbitofrontal cortex (OFC, p = 0.034) but did not significantly affect different measures of white matter integrity. finally, lower intracortical myelin in the OFC mediated the association between poor sleep quality and negative emotionality (p < 0.05). we conclude that intracortical myelination is an important mediator of the negative consequences of poor sleep on affective behavior
Decoding semantic content of visual stimuli from BOLD fMRI data
No full textIn vision, the brain is a feature extractor that works from images. We hypothesize that fMRI can mimic the latent space of a classifier, and employ deep diffusion models with BOLD data from the occipital cortex to generate images which are plausible and semantically close to the visual stimuli administered during fMRI. To this end, we mapped BOLD signals onto the latent space of a pretrained classifier and used its gradients to condition a generative model to reconstruct images. The semantic fidelity of our BOLD response to visual stimulus reconstruction model is superior to the state of the art
Enabling uncertainty estimation in neural networks through weight perturbation for improved Alzheimer's disease classification
Full text linkBackground: The willingness to trust predictions formulated by automatic algorithms is key in a wide range of domains. However, a vast number of deep architectures are only able to formulate predictions without associated uncertainty.
Purpose: In this study, we propose a method to convert a standard neural network into a Bayesian neural network and estimate the variability of predictions by sampling different networks similar to the original one at each forward pass.
Methods: We combine our method with a tunable rejection-based approach that employs only the fraction of the data, i.e., the share that the model can classify with an uncertainty below a user-set threshold. We test our model in a large cohort of brain images from patients with Alzheimer's disease and healthy controls, discriminating the former and latter classes based on morphometric images exclusively.
Results: We demonstrate how combining estimated uncertainty with a rejection-based approach increases classification accuracy from 0.86 to 0.95 while retaining 75% of the test set. In addition, the model can select the cases to be recommended for, e.g., expert human evaluation due to excessive uncertainty. Importantly, our framework circumvents additional workload during the training phase by using our network "turned into Bayesian" to implicitly investigate the loss landscape in the neighborhood of each test sample in order to determine the reliability of the predictions.
Conclusion: We believe that being able to estimate the uncertainty of a prediction, along with tools that can modulate the behavior of the network to a degree of confidence that the user is informed about (and comfortable with), can represent a crucial step in the direction of user compliance and easier integration of deep learning tools into everyday tasks currently performed by human operators
Cardiac autonomic changes in epilepsy
No full textThe term "Epilepsy" encompasses a broad spectrum of medical and social disorders that affect about 65 million people worldwide and is commonly defined as a tendency to suffer recurrent seizures. In patients with epilepsy, ictal discharges that occur in (or propagate to) the anterior cingulate, insular, posterior orbito-frontal, and the pre-frontal cortices, along with the amygdala and hypothalamus play a key role in influencing the autonomic nervous system (ANS) at the cortical level. In turn, this can result in cardiac effects which are widespread and range from subtle changes in heart rate variability (HRV) to ictal sinus arrest, and from QT-interval shortening to atrial fibrillation. In addition, cardiac events are the main hypothesized mechanisms underlying sudden unexpected death in epilepsy (SUDEP), which occurs in absence of a known structural cause. Patients with epilepsy also experience long-lasting changes in the regulation of the ANS and target organs. Heart rate (HR) and HRV can be easily measured/estimated when compared to other biomarkers that are commonly associated with seizures (i.e., long-term EEG), and are therefore potentially valuable biomarkers when it comes to characterizing seizures. In this context, a number of linear and nonlinear analysis techniques have been applied in order to detect and characterize epilepsy-related ANS changes. While the physiological and clinical applicability of nonlinear analyses like fractal and complexity measures of HR dynamics are not yet completely understood, in view of recent experimental findings it is reasonable to assume that such indices highlight abnormal patterns of RR interval behaviour that are not easily detected by commonly used moment statistics of HR variation. These findings may provide new insight regarding physiological and seizure- induced states of the complex brain-heart network underlying epilepsy and related autonomic modifications. A better understanding of the autonomic manifestations of seizures would provide practical added value to clinical epileptologists dealing with differential diagnosis of epilepsy and related disorders, as well as aiding in designing more sensitive seizure detection and prediction algorithms
Intra-cortical myelin mediates personality differences.
No full textOBJECTIVE: Differences in myelination in the cortical mantle are important neurobiological mediators of variability in cognitive, emotional, and behavioral functioning. Past studies have found that personality traits reflecting such variability are linked to neuroanatomical and functional changes in prefrontal and temporo-parietal cortices. Whether these effects are partially mediated by the differences in intra-cortical myelin remains to be established. METHOD: To test this hypothesis, we employed vertex-wise intra-cortical myelin maps in n = 1,003 people from the Human Connectome Project. Multivariate regression analyses were used to test for the relationship between intra-cortical myelin and each of the five-factor model's personality traits, while accounting for age, sex, intelligence quotient, total intracranial volume, and the remaining personality traits. RESULTS: Neuroticism negatively related to frontal-pole myelin and positively to occipital cortex myelin. Extraversion positively related to superior parietal myelin. Openness negatively related to anterior cingulate myelin, while Agreeableness positively related to orbitofrontal myelin. Conscientiousness positively related to frontal-pole myelin and negatively to myelin content in the dorsal anterior cingulate cortex. CONCLUSIONS: Intra-cortical myelin levels in brain regions with prolonged myelination are positively associated with personality traits linked to favorable outcome measures. These findings improve our understanding of the neurobiological underpinnings of variability in common behavioral dispositions
Physically constrained neural networks for inferring physiological system models
No full textSystems biology and systems neurophysiology in particular have recently emerged as powerful tools for a number of key applications in the biomedical sciences. nevertheless, such models are often based on complex combinations of multiscale (and possibly multiphysics) strategies that require ad hoc computational strategies and pose extremely high computational demands. recent developments in the field of deep neural networks have demonstrated the possibility of formulating nonlinear, universal approximators to estimate solutions to highly nonlinear and complex problems with significant speed and accuracy advantages in comparison with traditional models. after synthetic data validation, we use so-called physically constrained neural networks (PINN) to simultaneously solve the biologically plausible hodgkin-huxley model and infer its parameters and hidden time-courses from real data under both variable and constant current stimulation, demonstrating extremely low variability across spikes and faithful signal reconstruction. the parameter ranges we obtain are also compatible with prior we demonstrate that detailed biological knowledge can be provided to a neural network, making it able to fit complex dynamics over both simulated and real data
Septal and ventricular deformation in a shell model of the human heart: chamber wall geometry and ventricular interdependence
No full textVentricular interdependence is one of the principal controllers of heart function, and hence a key mediator of most pathological consequences of its impairment. It can only be explained by accounting for overall chamber deformation resulting from internal bending and twisting moments as well as cardiac dimensions and nonlinear material properties, and clinically useful interpretation of imaging data about pathological alterations in chamber geometry is hampered by lack of understanding of its significance to cardiac function. In order to characterise the influence of chamber geometry on ventricular function, a model was developed which describes the ventricles and septum as portions of generalised ellipsoid shells of non-uniform wall thickness whose interacting walls are subject to local and global stresses/strains as well as torques. Chamber configuration is derived as a function of pressure gradients by combining shell element equilibrium equations through static boundary conditions applied at the sulcus, where the walls exhibit a mixed state of stress. Diastolic pressure (P)-volume (V) curves are calculated for both ventricles as a function of contralateral (constant) pressure and ventricular interaction is characterized by calculating coupling coefficients represented by surfaces in pressure-volume-contralateral pressure space. Further, the alterations in PV relationships determined by a number of pathological conditions (constrictive pericarditis, septal or left ventricular wall hypertrophy, dilatative cardiomiopathy) are characterized. Our results are in good agreement with experimental literature and clinical data, and our model allows simulation/prediction of cardiac chamber behaviour in a variety of physiopathological circumstances
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