Propagation of external regulation and asynchronous dynamics in random Boolean networks

Boolean Networks and their dynamics are of great interest as abstract modeling schemes in various disciplines, ranging from biology to computer science. Whereas parallel update schemes have been studied extensively in past years, the level of understanding of asynchronous updates schemes is still very poor. In this paper we study the propagation of external information given by regulatory input variables into a random Boolean network. We compute both analytically and numerically the time evolution and the asymptotic behavior of this propagation of external regulation (PER). In particular, this allows us to identify variables which are completely determined by this external information. All those variables in the network which are not directly fixed by PER form a core which contains in particular all non-trivial feedback loops. We design a message-passing approach allowing to characterize the statistical properties of these cores in dependence of the Boolean network and the external condition. At the end we establish a link between PER dynamics and the full random asynchronous dynamics of a Boolean network.Comment: 19 pages, 14 figures, to appear in Chao

Message-passing algorithms for optimal utilization of cognitive radio networks

Cognitive Radio has been proposed as a key technology to significantly improve spectrum usage in wireless networks by enabling unlicensed users to access unused resource. We present new algorithms that are needed for the implementation of opportunistic scheduling policies that maximize the throughput utilization of resources by secondary users, under maximum interference constraints imposed by existing primary users. Our approach is based on the Belief Propagation (BP) algorithm, which is advantageous due to its simplicity and potential for distributed implementation. We examine convergence properties and evaluate the performance of the proposed BP algorithms via simulations and demonstrate that the results compare favorably with a benchmark greedy strategy

Network inference using asynchronously updated kinetic Ising Model

Network structures are reconstructed from dynamical data by respectively naive mean field (nMF) and Thouless-Anderson-Palmer (TAP) approximations. For TAP approximation, we use two methods to reconstruct the network: a) iteration method; b) casting the inference formula to a set of cubic equations and solving it directly. We investigate inference of the asymmetric Sherrington- Kirkpatrick (S-K) model using asynchronous update. The solutions of the sets cubic equation depend of temperature T in the S-K model, and a critical temperature Tc is found around 2.1. For T < Tc, the solutions of the cubic equation sets are composed of 1 real root and two conjugate complex roots while for T > Tc there are three real roots. The iteration method is convergent only if the cubic equations have three real solutions. The two methods give same results when the iteration method is convergent. Compared to nMF, TAP is somewhat better at low temperatures, but approaches the same performance as temperature increase. Both methods behave better for longer data length, but for improvement arises, TAP is well pronounced.Comment: 6 pages, 4 figure

Ocular Manifestations of the Sturge–Weber Syndrome

Sturge–Weber syndrome (SWS) or encephalotrigeminal angiomatosis is a non-inherited congenital disorder characterized by neurologic, skin, and ocular abnormalities. A somatic activating mutation (R183Q) in the GNAQ gene during early embryogenesis has been recently recognized as the etiology of vascular abnormalities in SWS. Approximately, half of the patients with SWS manifest ocular involvement including glaucoma as the most common ocular abnormality followed by choroidal hemangioma (CH). The underlying pathophysiology of glaucoma in SWS has not been completely understood yet. Early onset glaucoma comprising 60% of SWS glaucoma have lower success rates after medical and surgical treatments compared with primary congenital glaucoma. Primary angle surgery is associated with modest success in the early onset SWS glaucoma while the success rate significantly decreases in late onset glaucoma. Filtration surgery is associated with a higher risk of intraoperative and postoperative choroidal effusion and suprachoroidal hemorrhage. CH is reported in 40–50% of SWS patients. The goal of treatment in patients with CH is to induce involution of the hemangioma, with reduction of subretinal and intraretinal fluid and minimal damage to the neurosensory retina. The decision for treating diffuse CHs highly depends on the patient’s visual acuity, the need for glaucoma surgery, the presence of subretinal fluid (SRF), its chronicity, and the potential for visual recovery

Synthesis and characterization of a new zwitterionic palladium complex as an environmentally friendly catalyst for the Heck-Mizoroki coupling reaction in GVL

A new zwitterionic Palladium (II) complex has been synthesized by the one-pot mixing of Pd(OAc)2, 2-aminophenol and (3-formyl-4-hydroxy-5-methylbenzyl) triphenylphosphonium chloride, in refluxing ethanol. The metal complex formed was characterized by 1H NMR, 13C NMR, 31P NMR and X-ray crystallographic technique and its efficiency tested as a homogeneous pre-catalyst in Heck-Mizoroki cross coupling reaction using γ-Valerolactone (GVL) as a biomass-derived green medium. All the products were obtained in good to excellent yields

Dynamic mean-field and cavity methods for diluted Ising systems

We compare dynamic mean-field and dynamic cavity as methods to describe the stationary states of dilute kinetic Ising models. We compute dynamic mean-field theory by expanding in interaction strength to third order, and compare to the exact dynamic mean-field theory for fully asymmetric networks. We show that in diluted networks the dynamic cavity method generally predicts magnetizations of individual spins better than both first order ("naive") and second order ("TAP") dynamic mean field theory

DeepRetroMoCo:deep neural network-based retrospective motion correction algorithm for spinal cord functional MRI

Background and purpose: There are distinct challenges in the preprocessing of spinal cord fMRI data, particularly concerning the mitigation of voluntary or involuntary movement artifacts during image acquisition. Despite the notable progress in data processing techniques for movement detection and correction, applying motion correction algorithms developed for the brain cortex to the brainstem and spinal cord remains a challenging endeavor.Methods: In this study, we employed a deep learning-based convolutional neural network (CNN) named DeepRetroMoCo, trained using an unsupervised learning algorithm. Our goal was to detect and rectify motion artifacts in axial T2*-weighted spinal cord data. The training dataset consisted of spinal cord fMRI data from 27 participants, comprising 135 runs for training and 81 runs for testing.Results: To evaluate the efficacy of DeepRetroMoCo, we compared its performance against the sct_fmri_moco method implemented in the spinal cord toolbox. We assessed the motion-corrected images using two metrics: the average temporal signal-to-noise ratio (tSNR) and Delta Variation Signal (DVARS) for both raw and motion-corrected data. Notably, the average tSNR in the cervical cord was significantly higher when DeepRetroMoCo was utilized for motion correction, compared to the sct_fmri_moco method. Additionally, the average DVARS values were lower in images corrected by DeepRetroMoCo, indicating a superior reduction in motion artifacts. Moreover, DeepRetroMoCo exhibited a significantly shorter processing time compared to sct_fmri_moco.Conclusion: Our findings strongly support the notion that DeepRetroMoCo represents a substantial improvement in motion correction procedures for fMRI data acquired from the cervical spinal cord. This novel deep learning-based approach showcases enhanced performance, offering a promising solution to address the challenges posed by motion artifacts in spinal cord fMRI data