Decision-making algorithms for learning and adaptation with application to COVID-19 data.

This work focuses on the development of a new family of decision-making algorithms for adaptation and learning, which are specifically tailored to decision problems and are constructed by building up on first principles from decision theory. A key observation is that estimation and decision problems are structurally different and, therefore, algorithms that have proven successful for the former need not perform well when adjusted for the latter. Exploiting classical tools from quickest detection, we propose a tailored version of Page's test, referred to as BLLR (barrier log-likelihood ratio) test, and demonstrate its applicability to real-data from the COVID-19 pandemic in Italy. The results illustrate the ability of the design tool to track the different phases of the outbreak.

Graph Exploration for Effective Multiagent Q-Learning.

This article proposes an exploration technique for multiagent reinforcement learning (MARL) with graph-based communication among agents. We assume that the individual rewards received by the agents are independent of the actions by the other agents, while their policies are coupled. In the proposed framework, neighboring agents collaborate to estimate the uncertainty about the state-action space in order to execute more efficient explorative behavior. Different from existing works, the proposed algorithm does not require counting mechanisms and can be applied to continuous-state environments without requiring complex conversion techniques. Moreover, the proposed scheme allows agents to communicate in a fully decentralized manner with minimal information exchange. And for continuous-state scenarios, each agent needs to exchange only a single parameter vector. The performance of the algorithm is verified with theoretical results for discrete-state scenarios and with experiments for the continuous ones.

The emerging role of lncRNA MEG3 and MEG3 rs7158663 in hepatocellular carcinoma.

OBJECTIVE: Hepatocellular carcinoma (HCC) is the most common primary liver malignancy in Egypt. Genetic and environmental factors play a role in its development. This study explored the association between the long non-coding RNA (lncRNA) MEG3 rs7158663 polymorphism, MEG3 expression, and the risk of HCC and other clinicopathologic characteristics in an Egyptian population. PATIENTS AND METHODS: This case-control study included 114 patients with HCC and 110 healthy controls. TaqMan Real-time PCR was used to analyze lncRNA MEG3 rs7158663. Serum MEG3 expression levels were measured using RT-PCR. RESULTS: The AA, GA+AA, and A alleles were associated with increased risk for HCC (adjusted odds ratio (OR) 11.84%, 95% CI 4.07-34.45, p < 0.0001; adjusted OR 3.18, 95% CI 1.79-5.67, p < 0.0001; and adjusted OR 2.87, 95% CI 1.91-4.34, p < 0.0001, respectively). The mutant genotype and allele were linked to an increased risk in male patients and patients ≥ 50 years old. MEG3 serum expression level was downregulated in HCC patients. The rs7158663 G > A polymorphism and downregulated MEG3 were significantly associated with larger tumor size and advanced disease stage. CONCLUSIONS: MEG3 rs7158663 single nucleotide polymorphisms and downregulated lncRNA MEG3 were associated with HCC risk and may represent diagnostic and bad prognostic factors for HCC patients.

Revisiting correlation-based functional connectivity and its relationship with structural connectivity.

Patterns of brain structural connectivity (SC) and functional connectivity (FC) are known to be related. In SC-FC comparisons, FC has classically been evaluated from correlations between functional time series, and more recently from partial correlations or their unnormalized version encoded in the precision matrix. The latter FC metrics yield more meaningful comparisons to SC because they capture 'direct' statistical dependencies, that is, discarding the effects of mediators, but their use has been limited because of estimation issues. With the rise of high-quality and large neuroimaging datasets, we revisit the relevance of different FC metrics in the context of SC-FC comparisons. Using data from 100 unrelated Human Connectome Project subjects, we first explore the amount of functional data required to reliably estimate various FC metrics. We find that precision-based FC yields a better match to SC than correlation-based FC when using 5 minutes of functional data or more. Finally, using a linear model linking SC and FC, we show that the SC-FC match can be used to further interrogate various aspects of brain structure and function such as the timescales of functional dynamics in different resting-state networks or the intensity of anatomical self-connections.

Mathematical models of cerebral hemodynamics for detection of vasospasm in major cerebral arteries.

BACKGROUND: Vasospasm is a common complication of aneurismal subarachnoid hemorrhage (SAH) that may lead to cerebral ischemia and death. The standard method for detection of vasospasm is conventional cerebral angiography, which is invasive and does not allow continuous monitoring of arterial radius. Monitoring of vasospasm is typically performed by measuring Cerebral Blood Flow Velocity (CBFV) in the major cerebral arteries and calculating the Lindegaard ratio. We describe an alternative approach to estimate intracranial arterial radius, which is based on modeling and state-estimation techniques. The objective is to obtain a better estimation than that offered by the Lindegaard ratio, that might allow for continuous monitoring and possibly vasospam prediction without the need for angiography. METHODS: We propose two new models of cerebral hemodynamics. Model 1 is a more general version of Ursino's 1991 model that includes the effects of vasospasm, and Model 2 is a simplified version of Model 1. We use Model 1 to generate Intracranial Pressure (ICP) and CBFV signals for different vasospasm conditions, where CBFV is measured at the middle cerebral artery (MCA). Then we use Model 2 to estimate the arterial radii from these signals. FINDINGS: Simulations show that Model 2 is capable of providing good estimates for the radius of the MCA, allowing the detection of the vasospasm. These changes in arterial radius are being estimated from measurements of CBFV, and CBF is never being measured directly. This is the main advantage of the model-based approach where several interrelations between CBFV, ABP and ICP are taken into account by the differential equations of the model. CONCLUSIONS: Our results indicate that arterial radius may be estimated using measurements of ABP, ICP and CBFV, allowing the detection of vasospasm.

Post-translational regulation enables robust p53 regulation.

BACKGROUND: The tumor suppressor protein p53 plays important roles in DNA damage repair, cell cycle arrest and apoptosis. Due to its critical functions, the level of p53 is tightly regulated by a negative feedback mechanism to increase its tolerance towards fluctuations and disturbances. Interestingly, the p53 level is controlled by post-translational regulation rather than transcriptional regulation in this feedback mechanism. RESULTS: We analyzed the dynamics of this feedback to understand whether post-translational regulation provides any advantages over transcriptional regulation in regard to disturbance rejection. When a disturbance happens, even though negative feedback reduces the steady-state error, it can cause a system to become less stable and transiently overshoots, which may erroneously trigger downstream reactions. Therefore, the system needs to balance the trade-off between steady-state and transient errors. Feedback control and adaptive estimation theories revealed that post-translational regulation achieves a better trade-off than transcriptional regulation, contributing to a more steady level of p53 under the influence of noise and disturbances. Furthermore, post-translational regulation enables cells to respond more promptly to stress conditions with consistent amplitude. However, for better disturbance rejection, the p53- Mdm2 negative feedback has to pay a price of higher stochastic noise. CONCLUSIONS: Our analyses suggest that the p53-Mdm2 feedback favors regulatory mechanisms that provide the optimal trade-offs for dynamic control.

Adaptive models for gene networks.

Biological systems are often treated as time-invariant by computational models that use fixed parameter values. In this study, we demonstrate that the behavior of the p53-MDM2 gene network in individual cells can be tracked using adaptive filtering algorithms and the resulting time-variant models can approximate experimental measurements more accurately than time-invariant models. Adaptive models with time-variant parameters can help reduce modeling complexity and can more realistically represent biological systems.

Using an adaptive gene network model for self-organizing multicellular behavior.

Using the transient interleukin (IL)-2 secretion of effector T helper (T(eff)) cells as an example, we show that self-organizing multicellular behavior can be modeled and predicted by an adaptive gene network model. Incorporating an adaptation algorithm we established previously, we construct a network model that has the parameter values iteratively updated to cope with environmental change governed by diffusion and cell-cell interactions. In contrast to non-adaptive models, we find that the proposed adaptive model for individual T(eff) cells can generate transient IL-2 secretory behavior that is observed experimentally at the population level. The proposed adaptive modeling approach can be a useful tool in the study of self-organizing behavior observed in other contexts in biology, including microbial pathogenesis, antibiotic resistance, embryonic development, tumor formation, etc.