A unified approach of detecting phase transition in time-varying complex networks.

Deciphering the non-trivial interactions and mechanisms driving the evolution of time-varying complex networks (TVCNs) plays a crucial role in designing optimal control strategies for such networks or enhancing their causal predictive capabilities. In this paper, we advance the science of TVCNs by providing a mathematical framework through which we can gauge how local changes within a complex weighted network affect its global properties. More precisely, we focus on unraveling unknown geometric properties of a network and determine its implications on detecting phase transitions within the dynamics of a TVCN.

Inferring functional communities from partially observed biological networks exploiting geometric topology and side information.

Cellular biological networks represent the molecular interactions that shape function of living cells. Uncovering the organization of a biological network requires efficient and accurate algorithms to determine the components, termed communities, underlying specific processes. Detecting functional communities is challenging because reconstructed biological networks are always incomplete due to technical bias and biological complexity, and the evaluation of putative communities is further complicated by a lack of known ground truth.

Bursting Rate Variability.

In this paper, a new electromyographic phenomenon, referred to as , is reported. Not only does it manifest itself visually as a train of short periods of accrued surface electromyographic (sEMG) activity in the traces, but it has a deeper underpinning because the sEMG bursts are synchronous with wavelet packets in the D8 subband of the Daubechies 3 (db3) wavelet decomposition of the raw signal referred to as "-which are absent during muscle relaxation. Moreover, the db3 wavelet decomposition reconstructs the sEMG bursts with contiguous relatively high detail coefficients at level 8, suggesting a high incidence of two consecutive neuronal discharges.

Ollivier-Ricci Curvature-Based Method to Community Detection in Complex Networks.

Identification of community structures in complex network is of crucial importance for understanding the system's function, organization, robustness and security. Here, we present a novel Ollivier-Ricci curvature (ORC) inspired approach to community identification in complex networks. We demonstrate that the intrinsic geometric underpinning of the ORC offers a natural approach to discover inherent community structures within a network based on interaction among entities.

Gene Expression Is Not Random: Scaling, Long-Range Cross-Dependence, and Fractal Characteristics of Gene Regulatory Networks.

Gene expression is a vital process through which cells react to the environment and express functional behavior. Understanding the dynamics of gene expression could prove crucial in unraveling the physical complexities involved in this process. Specifically, understanding the coherent complex structure of transcriptional dynamics is the goal of numerous computational studies aiming to study and finally control cellular processes.

Progress Towards Computational 3-D Multicellular Systems Biology.

Tumors cannot be understood in isolation from their microenvironment. Tumor and stromal cells change phenotype based upon biochemical and biophysical inputs from their surroundings, even as they interact with and remodel the microenvironment. Cancer should be investigated as an adaptive, multicellular system in a dynamical microenvironment.

Quantifying differences in cell line population dynamics using CellPD.

Background: The increased availability of high-throughput datasets has revealed a need for reproducible and accessible analyses which can quantitatively relate molecular changes to phenotypic behavior. Existing tools for quantitative analysis generally require expert knowledge.

Results: CellPD (cell phenotype digitizer) facilitates quantitative phenotype analysis, allowing users to fit mathematical models of cell population dynamics without specialized training.

Quantum versus simulated annealing in wireless interference network optimization.

Quantum annealing (QA) serves as a specialized optimizer that is able to solve many NP-hard problems and that is believed to have a theoretical advantage over simulated annealing (SA) via quantum tunneling. With the introduction of the D-Wave programmable quantum annealer, a considerable amount of effort has been devoted to detect and quantify quantum speedup. While the debate over speedup remains inconclusive as of now, instead of attempting to show general quantum advantage, here, we focus on a novel real-world application of D-Wave in wireless networking-more specifically, the scheduling of the activation of the air-links for maximum throughput subject to interference avoidance near network nodes.

The labeling of cationic iron oxide nanoparticle-resistant hepatocellular carcinoma cells using targeted magnetoliposomes.

The in vitro labeling of cultured cells with nanomaterials is a frequent practice but the efficiency, specificity and cytotoxicity of labeling specific cell types using targeted nanoparticles has only rarely been investigated. In the present work, functionalized anionic lipid-coated iron oxide cores (magnetoliposomes (MLs)) bearing galactose moieties were used for the specific labeling of asialoglycoprotein receptor 1 (ASGPR-1)-expressing HepG2 cells. The optimal number of galactose moieties per particle (± 26) was determined and uptake efficiency was compared with galactose-lacking anionic and cationic MLs.

Magnetically levitated nano-robots: an application to visualization of nerve cells injuries.

This paper proposes a swarm of magnetically levitated nano-robots with high sensitivity nano-sensors as a mean to detect chemical sources, specifically the chemical signals released by injured nervous cells. In the aftermath of the process, further observation by these nano-robots would be used to monitor the healing process and assess the amount of regeneration, if any, or even the repair, of the injured nervous cells.

Visualization of a stationary CPG-revealing spinal wave.

Central Pattern Generator (CPG) is still an elusive concept that has a visual manifestation as a rhythmic oscillation commanded from the spine, but that also has another manifestation as a train of bursts in the surface electromyographic (sEMG) signals recorded on the para-spinal muscles. This leads to the challenging problem of correlating the visually observed spinal wave with the sEMG signals recorded during the session. This paper develops a mathematical model of the spinal wave phenomenon, which, when driven by the sEMG data, yields such visually observable features as wave nodes.

Dynamic neural-based buffer management for Queuing systems with self-similar characteristics.

Buffer management in queuing systems plays an important role in addressing the tradeoff between efficiency measured in terms of overall packet loss and fairness measured in terms of individual source packet loss. Complete partitioning (CP) of a buffer with the best fairness characteristic and complete sharing (CS) of a buffer with the best efficiency characteristic are at the opposite ends of the spectrum of buffer management techniques. Dynamic partitioning buffer management techniques aim at addressing the tradeoff between efficiency and fairness.

ChiroSensor -- an array of non-invasive sEMG electrodes.

It is shown that the statistical analysis of the sEMG signals recorded by the ChiroSensors along the paraspinal muscles during Network Spinal Analysis (NSA) provides objective confirmation of the physiological reality as perceived by the practitioner as it relates to levels of care and spinal injury recovery.