Distributed Community Detection in Dynamic Graphs

Inspired by the increasing interest in self-organizing social opportunistic networks, we investigate the problem of distributed detection of unknown communities in dynamic random graphs. As a formal framework, we consider the dynamic version of the well-studied \emph{Planted Bisection Model} \sdG(n,p,q) where the node set $[n]$ of the network is partitioned into two unknown communities and, at every time step, each possible edge $(u,v)$ is active with probability $p$ if both nodes belong to the same community, while it is active with probability $q$ (with $q<<p$) otherwise. We also consider a time-Markovian generalization of this model. We propose a distributed protocol based on the popular \emph{Label Propagation Algorithm} and prove that, when the ratio $p/q$ is larger than $n^{b}$ (for an arbitrarily small constant $b>0$), the protocol finds the right "planted" partition in $O(\log n)$ time even when the snapshots of the dynamic graph are sparse and disconnected (i.e. in the case $p=\Theta(1/n)$).Comment: Version I

Cytokine secretion in breast cancer cells – MILLIPLEX assay data

© 2019 The Author(s) Metastatic breast cancer is the most advanced stage of breast cancer and the leading cause of breast cancer mortality. Although understanding of the cancer progression and metastasis process has improved, the bi-directional communication between the tumor cell and the tumor microenvironment is still not well understood. Breast cancer cells are highly secretory, and their secretory activity is modulated by a variety of inflammatory stimuli present in the tumor microenvironment. Here, we characterized the cytokine expression in human breast cancer cells (MDA-MB-231, MCF-7, T-47D, and BT-474) in vitro using 41 cytokine MILLIPLEX assay. Further, we compared cytokine expression in breast cancer cells to those in non-tumorigenic human breast epithelial MCF-10A cells

Pressure-induced collapsed-tetragonal phase in SrCo2As2

We present high-energy x-ray diffraction data under applied pressures up to p = 29 GPa, neutron diffraction measurements up to p = 1.1 GPa, and electrical resistance measurements up to p = 5.9 GPa, on SrCo2As2. Our x-ray diffraction data demonstrate that there is a first-order transition between the tetragonal (T) and collapsed-tetragonal (cT) phases, with an onset above approximately 6 GPa at T = 7 K. The pressure for the onset of the cT phase and the range of coexistence between the T and cT phases appears to be nearly temperature independent. The compressibility along the a-axis is the same for the T and cT phases whereas, along the c-axis, the cT phase is significantly stiffer, which may be due to the formation of an As-As bond in the cT phase. Our resistivity measurements found no evidence of superconductivity in SrCo2As2 for p <= 5.9 GPa and T >= 1.8 K. The resistivity data also show signatures consistent with a pressure-induced phase transition for p >= 5.5 GPa. Single-crystal neutron diffraction measurements performed up to 1.1 GPa in the T phase found no evidence of stripe-type or A-type antiferromagnetic ordering down to 10 K. Spin-polarized total-energy calculations demonstrate that the cT phase is the stable phase at high pressure with a c/a ratio of 2.54. Furthermore, these calculations indicate that the cT phase of SrCo2As2 should manifest either A-type antiferromagnetic or ferromagnetic order.Comment: 6 pages, 5 figure

Origin of the Resistivity Anisotropy in the Nematic Phase of FeSe

The in-plane resistivity anisotropy is studied in strain-detwinned single crystals of FeSe. In contrast to other iron-based superconductors, FeSe does not develop long-range magnetic order below the tetragonal-to-orthorhombic transition at Ts≈90  K. This allows for the disentanglement of the contributions to the resistivity anisotropy due to nematic and magnetic orders. Comparing direct transport and elastoresistivity measurements, we extract the intrinsic resistivity anisotropy of strain-free samples. The anisotropy peaks slightly below Ts and decreases to nearly zero on cooling down to the superconducting transition. This behavior is consistent with a scenario in which the in-plane resistivity anisotropy is dominated by inelastic scattering by anisotropic spin fluctuations

Prospective Hybrid Molecules with Dual Anti-Viral and Anti-Thrombotic Activity Against the SARS-CoV-2 Infection and Its Associated Complications Employing in Silico Studies

Covid-19, a SARS-CoV virus-based disease, was identified in Wuhan, China, in December 2019. Initially, it was considered just an infection of the respiratory system, but due to its transmittable nature, it was declared a pandemic. A variety of treatment options were implemented, including antivirals like remdesvir, favipiravir along with vitamins and antioxidants. Further investigations revealed that the Covid-19 infection results in thrombotic cardiovascular complications, which are the major concern for the increased mortality associated with this disease. This study investigates the in Silico design of hybrid molecules with antiviral and an-tithrombotic properties. A docking study was performed using Autodock Vina software, and binding energies of the designed compounds were determined for papain-like protease (PDB: 3E9S) and 3-chymotrypsin-like cysteine protease (PDB: 6LU7). The docked poses and amino acids interactions were verified using Biovia Discovery studio 4.5. The binding energies of all designed compounds were compared with the standards, Compound RL1 (2-(5-(3-carbamoyl-1H-1,2,4-triazol-1-yl)-3,4-dihydroxytetrahydrofuran-2-yl)methoxy)-carbonyl)amino)(hydroxy)methyl)carbamoyl)phenyl acetate) and Compound FL2 (8-hydroxy-2-(3-hydroxy-4-methoxyphenyl)-4-oxochroman-6-yl(2-(6-flouro-3-oxo-3,4-dihydropyrazine-2-carboxamido)-1-hydroxy-3-phenylpropyl)carbamate) proved to be promising agents with strong binding interactions. Hybrid molecules that inhibit viral replication, possibly as transition state inhibitors, can be investigated further for use in the treatment of SARS-Co-V infection and its associated complications

Strong cooperative coupling of pressure-induced magnetic order and nematicity in FeSe

A hallmark of the iron-based superconductors is the strong coupling between magnetic, structural and electronic degrees of freedom. However, a universal picture of the normal state properties of these compounds has been confounded by recent investigations of FeSe where the nematic (structural) and magnetic transitions appear to be decoupled. Here, using synchrotron-based high-energy x-ray diffraction and time-domain Mössbauer spectroscopy, we show that nematicity and magnetism in FeSe under applied pressure are indeed strongly coupled. Distinct structural and magnetic transitions are observed for pressures between 1.0 and 1.7 GPa and merge into a single first-order transition for pressures ≳1.7 GPa, reminiscent of what has been found for the evolution of these transitions in the prototypical system Ba(Fe1−xCox)2As2. Our results are consistent with a spin-driven mechanism for nematic order in FeSe and provide an important step towards a universal description of the normal state properties of the iron-based superconductors

Cross-Platform Comparison of Microarray-Based Multiple-Class Prediction

High-throughput microarray technology has been widely applied in biological and medical decision-making research during the past decade. However, the diversity of platforms has made it a challenge to re-use and/or integrate datasets generated in different experiments or labs for constructing array-based diagnostic models. Using large toxicogenomics datasets generated using both Affymetrix and Agilent microarray platforms, we carried out a benchmark evaluation of cross-platform consistency in multiple-class prediction using three widely-used machine learning algorithms. After an initial assessment of model performance on different platforms, we evaluated whether predictive signature features selected in one platform could be directly used to train a model in the other platform and whether predictive models trained using data from one platform could predict datasets profiled using the other platform with comparable performance. Our results established that it is possible to successfully apply multiple-class prediction models across different commercial microarray platforms, offering a number of important benefits such as accelerating the possible translation of biomarkers identified with microarrays to clinically-validated assays. However, this investigation focuses on a technical platform comparison and is actually only the beginning of exploring cross-platform consistency. Further studies are needed to confirm the feasibility of microarray-based cross-platform prediction, especially using independent datasets