Persistence Diagrams to Visualise Damage to Biological Networks

One of the critical tools of persistent homology is the persistence diagram. We demonstrate the applicability of a persistence diagram showing the existence of topological features (here rings in a 2D network) generated over time instead of space as a tool to analyse trajectories of biological networks. We show how the time persistence diagram is useful in order to identify critical phenomena such as rupturing and to visualise important features in 2D biological networks; they are particularly useful to highlight patterns of damage and to identify if particular patterns are significant or ephemeral. Persistence diagrams are also used to analyse repair phenomena, and we explore how the measured properties of a dynamical phenomenon change according to the sampling frequency. This shows that the persistence diagrams are robust and still provide useful information even for data of low temporal resolution. Finally, we combine persistence diagrams across many trajectories to show how the technique highlights the existence of sharp transitions at critical points in the rupturing process.

Two 2D Co(II)/Mn(II) coordination polymers based on the quinoline-2,3-dicarboxylate ligand: synthesis, crystal structure, and fluorescence properties

A pair of two-dimensional (2D) isostructural coordination polymers (CPs), {[Co(2,3-qldc)(H2O)]} n (1) and {[Mn(2,3-qldc)(H2O)]} n (2), where 2,3-H2qldc = quinoline-2,3-dicarboxylic acid, were hydrothermally synthesized and characterized through IR spectroscopy, elemental and thermal analysis, power X-ray diffraction, and single-crystal X-ray diffraction. The results have revealed that the fully deprotonated 2,3-H2qldc ligand connects the Co(II)/Mn(II) atoms with a μ 3-bridge to form a square-wave 2D network, which is further extended into 3D stacks through O–H···O, C–H···O hydrogen bonds and π···π stacking interactions. Topologically, 1 or 2 can be simplified as a 4-connected sql type with a Schläfli symbol {44·62} and a Shubnikov tetragonal plane net, or as a 3-connected fes type with a Schläfli symbol {4·82} and a Shubnikov plane net. The thermal stability and the solid state fluorescence properties of 1 and 2 were investigated.

Different impact of suspended Al2O3 nanoparticles on microbial communities: formation of 2D-networks (without humic acids) or 3D-colonies (with humic acids)

The use of metal-based and, particularly, Al2O3 nanoparticles (Al2O3−NP) for diverse purposes is exponentially growing. However, the growth of such promissory market is not accompanied by a parallel extensive investigation related to the impact of this pollution on groundwater and biological systems. Pseudomonas species, ubiquitous, environmentally critical microbes, frequently respond to stress conditions with diverse strategies that generally include extracellular polymeric substances (EPS) formation. The aim of this study is to report that changes in the aqueous environment, particularly, the addition of Al2O3-NP without and with humic acids, induce different adaptive strategies of P. aeruginosa early biofilms. To this purpose, early biofilms were incubated in diluted culture media without (control) and with Al2O3-NP, and with humic acids (HA-control, HA-Al2O3-NP) for 24h. 3D colonies with EPS strings and isolated bacteria in their surroundings were detected in the control biofilms. Unlikely, an unusual adaptive behaviour was developed in presence of Al2O3-NP. Bacteria opt to disassemble the 3D arrangements, and to implement a 2D network promoting morphological and size changes of bacterial cells (small coccoid shapes). Remarkably, this strategy allows their temporarily non-EPS-depending survival without decreasing the number of cells. This behaviour was not observed with ZnO-NP, HA-Al2O3-NP, or HA-ZnO-NP. Physicochemical analysis revealed that HA were adsorbed on Al2O3-NP and promoted the Al(III) ions complexation. This supports the hypothesis that the reduction of toxicity of Al ions and the 3D colony formation in presence of HA-Al2O3-NP is promoted by the complexation of the metal ions with HA components.

On Topological Indices of Dual Graph of Benzene Ring Embedded in P-Type Surface in 2D Network

The structure of any finite molecular graph which represent numerical quantities are known as topological indices. The importance of topological indices is generally linked with QSAR/QSPR. In this paper, we compute general Zagreb (M஑_) index, general Randic connectivity (R஑_) index, general sum-connectivity (χ஑) index, atom-bond connectivity (ABC) index, and geometric-arithmetic (GA) index,ABCସ, GAହ, multiple Zagreb indices and Zagreb polynomial indices of the of dual graph of benzene ring embedded in P-type-surface in 2D network.

Temperature Acquisition System for Real Time Application of First Velocity Correction by EDM (Electronic Distance Measurement)

The first velocity correction is used to correct the measured distance affected by the velocity variation of the electromagnetic wave propagation in a medium. This correction depends on the refractive index of the propagation medium and reference refractive index. The influence of the temperature in the medium refractive index is critical; some estimates establish that variation 1°C causes 1ppm of error in distances. In the measuring processes with total stations, the temperature is usually collected at only one point, for example, in the position where the measuring instrument is setup. However, the wave propagates in a medium of non-constant temperature, where the extremes of the line can present variations and thus this measurement in only one point could be non-representative. In this context, it was developed a low-cost real-time temperature acquisition system. This system provides the temperature values in different locations allowing their monitoring through the time. Experiments realized during the geodetic monitoring of a dam, show variations up to 8°C among geodetic points on the dam and around it. An analysis was development to evaluate the influence of temperature variations on monitoring distances and geodetic coordinate of a 2d network with different approaches (temperature modeling). The results shows different values for distances (1.0 mm) and coordinates (0.5 mm) depending of the approach choose.

Multi-Dimension and Multi-Feature Hybrid Learning Network for Classifying the Sub Pathological Type of Lung Nodules through LDCT

In order to develop appropriate treatment and rehabilitation plans with regard to different subpathological types (PILs and IAs) of lung nodules, it is important to diagnose them through low-dose spiral computed tomography (LDCT) during routine screening before surgery. Based on the characteristics of different subpathological lung nodules expressed from LDCT images, we propose a multi-dimension and multi-feature hybrid learning neural network in this paper. Our network consists of a 2D network part and a 3D network part. The feature vectors extracted from the 2D network and 3D network are further learned by XGBoost. Through this formation, the network can better integrate the feature information from the 2D and 3D networks. The main learning block of the network is a residual block combined with attention mechanism. This learning block enables the network to learn better from multiple features and pay more attention to the key feature map among all the feature maps in different channels. We conduct experiments on our dataset collected from a cooperating hospital. The results show that the accuracy, sensitivity and specificity of our network are 83%, 86%, 80%, respectively It is feasible to use this network to classify the subpathological type of lung nodule through routine screening.

A morphospace of functional configuration to assess configural breadth based on brain functional networks

The quantification of human brain functional (re-)configurations across varying cognitive demands remains an unresolved topic. We propose that such functional configurations may be categorized into three different types: i) Network Configural Breadth, ii) Task-to Task transitional reconfiguration, and iii) Within-Task reconfiguration. Such functional reconfigurations are rather subtle at the whole-brain level. Hence, we propose a mesoscopic framework focused on functional networks (FNs) or communities to quantify functional (re-)configurations. To do so, we introduce a 2D network morphospace that relies on two novel mesoscopic metrics, Trapping Efficiency (TE) and Exit Entropy (EE), which capture topology and integration of information within and between a reference set of FNs. We use this framework to quantify the Network Configural Breadth across different tasks. We show that the metrics defining this morphospace can differentiate FNs, cognitive tasks and subjects. We also show that network configural breadth significantly predicts behavioral measures, such as episodic memory, verbal episodic memory, fluid intelligence and general intelligence. In essence, we put forth a framework to explore the cognitive space in a comprehensive manner, for each individual separately, and at different levels of granularity. This tool that can also quantify the FN-reconfigurations that result from the brain switching between mental states.