Graph Theory Applications to Comprehend Epidemics Spread of a Disease

Theory of Graphs could offer a plenty to enrich the analysis and modeling to generate datasets out of the systems and processes regarding the spread of a disease that affects humans, animals, plants, crops etc., In this paper first we show graphs can serve as a model for cattle movements from one farm to another. Second, we give a crisp explanation regarding disease transmission models on contact graphs/networks. It is possible to indicate how a regular tree exhibits relations among graph structure and the infectious disease spread and how certain properties of it akin to diameter and density of graph, affect the duration of an outbreak. Third, we elaborate on the presence of a suitable environment for exploiting several streams of data such as genetic temporal and spatial to locate case clusters one dependent on the other of a disease that is infectious. Here a graph for each stream of data joining all cases that are created with pairwise distance among them as edge weights and altered by omitting exceeding distances of a cutoff assigned that relies on already existing assumptions and rate of spread of a disease information. Fourth we provide an overview of epidemiology, disease transmission, fatality rate and clinical features of zoonotic viral infections of epidemic and pandemic magnitude since 2000. Fifth we indicate how the clinical data and virus spread data can be exploited for the creation of health knowledge graph. Graph Theory is an ideal tool to model, predict, form an opinion to devise strategies to quickly arrest the outbreak and minimize the devastating effect of zoonotic viral infections.

Rotation-based schedules in elementary schools to prevent COVID-19 spread: A simulation study

Little is known about how various interventions impact the spread of covid-19 in schools. Here, we examined effects of different types of rotations in various testing contexts using an agent-based modified SIER model run on real contact data acquired in an elementary school in Czechia (624 schoolchildren, 55 teachers, 27k social contacts). The results show that weekly rotations of in-class and distance learning reduce the spread of covid-19 by >75%; regular low-sensitivity (<40%) antigen testing twice a week significantly reduces infections; and the density of revealed contact graphs for older pupils is 1.5 times higher than the younger pupils graph, the teachers network is yet an order of magnitude denser. The results imply that teachers play a disproportionate role in spreading covid-19 and that weekly rotations with regular testing are an effective preventive intervention that can help to keep schools open during a worsened epidemic situation.

Accurate Protein Function Prediction via Graph Attention Networks with Predicted Structure Information

Experimental protein function annotation does not scale with the fast-growing sequence databases. Only a tiny fraction (<0.1%) of protein sequences in UniProtKB has experimentally determined functional annotations. Computational methods may predict protein function in a high-throughput way, but its accuracy is not very satisfactory. Based upon recent breakthroughs in protein structure prediction and protein language models, we develop GAT-GO, a graph attention network (GAT) method that may substantially improve protein function prediction by leveraging predicted inter-residue contact graphs and protein sequence embedding. Our experimental results show that GAT-GO greatly outperforms the latest sequence- and structure-based deep learning methods. On the PDB-mmseqs testset where the train and test proteins share <15% sequence identity, GAT-GO yields Fmax(maximum F-score) 0.508, 0.416, 0.501, and AUPRC(area under the precision-recall curve) 0.427, 0.253, 0.411 for the MFO, BPO, CCO ontology domains, respectively, much better than homology-based method BLAST (Fmax 0.117,0.121,0.207 and AUPRC 0.120, 0.120, 0.163). On the PDB-cdhit testset where the training and test proteins share higher sequence identity, GAT-GO obtains Fmax 0.637, 0.501, 0.542 for the MFO, BPO, CCO ontology domains, respectively, and AUPRC 0.662, 0.384, 0.481, significantly exceeding the just-published graph convolution method DeepFRI, which has Fmax 0.542, 0.425, 0.424 and AUPRC 0.313, 0.159, 0.193.

Identification and correction of phase switches with Hi-C data in the Nanopore and HiFi chromosome-scale assemblies of the dikaryotic leaf rust fungus Puccinia triticina

BackgroundMost animals and plants have more than one set of chromosomes and package these haplotypes into a single nucleus within each cell. In contrast, many fungal species carry multiple haploid nuclei per cell. Rust fungi are such species with two nuclei (karyons) that contain a full set of haploid chromosomes each. The physical separation of haplotypes in dikaryons means that, unlike in diploids, Hi-C chromatin contacts between haplotypes are false positive signals.ResultsWe generate the first chromosome-scale, fully-phased assembly for the dikaryotic leaf rust fungus Puccinia triticina and compare Nanopore MinION and PacBio HiFi sequence-based assemblies. We show that false positive Hi-C contacts between haplotypes are predominantly caused by phase switches rather than by collapsed regions or Hi-C read mis-mappings. We introduce a method for phasing of dikaryotic genomes into the two haplotypes using Hi-C contact graphs, including a phase switch correction step. In the HiFi assembly, relatively few phase switches occur, and these are predominantly located at haplotig boundaries and can be readily corrected. In contrast, phase switches are widespread throughout the Nanopore assembly. We show that haploid genome read coverage of 30-40 times using HiFi sequencing is required for phasing of the leaf rust genome (~0.7% heterozygosity) and that HiFi sequencing resolves genomic regions with low heterozygosity that are otherwise collapsed in the Nanopore assembly.ConclusionsThis first Hi-C based phasing pipeline for dikaryons and comparison of long-read sequencing technologies will inform future genome assembly and haplotype phasing projects in other non-haploid organisms.

Automorphisms of Contact Graphs of CAT(0) Cube Complexes

We show that, under weak assumptions, the automorphism group of a $\textrm{CAT(0)}$ cube complex $X$ coincides with the automorphism group of Hagen’s contact graph $\mathcal{C}(X)$. The result holds, in particular, for universal covers of Salvetti complexes, where it provides an analogue of Ivanov’s theorem on curve graphs of non-sporadic surfaces. This highlights a contrast between contact graphs and Kim–Koberda extension graphs, which have much larger automorphism group. We also study contact graphs associated with Davis complexes of right-angled Coxeter groups. We show that these contact graphs are less well behaved and describe exactly when they have more automorphisms than the universal cover of the Davis complex.

Modifi cation of the CGR-Algorithm on Data Routing in a Communication Network of Satellite Constellation

Communication networks in space systems involving the use of satellite constellations are DTN networks (Delay and Disruption Tolerant Networks). The establishment of communication channels in space communication networks has certain specifics: communication channels can be planned. In this regard, the CGR approach (Contact Graph Routing) is considered as the most promising solution to the problem of data routing. At the basis of this approach, taking into account this specificity, the calculation of the contact plan is considered. On the basis of this plan in the network nodes contact graphs are calculated, which are used to search the shortest data transmission routes. The paper proposes two interrelated solutions as a modification of this approach: the route search based on the contact plan, i.e. without calculation and use of the contact graph, and an adaptive method of finding the set of shortest routes required for routing. The essence of the first solution is as follows. In the standard CGR approach, the graph vertices correspond to the planned contacts between the network nodes, and the edges correspond to the data storage processes in the network nodes. In contrast, in the proposed approach, the vertices of the graph correspond to the nodes of the network, and the edges of the graph and their weight are determined dynamically, in the process of finding the shortest routes. The second solution is based on the concept of the planning front, which means a list of the closest contacts in time. The required routes are divided into a certain number of pools. Each pool combines the routes that use the specified contact from the planning front. The planning front is updated in two cases. If the network topology changes, the completed or not established contacts are replaced by subsequent ones with the same network nodes that are closest in time. If message traffic grows, a certain extension of the planning front and the use of additional route pools are performed. The article concludes with a description and justification of the expected advantages of the proposed approach.