A new species of aphid of the genus Nipponaphis (Hemiptera: Aphididae: Hormaphidinae) from China, inducing galls on the trunk of a witch-hazel (Hamamelidaceae)

Plants and insects have co-existed for millions of years. Although research has been conducted on various insect species that induce galls on various plant tissues, information is particularly scarce when it comes to insects that form galls on the tough trunk of their host plants. This contribution describes the gall-inducing aphid Nipponaphis hubeiensis sp. nov. from the Zhushan County, Shiyan City, Hubei Province of China. This aphid induces enclosed galls with woody external layer on the trunk of Sycopsis sinensis (Saxifragales: Hamamelidaceae), an uncommon ecological niche in the aphid-plant interaction system. Morphological features for the identification of new species are provided. In addition, a partial sequence of the nuclear gene EF1α was amplified and sequenced to construct a cluster graph. Based on the clustering graph combined with morphology traits, the gall-forming aphid was classified into Nipponaphis. The unique ecological habits of this new aphid will bring innovative perspectives to the study of the evolution and diversity in aphid-host interaction.

On Total Vertex Irregularity Strength of Hexagonal Cluster Graphs

For a simple graph G with a vertex set V G and an edge set E G , a labeling f : V G ∪ ​ E G ⟶ 1,2 , ⋯ , k is called a vertex irregular total k − labeling of G if for any two different vertices x and y in V G we have w t x ≠ w t y where w t x = f x + ∑ u ∈ V G f x u . The smallest positive integer k such that G has a vertex irregular total k − labeling is called the total vertex irregularity strength of G , denoted by tvs G . The lower bound of tvs G for any graph G have been found by Baca et. al. In this paper, we determined the exact value of the total vertex irregularity strength of the hexagonal cluster graph on n cluster for n ≥ 2 . Moreover, we show that the total vertex irregularity strength of the hexagonal cluster graph on n cluster is 3 n 2 + 1 / 2 .

Minimum Eccentricity Shortest Path Problem with Respect to Structural Parameters

The Minimum Eccentricity Shortest Path Problem consists in finding a shortest path with minimum eccentricity in a given undirected graph. The problem is known to be NP-complete and W[2]-hard with respect to the desired eccentricity. We present fpt algorithms for the problem parameterized by the modular width, distance to cluster graph, the combination of distance to disjoint paths with the desired eccentricity, and maximum leaf number.

Spatial structure of boreal woodland caribou populations in northwest Canada

Local population units (LPUs) were delineated in Canada’s recovery strategy for threatened boreal woodland caribou (Rangifer tarandus caribou). Population viability analyses central to contemporary integrated risk assessments of LPUs implicitly assume geographic closure. Several LPUs in northwest Canada, however, were in part delineated by geopolitical boundaries and/or included large areas in the absence of evidence of more finely resolved population spatial structure. We pooled >1.2 million locations from >1200 GPS or VHF-collared caribou from northeast British Columbia, northwest Alberta and southwestern Northwest Territories. Bayesian cluster analysis generated 10 alternative candidate LPUs based on a spatial cluster graph of the extent of pairwise co-occurrence of collared caribou. Up to four groups may be artifacts in as yet under-sampled areas. Four were mapped LPUs that were conserved (Prophet, Parker, Chinchaga and Red Earth). One small group between Parker and Snake-Sahtaneh known locally as the “Fort Nelson core,” and outside any mapped LPU, was also conserved. Finally, one large group, at >136000 km2, spanned all three jurisdictions and subsumed all of six delineated LPUs (Maxhamish, Snake-Sahtaneh, Calendar, Bistcho, Yates, Caribou Mountains) and part of southern Northwest Territories. These results suggest less geographic closure of LPUs than those currently delineated, but further analyses will be required to better reconcile various sources of knowledge about local population structure in this region.

SpatialCPie: an R/Bioconductor package for spatial transcriptomics cluster evaluation

Background Technological developments in the emerging field of spatial transcriptomics have opened up an unexplored landscape where transcript information is put in a spatial context. Clustering commonly constitutes a central component in analyzing this type of data. However, deciding on the number of clusters to use and interpreting their relationships can be difficult. Results We introduce SpatialCPie, an R package designed to facilitate cluster evaluation for spatial transcriptomics data. SpatialCPie clusters the data at multiple resolutions. The results are visualized with pie charts that indicate the similarity between spatial regions and clusters and a cluster graph that shows the relationships between clusters at different resolutions. We demonstrate SpatialCPie on several publicly available datasets. Conclusions SpatialCPie provides intuitive visualizations of cluster relationships when dealing with Spatial Transcriptomics data.

Estimating parameters associated with monotone properties

There has been substantial interest in estimating the value of a graph parameter, i.e. of a real-valued function defined on the set of finite graphs, by querying a randomly sampled substructure whose size is independent of the size of the input. Graph parameters that may be successfully estimated in this way are said to be testable or estimable, and the sample complexity qz = qz(ε) of an estimable parameter z is the size of a random sample of a graph G required to ensure that the value of z(G) may be estimated within an error of ε with probability at least 2/3. In this paper, for any fixed monotone graph property $\mathcal{P}= \text{Forb}\!(\mathcal{F}),$ we study the sample complexity of estimating a bounded graph parameter z that, for an input graph G, counts the number of spanning subgraphs of G that satisfy$\mathcal{P}$. To improve upon previous upper bounds on the sample complexity, we show that the vertex set of any graph that satisfies a monotone property $\mathcal{P}$ may be partitioned equitably into a constant number of classes in such a way that the cluster graph induced by the partition is not far from satisfying a natural weighted graph generalization of $\mathcal{P}$. Properties for which this holds are said to be recoverable, and the study of recoverable properties may be of independent interest.

Research Front Detection and Topic Evolution Based on Topological Structure and the PageRank Algorithm

Research front detection and topic evolution has for a long time been an important direction for research in the informetrics field. However, most previous studies either simply use a citation count for scientific document clustering or assume that each scientific document has the same importance in detecting the clustering theme in a cluster. In this study, utilizing the topological structure and the PageRank algorithm, we propose a new research front detection and topic evolution approach based on graph theory. This approach is made up of three stages: (1) Setting a time window with appropriate length according to the accuracy of scientific documents clustering results and the time delay of a scientific document to be cited, dividing scientific documents into several time windows according to their years of publication, calculating similarities between them according to their topological structure, and clustering them in each time window based on the fast greedy algorithm; (2) combining the PageRank algorithm and keywords’ frequency to detect the clustering theme, which assumes that the more important a scientific document in the cluster is, the greater the possibility that it is cited by the other documents in the same cluster; and (3) reconstructing the cluster graph where nodes represent clusters and edges’ strengths represent the similarities between different clusters, then detecting research front and identifying topic evolution based on the reconstructed cluster graph. To evaluate the performance of our proposed approach, the scientific documents related to data mining and covered by Science Citation Index Expanded (SCI-EXPANDED) or Social Science Citation Index (SSCI) in Web of Science are collected as a case study. The experiment’s results show that the proposed approach can obtain reasonable clustering results, and it is effective for research front detection and topic evolution.