A Novel GEP-Based Cluster Algorithm for Nearest Neighbors

2012 ◽  
pp. 243-252
Author(s):  
Hongguo Cai ◽  
Chang-an Yuan
Keyword(s):  
Cluster Algorithm ◽  
Nearest Neighbors

Separating Phages From Other Virus Families and Classifying the Different Phage Families By GI-Clusters

2021 ◽  
Author(s):  
Xingang Jia ◽  
Qiuhong Han ◽  
Zuhong Lu
Keyword(s):  
Test Data ◽  
Cluster Algorithm ◽  
Nearest Neighbors ◽  
Euclidean Algorithm ◽  
Training Data ◽  
Data Sets ◽  
Maximum Element ◽  
Clustering Techniques ◽  
Cluster Algorithms ◽  
Biological Entities

Abstract Background: Phages are the most abundant biological entities, but the commonly used clustering techniques are difficult to separate them from other virus families and classify the different phage families together.Results: This work uses GI-clusters to separate phages from other virus families and classify the different phage families, where GI-clusters are constructed by GI-features, GI-features are constructed by the togetherness with F-features, training data, MG-Euclidean and Icc-cluster algorithms, F-features are the frequencies of multiple-nucleotides that are generated from genomes of viruses, MG-Euclidean algorithm is able to put the nearest neighbors in the same mini-groups, and Icc-cluster algorithm put the distant samples to the different mini-clusters. For these viruses that the maximum element of their GI-features are in the same locations, they are put to the same GI-clusters, where the families of viruses in test data are identified by GI-clusters, and the families of GI-clusters are defined by viruses of training data.Conclusions: From analysis of 4 data sets that are constructed by the different family viruses, we demonstrate that GI-clusters are able to separate phages from other virus families, correctly classify the different phage families, and correctly predict the families of these unknown phages also.

Observation of Superlattice Dislocations on Cube Planes in Ni3Al

1973 ◽  
Vol 31 ◽  
pp. 174-175 ◽  
Author(s):  
J. M. Oblak ◽  
W. H. Rand
Keyword(s):  
Nearest Neighbor ◽  
Antiphase Boundary ◽  
Nearest Neighbors ◽  
High Energy ◽  
Cross Slip ◽  
Octahedral Plane ◽  
Trapping Mechanism ◽  
Slip Traces

The energy of an a/2 <110> shear antiphase. boundary in the Ll2 expected to be at a minimum on {100} cube planes because here strue ture is there is no violation of nearest-neighbor order. The latter however does involve the disruption of second nearest neighbors. It has been suggested that cross slip of paired a/2 <110> dislocations from octahedral onto cube planes is an important dislocation trapping mechanism in Ni3Al; furthermore, slip traces consistent with cube slip are observed above 920°K.Due to the high energy of the {111} antiphase boundary (> 200 mJ/m2), paired a/2 <110> dislocations are tightly constricted on the octahedral plane and cannot be individually resolved.

Probing vacancies and structural distortions at individual defects in ceramics using EELS

1996 ◽  
Vol 54 ◽  
pp. 678-679
Author(s):  
D. J. Wallis ◽  
N. D. Browning
Keyword(s):  
Structural Information ◽  
Core Loss ◽  
Nearest Neighbors ◽  
Ceramic Materials ◽  
Scanning Transmission Electron Microscope ◽  
Real Space ◽  
Atomic Scale ◽  
Structure Property ◽  
Scanning Transmission ◽  
Key Issues

In electron energy loss spectroscopy (EELS), the near-edge region of a core-loss edge contains information on high-order atomic correlations. These correlations give details of the 3-D atomic structure which can be elucidated using multiple-scattering (MS) theory. MS calculations use real space clusters making them ideal for use in low-symmetry systems such as defects and interfaces. When coupled with the atomic spatial resolution capabilities of the scanning transmission electron microscope (STEM), there therefore exists the ability to obtain 3-D structural information from individual atomic scale structures. For ceramic materials where the structure-property relationships are dominated by defects and interfaces, this methodology can provide unique information on key issues such as like-ion repulsion and the presence of vacancies, impurities and structural distortion.An example of the use of MS-theory is shown in fig 1, where an experimental oxygen K-edge from SrTiO3 is compared to full MS-calculations for successive shells (a shell consists of neighboring atoms, so that 1 shell includes only nearest neighbors, 2 shells includes first and second-nearest neighbors, and so on).

Ancient Israel's Neighbors

2020 ◽  
Author(s):  
Brian R. Doak
Keyword(s):  
Nearest Neighbors ◽  
Biblical Narrative ◽  
The Bible ◽  
The Social ◽  
Family Level ◽  
The Face ◽  
Social Turmoil

The purpose of this book is to tell the story of Israel’s nearest neighbors—not only discovering what the Bible has to say about them but also what we can know from archaeology, ancient inscriptions, and other sources. The Bible itself presents these neighbors in nuanced and conflicting ways; sometimes they are friends or even related to Israel at a family level, and sometimes they are enemies, spoken of as though they must die in order for Israel to live. We are left wondering how the biblical portrayal might have affected our thinking about these people as historical groups, on their own terms. How would an Aramaean have described her own religion? How would an Edomite have described conflict with Israel? This book explores both the biblical portrayal of the smaller groups surrounding Israel and what people can know about these groups through their own literature, archaeology, and other sources. By uncovering the identity of the Philistines as settlers along the coast at the same time that early Israel carved out their place in the land, for example, one can better understand the social turmoil and political maneuvering that lies just beneath the surface of the biblical narrative, and can see more clearly just how the authors of the Bible saw themselves in the face of others.

Temporal Changes in Habitat Selection and Nest Spacing in a Colony of Ross' and Lesser Snow Geese

The Auk ◽  
1983 ◽  
Vol 100 (2) ◽  
pp. 335-343 ◽  
Author(s):  
M. Robert McLandress
Keyword(s):  
Nearest Neighbor ◽  
Average Distance ◽  
Habitat Preferences ◽  
Nearest Neighbors ◽  
Nest Density ◽  
Snow Goose ◽  
Snow Geese ◽  
Lesser Snow Geese ◽  
Average Size ◽  
Island Habitats

Abstract I studied the nesting colony of Ross' Geese (Chen rossii) and Lesser Snow Geese (C. caerulescens caerulescens) at Karrak Lake in the central Arctic of Canada in the summer of 1976. Related studies indicated that this colony had grown from 18,000 birds in 1966-1968 to 54,500 birds in 1976. In 1976, geese nested on islands that were used in the late 1960's and on an island and mainland sites that were previously unoccupied. Average nest density in 1976 was three-fold greater than in the late 1960's. Consequently, the average distance to nearest neighbors of Ross' Geese in 1976 was half the average distance determined 10 yr earlier. The mean clutch size of Ross' Geese was greater in island habitats where nest densities were high than in less populated island or mainland habitats. The average size of Snow Goose clutches did not differ significantly among island habitats but was larger at island than at mainland sites. Large clutches were most likely attributable to older and/or earlier nesting females. Habitat preferences apparently differed between species. Small clutches presumably indicated that young geese nested in areas where nest densities were low. The establishment of mainland nesting at Karrak Lake probably began with young Snow Geese using peripheral areas of the colony. Young Ross' Geese nested in sparsely populated habitats on islands to a greater extent than did Snow Geese. Ross' Geese also nested on the mainland but in lower densities than Ross' Geese nesting in similar island habitats. Successful nests with the larger clutches had closer conspecific neighbors than did successful nests with smaller clutches. The species composition of nearest neighbors changed significantly with distance from Snow Goose nests but not Ross' Goose nests. Nesting success was not affected by the species of nearest neighbor, however. Because they have complementary antipredator adaptations, Ross' and Snow geese may benefit by nesting together.

scholarly journals GriSPy: A Python package for fixed-radius nearest neighbors search

2020 ◽  
pp. 100443
Author(s):  
M. Chalela ◽  
E. Sillero ◽  
L. Pereyra ◽  
M.A. Garcia ◽  
J.B. Cabral ◽  
...  
Keyword(s):  
Nearest Neighbors ◽  
Fixed Radius ◽  
Python Package
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