Abstract
Southeastern Canada is inhabited by an amalgam of hybridizing wolf-like canids, raising fundamental questions regarding their taxonomy, origins, and timing of hybridization events. Eastern ...wolves (Canis lycaon), specifically, have been the subject of significant controversy, being viewed as either a distinct taxonomic entity of conservation concern or a recent hybrid of coyotes (C. latrans) and grey wolves (C. lupus). Mitochondrial DNA analyses show some evidence of eastern wolves being North American evolved canids. In contrast, nuclear genome studies indicate eastern wolves are best described as a hybrid entity, but with unclear timing of hybridization events. To test hypotheses related to these competing findings we sequenced whole genomes of 25 individuals, representative of extant Canadian wolf-like canid types of known origin and levels of contemporary hybridization. Here we present data describing eastern wolves as a distinct taxonomic entity that evolved separately from grey wolves for the past ∼67,000 years with an admixture event with coyotes ∼37,000 years ago. We show that Great Lakes wolves originated as a product of admixture between grey wolves and eastern wolves after the last glaciation (∼8,000 years ago) while eastern coyotes originated as a product of admixture between “western” coyotes and eastern wolves during the last century. Eastern wolf nuclear genomes appear shaped by historical and contemporary gene flow with grey wolves and coyotes, yet evolutionary uniqueness remains among eastern wolves currently inhabiting a restricted range in southeastern Canada.
Recent trend of research is to hybridize two and several number of variants to find out better quality of solution of practical and recent real applications in the field of global optimization ...problems. In this paper, a new approach hybrid Grey Wolf Optimizer (GWO) – Sine Cosine Algorithm (SCA) is exercised on twenty-two benchmark test, five bio-medical dataset and one sine dataset problems. Hybrid GWOSCA is combination of Grey Wolf Optimizer (GWO) used for exploitation phase and Sine Cosine Algorithm (SCA) for exploration phase in uncertain environment. The movement directions and speed of the grey wolve (alpha) is improved using position update equations of SCA. The numerical and statistical solutions obtained with hybrid GWOSCA approach is compared with other metaheuristics approaches such as Particle Swarm Optimization (PSO), Ant Lion Optimizer (ALO), Whale Optimization Algorithm (WOA), Hybrid Approach GWO (HAGWO), Mean GWO (MGWO), Grey Wolf Optimizer (GWO) and Sine Cosine Algorithm (SCA). The numerical and statistical experimental results prove that the proposed hybrid variant can highly be effective in solving benchmark and real life applications with or without constrained and unknown search areas.
With the emergence of the Internet-of-Things (IoT) and seamless Internet connectivity, the need to process streaming data on real-time basis has become essential. However, the existing data stream ...management systems are not efficient in analyzing the network log big data for real-time anomaly detection. Further, the existing anomaly detection approaches are not proficient because they cannot be applied to networks, are computationally complex, and suffer from high false positives. Thus, in this paper a hybrid data processing model for network anomaly detection is proposed that leverages grey wolf optimization (GWO) and convolutional neural network (CNN). To enhance the capabilities of the proposed model, GWO and CNN learning approaches were enhanced with: 1) improved exploration, exploitation, and initial population generation abilities and 2) revamped dropout functionality, respectively. These extended variants are referred to as Improved-GWO (ImGWO) and Improved-CNN (ImCNN). The proposed model works in two phases for efficient network anomaly detection. In the first phase, ImGWO is used for feature selection in order to obtain an optimal trade-off between two objectives, i.e., reduced error rate and feature-set minimization. In the second phase, ImCNN is used for network anomaly classification. The efficacy of the proposed model is validated on benchmark (DARPA'98 and KDD'99) and synthetic datasets. The results obtained demonstrate that the proposed cloud-based anomaly detection model is superior in comparison to the other state-of-the-art models (used for network anomaly detection), in terms of accuracy, detection rate, false positive rate, and F-score. In average, the proposed model exhibits an overall improvement of 8.25%, 4.08%, and 3.62% in terms of detection rate, false positives, and accuracy, respectively; relative to standard GWO with CNN.
•We present a new improvement to the grey wolf algorithm.•The new improvement is tested with twenty-three benchmark functions.•The new improvement is compared with four published algorithms.•The new ...improvement is applied to grid-connected wind power plants.•The new improvement is verified by simulation results.
The grey wolf optimizer (GWO) is a new meta-heuristic algorithm inspired from the leadership and prey searching, encircling, and hunting of the grey wolves’ community. The GWO algorithm has the advantages of simplicity (less control parameters), flexibility, and globalism. In this paper, a simple and efficient augmentation for the GWO (AGWO) algorithm is proposed for better hunting performance. The AGWO algorithm focuses on increasing the possibility of the exploration process over the exploitation process by modifying the behavior of the control parameter (a) and position updating. The AGWO is suitable to the low number of search agents such as the electric power system application. The proposed AGWO algorithm is verified using twenty-three benchmark test functions and is applied to the grid-connected permanent magnet synchronous generator driven by variable speed wind turbine (PMSG-VSWT). The obtained results of the AGWO algorithm are compared with the results of the original GWO and other algorithms. The comparisons verified that the proposed AGWO is significantly augmented the performance of the original GWO algorithm without affecting its simplicity and easy implementation.
North American Canis genetics research varies in interpreting the Pre-Columbian distribution of Coyotes (Canis latrans). Many studies have relied on generalized species-distribution maps and a few ...actually cite earlier genetics works as secondary sources. I use archaeological, paleontological, and settlement era documents to demonstrate that Coyotes were present in portions of Minnesota, Wisconsin, and Illinois thousands of years prior to European arrival. This review provides important clarification of historical Coyote distribution in the region and may have implications on the various interpretations of introgressed Coyote haplotypes present in Gray Wolves (Canis lupus) throughout the Great Lakes region.
Wolf-Hirschhorn syndrome is a rare genetic disease caused by a chromosomal deletion of the distal short arm of Chromosome 4. It is associated with multisystem abnormalities, including delayed growth, ...characteristic facial features, epilepsy, and skeletal abnormalities. We report three patients who developed hip displacement, and describe the occurrence of delayed and nonunion in patients who underwent corrective proximal femoral osteotomy for hip displacement. We also performed a literature review identifying common musculoskeletal presentations associated with the condition. Patients with Wolf-Hirschhorn Syndrome are at risk of hip displacement (subluxation), and we would advocate annual hip surveillance in this patient group.
In this paper, a variant of gray wolf optimization (GWO) that uses reinforcement learning principles combined with neural networks to enhance the performance is proposed. The aim is to overcome, by ...reinforced learning, the common challenge of setting the right parameters for the algorithm. In GWO, a single parameter is used to control the exploration/exploitation rate, which influences the performance of the algorithm. Rather than using a global way to change this parameter for all the agents, we use reinforcement learning to set it on an individual basis. The adaptation of the exploration rate for each agent depends on the agent's own experience and the current terrain of the search space. In order to achieve this, experience repository is built based on the neural network to map a set of agents' states to a set of corresponding actions that specifically influence the exploration rate. The experience repository is updated by all the search agents to reflect experience and to enhance the future actions continuously. The resulted algorithm is called experienced GWO (EGWO) and its performance is assessed on solving feature selection problems and on finding optimal weights for neural networks algorithm. We use a set of performance indicators to evaluate the efficiency of the method. Results over various data sets demonstrate an advance of the EGWO over the original GWO and over other metaheuristics, such as genetic algorithms and particle swarm optimization.
The goal of this study was to observe the specific behavior of Carpathian wolf cubs, considering the importance of this specie from fauna balance and ecology point of view. Chronological observations ...of young cubs’ evolution from birth to wean period were made, in order to determine the mother-cubs relation and the relations between brother cubs from the same litter.
Wolf-Hirschhorn Syndrome (WHS) is a neurodevelopmental disorder characterized by mental retardation, craniofacial malformation, and defects in skeletal and heart development. The syndrome is ...associated with irregularities on the short arm of chromosome 4, including deletions of varying sizes and microduplications. Many of these genotypic aberrations in humans have been correlated with the classic WHS phenotype, and animal models have provided a context for mapping these genetic irregularities to specific phenotypes; however, there remains a significant knowledge gap concerning the cell biological mechanisms underlying these phenotypes. This review summarizes literature that has made recent contributions to this topic, drawing from the vast body of knowledge detailing the genetic particularities of the disorder and the more limited pool of information on its cell biology. Finally, we propose a novel characterization for WHS as a pathophysiology owing in part to defects in neural crest cell motility and migration during development.
•This review summarizes literature that has made recent contributions to the topic of Wolf-Hirschhorn Syndrome, drawing from the vast body of knowledge detailing the genetic particularities of the disorder and the more limited pool of information on its cell biology.•We propose a novel characterization for WHS as a pathophysiology owing in part to defects in neural crest cell motility and migration during development.
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