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Discover sea horses
\"This Level 3 guided reader introduces basic facts about sea horses, including their physical characteristics, diet, and habitat. Simple callouts ask the student to think in new ways, supporting inquiry-based reading. Additional text features and search tools, including a glossary and an index, help students locate information and learn new words.\"-- Provided by publisher.
Book
Seahorses
Presents various facts about seahorses, such as where they live, what they eat, and how they reproduce.
Book
The seahorse genome and the evolution of its specialized morphology
Seahorses have a specialized morphology that includes a toothless tubular mouth, a body covered with bony plates, a male brood pouch, and the absence of caudal and pelvic fins. Here we report the sequencing and
de novo
assembly of the genome of the tiger tail seahorse,
Hippocampus comes
. Comparative genomic analysis identifies higher protein and nucleotide evolutionary rates in
H. comes
compared with other teleost fish genomes. We identified an astacin metalloprotease gene family that has undergone expansion and is highly expressed in the male brood pouch. We also find that the
H. comes
genome lacks enamel matrix protein-coding proline/glutamine-rich secretory calcium-binding phosphoprotein genes, which might have led to the loss of mineralized teeth.
tbx4
, a regulator of hindlimb development, is also not found in
H. comes
genome. Knockout of
tbx4
in zebrafish showed a ‘pelvic fin-loss’ phenotype similar to that of seahorses.
Here, the genome sequence of the tiger tail seahorse is reported and comparative genomic analyses with other ray-finned fishes are used to explore the genetic basis of the unique morphology and reproductive system of the seahorse.
Evolution at a gallop
Seahorses are prime examples of the exuberance of evolution and are unique among bony fish on several counts, including their equine body shape and male brood pouch. An international collaboration reporting in this issue of
Nature
has determined the genome sequence of a seahorse (
Hippocampus comes
, the tiger tail seahorse). They find it to be the most rapidly evolving fish genome studied so far.
H. comes
is among the most commonly traded seahorse species—dried for traditional medicines and live for the aquarium trade—and is on the IUCN Red List as a 'vulnerable' species. Analysis of the genomic sequence provides insights into the evolution of its unique morphology. Of note is the absence of a master control gene,
tbx4
, which functions in the development of hindlimbs and pelvic fins. Pelvic fins are missing in seahorses, and
tbx4
-knockout mutant zebrafish also lack pelvic fins.
Journal Article
Enhanced Phishing Website Categorization Using Random Forest with Sea Horse and Jellyfish Search Optimization
In contemporary society, with advancements in science and technology, many global activities, ranging from financial transactions to information transfers, are conducted through the Internet via dedicated websites and applications. Unfortunately, the prevalence of online platforms has increased the proliferation of fake websites aimed at exploiting sensitive data, such as bank card information and personal details. It addresses the problem of cybersecurity w.r.t. the categorization of a set of 1353 websites by a machine learning algorithm into three categories, namely phishing, suspicious, and legitimate URLs. The dataset was gathered from published papers and divided into 70-30 in the training and testing phases. This will help keep members' banking and personal data much safer online. This paper uses the RFC model with two optimization schemes, Sea Horse Optimizer (SHO) and Jellyfish Search Optimization Algorithm (JSOA), to improve performance. After that, optimized versions of the schemes are tagged as RFSH and RFJS, respectively. After extensive training and testing on these three schemes, the best model was identified by comparing the performances of the three on the database in hand. The RFSH model performed better predicting, achieving 0.952 for all the data. It outperformed the RFJS model with a precision of 0.932 and the RFC single framework with an accuracy of 0.9106. Hence, it emerged as the best-predicting model.
Journal Article
The curious world of seahorses : the life and lore of a marine marvel
\"In this entertaining and informative book, science writer Till Hein shares the most tantalizing findings from the world of seahorses, opening up some of the secrets of these magical creatures of the sea. He reveals their intriguing biological features, such as their unique prehensile tails, their fins, and their lack of a stomach (seahorses only have intestines!). He speaks to experts about the fossil record of prehistoric seahorses, and examines their unique hunting strategy involving suction through their tubular (and toothless) snout. But the most unique aspect of the seahorses is their reproductive cycle, as it is the male of the species who becomes pregnant. Seahorses have become icons in feminist and transgender male communities for the way they can reshape human cultural notions of masculinity and fatherhood. Endlessly fascinating and charmingly approachable, The Curious World of Seahorses will captivate any reader looking to learn more about one of the most incredible creatures on Earth.\"-- Provided by publisher.
Book
GOG-MBSHO: multi-strategy fusion binary sea-horse optimizer with Gaussian transfer function for feature selection of cancer gene expression data
Cancer gene expression data has the characteristics of high-dimensional, multi-text and multi-classification. The problem of cancer subtype diagnosis can be solved by selecting the most representative and predictive genes from a large number of gene expression data. Feature selection technology can effectively reduce the dimension of data, which helps analyze the information on cancer gene expression data. A multi-strategy fusion binary sea-horse optimizer based on Gaussian transfer function (GOG-MBSHO) is proposed to solve the feature selection problem of cancer gene expression data. Firstly, the multi-strategy includes golden sine strategy, hippo escape strategy and multiple inertia weight strategies. The sea-horse optimizer with the golden sine strategy does not disrupt the structure of the original algorithm. Embedding the golden sine strategy within the spiral motion of the sea-horse optimizer enhances the movement of the algorithm and improves its global exploration and local exploitation capabilities. The hippo escape strategy is introduced for random selection, which avoids the algorithm from falling into local optima, increases the search diversity, and improves the optimization accuracy of the algorithm. The advantage of multiple inertial weight strategies is that dynamic exploitation and exploration can be carried out to accelerate the convergence speed and improve the performance of the algorithm. Then, the effectiveness of multi-strategy fusion was demonstrated by 15 UCI datasets. The simulation results show that the proposed Gaussian transfer function is better than the commonly used S-type and V-type transfer functions, which can improve the classification accuracy, effectively reduce the number of features, and obtain better fitness value. Finally, comparing with other binary swarm intelligent optimization algorithms on 15 cancer gene expression datasets, it is proved that the proposed GOG1-MBSHO has great advantages in the feature selection of cancer gene expression data.
Journal Article