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> Living Beings > Mammal > Chiroptera

Chiroptera

Chiroptera: The order of mammals that includes bats.
Bats are the only flying mammals and play important roles in ecosystems as pollinators, seed dispersers, and insectivores.
Cheir(hand) and ptera(wing) refer to the adaptaions that allow bats to fly using their webbed forelimbs.
Chiroptera research is crucial for understanding bat biology, ecology, and conservation.
PubCompare.ai can optimize your Chiroptera studies by providing easy access to reproducible protocols and leveraging AI-driven comparisons to identify the best approaches and products for your work.

Most cited protocols related to «Chiroptera»

1
Comprehensive Bleeding Phenotype Database
Bleeding score data (which were obtained for all subjects by expert-administration of the BAT) along with the von Willebrand factor (VWF) laboratory results, when available [VWF:Ag (VWF:antigen), VWF:RCo (VWF:ristocetin co-factor), FVIII:C (factor VIII coagulant)] and demographic data, were collected from 1422 normal subjects; adult data (n = 1079) were collected from individuals ≥18 years using the MCMDM-1VWD BQ (n = 294), Condensed MCMDM-1VWD BQ (n = 660), and ISTH-BAT (n = 125), while paediatric data (n = 343) were collected from individuals <18 years using the PBQ (n = 324) and ISTH-BAT (n = 19). A consistent definition of ‘normal’ was used across all studies. For adults and children, the specific wording was either individuals with no history of a known or previously diagnosed bleeding disorder or individuals with no known problem with bleeding or bruising, or both.
The data sets were merged using the Bleeding Phenotype Ontology (BPO), which was developed to explicitly represent the relationships among bleeding signs, symptoms, disorders, and treatments within the bleeding questionnaires [6 (link)]. The ontology is publicly available in the Bioportal ontology registry (http://bioportal.bioontology.org/ontologies/1166). Data elements (individual questions) from the four questionnaires were analysed to determine where they map to the BPO. From such analysis, a subset of common questions were identified and were utilized to compile data collected using different BATs for unified and standardized comparison. The aggregate data set was then stored on a MySQL database to facilitate data retrieval of various subgroups of patients. Data were separated into males and females for analysis. The mean and standard deviation (SD) were determined. Outlier values were defined as those above or below the mean ± 3 SDs. Once the outliers were removed, the middle 95th percentile was used to determine the normal range.
Elbatarny M., Mollah S., Grabell J., Bae S., Deforest M., Tuttle A., Hopman W., Clark D.S., Mauer A.C., Bowman M., Riddel J., Christopherson P.A., Montgomery R.R., Rand M.L., Coller B, & James P.D. (2014). Normal range of bleeding scores for the ISTH-BAT: adult and pediatric data from the merging project. Haemophilia : the official journal of the World Federation of Hemophilia, 20(6), 831-835.
Publication 2014
Adult Blood Coagulation Disorders Child Chiroptera Coagulants Factor VIII Factor VIII-Related Antigen Females Limulus clotting factor C Males Patients Phenotype Ristocetin
2
Inferring Evolutionary Rates in Influenza
We implemented the HSLC model in BEAST23 (link), allowing an arbitrary number of distinct clades to have rates inferred separately and simultaneously within a single Bayesian MCMC analysis. We constructed sequence alignments using all available full-segment IVA sequences encoding PB2, PB1, PA, HA (H1, H3, H7), NP, NA (N1, N7, N8), M1/2, and NS1/2 from birds, horses, pigs, humans, and fruit bats. A subset of sequences of a size amenable to molecular clock analyses (~300 sequences per segment) was sampled, preserving all major lineages and all the most basal sequences in major clades. Each major host group was allowed its own rate in the HSLC model. We inferred the rate of increase of U content through time for equine H3N8 by fitting a two-dimensional Scaled Log Transform: U content = a * ln(b * year + c). Age estimates for equine H7N7 were calculated assuming these rates and were then compared to the timing of the 1872 epizootic to test whether the high U content in equine H7N7 is consistent with continuous equine transmission from this period.
Worobey M., Han G.Z, & Rambaut A. (2014). A synchronized global sweep of the internal genes of modern avian influenza virus. Nature, 508(7495), 254-257.
Publication 2014
Aves Chiroptera Equus caballus Fruit Homo sapiens Pigs Sequence Alignment Transmission, Communicable Disease
3
Structured Timeline Follow-Back for Marijuana Use

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Publication 2010
Cannabis sativa Chiroptera Ethnicity Gender Joints Marijuana Use Origanum vulgare piperazine-N,N'-bis(2-ethanesulfonic acid) Plant Leaves TimeLine
4
Phylogeographic Analysis of Ebola Transmission
To study changes of host type in Ebola we used whole genome Ebola sequences from 78 patients recently obtained and aligned with sequences from previous outbreaks [39 (link)]. The authors of this study investigated the phylogenetic relationship of samples within or between Ebola outbreaks. We applied the three phylogeographic methods presented above to infer the contribution of zoonotic events to Ebola spread. We used the same alignment provided in [39 (link)] for the BEAST2 analysis, including sampling dates, but we also added information regarding host type. We defined two subpopulations, human and animal reservoir, and we allowed lineages to transmit forwards in time from the animal reservoir to a human host, but not vice-versa. So our phylogeographic model had two locations (respectively human and animal reservoir) but migration was only assumed to occur in one direction. This results in a structured coalescent model with three phylogeographic parameters for MTT and BASTA (one migration rate and two effective population sizes), but only two parameters for DTA, as only a single general effective population size can be defined in that model. A peculiarity of these analyses is that no samples from one of the two considered populations were available. While this might seem an impassable limitation, previous studies have shown that the structured coalescent can provide meaningful estimates even in the absence of samples from one populations (i.e. “ghost deme”, see [47 ]), suggesting that it is possible to perform statistical inference of zoonosis rates in this scenario.
Since the inclusion of no animal samples is unusual, we considered a second, more typical, analysis in which we included genetic sequences from bats. Relatively little sequencing has been performed in potential animal reservoirs, so we were able to include only partial Ebola virus sequences from a 265 bp region of the polymerase (L) gene from seven bats collected in [48 (link)]. In this analysis, it was necessary to allow a small but non-zero rate of migration from humans to the animal reservoir to avoid predetermining inference of the ancestral location of the root. Therefore we constrained the migration rate from humans to animals at a rate 105 times lower than the animal to human rate. This preserves the ability of the model to infer ancestral locations in either of the two subpopulations, once samples from the animal reservoir have been included.
De Maio N., Wu C.H., O’Reilly K.M, & Wilson D. (2015). New Routes to Phylogeography: A Bayesian Structured Coalescent Approximation. PLoS Genetics, 11(8), e1005421.
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Publication 2015
2,2-dichloro-1,1-difluoroethyl difluoromethyl ether Animals Basta Chiroptera Disease Outbreaks Ebolavirus Genes Genome Hemorrhagic Fever, Ebola Homo sapiens Patients Plant Roots Population Group Red Cell Ghost Reproduction Zoonoses
5
Genetic Determinants of Refractive Error
All studies participating in this meta-analysis are part of the Consortium for Refractive Error and Myopia (CREAM). All studies had a population-based design and had a similar protocol for phenotyping (Supplementary Table 1). Eligible participants underwent a complete ophthalmologic examination including a non-dilated measurement of refractive error of both eyes. Exclusion criteria were all conditions that could alter refraction, such as cataract surgery, laser refractive procedures, retinal detachment surgery, keratoconus, or ocular or systemic syndromes. Inclusion criteria were persons aged 25 years and over who had data on refractive error and genotype.
The meta-analysis of step 1 was based on 27 studies of European ancestry: 1958 British Birth Cohort, ALSPAC, ANZRAG, AREDS1a1b, AREDS1c, CROATIA-Korcula, CROATIA-Split, CROATIA-Vis, EGCUT, FECD, TEST/BATS, FITSA, Framingham, GHS 1, GHS 2, KORA, ORCADES, TwinsUK, WESDR, YFS, ERF, DCCT, BMES, RS1, RS2, RS3, and OGP Talana. The second step was formed by 5 Asian studies: Beijing Eye Study, SCES, SIMES, SINDI, and SP2.
General methods, demographics and phenotyping and genotyping methods of the study cohorts can be found in the Supplementary Note and Supplementary Table 1. All studies were performed with the approval of their local Medical Ethics Committee, and written informed consent was obtained from all participants in accordance with the Declaration of Helsinki.
Verhoeven V.J., Hysi P.G., Wojciechowski R., Fan Q., Guggenheim J.A., Höhn R., MacGregor S., Hewitt A.W., Nag A., Cheng C.Y., Yonova-Doing E., Zhou X., Ikram M.K., Buitendijk G.H., McMahon G., Kemp J.P., St. Pourcain B., Simpson C.L., Mäkelä K.M., Lehtimäki T., Kähönen M., Paterson A.D., Hosseini S.M., Wong H.S., Xu L., Jonas J.B., Pärssinen O., Wedenoja J., Yip S.P., Ho D.W., Pang C.P., Chen L.J., Burdon K.P., Craig J.E., Klein B.E., Klein R., Haller T., Metspalu A., Khor C.C., Tai E.S., Aung T., Vithana E., Tay W.T., Barathi V.A., Chen P., Li R., Liao J., Zheng Y., Ong R.T., Döring A., Evans D.M., Timpson N.J., Verkerk A.J., Meitinger T., Raitakari O., Hawthorne F., Spector T.D., Karssen L.C., Pirastu M., Murgia F., Ang W., Mishra A., Montgomery G.W., Pennell C.E., Cumberland P.M., Cotlarciuc I., Mitchell P., Wang J.J., Schache M., Janmahasathian S., Igo RP J.r., Lass J.H., Chew E., Iyengar S.K., Gorgels T.G., Rudan I., Hayward C., Wright A.F., Polasek O., Vatavuk Z., Wilson J.F., Fleck B., Zeller T., Mirshahi A., Müller C., Uitterlinden A.G., Rivadeneira F., Vingerling J.R., Hofman A., Oostra B.A., Amin N., Bergen A.A., Teo Y.Y., Rahi J.S., Vitart V., Williams C., Baird P.N., Wong T.Y., Oexle K., Pfeiffer N., Mackey D.A., Young T.L., van Duijn C.M., Saw S.M., Wilson J.E., Stambolian D., Klaver C.C, & Hammond C.J. (2013). Genome-wide meta-analyses of multi-ethnic cohorts identify multiple new susceptibility loci for refractive error and myopia. Nature genetics, 45(3), 314-318.
Publication 2013
Asian Persons Birth Cohort Cataract Extraction Chiroptera Europeans Genotype Genotyping Techniques Keratoconus Myopia Ocular Refraction Operative Surgical Procedures Refractive Errors Regional Ethics Committees Retinal Detachment Syndrome Vision

Most recents protocols related to «Chiroptera»

1
Agonistic Interactions in Male Horseshoe Bats
Two data sets were used in this study. For the first data set, we reanalyzed video recordings of dyadic agonistic interactions between male H. armiger originally described in Sun et al. (2019 ). Briefly, Sun et al. (2019 ) captured 230 adult males from Hanzhong (96), Simao (88), and Hekou (46) in August–November 2017. Eight bats were selected randomly and were introduced into a testing cage (0.5 m × 0.5 m × 0.5 m) to trigger agonistic interactions within each trial (Video S2). Trials were monitored until the first interaction between two of the eight bats terminated with a clear winner and loser. Otherwise, trials were terminated if no aggressive interaction occurred within 15 min. Only one aggressive interaction was allowed to occur in each trial. After the trial, we removed all bats and reintroduced another eight bats into the testing cage. Taken together, 115 agonistic interactions from 230 individuals were analyzed.
The second data set comes from Zhang, Sun, Lucas, Gu, et al. (2022 (link)) in which 96 adult males from Simao were caught in July–August 2019. There was no overlap between the Simao bats tested in Sun et al. (2019 ) compared with the bats tested in Zhang, Sun, Lucas, Gu, et al. (2022 (link)) because they were captured at different roosting cave sites. Indeed, the distance between these two caves was at least 20 km and it is very likely that they were isolated from one another. Agonistic interactions were performed between pairs in a box made of acrylic sheet plexiglass (long × wide × high: 1 m × 0.5 m × 0.5 m). Pairs of bats were chosen at random and placed in the center of the two pieces of wire mesh on opposite ends of a slide rail. The experimenter pulled the two pieces of wire mesh toward each other by means of ropes until the two bats arrived at the center of the box; this is a simulation of two bats invading each other's territory (Video S1). The criteria for termination of the experiment were the same as the criteria from Sun et al. (2019 ). Taken together, 48 agonistic interactions from 96 individuals were analyzed from the Zhang, Sun, Lucas, Gu, et al. (2022 (link)) study.
For the above two studies, each male was tested in only one agonistic interaction to avoid pseudoreplication. All of the agonistic interactions were monitored using night‐shot camcorders (FDR‐AX60, Sony Corp.). The experimental procedures are described in detail by Sun et al. (2019 ) and Zhang, Sun, Lucas, Gu, et al. (2022 (link)).
Zhang C., Lucas J.R., Feng J., Jiang T, & Sun C. (2023). Population‐level lateralization of boxing displays enhances fighting success in male Great Himalayan leaf‐nosed bats. Ecology and Evolution, 13(3), e9879.
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Publication 2023
Adult agonists Chiroptera Males Plexiglas Precipitating Factors
2
Laterality in Bat Forearm Preference
As noted above, data on bats from the Simao site were derived from two different studies (Sun et al., 2019 ; Zhang, Sun, Lucas, Gu, et al., 2022 (link)). We ran Pearson's chi‐square tests to examine whether the proportion of hand preference and the proportion of laterality index significantly differed between Simao bats from the two studies. We found no significant differences in either parameter between bats from the two studies (Pearson's chi‐square test: χ12 = 0.070, p = .791; χ12 = 0.682, p = .409, respectively). Therefore, we pooled these data sets for subsequent analysis.
We conducted the following statistical analysis for bats from Simao, Hekou and Hanzhong, respectively. Exact binomial probability tests were performed to compare the number of bats with a left‐forearm and a right‐forearm preference, and to compare the number of bats with a negative LI value and a positive LI value. We also used kernel density estimates of the frequency distribution of laterality indices to offer a potentially more robust estimate of the entire frequency distribution. The kernel density estimation is a nonparametric method that adopts a slipped peak function to fit the sample data and utilizes a continuous density curve to describe the distribution pattern of the variables. It does not involve setting a functional form and can include observed variability in the data set with a continuous curve.
We also used an exact binomial probability test to evaluate whether the winners tend to display a left‐forearm or a right‐forearm preference. Moreover, in order to further confirm whether forearm preference has an effect on fighting ability, an exact binomial probability test was used to test whether the losers tend to display a left‐forearm or a right‐forearm preference.
Zhang C., Lucas J.R., Feng J., Jiang T, & Sun C. (2023). Population‐level lateralization of boxing displays enhances fighting success in male Great Himalayan leaf‐nosed bats. Ecology and Evolution, 13(3), e9879.
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Publication 2023
Chiroptera Forearm
3
Urban Bat Ecology in Africa
We compiled a list of all mainland African insectivorous bat species using ACR (2018 ), Kingdon (2013 ), and Monadjem et al. (2020 ). We collected aspect ratio, wing loading, peak echolocation frequency, roost ecology, and functional group data for each of these species from these sources and other available literature (ACR., 2018 ; Aldridge & Rautenbach, 1987 (link); Kingdon, 2013 ; Monadjem et al., 2020 ; Norberg & Rayner, 1987 (link); Salsamendi et al., 2005 ). Available ecological trait data and presence in the phylogenetic super‐tree (Jones et al., 2005 (link)) reduced our data set from an initial 219 species to 54 species for statistical analyses [data available: https://doi.org/10.5061/dryad.k3j9kd5b9]. We then determined whether these species were present in urban (including suburban or peri‐urban) areas within their range (personal communication P. Webala, I. Tanshi and M.C. Schoeman; and Ancillotto et al., 2015 (link); Andreani et al., 2019 (link); Dekker et al., 2013 ; Fenton et al., 2002 ; Geldenhuys et al., 2013 (link); Hoye & Spence, 2004 (link); Jacobs & Barclay, 2009 (link); Kurek et al., 2020 (link); Lane et al., 2022 (link); Legakis et al., 2000 ; O'Malley et al., 2020 (link); Roswag et al., 2019 (link); Schoeman, 2016 (link); Schoeman & Waddington, 2011 (link); Taylor et al., 1999 ; Wojtaszyn et al., 2013 (link)) and recorded this information as presence (1) or absence (0) in urban areas. We categorized roost specificity for each species as: utilizing 1 roost type = high, 2 roost types = medium, and ≥3 roost types = low. Each of the following was considered a different roost “type”: caves and mines, tree crevices (behind bark and tree holes), foliage, rock crevices, exposed outer walls of houses/buildings, roofs of houses/buildings, and road culverts. Roost specificity was classified regardless of the surrounding habitat type (e.g., open vs. narrow space) or landscape (e.g., highly urbanized vs. more rural areas) where the roost was found.
We categorized bats into urban exploiters, adapters, or avoiders after Jung and Kalko (2011 (link)) and Schoeman (2016 (link)) based on wing morphology and roost habits. Urban exploiters are open‐air bats with high wing loading and aspect ratios and highly flexible roost habits that readily use anthropogenic resources; urban adapters are narrow‐edge space bats with intermediate wing loading and aspect ratios, and fairly flexible roosting habits; and urban avoiders are narrow‐space bats with restricted roosting requirements, such as obligate cave roosters (Jung & Kalko, 2011 (link); Schoeman, 2016 (link)).
Marsden G.E., Vosloo D, & Schoeman M.C. (2023). Urban tolerance is phylogenetically constrained and mediated by pre‐adaptations in African bats. Ecology and Evolution, 13(3), e9840.
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Publication 2023
Chiroptera Cortex, Cerebral Echolocation Insectivora Negroid Races Trees
4
Vocal Rhythm Development in Harbor Seal Pups
The ‘vocal learning-beat perception and synchronization’ (VL-BPS) hypothesis states that only vocal learning species—those capable of producing new vocalizations or modifying existing ones based on auditory experience—may possess advanced rhythmic abilities [28 ,29 (link)]. This hypothesis is inherently cross-modal: it suggests a strong link between audition and timed movement. For example, Snowball, a sulfur-crested cockatoo (Cacatua galerita eleonora), was shown to perceive auditory rhythms at different tempi and to predictively synchronize his body movements to them [30 (link)]. Parrots are phylogenetically distant from humans and, among mammals, pinnipeds (seals, sea lions and walruses) are one of the vocal learning groups (besides humans, bats, elephants and cetaceans). Pinnipeds may well be the best mammalian model for testing the VL-BPS hypothesis—the ability to extract a beat from periodic acoustic stimuli and entrain to it in a predictive and adaptive manner—since some species show vocal mimicry and plasticity [31 (link),32 (link)] and others can keep a beat [33 (link)]. These characteristics, parallelling human abilities, make pinnipeds an ideal animal clade for comparative research on the origins of rhythmic communicative behaviour.
Harbour seals exhibit both vocal flexibility [32 (link),34 (link)] and rhythmic interactivity [20 (link)], and are particularly vocal in the first few weeks of life [35 (link)]. During the lactation period, harbour seal pups emit ‘mother attraction calls' (hereafter ‘calls’) to draw their mothers' attention [36 (link)]. Mothers are silent and use the individual vocal signatures in these calls to recognize their pups [35 (link),37 (link)]. Against the acoustically complex backdrop of large mother–pup rookeries, rhythmically tuned pup calls could constitute a socio-ecologically selected trait that allows individual pups to avoid conspecific call overlap by adjusting the timing of their own call onsets. Such timing plasticity could allow a pup to be more acoustically conspicuous and increase its chances of successful reunions with its mother. Unlike cooperative types of turn-taking (e.g. in humans and in common marmosets [38 (link)]) harbour seal pups’ interactions are a by-product of neighbouring pups vocalizing to attract their silent mothers and are thus probably competitive.
To date, only two papers studied vocal rhythms in harbour seals, crucially both focusing on single individuals [20 (link),27 (link)]. The first study was a playback experiment in which a pup vocally interacted with sounds broadcasted from a loudspeaker [20 (link)]. The pup adjusted the timing of its calls in an asynchronous manner by responding to the broadcasted conspecific calls with a non-uniformly distributed response phase whose mean approximated 90° [20 (link)]. The second study looked at the presence and development of vocal rhythms in three harbour seal pups [27 (link)]. Complementary analytical approaches showed how the pups' individual calling patterns gained more rhythmic structure over time [27 (link)]. However, a major limitation of both studies was the lack of sociality (i.e. individuals were tested alone) and, by extension, interactivity (i.e. the stimuli did not adapt to the response of the tested animals).
Anichini M., de Reus K., Hersh T.A., Valente D., Salazar-Casals A., Berry C., Keller P.E, & Ravignani A. (2023). Measuring rhythms of vocal interactions: a proof of principle in harbour seal pups. Philosophical Transactions of the Royal Society B: Biological Sciences, 378(1875), 20210477.
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Publication 2023
Acclimatization Acoustics Animals Attention Auditory Perception Breast Feeding Callithrix jacchus Cetacea Chiroptera Cockatoos Delta Rhythm Elephants Enzyme Multiplied Immunoassay Technique Homo sapiens Mammals Maritally Unattached Mothers Movement Odobenidae Parrots Phocidae Pinnipedia Reproductive Behavior Seal, Harbor Sea Lion Sound Sulfur
5
Flower Pollination Algorithm for Optimization
FPA developed by Yang is a meta-heuristic algorithm that simulates the pollination process of blossoming plants [102 ]. The transport of flower pollen is referred to as flower pollination. Birds, bats, insects, and other animals are the principal actors in this transfer. Some flowers and insects participate in what is known as a pollinator relationship. These blooms can attract only the birds involved in this cooperation. These insects are regarded as the primary flower pollinators.
FPA takes into account four separate rules for flower constancy, pollination behaviour, and the pollination process [103 ].

Biotic pollination is cross-pollination in which the pollinator transports pollen. This is a global pollination process, and the pollinator movement complies with the Lévy flights.

Abiotic or self-pollination is the process of a plant or flower reproducing itself without the aid of a pollinator. Because the pollen transfer distance is typically less than that of biotic pollination, this procedure is known as local pollination.

Pollinators can acquire flower stability, favouring particular blooms. The flower constant is a mathematical expression for the likelihood of reproduction. The likelihood increases in direct proportion to how similar the related flowers are.

To manage the sort of pollination, \documentclass[12pt]{minimal}
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\begin{document}$$p\in [0, 1]$$\end{document}p∈[0,1] has the potential to be a key. These guidelines permit the use of both local and global search strategies. The greatest solutions are discovered nearby by using local search. Additionally, global pollination effectively prevents the problem from becoming trapped in a local optimum solution.

These rules must be used to construct updated equations. For instance, pollinators like insects transfer flower pollen gametes during the global pollination stage. Pollen can travel huge distances because insects can frequently fly and cover a bigger area. (2) can therefore be used to represent Rule 1 and flower constancy numerically (Rule 3). Xi(t+1)=Xi(t)++γL(λ)(g∗-Xi(t)) Here, the solution vector Xi for the pollen i or t iteration is Xi , and the best solution in the current generation or iteration is g∗ . Here, the scaling factor γ is used to regulate the step size.
The Lévy flight step size parameter is L(λ) . Insect migration can be depicted using the Lévy distribution as they travel great distances. The mathematical expression used by Lévy is presented in (3). LλΓ(λ)sin(πλ2)π1s1+λ,(s>>s0>0) The usual gamma function is Gamma(λ) in this instance, and the step size is s. This distribution holds true for significant steps s>0 . Although in theory s0>>0 must exist, in practice, s0 can be as low as 0.1. Rule 2 and Rule 3 are illustrated for local pollination in (4). Xi(t+1)=Xi(t)+∈(Xj(t)-Xk(t)) In (4), the pollen type xj(t) , xk(t) comes from various flowers of the same kind of plant. The Lévy distribution is used to search for several solution points throughout the search space, which is the algorithm’s most crucial property for optimization. The optimization logic of the algorithm consists of locating the solution points at a great distance using the biotic pollination model and examining the area around the solution points using the abiotic pollination model, just like in flowers.
Ay Ş., Ekinci E, & Garip Z. (2023). A comparative analysis of meta-heuristic optimization algorithms for feature selection on ML-based classification of heart-related diseases. The Journal of Supercomputing, 79(11), 11797-11826.
Publication 2023
Animals Aves Chiroptera Cloning Vectors Flowers Gametes Gamma Rays Gene Expression Insecta Movement Plants Pollen Pollination Simulate composite resin

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More about "Chiroptera"

Bats, the remarkable order of Chiroptera, are the only flying mammals on Earth, playing crucial roles in ecosystems as pollinators, seed dispersers, and insect predators.
These winged wonders, with their unique 'hand-wing' (cheir-ptera) adaptations, have captivated scientists for centuries.
Chiroptera research is vital for understanding the intricate biology, ecology, and conservation needs of these remarkable creatures.
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