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Apis

Apis is a genus of honey bees that are important pollinators and producers of honey.
These insects are found worldwide and are characterized by their distinctive black and yellow striped abdomens.
Apis species live in colonial hives and are known for their highly organized social structure and complex communication systems.
They play a vital role in the ecosystem by pollinating a wide variety of plants, contributing to the production of many agricultural crops.
Apis bees are also valued for their honey, which is a natural sweetener and has various medicinal properties.
The study of Apis bees provides insights into the evolution of social insects and their importance in maintaining a healthy environment.

Most cited protocols related to «Apis»

1
Integrating STRING Database with Cytoscape
The stringApp is implemented in Java utilizing the Cytoscape 3.6 App API. The app has two main functions: (1) to serve as a bridge between Cytoscape and the web service APIs of STRING and the related databases, and (2) to provide visualizations resembling the ones on the STRING web server as well as additional features like the side panel and enrichment visualizations. These two functions work together to bring much of the richness of the STRING website into Cytoscape, which then allows the network and all associated data to be analyzed with Cytoscape and its hundreds of other apps. For instance, the clusterMaker2 app4 can be very useful for clustering STRING networks, as shown in the use case below.
The bridge functionality of the stringApp uses several RESTful11 web service APIs to query the databases and retrieve networks. In case of protein and protein/compound queries, the app first resolves the entered query terms to the internal database identifiers using the standard STRING and STITCH API. For disease queries, it instead contacts the API of the DISEASES database twice, first to resolve the entered disease name to a disease identifier, and second to retrieve the list of proteins associated with the disease. For all three types of queries, stringApp provides the user with the ability to manually resolve any ambiguous names. The handling of PubMed queries was described in the previous section. Irrespective of the type of query, these steps result in a list of nodes, for which stringApp retrieves all node and edge data by calling the web service API of the dedicated PostgreSQL database. The latter API is also used to retrieve any node or edge data required when expanding an existing network, lowering the confidence cutoff, or adding additional nodes to a network.
The stringApp retrieves functional enrichment analysis results for a whole STRING network or a selected subset of it by sending a request to the STRING enrichment API. The results are stored and shown in a Cytoscape table called STRING Enrichment, which lists all enriched terms along with their gene counts, corresponding FDR values, and gene sets. Since the list of enriched terms can become very long, especially for large networks, the app allows the user to filter the enrichment results to show terms from any combination of six term categories as well as to eliminate redundant terms, which represent similar sets of genes.
The redundancy filtering takes the list of enriched terms sorted by FDR value and removes the terms that are too similar to any of the previous, better scoring terms that were not themselves removed (also referred to as the Hobohm 1 method12 (link)). The similarity between two terms is measured by the Jaccard index of the sets of genes annotated by the two terms. A term is added to the filtered list only if it has Jaccard similarity less than the user-specified redundancy cutoff to any other term already in the filtered list.
To retain the look and feel of STRING networks, the stringApp adds a new STRING Visual Style to the already existing set of Cytoscape styles. This style enables the glass ball effect and the optional visualization of the protein or compound structures within the nodes. These visual properties can be enabled or disabled by the user from the stringApp menu. The initial node colors are assigned arbitrarily by the app but can be easily substituted by a node color mapping of any node attribute. In addition to the node visual properties, the STRING style also includes a mapping of the interaction confidence scores to edge color and thickness.
Doncheva N.T., Morris J.H., Gorodkin J, & Jensen L.J. (2018). Cytoscape StringApp: Network Analysis and Visualization of Proteomics Data. Journal of proteome research, 18(2), 623-632.
Publication 2018
Apis CTSB protein, human Feelings Genes Proteins Strains
2
Genome-wide CRISPR Target Identification
Euchromatic regions of the dm3/BDGP release 5 Drosophila melanogaster genome were indexed as in Iseli et al. (2007) (link). PHP code was developed to (1) parse user-inputted DNA sequence to detect CRISPR targets on both strands, (2) execute fetchGWI (Iseli et al. 2007 (link)) to identify similar sequences elsewhere in the genome, (3) employ algorithms based on empirical rules and user-selected parameters to identify potential off-target cleavage sites, and (4) return CRISPR target sites ranked by specificity along with location information and a Gbrowse link for each potential off-target site. The following invertebrate genomes were processed identically: D. simulans (annotation DroSim1), D. yakuba (DroYak2), D. sechellia (DroSec1), D. virilis (DroVir3), two strains of Anopheles gambiae (AgamM1 and AgamS1), Aedes aegypti (AaegL1), Apis mellifera (apiMel3), Tribolium castaneum (TriCas2), and Caenorhabditis elegans (ce10). A detailed user manual is available at http://tools.flycrispr.molbio.wisc.edu/targetFinder/CRISPRTargetFinderManual.pdf.
Gratz S.J., Ukken F.P., Rubinstein C.D., Thiede G., Donohue L.K., Cummings A.M, & O’Connor-Giles K.M. (2014). Highly Specific and Efficient CRISPR/Cas9-Catalyzed Homology-Directed Repair in Drosophila. Genetics, 196(4), 961-971.
Publication 2014
Aedes Anopheles gambiae Apis Caenorhabditis elegans Clustered Regularly Interspaced Short Palindromic Repeats Cytokinesis Drosophila melanogaster Drosophila simulans Genome Invertebrates Strains Tribolium, monocots
3
Molecule Conversion and Standardization
The molecule inputted through the sketcher Marvin JS (version 16.4.18, 2016, www.chemaxon.com) are converted into SMILES by JChem Web Services (version 14.9.29, 2013, www.chemaxon.com) installed on one of our servers. This on-the-fly conversion allows seamless paste of SMILES in the input list. The user has the possibility to edit this list as a standard text, e.g. to modify SMILES or add a name to the molecule. Upon calculation submission by clicking the “Run” button, the SMILES of each molecule is canonicalised by OpenBabel (version 2.3.0, 2012, http://openbabel.org)9 (link) and processed individually. Several actions are performed through JChem Web Services APIs. First hydrogen atoms are added to the molecular structure, which is dearomatised (i.e. kekulised), neutralised and checked by the Standardizer API. Then a tridimensional conformation is generated though the StringMolExport function with the Clean3D option and stored in MOL2 format. Besides, a two-dimensional image created through the MolConverter API is displayed on demand when scrolling the output web page.
Daina A., Michielin O, & Zoete V. (2017). SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Scientific Reports, 7, 42717.
Publication 2017
Apis Hydrogen Molecular Structure Paste
4
Mutational Catalogs in Breast Cancer

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Publication 2013
Apis Chromosomes Dinucleoside Phosphates Exome Genes Genome Genome, Human INDEL Mutation Malignant Neoplasm of Breast Mutation Open Reading Frames Polynucleotides
5
Comprehensive Somatic Mutation Profiling
In all examined samples, normal DNA from the same individuals had been sequenced to establish the somatic origin of variants. Extensive filtering was performed to remove any residual germline mutations and technology specific sequencing artifacts prior to analyzing the data. Germline mutations were filtered out from the lists of reported mutations using the complete list of germline mutations from dbSNP60 , 1000 genomes project61 , NHLBI GO Exome Sequencing Project62 , and 69 Complete Genomics panel (http://www.completegenomics.com/public-data/69-Genomes/). Technology specific sequencing artifacts were filtered out by using panels of BAM files of (unmatched) normal tissues containing more than 120 normal genomes and 500 normal exomes. Any somatic mutation present in at least three well-mapping reads in at least two normal BAM files was discarded. The remaining somatic mutations were used for generating a mutational catalog for every sample. Prevalence of somatic mutations was estimated based on a haploid human genome after all filtering. Prevalence of somatic mutations in exomes was calculated based on the identified mutations in protein coding genes and assuming that an average exome has 30 megabases in protein coding genes with sufficient coverage. Prevalence of somatic mutations in whole genomes was calculated based on all identified mutations and assuming that an average whole genome has 2.8 gigabases with sufficient coverage.
The immediate 5′ and 3′ sequence context was extracted using the ENSEMBL Core APIs for human genome build GRCh37. Curated somatic mutations that originally mapped to an older version of the human genome were re-mapped using UCSC’s freely available lift genome annotations tool (any somatic mutations with ambiguous or missing mappings were discarded). Dinucleotide substitutions were identified when two substitutions were present in consecutive bases on the same chromosome (sequence context was ignored). The immediate 5′ and 3′ sequence content of all indels was examined and the ones present at mono/polynucleotide repeats or microhomologies were included in the analyzed mutational catalogs as their respective types. Strand bias catalogs were derived for each sample using only substitutions identified in the transcribed regions of well-annotated protein coding genes. Genomic regions of bidirectional transcription were excluded from the strand bias analysis.
Alexandrov L.B., Nik-Zainal S., Wedge D.C., Aparicio S.A., Behjati S., Biankin A.V., Bignell G.R., Bolli N., Borg A., Børresen-Dale A.L., Boyault S., Burkhardt B., Butler A.P., Caldas C., Davies H.R., Desmedt C., Eils R., Eyfjörd J.E., Foekens J.A., Greaves M., Hosoda F., Hutter B., Ilicic T., Imbeaud S., Imielinsk M., Jäger N., Jones D.T., Jones D., Knappskog S., Kool M., Lakhani S.R., López-Otín C., Martin S., Munshi N.C., Nakamura H., Northcott P.A., Pajic M., Papaemmanuil E., Paradiso A., Pearson J.V., Puente X.S., Raine K., Ramakrishna M., Richardson A.L., Richter J., Rosenstiel P., Schlesner M., Schumacher T.N., Span P.N., Teague J.W., Totoki Y., Tutt A.N., Valdés-Mas R., van Buuren M.M., van ’t Veer L., Vincent-Salomon A., Waddell N., Yates L.R., Zucman-Rossi J., Futreal P.A., McDermott U., Lichter P., Meyerson M., Grimmond S.M., Siebert R., Campo E., Shibata T., Pfister S.M., Campbell P.J, & Stratton M.R. (2013). Signatures of mutational processes in human cancer. Nature, 500(7463), 415-421.
Publication 2013
Apis Chromosomes Dinucleoside Phosphates Diploid Cell Exome Gene Products, Protein Genes Gene Therapy, Somatic Genome Genome, Human Germ-Line Mutation Homo sapiens INDEL Mutation Mutation Open Reading Frames Polynucleotides Tissues Transcription, Genetic

Most recents protocols related to «Apis»

1
Collecting Honey Bee Worker Samples
A single healthy and well-established honey bee colony, headed by a Carniolan Apis mellifera carnica queen, was used as a source for all worker samples of this study. Using sister bees originating from a single colony helps minimize variability in hive conditions and the genetic make-up of the workers. A total of eight capped worker brood frames ready to hatch were removed from this hive and placed in an incubator at 35 °C with 50–60% relative humidity. The following day, several thousand one-day-old sister bees were collected into a sterile plastic box for further use.
Alburaki M., Madella S, & Cook S.C. (2023). Non-optimal ambient temperatures aggravate insecticide toxicity and affect honey bees Apis mellifera L. gene regulation. Scientific Reports, 13, 3931.
Publication 2023
Apis Bees Honey Humidity Reading Frames Reproduction Sterility, Reproductive Urticaria Workers
2
Gut Microbiome Analysis of Maltese Honey Bees
Guts from Apis mellifera ruttneri were sampled from three different apiaries located in Malta during April 2016. Sampled honey bees, picked off the brood surface, were between 15–20 days old. The apiary in Għargħur (GH) had been established for more than 80 years as it belongs to a beekeeping family who still rear some of their colonies in terracotta hives, a practice unique to the Maltese Islands and other southern European countries (Supplementary Figure 1). This apiary is located in an urban location (35° 92′22.58″ N, 14° 45′39.58″ E) overlooking a small valley system. The apiary Campus Msida (CM) is located on the University of Malta grounds (35° 90′40.36″ N, 14° 48′33.56″ E) in Wied Għollieqa (Valley) and represents a recently established apiary with around 20 colonies of bees. The environment surrounding CM is best described as abundant agricultural land now dominated by carob trees (Ceratonia siliqua) and prickly pear (Opuntia ficus-indica). The apiary in Żejtun (ZT) is located at the outskirts of the village (35° 85′98.35″ N, 14° 53′74.71″ E), in an agricultural dwelling where occasional use of pesticides is practised. The main crops cultivated in the area include potatoes, tomatoes and courgettes. For bacteria isolation, a pool of 20 honey bee guts per sampling location were smashed and mixed. Following this, 0.5 mg of each pool was mixed with 4.5 ml of sterilized glycerol broth (meat extract 2.7 g/L, peptone 4.5 g/L, glycerol 100 ml/L) and 1:10 serial dilutions were carried out. For metagenomic analysis, 20 individual guts (both midgut and hindgut) were sampled from each apiary. All samples were immediately shipped on dry ice to the University of Bologna, Italy.
For comparative analysis, data obtained from Apis mellifera lineage C were used, samples of both subspecies ligustica and carnica. The ligustica data referred to samples collected in the Emilia-Romagna region (Italy) at Valsamoggia (Bologna, 44°29′45.3″N 11°06′10.4″E) and San Lazzaro di Savena (Bologna, 44°27′28.2″N 11°23′45.8″E) (Alberoni et al., 2021a (link),b (link); Baffoni et al., 2021 (link)), whereas the carnica data referred to samples previously collected in the South Tyrol region, Bolzano (46°22′47.7″N 11°14′14.6″E) (Baffoni et al., 2021 (link)). The full list of samples deriving from these studies can be found in Supplementary Table 1.
Gaggìa F., Jakobsen R.R., Alberoni D., Baffoni L., Cutajar S., Mifsud D., Nielsen D.S, & Di Gioia D. (2023). Environment or genetic isolation? An atypical intestinal microbiota in the Maltese honey bee Apis mellifera spp. ruttneri. Frontiers in Microbiology, 14, 1127717.
Publication 2023
Apis Bacteria carob Ceratonia Crop, Avian Dry Ice Europeans Glycerin Honey Intestines isolation Meat Metagenome Opuntia Opuntia ficus-indica Peptones Pesticides Solanum tuberosum Technique, Dilution Tomatoes Trees Urticaria
3
Honey bee stinging behavior experiments
Honey bee foragers were collected from various colonies of Apis mellifera ligustica, located in Rovereto, Italy from September 2019 to November 2019 and July 2020 to August 2020. The colonies were freely foraging and underwent routine beekeeping inspections during the entire period of the experiments. An equal number of bees from different colonies were included in the behavioural experiments. The bees were caught on sunny and cloudy days (but not on rainy days) in two rounds (around 10:30 AM or 14:00 PM).
Foragers were collected using a plastic container as they exited the hives, and they were brought back inside the lab and placed in an icebox. When the bees were motionless, they were placed in pairs into 50 ml centrifuge tubes modified into syringes. Two droplets of sucrose solution (50% sucrose water, vol/vol) were placed into the tube after the bees recovered completely. All honey bees were allowed to recover for at least 15 min (up to ~ 1 h for the last bees) before being tested in the set-up investigating stinging behaviour. If one or both bees showed signs of poor recovery when put in the setup (difficulty to hold upside down, disorientation and/or lethargic walk) the whole trial was excluded from further analysis. All the materials used to contain the bees were washed and cleaned with 80% ethanol, before the next use.
In total, 288 bees participated in the behavioural experiments, equally distributed between the 6 odour conditions (hence a sample size of 48 bees per group). This sample size was chosen based on previous studies19 (link),59 (link).
Scarano F., Deivarajan Suresh M., Tiraboschi E., Cabirol A., Nouvian M., Nowotny T, & Haase A. (2023). Geosmin suppresses defensive behaviour and elicits unusual neural responses in honey bees. Scientific Reports, 13, 3851.
Publication 2023
Apis Bees Disorientation Ethanol Honey Lanugo Lethargy Odors Rain Sucrose Syringes Urticaria
4
Comprehensive KED Gene Sequence Analysis
The nucleotide and amino acid sequences for tobacco (Nicotiana tabacum) KED [6 (link)] were used as a starting point for the initial search using BLASTN and BLASTP tools against the GenBank and OneKP database including non-redundant nucleotide and protein sequences, whole-genome shot gun, expressed sequence tags, high throughput genomic sequences, UniProtKB, transcriptome shotgun assembly proteins and protein data bank. Initial search using both the coding nucleotide sequences and the amino acid sequences identified 32 eudicots and at least one monocot (Elaeis guineensis). Subsequent searches were performed against the E. guineensis amino acid sequence through Liliopsida (monocotyledons) database to identify matching sequences of monocots. Likewise, using retrieved sequences to systematically search databases of the same orders and families of eudicotyledons yielded more species of possible matching sequences. Similar strategy was used to identified KED sequences from gymnosperms. Further searches were done sequentially by narrowing organism groups to find matches from more closely related species. However, it must be pointed out that the database search was aimed at surveying broadly the possible taxonomic presence of the KED gene and the retrieved sequences are not by no means an exhaustive outcome due to genomic sequence availability and the annotation quality of the public databases.
To search for possible KED-rich sequences in charophytes, bacteria and animals, KED protein and nucleotide sequences from plants were first repeatedly blasted through each of the intended organism groups in the databases. Then each match was further examined by retrieving its sequence from the database. Translation tool was used to generate open reading frames, followed by amino acid composition analysis, specifically for K+E+D content, to score the putative KED candidates. Once a KED sequence was identified from one taxon group (for example, charophyte), this sequence was used to search the entire available entries from this group. This way, sequences predicting KED-rich open reading frames in genomes of several charophyte, bacterial and animal species were identified.
During the course of searching animal KED candidates, a 6,229-amino acid microtubule-associated protein futsch from honeybee (Apis cerana) was found to contain an internal KED-rich region, whereas its N- and C-terminus portions have normal K, E and D contents. To illustrate examples of the presence of KED sequences in animal species, this 750-amino acid internal KED-rich region was arbitrarily taken out for demonstration in this study.
All retrieved sequences of possible matches were manually reviewed and verified for proper open reading frames and translated sequences. Wherever applicable, both genomic sequences and mRNA sequences were matched to verify the correct coding sequences. The full-length, translated sequences with considerable sequence identity and a high percentage of KED (K+E+D% greater than 30%) were designated as a candidate match.
Only partial KED sequences were available for two plants: cedar (Cryptomeria japonica, a gymnosperm; without C-terminus) and barley (Hordeum vulgare, a monocot, angiosperm; without N-terminus). However, they both still possessed the conserved domain (see “Results” below), therefore were included in sequence comparison analysis. But because their KED protein lengths were unknown and would distort the analysis parameters, they were excluded from the dataset for phylogenetic analysis described below.
Zhang X.H., Swait D., Jin X.L., Vichyavichien P., Nifakos N., Kaplan N., Raymond L, & Harlin J.M. (2023). Evolutionary analysis of KED-rich proteins in plants. PLOS ONE, 18(3), e0279772.
Publication 2023
Amino Acids Amino Acid Sequence Animals Apis Bacteria Base Sequence Cerana Charophyceae Cryptomeria Cycadopsida Exons Expressed Sequence Tags Genes Genome High-Throughput Nucleotide Sequencing Hordeum Hordeum vulgare Magnoliopsida Microtubule-Associated Proteins Nicotiana Nicotiana tabacum Nucleotides Open Reading Frames Plants Proteins RNA, Messenger Sequence Analysis Transcriptome
5
Quantifying Chemical Exposure Risks via QSTR Models
The data used to create the QSTR models
include predictors that have an impact on the likelihood that an EoL
activity will occur for a chemical, as illustrated in Figure 1 (e.g., environmental policy).
The main data source to develop the QSTR models is the PRTR_transfers
database built by Hernandez-Betancur et al.22 Data from siloed and publicly accessible database systems belonging
to OECD member nations were standardized and harmonized and stored
in this database.41 This database’s
information is chemical-centric, focusing on individual chemicals
rather than the total quantity of hazardous or non-hazardous wastes.
The aforementioned is appropriate for evaluating chemical exposure
and risk, as toxicologists usually evaluate chemicals separately instead
of mixtures. This database provides LCI facility-level data that include
off-site transfers from facilities and businesses manufacturing the
chemical, processing it as a reactant or intermediate, incorporating
it into formulations, mixtures, or reaction products, incorporating
it into articles, and using it industrially. The PRTR_transfers database
has the information to describe the generator industry sectors, EoL
activities or off-site transfer scenarios, the quantity of chemicals
in the off-site transferred flows (in kg/year), and the chemical substances.
The database contains 72 industry sectors, 643 chemical substances,
and a total of 3,116,211 records. The PRTR_transfers database contains
10 EoL activities or off-site transfer classes: surface impoundment,
sewerage, destruction, energy recovery, landfill, other disposal,
other treatment, recycling, storage, and underground injection.
Data associated with the chemical unit price and
the gross value
added by an industry sector are collected to represent the economic
bottom line of sustainability since the economic sustainability dimension
may affect the occurrence of an EoL activity for a chemical.21 These economic considerations are crucial because
a company can be willing to recycle a high-value chemical but destroy
a low-value one. Moreover, a business with high income or sales, which
are related to the gross value added,42 may afford to operate an EoL activity to handle or abate hazardous
chemicals and/or wastes.43 Using the CAS
numbers for the chemicals, their unit prices (in USD/g) are retrieved
automatically from e-commerce sources like SciFinder, Amazon, Alibaba,
and Fisher Scientific. Due to the massive inventory of chemical suppliers
around the world, these systems, particularly SciFinder, are utilized
as search engines, making it easier to find chemical prices.
The gross value added by the industry sector is obtained from the
OECD statistics,44 considering the year
and country of the reporting record. If the gross value added by industry
and country is not found for a particular year, it is imputed by using
linear extrapolation and country data. In addition, around 400 molecular
descriptors are obtained from RDKit cheminformatics software via its
Python API.45 However, before obtaining
the descriptors, each chemical’s SMILES is required. SMILES
is a notation to encode information on the molecular structure to
be understood by a computer.46 Through
automation, the SMILESs are obtained from the APIs of the US National
Library of Medicine and PubChem.
The environmental context that
may affect the probability of an
EoL activity occurrence is represented by the OECD environmental policy
stringency index obtained from OECD statistics.44 This index is a country-specific and internationally comparable
measure of the stringency of environmental policy.47 In cases where the index is not found for a country, linear
extrapolation, and the country data are used for item imputation.
Linear extrapolation has drawbacks, including the inability to take
causal elements into account in the calculus-based observations and
the failure to take into consideration qualitative values that can
affect future values.48 Gross value added
and environmental stringency indexes reported over time can help to
expand the QSTR model application domain, particularly for countries
that are not part of the PRTR_transfers database, but their industry
sector economy and environmental regulation could be represented by
any reporting country and/or sector in previous years.
Hernandez-Betancur J.D., Ruiz-Mercado G.J, & Martin M. (2023). Predicting Chemical End-of-Life Scenarios Using Structure-Based Classification Models. ACS Sustainable Chemistry & Engineering, 11(9), 3594-3602.
Publication 2023
Abate Apis Calculi Hazardous Waste Molecular Structure Pharmaceutical Preparations Recycling

Top products related to «Apis»

1
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2
Dimethyl sulfoxide (dmso) by Merck Group
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Ethanol is a clear, colorless liquid chemical compound commonly used in laboratory settings. It is a key component in various scientific applications, serving as a solvent, disinfectant, and fuel source. Ethanol has a molecular formula of C2H6O and a range of industrial and research uses.
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Melittin is a peptide found in the venom of honeybees. It is a key component of the laboratory equipment used in various research applications.
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9
Apis mellifera by Merck Group
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Apis mellifera is a laboratory equipment product. It serves as a device for the cultivation and study of honey bees, which are important pollinators. The core function of Apis mellifera is to provide a controlled environment for the observation and maintenance of honey bee colonies.
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Phosphoric acid is a chemical compound with the formula H3PO4. It is a colorless, odorless, and viscous liquid that is commonly used in various industrial and laboratory applications.

More about "Apis"

Honey bees, also known as Apis, are a genus of important pollinators and honey producers found worldwide.
These iconic insects are characterized by their distinctive black and yellow striped abdomens.
Apis species live in highly organized colonial hives and possess complex communication systems, playing a vital role in maintaining a healthy ecosystem by pollinating a diverse array of plants and contributing to agricultural crop production.
The study of Apis bees provides valuable insights into the evolution of social insects and their ecological significance.
Researchers utilize various tools and techniques to investigate these fascinating creatures, including the DNeasy Blood and Tissue Kit for DNA extraction, DMSO and Ethanol for sample preservation, and RNAlater for RNA stabilization.
Analytical methods such as Acetonitrile-based chromatography and spectroscopic techniques like the Milli-Q system are employed to analyze the chemical composition of bee products, including the bioactive compound Melittin and the antioxidant Quercetin.
Understanding the biology and behavior of Apis mellifera, the European honey bee, is crucial for developing effective conservation strategies and maintaining the delicate balance of our ecosystems.
By harnessing the power of AI-driven platforms like PubCompare.ai, researchers can optimize their protocols, locate relevant literature, and drive their investigations forward, ultimately enhancing our knowledge of these remarkable pollinators and their role in sustaining a healthy, thriving world.