The five main steps of the MITE-Hunter pipeline. Gray bars are genomic sequences, black and red triangles are TSDs and TIRs, respectively, blue bars are predicted TEs, white bars are homolog sequences, dashed lines are gaps and yellow bars are sequences that are similar to each other but not to those represented by green bars (and vice versa). (
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Living Beings
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Eukaryote
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Mites
Mites
Mites are small, eight-legged arthropods that belong to the class Arachnida.
They are found in a wide variety of habitats, including soil, plants, and even on the skin and in the homes of humans and animals.
Mites play important roles in ecosystems, acting as decomposers, parasites, and prey for other organisms.
Some species of mites can cause diseases in humans, animals, and plants, making them an important area of research and study.
Understanding the biology, ecology, and behavior of mites is crucial for developing effective strategies for managing and controlling mite infestations and mitigating their impacts on human and animal health, as well as agricultural production.
Reserach into mite biology and ecology can help inform the development of new treatments, prevention methods, and management strategies to address the challenges posed by these small but significant creatures.
They are found in a wide variety of habitats, including soil, plants, and even on the skin and in the homes of humans and animals.
Mites play important roles in ecosystems, acting as decomposers, parasites, and prey for other organisms.
Some species of mites can cause diseases in humans, animals, and plants, making them an important area of research and study.
Understanding the biology, ecology, and behavior of mites is crucial for developing effective strategies for managing and controlling mite infestations and mitigating their impacts on human and animal health, as well as agricultural production.
Reserach into mite biology and ecology can help inform the development of new treatments, prevention methods, and management strategies to address the challenges posed by these small but significant creatures.
Most cited protocols related to «Mites»
MITE-Hunter is a UNIX program pipeline composed mainly of Perl scripts. Given genomic sequences as the input data, MITE-Hunter identifies Class 2 non-autonomous TEs and produces outputs of consensus sequences classified into families. MITE-Hunter can use multiple processers (default 5 CPUs). The MITE-Hunter pipeline has five main steps that are summarized in Figure 1 : (i) identify TE candidates through a structure-based approach, (ii) identify and filter false-positives using an approach based on the pairwise sequence alignment (PSA), (iii) generate exemplars, (iv) identify and filter false-positives using an approach based on the multiple sequence alignment (MSA), generate consensus sequences and predict TSDs and (v) group consensus sequences into families. Details of each step are presented in the results section.
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Consensus Sequence
Genome
Homologous Sequences
Mites
Sequence Alignment
Strains
Tay-Sachs Disease
For comprehensive annotation of transposable elements, we designed a structural identification pipeline incorporating several tools, including LTRharvest40 (link), LTRdigest41 (link), SINE-Finder42 (link), MGEScan-non-LTR43 (link), MITE-hunter44 (link), HelitronScanner45 (link), and others (details in Supplementary Information ). The scripts, parameters, and intermediate files of each transposable element superfamily are available at https://github.com/mcstitzer/maize_v4_TE_annotation .
The MAKER-P pipeline was used to annotate protein-coding genes46 (link), integrating ab initio prediction with publicly available evidence from full-length cDNA47 (link), de novo assembled transcripts from short-read mRNA sequencing (mRNA-seq)48 (link), isoform-sequencing (Iso-Seq) full-length transcripts14 (link), and proteins from other species. The gene models were filtered to remove transposons and low-confidence predictions. Additional alternative transcript isoforms were obtained from the Iso-Seq data. Further details on annotations, core promoter analysis, and comparative phylogenomics are described inSupplementary Information .
The MAKER-P pipeline was used to annotate protein-coding genes46 (link), integrating ab initio prediction with publicly available evidence from full-length cDNA47 (link), de novo assembled transcripts from short-read mRNA sequencing (mRNA-seq)48 (link), isoform-sequencing (Iso-Seq) full-length transcripts14 (link), and proteins from other species. The gene models were filtered to remove transposons and low-confidence predictions. Additional alternative transcript isoforms were obtained from the Iso-Seq data. Further details on annotations, core promoter analysis, and comparative phylogenomics are described in
DNA Transposable Elements
Jumping Genes
Maize
Mites
Protein Isoforms
Proteins
RNA, Messenger
Short Interspersed Nucleotide Elements
Repbase Update is a well curated database of transposable elements (TEs) and other types of repeats in eukaryotic genomes. Sequences from Oryza Sativa were downloaded from the web page in EMBL format [27 (link)]. Of 2734 elements, 569 were filtered using a python script. Only elements labeled as MITEs or Class II DNA non-autonomous TEs that were shorter than 801 nt were kept. This database is usually used as a reference when comparing transposable elements detection programs. In this case we used Repbase Update to evaluate accuracy of the compared programs. The Triticeae TEs database TREP database was used to classify MITE families in wheat [28 ].
DNA Transposable Elements
Eukaryota
Genome
Mites
Oryza sativa
Python
Triticum aestivum
Custom repeat libraries were individually created for I. trifida and I. triloba by combining the putative repeat libraries predicted from MITE-Hunter46 (link) (v2011) and RepeatModeler (http://www.repeatmasker.org/ ; v1.0.8). Protein-coding genes were removed from the repeat library using ProtExcluder47 (link). The Repbase (v20150807) repeats for green plants (Viridiplantae) were then added to each library to create a final custom repeat library for each species. The pseudomolecules for I. trifida and I. triloba were repeat masked with the respective repeat library using RepeatMasker (http://www.repeatmasker.org/ ; v4.0.6).
For gene prediction, AUGUSTUS48 (link) (v3.1) was trained on the soft-masked assemblies using the leaf RNA-Seq alignments. Gene models were predicted with AUGUSTUS using the hard-masked assemblies and refined with PASA2 (v2.0.2) (ref. 49 (link)) using the genome-guided transcript assemblies from each tissue as transcript evidence (Supplementary Method1 ). Two rounds of gene prediction comparison were performed and gene models PASA identified as merged, but unable to split, were manually inspected and split as necessary. A third round of gene prediction comparison was performed to refine the structure of the manually curated models. The final output from PASA2 is the working set of gene models. Expression abundances for each gene model were determined based on the RNA-Seq read alignments using Cufflinks2 (ref. 50 (link)) (v.2.2.1). A high-confidence gene model set was constructed from the working gene model set by removing partial gene models and gene models with an internal stop codon, a hit to a transposable element, or an FPKM of 0 across the RNA-Seq libraries used for the annotation.
Functional annotations for the high-confidence gene models were assigned by comparing their protein sequences against the Arabidopsis proteome (TAIR 10), Pfam (v29), and the Swiss-Prot databases. Proteins that only matched a hypothetical Arabidopsis gene model and had no matches in the other databases were annotated as conserved hypothetical, while proteins with no matches in any of the databases were annotated as hypothetical.
For gene prediction, AUGUSTUS48 (link) (v3.1) was trained on the soft-masked assemblies using the leaf RNA-Seq alignments. Gene models were predicted with AUGUSTUS using the hard-masked assemblies and refined with PASA2 (v2.0.2) (ref. 49 (link)) using the genome-guided transcript assemblies from each tissue as transcript evidence (Supplementary Method
Functional annotations for the high-confidence gene models were assigned by comparing their protein sequences against the Arabidopsis proteome (TAIR 10), Pfam (v29), and the Swiss-Prot databases. Proteins that only matched a hypothetical Arabidopsis gene model and had no matches in the other databases were annotated as conserved hypothetical, while proteins with no matches in any of the databases were annotated as hypothetical.
Amino Acid Sequence
Arabidopsis
Codon, Terminator
DNA Library
DNA Transposable Elements
Gene Annotation
Gene Expression
Gene Products, Protein
Genes
Genes, vif
Genome
Green Plants
Mites
Plant Leaves
Plants
Proteins
Proteome
RNA-Seq
Tissues
In addition to the genome sequence, 15 publicly available BAC sequences for common bean were also downloaded from GenBank for a total of 2.2 Mb of sequence, including from accessions DQ205649 , DQ323045 , FJ817289 –FJ817291 and GU215957 –GU215966 . Transposon annotation was conducted using different methods according to the sequence structures and transposases of various transposons. To annotate LTR retrotransposons, the genome sequence was screened with LTR_Finder35 (link) using default parameters, except that we set a 50-bp minimum LTR length and 50-bp minimum distance between LTRs. All predicted LTR retrotransposons were manually inspected to eliminate incorrectly predicted sequences, including tandem repeats, nested transposons, incomplete DNA transposons and other sequences. The internal sequences of LTR retrotransposons were used to perform BLASTX and/or BLASTP searches to define superfamilies: Ty1-copia, Ty3-gypsy or other. LINEs (long interspersed elements) were predicted on the basis of the non-LTR retrotransposase and polyA sequences. SINEs (short interspersed elements) were annotated with the polyA structure feature and combined with BLAST searches. To find DNA transposons, conserved domains for transposases from different reported superfamilies were used as queries to search the common bean genome. The matching sequences and flanking sequence (10 kb on each side) were extracted to conduct BLASTN searches to identify complete DNA transposons by terminal inverted repeats (TIRs) and target size duplication (TSD). Furthermore, MITEs-Hunter software36 (link) was also used to identify DNA elements. The annotated transposons and two reported LTR retrotransposons, pva1-118d24-re-5 (FJ402927 ) and Tpv2-6 (AJ005762 ), were combined and used as a transposon library to screen the genome using RepeatMasker with default settings except that we used the 'nolow' option to avoid masking low-complexity DNA or simple repeats. Transposons were summarized according to names, subclasses and classes, and overlapping regions in the RepeatMasker output file were counted once (Supplementary Table 9 ).
To estimate the insertion times of LTR retrotransposons, the 5′ and 3′ LTRs for each full-length LTR retroelement were aligned and used to calculate the nucleotide divergence rate with the Kimura-2 parameter using MEGA 4. The insertion date (T) was estimated with the formula T = K/2r, where K is the average number of substitutions per aligned site and r is an average substitution rate. We used the average substitution rate of 1.3 × 10−8 substitutions per synonymous site per year55 (link) to calibrate the insertion times.
To estimate the insertion times of LTR retrotransposons, the 5′ and 3′ LTRs for each full-length LTR retroelement were aligned and used to calculate the nucleotide divergence rate with the Kimura-2 parameter using MEGA 4. The insertion date (T) was estimated with the formula T = K/2r, where K is the average number of substitutions per aligned site and r is an average substitution rate. We used the average substitution rate of 1.3 × 10−8 substitutions per synonymous site per year55 (link) to calibrate the insertion times.
DNA Library
DNA Transposons
Genome
Gypsies
Inverted Terminal Repeat
Jumping Genes
Long Interspersed DNA Sequence Elements
Mites
Nucleotides
Poly A
Retroelements
Retrotransposons
Short Interspersed Nucleotide Elements
Tandem Repeat Sequences
Transposase
Most recents protocols related to «Mites»
The PiRATE pipeline was used as in the original publication (Berthelier et al., 2018 (link)), including the following steps: 1) Contigs representing repetitive sequences were identified from the assembled contigs using similarity-based, structure-based, and repetitiveness-based approaches. The similarity-based detection programs included RepeatMasker v-4.1.0 (http://repeatmasker.org/RepeatMasker/ , using Repbase20.05_REPET.embl.tar.gz as the library instead) and TE-HMMER (Eddy, 2011 (link)). The structural-based detection programs included LTRharvest (Ellinghaus et al., 2008 (link)), MGEScan non-LTR (Rho and Tang, 2009 (link)), HelSearch (Yang et al., 2009 (link)), MITE-Hunter (Han and Wessler, 2010 (link)), and SINE-finder (Wenke et al., 2011 (link)). The repetitiveness-based detection programs included TEdenovo (Flutre et al., 2011 (link)) and RepeatScout (Price et al., 2005 (link)). 2) Repeat consensus sequences (e.g., representing multiple subfamilies within a TE family) were also identified from the cleaned, filtered, and unassembled reads with dnaPipeTE (Goubert et al., 2015 (link)) and RepeatModeler (http://www.repeatmasker.org/RepeatModeler/ ). 3) Contigs identified by each individual program in steps 1 and 2, above, were filtered to remove those <100 bp in length and clustered with CD-HIT-est (Li and Godzik, 2006 (link)) to reduce redundancy (100% sequence identity cutoff). This yielded a total of 155,999 contigs. 4) All 155,999 contigs were then clustered together with CD-HIT-est (100% sequence identity cutoff), retaining the longest contig and recording the program that classified it. 46,090 contigs were filtered out at this step. 5) The remaining 109,909 repeat contigs were annotated as TEs to the levels of order and superfamily in Wicker’s hierarchical classification system (Wicker et al., 2007 (link)), modified to include several recently discovered TE superfamilies using PASTEC (Hoede et al., 2014 (link)), and checked manually to filter chimeric contigs and those annotated with conflicting evidence (Supplementary File S2 ). 6) All classified repeats (“known TEs” hereafter), along with the unclassified repeats (“unknown repeats” hereafter) and putative multi-copy host genes, were combined to produce a Ranodon-derived repeat library. 7) For each superfamily, we collapsed the contigs to 95% and 80% sequence identity using CD-HIT-est to provide an overall view of within-superfamily diversity; 80% is the sequence identity threshold used to define TE families (Wicker et al., 2007 (link)).
BP 100
Chimera
Consensus Sequence
DNA Library
Mites
Multiple Birth Offspring
Repetitive Region
Short Interspersed Nucleotide Elements
Total RNA was extracted separately from testis (n = 4) and ovary (n = 4) tissues using TRIzol (Invitrogen). For each sample, RNA quality and concentration were assessed using agarose gel electrophoresis, a NanoPhotometer spectrophotometer (Implen, CA), a Qubit 2.0 Fluorometer (ThermoFisher Scientific), and an Agilent BioAnalyzer 2,100 system (Agilent Technologies, CA), requiring an RNA integrity number (RIN) of 8.5 or higher; one ovary sample failed to meet these quality standards and was excluded from downstream analyses. Sequencing libraries were generated using the NEBNext Ultra RNA Library Prep Kit for Illumina following the manufacturer’s protocol. After cluster generation of the index-coded samples, the library was sequenced on one lane of an Illumina Hiseq 4,000 platform (PE 150). Transcriptome sequences were filtered using Trimmomatic-0.39 with default parameters (Bolger et al., 2014 (link)). 30, 848, 170 to 39, 695, 323 reads were retained for each testis or ovary sample, and in total, 290, 925, 984 reads remained, with a total length of 42, 385, 060,050 bp. Remaining reads of all testis and ovary samples were combined and assembled using Trinity 2.12.0 (Haas et al., 2013 (link)), yielding 573,144 contigs (i.e., putative assembled transcripts). Contigs were clustered using CD-hit-est (95% identity). Completeness of this final de novo transcriptome assembly were assessed using the BUSCO pipeline (Simao et al., 2015 (link)).
Expression levels of contigs in each sample were measured with Salmon (Patro et al., 2017 (link)), and contigs with no raw counts were removed. To annotate the remaining contigs containing autonomous TEs, BLASTp and BLASTx were used against Repbase with an E-value cutoff of 1E-5 and 1E-10, respectively. The aligned length coverage was set to exceed 80% of the queried transcriptome contigs. To annotate contigs containing non-autonomous TEs, RepeatMasker was used with our Ranodon-derived genomic repeat library of non-autonomous TEs (LARD-, TRIM-, MITE-, and SINE-annotated contigs) and the requirement that the transcriptome/genomic contig overlap was >80 bp long, >80% identical in sequence, and covered >80% of the length of the genomic contig. Contigs annotated as conflicting autonomous and non-autonomous TEs were filtered out.
To identify contigs that contained endogenous R. sibiricus genes, the Trinotate annotation suite (Bryant et al., 2017 (link)) was used with an E-value cutoff of 1E-5 for both BLASTx and BLASTp against the Uniport database, and 1E-5 for HMMER against the Pfam database (Wheeler and Eddy, 2013 (link)). To identify contigs that contained both a TE and an endogenous gene (i.e., putative cases where a TE and a gene were co-transcribed on a single transcript), all contigs that were annotated both by Repbase and Trinotate were examined, and the ones annotated by Trinotate to contain a TE-encoded protein (i.e., the contigs where Repbase and Trinotate annotations were in agreement) were not further considered. The remaining contigs annotated by Trinotate to contain a non-TE gene (i.e., an endogenous Ranodon gene) and also annotated either by Repbase to include a TE-encoded protein or by blastn to include a non-autonomous TE were filtered out for the expression analysis.
Expression levels of contigs in each sample were measured with Salmon (Patro et al., 2017 (link)), and contigs with no raw counts were removed. To annotate the remaining contigs containing autonomous TEs, BLASTp and BLASTx were used against Repbase with an E-value cutoff of 1E-5 and 1E-10, respectively. The aligned length coverage was set to exceed 80% of the queried transcriptome contigs. To annotate contigs containing non-autonomous TEs, RepeatMasker was used with our Ranodon-derived genomic repeat library of non-autonomous TEs (LARD-, TRIM-, MITE-, and SINE-annotated contigs) and the requirement that the transcriptome/genomic contig overlap was >80 bp long, >80% identical in sequence, and covered >80% of the length of the genomic contig. Contigs annotated as conflicting autonomous and non-autonomous TEs were filtered out.
To identify contigs that contained endogenous R. sibiricus genes, the Trinotate annotation suite (Bryant et al., 2017 (link)) was used with an E-value cutoff of 1E-5 for both BLASTx and BLASTp against the Uniport database, and 1E-5 for HMMER against the Pfam database (Wheeler and Eddy, 2013 (link)). To identify contigs that contained both a TE and an endogenous gene (i.e., putative cases where a TE and a gene were co-transcribed on a single transcript), all contigs that were annotated both by Repbase and Trinotate were examined, and the ones annotated by Trinotate to contain a TE-encoded protein (i.e., the contigs where Repbase and Trinotate annotations were in agreement) were not further considered. The remaining contigs annotated by Trinotate to contain a non-TE gene (i.e., an endogenous Ranodon gene) and also annotated either by Repbase to include a TE-encoded protein or by blastn to include a non-autonomous TE were filtered out for the expression analysis.
DNA Library
Electrophoresis, Agar Gel
Genes
Genome
Genomic Library
Mites
Ovary
Proteins
Salmo salar
Short Interspersed Nucleotide Elements
Synapsin I
Testis
Tissues
TNFRSF25 protein, human
Transcriptome
trizol
Uniport
Crusted or profuse scabies were considered as “severe scabies”. Scabies diagnosis was performed at 2 different levels: (i) confirmed diagnosis; or (ii) suspected clinical diagnosis. A confirmed diagnosis was a compatible clinical presentation (see below) associated with at least 1 positive investigation (skin scraping, dermoscopic, or histological). Cases without paraclinical confirmation were discussed for inclusion with study investigators expert in the disease (MA, CG, CB) after reviewing the records and the images. Common scabies cases, doubtful cases, or cases where the records were uninterpretable or insufficient were excluded.
For the clinical presentation, the patient’s body was divided into 9 parts. Scabies was considered profuse when skin lesions extended to the whole body or there was a diffuse eruption involving the trunk, the back, the face, or the scalp. If ≥ 1 hyperkeratotic lesion(s) were reported, crusted scabies was diagnosed. A positive paraclinical examination was a skin scraping demonstrating Sarcoptes scabiei mites, eggs or faecal pellets on microscopy, a dermoscopic examination showing the delta sign, or a skin biopsy with mites, eggs or faecal pellets visible in the stratum corneum.
For the clinical presentation, the patient’s body was divided into 9 parts. Scabies was considered profuse when skin lesions extended to the whole body or there was a diffuse eruption involving the trunk, the back, the face, or the scalp. If ≥ 1 hyperkeratotic lesion(s) were reported, crusted scabies was diagnosed. A positive paraclinical examination was a skin scraping demonstrating Sarcoptes scabiei mites, eggs or faecal pellets on microscopy, a dermoscopic examination showing the delta sign, or a skin biopsy with mites, eggs or faecal pellets visible in the stratum corneum.
Biopsy
Dermoscopy
Eggs
Exanthema
Face
Feces
Human Body
Microscopy
Mites
Patients
Pellets, Drug
Sarcoptes scabiei
Scabies
Scalp
Skin
We assessed the efficacy of permethrin by estimating blow fly (Protocalliphora sialia) load in each control and treated nest. Following past work (De Simone et al., 2018 ; Grab et al., 2019 (link)), we collected all nests in sealed bags after fledging (19–21 days post‐hatch). Nests were then dissected in the laboratory, by one observer (SZ) blind to the experimental treatment group, to count blow fly larvae and pupae. Permethrin effectively reduced ectoparasite load (zero‐inflated negative binomial regression: p = .003), where blow flies were found in 0% of permethrin‐treated nests and 43.8% of water‐treated nests (9.5 ± 4.27 per nest across all control nests; 21.71 ± 7.76 per nest across control nests with blow flies; minimum = 1; maximum = 54). Note that feather mites and lice were likely present in small numbers but were not quantified, although we did note several random observations of lice on nestlings. Therefore, any effects of permethrin on morphology and telomere length can only be attributed to blow fly reduction, although it is possible that removal of other ectoparasites also contributed. In addition, our estimates do not distinguish sibling variation in ectoparasite exposure and therefore, only estimates a nest‐average exposure for our nestlings of interest.
Anoplura
Calliphoridae
Cocaine
Feathers
Maggots
Mites
Permethrin
Pupa
Telomere
Therapies, Investigational
Visually Impaired Persons
Tree swallows are obligate secondary cavity nesters that host a wide variety of ectoparasites, including mites, lice, fleas, and flies (Figure 1 ). These ectoparasites feed on blood, skin, and feathers (Janovy et al., 1997 ; Rendell & Verbeek, 1996 ) and can negatively impact offspring physiology, immune function, and survival (López‐Arrabé et al., 2015 (link); Martínez‐de La Puente et al., 2011 (link); Merino & Potti, 1995 ; Saino et al., 1998 ). Blow flies (Protocalliphora) have been found to infest 65.9% of tree swallow nests in northeastern US (Roby et al., 1992 ), with loads ranging from 4 to 54 parasites per nest (Grab et al., 2019 (link)). Previous work shows that blow flies feed on nestling blood and can cause anemia, hyperglycemia, and increased metabolic rates in avian hosts (De Simone et al., 2018 ; Grab et al., 2019 (link); Pryor & Casto, 2015 (link); Sun et al., 2020 (link)). One broad‐spectrum insecticide commonly used to remove ectoparasites is permethrin, which attacks the nervous system of larval and adult insects (Edwards, 2006 ). Permethrin treatment is effective against blow flies in nests of tree swallows (De Simone et al., 2018 ; Grab et al., 2019 (link)) and other bird species (Bulgarella et al., 2020 ) and also decreases the abundance of other ectoparasites like fleas and mites (Harriman et al., 2014 ; Pap et al., 2005 ; Pryor & Casto, 2017 (link)).
Any nest with a known hatching day was selected for our experiment, and then randomly assigned to one of two treatment groups, one in which ectoparasites were eliminated via the application of the insecticide permethrin (Permectrin II ©, diluted to 1% with distilled water), the other in which nests were treated with water as a control. In both treatment groups, nests were sprayed on day 0 and again on day 4. To do this, nestlings were temporarily removed from the nest, the bottom and sides of the nest were sprayed thoroughly (to minimize direct contact with nestlings), and the nestlings were returned once the nest had completely dried approximately 5 min later. To the extent possible, permethrin‐ and water‐treated nests were paired by hatch date to avoid the confounding effects that date has on many aspects of tree swallow reproduction (Winkler et al., 2020 ). The final number of nests in our study (n = 16 control, n = 16 insecticide) was less than the initial number sprayed due to brood loss.
Any nest with a known hatching day was selected for our experiment, and then randomly assigned to one of two treatment groups, one in which ectoparasites were eliminated via the application of the insecticide permethrin (Permectrin II ©, diluted to 1% with distilled water), the other in which nests were treated with water as a control. In both treatment groups, nests were sprayed on day 0 and again on day 4. To do this, nestlings were temporarily removed from the nest, the bottom and sides of the nest were sprayed thoroughly (to minimize direct contact with nestlings), and the nestlings were returned once the nest had completely dried approximately 5 min later. To the extent possible, permethrin‐ and water‐treated nests were paired by hatch date to avoid the confounding effects that date has on many aspects of tree swallow reproduction (Winkler et al., 2020 ). The final number of nests in our study (n = 16 control, n = 16 insecticide) was less than the initial number sprayed due to brood loss.
Adult
Anemia
Anoplura
Aves
BLOOD
Calliphoridae
Dental Caries
Diptera
Feathers
Fleas
Hyperglycemia
Immune System Processes
Insecta
Insecticides
Larva
Mites
Parasites
Permethrin
physiology
Skin
Swallows
Systems, Nervous
Trees
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More about "Mites"
Arachnids, Arthropods, Decomposers, Parasites, Zoonotic Diseases, Infestations, Mite Biology, Mite Ecology, Mite Behavior, Mite Research, TRIzol Reagent, DNeasy Blood and Tissue Kit, ImmunoCAP, RNeasy Mini Kit, Agilent 2100 Bioanalyzer, QIAamp DNA Mini Kit, pGEM-T Easy Vector, HiSeq 2000, Axioplan 2 Microscope, AxioCam Color HRc CCD Camera 412-312.
Mites are small, eight-legged arthropods that belong to the class Arachnida.
They can be found in a wide variety of habitats, including soil, plants, and even on the skin and in the homes of humans and animals.
These tiny creatures play important roles in ecosystems, acting as decomposers, parasites, and prey for other organisms.
Unfortunately, some species of mites can cause diseases in humans, animals, and plants, making them an important area of research and study.
Understanding the biology, ecology, and behavoir of mites is crucial for developing effective strategies to manage and control mite infestations and mitigate their impacts on human and animal health, as well as agricultural production.
Researchers can leverage tools like TRIzol reagent, DNeasy Blood and Tissue Kit, ImmunoCAP, RNeasy Mini Kit, Agilent 2100 Bioanalyzer, QIAamp DNA Mini Kit, pGEM-T Easy Vector, HiSeq 2000, Axioplan 2 Microscope, and AxioCam Color HRc CCD Camera 412-312 to study mite biology and ecology, and inform the development of new treatments, prevention methods, and management strategies to address the challenges posed by these small but significant creatures.
Mites are small, eight-legged arthropods that belong to the class Arachnida.
They can be found in a wide variety of habitats, including soil, plants, and even on the skin and in the homes of humans and animals.
These tiny creatures play important roles in ecosystems, acting as decomposers, parasites, and prey for other organisms.
Unfortunately, some species of mites can cause diseases in humans, animals, and plants, making them an important area of research and study.
Understanding the biology, ecology, and behavoir of mites is crucial for developing effective strategies to manage and control mite infestations and mitigate their impacts on human and animal health, as well as agricultural production.
Researchers can leverage tools like TRIzol reagent, DNeasy Blood and Tissue Kit, ImmunoCAP, RNeasy Mini Kit, Agilent 2100 Bioanalyzer, QIAamp DNA Mini Kit, pGEM-T Easy Vector, HiSeq 2000, Axioplan 2 Microscope, and AxioCam Color HRc CCD Camera 412-312 to study mite biology and ecology, and inform the development of new treatments, prevention methods, and management strategies to address the challenges posed by these small but significant creatures.