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Paste

Paste is a viscous, adhesive substance used for a variety of purposes, such as bonding materials together or applying to surfaces.
It is commonly made from a mixture of powdery or fibrous substances combined with a liquid, often water or an organic solvent.
Pastes can be used in a wide range of applications, including arts and crafts, construction, and industrial processes.
Their versatility and ease of use make them a valuable tool in many settings.
Effortlessly locate the best protocols from literature, pre-pints, and patents, while our intelligent algorithms provide side-by-side comparisons to help you identify the optimal solution for your research.
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Most cited protocols related to «Paste»

1
Efficient Species-Level Genome Comparison Using Mash
By default, Mash uses 32-bit hashes for k-mers where |Σ|k ≤ 232 and 64-bit hashes for |Σ|k ≤ 264. Thus, to minimize the resulting size of the all-RefSeq sketches, k = 16 was chosen along with a sketch size s = 400. While not ideal for large genomes (due to the small k) or highly divergent genomes (due to the small sketch), these parameters are well suited for determining species-level relationships between the microbial genomes that currently constitute the majority of RefSeq. For similar genomes (e.g. ANI >95 %), sketches of a few hundred hashes are sufficient for basic clustering. As ANI drops further, the Jaccard index rapidly becomes very small and larger sketches are required for accurate estimates. Confidence bounds for the Jaccard estimate can be computed using the inverse cumulative distribution function for the hypergeometric or binomial distributions (Additional file 1: Figure S1). For example, with a sketch size of 400, two genomes with a true Jaccard index of 0.1 (x = 40) are very likely to have a Jaccard estimate between 0.075 and 0.125 (P >0.9, binomial density for 30 ≤ x ≤ 50). For k = 16, this corresponds to a Mash distance between 0.12 and 0.09.
RefSeq Complete release 70 was downloaded from NCBI FTP (ftp://ftp.ncbi.nlm.nih.gov). Using FASTA and Genbank records, replicons and contigs were grouped by organism using a combination of two-letter accession prefix, taxonomy ID, BioProject, BioSample, assembly ID, plasmid ID, and organism name fields to ensure distinct genomes were not combined. In rare cases this strategy resulted in over-separation due to database mislabeling. Plasmids and organelles were grouped with their corresponding nuclear genomes when available; otherwise they were kept as separate entries. Sequences assigned to each resulting “organism” group were combined into multi-FASTA files and chunked for easy parallelization. Each chunk was sketched with:
mash sketch -s 400 -k 16 -f -o chunk *.fasta
This required 26.1 CPU h on a heterogeneous cluster of AMD processors. (Note: option -f is not required in Mash v1.1.) The resulting, chunked sketch files were combined with the Mash paste function to create a single “refseq.msh” file containing all sketches. Each chunked sketch file was then compared against the combined sketch file, again in parallel, using:
mash dist -t refseq.msh chunk.msh
This required 6.9 CPU h to create pairwise distance tables for all chunks. The resulting chunk tables were concatenated and formatted to create a PHYLIP formatted distance table.
For the ANI comparison, a subset of 500 Escherichia genomes was selected to present a range of distances yet bound the runtime of the comparatively expensive ANI computation. ANI was computed using the MUMmer v3.23 “dnadiff” program and extracting the 1-to-1 “AvgIdentity” field from the resulting report files [49 (link)]. The corresponding Mash distances were taken from the all-vs-all distance table as described above.
For the primate phylogeny, the FASTA files were sketched separately, in parallel, taking an average time of 8.9 min each and a maximum time of 11 min (Intel Xeon E5-4620 2.2 GHz processor and solid-state drive). The sketches were combined with Mash paste and the combined sketch given to dist. These operations took insignificant amounts of time, and table output from dist was given to PHYLIP v3.695 [50 ] neighbor to produce the phylogeny. Accessions for all genomes used are given in Additional file 1: Table S1. The UCSC tree was downloaded from [51 ].
Ondov B.D., Treangen T.J., Melsted P., Mallonee A.B., Bergman N.H., Koren S, & Phillippy A.M. (2016). Mash: fast genome and metagenome distance estimation using MinHash. Genome Biology, 17, 132.
Full text: Click here
Publication 2016
actinomycin D1 Escherichia Genetic Heterogeneity Genome Genome, Microbial GPER protein, human Marijuana Abuse Organelles Paste Plasmids Primates Replicon Trees
2
Age-Period-Cohort Analysis of Cancer Mortality
Online Help is available (click on Help in the web tool, or open http://analysistools.nci.nih.gov/apc/help.html). Input data for the web tool consist of age-specific numbers of events and person-years over time, in the form of a rate matrix of paired columns. Three sample datasets that describe prostate (43 (link)), lung (29 ), and breast cancer (32 ) mortality are linked to the web tool (click on Help, then Sample Data, or open http://analysistools.nci.nih.gov/apc/help.html#example). The input page is shown in Figure 1 for the prostate cancer mortality data (example 1). In general, user data can be input by copy-and-paste from an Excel worksheet or file upload of a comma-separated-values (csv) file. As shown in Figure 1, age groups correspond to rows and calendar periods to columns. The rates are defined by adjacent pairs of columns: the first column of each pair lists the numbers of events by age for a given calendar period, and the second column lists the corresponding persons-years. The age and period intervals must all be equal (44 (link)), i.e. if 5-year age groups are used then 5-year calendar periods must also be used. The intervals can range from 1 through 10 years inclusive. Data in this format can easily be obtained from publicly-available data resources with cancer case and population data, such as the Surveillance, Epidemiology and End Results (SEER) Program of the National Cancer Institute (http://www.seer.cancer.gov) and Cancer Incidence in Five Continents (CI5) of the International Agency for Research on Cancer (http://ci5.iarc.fr).
The web tool fits the APC model and calculates parameters and estimable functions summarized in Table 1. On the web site, each function is presented in its own tab in graphical and tabular format, as illustrated in Figure 2. A number of key hypothesis tests are also provided in the ‘Wald Tests’ tab located in the sidebar on the left-hand side of the web page. These hypothesis tests are summarized in Table 2.
Rosenberg P.S., Check D.P, & Anderson W.F. (2014). A Web Tool for Age-Period-Cohort Analysis of Cancer Incidence and Mortality Rates. Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology, 23(11), 2296-2302.
Publication 2014
Age Groups Inclusion Bodies Lung Malignant Neoplasm of Breast Malignant Neoplasms Paste Prostate Prostate Cancer Seizures
3
Aligning Sequences with M-Coffee
The server can be accessed at www.tcoffee.org. Following the M-Coffee link will either take the user to the regular or advanced mode. The regular mode merely requires the user to cut and paste a set of sequences in FASTA format. The advanced mode (Figure 1) offers more possibilities and guides the user with a series of bulleted points:

Cut and paste your sequences. Sequences should be in FASTA format. Duplicated names are now supported although not recommended.

Alignment computation. This section defines the way the primary library is computed. For instance, selecting only lalign_id_pair and slow_pair will lead to the computation of a regular T-Coffee MSA. The lower section (xxx_msa) displays the list of available MSA methods. Selecting only one of these methods will generate the corresponding alignment. Selecting several methods (or all of them, as in the regular mode displayed on Figure 1) will lead to a consensus T-Coffee MSA. If the MSA method one wants to combine is missing on this form, another server named ‘Combine’ should be used (accessible from www.tcoffee.org). The ‘Combine’ server works on the same principle as M-Coffee but does not compute the MSAs itself and requires the user to cut and paste pre-computed MSAs. At this point it should be used if one wants to incorporate specific constraints or structure-based sequence alignments.

Output. The Output section makes it possible to control the output format. The most notable element is score_html that will cause the server to produce a colored version of the final alignment (Figure 2). In this output, residues are individually colored according to the consistency of their alignment with the T-Coffee library. Residues in red are in perfect agreement with every constituting multiple alignment while those in blue have the lowest agreement (i.e. the lowest support in the individual MSAs). Previous analysis indicates that 90% of the residues having a score of 7 or higher (dark yellow, orange and red) are correctly aligned (24 (link)). A text version of this output is available as score_ascii where each residue is replaced with its consistency estimation on a scale between 0 and 9 (9 corresponding to the red-brick residues in the color-output). These score_ascii files can be used to process multiple alignments (block extraction) using seq_reformat, one of the utilities distributed along with T-Coffee. For this purpose, users can download their alignment, the score_ascii file and use the command line version of T-Coffee with the following syntax:

Method selection on the advanced M-Coffee server form. Each check box corresponds to either a pairwise (_pair) or a multiple sequence alignment method (_msa). Users should choose their methods of choice in order to combine them.

Typical colored output. This output was obtained by using the kinase1_ref5 from BaliBase. Correctly aligned residues (as judged from the reference) are in upper case, non-correct ones are in lower case. In this colored output, each residue has a color that indicates the agreement of the individual MSAs with respect to the alignment of that specific residue. Dark red indicates residues aligned in a similar fashion among all the individual MSAs; blue indicates a very low agreement. Dark yellow, orange and red residues can be considered to be reliably aligned.

        t_coffee -other_pg seq_reformat -in -struc_in -struc_in_f number_aln -action +keep ‘[5-9]’
Where is the name of the alignment and the name of the score_asccii file. This syntax will replace by a gap (‘-') every residue having an ascii_score lower than 5 (green and blue residues on the colored output).
Moretti S., Armougom F., Wallace I.M., Higgins D.G., Jongeneel C.V, & Notredame C. (2007). The M-Coffee web server: a meta-method for computing multiple sequence alignments by combining alternative alignment methods. Nucleic Acids Research, 35(Web Server issue), W645-W648.
Publication 2007
Coffee DNA Library Paste Sequence Alignment
4
Automated HIV-1 Recombination Analysis with jpHMM
The jpHMM is available online at http://jphmm.gobics.de/. The user can paste or upload up to five full-length HIV-1 genomic sequences or fragments at a time in FASTA format. A hyperlink to the results of the program run, which are stored on the server for 2 days, is returned to the user by e-mail. The result contains for each sequence the predicted recombination, including uncertainty regions and breakpoint intervals, in text format as well as a graphical representation of the predicted recombinant fragments within the HIV-1 genome. Additionally, the posterior probabilities of the subtypes for each sequence position are plotted. For uncertainty regions the originally predicted parental subtype is also provided. As thresholds for uncertainty regions and breakpoint intervals we use tBPI = tUR = 0.99. For each query sequence, the predicted recombination with precise breakpoint positions as well as the predicted recombination including uncertainty regions and breakpoint intervals, a list of the breakpoint intervals and uncertainty regions and the posterior probabilities of the subtypes can be downloaded. Additionally, the alignment of each input sequence to the HXB2 sequence (15 ), defined by jpHMM, is provided for download. HXB2 is the most commonly used HIV-1 reference sequence and is part of the multiple sequence alignment we use to build the model. Figure 1 shows an excerpt of the jpHMM output for an artificial recombinant HIV-1 sequence.

Extract of the jpHMM web server output for an artificial recombinant. The output contains a list of fragments from the input sequence that are assigned to different HIV-1 subtypes, including predicted breakpoint intervals and uncertainty regions. In the center, a graphical representation of the predicted recombinant fragments within the HIV-1 genome is given. At the bottom, the posterior probabilities of all HIV-1 subtypes are plotted.

Schultz A.K., Zhang M., Bulla I., Leitner T., Korber B., Morgenstern B, & Stanke M. (2009). jpHMM: Improving the reliability of recombination prediction in HIV-1. Nucleic Acids Research, 37(Web Server issue), W647-W651.
Publication 2009
Genome HIV-1 Parent Paste Recombination, Genetic Sequence Alignment
5
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.
Full text: Click here
Publication 2017
Apis Hydrogen Molecular Structure Paste

Most recents protocols related to «Paste»

1
Bioceramic Compositions for Biomedical Applications
Not available on PMC !

Example 2

The Bioceramic compositions in Table 2, below, were prepared by mixing the liquid component (carrier) with the solid components in a mechanical stirrer, in the following sequence: sorosilicate, radiopacifier, rheology control agent and setting agent with speed below 500 rpm, approximately 45 minutes until complete homogenization.

TABLE 2
Bioceramic compositions
Non-aqueous Paste
Rheology
Liquidcontrol
SampleSorosilicateRadiopacifiercarrieragentSetting agent
CB 3HardystoniteCalciumPolyethyleneSiliconCalcium
26%tungstateglycoloxidesulfate/potassium
37%25%2%sulfate
10%
CB 4Strontium-CalciumPolyethyleneSiliconCalcium
akermanitetungstateglycoloxidesulfate/potassium
35%35%25%2%sulfate
3%
CB 5AkermaniteZirconiumPolyethyleneSiliconCalcium
22%oxideglycoloxidesulfate/potassium
35%33%2%sulfate
8%
CB 6AkermaniteZirconiumPolyethyleneSiliconCalcium
30%oxideglycoloxidesulfate/potassium
28%29%4%sulfate
9%

US11857558B2. Dental and medical compositions having a multiple source of metallic ions (2024-01-02). ANGELUS INDÚSTRIA DE PRODUTOS ODONTOLÓGICOS S/A [BR]. Inventors: César Eduardo Bellinati [BR], Cristiane Vila [BR], William Pereira Dos Santos [BR], Maíra Bendlin Calzavara [BR].
Full text: Click here
Patent 2024
akermanite Calcium, Dietary Glycols Oxides Paste Polyethylenes Potassium-37 Silicon-29 Strontium Sulfates, Inorganic tungstate Zirconium
2
Aquatic Extract of Clitoria ternatea Flowers
Not available on PMC !

Example 1

Preparation of Clitoria ternatea flowers aquatic extract Clitoria ternatea flowers were purchased from Earth Circle Organics (USA). Clitoria ternatea flowers (0.5 and 1 g) were separately ground in a mortar and pestle for 1 minute in 50 mL of cold (4° C.) deionized water. The paste made from the Clitoria ternatea flowers was then centrifuged in a chilled centrifuge for 10 min. at 8000 g. The clear supernatant from each preparation (0.5 and 1 g) was collected into two separate volumetric flasks (1 L). Cold distilled water was used to top off each flask's volume to 1 L in order to obtain the corresponding Clitoria ternatea flowers at concentrations of 0.5 and 1 g/L.

US11871754B1. Promoting growth, yield, and salinity tolerance of Achillea fragrantissima (2024-01-16). KING FAISAL UNIVERSITY [SA]. Inventors: Fadia El Sherif [SA], Salah Khattab [SA].
Full text: Click here
Patent 2024
Clitoria Cold Temperature Flowers Paste Salt Tolerance Yarrow
3
Synthesis of Zn/DS1 Catalyst
Not available on PMC !

Example 3

Zinc nitrate hydrate (0.2275 g) was dissolved in 5 mL deionized water to form an aqueous zinc nitrate hydrate solution in a beaker, to which 5 grams of desilicated zeolite powder, DS1, was added. The mixture was stirred with a magnetic stirrer for 1 hour to obtain a homogenous slurry mixture. Water was subsequently removed from the mixture by keeping the mixture on a hot plate at a temperature of about 50° C. under stirring. The dry paste was transferred to an oven at a temperature of 110° C. for 12 hours, and was further calcined at 550° C. for 6 hours. The catalyst sample was pelletized and sieved to a particle size of from 500 μm to 1000 μm and labeled as Zn/DS1. The BET area, micropore volume, mesopore volume, and average pore diameter of the sample are reported in Table 1.

US11866389B1. Processes of forming metal-doped zeolite catalysts with enhanced mesoporosity (2024-01-09). Saudi Arabian Oil Company [SA], King Fahd University of Petroleum and Minerals [SA]. Inventors: Yaming Jin [SA], Ziyauddin S. Qureshi [SA], Abdullah M. Aitani [SA], Omer Refa Koseoglu [SA], Mohammad Naseem Akhtar [SA], Hassan Saeed Al-Awad Alasiri [SA].
Full text: Click here
Patent 2024
Homozygote Metals Paste Powder Zeolites zinc nitrate
4
Formulation of White Dielectric Ink
To 13.71 g of commercial
water-borne Baymedix CD102 PU dispersion (40 wt %) were added and
carefully mixed by hand, in order: 4.88 g of dipropylene glycol and
9.39 g of 2-hydroxyethyl cellulose solution (5 wt % in propylene glycol).
To the resulting mixture, 19.98 g of Kronos TiO2 2190 was
added. The obtained viscous paste was mixed in a SpeedMixer (German
Engineering by Hauschild, DAC 600.1 - CM 50) for 8.5 min (30 s at
800 rpm, 8 min at 1800 rpm). The obtained white dielectric ink was
then free from visible particles.
Boda U., Strandberg J., Eriksson J., Liu X., Beni V, & Tybrandt K. (2023). Screen-Printed Corrosion-Resistant and Long-Term Stable Stretchable Electronics Based on AgAu Microflake Conductors. ACS Applied Materials & Interfaces, 15(9), 12372-12382.
Full text: Click here
Publication 2023
DAC 1 Glycols hydroxyethylcellulose Paste Propylene Glycol Suby's G solution Viscosity
5
Formulation of Conductive AgAu Ink
To 959
mg of commercial
water-borne Baymedix CD102 PU dispersion (40 wt %), the following
was added and carefully mixed by hand, in order: 380 mg of dipropylene
glycol, 874 mg of propylene glycol, and 351 mg of 2-hydroxyethyl cellulose
solution (10 wt % in propylene glycol). The resulting mixture was
added to 6 g of dried AgAu flakes in a 15 mL jar with a lid. The obtained
viscous paste was mixed in a SpeedMixer (German Engineering by Hauschild,
DAC 600.1 - CM 50) for 8.5 min (30 s at 800 rpm, 8 min at 1800 rpm).
The obtained ink was then free from visible particles. For analogue
Ag ink, pristine Ag flakes from the bottle were used in the same quantities
described for the formulation of the AgAu flakes ink, with the exception
of the added propylene glycol amount, which was decreased to 166 mg
for the same weight of Ag to achieve a similar viscosity as the AgAu
ink.
Boda U., Strandberg J., Eriksson J., Liu X., Beni V, & Tybrandt K. (2023). Screen-Printed Corrosion-Resistant and Long-Term Stable Stretchable Electronics Based on AgAu Microflake Conductors. ACS Applied Materials & Interfaces, 15(9), 12372-12382.
Full text: Click here
Publication 2023
DAC 1 Paste Propylene Glycol Viscosity

Top products related to «Paste»

1
Dimethyl sulfoxide (dmso) by Merck Group
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DMSO is a versatile organic solvent commonly used in laboratory settings. It has a high boiling point, low viscosity, and the ability to dissolve a wide range of polar and non-polar compounds. DMSO's core function is as a solvent, allowing for the effective dissolution and handling of various chemical substances during research and experimentation.
2
Acetonitrile by Merck Group
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Acetonitrile is a colorless, volatile, flammable liquid. It is a commonly used solvent in various analytical and chemical applications, including liquid chromatography, gas chromatography, and other laboratory procedures. Acetonitrile is known for its high polarity and ability to dissolve a wide range of organic compounds.
3
Physical property measurement system (ppms) by Quantum Design
Sourced in United States
The Physical Property Measurement System (PPMS) is a versatile laboratory instrument designed for advanced materials characterization. The PPMS provides a controlled environment for measuring various physical properties of samples, such as electrical, magnetic, and thermal properties. It offers a wide range of measurement capabilities, including temperature and magnetic field control, to enable researchers to investigate the behavior of materials under different conditions. The PPMS is a valuable tool for researchers in fields such as condensed matter physics, materials science, and nanotechnology.
4
Sylgard 184 by Dow
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Sylgard 184 is a two-part silicone elastomer system. It is composed of a siloxane polymer and a curing agent. When mixed, the components crosslink to form a flexible, transparent, and durable silicone rubber. The core function of Sylgard 184 is to provide a versatile material for a wide range of applications, including molding, encapsulation, and coating.
5
Titanium diisopropoxide bis acetylacetonate by Merck Group
Sourced in India, United States
Titanium diisopropoxide bis(acetylacetonate) is a chemical compound used as a precursor in the synthesis of titanium-containing materials. It is a clear, colorless liquid with a molecular formula of Ti(OC3H7)2(C5H7O2)2. The compound is primarily utilized in the semiconductor and thin-film industries, where it serves as a source of titanium for the deposition of titanium-based materials.
6
Ethanol 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.
7
4 tert butylpyridine by Merck Group
Sourced in United States, Germany, Japan
4-tert-butylpyridine is a chemical compound used as a laboratory reagent. It is a pyridine derivative with a tert-butyl group substituted at the 4-position. This compound is commonly utilized in various chemical synthesis and analysis applications within research and development settings.
8
Sodium hydroxide (naoh) by Merck Group
Sourced in Germany, United States, India, United Kingdom, Italy, China, Spain, France, Australia, Canada, Poland, Switzerland, Singapore, Belgium, Sao Tome and Principe, Ireland, Sweden, Brazil, Israel, Mexico, Macao, Chile, Japan, Hungary, Malaysia, Denmark, Portugal, Indonesia, Netherlands, Czechia, Finland, Austria, Romania, Pakistan, Cameroon, Egypt, Greece, Bulgaria, Norway, Colombia, New Zealand, Lithuania
Sodium hydroxide is a chemical compound with the formula NaOH. It is a white, odorless, crystalline solid that is highly soluble in water and is a strong base. It is commonly used in various laboratory applications as a reagent.
9
S 4800 by Hitachi
Sourced in Japan, United States, China, Germany, United Kingdom, Spain, Canada, Czechia
The S-4800 is a high-resolution scanning electron microscope (SEM) manufactured by Hitachi. It provides a range of imaging and analytical capabilities for various applications. The S-4800 utilizes a field emission electron gun to generate high-quality, high-resolution images of samples.
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D8 advance by Bruker
Sourced in Germany, United States, Japan, United Kingdom, China, France, India, Greece, Switzerland, Italy
The D8 Advance is a versatile X-ray diffractometer (XRD) designed for phase identification, quantitative analysis, and structural characterization of a wide range of materials. It features advanced optics and a high-performance detector to provide accurate and reliable results.

More about "Paste"

Paste is a versatile, viscous, and adhesive substance used in a wide range of applications, such as arts and crafts, construction, and industrial processes.
It is commonly made by combining powdery or fibrous materials with a liquid, often water or an organic solvent like DMSO or Acetonitrile.
Pastes can be used for bonding materials together or applying to surfaces, making them a valuable tool in many settings.
Synonyms for paste include glue, cement, mastic, and putty.
Related terms include PPMS (Polyphenylene methane sulfonate), Sylgard 184 (a silicone elastomer), and Titanium diisopropoxide bis(acetylacetonate), which are all used in various paste-like applications.
Subtopics related to paste include its use in arts and crafts, such as for decoupage, paper mâché, and mixed media projects.
In construction, paste is used for tasks like grouting, plastering, and sealing.
Industrially, paste is employed in processes like adhesive bonding, surface coatings, and even as an electrolyte in lithium-ion batteries.
When working with paste, common solvents and additives include Ethanol, 4-tert-butylpyridine, and Sodium hydroxide.
These can be used to adjust the viscosity, drying time, and other properties of the paste.
Advanced analytical techniques like S-4800 scanning electron microscopy and D8 Advance X-ray diffraction can be used to study the microstructure and composition of paste-based materials.
Wheather you're a crafster, a builder, or an industrial engineer, paste is a versatile and indispensable tool that can help elevate your work.
Explore the power of paste and unlock new possibilites in your field.