Professional Excel Templates
Rithms can also easily be encoded into programming languages or spreadsheets and interfaced with numerous graphical software packages for plotting the hydropathy profiles (if graphics are not included in the particular prediction program). These are also discussed in unit 2.1.
Formats, together with what DAMBE can read in and convert to, are listed below. It is good practice to associate each file format with one particular file type. If you have used Microsoft Office, you will notice that WORD files are associated with the .DOC file type, EXCEL files with the .XLS file type, and PowerPoint files with the .PPT file type.
To effectively use temperature as an experimental parameter, it must be regulated in the experimental zone, its value recorded into the data file, and it must be tightly correlated with the measured optical parameters. The first two tasks are accomplished with appropriate hardware design (see Support Protocol 1), but the third task is complicated by the fact that current flow cytometry geometries limit the distance between the temperature regulation zone and the analysis point to several centimeters, which introduces a delay volume between the final point of thermoregulation and analysis. The delay volume can be precisely measured (see Support Protocol 2), and used to correctly correlate events with the actual temperature they experience in the thermoregulation zone via the use of standard flow cytometry analysis software and a spreadsheet program. Use of these programs, as described below, will allow accurate correlation of temperature and make it possible to use temperature as an...
Excel is a software program that uses spreadsheets organized into workbooks. A spreadsheet is an electronic worksheet composed of individual cells arranged as a grid of rows and columns. Each cell can contain data or a formula used for calculations from information in specified cells. Excel is used for a variety of purposes ranging from simple calculations to statistical analyses and producing charts and graphs, and even as a database. In this and following sections we shall be exploring the use of Excel for these functions, but we will make a start by finding out how a spreadsheet is organized and used.
It is now possible to model very complex and variable systems easily - the so-called stochastic 'spreadsheet models'. Whiting and Buchanan (1997) first presented that approach in the food microbiology literature to assess the risk from Salmonella enteritidis in liquid pasteurised eggs. However, while it is easy to develop spreadsheet models, it is also possible to introduce errors that are not immediately obvious, i.e. to develop models that are mathematically or logically incorrect. A common problem is to fail to include in the model relationships between variables, so that combinations of conditions that could never occur in practice are included in the model. For example, the range of storage times and storage temperatures for a food could be described independently by separate distributions. If the relationship between these factors were not explicit in the model, the model could generate predictions based on long storage times and high temperatures, situations unlikely to be...
If the information we are using in the spreadsheet needs to be used as a table in a report, the format ought to be more attractive. The butter y data can be formatted into a table (see Figure 3.14). Click on any cell in the list of data entered on the worksheet, e.g. click on cell C5. From the Format menu on the toolbar, select Autoformat.
The amino acid composition (unit 3.2) of the protein will also allow calculation of some basic physicochemical parameters. Using average pKa values for ionizable side chains in proteins (Matthew et al., 1978), the isoelectric point (pI) can be estimated by applying the well-known Henderson-Hasselbach relationship. The calculations can be performed using an electronic spreadsheet such as Excel or via the internet using one of the many molecular biology servers, e.g., ExPASy The values obtained, although only approximate, are useful for guiding the initial selection of
At a much higher strategic level of decision making, therapy area strategy, the choice of project and valuation within a portfolio has been addressed quantitatively for many years with the help of spreadsheets and more sophisticated decision support systems. These discount future costs and earnings back to present values and factor in risks, so that comparisons use expected net present value, or eNPV. All pharmaceutical companies now have sophisticated portfolio management groups advising which projects to take into development, in-license and out-license. Specialized software products are available to help them assess and provide for aggregate resource demands over a range of disciplines, allowing for different probabilities of failure in different projects and at different stages of R&D, for example, Planisware as recently implemented at Genentech 6 .
Input the data in Table 4.2 into an Excel spreadsheet. From the Tools menu select Data Analysis. If we wanted to check that the value of the standard error calculated in the Descriptive Statistics function was correct then we would insert the following formula into a cell on the spreadsheet, using the data from Group 1 as an example
Enter the data on a spreadsheet in Excel and perform the descriptive statistics on the data. Using the data for the mean and standard deviation for each sample, enter the following equation into one of cells on the worksheet, inserting the appropriate value for the mean and standard deviation in each case
Manipulation of information is also easier when it is in a digital format. While the cut-and-paste analogy comes from physical documentation, it takes on a new perspective when applied digitally. Electrophoresis images can be resized, cropped, and inserted into reports. Data can be passed to spreadsheets and statistical packages for analysis and later insertion into notebooks and reports. These reports can be passed out via the Internet to colleagues throughout the world. A single individual can do all this in a few hours.
In the relational database model, data tables relate to each other through common values. As in spreadsheets, data are stored in tables, independent of the way data are physically laid up. A row, corresponding to a record, represents a collection of information about a separate item, whereas a column represents the characteristics of an item. The relationship is a logical link between tables. The relational DBMS uses matching values in multiple tables to relate information in one table with information in other tables. The relational database model provides flexibility, allowing changes to the database structure without having to update any applications that rely on that structure. This data model permits the designer to create a consistent logical model of information, to be refined through database normalization. Basically, the rules of table schema normalization are enforced by eliminating redundancy and inconsistent dependency in the table designs. Codd provided a set of rules...
To export the measurement of the intensity under the line, click the right mouse button and select Copy. Open an Excel spreadsheet, and paste the data containing the intensity output of every corresponding position from the loading well to the 3 kb. Convert the distance (mm) to the DNA size based on the DNA marker migration.
O Scoring the PIADS is not difficult. It can be scored manually or with the aid of a spreadsheet. Scoring sheets are available to aid in the manual scoring process. To see an example of a completed scoring sheet, refer to Table 2. A blank scoring sheet is also included in the Table 1 and distributed with the PIADS questionnaire. An electronic version of the scoring sheet can be obtained by contacting the authors. Directions for calculating the three PIADS sub-scales are found on manual pages.
And adjust response factors in the integrator. Data reduction of experimental samples using the computer-based spreadsheets and methods of calculation presented in this unit requires up to 1 hr for 5 to 10 analyses. Protein identification using the Internet and PROPSEARCH and ExPASy search programs typically requires 15 to 30 min per search. Allow another 15 to 30 min per day for general instrument maintenance.
The secondary process, as described, is no longer fully automated, requiring human intervention between random access liquid handler and plate replicator, but due to the greatly increased throughput enabled by the combination of this with the fully automated process we currently have no requirement for additional overnight unattended running. Should capacity again be exceeded by increased demand, the decision to opt for semiautomation would have to be balanced alongside the requirement for an extended working day or shift system. We also have realized, quite early in this venture, that machine time may not be limiting but the ability of a single scientist to organize the multiple, varied outputs for different project teams will be. Relying as we do on a spreadsheet detailing the requirements (plate formats, volumes, concentrations, buffers, etc.) for many screens for each project limits the number of projects we can accommodate. Efficiencies may allow some slight increase, but to...
We identify several principles here that are most salient to the application of computers to pharmaceutical research. Pharmaceutical researchers can apply the following principles to help guide their behavior in using computers for pharmaceutical research. ACM principle 2.01 states that one should provide service in their areas of competence, being honest and forthright about any limitations of their experience and education. Thus researchers who do not have the appropriate expertise in developing computer applications should involve someone who does. Even for those who are appropriately qualified, ACM principle 3.10 says one should ensure adequate testing, debugging, and review of software and related documents on which they work. For example, most spreadsheet applications contain errors.
When the new slide is displayed you can double click on the Insert chart button to produce a datasheet and graph. You may enter the data directly onto the datasheet and the graph will be automatically plotted, or import data from a text file in Word, or from an Excel worksheet or insert a chart directly from Excel. It is usually more convenient to create a chart in Excel and then paste it into PowerPoint. Open Excel and insert the information given on the datasheet in Figure 6.3 and create the chart required for the slide. When you have completed the graph in Excel, copy and paste it into the space for the chart in PowerPoint. Re-size as appropriate and add the title.
In this appendix, we will consider some of the statistical tests most commonly used (and misused) in biological research. The tests discussed here are those used for comparisons among groups (e.g., t test and ANOVA). A number of other important areas (e.g., linear regression, correlation, and goodness-of-fit testing) are not covered. The purpose of the Appendix is to enable you to determine rapidly the most appropriate way to analyze your data, and to point out some of the most common errors to avoid. Toward this end, we have included a flow chart to provide a quick guide to choosing the right statistical test (Fig. A.3G.1). Instead of including the voluminous statistical tables necessary to perform these tests, we assume you will have access to a spreadsheet software program such as Microsoft Excel or to a statistical software package to do the actual calculations involved and to supply critical values. We include calculations for some simpler tests to aid in the interpretation of...
Export the data to a spreadsheet program such as Excel to facilitate analysis. Desirable clones will have two essential characteristics 1) they should be brighter than the wild-type or optimized control clones from previous round(s), and 2) each should be as bright or brighter at 27 C relative to 37 C (see Note 17). Typically, the top 1 3 of the set of optima is used for the subsequent round of shuffling and screening.
The decision of what software combinations will reside together on the same laboratory computer should be done with great care. In many cases it is better to purchase several computers and dedicate each to a particular problem, rather than run combinations of packages on the same machine. What a vendor claims can be done in an office environment (you can run word processing, spreadsheets, data base packages, send and receive FAXes, simultaneously), can cripple a data acquisition system. When combinations are run, the validation effort should include the behavior of the system with each combination running to avoid conflicts that may not be apparent when each is run alone.
Routine data analysis includes data gating, specification of plots to display signal distributions, and computation of statistical results. Records of these analyses should be retained with the rest of the information about the sample. Graphs and tables derived from data (e.g., fluorescence signal medians for export to a spreadsheet program) should include sufficient information to trace back to the original flow cytometer data records.
This procedure identifies proteins using near-UV spectrophotometry. Protein solution is placed in a UV-transparent quartz cuvette, absorption of light is measured as a function of wavelength, and the resulting spectrum is transferred either to a displaying device (e.g., CRT or printer) for inspection or to a computer that can employ a spreadsheet or other program for derivative calculations and or quantitative analyses (see Support Protocol). 6. Determine the exact peak positions by calculating the first-derivative spectrum in the 280- to 290-nm range. If the spectrophotometer has a built-in derivative calculation ability, choose derivative order 1, polynomial degree 2, and a window of five data points. If not, transfer the spectra in ASCII (text) format (or type it) into a spreadsheet (Excel, LOTUS-123, or equivalent) and perform the calculation using Equation 7.2.1 (Savitzky and Golay, 1964 Steiner et al., 1972), where FD(X) is the first-derivative value at the integral wavelength...
CambridgeSoft's BioAssay module has been designed to provide an easy-to-use method to upload assay data from multiple sources to a central, secure location. Once the data have been captured, users can perform various calculations, using the program's built-in calculation and curve-fitting abilities. The validated data can then be published to the larger research group with BioSAR Enterprise, which provides storage, retrieval, and analysis of the biological data. In BioSAR Enterprise, users define form and report layouts to combine biological and chemical data. It links the registration system to the BioAssay module to create customized structure-activity relationship reports. The results can be exported to a MS Excel spreadsheet. The fields exported are defined by the form definition, which allows the medicinal chemist to view both traditional numeric and textual data alongside structure data in the spreadsheet.
All of the work you do is contained within a rectangular area of the screen known as the window.The background on which the windows are placed is the desktop. Each application that you work with through Windows (such as the word processing package Word and the spreadsheet application Excel) are represented by small graphical symbols known as icons. Your actions in Windows are carried out by using either the mouse or the keyboard, depending on the task in hand. When using a program in Windows, the insertion point in a document or spreadsheet is shown by a flashing black line known as the cursor. When using the mouse a black line (J) or arrow may be moved across the screen to enable the user to reposition the cursor by clicking with the left mouse button the cursor is moved to the point indicated.
If the instrument's software cannot make such a correction, use a spreadsheet (Microsoft Excel, LOTUS-123, or equivalent see Basic Protocol 1, step 6 annotation, for an example) to generate the light-scattering curve using optical density values at 320 nm and 350 nm. Calculate the light-scattering correction from Equation 7.2.4, where m 64.32 - 25.67 log X.
Where k0 is the capacity factor of the solute in the absence of the organic solvent modifier, and S is the slope of the plot of lnk versus 9. The values of lnk0 and S can be calculated by linear regression analysis. The underlying principles of an intuitively performed optimization and manually achieved optimization (using Excel spreadsheets, for example, to calculate the ln k0 and S values), or, alternatively, optimization via computer simulation software (e.g., Simplex methods, multivariant factor analysis programs, DryLab G plus, etc.) are essentially the same. However, the outcomes result in different levels of precision. Two representative approaches are collectively outlined below.
Finally, a variety of comparisons are possible with large-scale ELISA data. For epitope mapping, scientists are typically interested in comparing the reactivity of several hundred peptides versus a single Ab. In this case, a simple line plot is usually helpful for visualizing the immunodominant epitopes. A careful scientist may choose to perform such a simple assay in triplicate, averaging the data. Thorough epitope mapping studies often include several Abs, tested against several hundred peptide epitope candidates, perhaps with repeats, extra controls, and varying reagent concentrations. Managing so much data and making sense of it are challenges. Plan out the entire series of experiments, to be sure, and have plenty of all necessary reagents, including peptides and Abs. Finally, use computer software, such as spreadsheets and databases, to facilitate data processing.
The beauty of unit activity costing, particularly if you are good with spreadsheets, is that it allows you to quickly remodel the budget using different assumptions (eg cost of increasing number of investigators, or working in a different territory). This may later become a lifeline as the project rolls out and you have to start dealing with reality.
Managing manpower, or what is more politically correctly termed human resources, is no easy juggling act. Nevertheless a project manager should be on top of this aspect of the job. Once again the approach is a continual cycling of the plan, track and adjust loop. If you like linked spreadsheets, then this activity will be a joy for the duration of the project.
Enter the observed frequencies onto your Excel worksheet from Table 5.6. On the Excel worksheet calculate the expected consumption using the above relationship, i.e. enter the formula (205+289) 2. An answer of 247 should be returned. If the selection of the chocolate pieces was completely random we would expect that exactly 247 pieces of both dark and milk chocolate would be eaten. We now have to test this against the observed results to find out whether our observations are significantly different from what we expected. Create a second column in the table and enter the expected results as shown in Table 5.7. We are now ready to perform the test.
Two versions of the centroid calculation program have been developed in the author's laboratory and are available upon request. The first version of the program requires the investigator to examine the y-axis data in a spreadsheet program and determine the average y-axis values that define the baseline before the leading edge, the plateau region at the leading edge, the plateau region at the trailing edge, and the baseline after the trailing edge. This version also requires inputs of the starting and ending times of the leading and trailing edges, respectively, as well as estimates for the centroid times of the leading and trailing edges. The second version of the centroid calculation program requires no input of parameters by the investigator. It uses empirically determined routines to calculate the parameters detailed above. This program cannot be used for large-zone profiles with baseline anomalies. A data collection rate of five data points per second results in very large ASCII...
Of data for each parameter to a single datum value. Most commonly, the reduced data values are either a percentage of the events or a number representing the distribution of events, such as the mean fluorescence channel. The next step is to plot these reduced data values for all the parameters in a single plot. This can be done by entering the data into a spreadsheet program and then displaying them as histograms. This
The PIADS Scale can be scored manually or with the aid of a computer, using an EXCEL spreadsheet. Scoring sheets are available to aid in the manual scoring process. To see an example of a completed scoring sheet, refer to Table 2. A blank scoring sheet is also included (Table 1) and distributed with the PIADS questionnaire. An electronic version of the scoring sheet can be obtained by contacting the authors.
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