Professional Excel Templates
A spreadsheet is a collection of data entries text, numbers, or a combination of both (alphanumeric) arranged in rows and columns used to display, manipulate, and analyze data (Atkinson et al., 1987 Diamond and Hanratty, 1997). Microsoft Excel (http www.microsoft.com ) is a spreadsheet software package that allows you to do the following Write user-defined macros for applications routines to automate or enhance a spreadsheet for a particular purpose. Launching Microsoft Excel brings you into the workspace of Excel and a new workbook. A workbook is a collection of related spreadsheets organized in rows (number headings) and columns (letter headings). The intersection of a row and a column is a cell that is addressed by row and column headings. To select a group of cells, click on the first cell and drag down to the last cell. This collection of cells is known as a range. Inserting cells, rows, and columns is done through either the Insert or shortcut Edit menu. Excel inserts the new...
There are three types of data that can be entered in the cells of the spreadsheet number, date time, and text. For multiple entries of serial number with constant increment, enter the initial value into the first cell, highlight the cells, and select Edit Fill Series. To fill multiple entries of the same value into a range, enter the value into the first cell, move the pointer to the right-hand corner of the active cell to activate fill handle (a bold crosshair), and drag it through the range. Data are edited with the usual cut, copy, and paste operations in the Edit menu and the decimal places of scientific numbers is controlled via dialog box in Format Cells. The fundamental operation of a spreadsheet is performing calculations on data. Excel performs mathematical operations through formula and functions. Formulas are written by using the formula bar and beginning with an equal sign ( ). Functions can be either user-defined or built-in Excel functions. Because the formulas and...
Alternatively, TCR analysis can also be depicted as a 'landscape', i.e. as a function of the CDR3 length (z-axis), the VB family (x-axis) and the percentage of each individual CDR3 in a distinct VA or VB family (y-axis) (14, 15). This is possible using regular excel spreadsheet functions, or alternatively, using the Immunoscope software (4, 16). This allows to describe the TCR repertoire at the time of evaluation. In order to compare either i) longitudinal differences (i.e. PBL or tissue samples collected at different time points) or ii) spatial differences (i.e. differences between a tumor sample and PBL or individual lymph node sections), the TCR CDR3 distribution pattern of individual VA or VB families can be used as a 'control' for other samples. This 'control' sample can be used from an individual patient (i.e. pre-, post-immunization, tumor normal tissue) or a representative Gauss-distributed sample from either CD4+ or CD8+ PBL from healthy blood donors.
Choice of spreadsheet entry or more sophisticated forms. Provides toggle buttons but no pull-down menus. Includes range and logic checks. PC-based and can be purchased from virtually any computer or office supply outlet. Paradox. SQL-like interface with spreadsheet views. Range and logic checks, but lacks ability to create other user-friendly features. Corel Corporation, 1600 Carling Avenue, Ottawa, Ontario, Canada K1Z 8R7.
The spots can either be counted manually using a standard dissecting microscope or imaged electronically using an automated imaging system. Several automated imagers are commercially available. Automated imaging systems are highly recommended for those laboratories dealing with a high-assay volume. Spots are counted according to specific manufacturer's recommendations. Each system has its individual means of dealing with differences in spot size and resolution of individual spots. The images for each experiment can be stored electronically and archived for future reference. Data are transferred into a spreadsheet format, and calculations are performed according to specific assay protocols. Among the calculated data fields are Mean spot numbers, standard deviation of triplicate wells, CV, background corrected mean spots (i.e., mean experimental value minus mean background value), SD, and corrected mean spots per 105 or 106 responder cells. Various statistical tests can be performed to...
In planning experiments in which many regimens can be evaluated (e.g., short-term studies in mouse models), simulations of serum drug concentrations vs. time using pharmacokinetic parameters derived from infected animals should be performed. The parameters, MICs, and dosage regimens can easily be programmed into a spreadsheet to generate PK-PD data. One can assess a number of ''what if'' scenarios for several doses, dosage regimens, and MICs and generate two- or three-dimensional plots showing the correlation for each regimen. An example of uncorrelated PK-PD parameters for several vancomycin regimens in a mouse is shown in Fig. 5. One can finalize selection of dosage regimens that will minimize the covariance among different PK-PD parameters. In selecting regimens, it is also important to be mindful of the relationships of dose and MIC with the number of hours serum concentrations will exceed the MIC. In some cases it may be virtually impossible to design dosage regimens that produce...
Table 1 shows the calculations required to prepare a Reppal PES 100-Breox 50A 1000 system with intermediate concentration, with added salts and protein (either pure or crude extract see Note 5). For further system compositions, this table should be reproduced in a spreadsheet program for ease of calculation. Systems may be prepared in a graduated test tube (see Note 6). Phase components should be mixed well to ensure a homogeneous mixture this can be carried out simply by inverting the test tube several times or using a vortex mixer. A blank system (i.e., containing all the phase components except protein), as well as a system for the partitioning should be prepared.
The traditional answer to some of these issues was the hierarchical database model. A hierarchical database is a series of flat files, each one similar to a spreadsheet, that are linked in structured treelike relationships (see Figure 11.2). Data are represented as a series of parent child relationships. A patient's record (the parent) might link to follow-up exam children, and each of these children might link to the records of specialized procedures (grandchildren).
The second method is to create two volumes, one for the balloon proper and the other the balloon plus the 10-mm margin. The volumes of the dose levels of interest can be extracted from each, and subtracted. The third method is to create the two volumes of interest as above, taking care that the bin width and number are identical in the DVH calculations. The histograms are then exported to a spreadsheet, and subtracted bin-for-bin. The resulting histogram will correctly depict the dose-volume characteristics of the intended PTV_EVAL.
As a practical matter, it is helpful to consider the V90, V150 and V200 together as constraints which determine the lower and upper bounds for adjustment of the normalization. We have found it convenient to export the two histograms on which these parameters are calculated, and use them in a spreadsheet to show the DVHs and constraints together. If adjustment of the normalization is needed (usually to accommodate V90), the trade-off can be readily visualized.
Object-oriented databases can work with images, spreadsheets, documents, CAD, email messages, and directory structures, as well as text. From the computer's point of view, data is merely a sequence of bits (1's and 0's) residing in some sort of storage structure perspective images are no more exotic than the characters that make up text. An object-oriented database could store ultrasound photos and ECG tracings alongside the numerical summary of results. It is difficult to visualize the practical value this still experimental approach could have for clinical trials. Let someone else be a pioneer.
We handle these calculations with a spreadsheet program that includes a test that the disintegrations per minute for an embryo equal or exceed the sensitivity of the assay (defined as the mean sham disintegrations per minute plus 2 standard errors), and any value below that is set to zero. The spreadsheet also calculates the embryo treatment group means and variances, which can be cut and pasted to other programs for data storage, statistical analysis, and graphing.
The most traditional, but still very widely used, method of calibration in chemistry is univari-ate, involving calibrating a single variable (e.g. a spectroscopic intensity) to another variable (e.g. a concentration). The literature goes back a century, however, with the ready availability of matrix based computer packages ranging from spreadsheets to more sophisticated programming environments such as Matlab, we can approach this area in new ways.
After brief introduction to biostatistics, Chapter 2 focuses on the use of spreadsheet (Microsoft Excel) to analyze biochemical data, and of database (Microsoft Access) to organize and retrieve useful information. In the way, a conceptual introduction to desktop informatics is presented. Chapter 3 introduces Internet resources that will be utilized extensively throughout the book. Some important biochemical sites are listed. Molecular visualization is an important and effective method of chemical communication. Therefore, computer molecular graphics are treated in Chapter 4. Several drawing and graphics programs such as ISIS Draw, RasMol, Cn3D, and KineMage are described. Chapter 5 reviews biochemical compounds with an emphasis on their structural information and characterizations. Dynamic biochemistry is described in the next three chapters. Chapter 6 deals with ligand- receptor interaction and therefore receptor biochemistry including signal
In test tubes, using the appropriate stock solutions, prepare systems with different compositions of known weight. Account for the additional volume owing to titration, e.g., if 5 g systems are prepared, 10 mL test tubes should be used (see Note 5). As an example Table 1 shows systems that can be used for various PEG-phosphate and PEG-dextran systems, and the necessary calculations. This table may be reproduced in a spreadsheet program to allow ease of calculation.
Figure 2-6 depicts the extensive form of a Markov chain. Although the chain is stopped after 4 months, it could have been continued for an endless time. This type of drawing provides an intuitive explanation for why the analysis is referred to as a chain, and it also allows the model to be transformed directly into a spreadsheet calculation. From a mathematical perspective, however, all the relevant information necessary to calculate the chain outcome is already contained in the transitions drawn for the first month only. Frequently, this relevant information can be condensed into a short form of a Markov chain, as shown in the upper drawing of Figure 2-7. The arrows pointing towards the
The emphasis of this chapter was directed towards the clinical application of decision analysis in the management of gastroenterology patients and its use as a bedside tool for optimizing clinical management of common medical problems. The examples were purposefully based on crude cost estimates and restricted to simple models that could be calculated without resorting to dedicated decision software. All models used in this chapter were actually drawn and executed on an Excel spreadsheet from Microsoft Office. The examples serve to demonstrate that there is still ample room for use of decision analysis as a bedside tool. Frequently the models can be solved without mathematical calculations. Numerical precision is not an issue, because the analysis is not meant to be published but to serve to make the best use of the clinician's current knowledge in deriving the most sensible medical decision.
Rated radioactivity, corrected for release. In other words, the data represent the dpm released as a percentage of total remaining radioactivity. To perform this calculation, we enter the dpm value for each fraction (including the lysis step) into an Excel spreadsheet. Using the summation of total dpm released, we determine the dpm release min as a percentage of total incorporation. This partially corrects for the rundown of 3H AA release with time during the experiment.
The findings of external committees often arrive well after the other results are in hand. They are often transcribed and kept in spreadsheet form. While such spreadsheets can be used as a basis for analysis, I'd recommend that they be entered into the database as soon as possible. Here's why The spreadsheet is often too convenient, with the result that multiple copies soon are made, each copy differing subtly from the next with none ever really being the master. A single location for the data makes it easier to validate each and every record against the original printed findings of the external committee.
100 years, and many scientists of yesteryear would be armed with statistical tables, that had been laboriously calculated by hand. The vast majority of texts still advocate the use of such tables, and it is certainly important to be able to appreciate how to use them, but like books of logarithm tables and slide rules it is likely that their days are numbered, although it may take several years before this happens. We have already introduced the normal distribution table (Section 3.4) and will present some further tables below. It is important to recognize that common spreadsheets and almost all good scientific software will generate this information easily and more flexibly because any probability level can be used (tables are often restricted to specific probabilities). Because of the widespread availability of Excel, we will also show how this information can be obtained using spreadsheet commands.
Many managers would feel more comfortable if clinical data could be stored and viewed in a format with which they are already familiar, an Excel spreadsheet, for example (see Figure 11.1). At first glance, the spreadsheet format seems ideal each row constitutes a different patient record, and each column a different field or variable. But as we start to fill in a mockup of our spreadsheet, two difficulties arise first, as the number of columns exceeds the width of the screen, we may easily forget just where a particular data item is located second, as the trial continues, we begin to accumulate multiple records for each patient pretreatment or baseline, one-week follow-up, one-month follow-up, and so forth. Will we run out of space FIGURE 11.1 Spreadsheet as an Example of a Flat File. Obviously we will need several spreadsheets to store our data, perhaps one for each record type or each set of screens. But then how are we to link them in such a way that we can search and retrieve...
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