Graphical Analysis 4 Download

The differential equations of compartmental analysis form the basis of the models describing the uptake of tracers used in imaging studies. Graphical analyses convert the model equations into linear plots, the slopes of which represent measures of tracer binding. The graphical methods are not dependent upon a particular model structure but the slopes can be related to combinations of the model parameters if a model structure is assumed. The input required is uptake data from a region of interest vs time and an input function that can either be plasma measurements or uptake data from a suitable reference region. Graphical methods can be applied to both reversible and irreversibly binding tracers. They provide considerable ease of computation compared to the optimization of individual model parameters in the solution of the differential equations generally used to describe the binding of tracers. Conditions under which the graphical techniques are applicable and some problems encountered in separating tracer delivery and binding are considered. Also the effect of noise can introduce a bias in the distribution volume which is the slope of the graphical analysis of reversible tracers. Smoothing techniques may minimize this problem and retain the model independence. In any case graphical techniques can provide insight into the binding kinetics of tracers in a visual way.

SPARKvue is a data-collection software solution that helps students investigate science and STEM concepts with tools for real-time data collection, visualization, and analysis, as well as coding and collaboration. It delivers a consistent user experience across devices, whether students are using the free app or a site license. SPARKvue is continuously improved with new features and integrations that enhance the user experience across devices.

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SPARKvue is uniquely designed to deliver the features educators love within a framework students can easily use. It provides a powerful all-in-one platform with intuitive tools for data collection, visualization and analysis, as well as coding and collaboration. This all-in-one approach enables science students to streamline experiments for deeper learning, while also equipping STEM and after-school programs with the tools they need to code, create, and explore.

Graphical Analysis is designed specifically for data collection, visualization, and analysis with Vernier sensors. It provides standard graph, table, and meter displays that work well for most science courses. Graphical Analysis Pro introduces a video display that allows an imported video to be synced with sensor data, which can be helpful for reviewing experiments. Both software options help structure science experiments by providing students with a standardized template for data collection, visualization, and analysis.

Graphical Analysis and Graphical Analysis Pro also provide built-in analysis tools, including statistics, curve fits, and calculated columns. Graphical Analysis Pro expands these features by allowing students to create custom calculated columns and curve fits. Students can download data as a .csv file from either software, but only Graphical Analysis Pro allows data to be exported in .pdf form.

In this paper we present a graphical analysis framework for the new neoclassical synthesis, which can be used to explain and interpret the behavior of the new neoclassical model under shocks. We elaborate the role of expectations on output and inflation as well as the influence of the monetary authority.

TREEFINDER is an easy-to-use integrative platform-independent analysis environment for molecular phylogenetics. In this paper the main features of TREEFINDER (version of April 2004) are described. TREEFINDER is written in ANSI C and Java and implements powerful statistical approaches for inferring gene tree and related analyzes. In addition, it provides a user-friendly graphical interface and a phylogenetic programming language.

Computational inference of molecular phylogenies has a wide spectrum of applications in the analysis of DNA sequences, ranging from systematic biology to population genetics and comparative genomics [1].

As a result, a large body of theoretical methodology has developed [2], along with numerous specialist software packages. However, often the most advanced of these computer programs typically provide only a very Spartan user interface and hence are too difficult to use without additional training, especially for novices in phylogeny. One notable exception is the popular commercially distributed PAUP* software [3] that implements both powerful probabilistic methods for modeling and inferring gene trees and at the same time offers a friendly graphical user interface (GUI). Unfortunately, this GUI is currently available only on the Macintosh platform.

On the other hand, a more experienced user will quickly outgrow the limits of a graphical user interface. Consequently, to facilitate complex sequence analysis corresponding scripting languages have been developed. For example, in PAUP* all elements of its GUI can also be invoked on the command line. However, for the rapid deployment of specialized phylogenetic analysis tools one still needs the additional flexibility of a programming rather than scripting language.

The development of the TREEFINDER software is an attempt to address these issues to provide a unified powerful framework for phylogenetic analysis for both occasional and experienced users across different platforms.

The TREEFINDER software has a modular design. It consists of a graphical frontend (written in Java) and computational kernel (written in ANSI C). Both communicate in the special-purpose language TL ("TREEFINDER's language"). The frontend translates mouse clicks and keyboard hits into TL commands that are sent to the kernel. The kernel evaluates these commands and sends the results back to the window interface, where they are displayed.

The Java frontend provides a tree and postscript viewer, a text editor, a graphical user interface for common tasks in phylogenetic analysis, and a command line terminal to enter TL commands (see Figures 1,2,3,4). The graphical user interface makes the use of TREEFINDER very intuitive. Data files and reconstruction parameters can be chosen interactively, and the tree viewer also offers basic tree rearrangement functionality.

The kernel performs the actual analysis. For an overview of the currently implemented phylogenetic procedures and algorithms see section "Results" below. In addition to these specialized tasks, the kernel implements many other general mathematical and statistical functions, including pdf, cdf, and quantile functions of common statistical distributions and most functions from the public-domain CEPHES library [4]. It is also possible to run the kernel without the graphical frontend. In this case TL commands may simply be typed in at the operating system shell prompt or may be read from a text file.

The phylogenetic analysis procedures currently implemented in TREEFINDER focus mainly on probabilistic and statistical approaches. One important reason for this choice is that these methods consistently provide the most powerful and accurate inferences [2]. The following is a non-exhaustive list of features present in the TREEFINDER version of April 2004.

The confidence of inferred evolutionary relationships may be assessed by bootstrap analysis [16]. Corresponding routines for computing consensus trees [17] with the option to count and output the distinct topologies in the set of samples are available. Further TL procedures include checks for compositional bias in the data and functions for reading, writing and manipulating sequence alignments.

Figures 1,2,3,4 give an impression of the graphical user interface for typical standard tasks: tree viewing (Figure 1), editing alignments (Figure 2), reconstructing trees (Figure 3), and the TL shell to enter commands (Figure 4). Examples for the inference of a chronogram [10] and the plot of a rate pro-file [15] are shown in Figures 5 and 6. Most GUI interface elements will be self-explanatory, but a detailed description of each button etc. is available in the TREEFINDER manual.

The TREEFINDER environment, while being an versatile analysis framework already in the present version, has many options for further enhancement. This includes, most importantly, substitution models for amino acids, e.g., the classic Dayhoff model [22] or the more recent WAG model [23]. Other desirable directions for extension are the implementation of modern population genetic methods, such as tools for coalescent simulation and estimation of demographic parameters [24]. These, and other procedures, are scheduled for inclusions in future releases of TREEFINDER.

The TREEFINDER analysis environment can be downloaded free of charge from the web page Packages are currently provided for the Windows, MacOS X, SUN Solaris, and Intel Linux platforms. TREEFINDER requires the prior installation of a Java virtual machine (preferably version 1.4 or later). The TREEFINDER software is provided "as is" with no guarantee or warranty of any kind. It may be distributed non-commercially, provided that neither its manual or any other components of the software are changed (for details refer to the web page or the manual).

Causal discovery is a subfield of causal inference that focuses on finding evidence in data for the existence of a causal path between two or more variables1,2. This is an essential preliminary step as it can be used to justify the assumptions made by statistical analyses. Algorithms for causal discovery identify patterns of conditional independencies (CI) with theoretical justification for refuting candidate models that are unlikely to have generated the observed data. This process requires two assumptions (1) graphical d-separation and the Causal Markov Condition (CMC), and (2) Causal Faithfulness Condition (CFC)2,3 (see Supplementary Note 1 for formal definitions). CMC states that whenever a pair of variables X and Y are separated in the graph given a set Z, then X and Y are conditionally independent given Z in every compatible distribution. CMC has been proven to hold in acyclic models and in linear models with cycles (also called feedback loops)4. Some results support CMC in other cyclic cases4,5. CFC deals with the opposite direction: it assumes that a conditional independence (CI) in the observed distribution entails separation in the graph. CFC has theoretical justification in that the set of models that do not satisfy it are extremely unlikely (i.e., have a zero Lebesgue measure2,6). ff782bc1db