Midterm

1. Biology of the heart (20 points)

During class we have used the lung and respiratory system as an example of the kind of information that is associated with classical biology. Part of the goal was to provide you with a framework for organizing information about other anatomical structures. Such a framework in anatomy class has been called a Knowledge Organization Template (KOT). Like a frame or object in programming, the attributes of such a KOT depend on the type of the anatomical structure. Part of the KOT for the class “organ” is as follows:

Name
Synonyms
Definition
External appearance (surfaces, shape, features)
Internal anatomy (parts)
Input/Output (blood, nerves, lymph, additional depending on the organ)
Relationships (location, adjacent organs, continuities)
Histological structure (kinds of tissues and cells)
Function

Your job is to fill in these attributes for the heart, in 1-2 pages, but no more. These pages can include up to two illustrations. You may use any source, including books from the library and online resources, but you should cite these sources at the end of the entire exam. The illustrations can be from any source, but the source of the illustrations must also be cited.

The values of the attributes should just be text, augmented by up to two illustrations. Note that the same structure can appear in more then one attribute value. In the case of function you should briefly describe the cardiac cycle, that is, the sequence of events that cause blood to flow into and out of the heart.

2. Querying the FMA (20 points)

In the remaining questions we will explore how to represent and use in computer applications the type of information described in question #1. As discussed in class one type of anatomical information is symbolic information about concepts and relationships, of which part-of relationships are particularly difficult because there are so many ways to divide something up.

If you have not already done so use the FME to look at the parts of the Heart. If you were to expand all the parts on the left-hand side you would be showing all the parts of the parts of the heart down to the most detailed part (this is called the transitive closure).

Suppose in this transitive closure that you are only interested in one kind of part, in this case all the parts that are epithelial tissues.

a. (5 points) Explain how you could use the FME to determine which of the parts of the parts of the heart are epithelial tissues. Which exact FMA term would you need to look for when you are looking for epithelial tissue? Is this a good way to answer this query?

b. (5 points) In order to build an application that uses the FMA we need a query processor and query language, much like SQL is a query language for a relational database. Write a high-level English language query that would answer the question, and that, when expressed in some formal query language, could be input to the query processor. (5 points).

c. (5 points) One such query processor we have developed is called OQAFMA. Express your query in the query language of OQAFMA, called StruQL, and show a screenshot of your result. The OQAFMA demo is here, the StruQL syntax with examples is here, and a paper describing OQAFMA is here. Hint: The following StruQL query was used last year to find only those parts of the heart that are left and right ventricles, and left and right atria.

WHERE
     X->":NAME"->"Heart",
     X->"part"+->Y,
     Y->":DIRECT-SUPERCLASSES"+.":NAME"->"Cardiac chamber",
     Y->":NAME"->Parts
     CREATE
     TheHeart(Parts)

d. (5 points) List the parts of the heart (transitive closure) that are epithelial tissues, ie the answer to the query. This is just a list of specific FMA terms.

3. Imaging (20 points)

The AnnoteImage program is an example of a tool for labeling regions on images. As shown in this screenshot the user loads an image, draws contours around regions of interest, and associates a name with each region. The resulting annotations are saved in a separate XML file associated with the image. The annotated image files can then be uploaded into a program such as our Image Manager program, where they can be used to automatically generate interactive atlases for education.

a. In the current downloadable version the names are simply typed into the program. Why might you want the user to be able to select from a controlled terminology (such as the FMA) when creating the labels? Describe how the OQAFMA query server, together with the types of queries you looked at in question 2, might be used by an enhanced version of AnnoteImage to provide the controlled list of terms. ( 7 points)

b. Next, look at the Digital Anatomist Image Manager application. This is basically a web front-end to a relational database, which contains the pathnames of image files, many of which have been annotated using AnnoteImage. Click on “Public Images” and try searching for images that show any of the four parts of the heart noted in question #2c (Right atrium, etc). Describe how the OQAFMA query server could be accessed to provide a more “intelligent” search feature for the Image Manager that would allow a single query to find images that depict any of Right atrium OR Left atrium OR Right ventricle OR Left ventricle. (7 points)

c. Finally, how might content-based retrieval (CBR) methods allow non-annotated images showing the right atrium to be retrieved? (6 points)

4. Simulation (20 points)

Annotations with controlled terminology from an ontology can be useful, not only for searching across image databases, but also for searching across model databases as a first step towards model integration. Assume that computational models like those presented in the Neal paper have been annotated using the methods described in the Gennari paper, and that the annotations for each model are saved as instances of an ApplModel. Sketch the design of a model database that would use the ApplModel annotations as a means for finding relevant models stored in the database. How would such a system answer queries like, "Retrieve all models that are related to the cardiovascular system", or "Retrieve all models that involve blood flow in the aorta?"

5. The Virtual Human (20 points)

You have decided to write a grant proposal to create the Virtual Human (VH), a complete model and simulation of an individual patient's anatomy and physiology. The model will establish a baseline 3-D model of the patient's anatomy using whole body CT scanning (a 3-D volume of the entire body), and of the patient's physiology and pathology based on measured variables such as EKG, blood pressure, standard lab tests, and clinical history. Given this baseline the model should be able to predict the response of the patient to trauma, disease, and therapeutic interventions.

Your job is to write the one page "Specific Aims" section of an NIH grant proposal to create the VH. The page should start with the problem that the VH should solve, and what use the VH would be for certain scenarios. It should then list from 3-5 "specific aims", which are research issues that need to be solved in order to create the VH. Each of these aims should be a few sentences at most, describing the issue, why its hard, and how you would approach solving the issue. The one page should end with an upbeat description of why building the VH is something significant that NIH should invest its money in.

After completing the one page specific aims, pretend you are a reviewer of this proposal and write a one or two paragraph critique, giving a score of 100-500 (100 being best and 500 being worst), and explaining your score.