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An interactive system for finding complementary literatures:
a stimulus to scientific discovery
Don R. Swanson* & Neil R. Smalheiser†
*Division of the Humanities, The University of Chicago
1010 E. 59th St., Chicago, IL 60637
†Department of Pediatrics, MC 5058, The University of Chicago
5841 S. Maryland Ave., Chicago, IL 60637
An unintended consequence of specialization in science is poor communication across
specialties. Information developed in one area of research may be of value in another
without anyone becoming aware of the fact. We describe and evaluate interactive
software and database search strategies that facilitate the discovery of previously
unknown cross-specialty information of scientific interest. The user begins by searching
MEDLINE for article titles that identify a problem or topic of interest. From downloaded
titles the software constructs input for additional database searches and produces a series
of heuristic aids that help the user select a second set of articles complementary to the
first set and from a different area of research. The two sets are complementary if together
they can reveal new useful information that cannot be inferred from either set alone. The
software output further helps the user identify the new information and derive from it a
novel testable hypothesis. We report several successful tests and applications of the
1. Introduction and background
An important problem in the growth of knowledge is brought to light by the following
type of literature structure: One set of articles (AB) reports an interesting association
between variables A and B, a different set of articles (BC) reports a relationship between
B and C, but nothing at all has been published concerning a possible link between A and
C, even though such a link if validated would be of scientific interest [6, 30]. For
example, "A" might represent intake of a substance that induces a specific physiological
change B, which in turn influences disease or body organ C. In broader terms, two
Swanson & Smalheiser p. 2
literatures such as AB and BC are complementary if useful information can be inferred
by considering them together that cannot be inferred from either one alone. If,
furthermore, the two literatures are mutually isolated in that the authors and readers of
one literature are not acquainted with the other, and vice versa, as may often be the case
for two different specialties, then no one at all may be in a position to notice the
implication that A and C might be related. To detect literature pairs that are mutually
isolated, we examine the citation pattern [7]. If two literatures are "noninteractive" —
that is, if they have never (or seldom) been cited together, and if neither cites the other,
— then it is possible that scientists have not previously considered both literatures
together [25, 35]. The two conditions, complementarity and noninteraction, describe a
model structure that shows how useful information can remain undiscovered even though
its components consist of public knowledge. Figure 1 illustrates how multiple
intermediate literatures might link a given A and C.
The concept of "undiscovered public knowledge" was developed and exemplified in
1986 [28, 29] based on an actual structure in the biomedical literature similar to the AB-
BC model shown in Figure 1. Articles on Raynaud's disease (C) and articles on
eicosapentaenoic acid (A) when considered together were suggestive that Raynaud
patients might benefit from dietary fish oils rich in eicosapentaenoic acid [29]. One B-
linkage, for example, was: dietary eicosapentaenoic acid can decrease
blood viscosity
(B); abnormally high
blood viscosity has been reported in patients with Raynaud's
disease. Neither of the two literatures (A, C) cited or mentioned the other, nor has
extensive searching turned up any prior suggestions that dietary fish oils might influence
Raynaud's disease. An independent clinical trial by medical researchers [2] subsequently
corroborated the predicted beneficial effects, as discussed in [5, 37]. This first structure
was found more or less by accident, but inspired an ongoing effort to systematize the
search process [32, 36], corroborate and replicate the structure [8], model it [3, 36],
extend it [3, 4, 8], trace historical antecedents [4], and examine citations to the project
itself [27, 37].
A second example of complementary noninteractive literatures began with the
literature on migraine (C), and subsequently identified a literature showing that
magnesium deficiency (A) led to certain physiological effects (B) that, in a different
context, were associated with migraine [31]. Eleven intermediate effects, B, provided the
linkage. (Two such linkages are, for example: magnesium can inhibit
spreading
Swanson & Smalheiser p. 3
depression in the cortex, and
spreading depression may be implicated in migraine
attacks; magnesium-deficient rats have been used as a model of
epilepsy, and
epilepsy has
been associated with migraine). Remarkably, even though the literatures on magnesium
and migraine that were identified as having extensive indirect linkages consisted of more
than 60 articles each, neither literature cited or mentioned the other. Moreover at that
time (1988) there were almost no records in the entire MEDLINE database that contained
both of the words "migraine" and "magnesium". Since 1988 [31], more than 12 different
groups of medical researchers have reported a systemic or local magnesium deficiency in
migraine or a favorable response of migraine patients to dietary supplementation with
magnesium [21, 37]. There is also one recent report of negative results [26].
To date we have found and described seven examples of complementary pairs of
literatures, using progressively improved methods for finding them [21-24, 29, 31, 33].
In each case we discerned a novel testable hypothesis that was implicit in a pair of
literatures considered together, but not previously made explicit in published form. Each
structure was found through extensive searching, exploring, and reading biomedical
literature [34]. The hypotheses evoked by these procedures can be evaluated by: 1) their
inherent plausibility, 2) their acceptance for publication in a refereed biomedical journal;
3) whether they stimulate biomedical researchers to conduct clinical trials or laboratory
experiments, and 4) whether the hypotheses are corroborated as a result of such tests. For
the two earliest cases [29, 31] all four criteria appear to have been met [2, 21, 37].
Our goal has been to create interactive software and database search strategies that can
facilitate the discovery of complementary structures in the published literature of science.
The universe of literature or search-space under consideration is limited only by the
coverage of the major scientific databases, though we have focused primarily on the
biomedical field and the MEDLINE database (8 million records). In 1991, a systematic
approach to finding complementary structures was outlined and became a point of
departure for software development [36]. The system that has now taken shape is based
on a 3-way interaction between computer software, bibliographic databases, and a human
operator. The interaction generates information structures that are used heuristically to
guide the search for promising complementary literatures. In this paper we describe and
evaluate the experimental computer software, which we call ARROWSMITH, and we
explain how it functions within the system.
Swanson & Smalheiser p. 4
2. Approach and overview
For the migraine study [31] we analyzed the form of the eleven relationships between
A and B and between B and C that we had found [36, p 282]. (For example, some variant
of "can or might influence" occurred repeatedly.) It was clear from the outset that, even
if we were able to fit most relationships into a limited number of seemingly simple
patterns, few or none of them were transitive; they were, however, often suggestive of a
plausible new inference. But to recognize relationships within the natural language text
of titles and abstracts and to draw inferences from them requires both common sense and
extensive background knowledge. We have not attempted to formalize or automate such
tasks. Our approach is based instead on automated procedures that present, for human
observation, suggestive juxtapositions of natural-language extracts (mainly titles, at
present) from database records. We attempt to stimulate, rather than codify, the
discovery process.
The user of the system begins by choosing a question or problem area of scientific
interest that can be associated with a literature, C. The ultimate target of the ensuing
search is a second literature, A, complementary to literature C. Two main procedures (I
and II) implement the foregoing approach in a working experimental system.
Procedure
I operates on a single specified "source" literature, C, identified by a database search of
title words or phrases. Figure 2 is a schematic diagram of title-word pathways from
literature C, proceeding via multiple intermediate B-terms that co-occur with title-word
"C", to various possible target title words denoted A1, A2, A3 . etc., each of which co-
occurs with one or more B-terms. There can be, potentially, thousands of intermediate
B-terms that link to C, and, for each B, thousands of possible candidates for A that link to
B. The problem of searching for one (or a few) initially unknown Ai within the entire
MEDLINE database presents a formidable explosion of possibilities. The interactive
system we describe evades this explosion by first restricting, by various means, the
number of different B-linkages to be traversed. From the resulting list of "A-candidates",
the user chooses one Ai judged to be biologically plausible. (Alternatively, the user may
choose A on some other basis and simply bypass
Procedure I.)
Procedure II starts anew from
two pre-selected literatures, A and C (irrespective of
how A was chosen). Focusing on a single target literature, A, makes it feasible now to
develop a much less restricted list of title-word pathways that connect A to C. This
Swanson & Smalheiser p. 5
procedure aims to identify all title-word pathways that might provide clues to the
presence of complementary arguments within those literatures. The output of
Procedure
II, a structured title-display (plus journal citation), serves as a heuristic aid to identifying
word-linked titles and serves as an organized guide to the literature.
ARROWSMITH (and its user) may be seen as a problem-generating system [16, 17].
The user chooses a relatively broad initial problem area (e.g. a disease for which neither
cause nor cure is known) and is guided by
Procedure I toward promising complementary
literatures and by
Procedure II toward a series of more specific problems concerning
biological pathways that might constitute mechanisms of action, and ultimately toward a
problem presented to the experimentalist in the form of a plausible testable hypothesis.
2.1 Procedure I: Forming a ranked list of A-word candidates
In this section we describe
Procedure I applied to a specific example in which C is
taken as the pre-1988 literature on migraine.
Step 1. Conduct a MEDLINE search for all titles that contain the word "migraine";
download these titles into a local computer file (FILE C). Derive automatically from
FILE C a list of the unique words that it contains (that is, words that co-occur in titles
with "migraine"). This list contains all possible candidates for B-words, (including
potentially interesting words such as "inflammatory", "platelet", "prostaglandin",
"serotonin", "vasospasm") but it contains also many unsuitable words. In order to evade
an explosion of search-paths and to limit the output to manageable size, four types of
restrictions are introduced, the first being: i) Words that are predictably unsuitable as B-
candidates are excluded (e.g. not only non-topic words such as "able", "about", "above",
but also words that are vague or too general such as "clinical", "comparative", "drugs",
"evidence", "experimental", "role", "treatment", "studies") [36]. A pre-compiled
exclusion list, or "stoplist" is provided to the computer. The list is human-constructed on
the basis of judgment applied
a priori concerning the suitability of each word, and at
present consists of about 5000 words.
Step 2. Each of the remaining B-word candidates is then searched in MEDLINE to
determine the total number of titles in which it occurs. Restriction ii): These words are
further screened automatically to retain only those that occur with greater relative
frequency in migraine titles than in titles from MEDLINE as a whole. The latter
Swanson & Smalheiser p. 6
frequency is determined from the information displayed in the online search which shows
each search statement and the corresponding number of items found. More specifically,
we retain only words for which the probability is small that a random allocation of words
to titles could lead to a number of co-occurrences with "migraine" equal to or greater than
the observed number [1]. (The cutoff probability in our migraine example is 0.09, as
calculated from a Poisson distribution, ignoring multiple occurrences of a given word
within the same title).
Step 3. We introduce Restriction iii): the human operator examines the filtered list of
B-terms, and removes entries that are judged not suitable — e. g. words that should be
put on the stoplist, or possibly words too broad to be useful in the particular problem
considered. Each word that remains becomes the basis for a new MEDLINE title-search.
Restriction iv is introduced into the search strategy: Each set of records formed by
searching titles for each B-word is to be narrowed down to certain
categories of records
(chosen in advance) that are likely to contain target words of particular interest. This
restriction is methodologically important for it provides the user flexibility in conducting
an exploratory process that may in many respects model the process of scientific inquiry
[11]. To date, most of our restriction categories have focused on exogenous agents
because of their potential for experimental manipulation in clinical tests. Thus,
categories such as deficiencies, dietary factors, toxins, or various subcategories of drugs
or environmental factors might be selected, strategies that can be designed and tested in
advance and built into the system [36]. In the present migraine example, the strategy was
designed to restrict the output to dietary factors or deficiencies induced by dietary
deprivation. Detailed search statements are given in [36]. Commands to form the
intersection of the restriction set with each B-word set and a command to form the union
of those intersections (U), are constructed automatically.
Step 4. The resulting search is uploaded into MEDLINE and executed. All titles that
correspond to set U are then downloaded to FILE U in the local computer. FILE U is
converted to a list of word occurrences (potential A-words), again using the stoplist as a
filter, repeating restriction i. Each A-occurrence is attached to the B-word that generated
it, thus forming an A-B list that is used at a later point. It is also used immediately to
create a list of unique A-words for the database search of Step 5.
Step 5. A database title-search is conducted for each unique A-word in order to
determine, for each A, the number of A-word titles within the restriction category.
Swanson & Smalheiser p. 7
Repeating restriction ii): the A-words are filtered according to probabilistic criteria, but
now based on their co-occurrences with each B-word (rather than with "migraine"). The
A-words thus selected constitute the list of A-candidates. Each candidate is then
assigned a rank according to the number of different B-words in the AB-BC co-
occurrence linkages in which it participates, as illustrated in Figure 2. This ranking
algorithm is based on a presumption that the greater the number of B-term linkages, the
greater the chance that some of them will be biologically important. The candidate list is
displayed for the human operator to use as a heuristic device (Fig. 3).
Step 6. From the ranked list, expert judgment is brought to bear in choosing words that
appear to be the best candidates for assisting the discovery of complementary arguments,
and so for defining a literature (A) complementary to that of C. Any one choice of A-
word from the candidate list can provide input to
Procedure II (Section 2.2).
The leftmost list in Figure 3 shows the first 24 ranked A-terms for our migraine
example. "Magnesium" is ranked first. Not only is it of high rank, it is biologically
highly plausible. Magnesium is an essential element in the human diet and is well known
as a modulator of neurotransmission. By these criteria "magnesium" emerged as the
most promising A-word candidate.
Other words on the A-candidate list also might merit further exploration. Some of
them are, however, too broad to be useful if taken alone ("hormone", "pressure",
"lipid(s)", "hepatic", "membrane"). For reasons of practicality, the A-list is at present
limited to single words, even though phrases may often be more appropriate and
informative. However, the titles in which any particular A-word occurs (in File U) can
be examined to determine whether a consistently more specific context can be identified.
For example, we found that "hormone" and its B-linkages referred in most cases to
"growth hormone"; the B-linkages, however, did not appear to be important enough to
pursue further. One could determine also whether a single letter such as "e" refers
consistently to vitamin E, apolipoprotein E, E-coli, or hepatitis E, and so on.
2.2 Procedure II: Forming a list of B-terms and a title display
This procedure develops a greatly expanded list of B-terms for a given A and C. The
probabilistic and category restrictions (ii and iv) are dropped, and phrases of up to 6
contiguous words are included. "A" represents any single agent which may have been
Swanson & Smalheiser p. 8
taken from the
Procedure I list of A-candidates, or "A" may simply be based on an
informed scientific conjecture concerning a possible A-C relationship.
Procedure II should always be preceded by a database search for all records that
contain both A and C (not restricting the search to just titles), in order to identify any A-
C or A-B-C relationships that are already explicitly published. Such known B-linkages
should be investigated in advance of executing
Procedure II to avoid unknowingly
rediscovering them as indirect linkages;
Procedure II can then focus on relationships that
are either novel or at least cannot be found by conventional searching. Understanding
strategies used in conventional searching is important at this point; finding the "direct"
literature is not always straightforward [12, 13]. The citation interaction pattern also can
play a key role in determining whether an A-C relationship exists and is already known, a
process described in more detail elsewhere [35].
The output of
Procedure II is to be a printed display of titles from the A and C
literatures organized according to the words, B, that they have in common. Figure 4 is a
schematic flowchart of the process that leads to this output, a process that begins with
downloading the two sets of titles (and journal citations) for literatures A and C. For the
migraine application (based on pre-1988 literature), MEDLINE was used to create a local
file (C) of all titles (about 2800) that contain the word "migraine", and a file (A) of all
titles (about 8000) that contain the word "magnesium". No title contained both words.
The computer then produced a list of all words and phrases (about 200, after excluding
stoplist words), called the "B-LIST", each of which appeared in at least 2 migraine titles
and 2 magnesium titles.
Application of the stoplist to phrases merits further comment. The appropriateness of
the stoplist for many words depends on the context in which they are used. "Protein", for
example, is too broad to be useful, and so is put on the stoplist. But individual proteins
are not, and the names of some of these include the word "protein", such as "amyloid
beta-protein" or "protein kinase C". We therefore divide the stoplist into two parts, a
short list of prepositions and other connectives and a long (5000-word) list. We apply
only the short stoplist to each word in a phrase. Only if every remaining word in the
phrase is on the long stoplist is the phrase dropped. (Figure 4).
The B-LIST is next edited by removing redundancies and non-useful terms, resulting,
for the migraine/magnesium case, in cutting the list from 200 to 150 terms. The editing
process in general may include adding, deleting, removing redundancies, or revising
Swanson & Smalheiser p. 9
entries in order to compensate for certain limitations in the mechanized rules.
After the editing has been completed, the computer produces a display or printed
output that shows titles within title-file A that share B-terms with titles in file C,
organized alphabetically by the 150 B-terms. A similar display is created for title-file C,
thus facilitating a comparison of titles A (magnesium) with titles C (migraine) that have
one or more B-words or phrases in common. Figure 5 shows the general format of the
display and some of the more interesting titles selected for their suggestiveness of
complementarity. This title display is the principal product of ARROWSMITH. In our
example it contained altogether about 920 migraine titles and 1350 magnesium titles.
In general, portions of the title-display output that appear promising on the basis of
expert judgment then become a heuristic point of departure for reviewing literatures A
and C in order to identify complementary statements or arguments (for example, AB-BC
relationships) that lead to novel testable hypotheses.
A study of the output display for magnesium and migraine revealed about 40 B-terms
(listed in Figure 6) that merit further investigation because they appear, from some of the
titles with which they are associated, to be related to both magnesium and migraine in a
way that suggests a physiological linkage.
3. Evaluating ARROWSMITH
In this paper we try to make clear how and why we found ARROWSMITH useful in a
search for complementary literatures. We have presented a step by step process and
detailed examples of output to permit others to form a reasonable judgment concerning
Prior to designing ARROWSMITH, we had available three completed and published
analyses of complementary noninteractive literatures [29, 31, 33]. These studies provide
an opportunity to determine whether ARROWSMITH can at least be helpful in
rediscovering complementary structures already known by the user to exist. Such an
outcome cannot be taken for granted without being put to a test -- principally because it
is not otherwise obvious that article titles alone contain enough information to put the
user on the track of complementary literatures.
For the migraine case study exemplified in the preceding section, we have noted that
"magnesium" was first on the ranked list of terms produced automatically as the output of
Swanson & Smalheiser p. 10
Procedure I. The title-word co-occurrence data that led to this ranking was not generated
at the time of the original study; magnesium was chosen on quite different grounds at that
time. Not only did magnesium rank high on the list of A-candidates, but within the B-list
generated by
Procedure II (Figure 6), one can recognize ten of the eleven connections
identified originally in the 1988 migraine/magnesium study (the eleventh did not co-
occur with "magnesium" as a title-word in MEDLINE). In addition, a substantial number
of new and plausible candidates for B-terms also appeared on the list. The 19 words and
phrases marked with an asterisk in Figure 6 correspond, with redundancies removed, to
the 10 terms discussed in the earlier study [31]. Thus, using
Procedure II, almost all of
the originally reported B-term connections were produced automatically, and in a matter
of hours, replacing weeks-long literature-searching and exploration that was previously
Up to this point, production of the ranked A-list and the B-list are automatic, provided
the user chooses the same search- restriction categories and strategy (based on terms
related to dietary intake) as we exemplified. Users do have the option of changing this
feature of the exploratory process however. In the next stage, the production of the
output title display (of which a small part is shown in Figure 5) is also automatic, for it is
fully defined by the B-list and the original set of downloaded titles. Figure 5 itself was
not automatically produced; the 67 titles shown were selected from the complete display
of about 2270 titles. This selection process is of course sensitive to what the user is
looking for, and other users might not see the same, or as many, linked titles in the output
display as we found. However, we invite readers to study Figure 5 closely and try to
form a judgment of the effectiveness of these word linked titles in suggesting a possible
link between magnesium deficiency and migraine. Successful use of the complete title
display does not depend on recognizing or selecting all of the titles we selected, but only
on seeing enough linked titles to impel further searching and analysis. Thus, the method
is probably quite robust with respect to individual variability among users. Accordingly,
we believe that the success of this retrospective application of
Procedure I in discovering
an already-known structure provides a reasonable basis for judging how the system
would have performed had the structure not been known in advance. That is, the
opportunity for favorably distorting the outcome because the users knew what to look for
is relatively limited.
The application of
Procedure I to the pre-1986 literature on Raynaud's disease [29]
Swanson & Smalheiser p. 11
was similarly successful, except that here the context revealed in File U played a more
central role. "Oil", one of the high-ranking but fairly broad words on the A-list (Fig. 3)
occurred (within File U) repeatedly in the same, and much narrower, context defined by
"dietary fish oil" (or equivalent terms). Figure 6 shows a B-list for the Raynaud's disease
analysis. We produced substantially the same outcome as in the original analysis
reported in 1986 [28, 29]. In addition, the main findings of that analysis have been
largely replicated, as well as extended, independently by other researchers [8].
The success of ARROWSMITH in the above two case studies was not matched in the
third study, that of somatomedin C and arginine [33]. This attempt failed because there
were no paired titles that were suggestive of complementary arguments within the
corresponding literatures. This failure to re-discover an already known structure
underscores an important point. We cannot and do not claim that the procedures we have
developed will always be successful, even in the hands of a prescient user. Some of the
many reasons why our procedures, as presently designed, might fail to reveal
complementary literatures that do exist are discussed in section 5.2.
The next section of this paper can also be seen as an evaluation of ARROWSMITH
(
Procedure II). But in this case, the test is focused on producing novel results rather than
on finding structures that we previously had discovered using more conventional
literature search methods.
4. Examples that use Procedure II independently of Procedure I.
Procedure II will be useful independently of
Procedure I if the user already has formed
hypotheses concerning plausible A-literatures. For example, a biomedical researcher
may have reason to think that a particular A-term is related to C without necessarily
knowing the intermediate pathways or mechanisms by which A and C are linked.
Procedure II can be regarded as a "higher order Medline search" (because it finds 2nd-
order indirect title-word connections). The following three examples fit this pattern. The
first two examples, on Alzheimer's disease (AD), show how previously unknown
connections can be discovered even between two literatures already known to interact in
other respects.
4.1 Case example 1: Indomethacin and Alzheimer's disease (AD).
Swanson & Smalheiser p. 12
The literatures corresponding to indomethacin (A) and Alzheimer's disease (C)
intersect and are interactive. Indeed, indomethacin appears to have a protective effect
against AD, as evidenced by epidemiologic and clinical data [22]. Nevertheless
indomethacin, an inhibitor of prostaglandin synthesis, affects many organ systems and its
effects have been reported in the literatures of many diverse specialties. Therefore it is
not an easy task to find all known effects of indomethacin which are likely to be relevant
to patients with AD.
Procedure II offers a useful aid to solving this problem by
displaying numerous plausible indirect (A-B-C) title-word linkages between the two
literatures on indomethacin and AD. In this case ARROWSMITH found 103 B-terms, of
which 5 referred to substances or physiologic processes affected by indomethacin in a
way that, as indicated separately in the AD literature, might possibly ameliorate AD [22].
In addition, we found a possible adverse effect: indomethacin had a well-documented
anti-cholinergic action, and separately it was clear from the AD literature that one would
expect anti-cholinergic action to affect AD patients adversely by exacerbating cognitive
dysfunction. The possibility that indomethacin might have adverse side effects in AD
patients apparently had not been considered explicitly in any scientific publication and so
we brought this to the attention of neuroscientists [22].
4.2 Case Example 2: Estrogen and Alzheimer's disease
Even though it has become relatively well established that estrogen replacement therapy
for postmenopausal women results in a lower incidence of AD, the mechanism of such
beneficial effects is not understood. We used
Procedure II to develop a list of potential
B-linkages between estrogen and AD; there were several hundred B-terms that appeared
in at least 2 titles in each literature. About 8 of these terms (calbindin D28K, cathepsin D
and other proteases, superoxide dismutase, antioxidants, apolipoprotein E, glutamate,
cytochrome C oxidase subunit III) refer to substances that are known to be modulated by
estrogen and are separately known to be implicated in AD, but which have not been
investigated as estrogen targets in the context of AD. In the process of investigating the
literature identified by the output title display, we encountered intriguing reports
indicating that estrogen has antioxidant activity. There is also an extensive AD literature
on free radicals (groups of atoms, usually highly active, that have one or more unpaired
electrons) which are thought to play a role in the development of AD. Because
Swanson & Smalheiser p. 13
antioxidants would tend to counteract such effects, it is remarkable that there was no
evidence in the database-accessible literature that AD researchers were aware of this
possible mechanism for estrogen's beneficial effects [23]. After our paper was submitted
for publication [23], one study did appear which mentioned this link.
4.3 Case Example 3: Phospholipases and Sleep
The hypothesis that phospholipases may regulate sleep has been raised implicitly
(phospholipases may regulate prostaglandin synthesis and several prostaglandins are
endogenous sleep modulators), but this idea has not been investigated experimentally nor
discussed explicitly in the biomedical literature. We employed
Procedure II in three
searches using "sleep" as the C-literature and "phospholipase A2", "phospholipase C", or
"phospholipase D" as A-literatures. (In this case we found that most B-terms did not
represent substances formed by phospholipase activity, but rather substances that
stimulate or inhibit phospholipases.) We found a number of cytokines and
neuromodulators among the B-terms, notably the major sleep-promoting substances
interleukin 1β, tumor necrosis factor, and endotoxin/lipopolysaccharide. These
substances stimulate phospholipase A2, C, and D activities in various systems, and this
stimulation is thought to be required for at least some of their biological activities. Thus,
Procedure II generated a list of promising agents whose effects on sleep may involve
phospholipases, and we suggest testing whether specific phospholipase inhibitors inhibit
the sleep-promoting effects of these agents [24].
5.1. Intelligence, implanted structures, and the role of heuristics.
ARROWSMITH provides three stages of output information structures that serve as
heuristic guides to complementary scientific arguments within the literature: i) the list of
A-candidates (Figure 3); ii) the B-LIST; and iii) the title display organized by B-terms
(Figure 5). The process is heuristic in that human choices at each stage not only are
assisted by the displayed structures [40], but these choices in turn influence the outcome
of each later stage. Each cycle through the three stages provides valuable information for
Swanson & Smalheiser p. 14
a retry, either with a new choice from the same A-candidate list, or possibly with a new
A-list derived from a different restriction category.
ARROWSMITH possesses no algorithm for recognizing "interesting" relationships
such as transitivity or complementarity, but it
seems to have such a capability. The
density of interesting title relationships revealed by the stage iii display appears to be
remarkably greater than the density of interesting relationships among titles selected
randomly even from two complementary literatures. The consequent impression of
intelligence arises primarily from three sources: the stoplist, the search strategy
restriction category, and the organization of the output display. These structures are
human created, then supplied to the computer, and can be thought of as implanted
intelligence. ARROWSMITH filters and organizes title data based on these implanted
5.2. Limitations, improvements and future directions.
The use of title-words as a basis for detecting complementary literatures is both a
strength and a weakness of ARROWSMITH. The advantage of titles lies in the fact that
they provide a constrained context within which the linkages between A and B terms, and
between B and C terms, tend to be biologically meaningful and easily perceived by the
viewer, thus facilitating the recognition of potential complementarity. On the other hand,
ARROWSMITH at present does not attempt automatic linkage detection in abstracts or
subject headings. Other investigators have shown that such information can be exploited.
Using full MEDLINE records (1983-1985 only), no restriction categories, and several
statistical criteria, Gordon and Lindsay have confirmed that fish-oil is a high-ranking A-
candidate in the A-B-C model for Raynaud's disease [8].
Each of ARROWSMITH's output heuristic information structures serves also as a
source of feedback that is potentially useful for improving the system, evaluating it, and
perhaps for stimulating ideas that can lead to better models and theories. Within the
framework of the present design, numerous incremental improvements appear to be
possible through augmenting the stoplist, improving phrase recognition by allowing for
more variation in word morphology and word position, organizing and grouping B-terms
with the help of subject headings, extending the A-candidate list to include phrases, and
developing a synonym-recognition capability.
Swanson & Smalheiser p. 15
The most practical and straightforward approach to improving ARROWSMITH
consists in juxtaposing pairs of titles not only on the basis of words they have in common
but in addition by common subject headings in the two respective MEDLINE records.
This kind of linkage supplies additional context for paired titles that may be meaningful
to the expert observer. Moreover, linking titles by common subject headings may also
reduce the number of near-synonymous or equivalent structures that are missed by word-
On a longer range basis, we envision applying ARROWSMITH to databases other
than MEDLINE and particularly to records that may be derived from different databases.
For such applications, the Metathesaurus of the Unified Medical Language System
(UMLS) offers a potentially valuable resource for developing a hierarchy/synonym-
recognition capability. The 1996 Metathesaurus is a synthesis of 38 different source
vocabularies (one of which is
Medical Subject Headings (MeSH)). It contains over
250,000 concepts named by 590,000 biomedical terms, and reflects all names, meanings,
and hierarchical and inter-term relationships that are present in the original source
vocabularies. In addition, it establishes new relationships between terms from different
source vocabularies [20].
Significant advances beyond the capabilities of existing database search systems
might follow from fundamental improvements in the indexing of relationships. Existing
index languages and subject heading systems (such as
MeSH) reflect context-independent
language structures, particularly synonymous and hierarchical relationships. These
systems are not designed or intended to represent adequately the relationships between
different subject headings that are appropriate for a particular article to which the
headings are applied (hence are context-dependent) but not appropriate for all articles and
contexts. For example, an article discussing arteriosclerosis as a possible cause of
hypertension would be indexed under the subject headings "arteriosclerosis" and
"hypertension"; there is no provision for linking the two terms together, in the process of
indexing a given article, to show that a cause-effect relationship between them is
discussed in that article. The hierarchical relationship between "arteriosclerosis" and
"coronary disease", on the other hand, is valid in all contexts and hence is built into the
MeSH structure itself. The requirement for improved indexing of context-dependent
relationships calls attention to these relationships themselves as entities that merit more
systematic study [9, 10], and has led to important research on interactive frame-based or
Swanson & Smalheiser p. 16
knowledge-based indexing [9, 10, 14, 15].
5.3. Comments on methodology
Any two literatures or sets of titles that share a common language will have many
words in common irrespective of whether the two literatures have substantial or
interesting scientific linkage. Hence we must address the question of whether
ARROWSMITH, in manipulating title words, really helps reveal linkage of scientific
interest. The A-list consists of words that co-occur in titles with other words (the B-
terms) that co-occur in titles with C-terms. The list is subjected to three filtering
processes -- an extensive stoplist, a probabilistic cutoff that retains only words strongly
correlated (A with B and B with C) and a category restriction. Our results at least
suggest that the more important and better-established A-B and B-C biological
connections tend to pass through the filters. Even though the problem of finding nuggets
of value among a large number of uninteresting connections persists, the above process
of enrichment appears sufficient to permit the more interesting or important connections
to be spotted easily by an expert. It is also our strong impression that the high-ranking
biologically-plausible A-terms lead to a more extensive and much richer B-LIST and title
display in
Procedure II than do words of equal
a priori plausibility but which are of low
rank or not on the A-list at all.
We have defined the complementarity of two literatures based on the scientific
arguments and discourse within those literatures, not on just shared language use. To
examine and illustrate this distinction further, we developed a new A-candidate list
(Figure 3), for the literature on Alzheimer's disease (AD), using a restriction category
based on toxins or poisons [36], and analyzed the underlying biologic connections
associated with one high-rank candidate, lead.
The appearance of the poisonous metal "lead" among the top-ranked words on the A-
list for AD invites attention. Lead is well known as an environmental hazard with
neurotoxic effects. Among the various high-ranking terms on the A-list that would seem
worth investigating (including ozone, ethanol, TNF (tumor necrosis factor), IL
(interleukin), cadmium) lead is a conspicuous and biologically-plausible choice. Even
though all A-words participate in A-B-C
word linkages, these linkages are not equally
plausible for AD. For example, the A-B linkages for ozone tend to affect the lungs but
Swanson & Smalheiser p. 17
those for lead tend to affect the brain. Thus lead is judged to be a more promising
But it should not be surprising that the quite large literatures on lead and AD share a
great deal of common language, including B-terms in titles, irrespective of whether lead
and AD have meaningful biologic interaction. To pursue this question further, we
examined not only the title linkages, but the underlying literatures as well. Specifically,
5 groups of effects could be identified in the separate literatures on lead and AD: i) Lead
treatment or lead exposure alters cholinergic function in a number of systems; deficient
cholinergic function is a hallmark of AD. Lead exposure was reported to cause a
decrease in choline acetyltransferase in the septum, a brain area known to be particularly
deficient in this enzyme in AD patients. ii) Lead has effects on calcium-dependent signal
transduction pathways involving adenylate cyclase, calmodulin, and kinase; all three
signal transduction pathways are reported to be abnormal in AD and are thought to
participate in its pathogenesis. iii) It is well established that lead affects brain vasculature
and glial cells, and alters the blood-brain barrier; these are also reported to be abnormal
in AD. iv) Lead exposure is associated with oxidative stress that generates free radicals
(see 4.2); many reports have indicated that excess free radical formation occurs in AD.
v) Lead exposure interferes with long-term potentiation in the hippocampus, a cellular
model of short-term memory; the latter is known to be abnormal in AD.
The connections that were found appear to go far beyond the matter of "just" shared
language, and indeed suggest that the cellular effects of lead exposure entail functional
deficits that also accompany AD. We cannot conclude on that basis that lead exposure is
necessarily a significant risk factor in AD, for this inference would require direct
evaluation of lead in living systems. But the foregoing argument does tend to support
our claim that the A-list for Alzheimer's disease identified agents having meaningful A-
B-C linkages with the source literature.
6. Application to other fields and disciplines
The question has often been raised of whether our techniques can be applied to fields
other than biology and medicine. In principle it is possible, but in practice the answer
may depend on the accuracy and specificity of article titles; in biology and medicine,
titles tend to be highly specific and informative. The same techniques could be applied
Swanson & Smalheiser p. 18
of course to abstracts, but we haven't tried it and do not know whether the advantages of
establishing more connections will be outweighed by the disadvantages of more "noise"
in the system. Moreover, all of the examples we have worked on so far have been in the
subset of biomedical literature that is relevant to mammalian physiology. In this corner
of the "real world", everything is richly interconnected. Thus each publication about
some aspect of that world is likely to create an intricate web of implicit and unintended
connections within the corresponding world of recorded knowledge. Fields that are not
as intimately interconnected may be less rewarding for our methods. In the field of
chemistry, our A-B-C model could refer to a chemical reaction pathway and would have
in that case interesting similarities and contrasts to work in AI on the automatic
generation of reaction pathways [38].
7. Conclusions and implications
Bringing together two complementary but noninteractive literatures from different
specialized areas of research can reveal undiscovered public knowledge — unnoticed
implicit relationships not apparent in the two literatures considered separately. This
paper has described an experimental "discovery support system"[8] called
ARROWSMITH which embodies a replicable database search procedure and related
software that produces heuristic aids to finding complementary literatures and to deriving
novel scientific hypotheses. Applying ARROWSMITH has led to testable hypotheses,
several of which have been experimentally corroborated, that demonstrate the
effectiveness and value of the system.
ARROWSMITH, operating in an environment characterized by the unruly problems
of natural language text and the immensity of the scientific record, is a practical system
that seeks immediate results in furthering the aims of biomedical research. At the same
time, it is a research tool for studying undesigned but human-created structures in the
literature of science. Each use of the system creates numerous examples of word-linked
titles suggestive of complementarity that are of potential value in examining the logic of
scientific discourse, in new approaches to the indexing of relationships [9, 10, 14, 15],
and as a source of ideas for modelling the process of discovery. Literature-based
discoveries may also open new lines of investigation in the growth of knowledge and the
drives toward specialization and fragmentation in science [18, 19, 39, 41, 42]. We invite
Swanson & Smalheiser p. 19
inquiries from those who might wish to try using the system on problems of their own.
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Swanson & Smalheiser p. 21
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Swanson & Smalheiser p. 23
FOOTNOTE -- page 1
A, B, and C refer either to physiological conditions or agents or to the search terms
based on them, or to the literatures that result from the search, the context indicating
which meaning is intended. "B" may also stand, generically, for Bi with i=1,2,3,., as in
A Venn diagram that represents sets of articles, or "literatures", A and C, that have no
articles in common, but which are linked through intermediate literatures, Bi (i=1, 2.).
Such a structure may contain unnoticed useful information that can be inferrred by
combining pairs of intersections ABi and BiC.
A schematic diagram of title-word pathways that, proceeding from right to left, lead
from a source literature C through intermediate title- words (B-terms) or literatures, to
one or more title words that can represent promising target literatures, Ai (i=1, 2, .). A
ranking algorithm for each Ai, based on the total number of B-pathways that lead to it, is
illustrated. The rank numbers (number of paths) are shown in the leftmost column.
Top twenty (approx.) entries on the A-candidate lists for migraine, Raynaud's disease,
and Alzheimer's disease, produced by
Procedure I (Figure 3). The ranking numbers
shown in the first column represent the number of title-word pathways connecting A to C,
and were generated automatically by the process illustrated in Figure 2. Not shown, but
included in the actual output, are the B-terms (illustrated in Fig. 2) that contribute to the
A schematic flowchart of
Procedure II. The procedure begins with a downloading of
titles from literatures A and C, and proceeds to the lower right where an output display is
produced as a heuristic aid for the human user of the system. The display is organized to
facilitate comparison of A-titles with C-titles for each B-term that they have in common,
and serves as a guide to the literature.
Selected entries from a printed display of the
Procedure II output for two sets of titles
that contain the word "magnesium" (A-left column) or "migraine" (C-right column),
respectively. The headings shown are B-terms (in alphabetic sequence). Under each B-
Swanson & Smalheiser p. C 2
term are titles containing that term in the two columns of the display. This arrangement
facilitates comparing "magnesium" titles with "migraine" titles for each B-term that they
have in common. Shown here are examples of B-terms in which biologically meaningful
relationships A⇒B and B⇒C are suggested by the titles; citations to the literature are
also identified. Although, for each B-term, only a few titles are shown, in general there
may be as many as several hundred. To avoid excessive output, a limit to the number of
titles displayed for any one B-term can be set interactively by the user.
Selected entries from the B-LIST for magnesium and migraine, and for fish-oil and
Raynaud's disease, produced by
Procedure II (Figure 6). The two numbers shown in the
two columns to the left of each word list represent the number of articles within the BC
and AB intersections, respectively, as illustrated in Figure 1. The asterisks mark entries
identified in the original studies [21, 23].
(CO-OCCUR WITH B IN TITLES)
NUMBER OF LINKS OR PATHWAYS
TITLE WORDS
* HIGHEST RANKED A-WORD
THAT CO-OCCUR WITH MIGRAINE
RAYNAUD'S DISEASE
ALZHEIMER'S DISEASE
3 polyunsaturated
plus journal citations
plus journal citations
and TO FILE C
APPLY SHORT STOPLIST
APPLY SHORT STOPLIST
Convert plurals to singular.
Convert plurals to singular.
SORT AND MATCH-MERGE TO FIND
WORDS AND PHRASES IN COMMON
includes journal citations
WORDS IN COMMON
PHRASES IN COMMON
ORGANIZED BY B-TERMS
APPLY LONG STOPLIST
Delete phrases that consist
plus optional probability filter
only of words on long stoplist
GUIDE TO THE LITERATURE
GO TO LIBRARY
MERGE WORDS AND PHRASES
TO PRODUCE B-LIST
Flow of information
MAGNESIUM - A
MIGRAINE - C
Mg2+-Ca2+ interaction in contractility of vascular smooth muscle:
Migraine treatment with calcium channel blockers. [Review]
Mg2+ versus organic calcium channel blockers on myogenic tone
Acta Pharmacol Toxicol (Copenh). 58 Suppl 2:161-7, 1986.
and agonist-induced responsiveness of blood vessels. [Review]
Can J Physiol Pharmacol. 65(4):729-45, 1987
Calcium-channel blockers in the treatment of migraine. [Review]
Am J Cardiol. 55(3):139B-143B, 1985
Blockade of current through single calcium channels by Cd2+, Mg2+,
and Ca2+. J Gen Physiol. 88(3):321-47, 1986
The pharmacology of calcium channel antagonists: a novel class
of anti-migraine agents?. Headache. 23(6):278-83, 1983
Microcirculatory actions and uses of naturally-occurring (magnesium)
and novel synthetic calcium channel blockers. [Review]
Flunarizine, a calcium channel blocker: a new prophylactic
Microcirc Endothelium Lymphatics. 1(2):185-220, 1984
durg in migraine. [Review] Headache. 23(2):70-4, 1983
Neurological consequences of magnesium deficiency: correlations
Migraine-epilepsy relationships. [Review]
with epilepsy. Clin Exp Pharmacol Physiol. 14(5):361-70, 1987
Epilepsy Res. 1(4):213-26, 1987
Preliminary report on the magnesium deficient rat as a model of
Is there a common pharmacological link between migraine and
epilepsy. Lab Anim Sci. 28(6):680-5, 1978
epilepsy? [Review] Funct Neurol. 1(4):515-20, 1986
The relation of migraine and epilepsy. Brain. 92(2):285-300, 1969.
Epileptic-type convulsions and magnesium deficiency.
Aviat Space Environ Med. 50(7):734-5, 1979
Association patterns between epileptic and migraine attacks.
Effect of magnesium on epileptic foci. Epilepsia. 19(1):81-91, 1978
Acta Neurol (Napoli). 3(4):587-98, 1981
A case of the epileptic form of latent tetany due to magnesium
Basilar migraine? Seizures, and severe epileptic EEG
deficit. Electroencephalogr Clin Neurophysiol. 23(4):388-9, 1967
abnormalities. Neurology. 30(10):1122-5, 1980
Specific change of histamine metabolism in acute magnesium-
Role of histamine in the pathogenesis of migraine. [Review]
deficient young rats. Drug Nutr Interact. 5(2):89-96, 1987.
Postepy Hig Med Dosw. 39(1):12-22, 1985
Reduction of histamine-induced bronchoconstriction by magnesium
Histamine metabolism in cluster headache and migraine.
in asthmatic subjects. Allergy. 42(3):186-8, 1987
Catabolism of 14C histamine. J Neurol. 216(2):105-17, 1977
Influence of magnesium on norepinephrine-and histamine-induced
Preventive treatment of migraine and histamine headache.
contractions of pulmonary vascular smooth muscle.
Tidsskr Nor Laegeforen. 86(5):322-3, 1966 [Review]
Pharmacology. 33(1):27-33, 1986.
The histamine-glucocorticoid relationship and its implications
Effect of parenteral magnesium on pulmonary function, plasma cAmp,
for migraine headache. Headache. 5(4):111-5, 1966
and histamine in bronchial asthma. J Asthma. 22(1):3-11, 1985.
Mild hypothermia and Mg++ protect against irreversible damage
Ischemia may be the primary cause of the neurologic deficits
during CNS ischemia. Stroke. 15(4):695-98, 1984
in classic migraine. Arch Neurol. 44(2):156-61, 1987
Pharmacologic inhibition of cerebral vasospasm in ischemia,
Impairment of cerebral serotonin and energy metabolism during
hallucinogen ingestion, and hypomagnesemia: barbiturates, calcium
ischemia: relevance to migraine. Adv Neurol. 33:35-40, 1982.
antagonists, and magnesium. Am J Emerg Med. 1(2):180-90, 1983
Migraine as a model of neurogenic ischemia [editorial].
Headache. 22(6):287-8, 1982
The ionic basis of the anti-ischemic and anti-arrhythmic properties of
magnesium in the heart. [Review] J Am Coll Nutr. 6(1):27-33, 1987
The ischemic hypotheses of migraine.
Arch Neurol. 44(3):321-2, 1987
Relation of myocardial magnesium deficiency to sudden death in
ischemic heart disease. Am Heart J. 103(3):449-50, 1982
Migrainous ischemic optic neuropathy. Neurol 35(1):112-4, 1985
Magnesium deficiency produces spasms of coronary arteries:
Migraine, a risk factor for ischemic cerebral stroke.
relationship to etiology of sudden death ischemic heart disease.
Med Welt. 34(8):233-4, 1983
Science. 208(4440):198-200, 1980
MAGNESIUM - A
MIGRAINE - C
Magnesium ions control prostaglandin reactivity of venous smooth
Use of a prostaglandin inhibitor in migraine crisis. Study of 40
muscle from spontaneously hypertensive rats.
cases. Arq Neuropsiquiatr. 38(2):140-3, 1980
Prostaglandins Med. 4(4):255-61, 1980
Ophthalmoplegic migraine: amelioration by Flufenamic acid, a
Effects of magnesium ion and oxytocin inhibitors on the uterotonic
prostaglandin inhibitor. Ophthalmologica. 175(3):148-52, 1977.
activity of oxytocin and prostaglandins E2 and F2alpha.
J Pharmacol Exp Ther. 190(1):77-87, 1974
Migraine attacks. Alleviation by an inhibitor of prostaglandinsynthesis and action. Neurology. 26(5):447-50, 1976
Vasomotor effects of magnesium: a comparison with nifedipine and
Extracranial and cardiovascular reactivity in migrainous subjects.
verapamil of in vitro reactivity in feline cerebral and peripheral
J Psychosom Res. 26(3):317-31, 1982.
arteries. Magnesium. 5(2):66-75, 1986.
Extracranial vascular reactivity in migraine and tension headache.
Magnesium ions control prostaglandin reactivity of venous smooth
Cephalalgia. 1(3):149-55, 1981
muscle from spontaneously hypertensive rats.
Prostaglandins Med. 4(4):255-61, 1980
Abnormal cerebrovascular reactivity in patients with migraine
and cluster headache. Headache. 19(5):257-66, 1979
Magnesium and vascular tone and reactivity.
Blood Vessels. 15(1-3):5-16, 1978.
Reactivity of the intra- and extracranial vessels to serotonin andits relation to migraine. Gac Med Mex. 100(12):1297-308, 1970
Aromatic amines (serotonin and histamine) and magnesium
Impairment of cerebral serotonin and energy metabolism during
deficiency in the rat. Int J Vitam Nutr Res. 50(2):185-92, 1980.
ischemia: relevance to migraine. Adv Neurol. 33:35-40, 1982.
Effects of cerebral intraventricular magnesium injections and a low
Platelet aggregability, disaggregability and serotonin uptake in
magnesium diet on nonspecific excitability level, audiogenic seizure
migraine. Cephalalgia. 4(4):221-5, 1984
susceptibility and serotonin.
Pharm Biochem Behav. 10(4):487-91, 1979
Serotonin precursors in migraine prophylaxis.
Adv Neurol. 33:357-63, 1982.
Cerebral arterial spasm. Part 4: in vitro effects of temperature,
serotonin analogues, large nonphysiological concentrations of
The evolution of thinking about the role and site of action of
serotonin, and extracellular calcium and magnesium on serotonin-
serotonin in migraine. Adv Neurol. 33:31-3, 1982.
induced contractions of the canine basilar artery.
J Neurosurg. 44(5):585-93, 1976
Low extracellular magnesium induces epileptiform activity and
Cerebral blood flow in migraine and cortical spreading
spreading depression in rat hippocampal slices.
depression. Acta Neurol Scand Suppl 113:1-40, 1987 [Review]
J Neurophysiol. 57(3):869-88, 1987
Characteristics of spreading depression and of its propagation.
The nature of the chick's magnesium-sensitive retinal spreading
Their possible role in migraine. Cephalalgia 7 Supp 6:65-8, 1987
depression. J Neurobiol. 15(5):333-43, 1984
Is migraine explained by Leao's spreading depression?.
Lancet. 2(8458):763-6, 1985
Experiments on spreading depression in relation to migraine andneurosurgery. An Acad Bras Cienc. 56(4):423-30, 1984 Dec.
Pharmacologic inhibition of cerebral vasospasm in ischemia,
hallucinogen ingestion, and hypomagnesemia: barbiturates, calcium
Bilateral cervical carotid and intracranial vasospasm causing
antagonists, and magnesium. Am J Emerg Med. 1(2):180-90, 1983
cerebral ischemia in a migrainous patient: a case of
"diplegic migraine". Headache. 24(5):245-8, 1984
Withdrawal of magnesium causes vasospasm while elevated
magnesium produces relaxation of tone in cerebral arteries.
Isolated benign cerebral vasculitis or migrainous vasospasm?.
Neurosci Lett. 20(3):323-7, 1980
J Neurol Neurosurg Psychiatry. 47(1):73-6, 1984
Vasospasm and vascular headaches: selective vasoconstriction inthe carotid vascular system measured by the Doppler ophthalmicmethod in migraineurs. Headache. 19(4):200-3, 1979
Cerebral vasospasm in migraine. J Lancet. 87(8):283-6, 1967
RAYNAUD-FISHOIL B-LIST
2 calcium antagonist
2 calcium channel
5 blood viscosity
11 11 hemodynamic
1 hemolytic uremic syndrome
4 hydroxytryptamine
3 11 hypertension
3 inhibition platelet
2 13 platelet aggregation *
8 muscle contraction
3 14 platelet function
1 polymorphonuclear
5 oral contraceptive
10 25 prostaglandin
5 platelet aggregation *
1 prostaglandin i2
2 spreading depression *
Source: http://www.macs.hw.ac.uk/~dwcorne/Teaching/swanson.pdf
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THE JOURNAL OF BIOLOGICAL CHEMISTRY Vol. 279, No. 53, Issue of December 31, pp. 55833–55839, 2004 © 2004 by The American Society for Biochemistry and Molecular Biology, Inc. Printed in U.S.A. Hyperphosphorylation and Aggregation of Tau in ExperimentalAutoimmune Encephalomyelitis* Received for publication, August 30, 2004, and in revised form, October 6, 2004