According to the integrated information theory, the quantity of consciousness isthe amount of integrated information generated by a complex of elements, and thequality of experience is specified by the informational relationships itgenerates. This paper outlines a framework for characterizing the informationalrelationships generated by such systems. Qualia space (Q) is a space having anaxis for each possible state (activity pattern) of a complex. Within Q, eachsubmechanism specifies a point corresponding to a repertoire of system states.Arrows between repertoires in Q define informational relationships. Together,these arrows specify a quale—a shape that completely and univocallycharacterizes the quality of a conscious experience. Φ— theheight of this shape—is the quantity of consciousness associated withthe experience. Entanglement measures how irreducible informationalrelationships are to their component relationships, specifying concepts andmodes. Several corollaries follow from these premises. The quale is determinedby both the mechanism and state of the system. Thus, two different systemshaving identical activity patterns may generate different qualia. Conversely,the same quale may be generated by two systems that differ in both activity andconnectivity. Both active and inactive elements specify a quale, but elementsthat are inactivated do not. Also, the activation of an element affectsexperience by changing the shape of the quale. The subdivision of experienceinto modalities and submodalities corresponds to subshapes in Q. In principle,different aspects of experience may be classified as different shapes in Q, andthe similarity between experiences reduces to similarities between shapes.Finally, specific qualities, such as the “redness” of red,while generated by a local mechanism, cannot be reduced to it, but requireconsidering the entire quale. Ultimately, the present framework may offer aprincipled way for translating qualitative properties of experience intomathematics. quantity of integrated information to describing thestructure or quality of the integrated information unfolded byinteractions in the system. We take a geometric approach, introducing the notionof a quale as a shape that embodies the entire set of informationalrelationships generated by interactions in the system. The paper investigateshow features of the quale relate to properties of the underlying system and alsoto basic features of experience, providing the beginnings of a mathematicaldictionary relating neurophysiology to the geometry of the quale and thegeometry to phenomenology.In prior work, we suggested that consciousness has to do with integratedinformation, which was defined as the amount of information generated by asystem in a given state, above and beyond the information generatedindependently by its parts. In the present paper, we move from computing the Bycontrast, the cerebellum - a part of our brain as complicated and even richer inneurons than the cortex – does not seem to generate much experience atall: if the cerebellum has to be removed surgically, consciousness is hardlyaffected. What is special about the corticothalamic system, then, that is not sharedby the cerebellum?Consciousness poses two main problems quality of consciousness? What makes an experience visual,auditory, or both? What makes a color a color, and red red, and what makes reddifferent from blue, a triangular shape, or a high C on an oboe? Again, empiricalevidence indicates that different parts of the cortex influence differentqualitative aspects of consciousness. For example, damage to certain parts of thecortex can impair the experience of color, whereas other lesions may prevent youfrom experiencing visual shapes, and other lesions may abolish auditory, rather thanvisual perception. Why is this so?Second, what are the necessary and sufficient conditions that determine theThe integrated information theory (IIT) information isgenerated when the discrimination is made. Information is classically defined asreduction of uncertainty. When the blank screen turns on, the mechanism in thephotodiode discriminates between 2 alternatives (the current from the sensor isabove rather than below a threshold) and thereby generateslog2(2) = 1 bit of information. Onthe other hand, when you “see” light, the mechanisms in yourcorticothalamic system discriminate among a much large number of alternatives:all other experiences you could possibly have had, but did not have . Thus,you generate a much larger amount of information.Informativeness is a key property of consciousness, as can be realized byconsidering the photodiode thought experiment 1,000,000, corresponding to 1,000,000 bits ofinformation), it is hard to imagine that it would be generating vividexperiences. According to the IIT, the key difference between you and the camerahas to do with integrated information. From the perspective ofan external observer, the camera chip has a large repertoire of states. From anintrinsic perspective, however, the sensor chip can be considered as acollection of one million photodiodes with a repertoire of two states each,rather than as a single integrated system with a repertoire of21,000,000 states. This is because, due to the absence ofinteractions among the photodiodes within the sensory chip, the state of eachelement is causally independent of that of the other elements. Indeed, if thesensor chip were literally cut down into individual photodiodes, the performanceof the camera would not change at all. By contrast, the repertoire of statesavailable to you cannot be subdivided into the repertoire of states available toindependent components. This is evident phenomenologically: when you consciously“see” a certain image, that image is experienced as anintegrated whole and cannot be subdivided into component images that areexperienced independently, such as the left half of the visual field of viewindependently of the right half. Underlying this unity is a multitude of causalinteractions among the relevant parts of your brain. Indeed, unlikedisconnecting the photodiodes in a camera sensor, disconnecting brain regionshas profound effects. For example, when the 200 million fibers linking the twocortical hemispheres are cut to alleviate severe seizures, consciousnessliterally splits in two Information is not enough, however, if it is not integrated. Consider anidealized digital camera whose sensor chip is made up of 1 million binaryphotodiodes. Though such a camera could discriminate among a very large numberof states  that can be discriminated by the system as a whole(integration). A measure of integrated information, called phi (Φ),can be used to quantify the information generated when a system is in aparticular state of its repertoire, above and beyond the information generatedindependently by its parts If the amount of integrated information generated by a system can in principleaccount for changes in the level of consciousness, what is responsible for thequality of each particular experience? For example, one can be aware of pure redon one instance, and of a piercing sound on another instance. In both instances,one is aware with roughly the same intensity – the quantity ofconsciousness is similar – but the quality of the experience isradically different. What determines that colors look the way they do, anddifferent from the way music sounds? And why do different cortical areasseemingly contribute different qualities to experience? Why does damage tocertain parts of the cerebral cortex forever eliminates our ability toexperience color , whereasdamage to other parts selectively eliminates our ability to experience visualshapes?The IIT claims that, just like the quantity of consciousness generated by acomplex of elements is determined by the amount of integrated information itgenerates, the quality of consciousness is determined by the set ofinformational relationships its mechanisms generate meaning: light as opposed to dark, not colored asopposed to colored (any color), diffuse as opposed to having a particular shape(any particular one), visual as opposed to auditory or olfactory, sensory asopposed to thought-like, and so on. To us, then, light is much more meaningfulprecisely because we have mechanisms that can discriminate this particular stateof affairs we call “light” against a large number ofalternatives.Consider a very simple example: a binary counter capable of discriminating amongthe 4 numbers: 00, 01, 10, 11. When the counter says binary“3,” it is not just discriminating 11 from everything elseas an undifferentiated bunch; otherwise it would not be a counter, but a 11detector. To be a counter, the system must be able to tell 11 apart from 00 aswell as from 10 as well as from 01 in different, specific ways. It does so, ofcourse, by making choices through its mechanisms, for example: is this the firstor the second digit? Is it a 0 or a 1? Each mechanism adds its specificcontribution to the discrimination they perform together. Similarly, when we seelight, mechanisms in our brain are not just specifying“light” with respect to a bunch of undifferentiatedalternatives. Rather, these mechanisms are specifying that light is what it isby virtue of being different, in this and that specific way, from every otheralternative. Thus, they specify at once that light is different not only fromdark, but also from any color, any shape, any movie frame, any sound or smell,and so on, in every instance in a very specific way. In this way, light acquiresits specific vs. dark or hotvs. cold.By contrast, when the photodiode signals light, what does it mean? The photodiodehas no mechanism to discriminate colored from achromatic light, even less totell which particular color the light might be. As a consequence, all light isthe same to it, as long as the intensity exceeds a certain threshold. So for thephotodiode “light” cannot possibly mean achromatic asopposed to colored, not to mention of which particular color. Also, thephotodiode has no mechanism to distinguish between a homogeneous light and abright shape – any bright shape - on a darker background. So for thephotodiode light cannot possibly mean full field as opposed to a shape– any of countless particular shapes. Worse, the photodiode does noteven know that it is detecting a visual attribute – the“visualness” of light - as it has no mechanism to tellvisual attributes, such as light or dark, from non-visual ones, such as hot andcold, light or heavy, loud or soft, and so on. As far as it knows, thephotodiode might just as well be a thermistor – it has no way ofknowing whether it is sensing light informational relationship.” Accordingto the IIT, these mechanisms working together generate integrated information byspecifying a set of informational relationships that completely and univocallydetermine the quality of experience.In short, generating a large amount of integrated information entails having ahighly structured set of mechanisms that allow us to make many nesteddiscriminations (choices) as a single entity. Each of the nested choices is an“quale corresponds to a particularset of arrows linking points in Q, that is, an experience is a shape in Q-space.We examine some of the properties of qualia, including entanglement, concepts,and modes. Finally, we show that the language of Q can capture, in principle,some of the basic distinctions that can be made in our own phenomenology, aswell as some key neuropsychological observations. Hopefully, this framework canhelp translate the seemingly ineffable qualitative properties of phenomenologyinto the language of mathematics.In the present paper, we set out to characterize mathematically the set ofinformational relationships generated by a complex of elements. First, we definequalia space (Q) as a space where each point is a probability distribution onthe possible states of the system. The informational relationships can then bethought as arrows between points in Q generated by causal mechanisms. We thenargue that each experience or In what follows, we consider isolated systems of binary elements, idealizing thesilence (0) and firing (1) of neurons. We further assume that elements arememoryless (first order Markov processes) and time passes in discrete instants (e.g.milliseconds). These simplifying assumptions are not essential and will be relaxedin further work. Elements are linked via directed connections and respond to theirinputs according to simple Boolean or probabilistic functions, which togetherconstitute the mechanism.Notation. We refer to systems and subsets of systems by capitalletters: X, S and so forth. Uppercase letters with subscripts  denote probability distributions of perturbations that arephysically imposed on the outputs of a subset at a given time, e.g. att = 0. Lowercase letters with subscripts denote events: the actual output of the subset inquestion at a particular time, e.g. at t = 1.Before we deal with the quality of consciousness, we must deal with its quantity.According to the IIT, the quantity of consciousness associated with a complex ofelements is given by the amount of integrated information it generates mech, being in the particularstatex1 = (n11n21) = at time t = 1? Prior to considering itsmechanism and current state, the system of two binary elements could havebeen in any of four possible stateswith equal probability p = ¼.This potential repertoire . Themechanism and current state of the system, however, reduce uncertainty, i.e.generate information, about the previous state of the system (att = 0). This is because only some previousstates  couldhave led to the current system state x1 through the mechanismX0(mech), while previous states[01],[00] could not(p = 0). In general, mechanism and currentstate specify an actual repertoire , theprobability distribution expressing how compatible previous system statesare with the system's mechanism and current state. The effectiveinformation generated by the system is the entropy of the actual repertoirerelative to the potential repertoire Consider the system in In integrated information? Integratedinformation is measured by comparing the actual repertoire generated by thesystem as a whole with the combined actual repertoires generatedindependently by the parts minimum information partition  is then the entropy of the actual repertoireof the system relative to the product of actual repertoires of its minimalparts MkOf the information generated, how much is generated by a single entity, asopposed to a collection of independent parts? That is, how much of theinformation is 1) = 0) above andbeyond what is generated by its parts.As an example, consider among rather than within its parts.Thus, a system can generate integrated information only to the extent thatit cannot be decomposed into informationally independent parts. A simpleexample of such a system is shown in 1) = 2 bits.Clearly, for integrated information to be high, a system must be connected insuch a way that information is generated by causal interactions1generates integrated information Φ>0 and is not containedin some larger set with strictly higher Φ it is called acomplex (a main complex if its subsets have strictlylower Φ). Indeed, only a complex can be properly considered toform a single entity and thus to generate integrated information.In short, integrated information captures the information generated by causalinteractions in the whole, over and above the information generatedindependently by the parts. If a system of elements in state xTo deal with the quality of consciousness, we must consider how the mechanism ofa complex specifies an actual repertoire by discriminating a given state (say“light”) not against an undifferentiated bunch of equivalentalternatives, but rather by discriminating that state, in a specific way,against each and every alternative. To do so, we must introduce some tools thatserve to characterize the set of informational relationships generated by themechanism of a complex .Conn among its elements and the rules implemented by theelements. Notation cij refers to a connection in X from elementni to nj. Elements are assumed to implementBoolean or probabilistic functions. A connection between two elements is theminimal meaningful unit of interaction in a system, but connections canmediate interactions among subsets of elements. The system in submechanism of the system, see Section 1of 1,m2,m3,m4],The mechanism of a complex is captured by the set of connectionslattice L(X), which conveniently captures allpossible combinations of causal interactions within X (bottom of the lattice (⊥) is thenull set ∅, which contains no connections. Thetop of the lattice (T) contains all connections . Going up the lattice, oneencounters all submechanisms: all combinations (subsets) of connections.The set of all submechanisms of the system, the powerset of Conn, is denotedas the within X . A lattiunion s = m∪r , thenadds a first submechanism, then two, then three, and finally all of them.Note, this does not correspond to travelling through thenetwork over time.fold. Each cyan arrow is drawn when adding submechanismm1 in different contexts. For example, thelowest cyan arrow is drawn when considering adding submechanismm1 in the null context, corresponding to thebottom of the lattice. The next cyan arrow is drawn when consideringsubmechanism m1 added in the context of submechanismm2. The highest cyan arrow is drawn when considering submechanismm1 added in the full context, correspondingto all the other connections together,which is where it reaches the top of the lattice. The union of all arrowsdrawn when adding a particular connection (or submechanism) in all contextsdefines the corresponding fold. Thus, all the cyan arrows in 1.Finally, given any subset r of Conn, we can construct two sublattices: thedown-set ↓r of all subsets included in r, and the up-set↑r of all subsets that include r .m of X discriminatesbetween potential prior states, distinguishing those that cause (lead to)state 1x from those that do not. Thediscrimination performed by m is explicitly described asthe 0actual repertoire Xspecified by that submechanism. The actual repertoire is computed byperturbing the system with states in the potential repertoire while usingBayes' rule to keep track of perturbations that cause (lead to) thecurrent state 1x. Connections not inm are treated as extrinsic noise and are independentlyaveraged over with the maxent distribution (see Section 1 of 0(maxH).Each submechanism Qualia space Q is a 2n dimensional space (for asystem of n binary elements and 2n possible states), having anaxis per state and coordinates corresponding to the probability of eachstate; the space of probability distributions in Q is studied in informationgeometry m mapsto the point in Q given by actual repertoire0X. Informational relationships (q-arrows) represent the“differences that make a difference” 0→X0in the quale. Informational relationships have counterparts in semanticsr generates in the context givenby submechanism m. As before, effective information is therelative entropy between the two repertoires:ei→X0) = H[X0∥X0].One can further resolve an informational relationship by considering itsinternal structure: the shape of sub-lattice↑m∩↓(m∪r) containing all submechanismsbetween m and m∪r, see Section 5 of 0, which constitutes the top of the quale.In 1)The quale Q of the information generatedby X. Points of the quale are given by the set of actual repertoires andrepresent the discriminations made by every submechanism of X. Informationalrelationships (q-arrows) capture the discrimination performed by asubmechanism in the context of other submechanisms  is not a measure of length. It follows thatthe quale cannot be accurately represented on a flat page.The figures that follow are not “to scale”. Instead, werelate geometric features of interest to important properties of the systemand show how they can be quantified.1 are taken as given. Each point is a perspective providedby a submechanism on the causal interactions that have occurred, and theq-arrows represent how perspectives differ from their subperspectives. Anatural question is: How do concepts arise? To answer we must firstintroduce the notion of entanglement.The quale shows a certain resemblance to graphical models A fundamental property of q-arrows is their entanglement (γ): the extentto which an informational relationship does not reduce to its componentrelationships (sub-q-arrows). A q-arrow is tangled (γ>0) if itssub-q-qarrows generate information differently taken together than they do takenseparately . As will be described below,entanglement is used to characterize concepts and modes.AND-gate. Themechanism of the system, given byT = {c13,c23}, rulesout the prior state[n1n2] = [11].As shown in 0(maxH)→X0 specified byT cannot be reduced to the q-arrows specified by {c12} and{c13} separately, since the actual repertoireX0 does not reduce to the product of actualrepertoires specified by submechanisms {c13} and {c23}separately. If the sub-q-arrows were not tangled the q-arrowX0(maxH)→X0 would reduce tothe diagonal of the parallelogram obtained by considering elements n1and n2 separately. That is, unentangled q-arrows are orthogonal toeach other; while entanglement “warps” the shape of thequale away from a simple parallelogram, see Section 4 of Entanglement γ of q-arrowX0→X0is the entropy of repertoire X0 relativeto the natural decomposition of the q-arrow:γ is the minimum informationpartition for entanglement, by analogy with the formula for Φ. The setrk contains the q-arrows in r outgoing from Mk. Thus, entanglement captures how much information a q-arrow generatesover and above its natural decomposition into its minimalq-arrows. Entanglement of a q-arrow is zero if and only if itdecomposes into a collection of independent component q-arrows. Theminimum information partition for entanglement is found by computingk = src(rk) be thesource elements for the connections in sk. Similar to Φ,the normalization l is the number of parts for whichSk≠∅. More details on entanglement are provided inSection 4 of Let RA conceptX0→X0is an indivisible informational relationship (γ>0). In otherwords, a concept is a discrimination performed by some mechanism r in context mthat cannot be decomposed into a product of simpler discriminations because theinformation generated by its constituent sub-q-arrows rely on each other forcontext. By contrast, a q-arrow withγ = 0 has not internal contextualdependencies, and reduces to its sub-q-arrows without any loss of information.The notion of concept is graded. For a submechanism to generate a concept itmust be highly interdependent; for example, a few connections in a system,chosen at random, will  not be tangled.1} and {probably notn2}, since in each case maxent noise has been introduced on the otherconnection. Taken together, however, they tangle and generate the indivisibleconcept {not both}. The concept {not both} does not reduce to the product of theelementary concepts {probably not n1} and {probability notn2}, 4 andn5 implement the operation NOISY COPY, i.e. havethe same mechanism as an AND gate in which one input connectionis given extrinsic noise. The informational relationship generated byc45 is the concept {probably not n4}, and similarly forc54 {probably not n5}. In this case, however, theq-arrow generated by both {c45, c54} is not tangled(γ = 0) as it decomposes into a productof the two smaller, independent q-arrows →X0,withγ→X0)>0that is not contained in some larger up-set of ¬b, which is (strictly)more densely tangled:0 contains the sourceelements in a, and similarly for b. As will be discussed below, modes play animportant role in understanding the structure of experience, especiallymodalities and submodalities. If a mode is contained within a larger mode, werefer to it as a sub-mode. By analogy with a main complex, anelementary mode is such that its component q-arrows havestrictly lower γ.AND and COPY gate. TheAND-gate tangles two of the connections in the quale,forming the pink shape in panel B: the concept {not both}. Similarly, theCOPY-gate generates the concept {not this}. The system as awhole does not generate a single concept, but rather two distinct concepts. Thiscan be seen in panel C where the system as a whole is depicted as aparallelogram, the {not this} and {not both} concepts are orthogonal to oneanother and do not interact. Since the concept {not both} is not contained in alarger, more densely tangled concept, it forms a mode.In what follows, we examine some general properties of qualia, and some implicationsof considering an experience as a shape in qualia space. We also examine howconsidering basic neurophysiological notions in terms of qualia space affects theirinterpretation. We then consider some consequences of entanglement and the meaningof concepts, and how learning new concepts affects qualia space. We consider basicexamples of how different aspects of phenomenology may be classified as differentbasic shapes in qualia space, and how, if experiences are shapes in qualia space,they can be compared just as shapes can be. Finally, we consider how a paradigmaticquale, such as “seeing red,” can be thought of in the presentframework, with relevant implications for neuropsychology.For computational reasons, as in We first consider some basic results that can be obtained by treating qualia asshapes specified by sets of informational relationships. We show that the amountof integrated information generated by a complex can be interpreted as the“height” of the quale. It also follows that onlyinformational relationships generated within a complex contribute to the shapeof the corresponding quale. Another consequence is that the state of a complexis meaningless without considering its mechanism. An intriguing corollary isthat different systems in different states may generate exactly the same quale.1)Q generated by asystem? To address this question, one must consider how partitions,including the minimum information partition (MIP), can be represented inqualia space. Recall that each submechanism specifies an actual repertoireby discriminating between potential prior states. Actual repertoireX0 can be interpreted as thatsubmechanism's perspective on the discriminations performed by theentire system. Each partition is just a point in Q; for example, suppose wehave a partition P. For each part Mk in P, let the intra-set ofMk be all connections for which the source and the target areelements in Mk. Let P ⊂ Conn (we reuse the symbol) be theunion of the intra-sets across the parts in P (the partition): it containsall connections within each part, and no connections between parts. In thisway partitions are mapped into L(X):0) is thenthe information generated by the extra-set of P (sincemech = T = P∪¬P),over and above the intra-set of P. The relations betweenpartition-repertoires inside Q geometrically realize the interactionsamongst parts that are performed by the various extra-sets of connections inX. The additional points in Q given by other submechanisms reveal the finerstructure of the discriminations performed by the system.How does the shape of the quale 0→X0has divergence:The minimum information partition (MIP) is thus just another point in Q, theone specified by the connections within the minimal parts only. The q-arrowXmost closely accounts for the perspective of thewhole. For this reason the down-set ↓PMIP, which unfoldsthe structure  of the perspective provided by the MIP, can bethought of the system's natural information-theoretic“base”. The Φ-q-arrow can then be thought ofas the height of the solid, quantifying how much the complex rises above acollection of independent parts. Therefore, Φ quantifies the difference between the perspectiveprovided by the entire system and that provided by the MIP, the partitionthat MIP: the actualrepertoire of the whole collapses onto its base (MIP), and Φ iszero. The solid is flat: looking at the quale from the natural perspective,level with the ground, nothing is visible. The perspective provided by theMIP completely accounts for the discriminations performed by the system.Indeed, there exists no complex corresponding to the double couple– as a whole, such a system does not generate any quantity ofconsciousness , nor anyquality . Informationally, andphenomenologically, it does not exist. What exists, instead, are two smallercomplexes, each corresponding to a couple of elements joined by a mechanism- for example, two separate photodiodes. Each of them generates a smallquale, corresponding to a single q-arrow with no further structure.Consider now The systems shown in n = 16 dimensional qualiaspace, it turns out, upon closer inspection, that its shape does not existin the full-dimensional space (the solid is flat). Rather, the shapecollapses into two simpler qualia living in lower-dimensional qualia spaces, oneper complex. In the case of overlapping complexes, informationalrelationships specified by the same mechanism may live in different qualia,a higher–dimensional one corresponding to the main complex, and alower–dimensional one corresponding to a larger complex of lowerΦ. In summary, only the informational relationships within acomplex contribute to giving the quale its shape.If experience is integrated information within a complex, it follows thatonly the informational relationships within a complex contribute toexperience 1 = 001,100). Since theconnections are engaged in different ways when the system is in twodifferent states, the interactions within the systems are qualitativelydifferent. As shown in When the same system (mechanism) is in a different state (firing pattern), itwill typically generate a different quale or shape, even for the same valueof Φ. 1 = 100); and bothgenerate Φ = 3 bits; in bothcases the minimum information partition is the total partition(MIP = 123|∅). However, the twosystems differ both in their connectivity and in the rules that the elementsimplement, so the quale generated by the AND/XOR-triple is structureddifferently from the PARITY/XOR-system. As an extreme example, a system thatwere to copy one by one the state of the neurons in a human brain, but hadno internal connections of its own, would generate no consciousness and noquale A quale is specified by a mechanism and a particular state - it does not makesense to ask about the quale generated by a mechanism in isolation, or by astate (firing pattern) in isolation. A consequence is that different systemsin the same state can generate different qualia. 1 = 00,01,10,11) have,respectively, probability   . This co0,0,½,½) . While tisomorphism between the twoqualia: the reflection in 1, flipping 1 and 0. Thus,a binary device like a photodiode or a thermistor generates the same qualiaregardless of the state it is in; the two qualia are equivalent. The systemis memoryless, so every input is a surprise ;to be a binary photodiode or thermistor is to rule two out of four potentialstates at each instant. As can be seen from the quale, there is noadditional structure to the system. An isomorphism between two qualia is anidentification of the qualia spaces, Q(X) and Q(Y), by relabeling elementsand outputs, that induces a lattice isomorphism from Q(x1) ontoQ(y1). A lattice isomorphism is a bijection preserving thelattice structure, see Section 2 of The symbols 0 (off) and 1 (on) are arbitrary labels given to interchangeableoutputs. In fact, there is an As another example, consider the qualia generated by a silent AND-gate and bya firing OR-gate . CompariConsidering the information generated by a complex of elements in terms of theshape they specify in Q has some implications for the way we interpretneurophysiologic data. Rather than trying to understand the meaning of theactivity of some elements (neurons) in isolation, or even of distributedpatterns of activity or of correlations, the IIT claims that meaning is onlygenerated in terms of shapes in Q, that is, in terms of the set of informationalrelationships generated by a complex. Below we examine a few representativeexamples that clarify the perspective provided by the IIT. For instance, we showthat, in Q, the same connections can specify different informationalrelationships in different contexts. Next, we show that removing a set ofconnections (mimicking a lesion) simplifies the shape of the quale by collapsingit along a q-fold. We also illustrate how, when an element (a neuron) turns on,it generates information by changing the shape of the quale. Moreover,informational relationships, and thus the shape of the quale, are specified bothby the elements that are firing and by those that are not. Finally,“inactivating” elements that are already inactive has majorconsequences on the shape of the quale, though the firing pattern remains thesame.context is a point in the lattice L corresponding to aparticular submechanism m. In Q, this point corresponds to the actualrepertoire generated by that submechanism. As shown in r betweenelements 1 and 2 generates a q-arrow of 1.1 bits pointing in a certaindirection. When considered in the full context provided by all otherconnections (¬r), the same connection r generates alonger q-arrow (1.8 bits) pointing in a different direction. Another exampleis shown in AND-gates. The four cyan elements generate 1.5bits of information in the null context and 4 bits of information in thefull context, and the informational relationships point in differentdirections. Panels CDEF show results averaged across many different statesof the same system, for different submechanisms. The results show that theinformation generated by a set of connections is higher in the full contextthan in the null context when a system generates high Φ. Thus,within an integrated system, a submechanism produces different informationaleffects in different contexts, and usually it produces larger effects thericher the context. Note that this result is fully compatible with empiricalwork on functional connectivity Informational relationships are context-dependent, in the following sense.Recall from the folds a quale. As shown in unfolds. Thus, within an integratedsystem, a connection produces informational effects in many differentcontexts, and these effects can be captured precisely by changes in theshape of the quale along a fold.Removing a set of connections from every possible context1 stands for a neuron selective for a“square” shape, which is currently firing due to thepresence of a gray square in the visual field (3), which was silent, becomesactive . From the extrinsicperspective of a neurophysiologist, if the n3 neuron becameactive every time a subject reports seeing red, it is natural to label theactivation of the “red” neuron n3 as theneural correlate of consciousness for red In neurophysiology, one often searches for neurons that fire for particularinputs. It is often assumed that, when such neurons fire, they“broadcast” the relevant information to a large publicof other neurons al field . Now asssactive ;integraThe assumption that neural elements that are active are broadcastinginformation often goes hand in hand with the corollary that inactiveelements are essentially doing nothing, since they are not broadcastinganything. According to the IIT, this is not correct. In the general case,being “off” is just as informative as being“on.” An element that fires specifies previous statesthat would have made it fire and rules out other states. Similarly, anelement that does not fire rules out previous states of affairs that wouldhave made it fire and thereby contributes to specifying the actualrepertoire. For example, in default” quale. A default qualehas the prerogative of expressing geometrically all relationshipsimplemented by the system's mechanisms, without weighing anymechanism more than any other. Whether the brain can sustain for sufficientperiods of time a state in which no neurons are firing ,remains to be determined. Possibly such a state may be reached in certainmeditation practices, and may correspond to a state of full consciousnesswith no particular content In a neurophysiologic context, constraints such as energy costs may dictatethat being “on” should be used more sparingly than being“off.” In that case, a system should reserve firing forstates of affairs that are less frequent and therefore more informative, so values of Φ may not beas high when all elements are silent compared to when an adequate fractionare active 2 is not merely inactive, but it has been inactivated, meaningthat its mechanism has been disabled and the connections with sourcen2 have been replaced with noise. It is evident that, despite theidentical “firing pattern,” the quale in As shown in the previous examples, inactive (i.e. silent) elements generateinformation and thus contribute to specifying the shape of the quale. Alongthe same lines, a somewhat counterintuitive prediction stemming from the IITis that if elements within a complex are inactivated, rather than merelybeing inactive, experience should change, although the firing pattern is thesame. Consider again An informational relationships is tangled if it does not reduce to its componentrelationships, see above. As introduced in the COPY of one input element. In doing so, theCOPY element generates 1 bit of information, whether it fires or not, andspecifies a single informational relationship (q-arrow), corresponding tothe simplest possible concept: that things are one way rather than anotherway, just like the photodiode in 4 = 16 possible inputpatterns), then extracting 1 bit of information is indeed the best a singleelement can do. By contrast, the “BAR”element in CONCEPTUAL system : it constitutes acomplex that knows both inputs as a single entity, above and beyond what itsparts know. Specifically, in this case the complex knows that the entiresensor layer is silent, since it knows at once that the input is <2and even. Integrating information would seem to be an advantage fororganisms that need to make unified decisions that are sensitive to context.As shown in Section 4 of This simple example also illustrates the importance of considering integratedinformation as opposed to just (effective) information. As previously shown and 6B, Experiences can be refined through learning and changes in connectivity Suppose that learning the difference between red and white wine causes thedetectors to become specialized by pruning some connections . Since tmodeswere defined, by analogy with complexes, as q-arrows that are more denselytangled than surrounding q-arrows. Whether a complex consists of a single modeor of multiple modes and submodes depends as usual on both its connectivity(mechanism) and activity pattern. In what follows we argue that the subdivisionof experience into modalities and submodalities corresponds to sub-shapes(modes) in Q. Moreover, we argue that qualia in the narrow sense are elementarymodes (not further decomposable); and that homogeneous/composite experiences arehomogeneous/composite shapes.In the Just like the shape of a quale can change depending on whether an element isactive or not, the modes or subshapes generated by a complex with a givenconnectivity can change depending on which elements are active. Experience seems to divide naturally into modalities, like the classic sensesof sight, hearing, touch, smell, and taste , as well assubmodalities, like visual color and visual shape. What do these broaddistinctions correspond to in Q? According to the IIT, modalities are setsof densely tangled q-arrows (modes) that form distinct sub-shapes in thequale; submodalities are subsets of even more densely tangled q-arrows(sub-modes) within a larger mode, thus forming distinct sub-sub-shapes. Asschematically represented in In a system such as the brain, two main modes might correspond for example tothe visual and auditory modalities. As would be expected, the visual andauditory system, especially early in the cortical hierarchy, are heavilyinterconnected within each system, and much less between systems. Asillustrated schematically in Sub-modes that do not contain any more densely tangled sub-sub-modes areelementary modes . According to the IIT, such elementary modes correspond toaspects of experience that cannot be further analyzed, meaning that nofurther phenomenological structure is recognizable. The term qualia (in anarrow sense) is often used to refer to such elementary experiences, such asa pure color like red, or a pain, or an itch .In the If an experience is a shape in Q, in principle it should be possible to classifydifferent experiences, or different aspects of the same experience, as one wouldclassify shapes. Moreover, it should be possible to compare experiences oraspects thereof the way one might compare shapes, and obtain some objectiveindication of how similar they are. At present, a comprehensive approach toclassifying and comparing qualia geometrically is not feasible, not only becauseof the obstacles to specifying qualia generated by realistic systems, but alsobecause mathematical tools for comparing different qualia have yet to bedeveloped. To provide an indication of how a geometry of phenomenology mightproceed, we offer two simple examples. Thus, we suggest thattopographic/categorical experiences may be organized like multidimensionalgrids/pyramids in Q; and that hierarchically organized experiences may betangled both “horizontally” and vertically” intohierarchically organized subshapes in Q. Finally, we argue that treatingexperiences as shapes suggests a principled way of assessing their similarityand dissimilarity.We recognize intuitively that the way we perceive taste, smell, and maybecolor, is organized phenomenologically in a“categorical” manner, quite different from, say, the“topographical” manner in which we perceive space invision, audition, or touch. According to the IIT, these hard to articulatephenomenological differences correspond to different basic sub-shapes in Q,such as grid-like structures and pyramid-like structures. In turn, theseemerge naturally from the underlying neuroanatomy and neuronal activitypatterns.Many sensory areas, especially early on the cortical hierarchy, are organizedtopographically However, if the two elements are neighbors, the concept generated by theirconnections tangle, since afferents that are topographically adjacentjointly specify an actual repertoire more precisely than if consideredindependently. The resulting tangled q-arrow, shown in beige, corresponds tothe concept “larger patch of local activity belowthreshold.” In this manner, one neighboring element after theother, entanglement progressively expands q-arrows in a topographicallycontinuous manner, until at the top of the quale all q-arrows  become tangled into the concept“activity below threshold everywhere.“In the end, thegeometry of the quale would reflect the nearest neighbor architecture of thegrid, building concepts from local pieces centered on elements, up to asingle global gestalt (the grid forms a single mode). An element firingwould then warp the tangled shape generated by the silent grid correspondingto the concept “local activity above threshold here and belowthreshold everywhere else.” In this vein, the example in Consider now a simple system that is organized like a categorizing pyramid. Here,eThis example is also meant to illustrate how basic features ofneuroanatomical organization contribute to determining the quality ofexperience. On one hand, there is overwhelming evidence that different brainareas contribute different aspects to the quality of consciousness. On theother hand, the present approach suggests that the contribution of differentneuroanatomical structures to experience is not direct (and mysterious).Instead, the contribution of different brain areas to experience would bemediated (and explained) by how their connectivity, together with theiractivity patterns, specifies shapes in qualia space.Much of experience is hierarchically organized context on topof which to tangle, “vertically,” the contribution ofneurons in a higher area whose connections specify the presence of eyes,nose, and mouth. On top of this richer context, “face”neurons in even higher areas are tangled, again vertically, to specify aface.Consider the diagram in The counter-clockwise edge of the quale illustrates how the“face” connections on their own specify that theretina-grid was presented with a face-like object, ruling out alternativessuch as house-like, car-like, and so on. However, the details of the faceare missing, and the face-neurons cannot specify how the face looks.Engaging the connections targeting the feature-neurons fills out some of thedetails of the face, the broad outlines of nose, mouth and eyes, and moredetails are added by the edge-neurons. In the context provided by the edgeand feature neurons, the face-neurons again rule out the alternatives,however now the alternatives are far more detailed. The q-arrows specifiedby these various sets of connections would be expected to be highly tangled,embodying relationships within and across levels, and generating moreinformation that the sum of their component q-arrows. For example,informational relationships constituting a “face” wouldbe more densely tangled than unnatural combinations such as one eye and alower lip. Back-connections and lateral connections may play a role (beyondtheir role in learning and attention) by allowing higher-order invariants toinform – and so tangle with – lower order invariants.Indeed, psychophysical experiments have shown that feature recognitioninvolves extensive filling-in of lower-order features Altogether, according to the present approach, the experience of aperson's face, with its faceness, its eyes, nose and mouth, itsprecise contour and spatial location, would not be captured by anyindividual neuron or population of neurons, whether face cells or not,whether firing or not, whether synchronous or not, but by the generation ofa set of informational relationships within a complex, i.e. a particularsub-shape in Q.Some experiences are more alike than others. Blue is certainly different fromred (and irreducible to red), but clearly it seems even more different frommiddle C on the oboe. In the IIT framework, colors correspond to differentsub-shapes of the same kind (say pyramids pointing in different directions)and sounds to very different sub-shapes in Q. In principle, such subjectivesimilarities and differences can be investigated by employing objectivemeasures of similarity between shapes Considering the quantity of consciousness as given by the repertoire ofstates that can be discriminated by a single system, and its quality by theshape of the set of informational relationships generated by itsconnections, may also shed some light on the effects of splitting the brainalong the corpus callosum in severely epileptic patients In this last section, we revisit the question of the quality of consciousness byconsidering a paradigmatic quale – say seeing red – anddiscussing how such an experience should be thought of, at least in principle,from the point of view of the IIT. We choose a color not only because it is atraditional example in philosophy, but because we can lend it a minimum ofconcreteness by referring to some evidence from neurology and neuropsychology that points in adirection representing how r by itself shapes the maximum entropydistribution into an actual repertoire. Schematically, this situationresembles that of a vegetative patient with V8 and its afferents intact butthe rest of the corticothalamic system destroyed. The shape of theexperience or quale reduces to this q-arrow, so its quantity is minimal(Φ for this q-arrow is obviously low) and its quality minimallyspecified: as we have seen with the photodiode, r by itself cannot specifywhether the experience is a color rather than something else, such as ashape, whether it is visual or not, sensory or not, and so on.The IIT provides a straightforward account for this difference. To see how,consider again context, that is, it foldsthe quale along the q-fold specified by r, as we saw inup-set of non-red (↑¬r). This q-arrowwill typically be much longer and point in a different direction than theq-arrow generated by r in the null context at the bottom of the quale, as wesaw in By contrast, subtract r from the set of all connections, so one is left with¬r. This “lesion” collapses all q-arrowsgenerated by r starting from any It is worth remarking that, while the quality of red specified by the q-foldof r in the above example refers to one particular experience, it is inprinciple conceivable to determine, in an objective manner, what differentexperiences described as red by a conscious subject, or even by differentsubjects, may have in common. Once again, one would need to establish whataspects of the shape of different qualia remain similar across differentexperiences of red from the same subject or different subjects.the “redness” of red, while generated by alocal mechanism, cannot be reduced to it. If an achromatopsicsubject without the r connections lacks precisely the“redness” of red, whereas a vegetative patient with justthe r connections is essentially unconscious, then the redness of red cannotmap directly to the mechanism implemented by the r connections. However, theredness of red can map nicely onto the informational relationships specifiedby r, as these change dramatically between the null context (vegetativepatient) and the full context (achromatopsic subject).The last example also shows why specific qualities of consciousness, such as1), which is a shape  in Q space. We argued that this shape completely and univocallycharacterizes the quality of a conscious experience. Φ – theheight of this solid – is the quantity of consciousness associatedwith the experience. High Φ allows “breathingroom” for the informational relationships within a complex to expressthemselves, while if Φ is reduced the quale collapses.In this paper, we have briefly reviewed the notion of integrated information, theamount of information generated by a complex of elements above and beyond theinformation generated by its minimal parts, measured by Φ. We havethen introduced the notion of qualia space (Q) as a space with an axis for eachpossible state of the complex. Each submechanism of the complex specifies aprobability distribution of system states, corresponding to a point in Q. Arrowsbetween points (probability distributions) in Q (q-arrows) define informationalrelationships among the elements of the complex  and the state of the elements (activity pattern) consideredtogether. Thus, two systems having exactly the same activity pattern may giverise to completely different qualia, depending on their mechanism. In the limitof no mechanism – a system that merely copies its state from anotherone – no quale is generated. Conversely, exactly the same quale may begenerated by two systems that differ both in terms of connectivity and activitypatterns. For example, a silent AND-gate and a firing OR-gate generateisomorphic qualia. On the other hand, in more complex systems many symmetriesare likely to be broken, making it extremely unlikely that two different systemsthat are sufficiently complex may generate the same quale.Some of the results derived from the present approach lend themselves to aneurophysiologic interpretation. For example, we have seen that both active andinactive elements specify a quale. Thus, a system in which all elements aresilent can still specify a quale with a complex shape. On the other hand, whileelements that are inactive contribute to specifying a quale, elements that areinactivated (incapable of becoming active) do not, even though, from anextrinsic perspective, the pattern of activity may not have changed. Also, whenan element within a complex becomes active, it changes the shape of the quale.The implication is that the meaning of the firing of a given element (neuron) isgiven not by what its extrinsically imposed label might be (a“red” neuron or a “face” cell), nor bythe information it broadcasts to other elements (‘red” or“face”), but by the new shape in Q it contributes tospecifying.Generating more concrete links between the techniques developed here andexperimental data requires robust causal models of neuronal activity. Recently,a large body of work on dynamic causal modeling (DCM) has been developedattacking exactly this problem, for example It should be pointed out that this paper investigates the quality of theinformational relationships generated by a system without any reference to theenvironment and to the issues posed by sensory processing or by learning. Theimportant problem of how a system can integrate information in such a way as tomatch its environment will be treated in future work.The present approach can help to rephrase basic phenomenological andneuropsychological observations in a geometrical language. For example, we haveseen how entanglement – which occurs when a submechanism gives rise toan informational relationship that cannot be decomposed into its componentrelationships, generates concepts and modes Entanglement is necessary fordimensionality reduction from many inputs to a single output, which is anessential informational requirement for neurons. Entanglement helps to increaseintegrated information, ensuring that a complex can make highly informativediscriminations as a single entity.We have also seen how informational relationships can be refined throughlearning, thereby generating a more differentiated experience or quale. Afterintroducing the notion of modes – sets of densely tangledinformational relationship, we have argued that the subdivision of experienceinto modalities and submodalities corresponds to subshapes (modes) in Q; thatqualia in the narrow sense are elementary modes (not further decomposable suchas the “redness” of red); and that homogeneous/compositeexperiences are homogeneous/composite shapes. Also, the notion of modesclarifies how some phenomenological aspects may appear largely orthogonal  and yet be part of the same experience or quale inthe broad sense.Finally, we have argued that it may in principle be possible to obtain ageometrical classification of aspects of experience in terms of the shape in Qof the underlying informational relationships. As a tentative example, wesuggested that topographic/categorical experiences may be like grids/pyramids inQ; and that hierarchically organized experiences are tangled both“horizontally” and vertically” into hierarchicallyorganized subshapes. We have also argued that the similarity between experiencesreduces to similarities between shapes. Finally, we have argued that specificqualities of consciousness, such as the “redness” of red,while generated by a local mechanism, cannot be reduced to it, but requireconsidering the shape of the entire quale, within which they constitute aq-fold.These abstract geometrical notions may seem at first to be far removed from theimmediacy of experience. At present, due to the combinatorial problems posed byderiving the shape of the quale produced by systems of just a few elements, andto the additional difficulties posed by representing such high-dimensionalobjects, the best one can hope for is to show that the language of Q cancapture, in principle, some of the basic distinctions that can be made in ourown phenomenology. Ultimately, however, the goal of the present framework is tooffer a principled way to begin translating the seemingly ineffable qualitativeproperties of experience into the language of mathematics.Text S1Supplementary information(1.40 MB PDF)Click here for additional data file.


Every morning and evening, millions of women in India spend an hour or two cooking their rice, dal, curry, and roti or other flat bread. Most will prepare their meals over a smoky, 3-stone open fire or a traditional clay or brick cook stove called a chulha. The stoves burn a mix of wood, hay, or cow dung that the women collect from around their homes or, at times, far from the safety of their villages. The old-fashioned chulhas cook slowly, imparting a delicious flavor to the food that many Indians love. But everyone—from the women cooking their meals to international health experts—knows the smoke from the fires has a dark side, literally and figuratively.Fuel for Life: Household Energy and Health.Users say the smoke burns their eyes and blackens their pots and kitchen walls. Climatologists such as Veerabhadran Ramanathan, a professor at the University of California, San Diego, say the black carbon (soot) from the smoke, which blankets the villages, contributes to anthropogenic climate change. Health experts report that smoke exposure increases the risk of numerous diseases. “Day in and day out, and for hours at a time, women and their small children breathe in amounts of smoke equivalent to consuming two packs of cigarettes per day,” the World Health Organization (WHO) reported in the 2006 report To date, the few nongovernmental organizations, companies, and development and public health agencies that have tried to replace these traditional stoves have met with only isolated successes in large-scale programs. Now India intends to launch one of the largest improved cook stove initiatives in the world, its government announced in December 2009. Although several other governments have stove replacement programs, India’s has a larger potential than most.Clean-stove experts warn there is no one perfect stove or stove program that will solve India’s—or any other country’s—problem with smoke exposure. “Almost certainly, we need a range of options for different groups defined by geography, socioeconomic level, and culture,” says Nigel Bruce, a consultant to the WHO and senior lecturer at the University of Liverpool. “It is possible that a common technology could be at the core of these, but I would not assume that either.”10 (which easily penetrates airways) and PM2.5 . According to Fuel for Life, 24-hour levels of PM10 in homes that use solid fuels routinely reach 300–3,000 μg/m3 and may spike to 10,000 μg/m3 during cooking. By comparison, the WHO recommends no more than an annual mean of 20 μg/m3 and a 24-hour mean of 50 μg/m3 for PM10.Researchers can now list the ingredients of the smoke flowing from open fires and old-fashioned cook stoves. Most—about 90%—is carbon monoxide (CO). The rest is a mix of volatile organic compounds, polyaromatic hydrocarbons, metals, and particulate matter including PMThe Global Burden of Disease: 2004 Update. The diseases and conditions associated with smoke exposure include pneumonia, chronic respiratory disease, heart disease, low birth weight, and probably tuberculosis and other diseases. Globally, an estimated 49% of deaths attributable to household use of solid fuel are due to pneumonia in children under age 5. In a meta-analysis of 24 studies in the May 2008 issue of the Bulletin of the World Health Organization, Bruce and colleagues reported that, despite “substantial” differences among the studies, “this analysis demonstrated sufficient consistency to conclude that risk of pneumonia in young children is increased by exposure to unprocessed solid fuels by a factor of 1.8.”Breathing the smoke from traditional cook stoves and open fires may have caused 1.96 million premature deaths worldwide in 2004, according to WHO risk factor estimates accompanying the 2008 report The data on illnesses related to cook stoves come primarily from observational studies; researchers have very little data from randomized controlled trials. To address this gap, a team led by Kirk Smith, director of the Global Health and Environment Program at the University of California, Berkeley, undertook a randomized cook stove trial in the rural highlands of San Marcos, Guatemala. Called RESPIRE (Randomized Exposure Study of Pollution Indoors and Respiratory Effects), the trial involved 534 families. Half the group received a cook stove with a chimney, and the other half continued to use their fires or stoves without a chimney. The researchers monitored smoke exposure via a passive diffusion CO tube attached to the mothers’ and children’s clothing. CO is a reliable surrogate for fine particles and is easier to measure over time than other pollutants, according to Smith.Journal of Exposure Science & Environmental Epidemiology. However, Bruce points out, children are exposed to smoke when they go outside and when they go to other homes, so their individual CO exposure levels don’t match levels in their own kitchen.The use of stoves with chimneys in Guatemala reduced CO levels in the kitchen by about 90% and children’s exposure by an average 50% over 48 hours, the researchers reported online 17 June 2009 ahead of print in the Data from studies such as RESPIRE will help answer an important question facing clean-stove advocates and public health experts: how much must concentrations of smoke in homes be reduced in order to improve families’ health? When tested in the field, few of the improved cook stoves used in India achieve more than a 50–60% reduction in indoor air pollution levels and a 50% reduction in fuel use, says Simon Bishop, policy and communications manager at the Shell Foundation, which promotes improved cook stoves as a primary solution to indoor air pollution.10 compared with traditional cook stoves, field conditions—particularly the significant natural variations in operator behavior and the type, size, and moisture content of the fuel used—can lead to substantially lower performance than might be predicted from lab tests, according to Smith. There are advanced stoves but “we don’t think any of them are truly as advanced as they should be,” Smith says.Whereas manufacturer testing in the laboratory may indicate a stove is capable of producing far less CO and suspended PM“The existing improved stoves have to go some way before they can meet a health-based standard, but they are much, much better than the traditional stoves we have now,” says Kalpana Balakrishnan, head of the Department of Environmental Health Engineering at Sri Ramachandra University in Chennai. Studies by Balakrishnan and colleagues suggest even existing cleaner cook stoves will contribute to an immediate improvement in children’s health. And for many populations, especially the poorest, it may be that current technologies are a reasonable intermediate step—just “not a policy end point,” says Bruce. He adds, “We have to be pragmatic.”None of the existing stove technology was commercially available 3 years ago, and even better devices will be introduced in the next 3 years, points out Jacob Moss, a senior advisor at the U.S. Environmental Protection Agency (EPA) and director of the Partnership for Clean Indoor Air, an international collaboration comprising more than 340 partners. “If supply and distribution networks can be developed now, the newer technologies will be able to be substituted into them as they reach the market,” Moss says.In the early 1980s, India and China both began ambitious countrywide clean-stove programs. Today, China has about 150 million improved stoves in use from that time, and India has none, says Smith, who has studied both programs in depth. China used an innovative approach of encouraging local development of sophisticated stove designs and a central production and distribution system for key parts of the stove, says Smith. The Indian program failed for many reasons, including the fact that the distributed stoves made by local artisans produced too much smoke and were not durable. In addition, stove designs did not always consider the availability of reliable fuel.But India is ready to try again with a whole new strategy. The new program, the National Biomass Cookstove Initiative, is intended to provide cleaner, more efficient biomass-fueled stoves to rural communities, the Indian Ministry of New and Renewable Energy announced in a 2 December 2009 press release. The new program will draw lessons from and build on the successes of the earlier program.The government appears committed to independent monitoring and evaluation of the stoves and to focusing on vulnerable groups, Smith says. “This is seen as an entirely domestic program—no foreign aid or carbon financing is being sought,” he explains. Technical committees of public health, clean-stove, and marketing experts have met to develop best practice recommendations, set up a timetable for the program, establish standards for stove performance, and share strategies for marketing the stoves, among other tasks. The goal of the program is to sell 150 million stoves in 10 years, says Harish Anchan, general manager of stove manufacturer Envirofit India. The government is expected to release more information on the program in March 2010.India decided to launch another program in part because “stove technology has improved considerably in the past few years,” the government announcement notes. The government will remain open to using better stoves as they become available—advances in stove technology “are still possible and, indeed essential,” the government release states. As part of the new initiative, a multimillion-dollar “innovation prize” will be awarded to encourage the development of improved cook stoves, says Smith.The initiative will also include an accreditation component. Knowing the health effects of different concentrations of smoke will give international agencies and governments the data necessary to establish “clean cook stove” standards that manufacturers must reach to be accredited. This ensures that customers who purchase an accredited “clean cook stove” can expect certain improvements in fuel use and reductions in indoor air pollution levels, says Anchan.Cleaner Hearths, Better Homes—Improved Stoves for India and the Developing World. But Barnes also warns against launching a heavily subsidized program without the budget to back it up long-term. “I am actually in favor of subsidies,” he says, “just not ones that have a negative impact on programs.” Heavily subsidized programs often are not self-sustaining, because when the budget for the program dries up, so does the program.The initiative is good news to stove experts such as Douglas Barnes, coauthor of the forthcoming book Subsidies can prevent the development of markets in general, and improved cook stoves are no exception, Barnes says. He predicts that a subsidy for the stove itself of more than 20–25% would prevent the market from taking off. People who use biomass-fueled stoves are generally poor, and the initial cost of purchasing a new stove is a major problem for them. The involvement of microfinance organizations—which loan money to people too poor to borrow from a regular bank—may be one solution to this problem. However, Barnes says the current effort by the government of India appears to steer away from past high-subsidy approaches, so if the new effort is done properly, it could give a tremendous boost to cook stove programs worldwide.The Indian government isn’t alone in its effort to expand Indians’ access to cleaner biomass cook stoves. International donors such as the Shell Foundation are increasing their support of cook stove programs that show the potential for economic sustainability and scalability. Corporations such as Royal Philips Electronics, First Energy (formerly a BP company), and Bosch-Siemens are developing cleaner cook stoves that can be customized for cooking needs around the world. Companies that can manufacture stoves include Envirofit, StoveTec, First Energy, WorldStove, and HELPS International.The Shell Foundation has invested $3.5 million in Envirofit to support its program to sell 5–7 million stoves in 7 states in India in the next 5 years and is investing several million dollars in a public awareness campaign. The Shell Foundation and Envirofit are also in “close talks with the Indian government to see [how] we would work with them,” says Bishop. “As part of our national [indoor air pollution] awareness-raising campaign, we planned on lobbying the government to get indoor air pollution higher up on the agenda, so the government has almost jumped ahead of us—and it’s great that they have.”The main objective of Indian consumers who purchase an improved cook stove is often quite different from the Indian government’s reason for promoting stoves. The government wants to improve the health of rural Indians. “But people buy [stoves] more for aspirational reasons,” says Mahesh Yagnaraman, CEO and managing director of First Energy. The energy-efficient stoves burn less, or burn different fuels, so women and children spend less time gathering solid fuel. As part of its original sales campaign, First Energy had doctors test the breathing capacity of interested customers to demonstrate how the old stoves had harmed their lungs. But instead of wanting a new stove, the prospective customers “just wanted medicine,” Yagnaraman says.To sell stoves, you have to explain how the stoves save time and fuel and don’t dirty the kitchen with soot, says Anchan. Selling improved stoves is “very difficult,” he says. Consumers know the smoke irritates their eyes and makes them cough, but they don’t always comprehend the long-term impact of breathing high levels of smoke.Customers “don’t trust any of these [stove] solutions very easily,” says Yagnaraman. No stove is entirely reliable, so some families will keep more than one type of stove in their homes, he says. Usage studies show that families cook at most about 70% of their meals on their improved cook stove, says Smith. In their studies, he and his colleagues attach microchip usage monitors to stoves instead of relying on self-reports from the family members. “If you ask people, they say, ‘Oh yes, we love it, we use it all the time,’” Smith says.Significant customer support is key to any cook stove program’s success, experts say. “You can’t drop a stove into a household and walk away,” notes Rita Colwell, a public health expert at the University of Maryland at College Park and the Johns Hopkins University Bloomberg School of Public Health. Envirofit provides that hands-on care, Anchan says. In addition to training customers how to use the stove and providing them with a manual, Envirofit calls customers to see how the stove is working for them.One of the biggest hurdles for stove engineers and manufacturers is producing a stove that is both affordable and well made. Companies know how to make a stove that has very low emissions, is sturdy, relatively easy to use, and reliable but not necessarily at a price that consumers can afford, notes Smith. So far most companies are marketing only to consumers who can afford to pay about US$20 for a stove, which excludes the very poor. But Envirofit plans to launch a new model that customers can purchase through monthly payments to a microfinance company, says Anchan. The user will pay about US$1 a month for around a year, he says. Another payment option that stove companies, including Philips, may pursue is to seek carbon credits for cleaner stoves.Although smoke from cook stoves is clearly a significant public health issue, another one is larger: malnutrition. “We’ve just started to look at programs that are seeking to integrate household environmental interventions for household energy, water, and sanitation with nutrition,” says Bruce.It’s unclear yet whether these integrated programs are more successful if run together or separately, Bruce says. There’s plenty of opportunity to find out: Experts estimate 600–800 million homes worldwide need improved cook stoves, and according to the United Nations Food and Agriculture Organization, more than 1 billion people were undernourished in 2009.


An approach is developed that utilizes randomized genotypes to rigorously infer causal regulatory relationships among genes at the transcriptional level. The approach is applied to an experiment in yeast, yielding new insights into the topology of the yeast transcriptional regulatory network. We develop an approach utilizing randomized genotypes to rigorously infer causal regulatory relationships among genes at the transcriptional level, based on experiments in which genotyping and expression profiling are performed. This approach can be used to build transcriptional regulatory networks and to identify putative regulators of genes. We apply the method to an experiment in yeast, in which genes known to be in the same processes and functions are recovered in the resulting transcriptional regulatory network. It is now possible to measure DNA variation, RNA expression levels, and protein expression levels from thousands of genes in a given biologic sample -3. Of grA number of integrative genomics studies have recently been conducted, in which large-scale genotyping and expression profiling is performed on individuals with randomized genetic backgrounds -15. TypiRandomization is the 'gold standard' for inferring causality of one variable on another -20. ThisThe concept of causal modeling has previously been considered within the context of genetic variation -32. SeveWe apply the proposed method to an experiment on yeast ,33, in wFor an individual organism, DNA has the useful feature that it is usually a static variable, meaning that it is fixed and will not change with changing RNA levels, protein levels, phenotypes, or environmental conditions. By performing designed crosses of genetically distinct inbred or isogenic lines, one can randomize the genotypes of an organism from two or more genetic backgrounds, thereby producing independent realizations of DNA content from offspring to offspring . At the We have developed Trigger as an approach for inferring regulatory relationships among all pairs of genes at the genome-wide level, based on these genetic cross experiments in which high-throughput expression profiling is also performed Figure . Howeverm genes with transcription levels measured on recombinant offspring from an experimental genetic cross.  The goal is to use the data from such an experiment to estimate the probability that the transcription of gene i has a causal regulatory effect on the transcription of any other gene j, which we denote by Pij, where 'causal regulatory effect' means that a change in the transcription level of gene i results in a predictable change in the level of gene j. This is not necessarily through a direct molecular interaction; however, if we directly modulate the transcriptional level of gene i, then this should result in a corresponding change in the transcriptional level of gene j. Trigger provides a conservative estimate of these probabilities, denoted by i = 1, ..., m and j = 1, ..., m.Suppose that there are i to gene j in the network is estimated by i to gene j if and only if These estimated regulatory probabilities can be used to build a regulatory network based on a directed graph. The probability that a directed edge exists from gene % Figure . The resi can be examined as a putative regulator, and hence a quantitative trait gene or 'quantitative trait transcript' [i is a regulator for each other gene j is estimated as In addition to constructing a regulatory network from these estimated probabilities, each gene nscript' . SpecifiL be the locus, Ti transcript i, and Tj transcript j.Trigger is based on a rigorous mathematical framework that we developed for utilizing randomized genetic backgrounds and genome-wide expression in order to test rigorously for causality among transcription levels. The approach starts with a pair of transcripts and a locus to which both are linked. Let L, Ti, Tj) such that L → Ti → Tj, where the arrow '→' means causation. The definition of 'causal' has been a topic of much interest [Ti → Tj means that the ideal manipulation of Ti will change the distribution of Tj, whereas the ideal manipulation of Tj will not disturb the distribution of Ti. 'Ideal manipulation' of a variable means to change the variable in a manner that leaves every other variable unchanged, at the moment when the manipulation occurs [The goal is to identify triplets  were crossed to produce 112 recombinant haploid segregant strains. Because of the random segregation of chromosomes during meiosis, the inheritance of L = BY or L = RM is random. Therefore, when measuring the alleles at a single locus L across 112 segregants, we observe 112 genotypes being generated from some probability distribution. With the genetic cross experimental design, the genotype at a fixed locus L takes place before the expression levels of Ti are measured, this implies that if Ti is linked to locus L then L → Ti. This property is due to the well established principles in statistics showing that an association between two variables when one of them is properly randomized implies causation [L is carried through to the variation in Ti whenever L → Ti. If L → Ti, then segregants with L = BY have a different mean expression for Ti than segregants with L = RM. Therefore, the randomization of L provides a randomization of the mean level of expression for Ti. Figure L. Because the inherited allele L = BY or L = RM is random for each segregant, the mean level of expression for Ti is random when L → Ti.Because the randomization of ausation ,20. AddiTi will not be explained by L, specifically the random fluctuations of the transcription levels within each genotype  Suppose that a hidden variable H is such that H → Ti and H → Tj. Because of this common hidden causal variable, any association between Ti and Tj would not allow us to conclude that Ti → Tj even though Ti has been partially randomized. In other words, the partial randomization of Ti caused by L is now confounded by the effect of H. The common causal hidden variable H does not prevent Ti → Tj from occurring; rather, we just are unable to draw any conclusion when this is the case, unless we are willing to model common hidden causal variables. Modeling common hidden causal variables has been shown to be particularly challenging in this high-dimensional setting [Importantly, some of the variation in e Figure . Therefo setting , and doiH that affects both Ti and Tj, then the Trigger method is designed to not make any conclusions about causality. However, if there is not a common hidden causal variable, then it is now possible, in a straightforward manner, to determine whether Ti → Tj. The following new theorem identifies three conditions that are equivalent to the case in which both L → Ti → Tj and no common causal hidden variable affects both Ti and Tj. If there is a common causal hidden variable L → Ti → Tj exists and there are no hidden variables causal for both Ti and Tj if and only if the following three conditions hold: L → Ti, L → Tj, and L ⊥ Tj | Ti.The causal relationship L → Ti, L → Tj, and L ⊥ Tj | Ti, then we may conclude that L → Ti → Tj exists and there are no hidden variables causal for both Ti and Tj. The fact that 'there are no hidden variables causal for both Ti and Tj' is not an assumption. Rather, it is a verified fact that follows when the three properties are true, as we show in the proof given in Materials and methods (below). We would prefer to detect all cases where L → Ti → Tj; however, as explained above, it is not yet possible to do so in the presence of common causal hidden variables.This theorem is used in the following manner. If L ⊥ Tj | Ti, denotes that Tj conditioned on the information in Ti is independent from L. The first two conditions basically ensure that both transcripts are subjected to a common randomization. The third condition is the key one for inferring causality based on these randomizations. Basically, what the third condition determines is whether the causal effect from L on Tj can entirely be captured by Ti. If so, then Ti is indeed a causal factor for variation in Tj, with no hidden variables.Figure L to be the locus of gene i , which we denote as Li. We call Li → Ti the primary cis linkage and Li → Tj for any other gene j the 'secondary linkage' here. Because Pr(Ti → Tj) ≥ Pr(L → Ti → Tj), we can obtain a conservative estimate of Pij by estimating Pr(L → Ti → Tj). From the causality equivalence theorem it follows that:For computational and statistical efficiency, we limit Pij by estimating each probability in the above product from left to right and taking their product. The Trigger algorithm conservatively estimates i to gene j if and only if Pij ≥ 90%. It can be seen from Figure We applied the Trigger algorithm to the yeast experiment  and found several interesting characteristics of the resulting regulatory probability matrix. Table CNS1 on chromosome 2, ILV6 on chromosome 3, SAL1 on chromosome 14, and NAM9 on chromosome 14. Each was highly significant for cis linkage, and the locus of each putative regulator had many significant secondary linking genes. At a 90% posterior probability cut-off (FDR = 6%), 144, 51 and 36 genes were significant for being regulated by CNS1, ILV6, and SAL1, respectively. At an 80% posterior probability cut-off (FDR = 11%), 14 genes were significant for being regulated by NAM9. The significant genes, posterior probabilities, and other relevant information for each putative regulator can be found in Additional data file 3. Note that each of these putative regulators is also a significant quantitative trait gene (or quantitative trait transcript) for each expression trait that it significantly regulates. Figure We examined in detail four genes as putative regulators: In order to determine whether the genes that are significant for each putative regulator show a coherent functional relationship, we employed the Gene Ontology (GO) database . For eacNAM9, which is a mitochondrial ribosomal component of the small subunit and inviable under deletion [NAM9. The other significant gene is heretofore uncharacterized. Translation, structural constituent of ribosome, and mitochondrial small ribosome subunit are all highly significant terms in the GO tree.Figure deletion . It is adeletion -41. For CNS1, which is an essential tetratricopeptide repeat (TPR)-containing co-chaperone, deletion of which is inviable [SSA1. CNS1 is involved in the protein binding process, and its cellular component is associated with cytoplasm [CNS1 in yeast. Many of the 144 genes were also found to be in the same pathway as CNS1; for example, TRM8 and CNS1 are both involved in a pathway for protein binding [Additional data file 1  shows the results for the putative regulator inviable . It bindytoplasm -45. Of t binding ,47.ILV6 and its 51 genes under statistically significant regulation. ILV6 is a regulatory subunit of acetolactate synthase, which catalyzes the first step of branched-chain amino acid biosynthesis [P values below 10-10. Cyclohydrolase activity and lyase activity are some other significant pathways identified by GO analysis.Additional data file 1  shows the significant GO results for ynthesis ,49. AminSAL1 is a probable transporter and a member of the calcium-binding subfamily of the mitochondrial carrier family, with two EF-hand motifs. It works in transporter activity and calcium ion binding [SAL1 are associated with the mitochondrian and membrane GO terms. Six of the 36 significantly regulated genes are involved in mitochondrial inner membrane with high statistical significance (P < 10-8), a trend that is consistent with previous findings [The putative regulator  binding , with it binding . From thfindings ,51.SAL1 no polymorphism exists in the immediate 500 base regions upstream or downstream of the SAL1 open reading frame. The linkage peaks occur approximately 13 kilobases and 21 kilobases on either side. This illustrates that linkage does not have to be due to an unequivocally cis-acting regulatory polymorphism in order for Trigger to work. On the contrary, there must simply be some locus to which both expression traits Ti and Tj are linked. We justified limiting the locus L to be in the 50 kilobases region of Ti based on computational and statistical increases in efficiency .It should be noted that in the case of YSW1, PHM7, and so on), were predicted to regulate a number of genes, with significant GO terms appearing for each set. Therefore, our results can potentially be used to predict properties of relatively unknown genes as well. Furthermore, several transcription factors significantly regulated a number of genes, including HAP1 [RAD16 [GPA1 and AMN1 lead to expression changes in genes whose expression exhibits linkage to each respective locus [GPA1 and AMN1 that appear to be the cause of the expression changes in the linking genes. In work to be reported in the future we examine the GPA1 and AMN1 cases in detail, showing that there appears to be common causal hidden variables involved. The Trigger approach is extended to take into account these common causal hidden variables, allowing us to recapitulate the previous findings regarding GPA1 and AMN1.In addition to these four well characterized putative regulators, we noticed that expression levels of a number of genes with relatively unknown function , the AIC model selection method [CNS1, about 2,800 genes are classified as having a causal relationship with CNS1 by model selection, as opposed to the 144 Trigger found to be significant with probability exceeding 90%. The advantages that Trigger has over AIC and other model selection criteria are as follows: there is no generally applicable method to obtain an interpretable measure of significance based on these criteria ; and these approaches force one to model directly all possible hidden variables, making typically unverifiable assumptions about their underlying model [We have made some simple comparisons between Trigger and the model selection and correlation based approaches Figure . In addin method  classifing model .We have presented Trigger within the context of inferring regulatory relationships based on gene expression data from organisms with randomized genetic backgrounds. However, this method may actually be applied to a much broader class of data types. Because the estimation is done in a nonparametric and scale-free manner , it is possible to combine any combination of expression, proteomic, metabolomic, and phenotypic data as the variables among which causal relationships are inferred. These may be considered separately or simultaneously, allowing one to discover regulatory relationships, say, among protein levels and transcriptions levels. The general requirement is that one must acquire organisms with random genetic backgrounds that are essentially stable as the expression levels and other potential traits are measured. The computational approach and statistical principles underlying the method remain the same for all of these data types.The Trigger algorithm allows one to infer transcriptional regulatory relationships among genes at the genome-wide level, based on experiments in which large-scale genotyping and expression profiling are performed among individuals with randomized genetic backgrounds. Moreover, the algorithm can be applied to any high-throughput phenotypic data in which genotypes or some other static regulatory mechanism has been randomized. Trigger works by identifying pairs of genes with expression levels both affected by a common randomized genotype and then testing for three key properties that we have mathematically demonstrated to be equivalent to a directed causal relationship among the pair of gene expression traits.CNS1, ILV6, SAL1, and NAM9).We applied Trigger to an experiment in yeast in which 112 independent recombinant segregants were subjected to genome-wide expression monitoring. The Trigger algorithm produced a regulatory probability matrix from this experiment that has been made available . This matrix can be used to build networks by a variety of techniques in which the noise level of any resulting network is easily assessed by the FDR. Our analysis of the results indicates that the proposed algorithm produces rich and biologically coherent information, mainly through a GO analysis of four putative regulators  However, we do not assume that the inheritance at several loci on a given chromosome occurs independently, and we make no other assumptions about independence of inheritance among loci. Segregation distortion, selection, and other traditionally problematic issues arising when performing genetic crosses for the purpose of genetic mapping do not invalidate Trigger.We simply point out here that the main assumption regarding random genotypes is that the Li and Lk are dependent . Suppose also that Li → Ti → Tj and Lk → Tj, but it is not the case that Lk → Ti. Because Li and Lk are dependent, it will not be the case that Li ⊥ Tj | Ti, as not all linkage information for Tj is captured by Ti. Specifically, Li contains some information about Lk because of their dependence, so Tj | Ti is not independent from Li. This is an example of how dependence of inheritance of different loci can reduce the power of Trigger. However, Trigger does not produce false positives because of this, so it is robust to linkage among loci on the same chromosome or other forms of dependence among loci.As in all genetic crosses, the more independent the inheritance of the loci is, the more information there is in the experiment. For example, suppose that loci X and Y, then the directed graph associated with X and Y can be represented by X → Y [The proof of the theorem follows from well-established theory in graphical and causal modeling ,61,63. SL → Ti → Tj with no hidden variables causal for both Ti and Tj, then L → Ti, L → Tj, and L ⊥ Tj | Ti. Under these assumptions, the first two properties (L → Ti and L → Tj) are trivially true. Because there are no hidden variables involved, Ti is the only direct cause of Tj, and L is a non-descendant of Tj, it follows by the causal Markov assumption that the third property (L ⊥ Tj | Ti) holds.We first show that if L → Ti, L → Tj, and L ⊥ Tj | Ti, then L → Ti → Tj and there are no hidden variables causal for both Ti and Tj. The third property (L ⊥ Tj | Ti) implies that there is no direct edge between L and Tj by the faithfulness assumption.We now show the more important direction of this equivalence: if Ti and Tj, so that the only variables involved in this causal graph are L, Ti, and Tj. Because of the second property (L → Tj), and there is no direct edge between L and Tj, it must follow that there is a direct edge between Ti and Tj. Otherwise, Tj is completely independent of L, which violates the second property. Thus, L → Ti - Tj, where an edge without arrowheads implies dependence. If any two variables are dependent, then one is a cause of the other or there must be a third variable causal for both [Ti is causal for Tj, or Tj is causal for Ti, or both cases are true. L cannot be the common direct cause for both Ti and Tj, because no direct edge exists between L and Tj. If L is an indirect cause of Tj, then Ti as the only other variable in the graph must be a direct cause of Tj, implying that Ti → Tj. If Tj → Ti and the first property (L → Ti) holds, then it cannot be the case that the third property (L ⊥ Tj | Ti) holds. Thus, Tj is not causal for Ti but it is true that Ti → Tj, implying that L → Ti → Tj.Let us first consider the case when there are no hidden variables causal for both for both . Thus, eL is an independently randomized, static variable, there cannot be any hidden variables causal for both L and Ti or both L and Tj. The only possible existence of hidden causal variable in this graph is one affecting both Ti and Tj. However, if there is a common hidden cause for Ti and Tj, then Ti → Tj [Tj | Ti is dependent with L, contradicting the third property (L Tj | Ti). Therefore, L → Ti → Tj with no hidden variables affecting either of the two.Now consider the second case in which there might be causal hidden variables in the graph. Because  Ti → Tj . If thisL → Tj and L ⊥ Tj | Ti, respectively) imply the first property (L → Ti). However, we have designed Trigger to test for all three properties because conditioning on the first property increases the power to detect the state of the second and third properties.Note that it can be shown that the second and third properties  Transform the expression data for each gene to follow a Normal distribution with mean 0 and variance 1.Ti , test the null hypothesis of no cis linkage to Li versus the alternative hypothesis of cis linkage to Li by performing a standard likelihood ratio test to obtain observed statistics Xi . Permute the expression data B times and perform the test on the permuted data to obtain null statistics b = 1, 2, ..., B). This is equivalent to testing Li → Ti.For each transcript, Li, Ti) from step 2, carry out the following. For all other transcripts Tj (j ≠ i), test the null hypothesis of no linkage to Li versus the alternative hypothesis of linkage to Li under the assumption that Li → Ti. Similarly to above, apply a standard likelihood ratio test to obtain observed statistics Yij. Permute the expression data B times under the assumption that Li → Ti, and perform the test on the permuted data to obtain null statistics b = 1, 2, ..., B).For each pair , carry out the following. Estimate the conditional distribution of Tj | Ti, which is tractable under the Normal transformation. Test the null hypothesis of independence between Li and Tj | Ti versus the alternative hypothesis of dependence between Li and Tj | Ti. Again, apply a standard likelihood ratio test to obtain observed statistics Zij for this test. Permute the expression data B times under the assumption that Li → Ti and Li → Tj, and perform the test on the permuted data to obtain null statistics b = 1, 2, ..., B).For each triplet  and null statistics i = 1, 2, ..., m; b = 1, 2, ..., B) from step 2 can be used to form an empirical Bayes estimate of Pr(Li → Ti), which is equivalent to an estimate of the probability that the alternative hypothesis is true for each i = 1, 2, ..., m. The statistics from step 3 are used to estimate Pr(Li → Tj | Li → Ti), and the statistics from step 4 are used to estimate Pr(Li ⊥ Tj | Ti | Li → Ti and Li → Tj).For each test from steps 2 to 4, the set of observed statistics and null statistics can be used to estimate the probability that the hypothesis of interest is true, based on previous methodology ,26,64. FPij = Pr(Li → Ti → Tj), where:Multiply the three estimated probabilities together to get an estimate of Li → Ti → Tj significant if and only if i, would be considered. The estimate of the FDR corresponding to λ, FDR(λ), is as follows:A significance threshold can be applied to the probabilities for either the entire regulatory probability matrix or for a specific putative regulator. For the entire probability matrix, this would entail applying a threshold λ to the Where 1 entry of this matrix is the probability that the expression level of gene i is causal for the expression level of gene j. Additional data file The following additional data are available with the online version of this paper. Additional data file Presented are supplementary text and figures, as referenced in the main text.Click here for filei,j) entry of this matrix is the probability that the expression level of gene i is causal for the expression level of gene j.Presented is the entire matrix of regulatory probabilities for all genes, where the rows are genes acting as regulators and the columns are genes under regulation. Thus, the (Click here for filePresented is a list of significantly regulated genes, posterior probabilities, and other relevant information for each of the four putative regulators considered in detail.Click here for file


Laminaria digitata) supplement. However, the authors failed to report that the product was used at two to four times the suggested amount, of potential significance because of the naturally occurring presence of iodine in kelp. Speciation of arsenic into organic and inorganic forms was not addressed, nor was the amount of arsenic consumed calculated from observed concentration levels. Also, there were errors in marketplace and regulatory descriptions.In their report of a 54-year-old woman with a 2-year history of worsening alopecia, memory loss, and fatigue, The supplement was identified as Icelandic Kelp. The patient “initially took two tablets,” which was later increased to “at least four pills per day” . The autCAUTION: Do not exceed recommended dosage without first consulting your healthcare practitioner, as excess iodine may adversely affect thyroid function.In neglecting to mention the product’s labeling and their patient’s decision to ignore it, Intake of iodine at least four times this product’s recommended dose must be considered a potential factor in evaluating the observed symptoms. Four tablets contain 900 μg iodine, 600% of its daily value. In their analysis Although Financial data that In conclusion, we have no disagreement with the authors’ implication that marketers have a responsibility to control the level of potentially harmful contaminants in herbal products. Inaccurate reporting and speculative science, however, have no place in safety evaluations of case reports associated with supplements.


Khimaira, Latin Chimaera; she-goat. In Greek mythology: a composite creature with the body and head of a lion, a goat’s head rising from its back, and a serpent’s tail. In science: an individual organism whose body contains cell populations derived from different zygotes, of the same or different species. Each population of cells keeps its own character, and the resulting animal is a mixture of tissues. Chimera also refers to a substance created from proteins or genes of 2 species, as by genetic engineering. Chimerism is rare in humans; ≈40 cases have been reported.From the Greek