Excess Years of Life Lost due to exposure is an important measure of health impact complementary to rate or risk statistics. I show that the total excess Years of Life Lost due to exposure can be estimated unbiasedly by calculating the corresponding excess Years of Potential Life Lost given conditions that describe study validity (like exchangeability of exposed and unexposed) and assuming that exposure is never preventive. I further demonstrate that the excess Years of Life Lost conditional on age at death cannot be estimated unbiasedly by a calculation of conditional excess Years of Potential Life Lost without adopting speculative causal models that cannot be tested empirically. Furthermore, I point out by example that the excess Years of Life Lost for a specific cause of death, like lung cancer, cannot be identified from epidemiologic data without assuming non-testable assumptions about the causal mechanism as to how exposure produces death. Hence, excess Years of Life Lost estimated from life tables or regression models, as presented by some authors for lung cancer or after stratification for age, are potentially biased. These points were already made by Robins and Greenland 1991 reasoning on an abstract level. In addition, I demonstrate by adequate life table examples designed to critically discuss the Years of Potential Life Lost analysis published by Park et al. 2002 that the potential biases involved may be fairly extreme. Although statistics conveying information about the advancement of disease onset are helpful in exposure impact analysis and especially worthwhile in exposure impact communication, I believe that attention should be drawn to the difficulties involved and that epidemiologists should always be aware of these conceptual limits of the Years of Potential Life Lost method when applying it as a regular tool in cohort analysis. This information is used to measure the effect of exposure on disease by comparisons of such statistics. Although these measures have been proven by practice and theory to be useful for this purpose, these frequency statistics are unable to reflect all causal effects of exposure in general . One reand 1998 , Greenlaand 1998 , Morfeldand 1998 ). An illTherefore, alternative measures that focus more directly on the time-shift of events or the time-shift of frequency statistics would be most welcome. One such approach aims at Years of Life Lost (YLL). Interestingly, even in the title of one of the very first articles about Years of Life Lost, Dempsey  expresseRecently, this approach was extended by Park et al. 2002 ) estimatHowever, in a letter to the editor, Morfeld 2003  claimed Here I try to explain the reasoning in detail. I expand the critique demonstrating additionally that the e-YPLL method is unjustified when applied to specific causes of death like lung cancer – the main topic of Park et al. 2002 YPLL. This is the first kind of contribution to the change in e-YPLL I have to consider. The second kind of contribution stems from the new exposed case dying at dx(1). This amount of change is simply the same as the Potential Years of Life Lost for this case: YPLL = dx(2) - dx(1) + [n/(n+1)]YPLL where n/(n+1) is the probability to survive dx(2) given the unexposed reference population. The third kind of contribution to the difference between e-YPLL(n+1) and e-YPLL(n) stems from the k exposed cases dying before dx(2). Their potential survival is shortened after introducing the new additional death at dx(2). Note that the reference-based probability to die at dx(2) is 1/(n+1) for each of these exposed cases. Therefore, the Years of Potential Life Lost is reduced for each of these cases by the amount [1/(n+1)]YPLL. This sums to [k/(n+1)]YPLL for all k exposed cases dying before dx(2). Now I can calculate how e-YPLL changes when the pair x is added to the n pairs:I assume that k of the n exposed in the study with n pairs may die before dx(2) - dx(1) +    e-YPLL(n+1) = e-YPLL(n) + d      + [n/(n+1) - (n-k)/(n+1) - k/(n+1)] YPLLx(2) - dx(1)      = e-YPLL(n) + dx(2) - dx(1)      = e-YLL(n) + d      = e-YLL(n+1).x(2) - dx(1) if the pair x is added to the n pairs I started with. Hence, I have proven that e-YPLL equals e-YLL in all ideal studies consisting of pairs of exchangeable twins given that exposure is never preventive. Note that the measures are always calculated with respect to deaths from all causes. It is remarkable that the equality e-YPLL = e-YLL holds although e-YPLL does not make use of the individual matching information.The second to last equation follows from the induction assumption. The last equality is based on the obvious fact that the true excess Years of Life Lost increases by dI have shown in Chapter 2 that the total e-YLL of the whole study group can be measured accurately by the total e-YPLL provided the assumptions of an ideal study hold including optimality criterion 2 (cf. last paragraph of Chapter 1) and provided the analysed response is death from all causes. However, this identity of e-YLL and e-YPLL no longer holds if I am interested in e-YLL conditional on age at death. To see why consider the scenarios illustrated in Figure a(1) are smaller in scenario 1 than in scenario 2 whereas it is vice versa at db(1). Irrespective of the scenario, the e-YPLL are always calculated in the same way. Therefore, we get in both scenarios the same e-YPLL at da(1) as well as the same e-YPLL at db(1). Note that the calculation of e-YPLL does not make any use of the individual matching information.In both scenarios Fig.  the totaHence, the true excess Years of Life Lost conditional on age at death are not identifiable without having access to a perfect control twin or without supposing a specific mechanism for how exposure causes death. Note that criterion 1 alone – as defined in the last paragraph of Chapter 1 and hopefully fulfilled in usual cohort data – does not render the excess Years of Life Lost stratified on age at death identifiable. Consequently, estimates based on e-YPLL conditional on age at death are potentially biased in all usual settings.Next, I analyse a theoretical birth cohort of 100,000 men to demonstrate this potential bias by a realistic life table example  for short. In contrast to the causal effect, e-YLL(lung cancer) comprises exactly that part of e-YLL from all causes of death that is due to the occurrence of lung cancer deaths among the exposed. In the example Figure  these e-The concept presented supposes that each subject has a set of hypothetical (deterministic) death times for each combination of level of exposure and cause of death. Just one of these death times is effective, the others are latent. For the exposed twin 1 lung cancer is effective as a cause of death and heart attack is latent. It is vice versa for the unexposed twin 2. Both causes of death compete within each twin for the effective position. Of course, the result of this competition depends on the exposure conditions applied. This result motivates to introduce a second (competing) exposure into the scenario, in which I deal with two competing responses (lung cancer and heart attack). Figure In Figure From Figure     e-YLL = e-YLL(lung cancer) = e-YPLL =a(2) - da(1) + db(2) - db(1).    dThe first equation holds because the only cause of death among all asbestos exposed is lung cancer and the second because I have shown that e-YLL = e-YPLL is true in an ideal study given the aforementioned assumptions. The third equation follows from a simple evaluation of e-YLL.Next, I calculate e-YPLL(lung cancer) according to Park et al. 2002 [lungcancer(t) - expectedlungcancer(t)) YPLL(t).    e-YPLL(lung cancer) = Σ  - da(1) + 0.5 (db(2) - da(2))    YPLL  = db(2) - db(1),    YPLL  = dfrom which I derivea(2) - da(1) + 0.5 (db(2) - da(2)) +    e-YPLL(lung cancer) = db(2) - db(1)      + db(2) - da(2)),      = e-YLL(lung cancer) + 0.5 (dsince no expected lung cancer deaths are to be subtracted.b(2) - da(2)) which is half of the causal effect of smoking on age at death from all causes  according to Figure Hence, e-YPLL(lung cancer) is biased upward by 0.5  for death from all causes can be accurately determined by calculating the corresponding excess Years of Potential Life Lost (e-YPLL) provided conditions hold that are also often cited for general study validity. However, the equality of e-YLL and e-YPLL does not hold in general under certain conditions of interest because under these certain conditions we need to assure criterion 2: each exposed subject has to have an ideal unexposed control partner so that the effect of exposure can be measured on the individual level . The equality also requires that this information about individual matching is used in the analysis. I pointed out that the e-YPLL conditional on age at death are potentially biased as they were published in some analyses  the aveStatistical measures that focus on the advancement of disease onset are assumed to be helpful in communicating risk factor impact on disease . FischhStatistics conveying information about the advancement of disease onset are therefore helpful in exposure impact analysis and especially worthwhile in exposure impact communication. However, attention should be drawn to the difficulties involved and that epidemiologists should always be aware of the conceptual limits of the Years of Potential Life Lost method when applying it as a regular tool in cohort analysis.Aside from Years of Life Lost, other approaches are available to convey information on the impact of a risk factor on the onset of a disease and may thus facilitate communication of epidemiological findings. One such concept is the risk and rate advancement period (RAP) introduced by Brenner et al. 1993  which coThe main conclusions about non-identifiability of e-YLL are derived as an application of a causal theory based on counterfactuals (for a review see Greenland 2000 ). Some aThe ideas of counterfactual reasoning can at least be traced back to philosophers in the eighteenth and nineteenth centuries, like Hume and Mill. Even the oldest clearly structured theory of causality, developed by Aristotle, has some similarities to counterfactual reasoning due to its manipulative four-causes concept (Vorländer 1979 ). In itsSome authors resist a counterfactual approach and argue against the speculative and metaphysical background of counterfactual worlds (Dawid 2000 ): How caNote further that the mathematically consistent analytical treatment of causal questions by counterfactual theory is obviously related to the so called multiverse approach (Everett 1957 ). The laIn addition, this link to quantum mechanics disproves the repeatedly made statement by Dawid  that coWhereas the counterfactual approach can help to clarify terminology and substance of causal relations, it points simultaneously at some ambiguities when discussing competing causes of death. In this scenario, it is unclear what kind of action should be taken to cause a suppression of competing risks (Greenland 2002 ). Hence,In conclusion, the excess Years of Potential Life Lost estimates the excess Years of Life Lost due to exposure unbiasedly if we are interested in a) death from all causes and b) total excess Years of Life Lost summed up across the whole cohort. However, the method of calculating excess Years of Potential Life Lost due to exposure is potentially biased if it is applied 1) to estimate the impact of exposure on specific causes of death, like lung cancer, in the presence of competing causes or 2) to estimate the impact of exposure  conditional on age at death. These potential biases can be rather severe in published analyses  declares that he has no competing interests.The following example is based on the counterfactual framework presented in Chapter 1. It demonstrates that neither relative risks nor relative rates can be used in general to estimate the probability of causation unbiasedly. In particular, I show that an estimate of the attributable hazard derived from a Cox model (Breslow and Day 1987 ) fails tend>0. The time scale can be chosen arbitrarily as age, calendar time or time since start of follow-up without affecting the following arguments. I assume that the exposed subject A may experience the event (death) during the follow-up period at t1 years (t1 > 0), if unexposed counterfactually (A's twin) at t2 > t1, t2 < tend. No event may occur in subject B and his/her twin during follow-up . Thus, among the two exposed subjects, A and B, only one case occurs and this case is causally affected by exposure since t1 < t2. It follows that the probability of causation  is exactly 100%.The cohort is supposed to comprise four subjects: A, A's twin, B, and B's twin. In addition, it is assumed that the cohort is followed up in mortality until the fixed censoring date tIn contrast, the incidence proportion (Rothman and Greenland 1998 ) is 0.5 1 + tend) and among the unexposed is 1/(t2 + tend). Consequently, the rate ratio is (t2 + tend)/(t1 + tend) > 1 because t2 > t1. The attributable rate among the exposed can be determined as (rate ratio -1) / rate ratio = 1 - 1/rate ratio. Thus, I conclude 0 < 1 - (t1 + tend)/(t2 + tend) < 1, again proving a systematic underestimate of the true probability of causation among the exposed by the attributable rate ratio. Note that the rare disease assumption is of no help. Assuming n exposed subjects (n>>1) that neither react nor do their n unexposed twins, we get an attributable rate among the exposed of 1 - (t1 + (n)(tend)) / (t2 + (n)(tend)) < 1. If n approaches infinity the attributable rate among the exposed decreases to zero whereas the true probability of causation remains always constant at 100%. Hence, the discrepancy is even sharpened under the rare disease assumption.Note that a change from risks to rates does not overcome the problem. The rate among the exposed is 1/ comprises 4 subjects and 2 risk sets. One set is generated by the exposed case (A), the other set by the unexposed (A's twin). Therefore, a Cox analysis of the cohort yields the following.Assuming a relative hazard (rate) in the Cox model of0 = exp (β exposure), exposure = binary exposure indicator    λ / λwe get the partial likelihood    which is maximized at b = ln 2/2.Therefore, the estimated hazard ratio is    exp (b) = √ 2yielding an estimated attributable hazard among the exposed of-1 < 1 = probability of causation.1 - (√2)Note that the rare disease assumption is again of no help to overcome this discrepancy because the estimated attributable hazard among the exposed approaches 0 when the number of controls is rising indefinitely.Greenland 1999  emphasizLife table analysis with calculation of excess Years of Potential Life Lost e-YPLL according to Park et al. 2002. Basic data (unexposed) from BEIR IV (1988), Table 2A-10, p. 133: death rates of the male US population, surviving at least 30 years, applied to a birth cohort of 100,000. Exposure impact: advancement of certain fractions of deaths. For details of assumed mechanism see Table 2 .Click here for fileTwo different exposure-response mechanisms compatible with the life table analysis in Table 1. Fractions of the unexposed deaths are advanced by 0 yr or 5 yr according to mechanism 1 and by 0 yr, 5 yr or 10 yr according to mechanism 2. The age distribution of all exposed deaths is identical under both mechanisms. The distribution of the true excess Years of Life Lost e-YLL differs between mechanisms and both diverge from e-YPLL , whereas the totals agree. (advcm = advancement)Click here for fileLife table analysis with calculation of excess Years of Potential Life Lost e-YPLL and true excess Years of Life Lost e-YLL for overall and lung cancer mortality (ICD9-162). Basic data (unexposed) from BEIR IV (1988), Table 2A-10, p. 133: overall and lung cancer death rates of the male US population, surviving at least 30 years applied to a birth cohort of 100,000. Exposure impact: advancement of factual and hypothetical lung cancer deaths by 5 years, mixture of advancements among deaths from all causes. It is assumed that the advancement of hypothetical lung cancer deaths leads to an excess of 50% of lung cancer deaths among exposed in each age category. The overall e-YLL for lung cancer are less than the number of exposed lung cancer deaths times 5 years. The e-YPLL for overall death and lung cancer death are determined according to Park et al. 2002. For all deaths e-YPLL must equal e-YLL, but e-YPLL is obviously biased for lung cancer death.Click here for fileExcel sheet explaining the calculations in Additional files 1 and 2.Click here for fileExcel sheet explaining the calculations in Additional file 3.Click here for file


Phospholipase D (PLD) is involved in many signaling pathways. In most systems, the activity of PLD is primarily regulated by the members of the ADP-Ribosylation Factor (ARF) family of GTPases, but the mechanism of activation of PLD and ARF by extracellular signals has not been fully established. Here we tested the hypothesis that ARF-guanine nucleotide exchange factors (ARF-GEFs) of the cytohesin/ARNO family mediate the activation of ARF and PLD by insulin.Wild type ARNO transiently transfected in HIRcB cells was translocated to the plasma membrane in an insulin-dependent manner and promoted the translocation of ARF to the membranes. ARNO mutants: ΔCC-ARNO and CC-ARNO were partially translocated to the membranes while ΔPH-ARNO and PH-ARNO could not be translocated to the membranes. Sec7 domain mutants of ARNO did not facilitate the ARF translocation. Overexpression of wild type ARNO significantly increased insulin-stimulated PLD activity, and mutations in the Sec7 and PH domains, or deletion of the PH or CC domains inhibited the effects of insulin.Small ARF-GEFs of the cytohesin/ARNO family mediate the activation of ARF and PLD by the insulin receptor.The online version of this article (doi:10.1186/1471-2121-4-13) contains supplementary material, which is available to authorized users. Small GTPases of the ADP-ribosylation factor (ARF) family play a major role in membrane trafficking in eukaryotic cells . ARF act2+, protein kinase C, tyrosine kinases, and G proteins  (1 μCi) in 20 mM Hepes buffer containing 2 mM MgCl2/ 0.1% Na-cholate / 1 mM ATP. At the indicated time points, the reaction was quenched by addition of 100 μM ice-cold, unlabeled GTPγS and the protein-bound nucleotide was determined by filtration through nitrocellulose filters as described [ARF activation was determined by the binding of GTPγS to the purified, myristoylated recombinant human ARF1 (mhARF1), as described by Shome and coworkers . The insescribed .HIRcB cells were plated on poly-L-lysine coated glass coverslips and transfected with the constructs as indicated above. Cells were serum starved overnight and stimulated with 100 nM insulin. Live cells were imaged in a LSM5 Zeiss laser scanning confocal microscope equipped with a 63X oil immersion objective.For ARF and ARNO colocalization experiments, HIRcB cells were plated on poly-L-lysine coated coverslips as described above and co-transfected with myc-ARNO and ARF-GFP constructs using Superfect transfection reagent according to the manufacturer's instructions. Following insulin stimulation, the cells were fixed with 4% fresh paraformaldehyde in PBS at 4°C for 30 min, and permeabilized in 0.1% Triton X-100 at room temperature for 2 min. After permeabilization, the cells were blocked with 3% bovine serum albumin in PBS at room temperature for 30 min, and immunostained with a monoclonal antibody 9E10 (Upstate Biotechnology) that recognizes the myc epitope. After extensively washing, the cells were incubated with a Cy5-conjugated donkey anti-mouse secondary antibody (Jackson Immunoresearch) and imaged using a Zeiss laser scanning confocal microscope with filters appropriate for the detection of GFP and Cy5.


Human faces provide important signals in social interactions by inferring two main types of information, individual identity and emotional expression. The ability to readily assess both, the variability and consistency among emotional expressions in different individuals, is central to one's own interpretation of the imminent environment. A factorial design was used to systematically test the interaction of either constant or variable emotional expressions with constant or variable facial identities in areas involved in face processing using functional magnetic resonance imaging.Previous studies suggest a predominant role of the amygdala in the assessment of emotional variability. Here we extend this view by showing that this structure activated to faces with changing identities that display constant emotional expressions. Within this condition, amygdala activation was dependent on the type and intensity of displayed emotion, with significant responses to fearful expressions and, to a lesser extent so to neutral and happy expressions. In contrast, the lateral fusiform gyrus showed a binary pattern of increased activation to changing stimulus features while it was also differentially responsive to the intensity of displayed emotion when processing different facial identities.These results suggest that the amygdala might serve to detect constant facial emotions in different individuals, complementing its established role for detecting emotional variability. Facial expressions and facial identities are important cues for the evaluation of social contexts ,2. Two mVisual analysis of human faces has been suggested to be achieved by a core system comprising the fusiform gyrus together with the inferior occipital gyrus, the superior temporal sulcus, and the amygdala ,8. The aRecent advances to assess the effects of facial identity and emotional expression include one report on increased left amygdala activation to blocks of multiple novel vs. single identical faces displaying neutral or emotionless expressions . AnimateICE), (b) variable identity, constant expression (VICE), (c) constant identity, variable expression (CIVE), (d) variable identity, variable expression (VIVE). To control for attentional effects during the procedure, an oddball task was included to avoid confounds by an emotional judgment or a gender differentiation task .fearful expressions (shown in different identities) would elicit the strongest amygdala activation given the evidence from lesion and imaging studies that this expression is a particularly potent activator of the amygdala  for the amygdala and masked it with the contrast  to exclude regions showing higher activations to CIVE than to VICE. For our hypothesis in the fusiform gyrus we used a contrast that compared conditions with at least one changing stimulus feature  with the condition in which the same stimulus was shown for the entire block [(VICE + CIVE+ VIVE) > 3 × CICE].In order to detect voxels that show elevated responses to the same expression displayed in different individuals we constructed the interaction contrast of our 2 × 2 design. Hence, we created the contrast [ which refer to the probabilistic behavior of Gaussian random fields . Our thrzer task ; see TabICE (Figure For the emotion-specific analyses of condition VEPI: echo-planar imagingfMRI: functional magnetic resonance imagingLOC: lateral occipital complexRT: reaction timeSPM: statistical parametric mapIVE: variable identity, variable emotionVICE: variable identity, constant emotionVICE: constant identity, constant emotionCIVE: constant identity. variable emotionCFear/Happy Max: maximally fearful/happy expressionFear/Happy Min: minimal fearful/happy expression.J.G. and O.T. designed, coordinated, and conducted data collection, analysis, and interpretation. C.B. and C.W. conceived of the study and participated in its design, analysis and interpretation. All authors read and approved the final manuscript.


Endurance exercise training can promote an adaptive muscle fiber transformation and an increase of mitochondrial biogenesis by triggering scripted changes in gene expression. However, no transcription factor has yet been identified that can direct this process. We describe the engineering of a mouse capable of continuous running of up to twice the distance of a wild-type littermate. This was achieved by targeted expression of an activated form of peroxisome proliferator-activated receptor δ (PPARδ) in skeletal muscle, which induces a switch to form increased numbers of type I muscle fibers. Treatment of wild-type mice with PPARδ agonist elicits a similar type I fiber gene expression profile in muscle. Moreover, these genetically generated fibers confer resistance to obesity with improved metabolic profiles, even in the absence of exercise. These results demonstrate that complex physiologic properties such as fatigue, endurance, and running capacity can be molecularly analyzed and manipulated. Engineered expression of the peroxisome proliferator-activated receptor δ in skeletal muscle increases type I muscle fibers allowing the modified mice to run twice the distance of wild-type littermates Skeletal muscle fibers are generally classified as type I (oxidative/slow) or type II (glycolytic/fast) fibers. They display marked differences in respect to contraction, metabolism, and susceptibility to fatigue. Type I fibers are mitochondria-rich and mainly use oxidative metabolism for energy production, which provides a stable and long-lasting supply of ATP, and thus are fatigue-resistant. Type II fibers comprise three subtypes, IIa, IIx, and IIb. Type IIb fibers have the lowest levels of mitochondrial content and oxidative enzymes, rely on glycolytic metabolism as a major energy source, and are susceptible to fatigue, while the oxidative and contraction functions of type IIa and IIx lie between type I and IIb . Adult sMuscle fiber specification appears to be associated with obesity and diabetes. For instance, rodents that gain the most weight on high-fat diets possess fewer type I fibers . In obesWe have previously established that peroxisome proliferator-activated receptor (PPAR) δ is a major transcriptional regulator of fat burning in adipose tissue through activation of enzymes associated with long-chain fatty-acid β-oxidation . AlthougA role of PPARδ in muscle fiber was suggested by its enhanced expression—at levels 10-fold and 50-fold greater than PPARα and γ isoforms, respectively (unpublished data). An examination of PPARδ in different muscle fibers reveals a significantly higher level in type I muscle (soleus) relative to type II–rich muscle (extensor digitorum longus) or type I and type II mixed muscle (gastrocnemius) A; this eTo directly assess the role of activation of PPARδ in control of muscle fiber plasticity and mitochondrial biogenesis, we generated mice expressing a transgene in which the 78-amino-acid VP16 activation domain was fused to the N-terminus of full-length PPARδ, under control of the 2.2-kb human α-skeletal actin promoter. In agreement with the previous characterization of this promoter , the VP1Type I muscle can be readily distinguished from type II or mixed muscle by its red color, because of its high concentration of myoglobin, a protein typically expressed in oxidative muscle fibers. We found that muscles in the transgenic mice appeared redder A, which A number of previous studies have shown that obese individuals have fewer oxidative fibers, implying that the presence of oxidative fibers alone may play a part in obesity resistance. To test this possibility, we fed the transgenic mice and their wild-type littermates with a high-fat diet for 97 d. Although the initial body weights of the two groups were very similar, the transgenic mice had gained less than 50% at day 47, and only one-third at day 97, of the weight gained by the wild-type animals A. The trp > 0.35, n = 4) were observed between the transgenic and control mice. Thus, the remarkable increase in endurance is the physiologic manifestation of muscle fiber transformation. This suggests that genetically directed muscle fiber switch is physiologically and functionally relevant. In addition, we looked at what effect the absence of PPARδ function has on exercise endurance. In the treadmill test, the PPARδ-null mice could sustain only 38% of the running time and 34% of the distance of their age- and weight-matched wild-type counterparts  of VP16 was fused in frame with the N-terminus of mouse PPARδ. The VP16-PPARδ fusion cDNA was placed downstream of the human α-skeletal actin promoter , and upsMouse EST clones were obtained from ATCC , verified by sequencing, and used as Northern probes. Antibodies were obtained from Santa Cruz Biotechnology . Total muscle protein extracts  and nuclPrior to the exercise performance test, the mice were accustomed to the treadmill  with a 5-min run at 7 m/min once per day for 2 d. The exercise test regimen was 10 m/min for the first 60 min, followed by 1 m/min increment increases at 15-min intervals. Exhaustion was defined when mice were unable to avoid repetitive electrical shocks.Muscle fiber typing was essentially performed using metachromatic dye–ATPase methods as described . Muscle p-values.Number of mice for each group used in experiments is indicated in figure legends. Values are presented as mean ± SEM. A two-tailed Student's t test was used to calculate Video S1This video shows the exercise performance of a representative of the transgenic mice (right chamber) and a representative of wild-type control littermates (left chamber) on the treadmill 15 min into the exercise challenge.(52.4 MB MOV).Click here for additional data file.Video S2This video shows the exercise performance of a representative of the transgenic mice (right chamber) and a representative of wild-type control littermates (left chamber) on the treadmill 90 min into the exercise challenge.(41.7 MB MOV).Click here for additional data file.


One recovery gradient undergoes natural succession to mature tropical rainforest, while the other involves plantation of jhum fallows with teak Tectona grandis monoculture.Community recovery following primary habitat alteration can provide tests for various hypotheses in ecology and conservation biology. Prominent among these are questions related to the manner and rate of community assembly after habitat perturbation. Here we use space-for-time substitution to analyse frog and lizard community assembly along two gradients of habitat recovery following slash and burn agriculture (Frog and lizard communities accumulated species steadily during natural succession, attaining characteristics similar to those from mature forest after 30 years of regeneration. Lizards showed higher turnover and lower augmentation of species relative to frogs. Niche based classification identified a number of guilds, some of which contained both frogs and lizards. Successional change in species richness was due to increase in the number of guilds as well as the number of species per guild. Phylogenetic structure increased with succession for some guilds. Communities along the teak plantation gradient on the other hand, did not show any sign of change with chronosere age. Factor analysis revealed sets of habitat variables that independently determined changes in community and guild composition during habitat recovery.jhum cultivation cycles and plantation forestry could result in landscapes without mature forest. Lack of source pools of genetic diversity will then lead to altered vegetation succession and faunal community reassembly. It is therefore important that the value of habitat mosaics containing even patches of primary forest and successional secondary habitats be taken into account.The timescale of frog and lizard community recovery was comparable with that reported by previous studies on different faunal groups in other tropical regions. Both communities converged on primary habitat attributes during natural vegetation succession, the recovery being driven by deterministic, nonlinear changes in habitat characteristics. On the other hand, very little faunal recovery was seen even in relatively old teak plantation. In general, tree monocultures are unlikely to support recovery of natural forest communities and the combined effect of shortened  Evaluation of the importance of various processes determining community structure and function is an important topic in ecology. Unlike just a decade or so ago, few studies today question whether or not community assembly is strictly random, recognizing the role of both stochastic and deterministic processes . This chThis newfound view of community ecology is an excitingly realistic one, and has the potential to make valuable contributions to conservation biology as well ,7. HowevCircumventing this problem is obviously very difficult. One possible approach is to study communities along gradients of habitat succession using space-for-time substitution (SFT) to obtain chronosequential communities . Thus, ijhum fallows giving way to mature forest, and (b) 1-yr jhum fallows planted over with teak, leading to monoculture stands. Slash-and-burn or shifting cultivation (jhum) agriculture involves clearing and burning of forest patches, so the original rainforest communities are effectively obliterated, and succession involves recovery of communities from scratch. The following questions were addressed in this study:This study takes an SFT approach to compare changes in frog and lizard community structure in two contrasting habitat succession gradients: (a) 1-yr 1. How much does frog and lizard community succession differ between the two gradients of habitat recovery?2. Does composition of the entire community change in synchrony, or does the recovery pattern differ between subcommunities such as frogs vs. lizards and guilds?3. What aspects of habitat change influence frog and lizard community recovery, and if habitat parameters are linked to niche axes, do they predict changes in guild composition?4. Do successional changes in guilds also show trends in phylogenetic structure? This last question is expected to yield interesting insights into possible evolutionary mechanisms underlying changes in community composition , but hasIn this paper, a chronosere is defined as a habitat that has recovered from perturbation for a known length of time, and can be assigned a place in the SFT. An assemblage is the set of all species of a taxonomic group in a landscape of interest. Ecological groups (EGs) are species' subsets of the assemblage with similar niche characteristics. Communities comprise species of the assemblage which share a habitat stratum  in the landscape. Guilds are members of the EGs that actually coexist in the same chronosere i.e., belong to the same community, and are thus likely to have ecological and evolutionary interactions (cf. ).To draw inferences about what aspects of habitat change determine sequential communities, habitat and frog-lizard community data were analysed hierarchically. As a first step, species richness and turnover of frog and lizard communities along habitat recovery gradients was summarised, and the entire assemblage classified into ecological groups (EGs) based on niche similarities. Guilds identified from this classification were then examined for phylogenetic structure. Using factor analysis, orthogonal combinations of variables that described biotic and abiotic aspects of habitat transition were extracted. We then tested for correspondence between these composite variables and composition of frogs and lizard communities and guilds. Based upon the relationships between different habitat factors and frog and lizard communities, variables were interpreted as composite adaptive zones, and we tested whether they predicted successional changes at different levels of community organization.jhum fallows (plots jh1A and B) were dominated by herbaceous plants, tall grass, shrubs and wild bananas, along with saplings and surviving crop plants. The 4 to 5-yr post-jhum plot (jh5) was dominated by almost homogeneous stands of the bamboo Melocanna baccifera, interspersed with a few shrubs and trees. Herbs were rare, and the understorey sparse. The 7 to 10-yr post-jhum plot (jh10) was very similar to jh5. However, here the bamboo culms were more sparsely distributed, and along with M. baccifera, two other bamboos- Dendrocalamus longispathus and Bambusa tulda were in greater abundance, and woody plants were relatively more common. Compared to the other plots, a larger area was included in the 30 to 35-yr jhum plot (jh35) because it contained a greater range of ages and hence perhaps more variability. Also, this was the only accessible site in the study area that represented a chronosere aged between 30–50 years. Although M. baccifera was common, this site had a greater abundance of other bamboos and trees than any of the previous stages. Though most trees were small, woody vegetation formed a significant part of the canopy. Herbs and shrubs were rare, and the understorey generally sparse.Table The three mature forest plots  were of untraceable age . They wLantana camara) and occasional herbs. The 22-yr teak plantation site (tk22) had a monotonous, uniform structure characteristic of a mature, managed teak monoculture. Undergrowth was sparse, consisting mostly of tall grass and Lantana sp. Table The 4-yr teak plot (tk4) was a young plantation characterized by a monodominant stand of teak trees. The understorey was sparse, with some tall grass, shrubs , and by Varimax rotation of the factor structure, which explained a cumulative 85.8% of the variation (see methods). Eigenvalues, factor loadings and factor scores are given in In general, although there is a change towards a tree dominated habitat in both recovery gradients, the end result is very different because the 22 year teak plantation is a monoculture, whereas the mature forest consists of a diverse tree community.jhum fallows and teak plantation communities on one hand, and the mature forest and the 35 year jhum fallows on the other.The three sampling techniques used in conjunction during the study (see methods) yielded sixteen frog and seventeen lizard species. Figure jhum to mature teak plantations on the other hand, seems to show little change in species richness or composition even after 22 years of plantation growth.The pattern of recovery is very different for the two gradients. For the mature forest succession gradient, the rate of frog and lizard community recovery is similar to that found for birds by Raman  in the sIt is worth noting that there are dissimilarities in the manner of species accumulation for frogs vs. lizards. There is much less augmentation of species number in the case of the latter, the main reason for this being that younger chronoseres support more lizard than frog species richness. Species accumulation curves see  show thaEcological groups defined by non-metric multidimensional scaling (NMDS) of the niche based dissimilarity matrix for the entire assemblage are shown in Figure Figure Figure These results on successional changes in guild structure and representation indicate a distinctly non-random sequence of community assembly, as certain guilds appear in later stages, followed by increase in their species richness and in many cases, phylogenetic structure. Habitat attributes that determine these changes are explored in the next section (see below).Table 2 = 0.85, and 0.97, respectively), and represents deterministic, linear aspects of vegetation succession. It has high positive loadings for tree species richness, and macro-habitat variables such as tree density, canopy cover, and canopy height, most of which increase deterministically along both gradients of habitat change. Among the measured variables, these are primary and independent, which over time drive changes in secondary (microhabitat) variables such as bamboo density, shrub abundance, and various measures of habitat heterogeneity (see methods). This factor clearly influences species composition at all levels of frog and lizard community structure.The strongest association is between factor 2 and overall species composition (frogs and lizards combined) across chronoseres. Factor 2 was strongly and non-linearly correlated with age along both teak and mature forest succession gradients  and diurnal-terrestrial (DT) groups, were correlated with factor 2. This suggests that in contrast to other guilds, these two, which are both made up only of lizards, are directly influenced by a hierarchically higher order of habitat attributes.These were also the two groups that showed non directional trends in species richness as well as phylogenetic trends along habitat recovery gradients Figures  and 6.nd order polynomial fit, R2= 0.99, and 0.82, respectively). This factor had high loading . This factor had no strong loadings, but is associated with shrub density, canopy height variability and tree density, all of which also affect ground cover, and can be considered macrohabitat variables.Along with factor 2, the crepuscular-nocturnal terrestrial (CT) group was correlated with factor 1 and 4. Factor 1 scores increase and then decrease with plot age along both habitat recovery gradients . It has high positive loading for CV of soil moisture, which as mentioned above, is highest in chronoseres with spatial and/or temporal variation in insolation. Among the measured variables, factor 2 probably subsumes most habitat parameters that affect both NA groups directly (see next section).The nocturnal arboreal frog group (NA(F)), was correlated with factor 7, which is non-deterministic with respect to chronosere age. This factor as a high loading for soil moisture, which by itself is difficult to interpret as a variable directly affecting this ecomorphological group. It is likely that this factor is a surrogate for an unmeasured or unclassified variable. Lastly, the nocturnal arboreal lizard group (NA(L)) is correlated with factor 8 along with factor 2. Factor 8 shows a weak negative linear relationship with recovery age along both gradients gradient . It canFactors 3, 5, and 6 showed no significant association with any level of community composition. The obvious reason for this appears to be that unlike other factors, these are completely non-deterministic with respect to age of succession, thus representing temporally and/or spatially stochastic attributes that were unlikely to show any influence on the conspicuously deterministic nature of frog and lizard community and guild (except for the DT and DA groups) succession  The two gradients of habitat recovery are very different and accordingly affect frog and lizard community assembly differently, (2) Although both groups increased in species richness with habitat recovery, lizards had higher species turnover, combined with lower species augmentation within each recovery gradient (3) Looking at a finer scale of community organization, assembly appears to be driven by changes in guild representation and composition, where some guilds change directionally with age of habitat recovery by species augmentation, while others change by species turnover (4) Guilds that showed directional increase in species richness also increased in phylogenetic structure (5) Hierarchies of community organisation were affected by composite, nested habitat attributes that correspond to particular niche axes, and (6) the increase in species richness along the mature forest gradient in contrast to lack of change along the teak gradient was due to availability (or lack thereof) of variables that comprise these complex adaptive zones. Also, the results show that a niche-based guild classification reveals patterns that would have been hidden in the gross response pattern of the entire community.Some indication of the qualitative nature of potential evolutionary and ecological processes in community turnover comes from the fact that changes in phylogenetic structure are tied to guild structure in the communities. Using phylogenetic techniques, recent work has demonstrated the importance of evolutionary adaptation in assembling ecological communities . It is cIt is an open question as to what extent vegetation succession leads to changes in the number of adaptive peaks and corresponding changes in mean fitnesses of species' populations such that multiple species can persist in the same habitat. In more ecological terms this is same as asking how habitat succession leads to changes in niche availability, occupancy, and overlap . Another related question, that was partly explored using the S/G ratios in this paper, is whether similar adaptive zones (or niches or adaptive peaks) tend to be occupied by more closely related taxa. The results here do indicate that this may be true for such gradients of community change, as phylogenetic and guild structure increase directionally and in tandem with succession towards mature forest. Whether this change is driven by immigration from the regional gene pool or due to local divergent adaptation is an interesting question . Reptilejhum-rainforest succession gradient, which is about 30 years for both frogs and lizards, and suggests that recovery of diverse communities can be relatively fast, as has been reported for other fauna [jhum chronoseres are gradually replaced by trees. This vegetation succession is obviously reliant on seed rain/dispersal from nearby mature forests. In this region and many other areas Southeast Asia, apart from continued pressure from shifting cultivation and shortening cultivation cycles, it has also become popular practice to plant and maintain monocultures of timber species. As the results of this study indicate, such plantations are unlikely to support natural recovery of faunal communities, and will harbour lower biological diversity compared to primary forest.The time scale of recovery on the er fauna . Howeverjhum cycles, plantation forestry and invasion by non-native species such as Lantana and Eupatorium will lead to the local extirpation of even remnant forest patches. This loss of recolonisation pools for flora and fauna, will alter natural trajectories of succession, and strongly impact the biological diversity supported by the landscape. It is therefore important that conservation and prioritisation agencies in these areas consider the value of habitat mosaics containing even small patches of primary forest vegetation.It is possible that the combined effects of short Tectona grandis plantations and abandoned shifting cultivation (jhum) fallows of varying ages. All primary forest is referred to as "mature forest" throughout the paper because it is often difficult to determine the age of ostensibly primary tropical forest, especially in areas with poorly known history of land use and recovery [The study was carried out from November 1998 to April 1999 in and around Ngengpui Wildlife Sanctuary  in south Mizoram, Northeast India. The study area covers about 200 sq. km. see  for mapsrecovery ,33. Furtrecovery .Ten sampling plots representing mature and successional vegetation stages of known ages were established  Table 11. To conVegetation composition and habitat structure variables were sampled on randomly located 10 × 25 m belt transects ,17. TranPrincipal Components Analysis (PCA) was used to identify different aspects of habitat change with vegetation succession and collapse the list of raw variables into composite factors that could potentially predict frog and lizard community structure. The factor structure was rotated using the Varimax method to obtain clear loading patterns . As addiAs the objective of the analysis was to combine variables into composite, orthogonal factors that could potentially account for community and guild structure, all factors with eigenvalues = 0.8 were extracted, irrespective of the number of factors thus extracted. Although somewhat arbitrary, in essence this eigenvalue threshold ensured that a factor was included only if it extracted approximately as much as one raw variable . AlthougThe low abundance of amphibians and reptiles and unstandardised sampling methodology in tropical Asia reduces the reliability of species diversity estimates and hence community structure analyses . Taking To improve detection and gather information for delineation of EGs (see below), the traditional transect method was modified by eliminating pseudoreplication and sampling both nocturnal and diurnal species on the same transect ,35. The within a chronosere, it varied considerably across plots. The DAW time on the other hand, was more or less consistent. This strategy was used because just as sampling effort needs to be proportional to habitat heterogeneity, higher microhabitat complexity calls for proportionally greater searching effort. Figure Although time taken for the NAW was more or less constant This technique was used to supplement species inventorying from the belt transects, and for an unbiased measure of the effects of weather on herpetofaunal activity and hence sampling efficiency. Comparisons of trapping frequency across plots over the study period are not used in this paper. Each pitfall array was, 'Y'-shaped, with three terminal (30 cm diam. × 60 cm depth) and one central (50 cm diam. × 70 cm depth) cylindrical aluminium funnel pitfall traps buried in the ground. The traps were connected with three opaque plastic-sheet fences (the arms of the 'Y') 0.4 m high and 5 m long, held up by bamboo stakes. In all, 22 arrays were placed, with two in each plot except for the large Jh35, which had four. Arrays were at comparable distance from plot edges, and on similar slope. Systematic trapping was initiated ten days after trap were established. Traps were opened for 5–10 consecutive days, and checked according to habitat characteristics, taking into consideration the level of exposure trapped animals were likely to be subjected to; plots with open habitat, such as jh1A were checked most  and those with relatively closed habitat such as matA were checked least frequently (every third day). Most specimens (95.2 %) obtained from pitfall trapping were released a minimum of 100 m away from the array, either in the same site, or in similar habitat elsewhere. A few were retained as voucher specimens.At the end of a sampling session in a plot, far ranging searches were carried out. This augmented species inventorying, and provided information crucial for EG classification (see below). Periodically, nocturnal searches were also made to collect information about the refuge of diurnally active animals, and also to confirm the presence or absence of species in different chronoseres.Irrespective of the sampling technique, animals detected were caught whenever possible, and identified in hand. All those that escaped were identified to a justifiable level or excluded from the analyses. A few individuals of taxonomically problematic species or taxa were preserved for later identification.The effectiveness of sampling was evaluated by species accumulation curves see , and theOverlap between recovering frog and lizard communities was measured with the Bray-Curtis measure between all possible pairs of chronoseres using presence absence data of all species see . The resLife history and behavioural traits were used to group species. These are often called guilds , but arPhylogenetic structure was measured as the ratio of number of species to the number of genera (S/G ratio) in each EG. A similar approach has been used in studies addressing questions about phylogenetic structure in ecological communities .To test which habitat attributes influenced community structure, Mantel tests of correspondence between dissimilarity (distance) matrices ,19,38,39To test whether the availability of composite variables (PCA factors) that predicted community and guild structure identified by the matrix correspondence tests did indeed influence community succession and phylogenetic structure along gradients of habitat recovery, correlations between sums of factor scores and species richness, ratio of species number/guild number and S/G ratios across chronoseres were tested.SSP conceived the study, carried out the fieldwork, performed the data analyses, and drafted the manuscript. BCC and GSR participated in design and coordination of the study. GSR also supervised the vegetation identification and habitat classification. All authors read and approved the final manuscript.Photographs of habitat types. Representative photographs of habitat typesClick here for fileResults of PCA along with list of habitat variables used. PCA ResultsClick here for fileSpecies accumulation curves and frog and lizard species' lists. Species accumulation curves and listsClick here for fileMaps of study area. Location map of study area sampling plots with respect to vegetation typesClick here for file


People sometimes solve problems with a unique process called insight, accompanied by an “Aha!” experience. It has long been unclear whether different cognitive and neural processes lead to insight versus noninsight solutions, or if solutions differ only in subsequent subjective feeling. Recent behavioral studies indicate distinct patterns of performance and suggest differential hemispheric involvement for insight and noninsight solutions. Subjects solved verbal problems, and after each correct solution indicated whether they solved with or without insight. We observed two objective neural correlates of insight. Functional magnetic resonance imaging  revealed Functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) are used to study neural activity in subjects during a verbal task for which they report solutions achieved by insight Insight is pervasive in human  when he suddenly discovered that water displacement could be used to calculate density. Since then, “Eureka!,” or “Aha!,” has often been used to express the feeling one gets when solving a problem with y animal ) cognitiAlthough many processes are shared by most types of problem solving, insight solutions appear to differ from noninsight solutions in several important ways. The clearest defining characteristic of insight problem solving is the subjective “Aha!” or “Eureka!” experience that follows insight solutions . This suPersistent questions about insight concern whether unconscious processing precedes reinterpretation and solution, whether distinct cognitive and neural mechanisms beyond a common problem-solving network are involved in insight, and whether the apparent suddenness of insight solutions reflects truly sudden changes in cognitive processing and neural activity.Recent work suggests that people are thinking—at an unconscious level—about the solution prior to solving problems with insight. Specifically, while working on a verbal problem they have yet to solve, people presented with a potential solution word read the actual solution word faster than they read an unrelated word . This “sProblem solving is a complex behavior that requires a network of cortical areas for all types of solving strategies and solutions, so solving problems with and without insight likely invokes many shared cognitive processes and neural mechanisms. One critical cognitive process distinguishing insight solutions from noninsight solutions is that solving with insight requires solvers to recognize distant or novel semantic (or associative) relations; hence, insight-specific neural activity should reflect that process. The most likely area to contribute to this component of insight problem solving is the anterior superior temporal gyrus (aSTG) of the RH. Language comprehension studies demonstrate that the RH is particularly important for recognizing distant semantic relations , and bilWe used functional magnetic resonance imaging (FMRI) in  and attempted to produce a single solution word (apple) that can form a familiar compound word or phrase with each of the three problem words . We relied on solvers' reports to sort solutions into insight solutions and noninsight solutions, avoiding the complication that presumed insight problems can sometimes be solved without insight  of their solutions, “no insight” for 41% (s.d. = 18.9) of their solutions, and “other” for 2% of their solutions. We marked a point about 2 s (rounded to the nearest whole second) prior to each solution button press as the solution event, and examined a time window 4–9 s after this event  to isolate the corresponding hemodynamic response. Solving problems and responding to them required a strict sequence of events , but this sequence was identical whether subjects indicated solving with or without insight, so differences in FMRI signal resulted from the degree to which distinct cognitive processes and neural systems led to insight or noninsight solutions.t < 1.0, p > 0.3). More importantly, the hemodynamic responses to both insight and noninsight solutions in the homologous area of the LH are about equivalent to the response to noninsight solutions in the RH aSTG—it is the strong response to insight solutions in the RH aSTG that stands out. There is no insight effect anywhere within temporal cortex of the LH. At statistical thresholds below significant levels (p < 0.1 uncorrected), there are as many voxels in LH temporal cortex showing a noninsight effect as showing an insight effect.The involvement of the RH rather than the LH for this verbal task is not due to greater difficulty in producing insight solutions: subjects produced insight solutions at least as quickly  as they produced noninsight solutions   as RH aSTG, the event-related signal within it was weak and the insight–noninsight difference (peak difference = 0.15%) was relatively small. (The insight effect may be attributable as much to a negative response for noninsight solutions as to a positive response for insight solutions.)After RH aSTG, the second largest area showing an insight effect in FMRI signal was the medial frontal gyrus in the LH B. AlthouThere also was an insight effect in small clusters in or near bilateral amygdala or parahippocampal gyrus. Again, regional signal was low (83% of the brainwide average), and the signal difference was small (peak = 0.16%). However, an amygdalar response may be expected, given the emotional sensation of the insight experience . Hippocampal or parahippocampal involvement is also plausible, if memory interacts with insight solutions differently from how it interacts with noninsight solutions. For instance, insight problems may encourage distinct memory encoding  or may rSeveral cortical areas showed strong solution-related FMRI signal, but approximately equally for insight and noninsight solutions. Some of these areas  relate to the response sequence rather than solution processes; other areas probably reflect component processes of a problem-solving network common to both insight and noninsight solving, such as retrieving potential solutions. Two areas that may be of interest for future studies are AC and posterior middle/superior temporal gyrus. Both these areas, in the RH only, showed strong, negative solution-related signal, approximately equal in the two solution types. AC is an area that might be predicted to be involved in reorienting attention as solvers overcome impasses, given its role in performance monitoring and cognitive control . RH postA separate group of subjects participated in fundamentally the same paradigm while we continuously recorded EEGs from the scalp. We then compared time-frequency analyses of the EEGs associated with insight solutions versus noninsight solutions. EEG provides temporal resolution greatly superior to that of FMRI and thus can better elucidate the time course and suddenness of the insight effect. Furthermore, complex EEG oscillations can be parsed into constituent frequency components, some of which have been linked to particular types of neural and cognitive processes .The high temporal resolution of EEG allows us to address one of the fundamental questions raised earlier: does insight really occur suddenly, as subjective experience suggests? For problems typically solved without insight, solvers report gradually increasing closeness to solution. In contrast, for problems typically solved with insight, solvers report little or no progress until shortly before they actually solve the problem . SimilarWe predicted that a sudden change in neural activity associated with insight solutions would produce an EEG correlate. Specifically, we predicted that high-frequency EEG oscillations in the gamma band  would reflect this sudden activity, because prior research has associated gamma-band activity with the activation of perceptual, lexical, and semantic representations . Gamma-bt[18] = 3.47, p=0.003); there was no difference in mean response times .Participants solved 46% (s.d. = 8.2) of the problems correctly within the time limit. Of correctly solved problems, subjects reported more insight solutions  than noninsight solutions ,  and insight × time window × Hemisphere  interactions. The overall interaction occurred because there was an insight × hemisphere interaction from −0.30 to −0.02 s  but no effect in the −1.52 to −0.36 s time window. Within the −0.30 to −0.02 s interval for these two electrodes, there was a significant insight effect at the right temporal (T8) site , but not at the homologous left temporal (T7) site or any other LH temporal electrode. Laplacian mapping of this effect (There was a burst of gamma-band activity associated with correct insight solutions (but not noninsight solutions) beginning approximately 0.3 s before the button-press solution response at anterior right temporal electrodes , with nos effect B is remaThe gamma burst in the right temporal area cannot be attributed to motor processes involved in making the response because (A) motor activity associated with the bimanual button press would have caused a bilateral gamma burst, not a unilateral one; (B) the location of the gamma burst as determined by Laplacian mapping B is not Other planned statistical tests (ANOVAs) examined possible insight-related frontal theta (5–8 Hz), posterior alpha (8–13 Hz), and fronto-central beta (13–20 Hz) activity. There were no statistically significant theta or beta effects.  There was a significant posterior alpha effect, which is discussed below.Complex problem solving requires a complex cortical network to encode the problem information, search memory for relevant information, evaluate this information, apply operators, and so forth. The FMRI and EEG results reported here conclusively demonstrate that solving verbal problems with insight requires at least one additional component to this cortical network, involving RH aSTG, that is less important to solving without insight. The insight effect in RH aSTG accords with the literature on integrating distant or novel semantic relations during language comprehension. When people comprehend (read or listen to) sentences or stories, neural activity increases in aSTG or temporal pole bilaterally more than when comprehending single words . Neural Like the results in language processing, the current results are predicted by the theory that the RH performs relatively coarse semantic coding . This thWe suggest that semantic integration, generally, is important for connecting various problem elements together and connecting the problem to the solution, and that coarsely coded semantic integration, computed in RH aSTG, is especially critical to insight solutions, at least for verbal problems . People come to an impasse on insight problems because their retrieval efforts are misdirected by ambiguous information in the problem or by their usual method for solving similar problems. Large semantic fields allowing for more overlap among distantly related concepts  may help overcome this impasse. Because this semantic processing is weak, it may remain unconscious, perhaps overshadowed by stronger processing of the misdirected information , and solA persistent question has been whether the cognitive and neural events that lead to insight are as sudden as the subjective experience. The timing and frequency characteristics of the EEG results shed light on this question. We propose that the gamma-band insight effect in Suddenly recognizing new connections between problem elements is a hallmark of insight, but it is only one component of a large cortical network necessary for solving problems with insight, and recognizing new connections likely contributes to other tasks, such as understanding metaphors  and deriF = 4.13, p = 0.027, with the Huynh-Feldt correction). Follow-up t-tests in each time window yielded significant effects of insight in the first time window at both electrode sites  and in the second time window only at the RH site , with a reversal of the direction of the effect. The third time window yielded no significant effects.We turn now to another result from the EEG time-frequency analysis, which was not predicted but nevertheless suggests a provocative interpretation. The gamma burst thought to reflect the transition of the insight solution from an unconscious to a conscious state was preceded by insight-specific activity in the alpha band (8–13 Hz). Specifically, there was a burst of alpha power (estimated at 9.8 Hz) associated with insight solutions detected over right posterior parietal cortex from approximately 1.4 s until approximately 0.4 s before the solution response, at which point insight alpha power decreased to the level of noninsight alpha power, or below . An ANOVAlpha rhythms are understood to reflect idling or inhibition of cortical areas . IncreasThis interpretation of the early insight-specific alpha effect is consistent with previous behavioral research suggesting that, prior to an insight, the solution to a verbal problem can be weakly activated , especiaIn sum, when people solve problems with insight, leading to an “Aha!” experience, their solutions are accompanied by a striking increase in neural activity in RH aSTG. Thus, within the network of cortical areas required for problem solving, different components are engaged or emphasized when solving with versus without insight. We propose that the RH aSTG facilitates integration of information across distant lexical or semantic relations, allowing solvers to see connections that had previously eluded them. In the two millennia since Archimedes shouted “Eureka!,” it has seemed common knowledge that people sometimes solve problems—whether great scientific questions or trivial puzzles—by a seemingly distinct mechanism called insight. This mechanism involves suddenly seeing a problem in a new light, often without awareness of how that new light was switched on. We have demonstrated that insight solutions are indeed associated with a discrete, distinct pattern of neural activity, supporting unique cognitive processes.Ten men and eight women were paid to participate in Following practice, subjects attempted 124 compound remote associate problems during FMRI scanning. These problems b can be Prior to the experiment subjects were told the following: “A feeling of insight is a kind of ‘Aha!' characterized by suddenness and obviousness. You may not be sure how you came up with the answer, but are relatively confident that it is correct without having to mentally check it. It is as though the answer came into mind all at once—when you first thought of the word, you simply knew it was the answer. This feeling does not have to be overwhelming, but should resemble what was just described.” The experimenter interacted with subjects until this description was clear. This subjective rating could be used differently across subjects , blurring condition boundaries; yet the distinct neural correlates of insight observed across the group demonstrate that there was some consistency.If subjects failed to solve problems within 30 s, the “Solution?” prompt appeared, and subjects pressed the “no” buttons and verbalized “Don't Know.” Then the “Insight?” prompt appeared, and subjects pressed the “no” buttons again. After the insight rating, subjects performed three line-matching trials (3 s each) to distract them from thinking about the problems, allowing the critical BOLD signal to return to baseline . The totImaging was performed at the Hospital of the University of Pennsylvania, on a 1.5 Tesla GE SIGNA scanner with a fast gradient system for echo-planar imaging and a standard head coil. Head motion was restricted with plastic braces and foam padding. Anatomical high-resolution T1-weighted axial and sagittal images were acquired while subjects performed practice trials. Functional images  were acquired in the same axial plane as the anatomical images using gradient-echo echo-planar sequences sensitive to BOLD signal . Each fu3.Images were coregistered through time with a three-dimensional registration algorithm . Echo pl3 in volume  in which each voxel was reliably different across subjects, . Monte Carlo simulations with similar datasets reveal low false positive rates with these criteria. RH aSTG was the only cluster to exceed these criteria, and converging evidence and the a priori prediction about RH aSTG strengthen confidence in this result.Data were analyzed using general linear model analysis that extracted average responses to each trial type, correcting for linear drift and removing signal changes correlated with head motion. Each TR was divided into two 1-s images to improve time locking of the solving event and the functional image data  from 128 tin electrodes embedded in an elastic cap (linked mastoid reference with forehead ground) placed according to the extended International 10–20 System. Prior to data analysis, EEG channels with excessive noise were replaced with interpolated data from neighboring channels. Eyeblink artifacts were removed from the EEG with an adaptive filter separately constructed for each subject using EMSE 5.0 . Induced oscillations were analyzed by segmenting each subject's continuous EEG into 4-s segments beginning 3 s before each solution response. 0, in the time domain has the formTime-frequency transforms (performed with EMSE 5.0) were obtained by the application of complex-valued Grossmann-Morlet wavelets, which are Gaussian in both time and frequency. Following 0 is a nondimensional frequency. In this case, ω0 is chosen to be 5.336, so that ∫ϕ0(t) ≅ 0. The constant π−¼ is a normalization factor such that ∫(ϕ0(t))2 = 1. For the discrete time case, a family of wavelets may be obtained aswhere ωt is the sample period (in seconds), s is the scale (in seconds), and n is an integer that counts the number of samples from the starting time. The Fourier wavelength λ is given bywhere δIn the frequency domain, the (continuous) Fourier transform of wheree-folding time and frequency may serve as quantitative measures of dispersion. Note that these dispersions are a function of the scale, s. For a selected frequency, 𝒻c = 1/λ, or from One reasonable way to measure the “resolution” of the wavelet transform is to consider the dispersion of the wavelets in both time and frequency. Since the wavelets are Gaussian in both domains, the e-folding time is c. From e-folding frequency is e-folding time is 0.12 s and the e-folding frequency is 2.6 Hz. For a 40-Hz ( gamma-band) center frequency, the e-folding time is 0.03 s and the e-folding frequency is 10.5 Hz. Note that these e-folding parameters imply that wavelet scaling preserves the joint time-frequency resolution , with higher temporal resolution but broader frequency resolution as the wavelet scale decreases.Then substituting into Segments corresponding to trials for which individual subjects produced the correct response were isolated and averaged separately according to whether or not the subject reported the experience of insight. Planned statistical tests (repeated-measure ANOVAs) were performed in order to detect insight-related effects on frontal midline theta (5–8 Hz), posterior alpha (8–13 Hz), fronto-central beta (13–20 Hz), and left and right temporal gamma (20–50 Hz). Response-locked event-related potentials (ERPs) were also computed using the same analysis epoch. Standard ERP analyses yielded no evidence of statistically significant effects, likely because ERPs reflect phase-locked activity rather than the induced  activity examined in the wavelet analyses; due to the long response times evident in this experiment, phase locking resulting from problem presentation would not be expected.EEG effects were topographically mapped by employing spline-based Laplacian mapping with an FMRI-derived realistic head model and digitized electrode positions. Localization of EEG/ERP signals is a form of probabilistic modelling rather than direct neuroimaging. In contrast to other techniques, source estimation by Laplacian mapping indicates the presence of superficial foci of neuroelectric activity with minimal assumptions.Figure S1The far left lane shows for each region a single slice best depicting the cluster activated above threshold; middle lane shows time course of signal following insight (red line) and noninsight (blue line) solutions, across the entire active cluster; right panel shows the “insight effect” .t[12] = 2.83, p < 0.015); (B–D) depict clusters of FMRI signal at the same t-threshold used in the main paper , but the clusters are too small to surpass cluster criterion.(A) depicts bilateral IFG with lowered threshold ((B) LH medial frontal gyrus;(C) LH PC gyrus;(D) LH amygdala . Spatial coordinates and other are details listed in (914 KB PDF).Click here for additional data file.


PLoS Medicine editors very useful [I find the arguments raised by the y useful  as I hadIt is not only colleagues in research and allied professions who need access but the global community, including members of the public wherever they live, those who participate in trials and those who will be on the receiving end of their outcomes.The annual reports of research ethics committees (RECs) are supposedly in the public domain after approval by Strategic Health Authorities in the UK. But very few members of the public know of their existence or how to access them. Approaches to individual committees even now can meet with varied reactions, from suspicious, defensive, or hostile—reluctantly sending one report, quizzing as to which organisation the enquirer belongs to and why they should want one—to extremely welcoming of interest and discussion.The annual reports should be easily accessible online by now, surely, but they are not. The activities of RECs and information on what research is being carried out in the name of society as a whole largely remain hidden from public view.www.corec.org.uk) or OREC . COREC has not been open about dealing with issues of concern raised with them in the past. They do state that public interest is welcome now, so it would show a real commitment to making research activity more open if they would show support for totally open access to a register and to promote that through their Web site.There is no information about public access on COREC (Central Office for Research Ethics Committees;


In its extreme form, methylation is involved in silencing one of the two X chromosomes in female mammals. Aberrant methylation underlies susceptibilities to several forms of cancer, and is likely to be involved in numerous other human diseases.For a recipe to become a meal, it's often necessary to embellish or modify the basic instructions—and to keep a note of the changes that work, so that it can be just as delicious next time around. The same is true for a gene, whose basic recipe—its nucleotide sequence—can be heritably annotated to “epigenetically” influence its level of expression without altering its sequence. Among the many epigenetic influences at work in the genome, methylation of cytosine is one of the most versatile and powerful. Addition of a methyl  is to map the methylation patterns of human genes, and to determine how they vary: among individuals, among tissues within an individual, and even over time within a single tissue. In this issue, Stephan Beck and colleagues describe the execution and results of a HEP pilot project, in which they analyzed methylation within the major histocompatibility complex (MHC), the set of genes that establish an individual's self-identity within the context of immune surveillance.The key to any such large-scale project is high throughput—a rapid, efficient set of technologies that produce the needed data with minimal human intervention. The strategy used by Rakyan et al. included bisulfite sequencing of DNA, in which unmethylated cytosines are chemically converted to uracils, while methylated cytosines are not. Software they developed detects the methylated sites and provides an overall measure of the methylation level within any given sequence. They confirmed the accuracy of their method with mass spectrometry, an alternative method also suitable for high-throughput screening.They initially analyzed 253 sequences within 90 genes in the MHC, about two-thirds of the total, from multiple tissues in multiple individuals. They found that most genes were either completely methylated or completely unmethylated, while relatively few had an intermediate value. The significance of this distribution pattern is not yet clear, although it does confirm similar results in smaller samples from other research groups. The researchers also confirmed that so-called CpG islands, regions rich in CG dinucleotides, are relatively hypomethylated, especially when they occur at the upstream end of a gene.Rakyan et al. also found differences in methylation levels among tissues, with some suggestion that the variations influence tissue-specific alternative splicing, at least in some genes. Intriguing inter-individual differences were also found, with median methylation levels differing significantly between individuals for at least one tissue at almost half the sites analyzed. For instance, such differences were found in liver for the regulatory region for the tumor necrosis factor gene.A major goal of the HEP is to identify methylation variable positions, sites whose methylation state is linked with some important biological state, be it tissue type, developmental stage, or disease state. The pilot project described here begins this undertaking, which will be greatly expanded as the HEP progresses. The first phase of the full-scale HEP, an analysis of 5,000 DNA sequences, is currently underway.


We compared two methods of rooting a phylogenetic tree: the stationary and the nonstationary substitution processes. These methods do not require an outgroup.Given a multiple alignment and an unrooted tree, the maximum likelihood estimates of branch lengths and substitution parameters for each associated rooted tree are found; rooted trees are compared using their likelihood values. Site variation in substitution rates is handled by assigning sites into several classes before the analysis.In three test datasets where the trees are small and the roots are assumed known, the nonstationary process gets the correct estimate significantly more often, and fits data much better, than the stationary process. Both processes give biologically plausible root placements in a set of nine primate mitochondrial DNA sequences.The nonstationary process is simple to use and is much better than the stationary process at inferring the root. It could be useful for situations where an outgroup is unavailable. The beginning of the alignments for the genes COX1, CYTB, ND1 and ND6 were slightly adjusted. The root positions are assumed to be on the (1) gorilla, (2) orangutan, and (3) frog branch, respectively. The branches on a tree are referred to by the organism names, except for the case of four taxa, where there is an internal branch , stationary (STA) and reversible (REV) substitution models to all available mitochondrial protein-coding genes, as well as the nuclear genes ganisms:  human, cIn group 1, the NONSTA and STA processes correctly placed the root in 8 and 6 genes respectively, out of 13 genes Table . In groualbumin and c-myc and three mitochondrial genes, COX1, COX2 and ATP6 from group 3 (with some mouse genes replaced with rat genes) were studied by Huelsenbeck et al. [ATP6, with NONSTA again noticeably more discriminative.The nuclear genes k et al. . For theet al. and Yang [Brown and Yang ,9 studieOur results confirmed earlier findings that the stationary process (STA) is not very good at discriminating among rooted trees corresponding to the same unrooted tree. In contrast, the nonstationary (NONSTA) process seems much more effective, with individual genes, and with combined genes. It is quite clear that the difference in log likelihoods between fitting STA and the reversible process (REV) is often small, and statistically insignificant, based on the likelihood ratio test, while those between NONSTA and STA, and between NONSTA and REV, are often large, and statistically very significant. Though the chi-square distribution may be inappropriate , it seemet al.'s analysis using STA failed to place the root correctly in any of the genes albumin, c-myc, COX1, COX2 and ATP6, there are some differences between the analyses. The raw data were different: the rat albumin and c-myc genes were used by Huelsenbeck et al.; since mouse and rat are very similar, this is not likely to matter much. Secondly, the alignments were probably different, though since the sequences are quite similar, this should not be too important. It is plausible that most of the discrepancies between the results is due to the difference in the estimation procedure (maximum likelihood vs. Bayesian) and to the fact that in Huelsenbeck et al., site variation was modeled by the gamma distribution [Although Huelsenbeck ribution , whereasalbumin, c-myc and ND2, showing the usefulness of the third codon position in this dataset, despite its markedly higher substitution rates. We also found that the pairwise identity at the third codon positions for all genes in groups 3 ranges from 34% to 61%. Base composition being generally nonuniform, the expected pairwise identity at saturation  is lower than 25%. This seems to indicate that the third codon position is not saturated, and hence the phylogenetic information from this position is not just the base composition at each taxon. In addition, the base composition at the third codon position for some genes is quite different from the other positions. Our model does not fit these genes as well as a model where separate processes are associated with the codon positions. Such a model will be investigated in future.Estimates of the relative rates are quite independent of the model used, and their relative magnitudes are largely within expectations. In particular, for group 3, the relative rates for codon positions 1, 2, and 3 fall between .2 and 1.1, .1 and .6, and 1.5 and 2.7 respectively. For all genes, the third codon position evolved the fastest, followed by the first and second positions. To gauge the contribution from the third codon position, we left out the corresponding bases in group 3 and reran the analysis with NONSTA. This gave the correct root placement in only three genes: The NONSTA process is only slightly more complicated to apply, compared to the STA and REV processes. The fact that it works quite well in the verification studies and predicts biologically plausible roots for the nine-primate data demonstrates its utility and perhaps argues for its use in routine phylogenetic analysis. In any case, if no suitable outgroup is available, it could be worthwhile to try it. Though the NONSTA process is the most general time-homogeneous Markov process, it is still simplistic and imposes a severe constraint on the evolution of base composition: if two leaf nodes are at the same distance from the root, then the process stipulates that the corresponding sequences must have the same composition. This is patently unrealistic: once lineages split, they should evolve quite independently, and may explain the failure of the process at estimating the root placement for some genes. However, it is still valuable even if it does not always work, in that it can serve as a base from which exploration of richer models can be launched. For instance, one could identify lineages where the evolution significantly deviates from expectations, and then allow these lineages to have different rate matrices, which brings us closer to the very rich models of ,7,13,14.The nonstationary substitution process is simple to use, has much greater power at estimating the root compared to the stationary process, and also fits data much better than the stationary and reversible processes. It seems feasible to use this process in analyses where a suitable outgroup is not easily available. It is also a good starting point for conducting more sophisticated phylogenetic analysis with richer models.Substitutions in DNA sequences are assumed to occur independently at each site according to a Markov process, i.e., given the present base, future substitutions are independent of past substitutions.a to b is the -entry in a 4 × 4 rate matrix Q; the diagonal entries are such that each row sums to 0. For any t > 0, the transition probability P(t) is given by P(t) = exp(Qt). Let π be a probability distribution on the DNA bases. The pair  defines a substitution process on a rooted tree, as follows: pick a base at the root according to π, then run the substitution process according to Q down the tree, splitting into independent copies whenever a branching is encountered. The joint probability of the observed bases at the leaf nodes can be computed using almost exactly the same algorithm by [Furthermore, it is assumed that the process is time-homogeneous, i.e., substitution rates stay constant in time. As usual, the substitution rate from base rithm by .π, Q). Associated with the rate matrix Q is a unique distribution πQ, called the equilibrium distribution of Q, such that the matrix product πQ × Q is the zero vector. The process  is stationary, i.e., the sequence composition remains unchanged through time, and is described by πQ. Q is said to be reversible if it satisfies the detailed balance condition:There are two important special cases of the time-homogeneous process  is then reversible, i.e., statistically the process looks the same in forward and backward time. In particular, as shown in [et al. [π and 12 off-diagonal entries in Q), 12  and 9 . Since the models are nested, the likelihood ratio test can be used to assess the relative goodness-of-fit of the MLEs. It is standard practice to allow only calibrated rate matrices, i.e., Q satisfieswhere Πshown in , the joishown in ,16,17. Sshown in ,18-22. Tshown in , and sub [et al. . Yang reπ, Q), with calibrated Q, since in general π ≠ πQ, it is not true that the expected number of substitutions in 1 time unit is 1, but the difference gets arbitrarily small as time goes to infinity.so that a branch length is the average number of substitution events per site. We adopt this practice, and remark that for the nonstationary process (i an unknown positive number ri, with the constraint thatThe sites in a DNA sequences can have very different substitution rates, the most well-known example being coding sequences, where the third codon positions evolved much faster than the others because of the degeneracy of the genetic code. In cases where the assignment of sites into several classes is known in advance, such as a coding sequence, the easiest way to deal with it is to associate to class ni is the number of sites in class i. The relative rate ri either expands or shrinks the tree depending on whether it is more or less than 1. The constraint gives a new interpretation of a branch length: it is now the average over all sites of their expected number of substitutions. Thus, this approach is similar to [where milar to : effectimilar to  or the hmilar to .Given a rooted tree relating aligned coding sequences, we seek the ML estimates of the branch lengths, the substitution parameters, and the relative rates. For other sequences, the relative rates are not estimated. Gradient-based methods are perhaps the most efficient at finding the maximum. The EM algorithm  is anothThe estimation procedure was implemented in C, and the source code can be requested from the first author.The idea was conceived by the first author and was inspired and refined by the second author. The first author composed the code and performed the data analysis.A text file containing the amino acid sequence alignments for group 1.Click here for fileA text file containing the amino acid sequence alignments for group 2.Click here for fileA text file containing the amino acid sequence alignments for group 3.Click here for file


Malaria remains one of the greatest threats to global health, infecting more people than ever before. Confined mainly to the tropical areas of Africa, Asia, and Central America, malaria hits Africa the hardest; the poverty-stricken lands of sub-Saharan Africa account for 90% of malaria infections worldwide. Despite ongoing efforts to battle the disease—by controlling mosquito populations, reducing human contact, and developing drug prevention and treatment—the crisis continues to worsen.The primary variables affecting risk of infection are the rate at which humans are bitten and the proportion of mosquitoes that are infectious. These two factors are often regarded as positively correlated, meaning that if the percentage of infectious mosquitoes increases, so will the human biting rate. But in a new study, David Smith, Jonathan Dushoff, and F. Ellis McKenzie challenge this assumption. Using a mathematical modeling approach to examine the relative contributions of the two factors across different landscapes and seasons, the authors show that the factors are not positively correlated. In fact, their calculations show that the rate humans are bitten and the proportion of infectious mosquitoes peak at different times and places.Their modeling results suggest that the standard metric to estimate risk of infection—the number of times an infectious mosquito bites a person per day, called the entomological inoculation rate (EIR)—is flawed when variable conditions are taken into account. Using the average EIR to estimate average risk of infection in variable environments generates biased estimates because there is not a direct correlation between EIR and the proportion of humans who are infected.The distribution of humans and suitable habitat for mosquito larvae varies across the landscape. And the density of mosquito populations varies seasonally, rising and falling with changes in rainfall, temperature, and humidity. Temporal and spatial variations in mosquito populations affect the rate humans get bitten, the number of infectious mosquitoes, and the risk of infection. To understand how these space- and time-induced variations in mosquito populations shape the epidemiology of human infection, Smith and colleagues developed a set of mathematical models that calculate the relative impact of different parameters, in order to determine which factors most influence where and when risk of infection is highest.First, they evaluated what factors affect the primary components of the EIR: the human biting rate and the proportion of infectious mosquitoes. As expected, the model predicts that fluctuations in mosquito density influence the EIR by changing the human biting rate. As more people are bitten, more people become infected; consequently, more mosquitoes feed on infected humans and so become infectious. Only adult mosquitoes transmit infection, so as mosquito populations age, the proportion of infectious mosquitoes increases. During the dry season, few mosquitoes are born, and so while the human biting rate and EIR decline, the proportion of infectious mosquitoes increases.Because mosquito populations are densest near breeding sites—where younger mosquitoes outnumber adults—the human biting rate and the number of bites by infectious mosquitoes per person per day reflect shifts in mosquito density, not in the proportion of infectious mosquitoes. The model predicts that human biting rate is highest shortly after mosquito population density peaks, typically either near breeding sites or where human density is highest. The proportion of infectious mosquitoes, on the other hand, reflect the age of the mosquito population: it peaks where older mosquitoes are found—farther from breeding sites—and when populations are declining.By mapping larval habitats against the local risk of mosquito-borne infections, Smith and colleagues conclude, epidemiological models can be developed to predict risk for local populations. Their results make the case that mathematical models can help public health officials calculate risk of infectious diseases in heterogeneous environments—that is, real world conditions—when vector ecology and the parameters of transmission are well characterized. Any plan to prevent and control the spread of mosquito-born infections would clearly benefit from paying attention to mosquito demography and behavior.