Plasmodium falciparum infection offers an attractive alternative to the conventional Ross-Macdonald methodology, as it allows simulation of heterogeneous communities subjected to realistic transmission (inoculation patterns).Agent-based modeling of Plasmodium falciparum. We calibrated the model using data from malaria-therapy (MT) studies, and developed a novel calibration procedure that accounts for a deterministic and a pseudo-random component in the observed parasite density patterns. Using the parasite density patterns of 122 MT patients, we generated a large number of calibrated parameters. The resulting data set served as a basis for constructing and simulating heterogeneous agent-based (AB) communities of MT-like hosts. We conducted several numerical experiments subjecting AB communities to realistic inoculation patterns reported from previous field studies, and compared the model output to the observed malaria prevalence in the field. There was overall consistency, supporting the potential of this agent-based methodology to represent transmission in realistic communities.We developed a new, agent based model that accounts for the essential in-host processes: parasite replication and its regulation by innate and adaptive immunity. The model also incorporates a simplified version of antigenic variation by Plasmodium falciparum infection.Our approach represents a novel, convenient and versatile method to model  Plasmodium falciparumMany attempts have been made to describe the complex in-host and population dynamics of malaria infection using mathematical models. Classical population-based models developed by Ross and MacDonald still provide the basis for many new approaches Agent-based (AB) approaches can overcome some of these drawbacks. Using AB methodology, individual agents are represented by dynamic processes, describing in-host interactions of the malaria parasite with target cells and host immunity. A community of such agents can then be constructed and subjected to realistic transmission in the form of inoculation patterns. Unlike SIR systems, agent based (AB) communities are computationally constrained by their size since computing time and resources increase with population size. This limitation is, however, more than compensated by greater accuracy and versatility. For instance, an AB community can be made completely heterogeneous, allowing multiple parasite strains and/or species, human hosts with different age-dependent immunity and different interventions, at little or no additional computational cost. Several in-host models for malaria have been developed in previous studies Plasmodium falciparum. The model was coded in Wolfram Mathematica 7.In the present study we developed a novel in-host agent model that accounts for the most salient features and biology of parasite-host interactions. This model and our calibration differ substantially from earlier work. In particular, we paid special attention to the parasite replication cycle (invasion and depletion of red blood cells (RBC), immune stimulation and parasite clearance. Mathematically, the model is implemented and run in discrete time steps based on the 48 h parasite replication cycle. Such discrete models behave in many aspects similar to models based on continuous differential equations, but they can be often implemented and simulated more efficiently, particularly for random processes. Our model combines deterministic and stochastic components of in-host dynamics, the latter resulting from random antigenic variation (AV) of Our calibration procedure also differs from earlier related models Several important biological factors are included in our model of asexual parasitemia: (i) homeostatic production/loss of uninfected RBC; (ii) parasite replication (invasion of uninfected RBC and release of merozoites); (iii) stimulation of innate and adaptive immunity effectors by parasite density; (iv) parasite clearance by immune effectors.x – uninfected RBC population (per µL of whole blood), y – infected RBC population (per µL of whole blood), a – innate immune effector; b – adaptive immune effector.A schematic view of the within-host processes is shown in 0 (5×106 µL−1) is maintained through stationary (homeostatic) production/loss terms. RBC are invaded by the newly released merozoite population (M). The probability of merozoite invasion depends on the available RBC pool per merozoite, x/M. The parasite burden stimulates an immune response consisting of innate and adaptive immune effectors a and b. We view these effectors as simplified proxies of the effector concentration  combined with the efficiency of the effector to clear infection. The immune effectors reduce parasite density by inhibiting parasite replication. Effectors are stimulated by parasite density, or through parasite-immune interactions above certain parasite density threshold levels.The normal RBC level xThe model was implemented using a discrete time-step based on parasite replication cycle rather than a process based on continuous differential equations. We utilize a novel approach to parasite antigenic variation (AV) and a novel calibration procedure.P. falciparum has evolved several immune evasion strategies, most notably AV, whereby it can vary (on each replication cycle) an important class of surface proteins expressed on infected RBC. These proteins play a double role. On the one hand they serve as immunogenic targets with stimulation of antibody production and consequent parasite clearance. On the other hand they mediate adherence of infected RBC to endothelial cells in the microvasculature (sequestration) and thus promote parasite survival. AV of P. falciparum has been the subject of considerable research var genes. Each parasite genome contains 50–60 of these var genes var gene but can switch expression in the next generation There are different ways to account for AV in mathematical models. A direct approach to multi-variant parasite dynamics assigns different population variables to each variant. In addition, suitable ‘variability’ (exchange) patterns need to be set among multiple variants and their possible immune interaction with ‘specific effectors’ assigned to each type. These two processes are typically described by ‘mutability’ (switching) and ‘cross-reaction’ matrices. Such multi-dimensional approaches have been developed and utilized in previous studies e.g. .var genes and the host develops antibodies against all antigenically distinct variants. We account for AV by random falls in adaptive effector b at each replication cycle. Randomness is essential for describing a typical ‘switching'/mutation’ process of any kind since, although they may not be truly random by nature, we typically lack detailed knowledge of their mechanisms and functions, and thus have to assume that they are random variables or parameters.In the present study, we propose a simpler way to account for AV in a single infected RBC population. Assuming all variants are nearly identical in terms of growth and invasion, the only essential difference between them are their antigenic properties, i.e. susceptibility to previously developed adaptive immune effectors. As new infected RBC populations may differ antigenically from the earlier ones, the effective adaptive immune response may be reduced at each replication cycle. However, the magnitude of such a change should diminish with time as the parasite gradually depletes its repertoire of expressed x(t) represents uninfected RBC (target cells) measured per µL of blood; y(t) represents infected RBC measured per µL of blood, a(t) and b(t) are dimensionless variables representing innate and adaptive immune effectors. On each time step the transition from the current state 0x = 5×106/µL, with survival rate δ = 0.98/cycle (based on a 100 day life-span). The last term x would be invaded and turned into the next generation of infected RBC (y′). Of those, only a fraction −(a+b)2 would survive through to the end of the cycle, depending on the combined immune level (a+b). Factor r in merozoite equation effective replication rate of the parasite and is equal to the average merozoite progeny per infected RBC times the maximal probability of RBC invasion by a merozoite. The latter refers to optimal invasion conditions with large pool of available target cells (x), or a small relative merozoite population z grows large, merozoites start competing for available RBC, and the effective probability of invasion (or invaded fraction of x) decreases according toz about each  RBC; (ii) exclusive competition where only one merozoite (among competing pool z) can establish successful invasion. This form of ‘invasion and resource depletion’ differs from the standard continuous formulation  are dimensionless variables measured in terms of clearance of y, so that a ‘unit effector’ would halve the parasite population over the 2-day cycle  or survival rate af. Adaptive effector (b) has longer memory  in our model is triggered by the product of infected RBC density (y) and the combined effector pool a+b relative to threshold B. Productb through adaptive immune memory. The efficiency factors  represent the maximum stimulation rates of a and b respectively under ‘high’ parasitemia. The corresponding maximal clearance levels become(y). Therefore, we impose a lower cut-off on parasite density at c = 10−6y µL−1 in our simulations, below which the parasite is considered cleared (y = 0).Immune effectors b) fall on each cycle by a random fraction b should diminish with each replication cycle, as the limited reservoir of antigenically distinct variants is depleted and the repertoire of host antibodies increases. In the present model we described this random process  represents the deterministic part of in-host processes. To account for AV, we let the adaptive immune effector , immune stimulation thresholds A and B for a and b respectively and parameter m .For numeric simulations and calibration of equation system (1), we fixed some model parameters, based on the available biological data and estimates and allowed others to vary. S – representing the number of merozoites released from the liver per life cycle.Equation system (1) is appropriate for asexual stage dynamics in the absence of external infectious sources. External inoculations can be added to (1) by augmenting the merozoite variableThe model was calibrated with data from MT studies for neurosyphilis, which provide a rare opportunity to examine host-parasite interaction over extended periods of time. The complete set of MT data used in the present study is given in The calibration procedure we propose involves two steps: a deterministic fit to the first wave of parasitemia, and a second stochastic step that attempts to accommodate irregular parasitemia patterns following the initial wave. Based on these steps we select ‘best choices’ of in-host parameters.P. falciparum, whereby new variants help to sustain infection over longer periods.The total number of datasets available was for 334 MT patients. From these, we selected 122 for which patients were either untreated or treated very late in the infection. The MT patients exhibit highly irregular dynamic patterns of parasitemia for several reasons. Firstly, due to cytoadherence of late stage parasites there is an apparent oscillation with a 2-day recurring pattern. Since these oscillations have little relevance for long-term infection outcome, they were smoothed out by taking the maximum/minimum parasitemia envelopes on consecutive odd and even days, and computing their geometric mean curve, as illustrated by a representative patient history in 0, y0, 0, 0x), i.e. normal RBC-level 0x, initial inoculum 0y = 0.001–0.01 µL−1, and no pre-existing immunity, −1) and was presumed cleared. Such cases typically had a short duration of patent parasitemia (10–20 days). These cases were calibrated using the first (deterministic) step, as AV played no less important role in the initial wave of infection, confirmed by our numeric experiments.As MT hosts had no prior exposure to malaria, equation system (1) was run starting with the naïve initial state  , as those are primarily responsible for the long term pattern of parasitemia and have only a minor effect on the primary wave of parasitemia   from eacasitemia .u<2 and 1<v<4, a random AV-ensemble of 50 realizations, then try to fit a given MT history within the ‘min/max ensemble envelop’ by minimizing its distance from the ensemble mean curve . The best-choice values of factors  and the adjusted 5-tuples We consider MT histories to be random realizations of a stochastic AV process rather than unique ‘individual patterns’. Therefore if the same host would be subjected to another inoculation he/she may exhibit a different infection pattern. In general, it is a challenging task to calibrate parameters of a stochastic process from its single realization and obtain statistically reliable results. The standard techniques would typically require a sufficiently ‘long history’ and ‘simple stochasticity’ (e.g. linear stationary process with ‘additive noise’). In the present model, we are dealing with a highly nonlinear system (1) and non-stationary, multiplicative noise, since the AV input is a decaying random sequence http://www.cwru.edu/artsci/math/gurarie/Malaria/In host calibration.nb (code) and http://www.cwru.edu/artsci/math/gurarie/Malaria/Sorted%20mean%20hist%20210 (filtered MT data in Mathematica format).The computational codes were implemented and run in Wolfram Mathematica 7. The code can be downloaded from: The Mathematica codes and procedures developed for the model are very efficient and require only modest computational resources. The efficiency is important for our calibration procedures (computing large ensembles of hosts and histories) and applications to agent based communities (AB communities).S-term in Equation (6)), based on entomological inoculation rates (EIR) as observed in field studies. External inoculation will produce different recurrent patterns that combine in-host regulation with external forces. In the present study, the inoculation patterns were generated as series of binaries (0 and 1) with 1 being an inoculum. The probability that an inoculation occurs on a specific time step is dependent on the EIR, which represents mean number of inoculations per agent in a specific time interval. Each agent was subjected to an individual inoculation pattern based on the same average EIR.AB communities were assembled from the best parameter sets resulting from the calibration process . The AB communities used in the present study typically consisted of 1000 agents. The communities (agents) were subjected to external inoculation , maxy = 2*103 to 3*103 at days or 13 to 15 for AV modified patterns (Panel B).Twenty-five datasets used for calibration in this study exhibited single wave patterns of parasitemia. For irregular patterns of longer duration, the qualities of the fits varied. Although AV, as modeled here, can account for a large number of irregular cases, some MT patterns are entirely different and fall outside the current setup. A typical stochastic pattern produced by our model exhibits an initial wave of parasitemia to a peak, with a subsequent decline until clearance.For each dataset, parameter ensembles were collected that resulted in the best fits of parasitemia patterns of MT patients. We compared characteristic statistics  of the MT data set with the same predicted characteristics from of a community generated using the best parameters collected for each MT dataset in the calibration process. The results of this comparison are presented in As a first test of validity of our calibration procedure, we created two AB communities. The first AB community was built from our collection of fitted parameters the second from random parameter choices within the calibration ranges . The preThe comparison with the data from Beier et al 1999 AB modeling of malaria requires the construction of a reasonable in-host model as a foundation. Depending on its intended use, the model is a compromise between reasonable simplicity and the complex mechanisms it seeks to describe on the in-host epidemiological scales. In this report, we present an agent based model that can be used to reproduce MT data with reasonable accuracy and is computationally efficient so that communities of 1000 or more agents can be run on a desktop computer without a long processing time.Several types of within-host malaria models have previously been developed etc.) or using informed trial and error methodology Some previous models were fitted to MT data by either formally fitting specific characteristic features –(4) are nonlinear, and randomness (AV) enters in a complicated nonlinear fashion. Therefore, we proposed that MT patterns are random realizations of the stochastic process and tried to ‘optimally’ fit them within a suitable ensemble envelope.The majority of the MT datasets exhibit a dominant first wave of parasite density followed by recurrent, diminishing waves. Our calibration procedure resulted in reasonable fits for all MT datasets exhibiting these basic features. We validated our scheme by comparing AB communities based on our calibrated parameter sets with purely random parameter choices, and found substantial differences in their predicted outputs (a and b). A further extension of the in-host model would include more detailed structure of the immune system with different B- and T-cell populations and relevant processes , along with antibodies, cytokines and fever.There are several limitations in our approach and results. On the in-host side, it may be desirable to account for variant diversity and more detailed structure of immunity. In the current setup all variants were considered to have identical growth and clearance characteristics. Furthermore, the immune regulation takes an abstract form represented by only two effector variables  parasite stages. Furthermore we modeled only a single parasite strain and a useful extension of the present model would include multiple strains with different fitness and drug susceptibility.The calibration procedure was limited mostly by the availability of data. Ideally, unperturbed (baseline) parasite density patterns from populations exposed to different endemicity levels and with different ages should be used to calibrate the model. However such data are not available. It remains to be determined whether MT-based calibration results are applicable to field data and how it should be modified to account for the important  differences in malaria infection.A major limitation of the AB community part of the present model is that there is no transmission. Therefore, the AB community is assumed to be a very small part of a larger transmission environment and changes in malaria prevalence within the AB community have no effect on this transmission environment. Future model developments and applications will include gametocyte production as a function of the asexual parasite density and calibrate gametocyte density using MT data or field data When comparing model predictions to observed field data it should be noted that the calibration proposed in the present study was conducted using data from adults who had never been exposed to malaria before. In reality, this is almost never the case. In the studies by Vercruysse and Gazin, only children were enrolled and we have to assume that nearly all of these children had been exposed to malaria before these studies commenced. Furthermore there was considerable usage of bed nets, chemoprophylaxis and insecticide spraying over the course of these studies. These are factors that are not taken into account by the present model. Extended versions of the model should address these issues. Nevertheless, it is evident that the model predictions and the observed prevalences are very similar over a wide range of compared data. We therefore concluded that this model can make some useful predictions and serve as a basis for future development.Figure S1Graphic representations of the six best fits to the first wave of parasitemia for datasets 35, 37, 38, 39, 40, 41, 42 and 43 (A–H). X axes are days, y axes are decadic logarithms of parasite density. The numbers above the graphs are the errors calculated using equation (6).(TIF)Click here for additional data file.Figure S2Graphic representations of the six best fits to the first wave of parasitemia for datasets 44, 45, 46, 48, 50, 51, 52 and 54 (A–H). X axes are days, y axes are decadic logarithms of parasite density. The numbers above the graphs are the errors calculated using equation (6).(TIF)Click here for additional data file.Figure S3Graphic representations of the six best fits to the first wave of parasitemia for datasets 55, 56, 57, 58, 59, 60, 61 and 62 (A–H). X axes are days, y axes are decadic logarithms of parasite density. The numbers above the graphs are the errors calculated using equation (6).(TIF)Click here for additional data file.Figure S4Graphic representations of the six best fits to the first wave of parasitemia for datasets 63, 64, 67, 69, 70, 71, 73 and 74 (A–H). X axes are days, y axes are decadic logarithms of parasite density. The numbers above the graphs are the errors calculated using equation (6).(TIF)Click here for additional data file.Figure S5Graphic representations of the six best fits to the first wave of parasitemia for datasets 76, 77, 78, 79, 80, 81, 82 and 83 (A–H). X axes are days, y axes are decadic logarithms of parasite density. The numbers above the graphs are the errors calculated using equation (6).(TIF)Click here for additional data file.Figure S6Graphic representations of the six best fits to the first wave of parasitemia for datasets 84, 85, 86, 87, 88, 89, 90 and 91 (A–H). X axes are days, y axes are decadic logarithms of parasite density. The numbers above the graphs are the errors calculated using equation (6).(TIF)Click here for additional data file.Figure S7Graphic representations of the six best fits to the first wave of parasitemia for datasets 92, 93, 94, 95, 96, 97, 98 and 99 (A–H). X axes are days, y axes are decadic logarithms of parasite density. The numbers above the graphs are the errors calculated using equation (6).(TIF)Click here for additional data file.Figure S8Graphic representations of the six best fits to the first wave of parasitemia for datasets 100, 101, 102, 103, 105, 106, 107 and 109 (A–H). X axes are days, y axes are decadic logarithms of parasite density.(TIF)Click here for additional data file.Figure S9Graphic representations of the six best fits to the first wave of parasitemia for datasets 110, 111, 114, 115, 116, 117, 118 and 119 (A–H). X axes are days, y axes are decadic logarithms of parasite density. The numbers above the graphs are the errors calculated using equation (6).(TIF)Click here for additional data file.Figure S10Graphic representations of the six best fits to the first wave of parasitemia for datasets 120 and 121 (A–B). X axes are days, y axes are decadic logarithms of parasite density. The numbers above the graphs are the errors calculated using equation (6).(TIF)Click here for additional data file.Figure S11Best ensemble fits to the entire course of infection for data sets 37, 39, 40, 41, 44, 45, 46, 48, 50, 51, 52 and 54 (A–L). Blue lines are the MT data, green lines are the ensemble means and shaded purple areas are ensemble envelopes. X axes are days, y axes are decadic logarithms of parasite density.(JPG)Click here for additional data file.Figure S12Best ensemble fits to the entire course of infection for data sets 55, 56, 57, 58, 59, 60, 62, 63, 64, 67, 69 and 70 (A–L). Blue lines are the MT data, green lines are the ensemble means and shaded purple areas are ensemble envelopes. X axes are days, y axes are decadic logarithms of parasite density.(TIF)Click here for additional data file.Figure S13Best ensemble fits to the entire course of infection for data sets 71, 73, 74, 76, 77, 78, 79, 80, 81, 82, 83 and 84 (A–L). Blue lines are the MT data, green lines are the ensemble means and shaded purple areas are ensemble envelopes. X axes are days, y axes are decadic logarithms of parasite density.(TIF)Click here for additional data file.Figure S14Best ensemble fits to the entire course of infection for data sets 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95 and 96 (A–L). Blue lines are the MT data, green lines are the ensemble means and shaded purple areas are ensemble envelopes. X axes are days, y axes are decadic logarithms of parasite density.(TIF)Click here for additional data file.Figure S15Best ensemble fits to the entire course of infection for data sets 97, 98, 99, 100, 101, 102, 103, 105, 106, 107, 109 and 110 (A–L). Blue lines are the MT data, green lines are the ensemble means and shaded purple areas are ensemble envelopes. X axes are days, y axes are decadic logarithms of parasite density.(TIF)Click here for additional data file.Figure S16Best ensemble fits to the entire course of infection for data sets 111, 114, 115, 116, 117, 118, 119, 120 and 121 (A–I). Blue lines are the MT data, green lines are the ensemble means and shaded purple areas are ensemble envelopes. X axes are days, y axes are decadic logarithms of parasite density.(TIF)Click here for additional data file.Figure S176 panels comparing random versus best parameter based community predictions of the model. The panels on the left hand side are community runs using random parameters. The panels on the right hand side are community runs using parameters from the model calibration. Community size is n = 2000. Panels A and B compare the community prevalences at an EIR of 1 per parasite reproductive cycle (182.5 per annum), Panels C and D compare the community prevalences at an EIR of 0.1 per cycle (18.3 per annum), and panels E and F compare community prevalences at an EIR of 0.01 per cycle (1.83 per annum). The dotted black lines denote fraction of uninfected RBC, the dashed black denotes iRBC, and the solid gray denotes infected but below limit of detection by light microscopy (10 parasites per microliter).(TIF)Click here for additional data file.Table S1Description of all variables, parameters and indices used in the model.(DOC)Click here for additional data file.Table S2Overview over the MT data used in the present study and allocation of numbers to each set of MT data. The columns labeled ‘#’ are the numbers assigned to each dataset over the course of this study to facilitate data processing.(DOC)Click here for additional data file.Table S3Comparative statistics between the output of our model, the MT data we used for calibration and data presented by Gatton et al 2006 .(DOC)Click here for additional data file.


Polyspermy is a common problem in bovine in vitro fertilization (IVF) and has a still unclear etiology. In this specie, after IVF, despite the lack of a biochemical post-fertilization hardening, the stiffness of the outer ZP layer is significantly increased. Therefore, polyspermy might be related to an incomplete or insufficient stiffening of the ZP. We obtained, by using atomic force spectroscopy in physiological conditions, a complete characterization of the biomechanical changes of the inner and outer ZP layers occurring during oocyte maturation/fertilization and correlated them to the ultrastructural changes observed by transmission electron microscopy using ruthenium red and saponin technique. In both the inner and outer ZP layers, stiffness decreased at maturation while, conversely, increased after fertilization. Contextually, at the nanoscale, during maturation both ZP layers displayed a fine filaments network whose length increased while thickness decreased. After fertilization, filaments partially recovered the immature features, appearing again shorter and thicker. Overall, the observed biomechanical modifications were substantiated by ultrastructural findings in the ZP filament mesh. In fertilized ZP, the calculated force necessary to displace ZP filaments resulted quite similar to that previously reported as generated by bovine sperm flagellum. Therefore, in bovine IVF biomechanical modifications of ZP appear ineffective in hindering sperm transit, highlighting the relevance of additional mechanisms operating in vivo. The zona pellucida (ZP) is a porous network of sulphated glycoprotein filaments surrounding mammalian eggs Of note, in all these previous reports, the above mentioned pre- and post-fertilization hardening – despite the term recalling the concept of an increased stiffness – were not correlated to a change in the biophysical properties of the ZP. By using atomic force spectroscopy (AFS), we recently demonstrated that, despite the lack of a biochemical post-fertilization hardening, the stiffness of the outer ZP layer is significantly increased after bovine oocyte IVF Cow oocytes collection, in vitro maturation and fertilization, and ZP isolation were performed according to the procedure already reported The physical properties of ZP were investigated by performing AFS measurements using a SPMagic SX Atomic Force Microscope , maintaining the samples on glass coverslips in an aqueous environment , at 37°C yield the critical indentation at which the ZP undergoes a plastic deformation.Estimation of the energy necessary for a plastic deformation. In ZP, local mechanical stress can induce an elastic reversible reaction, or a plastic irreversible filaments deformation Bovine immature, in vitro matured and fertilized oocytes (n = 10 each) were fixed in 1% (v/v) glutaraldehyde in 0.1 M cacodylate buffer (pH 7.4) and stored at 4°C until processed. Samples were washed  using a 0.02% saponin solution and 1.0% ruthenium red (RR) in cacodylate buffer, then postfixed with a solution of 1.0% osmium tetroxide containing 0.02% saponin and 0.75% RR in cacodylate buffer for 2 hours 2 each, for each of the ten samples investigated 2 each) for each of the ten samples investigated For the image analysis, sections were made at the midpoint of the oocytes, in order to minimize bias due to tangential sectioning. Mean thickness of filaments (FT) was measured in the middle of the filament in the outer and inner portions of the ZP. Reported measures were averaged over 60 different areas, of 1.65 µmMeasures were obtained on digital images, at original magnification 119.000 X, by using a Gatan digital micrograph apparatus . Measurement of filaments length was made by the “Line ROI” function only considering planar filaments with a clearly visible origin and end between two adjacent connecting knobs. Filament thickness was calculated by using the same function, with reference to their middle portion; also in this case only filaments entirely visible in their whole length were considered for evaluation.Data were reported as mean ± SD. The study variables are normally distributed. Differences between groups in comparison were analyzed by Student’s t test and Anova test. The presence of variance heterogeneity stressed by the Levene’s test and Welch’s test of equality of means, has led to the choice of the most conservative Tamhane’s test for post hoc multiple comparisons. Significance was defined as p<0.05. Statistical analysis was performed using MedCalc® 12.2.1  and IBM-SPSS® statistics 20.0  softwares.In Complete data for all samples of the Young modulus, E, calculated from the force indentation curves following Eq. 1, are reported in −16 J and (6.4±0.6) 10−16 J, in the outer layer of mature and fertilized samples, respectively.The calculated energy necessary for a plastic ZP transition, i.e. for an irreversible filament deformation, resulted of (3.8±0.7) 10TEM examination of the ZP from immature, mature and fertilized oocytes revealed a fine network of filaments anastomosed with each other and oriented in several different directions, sometimes forming small, rounded structures at the intersections. The filament arrangement was different between the inner and outer layers of the ZP and among the various maturation stages/fertilization of the oocytes studied.The inner surface of immature oocytes  was homoThe outer ZP layer of immature oocytes showed an indented and irregular aspect, due to the presence of typical fenestrations . SuperfiComplete FL and FT data with statistical evaluations are reported in The combined analysis by AFS and TEM showed that the variations in biomechanical properties recorded through the entire thickness of the ZP during the processes of maturation and fertilization are substantiated by ultrastructural changes.ZP biomechanical properties varied significantly among the examined conditions, with a relevant decrease in stiffness paralleling the achievement of maturation. On the other hand, fertilization led to an increased rigidity that involved the whole thickness of the ZP: really, the reported differences between the inner and outer surfaces, despite significant, were minor. This evidence contradicts theoretical expectations that foresee more pronounced variations in the inner side of the ZP, due to the direct exposition to cortical granules exudates Therefore, as a first consideration, we conclude that the high incidence of bovine IVF-related polyspermy is not due to an incomplete propagation of post-fertilization stiffening through the ZP depth.Recent findings suggest that modification in the overall organization of ZP structure, rather than sperm binding to a single ligand, is the major contributor of sperm binding regulation in the mouse In this paper, the use of the ruthenium red-saponin method allowed a correlation between biomechanical and ultrastuctural properties of the thin ZP filaments morphology as it prevented filaments shrinkage due to mechanical stress induced by dehydration For a rationale interpretation linking the observed ZP stiffening to changes in length and thickness of its constituent filaments we can refer to the classical theory of polymer −16 J and (6.4±0.6) 10−16 J, respectively. This evidence clearly argue against the hypothesis that ZP stiffening effectively seals up the ZP, thus preventing the occurrence of polyspermy through the hindrance of sperm transit. Our consideration is further supported by the recently calculated energy generated by a single pulse of the bovine sperm flagellum, of 4.6×10−16 J in vitro as compared to in vivo fertilization in cows An increased stiffness does not necessarily involve greater difficulties in sperm transit through ZP. Indeed, calculation of the force necessary to displace filaments of fertilized ZP, i.e. to induce the so called elastic-plastic transition, gave quite similar data for the outer layer of mature and fertilized ZP, of (3.8±0.7) 10In conclusion, combining two high resolution complementary techniques TEM and AFM


Oligoryzomys longicaudatus (Sigmodontinae), the major reservoir of Hantavirus in Chile and Patagonian Argentina, is widely distributed in the Mediterranean, Temperate and Patagonian Forests of Chile, as well as in adjacent areas in southern Argentina. We used molecular data to evaluate the effects of the last glacial event on the phylogeographic structure of this species. We examined if historical Pleistocene events had affected genetic variation and spatial distribution of this species along its distributional range. We sampled 223 individuals representing 47 localities along the species range, and sequenced the hypervariable domain I of the mtDNA control region. Aligned sequences were analyzed using haplotype network, Bayesian population structure and demographic analyses. Analysis of population structure and the haplotype network inferred three genetic clusters along the distribution of O. longicaudatus that mostly agreed with the three major ecogeographic regions in Chile: Mediterranean, Temperate Forests and Patagonian Forests. Bayesian Skyline Plots showed constant population sizes through time in all three clusters followed by an increase after and during the Last Glacial Maximum . Neutrality tests and the “g” parameter also suggest that populations of O. longicaudatus experienced demographic expansion across the species entire range. Past climate shifts have influenced population structure and lineage variation of O. longicaudatus. This species remained in refugia areas during Pleistocene times in southern Temperate Forests (and adjacent areas in Patagonia). From these refugia, O. longicaudatus experienced demographic expansions into Patagonian Forests and central Mediterranean Chile using glacial retreats.The long-tailed pygmy rice rat  Glacial cycles that have affected the distributional range of species during the Quaternary have been the focus of multiple phylogeographic studies intending to evaluate the occurrence of single or multiple gene genealogies The southern Andes were repeatedly glaciated during the Late Cenozoic. In particular, the Patagonian Andes were glacier-covered prior to 4.6 mya (Late Miocene) and icefields repeatedly expanded between 2.4 and 1.2 mya Nothofagus forests, the “southern beeches”) were strongly affected by glacial cycles of the Pleistocene Throughout the Quaternary, the mountain range of the Andes, the “Cordillera de los Andes” (the eastern geographic limit of Chile) experienced strong volcanic activity and glaciation events in the Temperate region Oligoryzomys longicaudatus . Latitudinally, it ranges between 27–54°S, encompassing three of the major ecogeographic regions in the southcentral portion of Chile: the Mediterranean, the Temperate, and the Patagonian Forests . Knowledge of phylogeographic patterns of reservoir populations can provide insight into factors responsible for the occurrence and spread of the virus in natural populations An important component of the small mammal fauna of southern forests in Chile and adjacent areas in Argentina is the “long-tailed pygmy rice rat” (“colilargo”)  Forests ; Fig. 1.Oligoryzomys longicaudatus occurs in areas that were glaciated during the Pleistocene, and in formerly non-glaciated zones that presumably constituted refugia  control region, and aligned sequences were analyzed using Bayesian approaches.This study evaluates the genetic consequences of Pleistocene glaciations on the phylogeographic structure of the “long-tailed pygmy rice rat” hypervariable region I (HVI), were sequenced from 223 specimens representing 47 localities in Chile and Argentina , rgentina . We recorgentina .O. longicaudatus was evaluated using the GENELAND v. 1.0.7 program The population structure for Regarding segregating sites per cluster, we found 51 in Cluster 1 Mediterranean, 46 in Cluster 2 Temperate Forests, and 47 in Cluster 3 Patagonia. The three groups showed similar haplotype diversity although Patagonia was slightly higher, whereas overall nucleotide diversity was low especially for Mediterranean Chile. The haplotype diversity (Hd) was 0.967 for Cluster 1 Mediterranean, 0.968 for Cluster 2 Temperate Forests, and 0.991 for Cluster 3 Patagonia . The nucP = 0.000), Temperate Forests , and Patagonia , indicating that the null hypothesis of population equilibrium is rejected in favor of a population expansion. Maximum likelihood estimations of the ‘g’ parameter were positive and significant  for each of the clusters along the O. longicaudatus's range . After a period of constant population size, analyses showed an increase in the effective population size starting about 12,000 years ago for the Mediterranean cluster , about 2's range : Mediter's range .Oligoryzomys longicaudatus has high haplotype diversity , but low nucleotide diversity for each of the clusters, suggesting recent differentiation with rapid population growth b mitochondrial sequences in a previous study O. longicaudatus between 13,000  and 25,000–26,000 years ago , which is in the range of the LGM event g’ parameter. According to our GENELAND results that recovered localities in glaciated and non-glaciated areas of southern Chile, we hypothesize that populations of the long-tailed mouse may have dispersed from these areas that might have constituted refugia in the Temperate Forests and Patagonia.O. longicaudatusFitzroya cuppresoidesHypochaeris palustrisNothofagus pumilioLiolaemusSamastacus spinifrons , and the sigmodontine mice Abrothrix olivaceusA. longipilisO. longicaudatus along its distribution in Chile and adjacent Argentina. Glacial cycles strongly affected Temperate Forests, the primary habitat of Oligoryzomys, which may have disturbed local populations of this species. Our results support the hypothesis that postglacial colonization of O. longicaudatus probably occurred from multiple refugia in southern Chile between 26,000 to about 13,000 ya, which is in agreement with palynological data At least three events of glacial cycles have been reported for southern Patagonian Forests that affected the biota of the area Oligoryzomys longicaudatus that closely correspond to the Mediterranean, the Temperate Forests and Patagonian steppes and forests. Since our results suggest that Temperate Forests samples constitutes the ancestral haplotypes because they are scattered through the network, we hypothesize that expansion of populations might have occurred from southern refugia, such as the localities assigned to the Mediterranean cluster within and outside the limits of the LGM  due to global cooling in the Pleistocene. For example, one hypothesis to explain the current relictual distribution of Temperate Forests in the coast of central Chile , which are currently separated by more than 1,000 km from southern forests, is that they are remnants of southern vegetation that expanded during glaciations In temperate areas of North America, dispersal of mammals to lower latitudes during glacial periods followed by demographic expansion during warm interglacials is well documented O. longicaudatus west of the Andes, former studies have suggested a colonization from the south (Temperate Forests) to Patagonia and to the central-north of Chile O. longicaudatus through the Andes in a west-east direction O. longicaudatus is part of an Andean-Chacoean clade O. andinus from the highlands Andes of Peru and Bolivia, O. fornesi from Bolivia, northern Argentina, southern Brazil, and the Paraguayan Chaco O. andinus and O. spB in the highlands of the central Andes, O. fornesi in the foothills of the Andes and Chaco region, and O. longicaudatus in the southern lowland that ranges from the Andes mountains of Argentina and Chile, southward to Patagonia et al.O. longicaudatus on populations west of the Andes, but they only included 4 localities  from the Chilean side. Their hypothesis is also based on the biogeography of another sigmodontine rodent, Abrothrix olivaceus, whose populations may have crossed the lower elevations of the Andes through the Nothofagus forests located in southern Chile and Argentina (about 40°S) in an west-east direction. The biogeographic history of O. longicaudatus, however, is different to that of A. olivaceus. While A. olivaceus ranges from southern Peru southward to Patagonia, Oligoryzomys longicaudatus ranges from the southern limit of the Atacama Desert  southward to Patagonia. Thus, we believe that the most parsimonious hypothesis for the entrance of O. longicaudatus into the Chilean Andes is from the east (Argentina), entering through the lower elevations of the Andes at about 40°S. Following predictions from the coalescent theory O. longicaudatus are ancestral to central Chile and Patagonian populations, and that the entrance of the species into the Chilean side could have been from the south.Regarding the biogeographic history of O. longicaudatus through GENELAND and Neighbor-net analyses are in agreement with the major ecogeographic regions recognized in Chile, the Mediterranean, the Temperate Forests and the Patagonian Forests. These three groupings concur with the three subspecies originally proposed by Osgood O. longicaudatus in each of these ecoregions, respectively: O. l. longicaudatus, O. l. philippii and O. l. magellanicus. An earlier study discarded the occurrence of subspecies within O. longicaudatuslongicaudatus and philippii as a synonym of the former and recognizing the species O. longicaudatus (Mediterranean and Temperate Forests) and O. magellanicus (Patagonia). However, we suggest that the latter proposition should be revised. Oligoryzomys longicaudatus has a broad distribution in Chile, south of the Atacama Desert (about 27°S) downwards to the Patagonia including Tierra del Fuego (Chile and Argentina), and the Navarino Island , and historical effects  O. longicaudatusO. l. longicaudatus to the Mediterranean ecoregion between Atacama and Biobio (27–38°S), O. l. philippii to the Temperate Forests (38–45°S), and O. l. magellanicus to the Patagonian Forests (45–55°S). As of O. l. pampanus from Bahía San Blas, Buenos Aires, Argentina, this taxon was recovered within the Patagonian cluster, but further analyses will be necessary to uncover the taxonomic status of this form.The three genetic clusters recovered within Oligoryzomys longicaudatus remained in multiple refugia within the Temperate Forests and Patagonia ecoregions of Chile and Argentina, and inside and outside of LGM icesheet. Subsequent dispersal to the north and south of previously proposed refugia and non-glaciated areas may have occurred during and after the LGM. Our results supported a population expansion scenario in the time period of glacial retreats, which may have been facilitated by the dispersal capabilities of the species. In addition, the low nucleotide diversity found among Oligoryzomys haplotypes suggests a recent history of expansion with high admixture between populations. A hypothesis of expansion from multiple refugia in the southern range of the species, and northern expansion due to global cooling during Pleistocene glaciation, with further retraction towards the south, was supported.Our analyses suggest that populations of Voucher specimens sequenced in this study were deposited in the Colección de Flora y Fauna Profesor Patricio Sánchez Reyes (SSUC), Departamento de Ecología, Pontificia Universidad Católica de Chile, Santiago, Chile; Colección de Mamíferos del Centro Nacional Patagónico (CENPAT), Puerto Madryn, Argentina (field numbers UP and LB); the Museum of Southwestern Biology (MSB), Department of Biology, University of New Mexico; and the Instituto de la Patagonia (CZIP), Universidad de Magallanes, Chile.O. longicaudatus, representing 42 localities in Chile and five in Argentina; O. longicaudatus is available O. longicaudatus in Chile and Argentina are given in We analyzed 223 samples from 47 localities throughout the range of We followed the guidelines of the American Society of Mammalogists during the collection and handling of the animals used in this work CGGAGGCCAACCAGTAGA-3)′ and DLO-H (TAAGGCCAGGACCAAACC-3′). The PCR program consisted of an initial denaturation time of 3 min at 94°C followed by 25 cycles of denaturation for 30 s at 94°C, annealing for 30 s at 57°C, and extension for 1 min 30 s at 72°C. Double-stranded PCR products were purified with QIAquick PCR Purification Kit (Qiagen). Cycle sequencing was performed using primers DLO-L and DLO-L2 (CACTTGGGGGTGTCTAACC-3′) labeled with the Big Dye Terminator kit of Perkin Elmer . Sequencing reactions were analyzed using an Applied Biosystems Prism 310  automated sequencer, and sequences were aligned using the ClustalW program DNA was extracted from frozen liver samples using QIAamp Mini Kit . The HVI mtDNA control region was amplified via polymerase chain reaction (PCR) using primers DLO-L . This model's objective is to infer and locate the genetic discontinuities between populations of geo-referenced genotypes, considering the uncertain localization of the sampled individuals. The number of clusters was determined by running MCMC (Markov chain Monte Carlo) iterations five times, allowing K  to vary, with the following parameters: 5×106 MCMC iterations, maximum rate of the Poisson process fixed to 100 , uncertainty attached to the spatial coordinates fixed at 5 km to account for the possibility that inaccurate coordinates were provided, and considering that the maximum home range reported for O. longicaudatus is 4.5 km K fixed to the inferred number of clusters, with the other parameters the same as above. The mean logarithm of the posterior probability was calculated for each of the 30 runs and the posterior probability of population membership for each pixel of the spatial domain was then computed for the three runs with the highest values.We used the DnaSP v 5.10.01 software O. longicaudatus we used a Bayesian coalescent-based framework. Coalescence explains how population genetic process through time have shaped an allelic or haplotypic genealogy To establish the relationships between haplotypes, we constructed a haplotype network using the Neighbor-Net BF = 3.073, cluster 2 log BF = 6.82 and cluster 3 log BF = 6.03). The Bayes factor compares two posterior probability distributions, by asking which is larger after having adjusted differences between the models (Mi vs Mj) in terms of the numbers of parameters that they use. Given marginal likelihoods for two different models, the log-Bayes factor is defined as log BF = −2 log [p (D|Mi)/p ]. We followed previously reported criteria to determine the support for the favored model j. The marginal likelihood was estimated using the method previously described Mus musculusWe used the coalescent-based Bayesian Skyline Plot approach BSP;  as impleeNμ  jointly with the population growth rate ‘g’, assuming an exponential model of growth, and using the Bayesian MCMC sampling. The ‘g’ parameter reflects the exponential growth model; positive values of ‘g’ indicate (population) growth, and negative values indicate population shrinkage. We set the program to compute the Watterson estimate of Θ, and allowed the population to change in size, with an initial value for ‘g’ set to 0.1, a random starting tree, and the transition/transversion ratio obtained from jModelTest 2The program LAMARC 2.1.3 Table S1List of geographic localities analyzed in this study. Refer to numbers depicted in the map for localities Click here for additional data file.Table S2List of haplotypes recovered through the mitochondrial control region (hypervariable I domain) for Oligoryzomys longicaudatus. In parenthesis the number of haplotypes. The localities (see (DOC)Click here for additional data file.


Information on the impact of cecal insertion time on colorectal neoplasm detection is limited. Our objective was to determine the association between cecal insertion time and colorectal neoplasm detection rate in colonoscopy screening.We performed a cross-sectional study of 12,679 consecutive subjects aged 40–79 years undergoing screening colonoscopy in routine health check-ups at the Center for Health Promotion of the Samsung Medical Center from December 2007 to June 2009. Fixed effects logistic regression conditioning on colonoscopist was used to eliminate confounding due to differences in technical ability and other characteristics across colonoscopists.The mean cecal insertion time was 5.9 . We identified 4,249 (33.5%) participants with colorectal neoplasms, of whom 1,956 had small single adenomas (<5 mm), 595 had medium single adenomas (5–9 mm), and 1,699 had multiple adenomas or advanced colorectal neoplasms. The overall rates of colorectal neoplasm detection by quartiles of cecal insertion time were 36.8%, 33.4%, 32.7%, and 31.0%, respectively (p trend <0.001).The odds for small single colorectal adenoma detection was 16% lower  in the fourth compared to the first quartile of insertion time (p trend 0.005). Insertion time was not associated with the detection rate of single adenomas ≥5 mm, multiple adenomas or advanced colorectal neoplasms.Shorter insertion times were associated with increased rates of detection of small colorectal adenomas <5 mm. Cecal insertion time may be clinically relevant as missed small colorectal adenomas may progress to more advanced lesions. Early detection and removal of colorectal adenomas by colonoscopy decreases the risk of colorectal cancer , and colHigher adenoma detection rates are associated with more careful examination of the proximal sides of folds and flexures, with adequate distension, with cleaning of pools of fluid and dirty areas, and with time for scope withdrawal ,10. TakiWe conducted a retrospective study of screening colonoscopies at the Center for Health Promotion of the Samsung Medical Center in Seoul, Korea, from December 2007 to June 2009. We restricted the study to male and female participants aged 40 to 79 years old as this is the recommended age range for screening colonoscopy in Korea. From a total of 14,370 consecutive colonoscopies performed at the Center for Health Promotion, we excluded 862 colonoscopies conducted in participants who were <40 or ≥80 years of age, 526 colonoscopies performed for therapeutic (non-screening) purposes, 65 colonoscopies performed outside of the colonoscopy unit, 29 colonoscopies performed in participants with a history of colorectal cancer, inflammatory bowel disease or who had undergone colonic resection, and 113 colonoscopies with incomplete examination due to poor bowel preparation. In subjects who had colonoscopy multiple times during the study period (n = 98), we chose the first colonoscopy results. Because some individuals met more than one exclusion criterion, the final sample size was 12,679 . The study protocol was reviewed and approved by the institutional review board at Samsung Medical Center. The informed consent requirement was exempted by the Institutional Review Board because researchers only accessed retrospectively a de-identified database for analysis purposes.Twenty three board-certified gastroenterologists performed the colonoscopies. Each colonoscopist had performed more than 500 examinations before the study period or before working at the Center for Health Promotion. Colonoscopies were performed after bowel preparation with 4 L polyethylene glycol solution .Cecal insertion time was defined as the time from insertion into the rectum to the time when the colonoscope tip passed to a point proximal to the ileocecal valve so that the base of cecum was visible. Withdrawal time was defined as the time taken for withdrawing the colonoscope tip from the base of cecum to across the anus. Cecal insertion and withdrawal times were recorded immediately after finishing the examination by the colonoscopist. Bowel preparation was assessed as excellent , good , fair , or poor .Colorectal neoplasms were further classified as small single non-advanced adenomas < 5 mm, medium single non-advanced adenomas 5 to <10 mm,and multiple or advanced colorectal neoplasms . Advanced adenoma was defined as a tubular adenoma with diameter ≥10 mm, an adenoma with villous component, or an adenoma with high grade dysplasia. The size of each lesion was estimated using open biopsy forceps.2). Waist circumference was measured at the midpoint between the inferior margin of the last rib and the superior iliac crest in a horizontal plane. Information on history of colorectal polyps, diabetes mellitus, hyperlipidemia, medication use including aspirin and non-steroidal anti-inflammatory drugs (NSAIDs), alcohol drinking and smoking and family history of colorectal cancer were collected using a self-administered questionnaire before endoscopy.A health questionnaire and a detailed physical exam were routinely completed as part of the screening program. Height and weight were measured using an Inbody 720 machine . Body mass index (BMI) was calculated by dividing measured weight (kg) by height squared (m2), waist circumference , family history of colorectal cancer, history of colorectal polyps, diabetes, hyperlipidemia, aspirin use, NSAIDs use, calcium use, alcohol drinking, and smoking .Colorectal adenoma proportions were compared across the quartiles of cecal insertion time. Odds ratios (ORs) and 95% confidence intervals (CIs) for the presence of any colorectal neoplasm for the 3 highest quartiles of insertion time compared to the first quartile were calculated using fixed effects logistic regression conditioned on colonoscopists. These regression models account for data clustering by colonoscopists and eliminate confounding due to differences across colonoscopists. Separate logistic regression models were used for the different colorectal neoplasms. In multivariable models, we adjusted for age , sex, withdrawal time , bowel preparation , BMI  age of study participants was 53.4(7.3) years and 62.9% of study participants were male Table . The proTheir median age of the 23 colonoscopists was 34 years . A majority of the colonoscopists were female (82.6%). The median number of colonoscopies performed by each colonoscopist during the study period was 592 . Before the study period, the 23 colonoscopists had performed a median of 1,400 colonoscopies .Colorectal neoplasms were detected in 4,249 participants .Click here for fileOdds ratios for colorectal neoplasm detection by cecal colonoscopy insertion times .Click here for file


The variational Bayesian independent component analysis-mixture model (VIM), an unsupervised machine-learning classifier, was used to automatically separate Matrix Frequency Doubling Technology (FDT) perimetry data into clusters of healthy and glaucomatous eyes, and to identify axes representing statistically independent patterns of defect in the glaucoma clusters.FDT measurements were obtained from 1,190 eyes with normal FDT results and 786 eyes with abnormal FDT results from the UCSD-based Diagnostic Innovations in Glaucoma Study (DIGS) and African Descent and Glaucoma Evaluation Study (ADAGES). For all eyes, VIM input was 52 threshold test points from the 24-2 test pattern, plus age.N contained primarily normal fields  and clusters 1G and 2G combined, contained primarily abnormal fields . For clusters 1G and 2G the optimal number of axes were 2 and 5, respectively. Patterns automatically generated along axes within the glaucoma clusters were similar to those known to be indicative of glaucoma. Fields located farther from the normal mean on each glaucoma axis showed increasing field defect severity.FDT mean deviation was −1.00 dB (S.D. = 2.80 dB) and −5.57 dB (S.D. = 5.09 dB) in FDT-normal eyes and FDT-abnormal eyes, respectively (p<0.001). VIM identified meaningful clusters of FDT data and positioned a set of statistically independent axes through the mean of each cluster. The optimal VIM model separated the FDT fields into 3 clusters. Cluster a priori information about class membership, and identified familiar glaucomatous patterns of loss.VIM successfully separated FDT fields from healthy and glaucoma eyes without  A number of previous studies have used supervised machine-learning techniques to separate healthy from glaucomatous eyes successfully, based on visual function and optical imaging data. a priori information regarding class membership. For instance, component analysis can decompose data by projecting multidimensional data onto n axes that meaningfully represent the data.An alternate class of MLCs, based on unsupervised learning, also has been employed to identify healthy and glaucomatous eyes, based on visual field data. Independent component analysis (ICA) We previously applied VIM to standard automated perimetry (SAP) results from glaucoma patients. Each axis identified by VIM represented a glaucomatous visual field defect pattern, and the severity of that pattern was organized from mild to advanced along each axis. Although identified automatically using mathematical techniques and no human input, VIM for SAP data identified patterns that were similar to those known to be indicative of glaucoma based on decades of expert visual field assessment Frequency Doubling Technology (FDT) stimuli test the responses of a subset of all available retinal ganglion cells that have different temporal and spatial summation properties compared to those tested using SAP. Individuals included in the current study were participants in the University of California, San Diego (UCSD)-based Diagnostic Innovations in Glaucoma Study (DIGS) and African Descent and Glaucoma Evaluation Study . In total, FDT results from 1,976 eyes of 1,136 individuals were studied.Each study participant underwent a comprehensive ophthalmologic evaluation including review of medical history, best-corrected visual acuity testing, slit-lamp biomicroscopy, intraocular pressure measurement with Goldmann applanation tonometry, gonioscopy, dilated fundus examination with a 78 diopter lens, simultaneous stereoscopic optic disc photography , and SAP using the 24-2 SITA Standard test strategy . To be included in the study, participants had to have a best-corrected acuity better than or equal to 20/40, spherical refraction within ±5.0 D and cylinder correction within ±3.0 D at baseline, and open angles on gonioscopy. Eyes with non-glaucomatous optic neuropathy, uveitis or coexisting retinal disease that could affect visual fields were excluded.This research followed the tenets of the Declaration of Helsinki and Health Insurance Portability and Accountability Act guidelines. All study participants provided written informed consent and the UCSD, UAB, and NYEE Human Research Protection Programs approved all methodology.Each participant was tested using FDT with the Humphrey Matrix (24-2 test pattern) FDT Visual Field Instrument  with Welch-Allyn technology  using the Zippy Estimation by Sequential Testing (ZEST) thresholding algorithm. For the purpose of assessing the specificity and sensitivity of VIM-defined clusters, FDT Matrix results from each study eye were classified as within normal limits  or abnormal [786 eyes with FDT Glaucoma Hemifield Test (GHT) outside of normal limits or Pattern Standard Deviation ≤5%], based on the instrument’s normative database. All FDT results were reliable, defined as false positives, fixation losses and false negatives ≤33%. Mean reliability results were 2.86%, 5.51% and 1.61% for false positives, fixation losses and false negatives, respectively.Previous studies using unsupervised classifiers to identify patterns of visual field defect in glaucoma eyes used glaucomatous optic neuropathy (GON), as determined by stereophotograph assessment, as an indicator of disease. FDT mean deviation was −1.00 dB (S.D. = 2.80 dB) in FDT-normal eyes and −5.57 dB (S.D. = 5.09 dB) in FDT-abnormal eyes, respectively . Individuals providing abnormal FDT results in at least one eye were slightly, but significantly, older than individuals providing normal FDT results from both eyes .This technique has been described in varying degrees of detail previously, by our collaborators and by us. First, several candidate VIM models were created using two to five possible Gaussian clusters, with a maximum of 10 or 20 possible axes per cluster and three, six or eight possible mixture components. These candidate VIM models were repeated with 30 random initializations to avoid finding a locally optimal solution. Thus, a total of 720 VIM models were created , and each model was subjected to 500 iterations of training  in an attempt to identify the models that provided the highest specificity versus sensitivity trade-off . The single best model (#202 with the highest specificity versus sensitivity trade-off) maximized specificity and sensitivity using three clusters, each including a maximum of 20 axes. The optimal number of axes within each cluster was manually chosen based on the previously described process of finding the “knee” points. The optimal  version of model #202 contained three clusters, C1, C2 and C3, composed of two, two and five axes, respectively. This version was retrained 500 times to determine the final best specificity and sensitivity of the model.The optimal VIM model (#202) had an initial specificity of 0.887 and sensitivity of 0.817 . After 5N) was composed of 1,109 normal fields (89%) and 135 abnormal fields (11%). The first “glaucoma” cluster  was composed of 474 abnormal fields (85%) and 81 normal fields (15%) and the second glaucoma cluster (G2) was composed of 177 abnormal fields and 0 normal fields. The three clusters identified by the best VIM model were one cluster composed mainly of normal FDT fields and two independent clusters composed mainly of abnormal  fields. The primarily normal cluster , with the values also expressed for each visual field location. Predictably, both of them appear as normal, or close to normal, fields because 89% of the actual FDT fields clustered there are normal, and the 11% of fields from glaucoma eyes differed little in appearance to the fields from normal eyes. The actual fields closest to Axis 1 form a set composed of 657 fields, 592 of which are normal and 65 of which are abnormal. The fields closest to Axis 2 form a set composed of 587 fields, 517 of which are normal and 70 of which are abnormal.G1. Regarding visual field patterns generated at +2 standard deviations from the cluster means , Axis 1 appears to represent primarily moderate superior hemifield defects and Axis 2 appears to represent primarily moderate inferior hemifield defects , both with diffuse loss in the opposing hemifield. The actual fields closest to Axis 1 form a set composed of 293 fields, 240 of which are abnormal and 53 of which are normal. The set for Axis 2 is composed of 262 fields, 234 of which are abnormal and 28 of which are normal.G2. Regarding visual fields placed +2 standard deviations from the cluster means, Axis 1 appears to represent diffuse moderate visual field loss and the fields closest to this axis form a set composed of 39 fields, all of which are abnormal. Axis 2 and Axis 3 appear to represent more severe superior nasal and inferior nasal defects, respectively. The set of fields closest to Axis 2 is composed of 39 fields, and the set of fields closest Axis 3 is composed of 38 fields . Axis 4  has the pattern of an “arrowhead” shaped defect, similar to the pattern observed using the VIM technique to identify patterns of visual field defect in SAP data. Axis 5 within this cluster  appears to represent a diffuse pattern of loss, primarily localized superiorly.a priori information regarding class membership. We believe this is important because mathematical techniques were used to define clusters with no human input . In addition, patterns of FDT visual field loss generated on the VIM axes were similar to the patterns uncovered by VIM with SAP reported in previous studies The variational Bayesian independent component analysis mixture model employed in this study to identify clusters of FDT Matrix visual fields discriminated between normal and abnormal fields with high specificity and sensitivity, without any As an unsupervised learning classifier, VIM relies on a probabilistic measure of the likelihood of class membership to identify normal and abnormal clusters instead of relying on labeling of normal and abnormal visual field “templates” against which classifiers are trained by supervised learning and later tested. Although VIM was not designed specifically to segregate normal and abnormal visual fields, the FDT clusters created by VIM from the structure of the data yielded clusters that classified normal and glaucoma fields with specificity and sensitivity similar to that accomplished by supervised machine learning techniques, using data labeled with the class identity. N) were abnormal based on our criteria . Upon inspection of Matrix printouts, most of these eyes had mild to moderate scattered decreases in thresholds across the visual field with no apparent pattern of defect , so they likely did not fit into a specific, identifiable glaucoma cluster. They were, however, outside normal limits when compared to the FDT normative database. Conversely, 15% of the eyes (n = 80) in glaucoma cluster 1G were within normal limits based on the FDT normative database. Upon inspection of Matrix printouts, many eyes had mild, scattered decreases in thresholds, although a hypothetical reason for misclassification was not obvious. These results are not wholly unexpected, because the VIM technique described herein does not rely on results from the FDT normative database to identify patterns of defect.Eleven percent of eyes (n = 135) assigned to the VIM normal field  within the glaucoma clusters.The identifiable patterns of defect  within each glaucoma cluster generally resembled those that have been shown to be indicative of glaucoma in SAP tests, based on many years of expert assessment. Age is a significant risk factor of glaucoma and its progression. To study how the age of study eyes influenced the defect patterns identified and generated by the VIM algorithm, we ran the VIM algorithm with the age parameter input set to zero for all study eyes. The VIM model generated without age provided a similar diagnostic accuracy and defect patterns. Therefore, it was evident that age did not significantly affect or bias how the FDT defect patterns were identified and automatically generated by the VIM algorithm.post-hoc examination of agreement between VIM-defined clusters and the presence of glaucomatous optic neuropathy  showed that in cluster N , 278 (38%) eyes had apparent GON and 649 (89%) had abnormal FDT results.Although the specificity and sensitivity of VIM-discovered clusters were very good relative to the result of the FDT test , cluster assignment did not always agree with characteristics of the optic nerve and retinal nerve fiber layer, as assessed by masked stereoscopic photograph assessment. This is not unexpected, because several studies have shown a disagreement between classification by visual function and structural assessments e.g., . This diIn previous studies In summary, VIM was applicable to FDT data. The outcome of the VIM process with FDT data was similar to the outcome with SAP data. Even without foreknowledge of the diagnosis of normal or glaucoma, unsupervised learning analyzing the internal structure of the data yielded separation of normal and glaucomatous eyes as well as can be achieved with supervised learning with foreknowledge of the diagnosis. Variational Bayesian independent component analysis mixture model can find statistically different visual field patterns similar to those identified by human experts. The axis representation of the internal structure of the data arranges mild to severe orientation of each pattern of visual field defect, thus permitting analysis for progression of disease. VIM, both for FDT and SAP, likely provides a good environment for the development of machine learning methods for detecting progression.


The aim of this study was to investigate whether insomnia and obstructive sleep apnea (OSA) were predictors of hospitalizations or emergency department visits during two years following the Sao Paulo Epidemiologic Sleep Study (EPISONO) sample.All participants  who underwent a baseline evaluation between July and December 2007 were contacted in December 2009 and asked to fill out a questionnaire about body weight changes, number of hospitalizations and visits to the emergency department. Participants lost during the follow-up period represented 3.2% (n = 35) and 7 subjects had died. Hospitalizations were reported by 116 volunteers (10.5%) and emergency department visits were reported by 136 participants (12.4%). The average body mass index (BMI) did not vary significantly between the first and the second assessment [26.7(95%CI:26.3–27.1) vs. 26.9(26.5–27.4) kg/m2]. After adjusting for confounders, a multiple logistic regression model revealed that female gender [1.4(1.0–1.9)], age ≥40 years, insomnia diagnosed according to the DSM-IV criteria [1.6(1.0–2.6)], and apnea-hypopnea index ≥15 [1.5(1.0–2.2)] were predictors of hospitalizations and/or demand for emergency services.Our study of a probabilistic sample of the Sao Paulo inhabitants shows that over a period of two years, insomnia and OSA were both associated with health impairment. Considering the high prevalence and public health burden of sleep disorders, the consequences of untreated disease for both the individual and society are undeniable and should be addressed. Sleep disorders are increasingly recognized as an important cause of medical morbidity and mortality. Many studies have indicated that the presence of untreated sleep disorders, especially insomnia and obstructive sleep apnea (OSA), is associated with an increased risk of fatal and nonfatal cardiovascular disease The purpose of the present study was to investigate whether insomnia and OSA were predictors of hospitalizations or emergency department visits after a period of two years following up the sample of the Sao Paulo Epidemiologic Sleep Study (EPISONO).The study protocol was approved by the Research Ethics Committee of the Universidade Federal de Sao Paulo (CEP 0593/06), and it was registered at ClinicalTrials.gov (number: NCT00596713). All participants read and signed a written informed consent form.The baseline evaluation was performed between July and December of 2007 on volunteers enrolled in the EPISONO, a large population-based survey conducted in the city of Sao Paulo, Brazil. The methodological details of this study have been described elsewhere Two years after the baseline evaluation (December 2009), all participants were contacted either by e-mail, telephone, or a home visit. Interviews included a questionnaire on changes in body weight, number and causes of hospitalizations and utilization of any emergency services during the follow-up period.2; diabetes that was self-reported in response to a yes/no question; hypertension in individuals who self-reported taking a prescribed anti-hypertensive medication or had high blood pressure (systolic >140 mm/Hg and/or diastolic >90 mm/Hg) measured in the sleep laboratory; symptoms of insomnia in individuals who self-reported difficulties in initiating, maintaining, and/or very early morning awakening at least three times a week; insomnia as defined by the Diagnostic and Statistical Manual of Mental Disorders (IV) in individuals reporting that the previously mentioned symptoms had occurred in the past thirty days and interfered ‘much’ or ‘extremely’ with daily functioning The following predictors were evaluated during the baseline study and were subsequently used to compare individuals who chose to seek medical care: overweight/obesity determined by a body mass index (BMI)>25 kg/mThe statistical package used in data analysis was SPSS (version 17) for Windows. A general linear model of repeated measures was applied to compare variations in weight and BMI. Complex sample cross-tabulations were applied to determine the weighed prevalence of ‘seeking medical care’ according to predictors in the 1,042 participants who had a PSG recording performed. To generate crude odd ratios, we used multiple, unadjusted logistic models. Because we decided to perform an exploratory analysis, we adopted the backward Wald method to enter variables in the fully adjusted model with all tested predictors.Of the 1,101 participants who comprised the total baseline sample, we were able to locate 1,021 by telephone or mail. The remaining 80 participants were visited at their homes and 45 were located. The total number of volunteers lost represented 3.2% of the initial sample.Seven participants died between December 2007 and December 2009. The causes of death reported by their relatives were heart disease (two), stroke (two), cancer (two) and murder during an assault (one).2; 95%CI: 26.3–27.1 vs. 26.9; 26.5–27.4).We found no significant changes in the variance of the participants' BMI. In 2007, weight and height were measured in the lab, whereas in 2009, weight was self-reported by the participants  were hospitalized, and 136 (12.4%) required health care or first aid for a variety of reasons. The most frequent causes of hospitalization and demand for emergency services were cardiovascular events and/or high blood pressure (27%), infectious processes or pulmonary diseases (14%), psychiatric problems (6%), diabetes (5%), thyroid dysfunction (4%), cancer (3%), and other (41%) including fracture or musculoskeletal problems, kidney and gastric conditions, and surgeries (one of them to treat OSA). The main predictors of these events were female gender, age ≥40 years, insomnia, and OSA (AHI≥15). Before adjustments were made, BMI>25 kg/mttention . We perfttention , the varWe showed that among a probabilistic sample, female gender, age ≥40 years, insomnia, and OSA were significantly associated with an increased number of hospitalizations and/or demand for emergency services during a short EPISONO follow-up period. Our sample included individuals from the general population and represents the inhabitants of the city of São Paulo between 20 and 80 years of age. In contrast to clinical populations, we evaluated individuals who are much less likely to receive a proper diagnosis or treatment of their sleep disorder.A recent review by Ohayon suggests that the prevalence of insomnia was already high two decades ago, affecting at least one-third of the population who in most cases, did not receive a proper diagnosis or proper treatment for their condition The prevalence of OSA syndrome in the EPISONO cohort is one of the highest ever found (32.9%) A recent study showed that among US adults who participated in the National Health and Nutrition Examination Survey, those who reported sleep complaints also had increased healthcare utilization. This study also suggested that sleep disorders appear to be highly underdiagnosed Interesting enough, our study showed that moderate and severe AHI could be a factor in association to other clinical conditions, which together, figured as predictor of poor health outcome, while the AHI between 5 and 15, with or without symptoms, was not found to be a risk factor associated with poor health outcome. In addition, more than ¼ of the causes of hospitalizations and/or demand for emergency services during the short follow-up period were linked to cardiovascular disease, as described in previous studies evaluating OSA and even insomnia Sao Paulo no doubt represents the worldwide trend of increasing levels of obesity (22%), OSA, insomnia, hypertension, and diabetes (among other diseases) in all metropolitan areas. It seems logical to think that promoting good sleep would regulate some metabolic and functional processes and might also treat some of the other conditions described herein.We must put forth an effort to recognize sleep disorders and also to raise awareness of the need to educate both physicians and the population about sleep, the diagnosis of sleep disorders, the benefit of behavioral and psychological treatments and interventions, and alternatives to pharmacology in individuals with chronic insomnia and/or a comorbid psychiatric disorder. Additionally, we must understand the potential therapeutic effects on obesity and adherence to nasal Continuous Positive Airway Pressure or to oral appliance treatments, which can to help us to treat OSA and other breathing-related sleep disorders.


A receptors. Cross- and auto-spectra from the two regions were used to optimise three DCMs based on biologically plausible neural mass models and specific network architectures. Consistent with known extrinsic connectivity patterns in sensory hierarchies, we found that a model comprising forward connections from A1 to PAF and backward connections from PAF to A1 outperformed a model with forward connections from PAF to A1 and backward connections from A1 to PAF and a model with reciprocal lateral connections. The parameter estimates from the most plausible model indicated that the amplitude of fast glutamatergic excitatory postsynaptic potentials (EPSPs) and inhibitory postsynaptic potentials (IPSPs) behaved as predicted by previous neurophysiological studies. Specifically, with increasing levels of anaesthesia, glutamatergic EPSPs decreased linearly, whereas fast GABAergic IPSPs displayed a nonlinear (saturating) increase. The consistency of our model-based in vivo results with experimental in vitro results lends further validity to the capacity of DCM to infer on synaptic processes using macroscopic neurophysiological data.Generative models of neuroimaging and electrophysiological data present new opportunities for accessing hidden or latent brain states. Dynamic causal modeling (DCM) uses Bayesian model inversion and selection to infer the synaptic mechanisms underlying empirically observed brain responses. DCM for electrophysiological data, in particular, aims to estimate the relative strength of synaptic transmission at different cell types and via specific neurotransmitters. Here, we report a DCM validation study concerning inference on excitatory and inhibitory synaptic transmission, using different doses of a volatile anaesthetic agent (isoflurane) to parametrically modify excitatory and inhibitory synaptic processing while recording local field potentials (LFPs) from primary auditory cortex (A1) and the posterior auditory field (PAF) in the auditory belt region in rodents. We test whether DCM can infer, from the LFP measurements, the expected drug-induced changes in synaptic transmission mediated via fast ionotropic receptors; i.e., excitatory (glutamatergic) AMPA and inhibitory GABA Neural mass models have been used to simulate the electrophysiological response of cortical regions and have recently served as generative models for empirical M/EEG and LFP data A receptors Pharmacological interventions can manipulate aspects of synaptic processing and can thus be used to validate model predictions: Here, we use isoflurane, a volatile anaesthetic agent that is used commonly in animal laboratory studies 1/f type spectral noise DCM is a generic modeling approach for inferring on the physiological mechanisms underlying measured neuroimaging data DCM for SSR assumes small perturbations about a dynamic equilibrium, where the perturbations are caused by endogenous fluctuations in cortical activity, i.e. white or coloured noise. The frequency response of a network of regions is described using the cross-spectral density of outputs, comprising auto- and cross-spectral components. Variational Bayesian techniques allow us to invert this generative model given real data and provide posterior densities over the parameters and the model evidence Previous validation studies of inference on synaptic processing using this DCM have used microdialysis measurements of extracellular glutamate levels to predict the parameter estimates that should be obtained by the model For recording LFPs in Lister hooded rats, a telemetric recording system (TSE Systems) was assembled, using chronically implanted epidural silverball electrodes above left and right auditory cortex in seven animals. In three of these animals, surgery and recordings were performed bilaterally and the results presented below use averages over both hemispheres.2) and 70% nitrous oxide (N2O). Once deeply anaesthetized, rats were transferred into a stereotactic frame and fixated using ear bars and a tooth bar. During surgery, animals inhaled a similar mixture of gases through a mask (isoflurane reduced to 2–3%). Body temperature was kept constant at 37°C using a heating pad feedback controlled by means of a rectal probe.Prior to surgery, rats were placed in an anesthesia box that was perfused with isoflurane (5%) mixed with 30% oxygen  and was increased to the next level after 40 min. Each level of anaesthesia was accompanied by LFP recordings with 20 min continuous white noise stimulation, followed by 20 min under silence. White noise stimuli had a level of 83 dB (sampling rate 25 kHz) and were delivered by an RX6 processor and two free field magnetic speakers  that were placed with a distance of 15 cm, on both sides of the rats head. Recordings started immediately after increase of anaesthesia to the next level.Electrode recordings were amplified (×1000) in the transmitter. Data were transferred to a receiver at a transmission frequency of 400 to 434 MHz, and amplified again (×10). Analogue LFP recordings were analyzed using a data acquisition system  at a sampling rate of 2 kHz. Digital filtering was applied online (0.6–60 Hz).p). Channel data y, from the two channels (A1 and PAF) was modelled as a VAR process  were extracted from the continuous time domain data and down-sampled to a sampling rate of 125 Hz. Frequency domain data-features were constructed from these epochs using a vector auto regression (VAR) model of order 8 ((n)A and channel noise covariance ijE, estimates were used to compute the cross-spectral densities for frequencies 1–30 Hz using the following transform:The autoregressive coefficients a priori to contribute less aggregate signal, comprising about 20% of the measured response Dynamic causal models treat distributed brain networks as a connected set of neuronal ensembles or sources, where each ensemble  is described by a set of differential equations. These equationscomputed . The meaesponse  . The secθ encode synaptic inputs in terms of the amplitude of excitatory and inhibitory postsynaptic potentials  and the parameters of the static sigmoidal firing curve . Forward, backward and lateral connections between regions  originate and terminate at specific cell layers (The parameters l layers . In thisyers  in . Forwardyers  in .BF>150 is considered very strong evidence in favour of one model over another (log Bayes Factor of ∼5). For larger systems, one could employ a network discovery approach whether connections played a role in generating the data but what type of connections generated the data. In model 1, we specified a two region network comprising A1 and PAF, where forward connections linked A1 to PAF and backward connections mediated the influences of PAF on A1, conforming to the hierarchical, forward and backward cortico-cortical connectivity structure of auditory cortex We tested whether we could infer the known hierarchical relation of the primary auditory cortex (A1) to the posterior auditory field (PAF) in the auditory belt region, from the steady state LFP data. This hypothesis was tested using Bayesian model selection (BMS) based on the evidence for competing models q(θ), by maximising the negative free energyp(y|m). Note that the model evidence, also known as the marginal likelihood, evaluates the relative goodness of models by taking account of both the accuracy with which it can explain (fit) the empirical data features and the complexity of the model. The complexity term accounts for both the “effective degrees of freedom” (number of parameters and their interdependencies) and differences between the parameter estimate and its a priori value. Simply speaking, a model is more complex (i) the more parameters it has, (ii) the more independent (low covariances) and “flexible” (low precision) these parameters are, and (iii) the more the posterior is required to deviate from the prior to account for the data The model was inverted (identified or fitted) by applying it to the cross-spectral densities from each of the ten hemispheres separately. In DCM, a variational Bayesian scheme is used, which factorises the conditional (posterior) density over unknown parameters into Gaussian marginal densities (here comprising model parameters and the log-precision of observation noise). Model inversion furnishes the (approximate) conditional density iκ) of regions A1 and PAF. The quantitative effects of anaesthesia on these animal-specific parameters (averaged over hemispheres where applicable) for each region were entered into an ANOVA with anaesthetic depth as a factor. As we predicted that excitatory parameters decrease and inhibitory parameters increase with level of anaesthesia, we used one-tailed probabilities at P<0.05. To test for particular parametric effects, first and second order polynomials were later fitted to these animal-specific modulatory effects. We tested for consistent parametric effects from the polynomial coefficients using a one-tailed t test.In our DCM, we modelled four conditions, corresponding to 1.4%, 1.8%, 2.4% and 2.8% isoflurane. The effect of isoflurane was modelled separately for each condition, allowing for unconstrained differences in specific synaptic parameters across increasing depths of anaesthesia. Three parameters were allowed to change across anaesthesia levels and thus explain condition specific effects on the spectral densities. These parameters were the maximum excitatory postsynaptic potential (EPSP), the maximum inhibitory postsynaptic potential (IPSP), and the inhibitory rate constants  , theta , alpha  and beta  bands. Similarly, the PAF auto-spectra showed a significant effect of anaesthetic depth . Finally, the cross-spectral densities comprising the off-diagonal components of p<10−7, silence: p<10−4, theta; noise: p<10−10, silence: p<10−5, alpha; noise: p<10−7, silence: p<10−5, beta; noise: p<10−5, silence: p<10−4).We examined frequency differences (across levels of anaesthesia) within traditional EEG bands by binning spectral measures per animal (n = 7) . Anaesth12 = 33.21) and silent (lnGBF12 = 80.63) conditions (These cross-spectra served as data features for model inversion (see Methods). We tested three models for each data set and averaged across hemispheres, where dual recordings had been obtained. Model 1 contained two sources representing A1 and PAF, with intrinsic dynamics as per nditions . As can iκ (see Methods). The maximum a posteriori (MAP) estimates  for condition specific effects were used for statistical analysis at the group level. We first examined the overall changes in excitation and inhibition relative to baseline and observed significant decreases (p<0.005) and increases (p<0.05), respectively, in all stimulus conditions and in both auditory regions. Our analyses did not indicate any significant changes in inhibitory rate constants in A1 or PAF.Having established the most probable model, we next examined its parameters encoding glutamatergic and GABAergic neurotransmission. Defining 1.4% isoflurane as a baseline, we modelled condition-specific effects for trials at 1.8%, 2.4% and 2.8% isoflurane on parameters controlling the maximal amplitude of EPSPs and IPSPs and inhibitory rate constants F3,24 = 6.11, p = 0.0031, silence: F3,24 = 9.26, p = 0.0003), F3,24 = 2.59, p = 0.076, silence: F3,24 = 1.88, p = 0.16 for both stimulus conditions) , a significant decrease for 2.4% compared to 1.8% , and a trend towards a decrease for 2.8% compared to 2.4% . The inhibitory trends were driven by significant increases for dose level differences at low doses . Within the PAF  and on IPSPs . Post-hoc we observed consecutive decreases in EPSP height in PAF similar to A1 , and this coincided with a significant increase in inhibitory neurotransmission as indexed by IPSP for 1.8% isoflurane compared to baseline  that remained high for higher doses . Note that EPSP effects in A1 (silence and white noise), EPSP effects in PAF (silence and white noise) and IPSPs effects in PAF (white noise) survive Bonferroni correction for 8 multiple comparisons.Dose-dependent analysis of synaptic parameters was performed using one-way ANOVAs for white noise and silent stimuli at A1 and PAF, with isoflurane depth as a single factor. In A1 there was a significant effect of isoflurane concentration on EPSP amplitude (noise: ditions) . Post ho the PAF , both exTo investigate the parametric effects of isoflurane depth, we estimated dose-response curves, using the parameter estimates above. We first used simple linear regressions to establish whether anaesthetic depth changes excitatory and inhibitory postsynaptic potentials in the expected direction. We then used a polynomial dose-response curve to assess the prediction that isoflurane produces a saturating nonlinear (decreasing) effect at higher doses . A linear curve was fitted to the dose responses of EPSP and IPSP MAP estimates. In A1, we found a consistent linear effect for EPSP estimates, where slopes were negative in all animals for noisy and silent conditions ; see In short, both inference on models and inference on the parameters of the model selected provide further endorsement of DCM as a way of accessing hidden architectures and synaptic physiology, given seemingly unresolved electrophysiological data. Our model comparison indentified the hierarchical architecture that was consistent with the known microanatomy of sensory brain systems, in terms of the laminar specificity of forward and backward connections. Furthermore, our pharmacological manipulation produced expected quantitative changes in hidden parameters encoding specific postsynaptic responses. We were able to generalise these parametric changes over two different contexts: the presence and absence of auditory noise in the environment. These two environments test the basic assumption in DCM for SSR that the cortical nodes can be understood as filters of surrounding cortical noise. In other words, even in silence there is sufficient cortical noise (white and pink components) to drive through the modulatory transfer function embodied by the neural state equations to produce the spectral output. The fact that no external perturbation was required to differentiate these models also points to a key difference between this DCM for steady state responses and models of evoked transients Using electrophysiological recordings of LFPs during different levels of isoflurane-induced anaesthesia, we have shown that by inverting a biologically plausible generative model of cortical dynamics one can recover latent quantities, such as the membrane responses to glutamate and GABA receptor binding. These conclusions were based on epidural recordings, which reflect, most prominently, membrane potential changes (net excitation and inhibition) of synchronous activity at pyramidal cell dendrites in an open field arrangement. Pyramidal cell apical dendrites are spatially aligned perpendicular to the cortical surface, producing a linear summation of currents DCM was originally designed for the analysis of fMRI time series Using electrophysiological recordings, we found that a model with forward, driving connections from primary auditory cortex to the posterior auditory field and backward connections in the opposite direction outperformed a model connected reversely and a model with lateral connections specifying two regions at the same hierarchical level. The (first) anatomically plausible model was inferred with strong evidence for both silent and noisy environments.A receptors The present view of isoflurane action is that it affects both excitatory and inhibitory synaptic transmission, influencing pre- as well as postsynaptic processes; the net effect is a decrease in excitation and an increase in inhibition It was reassuring to see that our model inversion results were consistent with these empirical findings, showing a linear decrease in peak, or maximum EPSP amplitudes with increasing isoflurane concentration. Moreover, our model parameter estimates also showed the expected changes in inhibitory synaptic processes with isoflurane depth, with nonlinear increases in GABAergic neurotransmission beyond the 1.4% baseline .Our model is limited by the receptor characteristics that determine its dynamic repertoire. The neural mass model employed here employs fast linear postsynaptic ion channels, both at excitatory and inhibitory synapses. Other receptor types not included in our model, such as glutamatergic NMDA and cholinergic receptors, have also been shown to be affected by isoflurane DCM works on the principle that model parameters identifiably contribute to the dynamic processes controlling measurable brain responses. In the case of DCM for steady state and evoked responses, the dynamic processes are described by a neural mass model and detail how excitatory and inhibitory cells within a given region interact but also how signals are passed between the regions themselves. In this work, we have validated the inference that is made on these unobservable hidden states. Future work will involve the validation of similar synaptic assays using non-invasive measures including MEG or EEG where manipulations are similarly performed using pharmacological agents with known synaptic consequences. Further work will also examine the validity of synaptic assays from more complex neural models, e.g. those including non-linear NMDA channels