Based on barriers to the use of computerized clinical decision support (CDS) learned in an earlier field study, we prototyped design enhancements to the Veterans Health Administration's (VHA's) colorectal cancer (CRC) screening clinical reminder to compare against the VHA's current CRC reminder.In a controlled simulation experiment, 12 primary care providers (PCPs) used prototypes of the current and redesigned CRC screening reminder in a within-subject comparison. Quantitative measurements were based on a usability survey, workload assessment instrument, and workflow integration survey. We also collected qualitative data on both designs.Design enhancements to the VHA's existing CRC screening clinical reminder positively impacted aspects of usability and workflow integration but not workload. The qualitative analysis revealed broad support across participants for the design enhancements with specific suggestions for improving the reminder further.This study demonstrates the value of a human-computer interaction evaluation in informing the redesign of information tools to foster uptake, integration into workflow, and use in clinical practice. The role of human factors and human-computer interaction (HCI) methods in the development of health information technology (IT), including electronic health records (EHRs) and computerized clinical decision support (CDS), is underutilized -3. IncorBased on an extensive field study conducted across multiple sites to understand common barriers to the use of CDS for colorectal cancer (CRC) screening ,9, we teThe computerized clinical reminders are the main form of CDS in the VHA's EHR, known as the Computerized Patient Record System (CPRS). We chose to focus this work on CRC screening because there is a robust evidence-base to support the efficacy of CRC screening ; yet, raSuccessful implementation and end-user acceptance of CDS requires high usability, low cognitive workload and integration of the CDS into workflow -16. To tThe VHA includes approximately 150 medical centers and many more community based outpatient clinics in the United States. The Department of Veterans Affairs (VA) is a leader in the transition to electronic patient records and clinical decision support, including the design and implementation of computerized clinical reminders. The clinical reminders function as part of the VA's EHR, which is an integrated program with multiple software packages designed to allow providers to order medications, laboratory tests, consultations, and document actions.The VA clinical reminder system evaluates available patient data according to a defined logic, based on the clinical topic addressed, such as CRC. If the data indicate that an intervention is potentially appropriate for the patient, the reminder is categorized as 'applicable'; if the data indicate the intervention has been provided, the clinical reminder 'satisfied' and if not provided, 'due' . In the The computerized clinical reminders in the VA's EHR are programmed to appear according to evidence-based guidelines. For example, the CRC screening reminder is triggered for patients 50 to 75 years old with no colonoscopy in the past ten years or no sigmoidoscopy in the past five years, and no record of three fecal occult blood test (FOBT) cards results reported within the past year. If the patient has a family history of colorectal cancer, the age criterion is adjusted downward to 40. If the record does include three FOBT card results in the past year, the reminder appears annually. If the record indicates that a colonoscopy was performed, the reminder will appear ten years after the colonoscopy date. Similarly, the reminder will appear five years after a sigmoidoscopy. Rank order logic is used so that the longest applicable period is chosen for the time to reappearance of this reminder. Figure The clinicians usually view a patient's due clinical reminders just before or during a patient visit. Each outpatient exam room has a desktop computer for the provider to access the EHR and the clinical reminders. Although the clinical reminders were designed to be used during the patient visit, some providers tend to complete the reminders after the patient visit, which iTo satisfy the clinical reminders, the clinician must first create a new progress note in the EHR, then click on an icon to display a list of reminders that are due, and then click on individual reminders to invoke a dialogue box to address each one. Once the dialogue is open, the clinician satisfies a reminder by selecting the appropriate dialogue options , although the actual clinical reminder system is populated with data from the EHR.The redesigned prototype for the CRC clinic reminder was constructed using AdobeThe redesigned prototype (design B) differed from the current design (design A) in the following two ways: (1) a timeline visual was created to display a complete, integrated history of a patient's colorectal cancer screening tests and results, including FOBTs, sigmoidoscopies, and colonoscopies; and 2) a general resource  was added to assist the clinician in providing patient education. Both design additions were implemented such that they could be initiated from the original reminder dialog box. Figure  a generaThis study received ethical approval by the Indiana University (IU) Institutional Review Board , as well as the Indianapolis VA Medical Center Research and Development (R&D) Committee.We recruited 12 primary care providers (PCPs) from five outpatient clinics at the VA Medical Center study site. We considered recruiting non-VA PCPs to remove the potential bias of users having experience with the current design of the CRC clinical reminder being more likely to perform well with the current system. However, information obtained from first-time users of a system is limiting, since it would not be possible to assess whether the new design enhancements affect the performance measures after the participants move past an initial learning phase. Therefore, current VA PCPs were recruited to participate.® usability testing software was used to capture the direct screen image of the participant's screen in conjunction with a Web camera to record the participant's face. The experimenter was able to view the participant's screen via the Morae® recording in real time from a different workstation, separated by a divider to reduce potential experimenter bias. Prototypes of the current and redesigned CRC computerized clinical reminder were displayed on the participant's computer.The experiment was set-up in the Human-Computer Interaction (HCI) & Simulation Laboratory at the Indianapolis VA Medical Center. The Lab provides an environment to capture performance and usability data and assess user interaction with clinical information systems. MoraeOne of the authors (JS) facilitated the experimental sessions, including all of the procedures described in this section. Brief demonstrations of the current (design A) and redesigned (design B) prototypes were provided to orient each participant. The demonstration, including both prototypes, lasted no longer than five minutes. Each participant was introduced to designs A and B for the demonstrations in a counter-balanced fashion . Then, for the experimental protocol, each participant worked with designs A and B in the same counter-balanced order as during the demonstrations . Each participant was given brief, written instructions to resolve the CRC clinical reminder  for two patient scenarios for each design , with relevant patient information necessary to complete the reminder.We developed a simple and complex patient scenario with the assistance of a practicing physician co-author (DH) to expose the participants to more than one type of patient case when working through the prototypes. Differences between the paired patient scenarios across designs A and B were 'surface-level' only  to reduce variability with user performance not related to the design of the CRC screening clinical reminder. In addition to designs A and B being presented to participants in a counter-balanced fashion, the presentation order of the simple and complex scenarios was also counterbalanced within designs A and B . An overview of the simple and complex scenarios included the following:• Simple: Patient is a 60 year-old male veteran who first came to the VA about 12 months ago. He is interested in discussing colonoscopy. Prior to seeking care at the VA, he completed FOBT cards for his family physician. He thinks they were negative, but he can't remember when he had them done. In addition, he believes he had a flexible sigmoidoscopy when he turned 50. He was not sedated for the procedure.• Complex: Patient is a 60 year-old male veteran who first came to the VA about 1 year ago. He wonders whether he is due for another colonoscopy. He had a colonoscopy 4 years ago. The previous colonoscopy wasn't cancer, but showed multiple polyps that the GI doctors said needed to be followed up.The prototypes included the fictitious patients' active problem list, medications, clinical reminders, current vitals, two previous progress notes, and previous test results (including CRC screening results). Since design A did not have the new timeline visual, previous CRC screening results were available as test results, which are currently displayed in the Labs or Reports section of the EHR.Participants completed both patient scenarios for one design (A or B) before completing the two scenarios for the other design. The computerized version of the NASA Task Load Index (TLX) ,20 was aThe experimental design was a within-subject 'A' (current design) vs. 'B' (redesign) comparison (the single factor was Design Type). To test the hypothesis that participants would perceive design B to be easier to use than with A, we grouped similar usability questions together. See additional file ® video recordings of the participant sessions, an open-ended portion of the CSUQ usability survey where participants were asked to list the three most positive and negative aspects of the CRC screening clinical reminder, and the open-ended debrief interview notes. The video recordings were reviewed and all participant comments and performance-related interactions with the new design features  were compiled. Each comment or interaction with a new design feature was coded as 'positive', 'neutral', or 'negative'. The same broad coding was applied to the open-ended usability survey comments and debrief notes. Then, we integrated the findings using all of these qualitative data for common occurrences across the 12 participants .Qualitative data included the MoraeParticipant characteristics, as well as quantitative  and two were nurse practitioners (NPs). Participants' experience in the VHA ranged from 1 to 25 years . The mean experience is noteworthy because the VA's EHR was implemented at the study site in 1998, about 13 years prior to the completion of this study. This indicates that average participant experience spanned the existence of the VA's EHR. Six participants had 1-10 years of experience with the VA's EHR and the other six had 11-25 years of experience. Participants with 11-25 years of experience consistency rated the usability of the current EHR higher than the participants with 1-10 years of experience on all items from the CSUQ survey. This same trend was not observed for the redesigned prototype. We suspect those with greater experience using the VA's system corresponded to higher usability due to their familiarity with the current system.Simple scenarios for designs A and B. Therefore, results reported in Tables Simple scenario and not the Complex scenario.Data were not collected for two participants for the current design (A) because it took longer than expected for them to complete the scenarios with the redesigned prototype (B); we were only IRB-approved to run 45-minute sessions. Both of these participants received design B to begin the experiment based on the counter-balancing of presentation order across participants. Furthermore, we did not include the CSUQ data from the first participant because s/he misinterpreted the survey instructions to answer the questions based upon the entire EHR rather than, specifically, upon the CRC screening clinical reminder. Therefore, the results reported in Table For the Workflow Integration Survey, design B was rated significantly higher (better) than design A for each of the four survey subscales Table . AnalysiTable There were fewer comments regarding the patient education feature in the redesigned prototype that included the one-page synopsis of CRC screening . The onlThese findings generally supported our redesigned prototype in multiple dimensions, but were mixed. Design enhancements to the VHA's existing CRC screening clinical reminder positively impacted PCPs' workflow integration and usability ratings in terms of finding the patient's relevant data, as well as in providing helpful patient education materials. However, the redesigned prototype showed no difference in terms of perceived workload, as measured by the NASA TLX. Also, the improvements demonstrated by the usability survey were related only to the three questions specific to CRC screening design enhancements. Of these three, only the question about patient education reflected a functionality that was available in the redesigned prototype, but not in the existing reminder. Both the redesigned and current CRC clinical screening reminder may inform the patient's cancer screening history and status. The specificity of these appended items to the CSUQ regarding CRC screening was likely main reason why the results differed from the validated CSUQ survey, since the three questions added were specifically targeted to assess the issues regarding CRC screening that our design changes were meant to address.The more general usability statements on the CSUQ about simplicity, efficiency, learnability, error recovery, overall satisfaction, and other dimensions of usability, did not produce significantly improved ratings for the resigned prototype over the current design. Adding the new design features in the redesigned prototype corresponded to additional mouse clicks and mouse movement, as recorded by the Morae software, and likely contributed to the lack of significant improvement in general usability. In terms of usability, potential advantages of the new timeline visual and patient education resource may have been off-set by the additional steps needed to access these features. These findings suggest that, while participants were supportive of the design changes to include the timeline visual and patient education resource, there is still room to improve the overall usability of the CRC screening clinical reminder and how it is integrated within the EHR."It would be better if you had it [the timeline] over here on the window itself [next to the reminder dialog] because otherwise you have to click back and forth and it's hard to remember. Especially which somebody with a complicated history and you got to go from one thing to another, that doesn't work. So why not put it over here, you know, same window."Specific participant verbalizations provide additional insight why general usability ratings for simplicity, efficiency, and other usability constructs, were not significantly improved with the redesigned prototype. For example, two participants directly commented that the timeline visual for CRC history should be integrated at the same level as the CRC reminder . One participant stated: The Workflow Integration Survey received significantly higher (better) ratings for the redesigned prototype than the current design for each of the four survey subscales . Interestingly, the NASA TLX did not show significant differences for workload. This suggests that perhaps the Workflow Integration Survey may have been more sensitive to detect differences in workload than the NASA TLX. Alternatively, 'workload' may have represented a different construct in the two instruments. The items from the Workflow Integration Survey that comprise the "workload" subscale are items 4, 8, and 12  increase the quality of the underlying data and (2) transparently provide information regarding the data's source. To increase trust in the data, we envision providing additional options in the visual timeline to increase the transparency of the data. In this way, providers can investigate, for example, the specific details of a patient's colonoscopy that was performed outside of the VA. These additional details may include the contact information for the facility and physician who performed the test. The additional details may also improve the overall quality of the data.In the case of the patient education resource, PCPs generally found the resource to be helpful, but five PCPs expressed a preference for the patient education form to be available in their clinics as a pre-printed form rather than having to access and print it from the CDS as needed. One obvious issue with our redesigned prototype was the inability of all but one PCP to recognize that the specific results of abnormal tests  were available from the timeline by clicking on a green box , which drives the VA's EHR. Future implementation of our proposed design changes will need to be coordinated with the future redesign of VHA's information system, which is underway.We should also note an important challenge of the approaches we used. By relying on experienced users, we achieved certain efficiencies and likely greater input and feedback, than we would have from users with little experience. However, recruiting providers for the project was quite challenging, due to multiple competing demands. Also, VA providers are prohibited from receiving monetary compensation as an incentive for participation in VA research. Methods are needed to allow rapid usability testing and iteration, particularly from users in the field. This is an important area for both innovation and further research and development.in situ. This type of qualitative field observation, followed by scenario-driven, comparative usability testing of experimental prototypes in a simulated setting, is complementary. Clinical software development and its uptake and integration into practice would benefit from such an approach if more widely followed. For a new CDS tool that is not yet implemented, rapid user-centered iterative design with laboratory-based simulation is critical. Adopting human factors input early and iteratively into clinical information system development can improve user performance and usability, as well as reduce cost by addressing important HCI and clinical workflow considerations pre-implementation, where the cost to redesign is much less than cost post-implementation [This study demonstrates the clear value of HCI evaluation in informing the redesign of information tools such as CDS. The redesign modifications tested here were informed by previous field study,9 to helentation . Use of The authors declare that they have no competing interests.JS led the conception and design of the study, facilitated all of the experimental sessions, led the analysis and interpretations of the data, and had principal responsibility for drafting the manuscript. DH contributed to the conception and design of the study, including development of the patient scenarios; he also contributed to the interpretation of the results, as well as critically edited the manuscript. LM contributed to the conception and design of the study, the interpretation of the results, as well as critically edited the manuscript. MF contributed to the conception and design of the study, the interpretation of the results, as well as critically edited the manuscript; she also co-developed the Workflow Integration Survey for this study. CK developed the prototypes for this study, helped interpret findings, and critically edited the manuscript. NA contributed to the conception and design of the study, the interpretation of the results, as well as critically edited the manuscript; she also co-developed the Workflow Integration Survey for this study. BD is the study's principal investigator; he was responsible for the overall design and supervision of the study. He contributed to the interpretation of the results and critically edited the manuscript. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1472-6947/11/74/prepubWorkflow Integration Survey. A list of the 12 items that comprise the Workload Integration Survey. Participants rate their responses to the items using a Likert-type scale.Click here for fileUsability Survey Questions and Groupings. A list of the usability survey questions and how they were grouped.Click here for file


The procedure is based on interactive machine learning and only requires a few labeled synapses for training. The statistical learning is performed on geometrical features of 3D neighborhoods of each voxel and can fully exploit the high z-resolution of the data. On a quantitative validation dataset of 111 synapses in 409 images of 1948×1342 pixels with manual annotations by three independent experts the error rate of the algorithm was found to be comparable to that of the experts . Our software offers a convenient interface for labeling the training data and the possibility to visualize and proofread the results in 3D. The source code, the test dataset and the ground truth annotation are freely available on the website  The chemical synapse is the predominant means by which information is transferred and stored in the central nervous system. Analysis of synapse size, shape and distribution contributes essential information to the understanding of neural circuitry, its function and its plasticity.Despite the advances in light microscopy, detailed structural analysis of synapses is still only possible with electron microscopy. With serial section transmission electron microscopy (ssTEM), synaptic density can be estimated by manually counting synapses within a large volume, or by stereological extrapolation from paired 2D images The recent introduction of focused ion beam/scanning electron microscopy (FIB/SEM)To detect synapses in EM images, human experts follow a set of morphological criteria: the presence of the pre- or post-synaptic densities, a visible synaptic cleft and a nearby cluster of at least two vesicles. If an automated protocol was to be based on these criteria directly, it would require a segmentation of the entire volume to find the membrane apposition sites and a full segmentation of ultra-cellular structures to detect vesicles. Although the problem of automated segmentation of neural tissue has advanced significantly in recent years, it is not yet fully solved Our contribution proposes an automated approach of this type and shows, through quantitative evaluation on a set of 111 synapses, that state-of-the-art machine learning methods can now achieve detection rates comparable to those of humans for asymmetric synapses in FIB/SEM data. Even though our approach does not explicitly implement the morphological criteria listed above, it finds enough evidence in the geometric features, extracted from a local neighborhood of each voxel, to mimic the decisions of the human expert.In the field of neuroscience, recent influential work along these lines has focused on tracing and segmentation of neurons  or automwww.ilastik.org) is a freely available interactive learning and segmentation toolkit, which relies on a rich family of generic (nonlinear) image features and a robust nonlinear classifier On the conceptual side, we rely on machine learning methods that are currently transforming all of image analysis. On the software side, we build on ilastik The quantitative validation of the automated synapse detection procedure, as well as the evaluation of the human experts' error rate, was carried out on a test dataset of 111 asymmetric, presumed glutamatergic, synapses Click here for additional data file.


All visual signals the cortex receives are influenced by the perigeniculate sector (PGN) of the thalamic reticular nucleus, which receives input from relay cells in the lateral geniculate and provides feedback inhibition in return. Relay cells have been studied in quantitative depth; they behave in a roughly linear fashion and have receptive fields with a stereotyped center-surround structure. We know far less about reticular neurons. Qualitative studies indicate they simply pool ascending input to generate non-selective gain control. Yet the perigeniculate is complicated; local cells are densely interconnected and fire lengthy bursts. Thus, we employed quantitative methods to explore the perigeniculate using relay cells as controls. By adapting methods of spike-triggered averaging and covariance analysis for bursts, we identified both first and second order features that build reticular receptive fields. The shapes of these spatiotemporal subunits varied widely; no stereotyped pattern emerged. Companion experiments showed that the shape of the first but not second order features could be explained by the overlap of On and Off inputs to a given cell. Moreover, we assessed the predictive power of the receptive field and how much information each component subunit conveyed. Linear-non-linear (LN) models including multiple subunits performed better than those made with just one; further each subunit encoded different visual information. Model performance for reticular cells was always lesser than for relay cells, however, indicating that reticular cells process inputs non-linearly. All told, our results suggest that the perigeniculate encodes diverse visual features to selectively modulate activity transmitted downstream. One often thinks of cortex as the earliest site where thalamic relay cells drive postsynaptic targets, but this is not the case. En route to cortex, thalamic afferents contact the perigeniculate sector (PGN) of the thalamic reticular nucleus  were first anesthetized with a dose of propofol and sufentanil  that was reduced to  for maintenance. The depth of anesthesia was monitored by the EKG, EEG and the absence of an autonomic response; body temperature was held near 37°C. After surgical procedures were complete, the animal was paralyzed with vecuronium bromide  and ventilated artificially; expired COWe first used a tungsten electrode to locate the LGN to guide placement of a multielectrode array  using a stimulus generator . We used two types of stimuli. One was spatiotemporal Gaussian white noise, for which the stimulus intensity of each square in the stimulus grid was sampled from a Gaussian distribution (33% RMS contrast). The stimulus sequence ranged between 4,096 and 16,384 frames, each frame was displayed for 20 ms and repeated 4–16 times. The second stimulus, sparse noise  using qualitative impressions of visual response properties such as binocularity and/or overlapping On and Off responses e.g., Uhlrich et al.  layers of the LGN. The A layers were typically encountered at a depth of less than 1 mm from a putative reticular cell, though there was some variation due to the angle of the electrode. Second, at the end of most experiments, we perfused the animal and made histological confirmation of the recording site by tracing the electrode tracks and/or electrolytic lesions. In all cases, the anatomy corroborated physiological assessment. Third, we quantified the differences between bursts in the LGN and PGN to confirm that they provide a signature of recording site, as below.We used the standard criteria to detect bursts in the LGN . This analysis revealed a unique U-shaped cluster mean that corresponded to bursts unique to neurons in the PGN, Figure Thus, to establish a quantitative measure to distinguish relay from reticular cells, we focused on differences in the distribution of spike times within bursts. We formed representations of bursts in PGN, Figure In order to recover the stimulus features  to which neurons in the PGN are sensitive, we used standard methods to compute the spike-triggered stimulus ensemble for intervals of 0–120 ms stimulus preceding each spike, see Schwartz et al.  for reviON by taking a spatiotemporal average of only the bright stimulus preceding each spike and an STAOFF using the analogous procedure for the dark stimuli. To determine the extent to which On and Off responses overlapped, we calculated the normalized dot product of STAON and STAOFF.We formed separate maps for responses to On vs. Off stimuli. Specifically, we formed an STAin vitro and in vivo has shown that this firing pattern is generated by the specialized intrinsic membrane currents that can be triggered by brief depolarizations  assembled from later spikes in the burst matched the map made from the cardinal spikes, simply shifted back in time, as illustrated in a schematic, Figure t = −20.8, −41.6, and −62.4 ms etc.). This time series shows that the receptive field emerged in the second frame, peaked in third and ultimately reversed polarity in the fifth and sixth frames and had a strong On subregion (red) and weaker Off (blue) components.We first illustrate STAs made by reverse correlating spikes in Group I to six frames of the stimulus, blue bordered images, Figure We then formed similar STAs for spikes from Groups II Figure , top andTo quantify the similarity of the STAs made from the different Groups  at different temporal displacements , we calculated the dot products between the STAs made for Group I with those made for Groups I, II, and III. The resulting correlations for the six combinations are plotted in Figure Thus, to avoid temporal blurring that correlations between spikes would cause, we excluded later spikes in the bursts from the data used to generate receptive fields. An example STA made from a mixture of tonic spikes and the first  spike of each burst is shown in Figure The results described in Figure We used a standard linear-non-linear (LN) model  derived from spike-triggered-analysis. The output of the linear stage of the model was the dot product between the linear filters for a given cell and the stimulus. This signal, often termed the filter output , we generated a peri-stimulus time histogram (PSTH) by averaging the spike trains evoked by multiple repeats of the same noise sequence. The response non-linearity was estimated by taking the averaged spike count (from the PSTH) for each instance in time in which the visual stimulus evoked the same instantaneous filter output, following procedures developed by others  models to explore the combined influence of the STA paired with a significant STC subunit on the neural response . For these 2D models, it was necessary to estimate a joint response non-linearity that mapped the outputs of the two subunits to the strength of neural response.Nσ) available for cross-validation as well as the number of free parameters (n) in the model. The equation for this modified version of traditional explained variance is written in Equation (1):dij: neuron's response measured in response to stimulus of length N which is repeated R times; mi: model response; Nσ: N(R−1); n: number of free parameters in the model.The ability of the model to predict the neural response was assessed in two separate ways. First, we estimated the percentage of stimulus-dependent variance in neural response that the model predicted. This metric, the explained variance, is biased by limitations in the length the stimulus sequence and number of repeated trials .The second, and more straightforward, method estimates the explained variance without accounting for the biases due to finite data or number of free parameters in the model  was estimated as the Kullback–Leibler divergence between the distribution of filter outputs during the entire stimulus period (prior stimulus distribution or unconditioned filter output) and the distribution of filter outputs that preceded spikes   of the stimulus on filters fK, P(sK|spike) is the spike conditional distribution and P(sK) is the prior stimulus distribution. Similarly the amount of information accounted for by the STA subunit was given by Equation (4) and that by the subspace spanning the joint STA + STCK subunits described by Equation (5).sSTA and sKSTCare the outputs of the STA and STCK subunits, respectively.P(sK), and the spike conditional distributions, P(sK| spike), differ. The precision with which the information can be estimated for each of these subunits can change as a function of the bin size used to sample the distributions. Hence we estimated the information for a range of bin sizes, calculated as a multiple of the standard deviation (σ) of the prior stimulus distribution (bin size ranged from 0.01 σ to 1σ). The smallest bin size that can be used, however, is limited by the amount of data that are available , we also estimated the information carried by the most non-significant subunit, Inonsig).Note that each significant subunit provides information about the neural response only if the prior, (STA) and paired information Inonsig) recovered from responses to Gaussian noise could be reconstructed by the weighted combination of two STAs obtained from responses to individually flashed dark and bright stimuli, STAOFF and STAON, respectively. We simulated the spatial structure (at peak amplitude) of the subunits of the receptive fields obtained with Gaussian noise by using a weighted combination of STAON and STAOFF.We asked if the spatial structure of the subunits  to minimize the sum of squared difference between the real and the modeled subunits (note we chose to use separate coefficients for the bright and dark STAs since using the same coefficient for both maps yielded poorer predictions). The pixel-wise difference between the real subunit and optimized model subunit formed the residual. The similarity between modeled and real subunits was measured using the signal to noise ratio, calculated as the logarithm of the ratio of power in the real subunit over the residual power.The modeled STA subunit was defined as: model STA = a × STAThe results were obtained from 23 adult female cats, ranging from 2.5–4.5 kg. We recorded from 121 cells in the PGN, 60 of which we held long enough for thorough study. Of these we were certain that 24 were binocular; 23 of the remaining cells were driven by the contralateral eye and 13 by the ipsilateral eye. In addition, we used 10 relay cells recorded from the same animals for control studies.Earlier studies described reticular receptive fields, qualitatively, as having diffuse shapes and having varying degrees of sensitivity to stimuli of opposite contrast  in the covariance matrix are found using principal component analysis. Transforming the eigenvectors back to the image space reveals the second order spatiotemporal features that a given cell encodes . Further, the presence or absence of significant STC subunits did not correlate with the changes in depth of anesthesia, as judged from the power spectrum of the EEG. Last, virtually all significant filters we extracted were excitatory rather than suppressive.By recovering the first and second order subunits of the receptive field, we were able to ask if similar patterns emerged across cells or if there were great heterogeneity in the population. We measured diversity in the receptive field by evaluating the relative magnitude of the On and Off subregions in the STA and STC subunits recovered from single cells. Our metric used the ratio between the absolute maxima of the On and Off subregions, setting the numerator to the highest peak and the denominator to the weaker one. A small value, near 1, indicated the presence of spatially separate On and Off peaks. We refer to subunits whose values for the “peak dominance” ratio was >2 as single peaked and those with values ≤2 as double peaked for ease of description. 1 in which the On and Off subregions were of roughly similar strength for both subunits; in this case the peak dominance ratios were near one, Figure 1 were different. Figure K's, are illustrated in Figure There was a wide range in the relative strength of On and Off subregions in both STA and STC subunits, Figure K recovered for the population, Figure To assess the extent of spatial diversity of reticular receptive fields, we plotted a histogram of values for the peak dominance ratio for each STA and STCQuantitative maps of the spatiotemporal receptive fields in the retina and the LGN allow one to build simple computational models that predict neural responses reasonably well  is shown as a contour plot at left, next to its associated non-linearity. The non-linearities are curves plotted as firing rate against the filter output (see “Methods” for greater detail). One can think of the non-linearity as a lookup table that maps a given value of the filter output to a (mean) firing rate.The components of the model are illustrated for a cell from which we recovered an STA and STCThe non-linearities for the STA and STC subunits have different shapes. For the STA, stimuli with the same polarity as the filter produced positive outputs and led to elevated spike rates; further, firing rate grew with the magnitude of the filter output. By contrast, filter outputs for stimuli that had the opposite polarity produced negative values that were not associated with notable changes in firing rate. Thus the shape of the non-linearity for the STA subunit was one-sided, or (approximately) half-wave rectified. The shape of the non-linearity associated with the STC subunit was qualitatively different; it was U-shaped. This is because both positive and negative values of the filter output were associated with elevated firing rates. In other words, firing rate grew larger as the absolute value of the filter output increased. For similar examples in the literature, see Rust et al. , Touryan1 are plotted alongside a square grid whose entries represent firing rates evoked by the coincident activation of both subunits as they are variously engaged by the stimulus.These non-linearities were generated separately for each filter. However, interactions between subunits have the potential to influence neural responses. Such mutual influence can be estimated by constructing a joint non-linearity, Figure It was necessary to estimate the joint non-linearity separately for each model because interactions between the STA and STC subunits were not merely additive or multiplicative. In other words, if the joint non-linearity simply resulted from the point-wise products of the two individual non-linearities, then the stronger entries in the grid would have formed rectangular contours. However, these stronger entries assumed curved patterns, see Rust et al. .Using these components, we were able to build two types of LN models to explore the relative contributions of each different subunit of the receptive field. These were 1D models that included only the STA, Figure How well did these models predict neural responses? Because the models were fitted using only half of the data recorded for each cell, we were able to test the performance using the remaining data, cross-validation  and the distribution of filter outputs just before each spike , using methods developed previously , as well as the joint information available from the pairs of two subunits, the STA and a (significant) STC subunit, IK) obtained from the paired subunits IK), Figure The initial step in the analysis was to address the problem of finite sampling of a continuous signal, which we did by resampling the filter output at different bin sizes, as described by Fairhall et al.  (see “MeK) and IK) + IK)(STC) from IK) for each reticular cell, Figure R2 = 0.91), indicating that both measures are equally good at assessing the performance of the LN models we made.Last, the information theoretic analysis served a second function, as a control for our methods of estimating the performance of the 1D and 2D models. Specifically, we compared values of explained variance obtained earlier with those for mutual information. A ratio of the explained variance for the 2D vs. 1D models is plotted against the ratio of the ISo far, we have discussed how we recovered subunits of reticular receptive fields, how we assessed their predictive power and estimated the amounts of information they encode. However, we have not addressed the question of how these subunits might be formed. In other words, we wondered how the subunits recovered from responses to Gaussian noise might reflect the On and Off inputs that relay cells supply. Recall that our previous analyses revealed a wide range in the relative strengths of On and Off contributions to filters recovered using STA and STC analysis, Figure ON) and (STAOFF), respectively . With the results of the sparse noise mapping, it was a simple matter to determine if the subunits (STA and STCK) recovered using Gaussian noise could be simulated by the weighted sum or product of the maps acquired with sparse noise, STAON and STAOFF.The simplest explanation for the shapes and signs of the various filters is that these were built from a linear combination of On and Off subregions whose peaks were spatially displaced to varying degrees. For many of the cells we mapped with Gaussian noise, we also collected companion datasets with sparse noise, individually flashed bright and dark pixels. The sparse noise allowed us to recover separate STAs for bright and dark responses; we refer to these specifically as . Specifically, the model was STA = a × STAON + b × STAOFF, where the coefficients, a and b, were optimized to reduce the mean square error between the model STA and the actual STA made from responses to Gaussian noise. The main features of the modeled STAs, prominent On and Off peaks and their relative positions, were similar to the actual STAs acquired using Gaussian noise, suggesting that the maps recovered from Gaussian noise approximate the weighted sum of On and Off inputs. This rough match between the modeled and real STAs was seen for all cells we were able to test, regardless of the relative strength or spatial displacement between On and Off subregions. The sparse noise analysis also allowed us to assess the degree of spatial and temporal overlap between STAON and STAOFF, which we did by calculating the normalized dot product of STAON and STAOFF. For our sample of eight cells, the spatial overlap was 64.26 ± 23.42%, mean ± standard deviation. The peaks of both STAON and STAOFF occurred during the same time interval.The results of this analysis are depicted for two different cells in Figures ON and STAOFF acquired using sparse noise(c × STAON × STAOFF). Non-zero values of the resulting product indicate the regions where On and Off subregions overlap. These models failed to reproduce the spatial structure of the STCs. At best, the filters produced by this simple operation captured one peak of the actual STC; even then, the modeled peak was often displaced from the actual. This result suggests that reticular cells do not pool their inputs in a simple fashion.The subunits recovered using STC analysis of responses to Gaussian noise reflect sensitivity to On and Off inputs. To model these subunits, we used a multiplicative model. Specifically, we modeled each STC as the weighted product of the STAThe next step in this analysis was to quantify the degree to which the real and modeled filters matched, which we did by calculating the signal-to-noise ratio. This metric is the logarithm of the ratio of power in the real subunit over the power of the error, with the latter defined as pixel-wise difference between the real and modeled subunit. As expected from visual inspection of the maps displayed in Figures K) of the receptive field from responses to dense noise (Gaussian checkerboards). These STAs could be explained by the weighted sum of maps obtained using individual dark and bright squares but STC subunits could not be approximated by simple combinations of On and Off maps, suggesting that complex mechanisms underlie selectivity for higher order features. Further, we built LN models to assess the predictive power of the receptive fields. For cells in the PGN, the performance of these models almost always fell below that achieved for relay cells, although including more than one filter improved predictions. Moreover, information theoretic analyses revealed that multiple subunits interact to encode information synergistically.The first feedback loop in the early visual pathway is formed by connections between the LGN and PGN. The PGN has long been thought to provide a non-selective form of gain control to relay cells  to assess the STC; independent component analysis [for which filters need not be orthogonal,  characteristic of the cortical cells that project to thalamus  to the models always bettered performance but rarely brought the total explained variance near levels, achieved for relay cells.Simple LN models successfully predict responses recorded from retina  to the amount estimated from either subunit alone. The pairs often encoded significantly more information than predicted from the sum of that encoded by single filters, suggesting that subunits interact synergistically. All told, understanding the function of reticular neurons seems no less challenging than exploring the visual cortex—the ultimate destination for relay cells that innervate and receive feedback from the PGN.Last, we asked if the different subunits conveyed information independently. We compared the amount of information encoded by pairs of filters (STA and STCVishal Vaingankar and Cristina Soto-Sanchez performed the experiments with help from Judith A. Hirsch and Xin Wang. Vishal Vaingankar, Cristina Soto-Sanchez, and Friedrich T. Sommer contributed to most analyses with additional assistance from Xin Wang. Vishal Vaingankar, and Judith A. Hirsch wrote the manuscript. Vishal Vaingankar prepared the figures.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Toxoplasma gondii, has cumulative effects on the behaviour of hosts, including humans. The most impressive effect of toxoplasmosis is the “fatal attraction phenomenon,” the conversion of innate fear of cat odour into attraction to cat odour in infected rodents. While most behavioural effects of toxoplasmosis were confirmed also in humans, neither the fatal attraction phenomenon nor any toxoplasmosis-associated changes in olfactory functions have been searched for in them.Latent toxoplasmosis, a lifelong infection with the protozoan Toxoplasma-infected and 134 noninfected students rated the odour of urine samples from cat, horse, tiger, brown hyena and dog for intensity and pleasantness. The raters were blind to their infection status and identity of the samples. No signs of changed sensitivity of olfaction were observed. However, we found a strong, gender dependent effect of toxoplasmosis on the pleasantness attributed to cat urine odour (p = 0.0025). Infected men rated this odour as more pleasant than did the noninfected men, while infected women rated the same odour as less pleasant than did noninfected women. Toxoplasmosis did not affect how subjects rated the pleasantness of any other animal species' urine odour; however, a non-significant trend in the same directions was observed for hyena urine.Thirty-four Toxoplasma-infected subjects in this population rather than by schizophrenia itself. The trend observed with the hyena urine sample suggests that this carnivore, and other representatives of the Feliformia suborder, should be studied for their possible role as definitive hosts in the life cycle of Toxoplasma.The absence of the effects of toxoplasmosis on the odour pleasantness score attributed to large cats would suggest that the amino acid felinine could be responsible for the fatal attraction phenomenon. Our results also raise the possibility that the odour-specific threshold deficits observed in schizophrenia patients could be caused by increased prevalence of  Toxoplasma gondii, has cumulative effects on the behaviour of hosts, including humans. The most impressive effect of toxoplasmosis is the so-called “fatal attraction phenomenon,” the conversion of innate fear of odour of the definitive host, the cat, into attraction to cat odour in rodents infected with Toxoplasma. While most behavioural effects of Toxoplasma infection were confirmed also in humans, neither the fatal attraction phenomenon nor any toxoplasmosis-induced changes in olfactory functions have been searched for in them. Our study performed on 34 Toxoplasma-infected and 134 noninfected students showed that the infected men rated odour of cat urine as more pleasant than did the noninfected men, while infected women rated the same odour as less pleasant than did noninfected women. No significant effect of toxoplasmosis on the urine odour pleasantness was found for horse, tiger, brown hyena and dog. The possible absence of the effects of toxoplasmosis on the urine odour pleasantness score attributed to tiger would suggest that the amino acid felinine, which is absent in urine of large cats, could be responsible for the fatal attraction phenomenon.Latent toxoplasmosis, a lifelong infection with the protozoan  Toxoplasma gondii is well known for changing behaviour of intermediate hosts, for review see Sarcocystis and Frenkelia) The protozoan parasite Toxoplasma infection on the behaviour of rodent hosts is the so-called “fatal attraction phenomenon” Toxoplasma-free rodents that have an innate fear of cat odour and avoid the places containing traces of cat urine, the Toxoplasma infected animals, both mice and rats, lose this fear and even gain attraction to cat odour instead. In the arena experiments, these animals spend more time in places containing a cat urine sample or piece of a worn cat collar. This effect is highly specific, the fatal attraction concerns only the odour of cat or bobcat and not of a rabbit, mink or dog Toxoplasma infection disappears when the infected animals are treated with drugs that specifically inhibit proliferation of ToxoplasmaToxoplasma infection-induced behavioural changes, the fatal attraction phenomenon is rather stable and remains detectable for a longer period of time than is the average life expectancy of rodents in their natural environment The most impressive effect of Toxoplasma infects any warm-blooded animal, including about one third of the human population of developed countries Toxoplasma. Acute toxoplasmosis is a relatively mild and short-term disease in most of the immunocompetent subjects. Acute toxoplasmosis goes spontaneously into the latent phase, characterized by the lifelong presence of dormant stages of the parasite, the bradyzoites, mainly in the neural and muscular tissues, usually also by the lifelong presence of low but protective concentrations of specific antibodies in the serum of infected subjects It was observed that infected humans have prolonged reaction times, increased risk of traffic accidents – analogy of the “predation accidents” that are studied in predation experiments in animals Toxoplasma than the general population Toxoplasma-infected and Toxoplasma-free university students were asked to rate the odour of urine samples from five animals, the cat, horse, tiger, brown hyena, and dog, for the intensity and pleasantness. The raters were blind to the Toxoplasma infection status and identity of the samples rated.To the authors knowledge, no results concerning the effect of latent toxoplasmosis on human olfactory function have been published yet. However, it was shown recently that schizophrenic patients who are reportedly more often infected with The project and specific subprojects were approved by the IRB of the Faculty of Science, Charles University. All participants signed a general informed consent form to participate in a toxoplasmosis study before the blood test and all subsequent evaluations. The samples of animal urine for testing olfactory ability and preferences of students were obtained from spontaneously urinating animals without any contact with the animals, therefore, according to international and national guidelines and commendations of the IACUC of Faculty of Science, Charles University, no formal approval of this part of experimental protocol by an IACUC or an ethic committee was necessary.The students of the Faculty of Science of Charles University were invited, during regular baccalaureate courses of biology, to participate voluntarily in an unspecified study aimed to search for phenotypic, mostly behavioural effects of latent toxoplasmosis. More than 50% of attendants of these courses participated in the first testing session and provided a blood sample for serological testing for toxoplasmosis and also an e-mail address and mobile phone number. Later, they were invited by e-mail or text message for half-day testing sessions, including the test of olfactory preferences. Some of these sessions  were remunerated, while others were not (e.g. reaction time and intelligence testing sessions). The students received 200 CZK (about $ 10) for their participation in the olfactory functions experiment.To gather all target urine samples, we solicited a veterinary surgery (cats and dogs), the Prague zoo  and the second author's friends (horses and dogs). The collaborators were instructed to collect clear urine preferably directly from animals while urinating, if possible. Immediately after collection, the samples were aliquoted into 1 ml plastic tubes (to facilitate handling) and subsequently deep frozen (−72°C) until used in the testing sessions.The available urine samples from various animal species  were used as specified below and carefully randomized. Thus, in each session, a specific counterbalanced set of 10 vials from different individuals  were evaluated. Based on our previous experience and on the fact that the effect of toxoplasmosis on olfactory preferences qualitatively differs depending on the intensity of cat odour The experiment consisted of 17 rating sessions. Each session started in the morning (after the samples had been thawed for 1.5 h and prepared for testing) and continued to approximately 2 pm. It took place in a small but convenient experimental room at a stable room temperature and humidity (average temperature ranged from 20.6 to 25°C and average humidity from 42.75 to 56.6%).A specific amount of each sample was applied on a cotton pad  that was subsequently placed in a 500-ml opaque laboratory jar with a glass lid and labeled with an alphanumeric code that changed with each session. The samples used for a session were divided into 2 identical sets, with the less and more intense odour samples evenly distributed. After sniffing half of the urine samples, the raters were recommended to have at least a 5-minute break to avoid possible odour habituation. During the break, they were encouraged to drink water and smell coffee beans for olfactory recovery.The stimuli were assessed on 7-point scales for the pleasantness and intensity. The two ends of each scale were anchored by the verbal descriptors . The rating scores were written down immediately after sniffing the respective stimulus and the time spent by sniffing was not restricted. If the raters found any of the samples too weak, they were instructed to select the answer “I cannot smell the sample” instead of using the scales.Toxoplasma antibodies with IgG ELISA  in the National Reference Laboratory for Toxoplasmosis of the Czech Republic. The subjects with very high concentrations of antibodies were tested for acute toxoplasmosis with IgM ELISA . Only the subjects with positive result of IgG ELISA (concentration of specific antibodies>10 IU/ml) and negative in IgM ELISA (positivity index less than 0.9) were considered to be latent toxoplasmosis positive and were included in the present study. One female student with acute phase toxoplasmosis was informed about the serological test result and was not used for any part of our experiment.The sera of students were tested for the presence of anamnestic concentrations of anti-All statistical testing, including analyses for the tests assumptions, namely those of the normality of data distribution, normality of residuals and equality of error variances, was performed with Statistica 9.0. The differences in the attributed intensity and pleasantness scores were estimated with the General Linear Models method (GLM), using the intensity or Z-score of the odour pleasantness scores attributed to a particular sample as dependent variables and the rater's sex and toxoplasmosis status as independent factors. To eliminate any potential effect of a strong negative correlation between the odour intensity and pleasantness, the odour intensity of a particular sample was included into the models as a covariate. However, when not included, the results were qualitatively the same. Although there was only a little inter-individual variability in the odour intensity rating scores, the study subjects differed in the odour pleasantness scores – some only used the lower part of the 7-point scale while others used the whole range of the scale. To control for individual rating habits, we used the Z-scores instead of raw ratings (numbers 1–7) for the odour pleasantness assessment. The Z-score was obtained by dividing the difference between the subject's rating score for a particular sample and mean rating score for all samples by the standard deviation of all his/her rating scores.Toxoplasma-infected) and 66 men (15 Toxoplasma-infected). The difference in the frequency of toxoplasmosis between women and men was not significant . There were no sex differences in the mean odour pleasantness and intensity scores for all 10 samples . The urine odour pleasantness scores varied with the animal species, see Toxoplasma-free men, while Toxoplasma-infected women rated it as less pleasant than did Toxoplasma-free women; a non-significant trend in the same directions was observed also for hyena . This effect for the cat odour was also significant when a simpler GLM model without any covariate was analysed; however, the p value for the interaction increased from 0.0025 to 0.0043. The observed toxoplasmosis-sex interaction remained significant even after Bonferroni correction for multiple tests. Separate analyses showed that the effect of toxoplasmosis was significant both for women  and men , see The final set contained data on 102 women (19 2 = 0.027); however, this effect was not significant after Bonferroni correction for multiple tests.There was no effect of toxoplasmosis, sex or toxoplasmosis-sex interaction on the odour intensity scores attributed to any of ten samples, except the more concentrated dog urine , increased suspiciousness (factor L) and decreased affectothymia (factor A) while the opposite is true for these factors in Toxoplasma-infected women. In behavioural tests and observational studies, infected men expressed less and infected women more selfcontrol than Toxoplasma-free subjects The opposite reactions of infected male and female rodents to cat urine odour have not been reported yet. The phenomenon of fatal attraction to cat urine odour in infected mice or rats has been observed in both males and females. However, the experiments were conducted exclusively in either male rats Toxoplasma-free subjects) could express opposite reaction on danger stimuli, e.g. a smell of predator, namely, the men could have increased and women decreased tendency to risk a contact with a predator.Lindová et. al. (2006) suggested so called stress hypothesis as a possible explanation of the opposite effects of latent toxoplasmosis on men and women personality and behaviour. This hypothesis is based on the assumption that latent toxoplasmosis, via deteriorated physical functioning, causes long-term subliminal stress. It is known that men and women cope differently with stress. In contrast to men, who seem to use more individualistic and antisocial  forms of coping with stress, women are more likely to seek and provide social support, join with others, verbalize towards others or the self. A recent biological (evolutionary) theory similarly distinguishes between the male “fight-or-flight” response and the female “tend-and-befriend” reaction to stress following from the strong need of women to protect children and maintain social relationships. For detailed discussion of the theoretical background of the stress hypothesis, see Toxoplasma. Moreover, the Toxoplasma bradyzoites had higher probability to enter the stomach of a tiger than that of a domestic cat in our recent evolutionary past. Presently, the urine component responsible for the fatal attraction phenomenon is not known. Based on our results, it can be suggested that felinine  might be implicated in the olfactory preference changes in Toxoplasma-infected intermediate hosts. This characteristic volatile component of the feline urine is involved in scent marking and, at natural temperature, is slowly released from its transporter proteins in urine for more than 30 days. Felinine is present only in the urine of small cats (Felinae subfamily) and not in that of large cats, tigers, lions, etc. (Pantherinae subfamily) We can only speculate on the reasons for the absence of any effects of toxoplasmosis on the scores attributed to the second feline species, the tiger, in our urine sample set. Both cat and tiger (in contrast with other species in our study) are definitive hosts of Toxoplasma-infected students among the study population. This number (fifteen men and nineteen women) was similar or higher than that of infected mice used in the previous animal studies; however, in various human behavioural studies, a higher number of infected subjects have always been used. The main reason was the substantial decrease in the prevalence of latent toxoplasmosis from 24% to about 15% among the students of the Faculty of Science of Charles University in the last decade. We tried to “enrich” our experimental set with Toxoplasma-infected individuals (our study is designed as a case control study rather than a cohort study); however, we had not enough infected students willing to participate voluntarily in our olfactory experiment in our file. The moderate number of infected students in our experiment could not lead to false positive results of statistical tests, e.g. to a detection of statistically significant but in fact non-existent effect of toxoplasmosis on the score attributed to the cat urine odour pleasantness. However, it could lead to false negative results, for example a missed effect of toxoplasmosis on urine odour pleasantness scores attributed to other animal species. A possible candidate for such a missed effect was the brown hyena which expressed a similar, but nonsignificant (p = 0.074) trend as the domestic cat.We are aware of some particular limitations of the present study. There was a relatively low number of The general aim of the study was explained in the informed consent. The study subjects thus knew that the focus was on possible influence of latent toxoplasmosis on olfactory sensitivity and preferences. Although the fatal attraction phenomenon was not referred to, some of the students may have been aware of it from e.g. some Czech textbooks, Wikipedia, mass media, etc.. However, in the rating form, the subjects were asked not only to score the intensity and pleasantness of particular odours but also to guess the nature of every particular sample as well as the aim of the whole experiment. Most of them guessed wrong – for example, cat urine odour was often mistaken for the smell of the sea, mushrooms or hospital. Only one student (out of 168) guessed right the specific aim of the study. However, none of the study subjects was aware of his/her toxoplasmosis status before or during the experiment and therefore, even hypothetical knowledge of the specific aim of the study could not result in systematic bias and consequent false positive results.Toxoplasma infected subjects in each section to block the possible effect of interindividual differences in the quality of urine samples from the same animal species. Due to the already mentioned effect of odour-specific hyposmia on the completeness of data, it would be difficult to eliminate the effect of the individual specimens during our analyses by using a more complex statistical models and more complex statistical techniques . Therefore, even in future studies, the effect of pseudoreplications cannot be fully avoided .The observational studies can reveal existence of statistical association between two factors, e.g. between the toxoplasmosis and changed olfactory functions, however, they cannot determine causality. As one anonymous reviewer suggested, a possible explanation of the observed association is that individuals with past experience and familiarity with domestic cats might find the odour of their urine less unpleasant and also have had a higher chance of being exposed through their past contact with cats . However, this explanation seems to be less probable because: 1) the same association was observed also in the experiments with artificially infected animals 2) the odour of cat was less, not more, pleasant for the infected women than for uninfected controls. We used five  or three  independent urine samples for each animal species to reduce the effect of pseudoreplications. To fully eliminate such an effect, it would be necessary to use the urine of a different animal for each rater, which would be technically difficult or even impossible for some animal species (there are probably not enough tigers in the Czech zoos for this design). We regularly rotated the urine samples in 17 experimental sessions and always focused on the similar frequency of Toxoplasma – the domestic cat, for latently infected individuals, in taxonomically unrelated host species, humans (Primates). In contrast to other studies dealing with this phenomenon, we searched for the effect of the same urine samples on olfactory preference in both male and female hosts, which enabled us to recognise the opposite directions of the toxoplasmosis-associated preference shift in males compared to females. Because the strength of the effect of toxoplasmosis on the olfactory preferences varies non-monotonically with the concentration of urine (and can be expressed as an inverted U function Our results confirmed the existence of specific changes in the attractiveness of urine odour of a definitive host of Toxoplasma-infected subjects in this population Toxoplasma gondii.The possible absence of the effects of toxoplasmosis on the urine odour pleasantness score attributed to large cats would suggest that the amino acid felinine Toxoplasma-infected men and women in general population have probably only minor impacts on their life However, it might have an important impact on the progression of schizophrenia by contributing to symptoms of the disease such as olfactory hallucinations and paranoia. Therefore, the mechanism and nature of latent toxoplasmosis-associated changes in human olfactory functions deserve further attention in future studies.The change of olfactory functions in


Public cooperation plays a significant role in the survival and maintenance of biological species, to elucidate its origin thus becomes an interesting question from various disciplines. Through long-term development, the public goods game has proven to be a useful tool, where cooperator making contribution can beat again the free-rides. Differentiating from the traditional homogeneous investment, individual trend of making contribution is more likely affected by the investment level of his neighborhood. Based on this fact, we here investigate the impact of heterogeneous investment on public cooperation, where the investment sum is mapped to the proportion of cooperators determined by parameter  Understanding the sustenance and emergence of cooperation within groups of egoistic agents represents one of long-standing puzzles across various disciplines However, to our surprises, the above-mentioned prediction is different from many experimental findings in the literature Except for the well-mixing assumption, in reality the spatial PGG also draws a lot of attention of statistical physicists who focused on the phase transition behaviors in the evolution of cooperation Aiming to answer this question, in this work we consider a spatial PGG model, where the cooperator's contribution will be non-uniformly distributed over all PGG groups it involves. While its contribution to each group can be mapped to a function of proportion of cooperations tuned by the parameter Here, we consider the spatial PGG: all participants are placed on a regular interaction graph which is a square lattice with Von Neumann neighborhood as described in Ref.Initially, each participant or player on site Since the system evolves according to the Monte Carlo Simulation (MCS) procedure, the player's strategy will be updated by a specific rule after acquiring the total payoff. Here, within one elementary MCS time step, we will randomly choose a player We start by presenting how the heterogeneous investment affects the evolution of cooperation on spatial cooperations. To further illustrate the origin favoring the cooperation in our PGG model, Moreover, it is also interesting to examine the time course of cooperation under this novel mechanism. According to the recent investigate prediction The robustness of cooperation against the noise can be discussed through the full Finally, it is still of interest to examining the universality of this simple mechanism under different topologies. To answer this question, in To sum, a novel spatial PGG model with heterogeneous payoff investment is proposed to illustrate the public cooperation behavior among selfish agents. Distinguishing from the traditional version, the investment of cooperator for a given PGG group (i.e.,


Cholera data were derived from the ICDDR,B health records and linked to socioeconomic and geographic data collected as part of the Matlab Health and Demographic Surveillance System. Longitudinal zero-inflated Poisson (ZIP) multilevel regression models are used to examine the impact of environmental and socio-demographic factors on cholera counts across baris. 01γ = −0.147, p = 0.041) and a higher probability of reporting no cholera cases . Populations in baris characterized by low SES are more likely to experience higher cholera morbidity at the beginning of an epidemic than populations in high SES baris.There has been little evidence of a decline in the global burden of cholera in recent years as the number of cholera cases reported to WHO continues to rise. Cholera remains a global threat to public health and a key indicator of lack of socioeconomic development. Overall socioeconomic development is the ultimate solution for control of cholera as evidenced in developed countries. However, most research has focused on cross-county comparisons so that the role of individual- or small area-level socioeconomic status (SES) in cholera dynamics has not been carefully studied. Reported cases of cholera in Matlab, Bangladesh have fluctuated greatly over time and epidemic outbreaks of cholera continue, most recently with the introduction of a new serotype into the region. The wealth of longitudinal data on the population of Matlab provides a unique opportunity to explore the impact of socioeconomic status and other demographic characteristics on the long-term temporal dynamics of cholera in the region. In this population-based study we examine which factors impact the initial number of cholera cases in a  Cholera is a bacterial disease usually spread through contaminated water that causes severe diarrhea and dehydration. Modern sewage and water treatment have virtually eliminated cholera in industrialized countries but cholera is still present throughout much of SE Asia, Latin America and sub-Saharan Africa. One of the reasons cholera is still problematic is that genetically distinct forms of the bacteria  have developed and spread rapidly because the population has no natural immunity to the new biotype. In Bangladesh, the 0139 biotype developed in 1993 and caused a large epidemic. Although it is widely accepted that poor conditions place people at risk for cholera, very few studies have examine what role low socioeconomic status plays in cholera risk, especially during a new epidemic of the disease. In this paper, we explore how local-level socioeconomic status, measured using assets, education and sanitation, affect the severity of the cholera outbreak experienced during the O139 epidemic in Matlab, Bangladesh. We believe our study highlights the importance of improving overall socioeconomic status, not just sanitation and water treatment, in controlling the spread of cholera. Despite global efforts to improve drinking water quality and sanitation in developing countries, cholera continues to pose a serious public health problem. In 2010, 317,534 cases were reported from 48 countries, an increase of 130% from just 10 years earlier vibrio bacteria Cholera is endemic in Bangladesh, meaning it is consistently present in the population at relatively low levels. The actual number of cases in the population fluctuates over time, due to a variety of population and environmental factors. Most notably, cholera has a seasonal component, peaking just before and just after the annual monsoons While there is general agreement among researchers that SES plays a role in vulnerability to and transmission of cholera, little research has directly examined the role of individual-or household-level SES in cholera dynamics. Cross-country level analyses indicate that low-income countries have higher rates of cholera than middle- or high-income countries SES or social class is a complex concept that is often conceptualized as a combination of occupational, educational, financial and locational influences V. cholerae 0139) into the region in 1993. The rich longitudinal data collected on the population of Matlab provides a unique opportunity to explore the impact of SES and other demographic characteristics on the long-term temporal dynamics of cholera in the region. We suggest that in the presence of a new serotype, we can investigate the importance of socioeconomic and socio-demographic factors in determining the severity of the initial outbreak of the disease. In this study we use longitudinal multilevel models to examine two study questions: 1) what is the effect of SES on the initial number of cholera cases at the beginning of the 0139 epidemic and, 2) what is the effect of SES on the trajectory of decline in cholera cases over the subsequent 15 years? We hypothesize that baris with lower overall SES will experience a greater number of cholera cases at the beginning of the study period, and that the decline in cases in these low SES baris over time will occur more slowly than high SES baris.Reported cases of cholera in Matlab, Bangladesh have fluctuated greatly over time and epidemic outbreaks of cholera continue, most recently with the introduction of a new serotype  and latrine facilities. An average of six distinct households constitute a bari and the different households in a bari are typically comprised of related individuals.Matlab is located in south-central Bangladesh approximately 50 km south-east of Dhaka. Most residents of Matlab are engaged in agricultural production, though increasingly young men and women migrate to Dhaka for work in the textile industry. The study area is 184 kmbari within which each individual lives. Detailed information on the MHDSS is available elsewhere bari was used as the unit of analysis in this study because oral-fecal transmitted diseases often affect several households in a bari because of close contact and sharing of resources among households within a bari. Cholera cases were assigned to the bari location from which they occurred, creating a bari-year dataset which contained a count of cholera cases in each bari for each year between 1993 and 2007. Cholera cases for which no bari was recorded – either due to reporting error or because the patient lived outside Matlab – were excluded from the analysis.Identification and surveillance of cholera cases in Matlab has been ongoing since 1964 when data collection began in conjunction with several early cholera vaccine trials. Detailed demographic, socioeconomic and disease data are currently collected by the International Center for Diarrheal Disease Research, Bangladesh . The icddr,b was preceded by the Pakistan-SEATO Cholera Research Laboratory, which collected surveillance data prior to the establishment of icddr,b. In this study, we only use data from 1993 onward and icddr,b has been responsible for cholera surveillance and health and demographic surveillance system activities since this time. The icddr,b maintains a hospital at their Matlab research site which is well known as a regional diarrhea treatment center. Patients admitted with diarrhea are tested at the on-site laboratory for cholera, shigellosis and other pathogenic causes of diarrheal disease. From the icddr,b health records, we obtained data on 3,541 laboratory-confirmed cholera cases that occurred between January 1, 1993 (the year 0139 was introduced) and December 31, 2007. All cases of cholera that occurred during the study period were eligible for inclusion in the study. These cholera laboratory data were linked to the Matlab Health and Demographic Surveillance System (MHDSS), a comprehensive demographic surveillance system also maintained by icddr,b which contains a unique ID for the bari for each year from 1993 to 2007. All baris in existence the study area between 1993 and 2007 were initially eligible for inclusion in the study. Socioeconomic data are only collected approximately every 10 years when a comprehensive household-level census is taken on the population of Matlab. Although income and consumption measures are not currently available for the population of Matlab, data on household assets, education and sanitation are. Therefore, contextual variables pertaining to household assets and sanitation were obtained from the 1996 and 2005 censuses. Since SES changes occur slowly in Matlab, we felt that the 1996 census data accurately reflected the economic and sanitation conditions in the bari at the beginning of the study period, just 3 years earlier, and that the 2005 data accurately reflected the SES conditions for the 3 years at the end of the study period. To create a time-varying predictor of SES, we interpolated the bari-level SES for 1997 through 2004 using linear interpolation methods. We used locational information on each bari contained in the Matlab GIS to calculate the distance from each bari to the ICDDR,B hospital and the distance to the river. Using Hawth's Tools in ArcGIS v9.3 we also calculated the total population and cholera case count for both 500 and 1,000 meter radius neighborhoods around each bari. These variables captured the impact of population density and cholera case load around each bari, regardless of the respective sizes of individual baris.From the MHDSS we obtained the total population count and mean age of each bari-years). 6,850 of these baris (95.7%) had all 15 years of data.Baris were excluded from this analysis if: 1) they did not exists in 1993 (the beginning of the epidemic), 2) no data were recorded for the 1996 census and were therefore missing SES and sanitation variables, and 3) they had fewer than 4 years of data because multilevel longitudinal models provide more stable estimates with three or more years of data. This resulted in a sample of 3,413 cholera cases nested within 7,161 baris for a total of 105,678 observations  in SAS v9.2, creating a single household-level measure of SES from multiple census variables. The first principle component was the only one with an eigenvalue greater than 1 and captured approximately 41% of the variability in the index measurement. The SES measure reflects a composite of seven dummy variables of ownership of household assets , two ordinal variables of household wall material and type of latrine facility and one continuous variable of years of education for the head of household . The houWe conducted a sensitivity analysis of our SES variable by creating several composite SES variables using different combinations of household asset, years of education and sanitation variables and including each (in combination with sanitation variables) to examine the impact on model results. The first SES variable was a PCA of household assets only. When we included this variable along with the latrine facility variable in the regression analysis, only SES was statistically significant  regression models. We chose to model cholera cases using a Poisson distribution rather than creating bari-level rates because cholera is a rare disease event, which leads to small numbers and unstable rates which are not normally distributed. The ZIP model allows for a large number of zero cases without compromising the model. Count data, such as the bari cholera counts examined in the present study, are often characterized by overdispersion . With rare disease events, overdisperson is often the result of excess zero counts, causing the data to exhibit a bimodal distribution The number of cholera cases in each bari experiences over the time period under study. It also includes other time varying predictors, such as population density, average age or the interpolated SES value. The level-2 component examines the effect of time-invariant predictors, such as distance to hospital, on between bari differences in the change trajectories. Thus, measurements of cholera at different times are nested within baris. Just like other multilevel models, longitudinal multilevel models consist of a fixed and a random part. The fixed effects show the shape of each bari's trajectory of change over time and the bari's initial number of cholera cases at the beginning of the study period and the factors that modify these things. The random components of the model allow the value of each bari's growth parameters to vary around these population averages.Longitudinal multilevel models, often called “growth curve” or “growth trajectory” models, examine the change in an outcome (cholera cases) over time ith bari's true intercept  and slope (rate of change), ijPOPDEN is an explanatory variable (population density) for the ith bari at time j, bari growth model, iHOSPDIST is a time invariant explanatory variable of the distance to the icddr,b hospital, and bari-specific residual terms, which capture variation of each bari's intercept and slope around the population average intercept and slope. We assume that covariates are uncorrelated with residuals, and that The first portion of the ZIP model assesses level and change in the logged Poisson counts of cholera over time. The Poisson portion of our model was specified as:ijp is the probability of the bari being an inflated zero, bari-level residuals. The aforementioned assumptions about the residuals from multilevel models apply here also .The second portion of the ZIP model asses the change in zero-inflation and is a growth model based on a logistic regression model. The ZI portion of our model was specified as:bari, the cholera rate within a 500-meter and 1000-meter radius buffer around each bari (to examine disease environment), average education of the household head and type of latrine facility. We tested the effect of each of these on both the slope and the intercept of the growth model. Two-way interaction terms were also included, but in most instances the equations would not converge or estimates were zero. The age and disease environment variables were not statistically significant in the longitudinal models. All covariates, except the SES index, were centered on the grand mean. The PROC NLMIXED procedure in SAS v9.2 was used for all model estimation.We conducted both bivariable and multivariable analyses of the data. A variety of different models were fitted but only the final model with the best fit is presented here. Additional covariates we considered include: average age of residents in each This study was reviewed by the University of North Carolina at Chapel Hill Institutional Review Board and found exempt. All data were anonymized by icddr,b prior to being released to the investigators for analysis.bari per year, or 1.5 cholera cases over the 15 years of the study. There was also significant variation in the random intercept and slope for the Poisson cholera counts, but only significant variation in the random intercept for the ZI portion of the model.bari has a higher mean number of cholera cases while the negative coefficient in the ZI portion of the model indicates that the larger a bari's population, the lower the probability of it being cholera-free. In addition, cholera counts decline with increasing distance to the ICDDR,B regional hospital. This is most likely an indication of accessibility problems; people living further from the hospital are less likely to travel to receive treatment for cholera, preferring to administer Oral Rehydration Therapy (ORT) in the home setting. The ZI portion of the model further suggests that as distance to the regional hospital increases, the number of baris reporting no cholera cases increases. We also provide an intermediate model (Model B) which shows the effect of SES in 1996 (time invariant) on both the slope and intercept. The AIC, BIC and Log Likelihood scores indicate that this is not the best fit model, and that the time varying measure of SES (interpolated between 1996 and 2005) best measures the modifying effect of SES on the trajectory of cholera over time.The full final model (Model C) controls for socioeconomic and locational factors that may affect the initial number of cholera cases and the decrease in cases over time. Population has a significant control effect in both the count and zero-inflated part of the model. The positive coefficient in the Poisson portion of the model indicates that a larger bari at the beginning of the study period (intercept) but not on the trajectory of change over time (slope). On average, baris with a high SES had lower initial rates of cholera  and a higher probability of reporting no cholera cases , though these results were only marginally significant. The rate of decline in cholera cases (the slope) was not significantly affected by SES, though the coefficient for the Poisson model was in the expected direction (negative). For example, baris with a higher initial SES experienced rates of change that decelerate with time, suggesting that the overall rate of change was slower than baris with a lower initial SES. bari to 1.25 for the highest SES bari – and the overall decrease in cholera cases over time across all levels of SES. bari with a low initial SES score that stays low over the study period (solid blue line), is less than the decrease in cases for a bari with a low initial SES that improves over time . This relationship holds true across all levels of initial SES. A bari with an initial SES at the mean that experiences lower SES over time  will initially see a decrease in the number of cholera cases, which increases toward the end of the study period. The key covariate of interest in this analysis, SES, provides the most interesting findings from the study. The addition of SES has an effect on the initial number of cholera cases in a bari variance in cholera counts. Because it is still statistically significant, potentially explainable residual variation in initial status remains. The random effect for the slope declines by 30.4% from Model A to Model C, though it too is still statistically significant, suggesting the presence of potentially explainable residual variation in rates of change. Since the effect of SES on time is not statistically significant, but the variance component is, this indicates that SES does modify the slope for some baris, but not all.The random effect for initial status declines by 54.8% from Model A to Model C suggesting the additional variables explain a significant portion of the between-bari-level socioeconomic status on the longitudinal change in cholera and second it identifies the role of SES during the introduction of a new biotype of cholera into Bangladesh. Earlier studies only examined the change in cholera over time, without considering other socio-demographic factors bari-level socioeconomic status on cholera rates over time.The longitudinal nature of the cholera data that has been systematically collected in Matlab over the past 28 years coupled with the wealth of longitudinal demographic, economic and geographic data available for the population, allows for the innovative analysis of the effect of a variety socio-demographic factors on cholera dynamics over time. This study is unique in two respects: first it examines the effect of bari at the beginning of the 0139 epidemic, net of all other covariates. However, since SES had no discernible effect on the rate of change over time, SES does not appear to play a strong role in the rate at which cholera cases decline to pre-epidemic levels. This is not surprising given that the decline in cholera is due to a variety of factors, including the population gradually building immunity to the disease. Therefore, it is likely that we do not have the necessary data to properly model the decline in cases over time.Our analysis shows that socioeconomic status had a significant impact of the initial number of cholera cases in a bari are able to afford such improvements, risk of cross-household contamination is even less. This is consistent with an earlier study by Emch SES is an indicator of several factors that directly impact cholera transmission, namely sanitation and education. Income (measured by assets here) allows households to purchase adequate housing, upgrade sanitation systems and improve drinking water. Higher SES households are more likely to have a latrine with a septic system  or a deep tube well, both of which protect household members from fecal-oral contamination. When several households in a bari. If education is a predictor of cholera risk, then we felt we must consider the average level of education attained by members of the bari for which we are predicting cholera cases, not just the educational levels of women. Finally, single variable measures of education were highly collinear with SES Indices, creating problems with model estimation.Women's education in particular is often strongly correlated with child health V. cholera 0139 epidemic is remarkable given that most of the population in rural Bangladesh is very poor. This study shows disparities exist in cholera transmission even among the very poor. Populations in baris characterized by low SES are more likely to experience higher cholera morbidity at the beginning of an epidemic than populations in high SES baris. The policy implication of this finding is that local level poverty alleviation programs which include improvements to sanitation and drinking water access - as well as other strategies to improve overall SES - will likely have an impact on cholera, especially during the introduction of new biotypes.Since the SES index explained more of the variation in cholera than the sanitation variable alone, we suggest that there is some aspect of high socioeconomic status, above and beyond simply improving sanitation, which affects cholera risk. We are not able to say from our models the exact mechanisms by which high SES reduces cholera risk, but we hypothesize it may be due to a combination of education, hygiene knowledge and practices, better housing quality and access to clean drinking water. The finding that SES modifies the initial effect of the Checklist S1STROBE checklist.(DOC)Click here for additional data file.Table S1Additional model specifications with random slope and intercept terms for both the Poisson and ZI portions of the ZIP model.(XLS)Click here for additional data file.Table S2Additional model specifications showing robusteness check of SES indices, sanitation variable and age.(XLS)Click here for additional data file.