Recent studies have raised the question of whether dogs, like human infants, comprehend an established rule as generalizable, normative knowledge or rather as episodic information, existing only in the immediate situation. In the current study we tested whether dogs disobeyed a prohibition to take a treat (i) in the presence of the communicator of the ban, (ii) after a temporary absence of the communicator, and (iii) in the presence of a novel person. Dogs disobeyed the rule significantly more often when the communicator left the room for a moment or when they were faced with a new person, than when she stayed present in the room. These results indicate that dogs “forget” a rule as soon as the immediate human context becomes disrupted. Canis lupus familiaris) has become increasingly popular as a model organism for comparing cognitive abilities across species boundaries. This is because dogs are especially skilled at understanding communicative signals given by humans. Considering the multiple ways dogs work and interact with us, understanding human signals should be highly adaptive In the past two decades, the domestic dog  in such socio-cognitive studies is required in Germany, wherefore IRB approval was not necessary. All procedures were performed in full accordance with German legal regulations and the guidelines for the treatments of animals in behavioral research and teaching of the Association for the Study of Animal Behavior (ASAB) Canis lupus familiaris) of various ages and breeds were tested. All subjects lived as pets with their owners in Leipzig, a medium-sized German city. Dogs were recruited from the database of the Max Planck Institute for Evolutionary Anthropology, where their owners had registered and volunteered them for testing. Most dogs had taken part in various studies before but were unfamiliar with the procedures used in this study. Testing took place between 25 June and 7 September 2012. Five dogs had to be excluded from the experiment: four dogs were distracted by the separation from their owner or afraid of the Plexiglas door; one dog was deaf and we decided that he might not perceive the ostensive cueing in the same way as other dogs. Data from 42 dogs were included in an initial analysis, whereupon three more dogs were excluded because they did not display an unambiguous choice behaviour. Thus, data of 39 dogs- 19 males and 20 females- were included in the final analysis  were placed on the floor. One dish was placed 120 cm and the other 350 cm from the Plexiglas door . The disThe general idea of the study was to see whether dogs would transfer a rule set up by one person  to a second person  if following the rule would mean rejecting a preferred solution. Therefore we used a setup based on prohibition. Dogs became familiar with the setting during pre-training. This was followed by four Preference trials (PT) to see which location (nearby vs. distant) dogs naturally preferred. The rule that it was forbidden to take a treat from the nearby location was established during demonstration trials and repeated before every experimental trial to avoid ceiling effects. Experimental treatments were presented in three distinct blocks. The sequence of these treatments was randomized and counterbalanced across all dogs to control for learning effects . Each trIn the pre-training dogs were familiarized with all four locations from where food could be obtained  and also learned to walk through the Plexiglas door comfortably.Regardless of the diagonal arrangement, baiting always started on the left-hand side from the experimenter’s perspective. The experimenter stood in front of the dog, showing it the treat and then walked to the location on the left side. Standing behind the dish, she called the name of the dog and said “Watch it!”. Then, while alternating her gaze between the dog and the dish she placed the treat in the dish, before walking to the location on the right side and repeating the procedure. Baiting was conducted by E1 in all experimental trials.The second experimenter baited both locations. She then left the room without giving any positive or negative cues and the Plexiglas door was opened. The dog could choose freely between the two locations without interference. After having chosen one location the dog was brought back behind the fence. A choice was defined as the dog touching the dish with its muzzle or paw and eating the treat. If a dog did not choose within 30 seconds, the trial was classified as ‘no choice’. Dogs were considered not motivated enough if they chose less than three times.Demonstrations served to establish the rule that “it is forbidden to take food from the nearby location”. E1 therefore claimed ownership of the nearby dish by standing behind it and banning it with ostensive communication. She bent over the dish, guarding it with her hands and saying “This is mine!” with a firm voice, accompanied by gaze alternation. Then she stood straight and looked down at the dish. The Plexiglas door opened and the dog was allowed to roam freely through the room. Every approach towards the forbidden dish prompted a sharp “This is mine!” and if necessary was hindered by physically guarding the food. If however the dog decided to take the distant dish, no cueing or interference took place. Demonstration phases lasted 30 seconds and were repeated before every experimental trial. Placement of the dishes in the demonstration trials was always the same as in the following trial.After the baiting process, E1 stood behind the nearby dish, prohibiting it by saying “This is mine!”. Dogs were allowed to make one choice only. There were three treatments:Stay: E1 kept standing behind the forbidden location, looking down at it. After 8 seconds the Plexiglas door opened and the dog was allowed to choose one location.Return: After having expressed the rule, E1 left the room and returned after 5 seconds without looking at the dog. She stood behind her initial position and looked down at it. The Plexiglas door opened and the dog was allowed to choose.Switch: E1 left the room after having expressed the rule. After 5 seconds E2 entered and took over her position without giving any communicative cues. The Plexiglas door opened and the dog was allowed to choose in E2’s presence.In the experimental trials dogs were always allowed to take the treat from the location they chose first. There was neither positive nor negative cueing when the dog made its choice. The trial was over after the dogs made a choice or 30 seconds had elapsed.P<0.001).For preference and experimental trials we coded whether dogs took the nearby dish, the distant one or none at all. We also noted whether the choice made was for the left- or right-hand side. A dog’s response was scored as “disobey” when it took the nearby dish. 20% of all trials were re-coded by a person unfamiliar with the study for reliability analysis. Inter-rater reliability was assessed using a weighted Cohen’s Kappa coefficient with squared weights of differences. Reliability between the two coders was excellent  were used for model optimisation. 2 = 10.078, df = 1, P = 0.003), therefore treatment had a significant effect on the probability that a dog displayed a no choice behavior. Also position had a significant effect . The negative estimate indicates that dogs were less likely to display a no choice response at the end of the experiment than at the beginning. Posthoc pairwise comparisons revealed that dogs were significantly more prone not to choose when the experimenter stayed in the room than when experimenters switched (P = 0.002). They were also less prone not to choose when the experimenter left and returned than when she stayed (P = 0.012). There were no significant differences in the probability not to choose a dish between the Return and the Switch treatment (P = 0.607). No significant effect of the interaction between treatment and position on the probability not to choose was found .After a first data examination, three dogs were excluded from further analysis because they did not choose a dish in all four trials of two or more treatments. Although this behavior is in line with obeying the rule we could not exclude the possibility that dogs either did not understand the matter of the experiment or were too intimidated to choose. Results for the 39 remaining dogs are depicted in z = 1.429, P = 0.153). Age however did have an effect. With increasing age dogs were more prone to choose the forbidden dish . Treatment had a highly significant effect on the dogs’ responses, indicating that the probability to disobey the rule varied with the testing situation .A GLMM revealed no significant effect of the sex of a dog on its probability to disobey . Dogs disobeyed the ban significantly less often when E1 stayed in the room than when she left for a moment and returned (Stay – Return: P = 0.003) and or when experimenters switched (Stay – Switch: P<0.001). Also, the dogs took the nearby dish significantly more often and thus disobeyed the rule when the experimenter was replaced by another person than when the same person returned (Return – Switch: P = 0.031)  .P<0.001; mean correct 3.128). When confronted with the rule not to take the preferred nearby dish and in presence of the rule communicator, dogs chose the nearby dish and thus disobeyed significantly below chance level . Now, only 17% of dogs took the nearby dish either 3 or 4 times, while 19% disobeyed twice and 62% only once or not at all. In both the Return and Switch treatments this effect vanished and dogs chose the nearby dish at chance level . However, in the Return 31% of dogs chose the nearby dish three or four times, while 31% performed at chance and 38% chose it once or not at all. By contrast, in the Switch treatment 46% of dogs chose the nearby dish three or four times, 31% twice and 23% once or not at all . They also disobeyed more often, although not significantly so, when the treatment was received first compared to second (P = 0.071) or second compared to last (P = 0.071). Experience therefore increased the probability not to disobey significantly in this treatment. Sex (z = −1.164 P = 0.245) and age  did not have a significant effect on the probability to disobey in this model and where thus stepwise eliminated.We found a significant effect of the position in which a treatment was received on the dogś probability to disobey. A model including the interaction term between treatment and position was superior over a reduced model without the interaction .Dishes were distributed in a counterbalanced way to the left and right in all treatments, meaning no differences were expected in terms of dishes being taken on the left or the right – regardless of their being placed closer or farther away. Nevertheless, side had a significant effect on the probability of taking a dish. Dogs took a dish placed on the right side significantly more often than one on the left side Click here for additional data file.


Family Medicine Groups (FMG) were introduced in Quebec in 2002 to re-organize primary care practices and encourage inter-professional service delivery. We measured visits to the emergency department (ED) for acute complications related to diabetes as a proxy for access to and quality of primary care, before and after the reform using an open cohort of individuals diagnosed with type 1 and type 2 diabetes.The weekly rate of ED visits between April 1, 2000 and March 31, 2012 were derived from administrative databases. We performed an interrupted segmented regression analysis to obtain the estimated and predicted rates of visits in the years following the introduction of the reform. An outcome control series of diabetic patients visiting the ED to treat appendicitis was incorporated to strengthen the study’s internal validity.After 9 years of reform implementation, we observed a statistically significant absolute decrease of 2.12 and 2.25 ED visits per 10,000 diabetic patients per week to treat acute diabetes-related complications in urban and rural areas, respectively. However, the magnitude of the changes between the estimated and predicted rates did not differ significantly over time. No statistically significant change in the rate of ED visits for appendicitis was observed.Our findings suggest that the introduction of the FMG model produced reductions in the weekly rate of avoidable visits to the ED. Our results also imply that despite a greater proportion of the diabetes population being enrolled with FMG physicians across the province over time, the added benefit may be minimal. More studies examining this issue are needed to inform future policy. In 2000, Quebec’s Clair Commission called for the re-organization of primary care to place an emphasis on population health, in addition to continuity of and accessibility to care . Since 2The rise in the prevalence of diabetes over the last two decades has made it a priority for chronic disease surveillance in Quebec , 8. As iIn the context of an aging population, it is of relevance to policy makers to determine whether reforms to primary care have changed access to and quality of care among those with chronic ambulatory care sensitive conditions (ACSC) that are largely managed by family physicians. Our objective was to examine the change in the rate of visits to the ED produced by the FMG model within the province’s diabetic patient population.We conducted a population-based retrospective study using a segmented regression analysis of an interrupted time series. The segmented regression analysis is useful for determining whether the reform produced any changes in the outcome, and if so, whether these changes were abrupt or gradual . The impData from the Quebec Integrated Chronic Disease Surveillance System (QICDSS) were used for the development of healthcare utilization indicators . The anaWe defined the rate of ED visits for acute diabetes-related complications and appendicitis among patients aged 20 and over as our outcomes of interest. We selected ICD-9 codes based on the Canadian Institute of Health Information’s list of ambulatory care sensitive codes specific to diabetes (eg.: hyperglycemic and hypoglycemic emergencies) . Using tThe time series was produced from daily ED records aggregated to the number of visits per week for each outcome. This produced 626 weeks (observations) in our time series, which is consistent with the number of weeks between April 1, 2000 and March 31, 2012. We fit a regression line to each segment of the series (before and after November 2002) using a negative-binomial distribution and the log of the mid-year diabetic patient population as the offset term. Our model included an intervention variable and a variable denoting time since the intervention (number of weeks). We included demographic covariates measured at the province-year level: average age and sex. We included fixed effect terms for year and season to control for secular trends in the rate of avoidable visits to the ED since long-term temporal changes may be correlated with the FMG practice model. For instance, fixed effects for year would control for secular trends in the family physician labor force if there were a growing preference for practicing part-time. We also included a linear time variable to address residual confounding due to unmeasured regional characteristics that could produce gradual trends in the rate of avoidable visits to the ED. The Durbin-Watson test revealed no significant autocorrelation in the data and robust standard errors were specified for all models.T3, T6 and T9, respectively, in Figs. Ŷt (with reform) − Ŷt (without reform) [p = 0.05 level. Analyses were performed using SAS version 9.3.The literature recommends at least 12 data points before and after the intervention, and at least 100 observations making up each data point, in order to have sufficient statistical power to detect intervention effects . Accordi reform) . We repoGovernment bodies in legal possession of the databases, in addition to Quebec’s Comité d’éthique de santé publique and the Commission d’accès à l’information du Québec, have approved the creation of the QICDSS and its use for chronic disease surveillance. The creation of the QICDSS and access to its data meet stringent standards of security and privacy. This study is part of a doctoral project that was approved by the Faculty of Medicine’s Institutional Review Board at McGill University. Individual participant consent was not required in accordance with the Canadian Tri-Council Policy Statement: Ethical Conduct for Research Involving Humans.Figure 1 + β3). The results indicate that the FMG model produced a significant 1 % decrease in the post-reform rate of avoidable visits to the ED per 10,000 diabetic patients per week . Similarly to the urban regions, a significant reduction of 1 % in the rate of avoidable visits to the ED per 10,000 diabetic patients per week was found in the post-reform period. Per annual quarter, this represents a 3 % reduction in the number of avoidable visits to the ED per 10,000 diabetic patients. The appendicitis control series indicated no statistically significant effect of the reform . Model estimates were robust to intervention lag terms and control for potential wild data points.Table T6 onwards, the differences between the estimated and predicted rates were significant in both regions. A statistically significant reduction of 2.12  and 2.25  cases per 10,000 diabetic patients per week was observed in urban and rural regions at T9, respectively. This meant that at T9, 469 weeks post-intervention, there was a difference of 2.12 visits per 10,000 diabetic patients per week between the estimated and predicted rates. The incremental decrease from T3 to T9 was roughly similar between urban and rural areas. For the appendicitis series, the differences between estimated and predicted rates were consistently non-significant up to and including T9 .Estimates from the regression model indicate decreases in the weekly rate of avoidable ED visits among diabetic patients. To contextualize these results, we calculated the difference between the estimated and predicted rates of avoidable ED visits, expressed as the number of avoidable visits to the ED per 10,000 diabetic patients per week, at distinct time points in the post-reform period. Table T3 to T6 and from T6 to T9. Table We also assessed whether the increases in the magnitude of the differences between the estimated and predicted rates in urban and rural areas were statistically significant over time from Given that the rate of ED visits was modeled as a function of time before and after the introduction of the reform, an important consideration was our approach to modeling time trends. The segmented regression analysis assumes that the intervention produced a sudden change in the rate of avoidable visits to the ED. To explore whether our results were sensitive to how our main model was specified, we modeled the effect of the reform using a linear spline with a knot located at the point when the FMG model was introduced. The results were similar to those produced by the segmented regression. In urban areas, there was no change in the weekly rate of avoidable ED visits per 10,000 diabetic patients in the pre-reform period . In the post-reform period, there was a significant 1 % decrease in the weekly rate of avoidable ED visits per 10,000 diabetic patients . Similar results were observed for the rural regions. In each model for the urban and rural areas, the estimates for the pre and post reform slopes were significantly different from one another. There was no change in the weekly rate of visits to the ED for appendicitis per 10,000 diabetic patients in the pre-reform period  or the post-reform period .Our results indicate that the introduction of the FMG model produced reductions in the weekly rate of avoidable visits, suggesting some features of the FMG model are conducive to either increasing quality of or access to primary care that in turn contribute to less ED use. In comparison to other Canadian provinces, primary care reform in Quebec did not result in an overwhelming restructure of services. A 1 % decrease in the weekly rate of avoidable ED visits per 10,000 diabetic patients suggests that a greater reduction could be possible under a more encompassing reform. To place our results in context, a previous study found a 7 % reduction in the rate of total ED use in the first 3 years of the FMG reform among vulnerable patients who were enrolled with a physician practicing in a FMG versus those that were not .The increase in the rate of avoidable visits in rural regions in the weeks closely following the introduction of the reform could be explained if FMG clinics do indeed increase access to primary care and therefore address previous unmet needs. While this would likely have a greater effect on the use of primary care services versus the ED, it is common in rural regions in Quebec for the ED to be used as a source of ambulatory care consultation. In this context, greater access to primary care may have resulted in greater diabetes screening or management in the ED based on pre-existing patterns of health service utilization.T3 onwards. This is evidenced by the non-significant post-reform slope  . InsofarOur use of administrative data to conduct an interrupted regression analysis that measured the reform’s effect on both quality of and access to primary care is relevant for policymakers, physicians, and to the development of diabetes surveillance in Quebec. Previous studies have provided important insight on issues of access, unmet need and patient experiences with primary care in the province using patient surveys , 33–35, The QICDSS contains administrative data from 1996 onwards and applies a previously validated 4-year clearance period before distinguishing between incident and prevalent cases of diabetes , 38. As This was a physician-led reform and therefore take-up of the FMG model was, and remains, voluntary with staggered implementation across regions and time. Factors such as concurrent reforms are potential threats to study validity. A limitation of this study was the extent to which we could separate the effects of changes to the organization of primary care (the FMG model) from the effects of other changes that specifically targeted pathways of care to emergency health services. Caution when interpreting results from long post-reform periods is required since delayed and time-varying effects can complicate the analysis . Given t1 + β3 in Table T3 to T9 in Table We attempted to address this concern using visits to the ED for appendicitis as a negative control series that would not be associated with the FMG reform yet is still sensitive to changes in the health system that are also related to our main outcome series. For instance, network clinics were introduced to facilitate the creation of a service corridor between primary care practices and specialized care in hospitals for a given territory . Among tT0 for the year each practice switched to FMG status would have provided an added safeguard against bias. However this information was not available to us.This analysis used a single point in time to define the post-reform period. Although the policy was introduced in 2002, it was rolled-out over time as more physicians joined the model. Consequently, despite our use of a control series we cannot rule out the possibility of bias due to concurrent interventions. For instance, in 2008 a centralized waiting list for individuals without family physicians was introduced which may have also contributed to reduced use of the ED. A multiple baseline analysis that incorporated a different Our findings suggest that the introduction of the FMG model produced a reduction in the weekly rate of avoidable visits to the ED. In extending this research further, it would be useful to assess relevant dimensions of access to and quality of care separately for diabetes and other conditions that are largely managed in primary care.Our results also imply that despite a greater proportion of the diabetes population being enrolled with FMG physicians across the province over time, the added benefit may be minimal. Future studies should account for the time-varying nature of the expansion of the FMG model across the province and whether there is indication of a saturation effect. Evidence of this may suggest that FMG implementation differs according whether physicians adopted the model in the early or late post-reform reform period. This in turn may be important for policy makers if these groups systematically differ from each other. These considerations are applicable to other contexts in which reform uptake is voluntary and diffused over time.


Synechocystis sp. PCC 6803 moves with Type IV pili and measures light intensity and color with a range of photoreceptors. Here, we show that individual Synechocystis cells do not respond to a spatiotemporal gradient in light intensity, but rather they directly and accurately sense the position of a light source. We show that directional light sensing is possible because Synechocystis cells act as spherical microlenses, allowing the cell to see a light source and move towards it. A high-resolution image of the light source is focused on the edge of the cell opposite to the source, triggering movement away from the focused spot. Spherical cyanobacteria are probably the world’s smallest and oldest example of a camera eye.Bacterial phototaxis was first recognized over a century ago, but the method by which such small cells can sense the direction of illumination has remained puzzling. The unicellular cyanobacterium DOI:http://dx.doi.org/10.7554/eLife.12620.001 Synechocystis is a spherical single-celled cyanobacterium that measures about three thousandths of a millimetre across. Because Synechocystis needs sunlight to produce energy, it is important for it to find places where the light is neither too weak nor too strong. Unlike some bacteria, Synechocystis can’t swim, but it can crawl across surfaces. It uses this ability to move to places where the light conditions are better.Cyanobacteria are blue-green bacteria that are abundant in the environment. Cyanobacteria in the oceans are among the world’s most important oxygen producers and carbon dioxide consumers. Synechocystis cells move towards a light source that is shone at them from one side, which implies that the cyanobacteria can “see” where the light is. But how can such a tiny cell accurately detect where light is coming from?It was already known that Synechocystis moved in response to different light conditions, and found that the secret of “vision” in these cyanobacteria is that the cells act as tiny spherical lenses. When a light is shone at the cell, an image of the light source is focused at the opposite edge of the cell. Light-detecting molecules called photoreceptors respond to the focused image of the light source, and this provides the information needed to steer the cell towards the light. Although the details are different, and although a Synechocystis cell is in terms of volume about 500 billion times smaller than a human eyeball, vision in Synechocystis actually works by principles similar to vision in humans.Schuergers et al. tracked how Schuergers et al.’s findings open plenty of further questions, as other types of bacteria may also act as tiny lenses. More also remains to be learnt about how the cyanobacteria process visual information.DOI:http://dx.doi.org/10.7554/eLife.12620.002 Escherichia coli  alternates with random changes of direction (tumble). Tumbles become less frequent when cells sense a temporal increase in attractant concentration, introducing a bias to movement up a concentration gradient  has spherical cells about 3 µm in diameter and moves using Type IV pili (T4P)  . The locli (T4P) , as has li (T4P) . It has li (T4P) . One likpendages  apart fr strains .Synechocystis T4P-dependent phototaxis can be observed microscopically at the single cell level and macroscopically through the migration of cell colonies. Genetic studies have identified a number of photoreceptors that influence phototactic behavior under different light regimes  at an angle oblique to the surface  evenly distributed in the periplasm (n = 13 cells from one representative experiment) and the observed mean full width at half maximum (FWHM) of the focused spot was 609 ± 30 nm . Since the measured FWHM for the point-spread function of the microscope was 270 nm, the true FWHM for the focused spot of 488 nm light at the cell periphery can be estimated to be about 550 nm. This experiment shows that for 488 nm light, lensing effects leading to concentration of light at the opposite side of the cell from the light source are overwhelmingly greater than shading effects, which would lead to higher light intensity on the illuminated side of the cell. Shading effects at other wavelengths in the range from 400 to 750 nm could be only marginally greater than at 488 nm  was projected vertically down onto them   . This exe shown) , the lene shown) .Synechocystis have been experimentally shown to produce similar sharply focused light beams at the edge of the object opposite to the light source: these are termed “photonic nanojets”  simulations, a method that uses the electromagnetic Maxwell equations without any geometrical simplifications . This elanojets” .Synechocystis cells that are opposite in orientation and at least 20 times greater than those predicted from shading due to light absorption by the photosynthetic pigments  light emitting diode (LED) light source were visualized by fluorescence from the photosynthetic pigments excited by the LED light. Focused spots of light at the rear periphery of the cell were again observed under this illumination regime  is actually triggered as a negative response to the focused spot of light at rear periphery of the cell. We directly tested this idea by exposing cells to a spot of red laser light that was sharply focused enough to selectively excite one edge of the cell  also show only negative phototaxis  of about 21°, essentially limited by light wavelength and the area of the plasma membrane, which is tiny in comparison to an animal retina. However, this resolution is sufficient to incorporate quite complex spatial information into a 360° image of the cell’s surroundings, and our data in From the observed dimensions of the spot of 488 nm light focused by is cells  we can eSynechocystis and Gloeocapsa are described as motile and phototactic, and these species do indeed have spherical or near-spherical cells  is clustered at the cell poles  in 1993. This sub-strain (PCC-M) was recently characterized by complete genome sequencing (Synechocystis cells were grown for at least 24 h on motility medium (0.3% (w/v) agarose in BG11 medium .We used a motile sub-strain of the original quencing . Synecho1 medium  supplemeCells from the moving front of a colony on a motility plate were resuspended in fresh BG11 medium and 3 µl aliquots were directly spotted on top of 5 ml motility medium in 35 mm glass-bottom plates. When liquid droplets were no longer visible (5–10 min) a cover-slip was carefully placed on top of the cells and the surrounding surfaces of the plates were covered with a silicone ring to minimize surface oscillations and evaporation. Time-lapse videos were captured at room temperature (ca. 22°C) using an upright Nikon Eclipse Ni-U microscope  fitted with a 40× objective . Gradient illumination was with white light from the microscope condenser lamp. RGB LEDs   for directional illumination were mounted in boreholes of a black plastic cylinder surrounding the motility plates. Light intensities were measured with a LiCOR light-meter with a planar quantum sensor  with a detection window from 400–700 nm. Single cell movement was captured at 1 frame per 3 s. Unless otherwise stated, the exposure time was 200 ms to reduce the background light needed for visualizing the cells.https://web.fe.up.pt/~dee11017/software/BacterialMobilityQuant.zip) implemented in MATLAB for tracking single cells in time lapse videos. It is based on a detection-association tracking approach that relies on the Laplacian of Gaussian filter (LoG) for the task of bacteria detection (–1 and a maximum displacement of less than 8 µm between two frames (3 s) were considered. Cells with an average velocity lower than 0.05 µm s–1 were regarded as immotile and discarded. Displacement and orientation were calculated for all cells that could be continuously tracked for 60 frames, during 5 min after the onset of the directional light. All the data shown in We developed the BacteriaMobilityQuant software , which is equipped with a wide detection window to minimize distortion due to light scattering. After subtraction of the optical density due to light scattering at 750 nm, the absorption spectrum was deconvoluted into chlorophyll and phycobilin components and chlorophyll and phycocyanobilin concentrations were estimated according to the formulae of 7 chlorophyll molecules and 3.70 × 107 phycocyanin-coupled phycocyanobilin molecules per cell. These numbers are slightly higher than previously estimated  and tracing in an anti-clockwise direction back to the origin. The line width was 3 pixels and the analysis was done in ImageJ.The escribed . Cells wcope see . FluoresSynechocystis culture in BG11 medium were dispensed on top of the 5 µm thick photoresist. The BG11 medium evaporated under standard clean-room conditions after 30 min, leaving the Synechocystis cells on top of the wafer. Flood exposure of the photopolymer was performed at a center wavelength of 365 nm . The exposure dose of 85 mJ cm–2 was found by a lithographic series, in order to achieve sharp rendering of scattering patterns on the SU-8 film. However, the SU-8 layer was not exposed entirely and rendered a height-dependent cross-linked structure similar to grayscale lithography  with outstandingly low absorption in the near-UV range  was applhography . Height –8 s).The optical field distribution was computed by the FDTD method. The algorithm solves the time-dependent Maxwell curl equations by using discrete time steps and leads to a time-resolved evolution of the spatial electromagnetic field distribution . We usedSynechocystis cells were freshly plated in a line on motility plates and grown overnight at 30°C with directional illumination (~10 µmol photons m–2 s–1) in a standing incubator. Fingers of cells projecting 0.3–0.5 cm were seen after ~16 hrs. The leading edges of the fingers were collected and resuspended in 20–50 µl of BG11 medium and spotted onto freshly made motility plates and left to adsorb. Blocks of the motility agarose containing the spots were excised from the plate and inverted onto 35 mm glass-bottomed tissue-culture dishes for imaging in a custom-built inverted microscope. The time lapse microscope  and Carol Dieckman (peer reviewer).Thank you for submitting your work entitled "Cyanobacteria use micro-optics to sense light direction" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.Synechocystis operates via the cell acting as a microlenses. All reviewers felt this conclusion was well supported by the data presented, particularly the laser spot experiments in This paper reaches an interesting and novel conclusion – that phototaxis in the cyanobacterium 1) The paper would benefit from a more detailed description of the signal transduction process between light absorption and the pili, as well as more details about how movement of the pili lead to cell movement. This should include a discussion of what is known about the photoreceptors themselves – e.g. what is their distribution? This could be added to the figure showing the proposed model.2) Thermosynechococcus data in 3) The Individual reviewer comments:Reviewer #1:This paper presents a very interesting idea about the mechanisms underlying phototaxis in cyanobacteria. The central idea – that the cell membrane acts as a lens to focus light on the opposite side of the cell and that this leads to changes in directed movement – is well supported by several independent analysis approaches. There are some areas in which I think the paper can be improved. Specific comments follow.Light gradient experiment. The explanation of the light gradient experiment  is confusing, and I did not fully understand it until watching the movie. I would suggest including a schematic like that in the movie in the main text to make sure the light configuration is clear. It might help to state that the bacteria (presumably) try to swim towards the condenser.Thermosynechococcus: this is an interesting addition to the paper, but is quite underdeveloped. As a consequence it raises more questions than it answers – e.g. can they still integrate two light stimuli and if so how? Some quantification of where the spots are on the cell relative to the orientation of the light would strengthen this section. Alternatively, I think it could be left out without causing the main message to suffer.Dispersal mechanisms : how is Reviewer #2:Synechocystis, a spherical cell a few μm in diameter, is just that: the migration of cells directly toward the source of light. The cells are shown to act as microlenses, generating a spot of light at the back surface of the cell, which is presumed to disperse the type-IV pili, so that the cells are pulled toward the front. The most direct and convincing experiment that shows this is the exposure of the back side of the cell to the edge of a spot of laser light, which causes a cell to move away from the light.This paper shows that phototaxis in the cyanobacterium Some comments:Synechococcus swims without any obvious surface appendages. Samuel and Reese showed that these cells are covered with fine filaments that extend from the inner membrane out into the external medium.  Envelope structure of Synechococcus sp. WH8113, a nonflagellated swimming cyanobacterium. BMC Microbiology 1:4.)1) In the Introduction, the authors assert that a marine Synechocystis cells act as microscopic spherical lenses”) to the subwavelength focusing of light. Does this mean that the focused spot is smaller in diameter than the wavelength of light, that the focused light is at a smaller wavelength than the incident light, or something else?2) The authors refer  Type IV pili are known to pull rather than push cells, which might be mentioned in the Discussion. Perhaps I missed it.4) More should be said about computation of the distribution of the optical field (FDTD), subheading “Modeling of micro-optic effects”. In particular, the reader does not know what to make of the wiggles (waves?) shown in 5) Can we be told that the peak in Reviewer #3: The main, novel conclusion of this work is that Synechocystis cells are lenses. Light hitting the cell on one side is focused on the other side of the cell, triggering the cell to move away from the focused spot of light and toward the environmental light source.I was convinced by the data presented; particularly compelling were the data presented in eLife readers. Foremost, I found the text to be very terse, with not much explanation of the biological context and little summary of the signal transduction pathway from light reception to the response by the pili. Though I endorse including the details in the Methods and legends to satisfy photoreception, phototaxis, and optical biology experts, I would encourage the authors to envision presenting the work to a much larger audience with rudimentary optics knowledge, but interest in many aspects of biology. Reviewers most often ask for manuscripts to be condensed, but in this case, I ask that the text be expanded with the goal of making the study more accessible. I have a few suggestions:There are several improvements that could be made to make this story accessible to the greatest number of 1) Thermosynechococcus data in Synechocystis, but the introduction and presentation were a distraction rather than an enhancement. My vote would be to leave it out, perhaps mentioning it in the text.2) The inclusion of the  This paper reaches an interesting and novel conclusion – that phototaxis in the cyanobacterium Synechocystis operates via the cell acting as a microlenses. All reviewers felt this conclusion was well supported by the data presented, particularly the laser spot experiments in 1) The paper would benefit from a more detailed description of the signal transduction process between light absorption and the pili, as well as more details about how movement of the pili lead to cell movement. This should include a discussion of what is known about the photoreceptors themselves – e.g. what is their distribution? This could be added to the figure showing the proposed model.Synechocystis”) and present in revised In revised paper we have focussed our Discussion only on positive phototaxis, as recommended by Reviewer 1. We must stress that even here the information available on the signal transduction pathway is very incomplete, but we can at least present a plausible model for the pathway, which we now discuss  We have made changes to all these figures as recommended by the reviewers. The details are listed below.3) The Thermosynechococcus data in Thermosynechococcus data are quite preliminary and therefore we decided to remove them, together with associated discussion, as reviewers do not consider these data essential to the story. We would be interested in submitting a more complete set of data for Thermosynechococcus as an eLife Research Advance at a later stage.We agree that the Individual reviewer comments:Reviewer #1:This paper presents a very interesting idea about the mechanisms underlying phototaxis in cyanobacteria. The central idea – that the cell membrane acts as a lens to focus light on the opposite side of the cell and that this leads to changes in directed movement – is well supported by several independent analysis approaches. There are some areas in which I think the paper can be improved. Specific comments follow.Light gradient experiment. The explanation of the light gradient experiment  is confusing, and I did not fully understand it until watching the movie. I would suggest including a schematic like that in the movie in the main text to make sure the light configuration is clear. It might help to state that the bacteria (presumably) try to swim towards the condenser.Synechocystis phototaxis is based on direct light perception, rather than a biased random walk”). In fact the bacteria are “walking” in 2 dimensions on the agar surface rather than swimming, and therefore they are not able to move towards the condenser light, which is perpendicular to the surface.We have added a schematic similar to the one in the movie to Thermosynechococcus: this is an interesting addition to the paper, but is quite underdeveloped. As a consequence it raises more questions than it answers – e.g. can they still integrate two light stimuli and if so how? Some quantification of where the spots are on the cell relative to the orientation of the light would strengthen this section. Alternatively, I think it could be left out without causing the main message to suffer.Thermosynechococcus data are quite preliminary in nature and therefore better kept for a future publication after we have developed the work further. We have removed the Thermosynechococcus images and associated text.We agree that the Dispersal mechanisms : how is The cells that encounter the laser spot are presumably integrating information from the laser spot and also from the oblique light source from the bottom of the frame. We think that this is apparent from the behaviour of cells that receive just a glancing blow from the laser rather than encountering it head-on. However, since we only have a fully quantified set of data for the situation in Similarly I was not sure why you assume that the dispersal signal occurs at the location of the strongest light spot – as opposed to the dispersal signal being graded and integration simply reflecting dispersal anywhere light is focused in the membrane (and hence motility occurring on the opposite side of the cell); this seems more consistent with Actually the reviewer has given a clear and succinct explanation of exactly the model that we were trying to put forward! We have clarified our explanation with help from the reviewer’s text .Related to this point, if you have two light sources as in , do the The length scales of these measurements (which are limited by the field of view of the microscope) are very short, and therefore there is no significant change in light intensity from either source across the field of view. We would therefore not expect any observable dependence of motility direction on the position within the field of view, and indeed there is no sign of such an effect  In the Introduction, the authors assert that a marine Synechococcus swims without any obvious surface appendages. Samuel and Reese showed that these cells are covered with fine filaments that extend from the inner membrane out into the external medium.  Envelope structure of Synechococcus sp. WH8113, a nonflagellated swimming cyanobacterium. BMC Microbiology 1:4.)Synechococcus . It is worth noting however that these “spicules” have only been detected in one of the motile marine Synechococcus strains.Agreed – we have modified our description of 2) The authors refer  to the subwavelength focusing of light. Does this mean that the focused spot is smaller in diameter than the wavelength of light, that the focused light is at a smaller wavelength than the incident light, or something else?Synechocystis cells act as microscopic spherical lenses”).We meant that the focused spot is smaller in diameter than the wavelength of light, and have clarified this point in the text  Type IV pili are known to pull rather than push cells, which might be mentioned in the Discussion. Perhaps I missed it.We now mention this point in the Introduction (paragraph two).4) More should be said about computation of the distribution of the optical field (FDTD), subheading “Modeling of micro-optic effects”. In particular, the reader does not know what to make of the wiggles (waves?) shown in Synechocystis cells act as microscopic spherical lenses”).We have now extended our discussion of these simulations and the presentation of them to make them more comprehensible to non-specialists (see legend to 5) Can we be told that the peak in In fact we chose a good representative trace (close to the mean for intensity profile) and a similarly representative set of cell images – but they weren’t actually for the same cell as the profile. We have corrected this in the revised paper, which now shows in Reviewer #3:The main, novel conclusion of this work is that Synechocystis cells are lenses. Light hitting the cell on one side is focused on the other side of the cell, triggering the cell to move away from the focused spot of light and toward the environmental light source. I was convinced by the data presented; particularly compelling were the data presented in There are several improvements that could be made to make this story accessible to the greatest number of eLife readers. Foremost, I found the text to be very terse, with not much explanation of the biological context and little summary of the signal transduction pathway from light reception to the response by the pili. Though I endorse including the details in the Methods and legends to satisfy photoreception, phototaxis, and optical biology experts, I would encourage the authors to envision presenting the work to a much larger audience with rudimentary optics knowledge, but interest in many aspects of biology. Reviewers most often ask for manuscripts to be condensed, but in this case, I ask that the text be expanded with the goal of making the study more accessible.Synechocystis cells” and “Synechocystis cells act as microscopic spherical lenses”.Agreed – we have been through the text and tried to provide such clarification wherever possible. See subheadings “Shading is minimal in single I have a few suggestions:1) Synechocystis cell absorbs only a relatively small proportion of the light passing through it, is important when considering how light intensity will vary within and around the cell, which is a theme central to our paper. Therefore we would prefer to present this figure prominently so that we can be up-front about our arguments. The reviewer does not raise any specific technical problems with the estimate of a single-cell transmission spectrum but is clearly not comfortable with the discussion. In the revised paper we have tried to explain more clearly how we derived the transmission spectrum . Therefore our estimate of a minimum transmittance for a Synechocystis cell of about 0.8 is not at all out of line.We feel that the conclusion from this figure, that a single 2) The inclusion of the Thermosynechococcus data in Thermosynechoccus data for a future publication. We feel that it is better not to mention it in the text if we don’t present the data.Agreed – as discussed above we decided to leave the


Subjects had to estimate the duration of temporal intervals of 500 ms defined by a tactile and a visual, masked stimulus. We observed a strong (~100 ms) underestimation of the temporal interval when the stimuli from both senses appeared to occur at the same position in space. In contrast, when the positions of the visual and tactile stimuli were spatially separate, interval perception was almost veridical. Temporal compression furthermore depended on the correspondence of probe features and was absent when the orientation of the tactile and visual probes was incongruent. An additional experiment revealed that temporal compression also occurs when objects were presented outside the attentional focus. In conclusion, these data support a role for spatiotemporal binding in temporal compression, which is at least in part selective for object features.Multisensory integration provides continuous and stable perception from separate sensory inputs. Here, we investigated the functional role of temporal binding between the visual and the tactile senses. To this end we used the paradigm of compression that induces shifts in time when probe stimuli are degraded, e.g., by a visual mask (Zimmermann  The high accuracy of temporal event perception by the human visual system is illustrated by, e.g., ball sport activities like tennis or baseball. Other obvious examples can be derived from the animal kingdom where an accurate estimate of the predator’s arrival may be decisive about survival. Despite the ecological relevance to correctly judge temporal durations, several illusions demonstrate a surprisingly strong susceptibility to interference resulting in altered time perception: Temporal intervals appear compressed during voluntary actions34Many phenomena of temporal plasticity can be explained by the amount of attentional resources allocated to a time intervalHow does plasticity of time perception fit with the ecological demand of accurate time perception in order to ensure survival? Is this plasticity only the by-product of a reduction of processing resources or does it subserve the need to classify stimuli in time? The so-called object correspondence problem is a constant challenge for the visual system, whenever the eyes move or blink or whenever a distracting flash of light or a mask interrupt the visual input. Plasticity of time perception and in particular time compression might be an important prerequisite for solving the correspondence problem. Matching stimulus correspondence becomes particularly pressing in a cross-modal situation. In order to provide coherence across multisensory stimulation, the brain needs to actively organize the perceptual experiencesWe have recently demonstrated that apparent time and space are compressed between two consecutively flashed objects if one of the stimuli is degraded by a briefly presented visual whole field maskIn our current study, we investigated whether temporal compression also occurs for intervals defined by multisensory stimuli. We presented a tactile stimulus to the unseen hand and a masked visual stimulus in the same or a different position shortly thereafter. Subjects had to report the apparent temporal interval between the two stimuli. We found that the temporal interval was compressed when tactile and visual stimulation coincided spatially. The effect disappeared as a function of spatial distance between tactile and visual stimulus. The effect also depended on the correspondence between the stimuli, i.e., the effect disappeared when the tactile stimulus did not match the visual stimulus.We first sought to investigate whether binding of stimuli across the visual and the tactile senses might produce temporal compression. We asked subjects to judge the duration of a 500 ms interval, defined by a tactile impulse acting as the marker of its beginning and a visual impulse acting as the marker of its end see . The visTemporal estimations for purely visually defined intervals are shown in We calculated a two-way ANOVA of the perceived duration of the first interval with the factors “modality”  and “position” . Please note that we used those visual-visual data bins for statistics whose positions corresponded to the visual-tactile data bins. We found a significant factor “position”  = 6.63, p < 0.001). No significant difference in the factor “modality”  = 0.80, p < 0.37) was found.We next aimed to further corroborate the functional role of temporal compression by testing object correspondence when visual-tactile stimuli are close to each other. A temporal interval was defined again by a tactile and a visual marker. The tactile interval marker consisted of a simultaneous impulse to the thumb and the index finger see . The visThe aim of Experiment 3 was to unravel why the mask induces temporal interval compression. We tested the idea that the mask might withdraw visual attention from the stimulus marking the interval’s end. We presented one stimulus in the left and one in the right part of the screen as an interval start marker. After 500 ms an interval end marker appeared. In 80% of all trials, the interval end marker was shown in the right part and in 20% of all trials in the left part of the screen. With this manipulation we aimed to direct subject’s spatial attention to the right part of the screen where the stimulus occurred in the majority of the trials. This manipulation was tested in trials with and without masking the interval end marker.Average data from Experiment 3 are shown in The main finding of this study is that multisensory temporal interval compression is selective for the correspondence of the interval markers. The estimation of temporal duration was strongly modulated by the spatial congruency of the interval markers. When two visual interval markers (dots) were flashed closely together in space and the last dot was masked the interval duration was underestimated by approximately 29%. Importantly, however, compression magnitude reduced as a function of spatial disparity between the interval markers. A very similar finding was observed when temporal compression was tested using visual and tactile stimuli to mark the interval. Interval duration was compressed by approximately 26% when the visual and the tactile stimulus were perceived to originate from the same position. In contrast, duration perception was close to veridical for large spatial separations between the visual and the tactile stimuli. In the multisensory setup, compression magnitude also depended on the feature correspondence between tactile and visual stimulation. When visual stimuli were close in space but non-correspondent to the tactile stimuli, the interval formed by these two stimuli appeared less compressed than when they corresponded.What might be the neural basis of masked-induced time compression? In order to estimate the interval duration, the occurrences of the interval markers must be compared to neural activity which somehow codes the elapsed time. The most prominent account of neural time representation is the ticking clock model16et al.2125These results are reminiscent of the literature on temporal order judgments in visual-tactile ventriloquismThe data from our Experiment 3 showed that temporal compression occurred when attention was drawn away from the interval end marking stimulus. The mask, acting as a salient distractor, might similarly have reduced attention toward the interval end marking stimulus. However, this interpretation has to be treated cautiously since the absence of an interaction effect in Experiment 3 suggests additive effects of the spatial attention manipulation and the masking. In this view, spatial attention drawn away from the interval end marking stimulus led to a compression of the interval duration. This account is consistent with studies in which spatial attention shifts away from the temporal interval stimulus led to duration underestimation3427In cases of visual disruptions, like eye movements or shifts of attention, the problem of object continuity occurs. As experiments on change detectionSeven subjects , mean age 25 years) participated in the condition where the right hand was tested and six subjects , mean age 25 years) in the condition where the left hand was tested. All had normal or corrected to normal vision and all except the authors were naive to the purpose of the experiment. Written informed consent was obtained prior to each experiment in accordance with the Declaration of Helsinki. Participants were remunerated for their time. All experiments of the study was approved by the ethics committee of the German Society of Psychology (DGPS) and conducted in accordance with their guideline.Stimuli were presented against a grey background. A fixation point (0.86 × 0.86°), illustrated by the square in If bimodal neurons are involved in the putative effect of visual-tactile temporal compression then the spatial principle of multisensory integration should govern the effect. This principle states that multisensory neurons are activated only if stimulation enters the separate modalities from the same region in external space, since their receptive fields are in spatial registerIn total, we manipulated three factors: The hand to which we presented the tactile stimulus (left vs. right hand), the side of fixation (left vs. right side), and the three positions at which we presented tactile stimuli (0° vs.12° vs. 24°). This resulted in a total of 12 experimental conditions, which were tested in blocks of 40 trials each with a duration of 3 minutes. We divided subjects into two groups by side of fixation. The factors hand and position of tactile stimuli were tested as within-subjects factors. The presentation order of the experimental conditions was counterbalanced between subjects and each subject did one preparation block that was also counterbalanced and not used for further calculations. For our analyses, we averaged over the last 10 trials of each block to obtain a reliable threshold value.Nine subjects ) with normal vision participated in the experiment. All participants except for the authors were naive to the purpose of the experiment.The trial setting was the same one as in Experiment 1. The two intervals were now initiated by two tactile stimuli, presented simultaneously for 50 ms. Intervals were ended by two visual stimuli, presented simultaneously for 16 ms. Tactile stimuli were always presented at the same positions: one 2.16° right and 2.16° above the center of the screen, the other 2.16° left and 2.16° below the center of the screen. Visual stimuli were presented either at the same positions as the tactile stimuli (same condition) or on the opposite side at an equivalent eccentricity as the tactile stimuli (different condition). In the different condition, visual stimuli were presented 2.16° right and 2.16° below the center of the screen, and 2.16° left and 2.16° above the center of the screen, respectively. The positions of the visual and tactile stimuli are shown in Fifteen subjects  with normal vision participated in the experiment. All participants except for the authors were naive to the purpose of the experiment.Participants were required to keep their gaze on a fixation point (0.88°x0.88°) which was shown at the screen center. After 1000 ms two dots (radius: 1.36°) were presented, one in the left and one in the right part of the screen at a horizontal eccentricity of 9° and 6.8° above the horizontal meridian. The dots marked the start of the probe interval and were shown for 16 ms. After 500 ms a single dot (radius: 1.36°) was shown, which marked the end of the interval. In 80% of all trials this dot was shown in the right part of the screen and in 20% of all trials in the left part of the screen at the same horizontal eccentricity as the first two dots and 6.8° below the horizontal meridian. In half of all trials, the bar marking the interval end was masked by a visual whole field mask, which was presented for 50 ms . After 9Written informed consent was obtained prior to each experiment in accordance with the Declaration of Helsinki. Participants were remunerated for their time. All experiments of the study was approved by the ethics committee of the German Society of Psychology (DGPS) and conducted in accordance with their guideline.How to cite this article: Zimmermann, E. et al. The functional role of time compression. Sci. Rep.6, 25843; doi: 10.1038/srep25843 (2016).


Methods for retrieving protein tertiary structures from such a large database have turn out to be the key to comparative analysis of structures that plays an important role to understand proteins and their functions. In this paper, we present fast and accurate methods for the retrieval of proteins having tertiary structures similar to a query protein from a large database. Our proposed methods borrow ideas from the field of computer vision. The speed and accuracy of our methods come from the two newly introduced features- the co-occurrence matrix of the oriented gradient and pyramid histogram of oriented gradient- and the use of Euclidean distance as the distance measure. Experimental results clearly indicate the superiority of our approach in both running time and accuracy. Our method is readily available for use from this website:  Proteins perform a large array of functions within a living organism. Proteins are polymers of amino-acid monomers. Linear sequence of amino acids is called the primary structure of a protein. There are three other levels of structural complexity: secondary, tertiary and quaternary. Secondary structure is the local spatial arrangement of the backbone atoms formed by the intramolecular and intermolecular hydrogen bonding of amide groups. Tertiary structure refers to the three-dimensional structure of an entire polypeptide chain and quaternary structure is the spatial arrangement of two or more polypeptide chains known as sub-units.Every protein has a unique, stable and kinetically accessiblehttp://wwpdb.org/). RCSB PDB is the primary repository for known protein structures. As of February 11, 2014, the number of protein structures stored in PDB has been more than 97,789. With this increase in the number of known protein structures, the need for fast and accurate protein tertiary structure retrieval algorithm is now greater than ever. In the protein tertiary structure retrieval problem, the task is to retrieve proteins having the most similar structure to the query protein from the known structure database. The results are ranked based on similarity or distance measures.Following the determination of the first tertiary structure in 1958 using crystallographyα carbon distance matrix is widely used in the literature8α carbon distance matrix gives the intra-molecular distances of α carbons in a protein chain. This α carbon distance matrix resembles the backbone of the tertiary structure of a protein and the secondary structure elements conserved in it. Notably, α carbon distance matrix based exact algorithms run in O(N!) time assuming that the input matrix is of size N × N.There exist numerous approaches in the literature focusing on fast and accurate retrieval of protein tertiary structures. The traditional way to compare structures is to treat each one as a rigid three-dimensional object and superimpose one on the other. Differences are then calculated using different distance metrics, e.g., least-squares method. In the pattern recognition literature, the structures are often represented by feature vectors and similarity or dissimilarity is measured by comparing the feature vectors with one another. As a feature to represent the tertiary structure of a protein chain, the DALIα carbon distance matrices. Each distance matrix is decomposed into sub-matrices of fixed size that are called the elementary contact patterns. It compares those contact patterns (pair-wise), and store the matching pairs in a list with a matching score. Then, it assembles pairs in the correct order using Monte Carlo optimization to yield the overall alignment and the final matching score. In a later version, it has used branch and bound method to assemble the pairsCE methodα carbon distance matrix as the feature vector and uses combinatorial extension and Monte Carlo optimization to compare protein structures in a way that is quite similar to DALI. Both DALI and CE require lots of computation and hence the corresponding web servers respond to the query requests via email only after a certain period that is required to complete the costly processing. A faster approach based on the α carbon distance matrix as a feature is MatAlignO(N4) dynamic programming solution where N is the dimension of the distance matrix. MatAlign compares distance matrix of the query protein and the target protein row by row and builds up a dynamic programming table based on a row by row matching score. Then, it aligns rows of the query protein with the rows of the target protein to maximize the corresponding matching score. So, higher matching score indicates higher similarity between the protein structures.A celebrated web server named ProteinDBS for protein structure retrieval algorithm has been reported inet al.et al. have introduced MASASWDaubechies-2 wavelets can provide improved accuracy over others. MASASW transforms all the α carbon distance matrices into a 32 × 32 matrix by interpolation and wavelet transformation. It then compares them like MatAlign but with a sliding window to reduce the number of comparisons. The time complexity of MASASW is O(wWN2) where w and W are window sizes and N is the dimension of the distance matrices. As has been mentioned above, MASASW assumes N = 32.Marsolo Co-occurrence Matrix of the Oriented Gradient of Distance Matrices (CoMOGrad) and the Pyramid Histogram of Oriented Gradient (PHOG). Additionally, as will be reported later, our proposed algorithm gives more accurate results than the state of the art methods. Very briefly, the speed and accuracy of our method can be attributed to the novel features from the field of computer vision and pattern recognition. Our aim has been to introduce a feature which does not require any complex algorithm to compare the tertiary structures. And indeed, a simple distance measure to calculate the distance between the two vector quantities has been used in our approach.Although several methods are found in the literature for protein structure retrieval, the quest for even faster and more accurate methods still continues as the number of known protein structures grows at a rapid pace. In this paper, we present an extremely fast and highly accurate method of retrieving proteins from a large database. In particular, here we present an ultra fast algorithm based on two novel feature vectors. These are the α carbon distance matrix as their feature vector. Upon analyzing the tertiary structure and α carbon distance matrix represented as a gray-scale image, we have observed that not all data in the matrix are equally important. We further have realized that the co-occurrence matrix of the oriented gradient of the distance matrix is the most important feature with respect to the comparison of tertiary structures. These observations and findings motivate us to use these as our feature vectors. Finally, we have anticipated that the Euclidean distance or α carbon distance matrices. In the sequel, the combination of the above ideas has given us an extremely fast method without sacrificing the accuracy.At this point a brief discussion on our rationale to use CoMOGrad and PHOG as our feature vectors is in order. As has already been mentioned above, some previous methods in the literature have used the et al. also used a similar approach inet al. start from generating distance matrix images. In particular, Chi et al. extracted some texture features of the images from the original size images and diagonally partitioned images. Subsequently, they used well known features of image processing. The key difference between the two methods lies in that, unlike their method, we have taken gradients of the image first and then have used the co-occurrence matrix and histogram. There exist differences in the implementation level as well: Chi et al. used multiple CPU clusters to improve speed and their search space is also distributed, whereas, our method gives a single CPU solution.Note that the idea of borrowing features from the field of computer vision and applying it in protein structure retrieval is not new. In particular, Chi n atoms, among Another interesting technique from computer vision called geometric hashing22232526α carbon distance matrices and the tertiary structures. We have observed that the α helices and the anti-parallel beta sheets appear as dark lines parallel to the diagonal dark line and parallel beta sheets appear as dark lines normal to the diagonal dark line. Beta sheets of two strips appear as one dark line normal to the diagonal; beta sheets of three strips appear as two dark lines normal to the diagonal and one dark line parallel to the diagonal. In general, for a standard beta sheet, the number of points of co-occurrence of parallel and anti-parallel diagonal lines depends on the number of strips in the beta sheets. Again, the number of single parallel lines depends on the number of standard α helices. Moreover, lengths of those lines near the diagonal region are proportional to the lengths of the α helices. The distance of the parallel lines from the diagonal dark line is proportional to the radius of the α helix. α carbon distance matrix of a tertiary structure of a protein with beta sheets as a gray scale image. In the gray scale image, the 7 anti-parallel dark lines near the diagonal dark line correspond to the presence of 8 beta sheets in the corresponding protein structure and the lengths of those dark lines are proportional to the lengths of the beta sheets. α carbon distance matrix of a protein tertiary structure with two alpha helices. Here in the image, the two parallel dark lines near the diagonal dark line correspond to the presence of two α helices in the protein structure and the lengths of the dark lines are proportional to the lengths of the α helices.We have carefully analyzed the gray-scale images from the α carbon distance matrix gray-scale image, it is clear that the SSEs are represented by the orientation of the dark lines at the near diagonal region of the α carbon distance matrix image. The positions of the SSEs in a protein chain are represented by the positions of the dark lines at the near diagonal region of the image. Here, near diagonal region refers to the region nearby the diagonal dark line. The sizes of the SSEs are represented by the lengths of the dark lines at the near diagonal region. The orientation of the SSEs are represented by the presence of the dark lines at the far diagonal regions and the darkness and orientation of those dark lines. Here, far diagonal region is the region in the image that is distant from the main diagonal dark line. Therefore, we need to incorporate the gradient orientation angle along with the gradient magnitude and gradient spatial orientation to incorporate the orientation of the SSEs in the feature vector. We also need co-occurrence of gradient angles. Based on our study and analysis, we introduce Co-occurrence Matrix of the Oriented Gradient (CoMOGrad) as our first feature vector which incorporates gradient orientation angles and the co-occurrence of the gradient orientation angles. This feature enables us to devise an algorithm that can facilitate extremely fast comparison. However, this speed is achieved at the cost of a slight decrease in the accuracy. Subsequently, we introduce another feature vector, namely, the Pyramid Histogram of Oriented Gradient (PHOG) by incorporating the spatial information and gradient magnitude. The combination of both CoMOGrad and PHOG features results in an algorithm that is not only faster but also highly accurate.In the contemporary literature of computer vision and digital image processingα carbons in residues is a good candidate to transform the 3D structure to the corresponding 2D vector representation as shown in MASASWet al.α carbons in the polypeptide chain. Proteins with similar tertiary structures will have similar distance matrices and vice versa. As stated earlier, if we consider the matrix as a monochromatic image, α-helices and parallel β-sheets will appear as dark lines parallel to the main diagonal and antiparallel beta sheets will appear as dark lines normal to the main diagonal. This distance matrix feature also has a very appealing property: this is translation, scaling and rotation invariant.Distance matrix of the Interestingly, since it is a two dimensional matrix like a digital image we can easily apply image processing and computer vision algorithms on it. Similarly, we can use this matrix as an adjacency matrix and interpret it as a graph. Subsequently, graph theory techniques may also be applied to solve the tertiary structure retrieval problem with this feature. In this paper, we apply ideas from the field of image processing and computer vision. Most recently these ideas have got their niche in pedestrians and car detection29α carbons, the dimensions of their α carbon distance matrices vary. Therefore, a natural approach is to scale the distance matrices to the same dimension. For scaling the distance matrices, we use the methods of digital image processing used for image resizing. At first, we scale all the images to the dimension that is a power of 2 and the nearest to their original dimension. As an example, if the image dimension is 80 × 80 we scale it to 64 × 64 and if the original image dimension is 100 × 100 we scale to 128 × 128. For this step, we use bi-cubic interpolation.As different protein chains have different number of Note that similar scaling approaches have also been adopted by other researchers in the literature e.g.,. HoweverMASASWapproximate detail, horizontal detail, diagonal detail and vertical detail. Each of these images is half of the original image in dimension. We take the approximate detail to scale a large image to a smaller size since this is the approximate sub sampled image. For the images with dimension greater than 128 × 128, we perform wavelet transform on each image multiple times to get the approximate coefficient of size 128 × 128. For an image with dimension less than 128 × 128, we first perform wavelet transform on it. Then using bi-cubic interpolation, we scale all four coefficients to twice their initial size. After that, applying inverse wavelet transform on the four scaled wavelet coefficients, we get the original image scaled up to twice its initial size. With repeated application of scaling up (down) the images that are smaller (higher) than dimension 128 × 128, we finally get all of them in the desired dimension of 128 × 128.After scaling the images as mentioned above, we apply wavelet transform to transform all the images to the same dimension. Notably, wavelet transform is the most widely used technique for image scaling or resizing in digital image processing. Using wavelet transform, we scale all images to 128 × 128 dimension as, during our study, most of the images in the previous step have been found to be of that dimension. For wavelet filter we use Daubechies-2 waveletα carbon distance matrices used by MASASW and MatAlign. As anticipated, introduction of this feature makes the comparison method much simpler and faster.After having all the images at dimension 128 × 128 we extract our CoMOGrad feature. First we take the gradient of each of the images and compute the gradient angle and magnitude. As the angle values are continuous quantities, we have to quantize those values. For quantization, we have tuned the number of quantization bins as a parameter. With experiments using various bin sizes , we have found that using 16 bins with a bin size of 22.5 degree gives excellent results. After quantization to 16 bins, we compute co-occurrence matrix which is a 16 × 16 matrix. We convert this 16 × 16 matrix to a vector of size 256. This is our CoMOGrad feature vector. With this feature, we can simply take Euclidean distance to compare structures rather than using the alignment technique of et al. intop-left, top-right, bottom-left and bottom right. Each of these images is of size one fourth of the original image. In The use of CoMOGrad gives us an ultra fast structure retrieval algorithm. However it achieves this speed at the cost of some reduction in accuracy. From the discussion in the previous sections, it is clear that we have to incorporate the gradient magnitude and spatial orientation of gradient along with the angular orientation of gradient to accurately capture the tertiary structure of a protein. The CoMOGrad feature only includes the angular orientation of gradient and the co-occurrence of angular orientation of gradient. To incorporate the gradient magnitude and the spatial orientation of gradient along with the angular orientation of gradient, we extract another feature named pyramid histogram of oriented gradient (PHOG) together with our CoMOGrad feature to improve the accuracy. PHOG was first proposed and successfully used in object classification and pattern recognition by Bosch fq and fi denote the feature vectors of the query protein q and a protein i in the database, respectively. Then the distance score q and i would be calculated according to We use the Euclidean distance or O(N) time, where N = 1021 is the size of our feature vector. Also, recall that for CoMOGrad alone, N = 256 only. Our algorithm needs to compute i in the database and then sort the results to rank them.Clearly, the above distance measure can be calculated in MATLAB R2012. The experiments have been conducted on GNU/Linux debian ubuntu i686 operating system. We have used a machine having Intel (R) Core(TM) i5 3470 CPU 3.20 GHz with 4 GB RAM. We have compared our methods with the tertiary structure retrieval method, MASASWhttp://research.buet.ac.bd:8080/Comograd/. The source code of our implementation can be found at: https://github.com/rezaulnkarim/protein_tertiary_structure_retrieval-.We have implemented our algorithm in Java (jdk 1.6) with Netbeans IDE and MySQL database. The feature extraction has been done Using SCOP33SCOP domain as the search space and extracted features from them. As the query proteins, we have used 4965 protein chains from 417 SCOP family, 330 superfamily, 234 folds and 11 classes. We have run all the queries over the search space using our methods and sorted the results in ascending order based on our distance measure. We also have run MASASWSCOP labels, namely, class, fold, superfamily and family. Lists of the query sequences as well as the search space sequences are provided as For our experiments, we have used When a query protein is searched, the system  returns a number of top ranked results. Suppose, we are searching Protein family are 40, 42 and 48 respectively. Then the average number of family match is (40 + 42 + 48)/3 = 45 and the percentage of family match is (45 × 100)/(50) = 90 percent.The accuracy defined here is similar to the term precision used in statistics and machine learning. However, we prefer to use the term accuracy. In our experiments we measure average accuracy by taking the average of the numbers of a SCOP label match from multiple queries  and using it in the numerator of number of top results” in each line graph, gives the number of top ranked query results considered whereas the vertical axis entitled “% of ‘label’ match” gives the average number of query results that have matched the SCOP ‘label’ with the corresponding query protein. So, each point in a graph reports the average number of query results having the same SCOP ‘label’ as the corresponding query protein for a specific number of top results. Each line graph reports results for 4 methods: the method of SCOP labels, i.e., class, fold, family and superfamily in the line graphs presented in The comparison of the accuracy of the query results is provided in Loading Time is the time needed to load the feature vectors from disk to memory which is done once when the system starts. The Time Per Query in the table is the time needed to compare the feature vector of a query protein with that of all the 152,487 proteins in our protein database and sort the results based on the distance/similarity measure and to return the sorted top ranked results. Note that the query time reported excludes the loading time. The Query Time in the table indicates the total time needed for 100 query structures. The results indicate that the query time for the variant with only the CoMOGrad feature is ultra fast albeit at the cost of some reduction in the accuracy as is evident from MASASW.The time required to retrieve the results for all three methods are reported in To measure the quality of our distance measure, we have conducted a separate set of experiment as follows. We have examined the performance of a binary classifier designed with our feature sets. In particular, we have evaluated the Matthews correlation coefficient (MCC) values with the binary classifier partition at various distances. The Matthews correlation coefficient is a measure of the quality of binary classificationsPlotting MCC for binary classification with discrimination at various distance values shows that the MCC graph is convex which indicates that this measure is good for binary classifiers. And MCC have highest value at distance 0.011 which indicates that binary classification at distance 0.011 will give the best classification. Also MCC value there is nearly 0.9 which is very good. From this observation it is clearly evident that the distance measure is an effective one See .α carbon distance matrix runs in O(N!) time assuming that the input matrix is of size N × N. The time complexity of MASASW for comparing two features is O(wWN2), which assumes N = 32 as the dimension of the distance matrix. Here, W and w are the size of the sliding windows to align matrices and to align rows, respectively. The authors of MASASW have empirically obtained the most reasonable and suitable values for W and w which are 5 and 8 respectively. Our CoMOGrad feature is a vector of size 256 and the combination of CoMOGrad and PHOG gives us a feature vector of size of 1021. For both of our methods, the run time for comparing two features is just O(N) where N is the length of feature vector. Therefore, when only the CoMOGrad feature is used, the time to compare two features is approximately (32 × 32 × 5 × 8)/(256) = 160 times faster than MASASW. And, the combination of CoMOGrad and PHOG is approximately (32 × 32 × 5 × 8)/(1021) = 40 times faster than MASASW in this respect. The feature extraction of the query protein as and when it is submitted as a coordinate file in PDB format does not have noticeable effect on the running time as this operation is done for one, i.e., the query protein only; the features of all the proteins in the target search space  are made available beforehand as they have already been preprocessed.The exact algorithm for the matching of tertiary structures with MASASW, the query time of the variant with CoMOGrad is almost 30 times and the variant with CoMOGrad and PHOG is more than 7 times faster. As mentioned in our earlier discussion, theoretically the variant with CoMOGrad is 160 times and the variant with CoMOGrad and PHOG is 40 times faster than MASASW in comparing two features. The apparent discrepancy between the theory and practice with respect to running time can be attributed to the fact that, in addition to the feature comparison, the actual retrieval algorithm needs to perform a sorting operation on the results of the distance/similarity values of all the proteins in the database against the query protein. Note that both MASASW and our two methods need to use this sorting algorithm. MASASW however sorts in descending order because it uses a similarity measure. As a result the actual improvement in the query time achieved by our methods does not completely match with the theoretical deduction.As can be see from MASASW. On the other hand, the combination of CoMOGrad and PHOG, both with or without scaling the alpha carbon distance matrix images, achieves a higher degree of accuracy. We notice that for the class label, the non-scaled version shows better performance than the scaled version. However, for the other three SCOP labels, the non-scaled version performs better up to a cutoff point (around 20) after which the scaled version seems to be superior. Note that in most practical scenarios, only the top few results of the returned result set are of interest. Hence, from the results reported in In terms of accuracy, the performance of our method using only the CoMOGrad feature is almost similar to that of MASASW. This suggests that our methods are faster than ProteinDBS v2.0. Nevertheless, it would be interesting to conduct a direct comparison with ProteinDBS v2.0 or a later version on a level-playing ground. This seems possible in near future when the implementation of a parallelized version of ProteinDBS series in Hadoop is expected to be released.In our experiments, we have only compared our methods with MASASW which to the best of our knowledge is the best algorithm in the literature to date. Another candidate for comparison perhaps is ProteinDBS v2.0, the successor of the celebrated web server ProteinDBS. However, as it turns out ProteinDBS v2.0  is a complex system and hence it is not possible to simply package the codes for comparison under the current implementation.In the absence of a running interface or any implementation we are not able to include ProteinDBS v2.0 in our experiments. Since direct comparison through experiments are not possible, we here make an effort to do an indirect comparison as follows. While presenting ProteinDBS inn, that of alpha carbons by m and that of chains in the database by N. First of all, geometric hashing technique first requires to examine all the O(m3) triplets for a protein with m alpha carbons, which incurs huge computational cost for large proteins. Then, before the actual comparison is done, a hash table is prepared to contain all the selected triplets with the sides of the corresponding triangles as keys to the hash table. Then, triplets are selected from the hash table one after another using appropriate filtering constraints. For each selected triplet, an orthogonal system represented by the 3 atoms as basis is computed. Then the coordinates of other m − 3 atoms are transformed to that orthogonal system and the new coordinates are compared to the alpha carbon coordinates of the target structure to compute the score. This process is repeated for all the triplets to optimize the score. These iterations also incur huge computational cost. Clearly, our methods require less computing in preprocessing/learning as well as in the comparison step. Additionally, our methods also do not require any complex data structure like nonlinear hash table required by the geometric hashing technique. Informatively, the matching and comparison stage of geometric hashing is also computation and memory intensive procedure as it employs a voting technique requiring a lot of memory for the voting entries.Another candidate for comparison is the geometric hashing method used in2526SCOP classification hierarchy. We have reported average percentage of matching for class, fold, super family and family of our retrieval results with the query protein while most of the works in the literature have only shown similarity on only class and fold; very few in fact have worked on automated similarity match for the lower levels. Our results are in good compliance with the SCOP classification. CoMOGrad feature is ultra fast as compared to the state of the art methods but this extreme speed is achieved at the cost of a slight reduction in the accuracy. The combination of CoMOGrad with PHOG is also very fast and at the same time is superior in terms of accuracy as compared to state of the art methods. This creates the opportunity to implement a web based service for the protein tertiary structure retrieval with a truly online behaviour, i.e., a web-server that can provide the results in seconds, while the present web services usually provide query results via email only.In this paper, we have presented two novel features, namely, CoMOGrad and PHOG, for faster and accurate retrieval of the protein tertiary structures. We have compared our results considering all the levels of How to cite this article: Karim, R. et al. CoMOGrad and PHOG: From Computer Vision to Fast and Accurate Protein Tertiary Structure Retrieval. Sci. Rep. 5, 13275; doi: 10.1038/srep13275 (2015).


The practical advantages of this visualization method are its rapid and simple implementation, with many potential applications including in characterising acoustic pumping within microfluidic channels. It also enables low-cost characterisation of IDT designs thereby allowing the determination of anisotropy and orientation of the piezoelectric substrate without the requirement for sophisticated and expensive equipment. Here, we show that the optical visibility of the sound path critically depends on the physical properties of the liquid film and identify heptane and methanol as most contrast rich solvents for visualization of SAW. We also provide a detailed theoretical description of this effect.We demonstrate that the propagation path of a surface acoustic wave (SAW), excited with an interdigitated transducer (IDT), can be visualized using a thin liquid film dispensed onto a lithium niobate (LiNbO Surface acoustic waves (SAW), excited and detected by interdigital transducers (IDTs), are used in a wide range of common devices, such as wireless network systems, mobile phones and navigation systems. Other fields of application include gas-sensors458101113141516181920222325272131728in situ, requiring techniques like laser vibrometry34To date, such characterisation tasks remain expensive, time-consuming, and often difficult to perform In order to analyse an IDT, we deposited a microliter volume of liquid onto the chip using a pipette tip to wet the chip completely with a thin film as shown in First we excited two opposed tapered TIDTs  at different frequencies and isopropanol for the liquid layer . The resTo verify our new method, the position 3 is an anisotropic substrate, the velocity of sound varies with the direction of travel. The experimental values are in good agreement with the literature values of 3772 m/s, 3830 m/s and 3864 m/s for the different angles at each of the positions (piezoelectric shorting is included in these velocities)We used this new visualization method to determine the velocity of the SAW on the piezoelectric substrate accordingly. The wavelength λ of a TIDT at the excited frequency f can be measured, using the visible sound path position. The results shown in 3 and hence the velocity of the SAW was anisotropic, leading to a shift Z = 900 ± 77 μm of the focal point towards the IDT for the focusing IDT . Our simple measurements deliver an anisotropy factor bexp = −0.216 ± 0.026. The deviation between theoretical and experimental values was less than 6%.The literature valueTo explore the dependence of the effect on the acoustic properties of the fluid layer, different solvents were used in a series of experiments . The experimental set-up involved using a single focusing IDT, as described above. All experiments were performed with the same power of 18 dBm. A contrast increase with experimental time is caused by evaporation of the fluid. To avoid the influence of the thickness of the fluid layer over time (where a contrast increase occurs with decreasing thickness of the fluid layer) a standardized volume of liquid (20 μl) was dispensed on the whole area of the chip. A single image was extracted of the video shortly (<1 s) after application of the RF-signal. To determine the optical contrast, the grey value in the area in front of the IDT was averaged in vertical direction  and the Therefore heptane and methanol were the most suitable fluids for visualizing the SAW with this new method, in that they provided the highest contrasts.In order to explain the optical contrast in the experiments, the coupling and reflection of the SAW has to be understood. The excited SAW on the substrate couples into the fluid under the Rayleigh angle 3 and the fluid, respectively. Since the acoustic wave velocity on LiNbO3 was more than two times larger than the velocity in the fluid, the Rayleigh angle is 30° or lower, most of the acoustic pressure is pointing towards the surface of the fluid. A large acoustic impedance mismatch exists at the interface of the fluid and air, resulting in reflection of most of the acoustic energy. The reflection coefficient R was given byThere by The acoustic radiation pressure at the surface leads to a deformation of the fluidic surface and causes an optical contrast between excited and non-excited sections of the film visible with the microscope52As an example for a qualitative explanation, the acoustic path of a focus IDT is visualized  and the Inset of et al., who examined deformation caused by acoustic radiation pressure in their works52This explanation is based on the observations and explanations by Issenmann They found that the deformation height is a stationary state, where acoustic radiation pressure, gravity forces and surface tension are in balance. The deformation height depends on the pressure amplitude and applied frequency of the transducer and on the properties of the fluid: surface tension E1 is the first argument of the exponential integral function. ω is the beam waist, roughly approximated by three wavelengths in our experiments (3∙60 μm) and g the gravitational constant. The acoustic pressure amplitude contrast .−3 to 21∙10−3For our experiments the Bond number 3) via electron beam evaporation. The specific cut is chosen to ensure that only Rayleigh waves  are excited by the IDT and that other modes are almost completely supressed. Optionally, a 200 nm thick SiO2 layer was sputtered onto the LiNbO3 chip with the IDT for mechanical protection. The device was connected to a frequency generator  and mounted on a microscope . For the illustrating 2-plasma. For evaluating the optical contrast the software ImageJWe used different designs to excite the SAW . To fabricate the IDTs, layers of 10 nm Ti, 50 nm Au and 10 nm Ti were deposited onto a 17.5 mm x 17.5 mm piezoelectric substrate .