Mice have a large visual field that is constantly stabilized by vestibular ocular reflex (VOR) driven eye rotations that counter head-rotations. While maintaining their extensive visual coverage is advantageous for predator detection, mice also track and capture prey using vision. However, in the freely moving animal quantifying object location in the field of view is challenging. Here, we developed a method to digitally reconstruct and quantify the visual scene of freely moving mice performing a visually based prey capture task. By isolating the visual sense and combining a mouse eye optic model with the head and eye rotations, the detailed reconstruction of the digital environment and retinal features were projected onto the corneal surface for comparison, and updated throughout the behavior. By quantifying the spatial location of objects in the visual scene and their motion throughout the behavior, we show that the prey image consistently falls within a small area of the VOR-stabilized visual field. This functional focus coincides with the region of minimal optic flow within the visual field and consequently area of minimal motion-induced image-blur, as during pursuit mice ran directly toward the prey. The functional focus lies in the upper-temporal part of the retina and coincides with the reported high density-region of Alpha-ON sustained retinal ganglion cells. Mice have a lot to keep an eye on.To survive, they need to dodge predators looming on land and from the skies, while also hunting down the small insects that are part of their diet. To do this, they are helped by their large panoramic field of vision, which stretches from behind and over their heads to below their snouts.To stabilize their gaze when they are on the prowl, mice reflexively move their eyes to counter the movement of their head: in fact, they are unable to move their eyes independently. This raises the question: what part of their large visual field of view do these rodents use when tracking a prey, and to what advantage?This is difficult to investigate, since it requires simultaneously measuring the eye and head movements of mice as they chase and capture insects. In response, Holmgren, Stahr et al. developed a new technique to record the precise eye positions, head rotations and prey location of mice hunting crickets in surroundings that were fully digitized at high resolution. Combining this information allowed the team to mathematically recreate what mice would see as they chased the insects, and to assess what part of their large visual field they were using.This revealed that, once a cricket had entered any part of the mice’s large field of view, the rodents shifted their head – but not their eyes – to bring the prey into both eye views, and then ran directly at it. If the insect escaped, the mice repeated that behavior. During the pursuit, the cricket’s position was mainly held in a small area of the mouse’s view that corresponds to a specialized region in the eye which is thought to help track objects. This region also allowed the least motion-induced image blur when the animals were running forward.The approach developed by Holmgren, Stahr et al. gives a direct insight into what animals see when they hunt, and how this constantly changing view ties to what happens in the eyes. This method could be applied to other species, ushering in a new wave of tools to explore what freely moving animals see, and the relationship between behaviour and neural circuitry. The visual system of mice serves a variety of seemingly opposing functions that range from detection of predators, to finding shelter and selection of food and mates, and is required to do so in a diverse set of environments . For exaGiven the VOR stabilized panoramic field of view, it is not clear what part of the visual field mice use to detect and track prey but see: . Mouse rThe challenge is to measure what part of the visual field the mouse is attending to during a visually based tracking task  and the Here, we measured the position of a cricket in the visual fields of freely moving mice performing a prey pursuit behavior, using head and eye tracking in all three rotational axes, namely horizontal, vertical, and torsional. Eye tracking included an anatomical calibration to accurately account for the anatomical positions of both eyes. To quantify object location in the animal’s field of view and generate optic flow fields, head and eye rotations were combined with a high-resolution digital reconstruction of the arena to form a detailed visual map from the animal’s eye perspective. Given that mice use multisensory strategies during prey pursuit  and can To measure what part of the visual field mice use during prey capture while also considering that mice can use multisensory strategies during prey pursuit , we firsAcheta domesticus), shown previously to be readily pursued and preyed upon by laboratory mice , and a rapid reduction in the Euclidean distance to the cricket , consistent with previous findings . Thus, during the tracking and pursuit behavior the image of the prey consistently fell on a local and specific retinal area that we refer to from here on as the functional focus. The functional focus fell within the binocular field, while the region of elevated density of RGCs has been found to be located near the optical axis , and a ~54% increase in the mean alignment error over all frames for the reference location  were all significantly smaller in the tracking epochs .If mice successfully track and capture prey by retaining the target in front of them then this should be reflected in the trajectories taken by the mice during the tracking epoch compared to the non-tracking behavioral epochs. During cricket tracking periods, mice ran directly toward the cricket, and their trajectories were significantly straighter than during equivalent non-tracking phases . LateralTogether this suggests that mice do not make compensatory vertical head movements, tracking eye movements or vergence eye movements to keep prey within their functional foci, but instead retain their target within a restricted bearing by running straight towards it. This raised the question of what is unique about the position of the functional focus on the cornea?−20, lower right eye visual field: 361.91 ± 168.80 °/s, upper right eye visual field: 40.59 ± 22.79 °/s, p = 6.68x10−19, Two-sided t-test, unequal variance, N=2 mice), due to the greater distance between ceiling and mouse . Given the advantage of low optic flow to mammalian vision, we next quantified the position of least optic flow during prey tracking. We calculated the location of the translational optic flow minimum in each frame for each eye, and created a probability map of this location over the visual field  of both eyes, with nose-down pitch counteracted by excyclotorsion . These rIn summary, we show here that during pursuit in mice the image of the intended prey falls consistently in a localized region of their visual fields, referred to here as the functional focus, and that this occurs through the animal orientating their head and body and running directly toward the prey rather than with specific eye movements. The location of the functional focus is within the binocular visual field, but in addition also coincides with the region of minimal optic flow during the pursuit, and presumably also minimally distorted by motion blur.Acheta domesticus, Bugs-International, Germany) were housed in 480x375x210 cm cages with ad lib water and food (powdered mouse chow). During experiments in which head and eye rotations were recorded cricket body sizes ranged from 1 cm to 2 cm .Experiments were carried out in accordance with protocols approved by the local animal welfare authorities . Experiments were carried out using male C57Bl/6J mice acquired from Charles River Laboratories. At the time of the cricket hunting experiments, mice (n=9) were between 2 and 8 months old, and weighed between 21 and 29 g. Mice were maintained on a 12 hr light/dark cycle. Crickets (2O2 (3%). A custom-made implant, consisting of a flat circular attachment surface for attachment to the skull, and implant body with three anti-rotation pins and a magnet , and analgesia was provided with carprofen (7 mg/kg delivered s.c.). Body temperature was maintained using a thermostatically regulated heating pad. Respiration rate and depth of anesthesia was monitored throughout the procedure. Following opening of the skin and removal of connective tissue overlying the sagittal suture and parietal bones, the skull was cleaned with Ha magnet , was fixThe eye cameras for oculo-videography were mounted on mounting arms which were attached to a baseplate with complementary holes to the anti-rotation pins on the implant and fitted with a magnet of complementary polarity. During positioning of the head-camera, mice were anesthetized with isoflurane . Anesthetic depth and body temperature were monitored as above. The cameras were positioned to have a sharp image of the entire eye, with the mounting arms adjusted such that the cameras and mounting system caused minimal disruption to the mouse’s lateral and frontal field of view. Mounting arms were secured with cyanoacrylate adhesive glue . The eye-camera system was then removed and the animal allowed to recover.Mice were acclimated to cricket capture in their home cage. Individual crickets were placed in the mouse’s home cage overnight, in addition to their standard ad lib mouse food. Mice were handled and habituated to the experimenter, the head cameras, and the head tracking mounts. The ability of each mouse to visually track the crickets was assessed using the protocol of 2) paste dots (~300 μm) applied to ventral and/or temporal locations on the cornea as described in 2 paste consisted of TiO2 powder  mixed with a small quantity of sterile artificial cerebrospinal fluid solution with the following composition (in mM): 135 NaCl, 5.4 KCl, 1.8 CaCl2, 1 MgCl2, 5 HEPES, pH balanced to 7.2 (300 mOsm/l). Application of the TiO2 spots was performed with the animal anesthetized with isoflurane . Anesthetic depth and body temperature was monitored as above. Following application of TiO2 spots, mice were allowed to recover for >45 min prior to a cricket hunt. The presence of the TiO2 marks did not significantly change the animal’s cricket hunting performance as assessed by the average time taken to capture crickets  without head camera mounts. During subsequent training sessions, mice were acclimated to cricket hunting, with the head cameras on, in the experiment arena. Auditory white noise  was provided through four speakers , one on each wall of the arena. Olfactory noise was provided by ventilating the arena  through a perforated floor (5 cm perforation spacing) with air blown through a cage containing live crickets (cricket cage dimensions 480x375x210cm). During experiments the arena was lit by a single lamp  positioned ~1 m above the arena. During each experiment, the mouse was given 5 min to explore the arena without head cameras. After this period, the mouse was removed from the arena and the head cameras were mounted. At the commencement of each hunt, the cricket was released at a variable location into the central region of the arena.Head and eye tracking was performed as described in The positions of the cricket within the arena were recorded using four cameras  fitted with NIR-blocking filters . Cameras were located ~1.5 m above the arena and were positioned so that the arena was covered at all points by two or more cameras from differing vantage points. Mouse IR-head tracking LEDs were recorded at 200 Hz using four cameras . Image acquisition, synchronization, and mouse head rotation calculations were performed as described previously .i-led.co.uk, Berkshire, UK) run at 1 A. Position tracking LEDs, medial canthi, nares, mirror corner and camera chip corner positions were marked in two or more camera views, in multiple synchronized frames. Based on the triangulated positions of anatomical features, head cameras and position tracking LEDs the mouse’s eye position could be placed a common coordinate system.Head mount features and mouse anatomical features  were recorded at 50 Hz using four synchronized cameras  fitted with 25 mm focal length objectives  calibrated as described below. Cameras were positioned to provide images of the animal and headset from different angles to allow triangulation of the anatomical features . During To establish the animal’s horizontal plane from the head tracking LEDs, a position for the animal’s nose was first defined by averaging to 3D positions of the marked nostrils. A pre-forward vector was calculated using the direction between mean of eyes and nose and a pre-up vector as vector orthogonal to the pre-forward and vector between the eyes. Next, the left vector was defined as orthogonal to pre-forward and pre-up. Finally, the system was rotated by 40° around the left vector such that forward vector was elevated. This established a head-fixed forward-left-up coordinate system that was based on the bregma-lambda sagittal plane by tilting the eyes-nose plane by an angle of 40°.Head tracking frame rates were 200 Hz, while eye tracking cameras recorded at 60 Hz. Eye positions were consequently interpolated as follows: LetOverhead cameras for animal position and cricket tracking, were calibrated as previously described for the overhead cameras , with thPupil boundary tracking, compensation for eye image displacement, and gaze vector calculation was performed as described previously in 2 spots for tracking ocular torsion were tracked manually in each image frame. Torsional rotations were determined based on the tracked TiO2 spot positions as follows. Total rotation of the eye was defined as previously described in The TiO2 spot is located in the x-y plane of the eye camera  for capture periods were identified by manual inspection of the tracking videos. Further identification of the behavioral start points (tstart) and tend points for the different hunt sequence epochs were then identified as described below.The criteria used to classify the different hunt phases were based on those described in end points were defined as:The tend point for a detect period was defined as the last frame before (1) Mouse head velocity in the direction of the cricket was >= 20 cm/s, (2) The mouse’s bearing towards the cricket was constantly below 90° and (3) the Euclidean distance between the mouse and cricket was continuously decreasing.The tend point for a tracking period was identified by locating local minima in the mouse-cricket Euclidean distance time plots, where local minima were defined as points at which the mouse came within a contact distance of 6 cm . These were followed either by a capture period (see below) or were followed by a ⩾ 5 cm increase in inter-animal Euclidean distance, which were defined as cricket escapes. In cases where the absolute value of the target bearing was > 90° before the mouse turned towards the prey, the start of the tracking period was taken as the first frame in which the bearing to the target was <90°. Only tracking periods, in which the initial Euclidean distance between the mouse and cricket was >20 cm were analyzed.The tend point for the capture period was taken to be the point 6 cm away from the cricket, following which a cricket captured and consumed.The tThe start points of the hunt epochs were defined as follows:start for the detect period was the frame 500 ms prior to the detect tend point.The tstart for the tracking period was the first frame after the tend detect frame.The tstart for the capture period was either; (1) the first period in which the mouse approached the cricket and directly caught it, or (2) the first frame in which the mouse approached the cricket and all subsequent cricket escapes  were less than 5 cm outside the contact zone .The tCases in which the eye cameras were dislodged by the animal during the chase (n=4 hunt sequences) were included in the dataset up until the point where the cameras were dislodged.Target bearing was defined as the angle between the cricket position and the mouse’s forward head direction in the horizontal plane.For the digital reconstruction, the company 3dScanlab  was engaged to create a complete scan, photo series and 3D mesh model of the arena and room, which they performed using an RTC 360 3D laser scanner . The 3D point cloud produced by the laser scanner was converted to a 3D mesh model, to which textures of the experiment arena obtained from photographs  were baked.The camera tracking coordinate system, in which the mouse and cricket positions were tracked, and the scanned coordinate system of the 3D mesh model were aligned based on 16 fiducial points which could be clearly identified in both tracking camera images and the scan. Crickets were modeled as 2 cm diameter, 1 cm thick disks centered on their tracked position with the disk's axis oriented parallel to gravity.Each eye was modeled as a hemisphere with a 180° field of view whose equator was perpendicular to the animal’s gaze vector. For the projection of the environment onto the cornea, frame-wise animal’s eye views for both eyes were created with custom written software in C++  on a GeForce RTX 2070 (NVidia driver 450.66), using first cube mapping followed by a transformation into a spherical coordinate system. To do this, individual frame-wise coordinate transformations were made using the eye locations and orientations determined as described above to transform the mesh model of the arena and cricket to a static eye coordinate system using custom written vertex shaders to perform the coordinate transformation and the fragment shaders to texture the mesh. A cube-map (1024 x 1024 pixels per face) was created by performing such coordinate transformations for a 90 degree view in the direction of the optical axis of the eye and four mutually orthogonal directions. Custom written code was then used to transform the cube-map into a spherical coordinate system, with a 180 degree opening angle, using fragment shaders, resulting in a 1024 x 1024 pixel frame exported as png and OpenEXR files. In addition to the color map, maps of depth (pixel-wise object intersection distance), object identification, optic flow and 3D position of the object intersection point in the contralateral eye’s coordinate system were also generated.For generation of the prey image probability density maps, animal’s eye views were rendered that contained the cricket only (i.e. without inclusion of arena and room). Density maps from multiple detect-track sequences, and multiple animals, were made by averaging.Ocular alignment was defined as the consistency of the projection of a given point in the eye view of one eye into the other in an infinitely distant environment. This is equivalent to a projection in an idealized finite-distant spherical environment while assuming a distance between the animal’s eyes of 0. For calculation, the radius of the sphere can then be set to 1 . A point, located at the center of mass of the functional focus in each eye, was chosen from which to calculate the degree of inter-ocular alignment. This point was projected from one eye to the sphere surface and into the contralateral eye. The degree of alignment between the two eyes was calculated as follows:2 we calculate the projection into the right eye 3 by:LetThe average alignment is then calculated using the formula:Generation of animal’s eye view’) generated for the ocular alignment analysis: pixels whose 3D object intersection points had an angle of less than 90° to the optical axis were considered part of the overlapping field of view. Probability maps of overlap were calculated by averaging.Visual field overlap was analyzed in the idealized finite-distant spherical environment described above for ocular alignment. Visual overlap was calculated from the frame-wise maps of 3D object intersection points in the contralateral eye  were set to the mean rotation in one eye, and the effect quantified in the other eye view.To calculate the optic flow in a given pixel for a given eye, we consider the difference vector between the 3D positions in the static eye coordinate system of the object intersection point for this pixel one frame before and after the frame of interest, divided by In In The points in the scatter plot of optic flow poles in mouse corneal views were color-coded for the density of neighboring points using a two-dimensional Gaussian smoother with standard deviationp in frame i, in the coordinate system of the respective eye, evaluated over h=4 frames.For a given point, the density was calculated as:When constructing the eye model, we took experimentally determined values from  indices  were witTo test the sensitivity of the model to changes in assumed physical parameters, we systematically changed the radius of curvatures listed in The refractive elements in the rodent eye do not behave like ideally corrected optical elements, with the result that there is a distribution of incident rays with slightly varying angles of incidence on the cornea which converge on any given point on the retina. Projection from retina to cornea therefore requires an estimate of the distribution of outside world angles of incidence for any point of interest on the retina. To do this, we used a Monte-Carlo simulation to back-trace through the optics a set of randomly chosen rays emerging from the point of interest on the retina. Since the intensity of light on a surface with an incoming angle of θ is proportional to Refraction on boundary layers between different indices of refraction was performed analytically according to Snell’s law. In volumes with a continuous variable refractive index (i.e. gradient-index (GRIN) optics), we used a finite-elements model. We first discretized the lens as a 40x40x40 lattice of side length 2.4 mm. We then started from initial conditions where s(0) is the point of incidence and v(0) is the vector of incidence multiplied by the speed of light c. The subsequent discrete trajectory and direction of propagation is then calculated step-wise according toThe gradient is calculated in the lens lattice as the three-dimensional difference quotient, and then trilinearly interpolated to the exact position To determine the corneal location corresponding to the histologically identified retinal specialization in the mouse, isodensity lines were redrawn from To quantify the effect of head rotations on VOR evoked eye movements in a common coordinate system, head rotations were normalized such that the average pitch and roll were 0. Axes were labeled X and Y respectively and eye rotations were represented using this horizon-aligned X-Y coordinate system. Positive head X values indicate head pitched up, while negative head X values indicate head pitched down. Negative head Y values indicate roll left, while positive Y values indicate roll right. Comparisons of the relationship between head and eye rotations were carried out using differential rotations between frame and average pose, defined in the following way:The transformations from We calculate the left and right eye differential rotations as:Within one experimental trial, the experimentally measured variables of interest are highly correlated with each other. This fact prevents us from using standard statistical tests on the whole time-trace to establish if any difference we observed in the data across different experimental conditions are significant or not, as one requirement of these kind on tests is that the samples from the populations being compared are independent of each other. However, we realized that trial-to-trial variability is the dominant source of variability in the data, whereas within-trial variability explains a smaller fraction of the total variance observed  A major concern is that readers will come away with the idea that the mouse is "foveating" the cricket and that there is something special about this location for optic flow, when both of these are consequences of the fact that the mouse is running toward the cricket. The authors should clarify what is active gaze control vs what is just a consequence of cricket pursuit. We encourage the authors that if they want to make a strong claim that this region is being actively used, there should be some causal support.2) It would be useful to quantify whether the eye movements are strictly compensatory for head movements, or whether there are larger abrupts shift in gaze, as found in Michaiel.3) Figures can be improved by labelling subpanels, as it is unclear what eg many of the circular plots represent without reading the caption.4) The title is a bit misleading – it implies that mice are making active effort to use this region. Likewise, the abstract should note that both the specific retinal area and the optic flow pattern are direct consequences of running toward the cricket. And overall there is a lot of language that implies active targeting – the "the image of the prey is kept on a specific area". Since this is a consequence of the pursuit, rather than active maintaining the gaze, we suggest language more like "the image lands on a specific area" or "the image remains in a specific area".5) If the authors want to support the inherent claim that this region is important for visually guided pursuit, which would provide more a significant advance, then some type of causal test would be helpful. Most conclusive would be a precise manipulation such as Johnson et al., 2020, e.g. manipulating α ganglion cells, or alternatively a lesion of the functional focus or blinders to occlude the functional focus.6) The fact that the cricket does not land on the region of highest overall RGC density is a bit of an implausible strawman. Given that the cricket is in front of the mouse and that this region is located laterally (further than the amplitude of mouse eye movements) it's fairly clear that these will not overlap.7) Johnson et al., (2021) provided a causal test demonstrating that mice not only use the binocular zone to pursue prey, but that the ipsi projecting RGCs that generate binocular receptive fields are necessary. This provides causal support for the findings demonstrated correlatively in this paper, yet this finding is not discussed beyond a reference to the fact that Johnson et al., also tracked head position during prey capture.8) The authors emphasize that their setup eliminates auditory and olfactory cues. Is this essential to their results, i.e. do they find different targeting when these are not eliminated. Likewise, the title emphasizes visual pursuit, but does the cricket land on the same region when performing auditory pursuit (when controlled for pursuit accuracy?)Reviewer #1 (Recommendations for the authors):Holmgren et al., investigate how the head and eye movements in mice are coordinated during prey capture of crickets. A technical tour-de-force with eye and head tracking in freely moving animals allows for precise quantification of a cricket's position in the retinal field of view. Mice oriented towards the cricket using head rotations, maintaining the cricket in a narrow functional focus in the binocular, upper-temporal part of the retina. Eye movement were, as found in previous studies, primarily compensatory for head movements. The functional focus region has minimal optical flow and a high density region of α-ON retinal ganglion cells.This is an impressive study, establishing a rich set of techniques for reconstruction of what the mice see during freely moving behavior, an approach that can be used for many different questions in the future. While some of the findings of the study are extensions of previous work, in particular, the compensatory head movements, the finding that during approach of the cricket it is kept within a relatively narrow functional focus with specialized properties is highly interesting. The finding will appeal to vision researchers, as well as researchers interested in the way in which our body optimizes sensory sampling of our environment according to ecological demands. The paper is well written and accessible to a broad audience.An interesting question for future work would be to assess whether the head movements of the mice predict future trajectories of the cricket, thereby bringing the cricket into the predicted field of view, or whether the head movements are merely reactive to the cricket's behavior.It would be useful to quantify whether the eye movements are strictly compensatory for head movements, or whether there are larger abrupts shift in gaze, as found in Michaiel.A causal demonstration that silencing SC or V1 activity in this field of view disrupts behavior seems feasible and would provide direct support for the functional interpretations of the authors, but is not necessary for acceptance of the paper.Figures can be improved by labelling subpanels, as it is unclear what eg many of the circular plots represent without reading the caption.Reviewer #2 (Recommendations for the authors):This study aims to determine whether mice use a specific region of their visual field, and consequently retina, to pursues a target during natural behavior, prey capture. This is an interesting question because mice do not have a clear fovea like primates, and it is significant in the field since the mouse Is a common model system for vision. Recent studies have shown that eye movements in the mouse are mostly compensatory with saccades serving to reset eye centering, and that eye movements and direction of gaze are primarily driven by head movements , including in prey capture . Likewise, mice accurately aim towards prey during pursuit , and thus the prey is in the binocular zone based on amplitude of eye movements . Furthermore, a recent study  used ablation of ipsi-projecting retinal ganglion cells to show that the binocular visual field is necessary for prey capture.The authors use a tour-de-force computational approach to determine the visual scene as projected on to the retina, based on 3-D scanning and virtual reconstruction of the environment, improved eye tracking methods, and computational projection of the scene onto the retina, all while mice pursue and capture crickets. This novel and rigorous approach, which will likely be useful to others in the field, is a significant advance and the real strength of the paper. However, as discussed below, most of the results they obtain from this effort do not provide a clear advance beyond what was known from previous studies, and are largely a direct consequence of mice running towards crickets during pursuit, though the impression one gets from the title and abstract is that this is an active and optimal gaze targeting strategy. One significant new piece of knowledge is that the cricket image lands on the region of highest density of α retinal ganglion cells, and this might be something to emphasize or explore more fully.1. A main finding is that the cricket is generally maintained in a specific location in the lower binocular zone, which they term a "functional focus". Furthermore, the authors do several tests as to whether this is an active centering process and conclude that is not. "Mice do not make compensatory vertical head movements, tracking eye movements or vergence eye movements to keep prey within their functional foci but instead retain their target within a restricted bearing by running straight towards it". Thus, this main finding is just a direct consequence of the fact that mice are pursuing crickets by running towards them. And indeed two previous papers  showed that mice keep prey in their binocular zone and maintain their head tilted downward, so this is not necessarily new information.2. The authors also ask "what advantage is this behavior to the mouse?" The obvious answer is that running straight toward crickets is how to catch them. In other words, this is not a gaze targeting strategy but simply a consequence of that fact that if you want to catch something, you run towards it. However, instead the authors analyze the pattern of optic flow and demonstrate that this places the cricket in the region of least optic flow. Again, this is likely a trivial consequence of the fact that mice are running straight towards the cricket. Translational motion results in a focus of expansion (FOE) of optic flow in the heading direction, and the FOE itself is a minimum in the optic flow. Hence, as long as the mice are running toward the cricket, it will be in a region that is on average (given distortions of optic flow resulting from eye movements) a minimum of optic flow. Therefore I don't see this as a significant finding, and particularly as the title, unless they can demonstrate that this is not trivially true.3. The title is a bit misleading – it implies that mice are making active effort to use this region. Likewise, the abstract should note that both the specific retinal area and the optic flow pattern are direct consequences of running toward the cricket. And overall there is a lot of language that implies active targeting – the "the image of the prey is kept on a specific area". Since this is a consequence of the pursuit, rather than active maintaining the gaze, I'd suggest language more like "the image lands on a specific area" or "the image remains in a specific area".4. If the authors want to support the inherent claim that this region is important for visually guided pursuit, which would provide more a significant advance, then some type of causal test would be helpful. Most conclusive would be a precise manipulation such as Johnson et al., 2020, e.g. manipulating α ganglion cells, or alternatively a lesion of the functional focus or blinders to occlude the functional focus.5. The fact that the cricket does not land on the region of highest overall RGC density is a bit of an implausible strawman. Given that the cricket is in front of the mouse and that this region is located laterally (further than the amplitude of mouse eye movements) it's fairly clear that these will not overlap.6. Johnson et al., (2021) provided a causal test demonstrating that mice not only use the binocular zone to pursue prey, but that the ipsi projecting RGCs that generate binocular receptive fields are necessary. This provides causal support for the findings demonstrated correlatively in this paper, yet this finding is not discussed beyond a reference to the fact that Johnson et al., also tracked head position during prey capture.7. The authors emphasize that their setup eliminates auditory and olfactory cues. Is this essential to their results, i.e. do they find different targeting when these are not eliminated. Likewise, the title emphasizes visual pursuit, but does the cricket land on the same region when performing auditory pursuit (when controlled for pursuit accuracy?)eLife. Your revised article has been evaluated by Tirin Moore (Senior Editor) and a Reviewing Editor.Thank you for resubmitting your work entitled "Visual pursuit behavior in mice maintains the pursued prey on the retinal region with least optic flow" for further consideration by The manuscript has been improved but there are some remaining issues that need to be addressed before final acceptance, as outlined below. These can be addressed with textual changes. Please clearly indicate those changes so we can proceed with acceptance as soon as possible.Reviewer #1 (Recommendations for the authors):The authors have addressed all of my comments.Reviewer #2 (Recommendations for the authors):The authors have overall done an excellent job of revising their claims to match the data and avoid misleading conclusions by readers. I have two remaining points that weren't fully addressed.1. The authors have generally improved the wording in the text to avoid implying that mice are actively targeting the cricket to this region. However, this is still not very clearly stated in the abstract, which is what most people take away from a paper. Indeed, in their response, the authors state that their findings are best stated in the first paragraph of the discussion as "The positional maintenance of the cricket was not achieved by active eye movements that followed the prey, but rather by the animal's change in behavior, specifically the head-movement and orientation towards the prey during pursuit." A statement along these lines in the abstract would be very effective, so that readers don't have to wait until the discussion to have this stated explicitly. If word length is the issue, I suggest finding something else to trim since as the authors themselves note, this is the best statement of the conclusion from their findings.2. In the public section of the original review, I noted that the fact that the cricket lands in the region of least motion blur was an almost trivial consequence of the fact that the mouse is running towards the cricket, and hence the cricket is at the focus of expansion (minimum of optic flow). However, this has not been addressed in the revision – I apologize if that is because it was in the public comments rather than author comments. However, it seems essential to address this so that readers do not think that this is an "design principle" of mouse vision. Unless the authors can provide a compelling argument for how this could not be the case, given the geometry of optic flow, then the statement that it lands on the region of least motion blur should be accompanied, in the abstract and elsewhere, by the clarification that this is a direct consequence of motion towards the cricket rather than a specific optimality. I also suggest revising the title to emphasize a more compelling finding (e.g. α RGCs), but if the authors want to continue to use this title, accompanied by appropriate explanation in the abstract, then that is their prerogative. Essential revisions:1) A major concern is that readers will come away with the idea that the mouse is "foveating" the cricket and that there is something special about this location for optic flow, when both of these are consequences of the fact that the mouse is running toward the cricket. The authors should clarify what is active gaze control vs what is just a consequence of cricket pursuit. We encourage the authors that if they want to make a strong claim that this region is being actively used, there should be some causal support.We thank the reviewers for raising this point, because this is a central and important conclusion of the study. To state it clearly here, our conclusion is that mice do not “foveate” or have pursuit-like eye movements for following the cricket. We found no evidence to support the presence of pursuit-like movements, neither vergence movements  nor combinations of movements that would fulfill the role of keeping the cricket image in a consistent location on the cornea  during the tracking phase of the behavior. Our conclusion was probably best stated in the first paragraph of the Discussion of the initially submitted manuscript as:“…The positional maintenance of the cricket was not achieved by active eye movements that followed the prey, but rather by the animal’s change in behavior, specifically the head-movement and orientation towards the prey during pursuit. While eye rotations stabilized the visual field via the vestibulo-ocular reflex by countering head rotations, the rotations were not specific to either prey detection or prey tracking. This strongly suggested that eye-rotations in mice, like in rats, primarily stabilize their large field of view and that all three rotational axes, including ocular torsion, combine to counter head rotations. …”.We agree with the reviewers that there were several sections of the submitted text that have the potential to give a reader an incorrect impression of our conclusion in this regard, and we have modified the text with the aim of making this point clearer and more consistent throughout the document. The text modifications are outlined below.Abstract:“…the behavior. By quantifying the spatial location of objects in the visual scene and their motion throughout the behavior, we show that the prey image consistently falls within a small area of the VOR-stabilized visual field. This functional focus coincides with the region of minimal optic flow in the visual field and consequently minimal motion-induced image blur during pursuit. The functional focus lies in the upper-temporal part of the retina and coincides with the reported high density-region of Α-ON sustained retinal ganglion cells.”Introduction:page 5: “…the binocular field and undertake direct pursuit. Prey objects remain in the functional foci through the stabilizing action of the VOR, and not through active prey-pursuit eye movements. The stabilized…”Results:page 7, heading: “During pursuit the image of the prey consistently falls in a localized visual region”page 9: “…Mean Absolute Difference with bootstrapping, N=57 detect-track sequences, N = 3 mice. Thus, during the tracking and pursuit behavior the image of the prey consistently fell on a local and specific retinal area that we refer to from here on as the functional focus. The functional focus fell within the binocular field, while the region …”page 18: “… N=3 mice, Figure 6E. Together this shows that the behavioral strategy employed by mice during hunting, consisting of orienting themselves to directly face the prey and following a straight, direct course to it, results in the image of their prey coinciding with the region of reduced optic flow during pursuit, where the retinal image of their prey is least distorted due to motion induced image blur.”page 19: “…environment, cricket and the mouse. Using this approach, we show that during pursuit of crickets the hunting behavior employed by mice results in the image of the prey consistently falling within a localized region of their visual field, termed here the functional focus. …”page 23: “In summary, we show here that during pursuit in mice the image of the intended prey falls consistently in a localized region of their visual fields, referred to here as the functional focus, and that this occurs through the animal orientating their head and body and running directly towards…”2) It would be useful to quantify whether the eye movements are strictly compensatory for head movements, or whether there are larger abrupts shift in gaze, as found in Michaiel.We did observe the abrupt shifts in gaze reported by Michiael et al., and by Meyer et al., 2020 and these are included in the trajectories and calculations. However, as these eye movements occur in conjunction with the animal’s yaw turns they neither formed a large fraction of total duration of the data we analyzed nor were they of direct consequence to the location of the cricket in the eye view during the tracking phase of the behavior, which was the primary interest in this manuscript. In addition, as these eye movements had been described previously in the above mentioned publications we elected not to isolate and analyze them. However, in response to this reviewer comment we have added a reference to these movements in the text and an example trace in figure 4 supplement 1. The text now reads as follows:page 11: “…and counter head rotations through the VOR, enabling the large field of view around the animals head to be stabilized while the animal is moving. The relationships between head rotations and both the horizontal and vertical eye rotations have recently been quantified, and in addition it has been reported that both during exploration and hunting, mice also have abrupt gaze shifts brought about by the combination of head rotation and conjugate saccade-like horizontal eye rotations. We also observed both forms of eye movements in the current study . However, how these rotations combine with torsional rotations is not known. If mouse …”3) Figures can be improved by labelling subpanels, as it is unclear what eg many of the circular plots represent without reading the caption.With the aim of making the figures clearer we have now added labels on figure panels 1IandJ, 2E-H, 3BandE, 4D, F and I, 5A and 6C-E, as well as figure 1 supplement 1 panel H, all panels in figure 2 supplement 1 and figure 4 supplement 1 panels H, J, KandL. We feel this is a reasonable balance between adding clarity to the figures while not overcrowding.4) The title is a bit misleading – it implies that mice are making active effort to use this region. Likewise, the abstract should note that both the specific retinal area and the optic flow pattern are direct consequences of running toward the cricket. And overall there is a lot of language that implies active targeting – the "the image of the prey is kept on a specific area". Since this is a consequence of the pursuit, rather than active maintaining the gaze, we suggest language more like "the image lands on a specific area" or "the image remains in a specific area".We appreciate the reviewers concerns regarding implications about active targeting, and have made a number of modifications to address this throughout the document.First, we have modified the title to “Visual pursuit behavior in mice maintains the pursued prey on the retinal region with least optic flow”.With respect to the abstract, we would like to point out that the localization of the functional focus is not only a consequence of the mouse running directly toward the cricket, but rather a consequence of this behavior and the visual-stabilizing effect of the vestibular-ocular reflex. We have modified the abstract with this point in mind, as described in our response to point 1. Further, we have also made numerous modifications throughout the text to address this concern as also described in our response to point 1.5) If the authors want to support the inherent claim that this region is important for visually guided pursuit, which would provide more a significant advance, then some type of causal test would be helpful. Most conclusive would be a precise manipulation such as Johnson et al., 2020, e.g. manipulating α ganglion cells, or alternatively a lesion of the functional focus or blinders to occlude the functional focus.While we agree with the reviewers that a causal demonstration would reinforce the conclusion of the study, we do not think that there is an experimental design which allows the causality to be unequivocally established in this case, and further, the additional time delay imposed by the need to obtain both approval for new experiments and to acquire and control the required mouse lines or other experimental devices would preclude timely publication of the results in this study.In addition, the primary difficulty in performing an experiment designed to demonstrate the causal link between a retinal region or ganglion cell type or subtype and a behavioral outcome, is the requirement that it can be unequivocally shown that any behavioral impairment is really due specifically to the intended manipulation and not to simply the loss of vision in part of the visual field. To take the example of the study by Johnson et al., 2021, while their manipulation should indeed cleanly eliminate the function of the ventro-temporal ipsilaterally projecting RGCs, the wide distribution of these neurons across the retina shown in their figure 3 would indicate that this inhibition may cause a very broad loss or impairment of vision across the animals visual field. This visual loss alone may disturb the animals’ behavior rather than the behavioral impairment being due to a deficit specifically in binocular vision or stereopsis. That this hunting behavior is predominantly a visual behavior would seem to be clear, and consequently showing that the behavior was impaired by a manipulation causing some form of loss of vision is perhaps not particularly compelling. The same logic applies also to manipulations involving either blinkering of the visual field or a lesion. A manipulation that selectively targeted the Α-ON sustained subset of Α RGCs ) would provide considerable strength to the conclusion, however, we are not currently aware of a mouse line that would allow this specific manipulation and even if it were available the administration associated with performing the experiment would prevent timely publication of the current result. We will of course try to follow up on this in future studies.6) The fact that the cricket does not land on the region of highest overall RGC density is a bit of an implausible strawman. Given that the cricket is in front of the mouse and that this region is located laterally (further than the amplitude of mouse eye movements) it's fairly clear that these will not overlap.We acknowledge the reviewers point, but maintain that the comparison of the locations of the functional focus and density contours for all ganglion cells is a reasonable and impartial comparison which is of general interest. The abundance of RGCs contributing to the density distribution for all RGCs is numerically two orders of magnitude greater, and presumably has a substantial biological significance, for what, is now not clear.We have modified the introductory heading to this part of the Results section and substantially re-written it with this criticism in mind, and have removed the section dealing with the rotations and errors required for the functional focus and overall RGC density contours to overlap, as we agree with the reviewers that this comparison is not necessary. We have also modified a section of the discussion dealing with this topic, and removed 4 panels from figure 3 supplement 1 (panels H-K) which also dealt with this.This section now contains numerous small and some larger modifications and has not been reproduced here for the sake of space, however the section begins on page 9 with the modified heading “Relative locations of functional foci and ganglion cell density distributions”.The modified Discussion reads:page 19: “…of the mouse eye and optics. Using this we show that the location of the functional focus occurs within a dorso-temporal retinal region in an area with the highest density of Α-ON sustained RGCs, whose general properties have been previously proposed to be well suited for this purpose . Finally,…”7) Johnson et al., (2021) provided a causal test demonstrating that mice not only use the binocular zone to pursue prey, but that the ipsi projecting RGCs that generate binocular receptive fields are necessary. This provides causal support for the findings demonstrated correlatively in this paper, yet this finding is not discussed beyond a reference to the fact that Johnson et al., also tracked head position during prey capture.We understand the point of the reviewers but we somewhat disagree with the strength of the conclusions possible from the Johnson et al., (2021) manuscript. Firstly, ablation of the ipsilaterally-projecting RGCs did not extinguish the behavior, but rather reduced the probability of a successful capture when the mouse was in close proximity with the cricket and extended the total time required for capture. They provide strong evidence that these RGCs are involved in close-range interaction between the mouse and cricket, and it may be the case that these RGCs are required for binocular viewing, binocular correspondence or stereopsis at this range. By the same argumentation, we further think that to call this a “causal” demonstration is perhaps extending the reasonable conclusions from the Johnson et al., study a little beyond their intention; the mice could still successfully pursue and capture crickets in the absence of functional ipsilaterally-projecting RGCs and it was not determined by the Johnson et al., study whether this was residual visual ability or alternatively guided by tactile or olfactory cues.With regard to methodology, Johnson et al., do not measure eye position directly but infer the animals’ visual fields from its head orientation, which they measure from lateral imaging. Without high resolution imaging and 3D triangulation it is difficult to accurately measure 3D changes in head orientation, and with this in mind we note that their estimate of average pitch in their study is close to double that reported in the current study and by other studies .We do acknowledge that the Johnson et al., study is very complimentary to the current study, in that they focus on the final phase of the capture behavior (mouse-cricket distance < 6cm) while we specifically exclude this data (mouse-cricket distance < 3cm) to avoid any misinterpretation arising from the animal using its tactile sense through the mystacial whiskers. Further, the data presented in our Figure 5A suggests that as the mouse approaches the cricket, the cricket image systematically drifts further and further toward the dorsal tip of the retinal region in which the ipsilaterally-projecting RGCs reside, which is supportive of a potential role for these RGCs in the final phase of the hunt where the cricket is in close proximity to the mouse.We have now included an extended description of the points outlined in the paragraph above in the Discussion section as follows:page 21: “…and employed by the mouse . Further supportive of the importance and relevance of the region of binocular overlap, another recent study provides strong evidence to suggest that ipsilaterally-projecting RGCs in the ventro-temporal retina are important in the final phase of cricket pursuit (mouse to cricket distance less than 6 cm), with selective ablation of these RGCs reducing the probability that coming into close proximity with the cricket resulted in its capture . This finding is complimentary to the current study, in that it deals with the section of the hunting behavior excluded from analysis in the current study, that being behavioral segments where the distance between mouse and cricket is < 3cm. This criteria was used in the current study to mitigate the possibility that the mouse was using its mystacial whiskers to detect or assist in detection of the cricket location. In the current study we find that the location of the image of the cricket systematically shifts nasally and slightly ventrally on the cornea  as the mouse closes in on the cricket, and this may place the cricket’s image within the retinal region containing the ipsilaterally-projecting RGCs. As the mouse closes further, beyond our distance threshold, these RGCs may become increasingly important, particularly when the cricket is within grasping distance, where binocular vision and stereopsis may be most relevant.”8) The authors emphasize that their setup eliminates auditory and olfactory cues. Is this essential to their results, i.e. do they find different targeting when these are not eliminated. Likewise, the title emphasizes visual pursuit, but does the cricket land on the same region when performing auditory pursuit (when controlled for pursuit accuracy?)Animal Behaviour, 31, 199-205; Langley 1988, Behav Processes, 16, 67-73; Gire, Kapoor et al., 2016, Curr Biol, 26, 1261-1273) we removed any potential confound by conducting the experiments in the presence of olfactory and auditory noise. We did measure the time to capture with and without olfactory cues from the same mice and found that, as expected from the literature, without olfactory cues the mice took significantly longer than with olfactory cues present . There were no overt differences in behavior, but we did not record eye movements during these control experiments. Unless the animals changed their behavioral strategy when they have useful information from all senses available to them, we cannot see how the targeting would change, given the strong influence of the VOR on their eye position.We conclude here that the functional focus observed is a consequence of both the behavioral strategy employed by the mice during pursuit and the VOR-based stabilization of the visual field during the animal’s movement. As mice use auditory, visual and olfactory cues during prey pursuit and can swap between modalities :The authors have overall done an excellent job of revising their claims to match the data and avoid misleading conclusions by readers. I have two remaining points that weren't fully addressed.1. The authors have generally improved the wording in the text to avoid implying that mice are actively targeting the cricket to this region. However, this is still not very clearly stated in the abstract, which is what most people take away from a paper. Indeed, in their response, the authors state that their findings are best stated in the first paragraph of the discussion as "The positional maintenance of the cricket was not achieved by active eye movements that followed the prey, but rather by the animal's change in behavior, specifically the head-movement and orientation towards the prey during pursuit." A statement along these lines in the abstract would be very effective, so that readers don't have to wait until the discussion to have this stated explicitly. If word length is the issue, I suggest finding something else to trim since as the authors themselves note, this is the best statement of the conclusion from their findings.Thank you for the additional comments for clarification. We have now changed the abstract to include the following sentence:“This functional focus coincides with the region of minimal optic flow within the visual field and consequently area of minimal motion-induced image-blur, as during pursuit mice ran directly toward the prey.”2. In the public section of the original review, I noted that the fact that the cricket lands in the region of least motion blur was an almost trivial consequence of the fact that the mouse is running towards the cricket, and hence the cricket is at the focus of expansion (minimum of optic flow). However, this has not been addressed in the revision – I apologize if that is because it was in the public comments rather than author comments. However, it seems essential to address this so that readers do not think that this is an "design principle" of mouse vision. Unless the authors can provide a compelling argument for how this could not be the case, given the geometry of optic flow, then the statement that it lands on the region of least motion blur should be accompanied, in the abstract and elsewhere, by the clarification that this is a direct consequence of motion towards the cricket rather than a specific optimality.We thank the reviewers for raising this point, which we had unfortunately overlooked and not directly responded to in our previous author comments. We had/have amended sections of the text to clarify for the reader that the overlap of the prey image and the region of minimal optic flow arises as a result of the animal’s motion towards the prey. In particular:1. We have changed the wording of the abstract (please see point 1).We have added an ending to the following sentence:2. Introduction, Line (83): “The stabilized functional foci are spatially distinct from the regions of highest total retinal ganglion cell density, which are directed laterally, but coincides with the regions of the visual field where there is minimal optic flow and therefore minimal motion-induced image disturbance during the behavior as the mouse runs towards the cricket.”In addition, in light of the previous reviewer comments, we had modified the text to:3. Results: “Together this suggests that mice do not make compensatory vertical head movements, tracking eye movements or vergence eye movements to keep prey within their functional foci but instead retain their target within a restricted bearing by running straight towards it.4. Results: “Together this shows that the behavioral strategy employed by mice during hunting, consisting of orienting themselves to directly face the prey and following a straight and direct course to it, results in the image of their prey coinciding with the region of reduced optic flow during pursuit, where the retinal image of their prey is least distorted due to motion induced image blur.”5. Discussion: “The positional maintenance of the cricket was not achieved by active eye movements that followed the prey, but rather by the animal’s change in behavior, specifically the head-movement and orientation towards the prey during pursuit.”6. Discussion: “Finally, we show that the region that contains these Α-ON sustained RGCs also coincides with the region of minimum optic flow and therefore reduced image blur during translation pursuit, a feature which would supports accurate localization of small targets by Α-ON sustained RGCs.”7. Discussion: “In summary, we show here that during pursuit in mice the image of the preferentially keep their intended prey falls consistently in a localized region of their visual fields, referred to here as the functional focus, and that this occurs through the animal but do so by orientating their head and body and running directly towards the prey rather than with specific eye movements.”As to the question as to whether or not this is a “design principle” of mouse vision we would suggest that it is hard to differentiate what is by design and what is not. In general, there is a tradeoff between panoramic field of view and eye-position in the head, which in addition, at least for the mouse and rat, is stabilized by a strong VOR. These factors undoubtably aid the rodents in survival and in many respects dictate how vision is used by the animal. Together with the topographical differences in the retinal densities of some functionally important retinal cell types, this means that cells tuned for tuned some specific tasks will sample different regions of visual space . Dorso-ventral differences in cone subtype aid contrast detection of objects in the sky (Baden 2013), W3 cells in the ventral retina aid detection of prey in the sky  and the orientation and location of direction selective RGCs in the retina align with the orientation of idealized optic flow fields . Spectral differences in UV in ground vs sky and specific features of the aerial predator may be selective evolutionary pressures in the first two cases, while in the latter case, the characteristics of optic flow during forward translational motion may be a part of the evolutionary pressure influencing the direction selective RGCs. Similarly, for visualization of objects in the ventro-medial visual field, the properties of the optic flow fields produced as the mouse runs forwards towards its target may be a selective pressure on VOR-stabilized RGCs in the dorso-temporal retina.With regards to the point that it seems ‘trivial’ that the location of the functional focus and the region of least optic flow coincide, our suggestion is that this results from a behavioral strategy employed by the mouse, that being to run directly at the cricket. This need not have been the case. The mouse could theoretically instead have used a strategy of predicting the future position of the cricket based on the cricket’s current trajectory and speed and run at that. This would result in the cricket being outside the region of minimal optic flow.In both cases these points are more speculative and we have therefore not included them in the Discussion section.I also suggest revising the title to emphasize a more compelling finding (e.g. α RGCs), but if the authors want to continue to use this title, accompanied by appropriate explanation in the abstract, then that is their prerogative.While we are very curious about the overlap of the functional focus and the reported high density-region of Α-ONsustained retinal ganglion cells, we think that it is a bit premature at this stage to put this in the title. Consequently, we have elected to keep the current title but have changed the abstract as requested (please see point 1).


Nigella sativa L.) pressing waste (BCW) and black cumin seeds (BCS) on the properties of starch bread. The control bread was prepared from wheat starch (100%) with a gluten-free certificate, plantain husk (5%), and guar gum (2%). BCS and BCW were added between 0 and 10% of wheat starch. We determined the physicochemical properties, color, texture, and sensory properties of the prepared bread. Gas chromatography–mass spectrometry (GC–MS) analysis was performed to detect the phenolic compounds in the bread. The bread prepared with 6% BCS and 4% BCW had a significantly higher volume than the starch control bread did. Sensory analysis (taste) showed that BCS and BCW could be added up to 4% and 8%, respectively. The addition of BCS and BCW reduced the brightness of the crumb. A significant decrease in the L * index of the crumb was observed from 50.9 for the control bread to 34.1 and 34.0 for bread with 10% BCS and BCW, respectively. The addition of BCS and BCW decreased the hardness, elasticity, and chewiness of the starch bread crumb. Starch bread enriched with BCS and BCW was characterized by a higher content of 2-hydroxybenzoic acid, 2-hydroxyphenyl acetic acid, and 4-hydroxyphenyl acetic acid.The aim of the study was to determine the effect of the addition of black cumin ( Bread is one of the most popular food products around the world. Most people today suffer from various forms of allergies and health conditions; therefore, it is necessary to develop adequate dietary products. Carbohydrates, mainly starch from cereals, play an important part in our diet ,2, and aIn this study, we proposed the enrichment of starch bread with black cumin pressing waste (BCW). Previous research has shown that waste generated from various food materials has a high nutritional value and is rich in dietary fiber, minerals, and antioxidants .Researchers have attempted to enrich bakery products with apple pomace, buckwheat pomace, grape pomace , carrot Nigella sativa L.) has been used as a traditional medicine for centuries [In this study, we aimed to utilize BCW in the making of bread. Black cumin (BC)  and Plantago ovata husk  were used as the raw materials for the production of control bread. Black cumin seeds were sourced from India . The same batch of seeds was used for BCS and BCW. Other materials used in this study were guar gum, dried instant yeast , and salt.The chemical composition of wheat starch, BCS, BCW, and bread, such as protein content, was determined by the Kjeldahl method Kjeltec 2300, Foss) , fat by 0, Foss ,The seed waste was obtained as a by-product of the cold pressing of N. sativa seeds using a DUO-type screw press . Both raw materials were ground in a knife grinder before baking.Starch bread was baked using a single-phase method . The conThe control bread recipe was enriched with the addition of N. sativa seeds  and waste material obtained after pressing the oil from these seeds .The dough was mixed for 5 min , and then divided into 300 g portions, which were fermented and proved in molds in a fermentation chamber  , and then baked in a laboratory oven  . After baking and cooling, the loaves were packed into polyethylene bags and analyzed after 24 h. Baking tests were performed in triplicate and used for physical and chemical analysis. An additional 5 loaves were made from each sample for sensory analysis.The bread volume was measured by using the millet seeds displacement method, and then the specific volume of bread (bread volume divided by weight) was calculated. The pH value of the breadcrumb was determined using a pH meter . Color measurements were performed on the L *a * b * scale (4Wave CR30-16) , and ΔE was calculated ,43. Meas−1 . The hardness, springiness, cohesiveness, and chewiness were calculated from the graphs obtained during the TPA test . The texture measurements were performed in 9 replicates in the middle of the central slices of the bread.The texture parameters of breadcrumbs (30 mm × 30 mm × 20 mm) were determined in a double compression test to a depth of 50% with a speed of 1 mm∙sThe sensory analysis was performed by a panel of 75 untrained consumers (21–55 years old), who rated the taste, aroma, appearance, texture, and overall acceptability of the bread samples. The responses were obtained with the use of a 9-point hedonic scale  .n-hexane to remove the lipid fraction [The 5 g powdered seeds, waste material, and dried bread were extracted thrice with 40 mL of 80% acidified methanol in 40 °C with sonification. In the next step, the supernatant was evaporated under reduced pressure to remove all methanol. Aqueous fractions were extracted with fraction ,46.v/v 1:1). The collected eluent was dried over anhydrous sodium sulfate and then evaporated to dryness on a rotary evaporator under vacuum. The extracted dry residue was derivatized with 100 µL of N,O-bis(trimethylsilyl) trifluoro acetamide (BSTFA) with 1% trimethyl chlorosilane (TMCS)  and 200 µL of pyridine . The content was heated at 60 °C for 1 h. Trimethylsilyl (TMS) derivatives were subjected to GC-MS analysis.Phenolic compounds were extracted with 2 × 10 mL portions of diethyl ether/ethyl acetate (−1 (helium). The chromatographic analysis was based on a validated procedure [−1 (1:10 split) to separate compounds. The analyses were carried out in full scan mode, and we wanted the best possible separation of the mixture of analytes, some of which are isomers, with slightly different retention indices, while others had a significant molecular weight and retention rates above 3000. The detection process was performed in the full scan mode from 45 to 600 m/z. All compounds were calibrated using the same parameters.The separation and detection of phenolic compounds were conducted using a 7890B GC System with a 7000C GC/MS Triple Quad mass detector . The determination of phenolic acids was made based on calibration curves of individual standard compounds. The identification was carried out based on the mass spectra and the chromatogram of a standard mixture of 30 different phenolic acids analyzed under the same conditions of the chromatographic procedure. A HP-5 ms fused silica capillary column  was used for the separation process. The injection temperature was maintained at 260 °C, and the carrier gas flow rate was maintained at 1 mL∙minrocedure . TemperaStatistical analysis was conducted using Statistica software version 12.0 considering a significance level of α = 0.05. Analysis of variance (ANOVA) was performed, and Tukey’s test was used to compare the mean values.The bread was supplemented with BCS or BCW. The composition of the BCS was as follows: protein, 20.0 ± 0.8%; fat, 34.6 ± 1.3%; fiber, 12.6 ± 0.9%; and carbohydrates, 26.3 ± 0.8%. The composition of the BCW was as follows: protein, 26.0 ± 0.6%; fat, 18.3 ± 0.5%; fiber, 16.3 ± 1.5%; and carbohydrates, 34.1 ± 2.1%.Among the fatty acids detected  in BCS aThe results showed that the starch bread with the addition of BCS and BCW increased its specific volume . The breA significant decrease in the pH value was observed  after thThe addition of BCS and BCW reduced the brightness of the crumb , which wThe addition of BCS and BCW decreased the hardness, elasticity, and chewiness of the starch bread crumb a–d. OnlyIn a previous study, the BCS protein concentrate significantly increased the firmness and decreased the springiness of the crumb of gluten-free bread . The oilSensory analysis  of the sThe control starch bread had the following characteristics: protein, 0.15%; fat, 0.10%; fiber, 2%; and carbohydrate, 44.9%. The addition of BCW compared to BCS increased the levels of protein and fiber in the bread , while tControl starch bread had a low caloric value of 185.2 kcal/100 g . This miThe analysis of phenolic compounds showed that the enrichment of starch bread with BCS and BCW resulted in a significant increase in the content of 2-hydroxybenzoic acid . Salicylic acid levels were increased with the addition of 4% BCS. Its content was 1.87 times more than in BCS bread than that of control bread. The addition of 4% BCW resulted in 3.6 times increase in the level of salicylic acid when compared to the control bread. In contrast, salicylic acid in bread with 8% BCW was 5.4 times than that of the control bread. In starch control bread, 2-hydroxybenzoic acid was 0.2004 μg/g d.m, and in bread with 4% BCS and 8% BCW, it was, respectively, 0.3767 μg/g d.m and 1.0903 μg/g d.m.Recent data indicate that in addition to antioxidant properties, hydroxybenzoic acids are capable of inhibiting α-amylase and α-glucosidase, enzymes that break down complex carbohydrates by keeping blood sugar levels low . The incThe obtained results confirm the possibility of the utilization of waste from black cumin pressing in the production of gluten-free starch bread. These wastes added to the bread recipe in an amount not exceeding 8% made the bread more interesting in taste and appearance. Moreover, such bread was beneficial in phenolic content. However, the higher amounts of BCW negatively affected the taste and odor of bread (unpleasant aroma and bitter taste). Bread with 4% BCS was characterized by a caloric value of 188.9 kcal/100 g and that of 8% BCW was characterized by a calorific value of 200 kcal/100 g. Among the fatty acids identified in BCS and BCW, the highest quantities were recorded for C18:2 linoleic (n − 6) acid, followed by C18:2 oleic (n − 9) acid, and C16:0 palmitic acid. The enrichment of starch bread with BCS and BCW resulted in a significant increase in the content of 2-hydroxybenzoic acid , 2-hydroxyphenylacetic acid, and 4-hydroxyphenylacetic acid.


GAA (nGAA). The goal of this study was to develop a tool for personalized immunogenicity risk assessment (PIMA) that quantifies T cell epitopes that differ between nGAA and rhGAA using information about an individual’s native GAA gene and their HLA DR haplotype, and to use this information to predict the risk of developing ADA. Four versions of PIMA have been developed. They use EpiMatrix, a computational tool for T cell epitope identification, combined with an HLA-restricted epitope-specific scoring feature (iTEM), to assess ADA risk. One version of PIMA also integrates JanusMatrix, a Treg epitope prediction tool to identify putative immunomodulatory (regulatory) T cell epitopes in self-proteins. Using the JanusMatrix-adjusted version of PIMA in a logistic regression model with data from 48 cross-reactive immunological material (CRIM)-positive IOPD subjects, those with scores greater than 10 were 4-fold more likely to develop ADA (p<0.03) than those that had scores less than 10. We also confirmed the hypothesis that some GAA epitopes are immunomodulatory. Twenty-one epitopes were tested, of which four were determined to have an immunomodulatory effect on T effector response in vitro. The implementation of PIMA V3J on a secure-access website would allow clinicians to input the individual HLA DR haplotype of their IOPD patient and the GAA pathogenic variants associated with each GAA allele to calculate the patient’s relative risk of developing ADA, enhancing clinical decision-making prior to initiating treatment with ERT. A better understanding of immunogenicity risk will allow the implementation of targeted immunomodulatory approaches in ERT-naïve settings, especially in CRIM-positive patients, which may in turn improve the overall clinical outcomes by minimizing the development of ADA. The PIMA approach may also be useful for other types of enzyme or factor replacement therapies.Infantile-onset Pompe disease (IOPD) is a glycogen storage disease caused by a deficiency of acid alpha-glucosidase (GAA). Treatment with recombinant human GAA  enzyme replacement therapy (ERT) significantly improves clinical outcomes; however, many IOPD children treated with rhGAA develop anti-drug antibodies (ADA) that render the therapy ineffective. Antibodies to rhGAA are driven by T cell responses to sequences in rhGAA that differ from the individuals’ native  Infantile-onset Pompe Disease (IOPD) is a fatal autosomal recessive glycogen storage disorder caused by a deficiency of the enzyme acid alpha-glucosidase (GAA), which breaks down lysosomal glycogen. The deficiency of lysosomal GAA leads to the accumulation of glycogen and damage to skeletal, cardiac and smooth muscles . Childreversus those who can be carefully watched instead, are needed.The development of ADA to rhGAA is influenced by the presence or absence of endogenous GAA, defined as cross-reactive immunologic material (CRIM). Individuals who are CRIM-negative have a complete absence of GAA and are at the highest risk of ADA, whereas those who are CRIM-positive may be more immune tolerant to ERT, due to prior exposure to endogenous GAA . Howeverpersonalized immunogenicity risk assessment  that quantifies T cell epitopes that differ between nGAA and rhGAA , which included more precise definition of HLA DR alleles for each subject and a correction for T cell epitopes in nGAA that may be inducing tolerance to the recombinant replacement enzyme (rhGAA), performed better than the intermediate versions. This version of PIMA integrates information generated by JanusMatrix, a computational tool that identifies T cell epitopes that have extensive conservation in the human genome (at their TCR face), which may be epitopes that activate circulating regulatory T cells .in vitro. Several of the putative Treg epitopes identified in GAA significantly suppress effector memory T cell response in a standardized Treg bystander assay. This important discovery of potential regulatory T cell epitopes in nGAA may improve the assessment of immunogenicity risk for IOPD and for a range of enzyme replacement therapies.We also investigated the potential for selected epitopes identified by JanusMatrix to induce regulatory T cell responses And finally, a first-generation PIMA website (Pompe-PIMA) has been developed for use in clinical decision making. Once the clinician inputs the patient-specific nGAA sequence(s) and HLA DRB1 alleles, an ADA risk estimate that is based on V3J can be calculated. After further validation and regulatory approval, the website may be used by clinicians to assess the relative risk associated with ERT therapy for their individual CRIM-positive IOPD patient.ClinicalTrials.gov NCT01665326]. Subjects were selected for the present study based on the following inclusion criteria: 1) a confirmed diagnosis of Infantile-onset Pompe disease (IOPD), 2) CRIM-positive status determined as described previously  [ Status and Longitudinal Follow-up of Individuals with Pompe disease; LDN6709 Site 206; eviously , 3) receCRIM status was assessed by Western blot reactivity to a pool of monoclonal and polyclonal anti-GAA antibodies capable of recognizing both native and recombinant GAA , 12 fromADA titers were determined by Sanofi Genzyme using enzyme-linked immunosorbent assays (ELISA) and confirmed using radioimmunoprecipitation as described previously . SubjectThe PIMA approach to assessing an individual patient’s HLA-specific risk for immunogenicity has been described previously . Each paEpiMatrix focuses on HLA DR, as it is usually expressed at the highest levels on antigen presenting cells , 17 and In this study, three additional candidate scoring approaches were evaluated: PIMA V2, V3 and V3J. For PIMA V1, predictive models for the following supertype alleles were available: HLA DRB1*0101, *0301, *0401, *0701, *0801, *1101, *1301 and *1501. Since the publication of V1, many new HLA DR allele epitope prediction models have been developed for use in EpiMatrix. PIMA V2 evaluated whether expanding the set of HLA DR alleles used for T cell epitope prediction improved the performance of the tool. In V2, IOPD subject-specific subtypes were used instead of the eight supertypes (e.g. HLA DRB1 subtype 0403 instead of DRB1 supertype 0401). If a prediction model for a patient’s HLA DR allele is not available in our EpiMatrix tool, a close proxy was selected based on HLA DR binding pocket similarity (by examining the preferred side chains) and that was used for T cell epitope prediction. The new, more precise subject-specific subtypes were also incorporated into the subsequent V3 and V3J analyses.The next step was to refine the selection of T effector epitopes for the third PIMA model (V3). In previous models, we considered all epitopes that were not identical in sequence to nGAA to be potential T cell epitopes. We re-assessed these potential T effector epitopes in V3. Instead of automatically counting non-identical sequences as T cell epitopes, we considered only whether the epitopes were different from nGAA at the T cell receptor face. If so, they counted as T effector epitopes, but if they were conserved at the TCR face , we considered the epitopes to be ‘null’ or not T effector epitopes . In retrIn some subjects, mutations are caused by frameshifts. For each frameshift and non-sense mutations, the anticipated expressed GAA protein product is compared to the therapeutic rhGAA sequence. The sequence of the rhGAA which does not align with the truncated protein product is considered to be mismatched and is scored as a foreign protein as follows: The mismatched sequence is parsed into 9-mer frames and evaluated for potential HLA binding hits to the patient’s specific HLA DRB1 haplotype. For PIMA V1 and PIMA V2, the HLA-epitope hit values are then added up to calculate to overall PIMA score. In the case of V3 and V3J, the predicted binding epitopes are further evaluated for T cell receptor facing residues and cross-conservation to the human proteome (V3J).other human proteins , while nine putative GAA regulatory T cell epitopes were more HLA DR restricted by the HLA DRB1 of the individual IOPD subjects included in the study cohort. T cell assays were performed using a diverse panel of healthy donor PBMCs (as subject-specific PBMCs were limited for in vitro studies). The positive (regulatory T cell epitope) control for this assay was a Treg epitope similar to the previously identified Tregitopes (in vitro in a standardized Tetanus Toxin Bystander Suppression Assay (TTBSA)  .in vitro binding to HLA DRB1*0101, *0301, *0401, *0701, *1101, *1301 and *1501 alleles. The HLA DRB1 alleles selected for the HLA binding assays represent families of Class II HLA DRB1 alleles that share similar binding peptide side-chain preferences for their binding pockets  play a critical role in the development of an effective immune response or in activating both effector and regulatory T cells to induce, or diminish immune responses, respectively. The twenty-one putative regulatory T cell epitopes from GAA were tested for  pockets .in silico binding predictions. This binding assay has been described in detail in previous publications  in Providence, RI. High-resolution HLA Class II DRB1 haplotyping of donor PBMCs was performed at the Transplant Immunology Laboratory at Hartford Hospital in Hartford, CT. Donors’ age and sex are provided however race, ethnicity, and medical history are not available due to the anonymous nature of the blood donation process.All assays were performed in RPMI complete medium: RPMI-1640 + GlutaMax (Life Technologies) containing 10mM HEPES buffer (Life Technologies), 2mM L-glutamine (Life Technologies), 50µg/ml Gentamicin (Life Technologies), 10% Human AB serum (Sigma), MEM Non-essential amino acids (Gibco) and 55µM β-Mercaptoethanol (Gibco).in vitro assays that require TT-specific memory T cells.The TTBSA measures the inhibitory capacity of potential regulatory peptides on the recall response of human CD4 T cells to the tetanus toxoid (TT) antigen was adapted for validation of Treg epitopes and previously described , 31. TT 2. The following day cells were stimulated with Tetanus toxoid (TT)  at 0.5 μg/ml alone and in combination with the putative regulatory peptides or control peptide at 8, 16 or 24 μg/ml, then incubated for 6 days and analyzed by flow cytometry on day 7. CD4+ T cell proliferation, T effector activation and the ratio of regulatory to effector T cells were measured.Briefly, PBMCs were labeled with CFSE cell proliferation dye (eBioscience) and rested overnight at 37°C, 5% COt-test  was used to compare the significance of differences between TT stimulated cells to Tregitope treated cells or the indicated experimental groups. Differences were considered significant when p < 0.05 (*), very significant when p < 0.01 (**), highly significant when p < 0.0002 (***), and extremely significant when p < 0.0001 (****).Association between predictors of ADA response and outcome were evaluated by Chi-square test, or Fisher’s Exact test in the case of small sample sizes, using GraphPad online tools (GraphPad Software). Prediction metrics  were evaluated using Microsoft Excel (2016) . StatistHLA and GAA genotype data were collected from 62 individuals with IOPD in an international cohort of IOPD subjects enrolled in the Duke University IRB-approved study. These individuals include 19 CRIM-positive participants analyzed as part of our published (V1) pilot study . At thisUsing a score threshold of +10, PIMA V1 correctly predicted ADA status for 27 (56%) of 48 subjects, the intermediate versions V2 and V3 correctly predicted for 54% of the subjects and PIMA V3J correctly predicted for 64% of the subjects Figure 3The four PIMA scoring algorithms were further evaluated as predictors of high ADA development in IOPD using univariate and multivariable logistic regression analysis. Odds ratios (OR) and 95% confidence intervals (95% CI) for the development of high ADA titers were calculated. Two-sided P-values <0.05 were employed to assess significance. In the univariate logistic regression analysis, IOPD subjects with PIMA scores above 10 had increased odds of developing high ADA titers compared to subjects with scores below 10.As shown in Considered together, subjects with PIMA V3J scores >10 and initiation of ERT after 19 weeks of age were 8.23 times more likely to develop high ADA than all other subjects Table 2.in vitro, as were other epitopes that were more restricted by HLA DRB1 class. To confirm HLA binding before testing in vitro bystander T cell assay, we performed 7-point HLA DR binding assays. See in vitro. Within twenty-one tested peptides, we found that the 12 GAA peptides showed moderate to strong binding to the panel of multiple HLA alleles, whereas others were somewhat more restricted in the breadth of binding to the full range of HLA DR alleles (data not shown). To investigate the hypothesis that there may be tolerogenic peptides in GAA, we used JanusMatrix to identify putative tolerogenic epitopes. Epitopes that were predicted to bind promiscuously across multiple human class II HLA-DRB1 molecules were selected for testing To determine the magnitude of immune tolerance induced by the 21 pre-selected putative Treg epitopes and 2 IOPD patient-specific Teff epitopes Figure 5As shown for one representative donor in low, CD25hi and FoxP3hi   in the presence or absence of GAA-derived peptides or FV621 (as a positive control peptide) for 6 days and the proliferation of T effector and T regulatory cells was assessed. Tregs were identified by the expression of CD127r cells)  while CDin vivo converted (OVA) antigen-specific T effector cells to regulatory T cells (in vitro converted tetramer-stained Birch Pollen specific T effector cells to adaptive Tregs (We hypothesize that the increased frequency of Tregs that is observed when PBMC cultures of activated TT-specific effector T cells are treated with increasing concentrations of GAA-peptides may be due to (1) conversion of T effectors to adaptive Tregs; (2) elimination of T effectors from the mixed population of cells due to killing by Granzyme B ; or (3)  T cells . The potve Tregs .The ratio of activated T regulatory cells to T effector cells may be a determining factor in the maintenance of tolerance and in the potential for tolerance to ERT as well as for treatment of allergic and autoimmune diseases , 38. HerAmong the 48 IOPD subjects evaluated in this study, 19 (40%) developed high ADA titers, which included patients who exhibited sustained ADA titers of ≥12,800. Using the existing personalized immunogenicity risk assessment (PIMA) Version 1 (V1) (in vitro T cell assay (The Tetanus Toxoid Bystander Suppression Assay (TTBSA) (in vitro appear to be significantly immunomodulatory. These four peptides inhibited the expansion of Tetanus Toxoid-specific memory CD4+ T cells in a standardized Treg assay (the TTBSA), similar to other well-defined Treg epitope peptides. Others have reported an absence of immune responses to some of the epitopes that have been confirmed to be regulatory here  adjusted for potential regulatory T cell epitopes using JanusMatrix was also tested. This improved version that integrated putatively tolerated and tolerogenic epitopes predicted ADA status accurately for 64% of subjects. To explore the interesting hypothesis that these epitopes might down-modulate T effector responses to ERT we used a validated  (TTBSA)  to examiory here . The reaIn subjects who have circulating Tregs that recognize these epitopes in GAA, treatment with the drug may activate antigen-specific T regulatory cells, contributing to the induction of tolerance to the GAA therapy while limiting the development of ADA. In future studies, we intend to assess the effect of these peptides in the TTBSA with blood samples from IOPD subjects, while also testing additional GAA T effector and GAA Treg epitopes using naïve donor and IOPD subjects’ T cells. In keeping with the hypothesis, tolerance to GAA has been observed in late-onset Pompe Disease , may not have Tregs that respond to these sequences. Tolerance to GAA may be ‘personalized’ since it is both native GAA-sequence-specific and HLA DR-haplotype dependent. Therefore, a personalized immunogenicity risk assessment such as the PIMA V3J tool may be the most accurate means of assessing the risk of an immune response to replacement rhGAA.The means by which the GAA-specific immunomodulatory T cells modulate immune responses deserves further study. Their effect may be due to i) production of immunosuppressive cytokines, e.g., TGFβ, IL-10 and IL-35, ii) upregulation of effector T cell-specific transcription factors important for the expression of CXCR3 and survival of Tregs e.g., T-. As blooin vitro test (TTBSA) could be developed using their peripheral blood cells that could guide their ADA risk assessment.ADA also develop in IOPD individuals who have splice site mutations that result in indeterminate protein products . FourteeNo comparison to publicly available tools was made because the type of analysis performed by JanusMatrix is not available elsewhere. These tools have been compared to on-line tools in other settings such as for cancer, please see reference . SeveralPersonalized IMmunogenicity Assessment” tool (PIMA)  has been incorporated into a web-based “l (PIMA) Figure 7All relevant data generated for this study are included in the article/The studies involving human participants were reviewed and approved by Duke Institutional Review Board. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.in silico tool development. Experiments, in silico implementation of algorithms were performed by MA, WDM, FET, SK, SL, JSY and SM. IOPD patient cohort data was collected by AKD, CL, and PSK. All authors contributed to the article and approved the submitted version.PSK, ADG, MA, and WDM conceptualized and designed the study, experiments and Research reported in this publication was supported by the National Center For Advancing Translational Sciences (NCATS) of the National Institutes of Health under Award Number R43TR002441. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Rhode Island Commerce provided support as part of the Innovate RI Small Business Fund SBIR/STTR Phase I Matching Grant Program, STAC/RI Commerce.ADG and WDM are senior officers and shareholders, and SM, FET, SK, MA, and SL are employees of EpiVax, Inc., a company specializing in immunoinformatic analysis. EpiVax, Inc. own patents to technologies utilized by associated authors in the research reported here.AKD has received grant support from Sanofi Genzyme and the lysosomal disease network. PSK has received research/grant support from Sanofi Genzyme, Valerion Therapeutics, and Amicus Therapeutics. PSK has received consulting fees and honoraria from Sanofi Genzyme, Amicus Therapeutics, Maze Therapeutics, JCR Pharmaceutical and Asklepios Biopharmaceutical, Inc. (AskBio). PSK is a member of the Pompe and Gaucher Disease Registry Advisory Board for Sanofi Genzyme, Amicus Therapeutics, and Baebies. PSK has equity in Asklepios Biopharmaceutical, Inc. (AskBio), which is developing gene therapy for Pompe disease and Maze Therapeutics, which is developing small molecule in Pompe disease.The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.