The role of glutathione (GSH) and GSH-S-transferase (GST) activity in modulating the cytotoxicity of four platinum drugs and melphalan was evaluated in eight human ovarian carcinoma cell lines. The cell lines were established from solid and ascitic tumours from pretreated and untreated patients, and showed a wide spectrum of sensitivity to several platinum II and platinum IV drugs; cisplatin, carboplatin, CHIP and tetraplatin. Intracellular glutathione concentration measured in the cell lines showed a significant (P = 0.05) correlation with IC50 values for cisplatin (r = 0.91), carboplatin (r = 0.87) and CHIP (r = 0.88). The correlation between GSH levels and IC50 values for melphalan (r = 0.76) or tetraplatin (r = 0.60) was not as significant. GST activity showed no correlation with IC50 values, for the four platinum drugs. To determine the significance of the elevated GSH concentration in the refractory cell lines, the effect of D,L-buthionine-S, R-sulfoximine (BSO) mediated GSH depletion on platinum drug cytotoxicity was examined in one of the most sensitive (CH1) and two of the least sensitive  cell lines. Comparison was made with the effect of GSH depletion on melphalan cytotoxicity in these three lines. These lines were differentially sensitive to BSO, with the two most platinum drug resistant lines being more tolerant to BSO than the sensitive CH1 line. Depletion of cellular GSH, ranging between 61 and 88%, had a differential effect on the sensitivity to PtII vs PtIV drugs in the three cell lines: cytotoxicity of the PtIV drugs, tetraplatin and CHIP, was substantially enhanced in both the resistant and sensitive cell lines; in contrast, the cytotoxicity of the PtII drugs, cisplatin and carboplatin, was only significantly increased in one of the two relatively resistant lines (SKOV-3) and in the sensitive (CH1) line after GSH depletion. Moreover the dose modification factor (DMF) for the PtII agents were lower than those for PtIV agents in the three cell lines. The dose modification factor for tetraplatin after BSO treatment was similar to that observed for melphalan in all three cell lines. In the SKOV-3 cell line extending the BSO pretreatment period to 48 h from 24 h marginally reduced the cytotoxicity of cisplatin, whereas the cytotoxicity of the other three drugs remained similar to that observed after 24 h BSO pretreatment. In contrast, extending the BSO treatment to 24 h after drug exposure potentiated the cytotoxicity of cisplatin, CHIP and tetraplatin.(ABSTRACT TRUNCATED AT 400 WORDS)


Cell lines selected (CHRC5) and transfected (LR-73-1A) for multi-drug resistance have total lipid compositions which are indistinguishable between resistant and parental cells. Lipid composition was evaluated by 1H NMR and the total fatty acid content by GLC. No change in surface hydrophobicity, as measured with the fluorescent probe dansyl-PE, was observed as a result of transfection of CHO cells with the mdr1 gene. However, the selected cell line, CHRC5, showed a decreased surface hydrophobicity. This decreased surface hydrophobicity was indicated by an 8 nm increase in the fluorescence emission of dansyl-PE in the CHRC5 cell line compared with the AB1. Both resistant cell lines showed an increase in the polarisation of the fluorescent probe, TMA-DPH in the plasma membranes corresponding to a 14% and a 24% change in fluorescence polarisation for the selected and transfected cell lines, respectively. This is indicative of reduced mobility of the acyl chains in the resistant cell lines. Both the CHRC5 and the transfected cell lines showed almost a 2-fold increase in the initial rate of membrane cycling. The membrane cycling could be inhibited by a known bilayer stabiliser, the N-carbobenzoxy-D-Phe-L-Phe-Gly. These results indicate that the properties of the plasma membrane from resistant cells are altered compared with their parental cell line.


Congenital arhinia is an extremely rare anomaly consisting of an absence of external nasal structures and nasal passages. Fewer than 30 cases have been reported. Patients with a familial absence of the nose have been reported, but the effects of genetic and maternal factors are unknown. Midface hypoplasia may accompany arhinia. Accompanying malformations are thought to be caused by an absent or rudimentary nose. A patient with partial congenital arhinia is presented and the embryology and literature review are discussed. Arhinia may be accompanied by midline defects, or ear, palatal, ocular, or facial abnormalities. Severe airway and feeding problems may accompany arhinia in newborns.Congenital absence of the nose, Our case is a female patient, born to a 28-year-old mother. This was the mother's first pregnancy. The mother denied any antenatal problems, and the baby was born vaginally at full term. Family history revealed no interrelative marriage or history of congenital malformation. Results of a chromosomal analysis showed a normal female. The baby was born with severe nasal deformation. On gross inspection the external nose was absent. The nasal dorsum structures and remnants of the alar cartilages were barely palpable. The columella and right nostril were absent; the left nostril was severely stenotic Figure . ProbingDuring facial development, cranial neural crest cells migrate from the trigeminal nerve region to the face . DevelopThe pathogenesis of arhinia is not clearly understood. The proposed mechanism may be a developmental defect in the medial and lateral nasal processes or overdevelopment and early fusion in the medial nasal processes . Arrest Olsen and associates reviewed the literature through 2001 and collected 22 additional cases . McGloneCongenital arhinia is a very rare defect during embryogenesis. Most cases are sporadic but familial cases have been reported. Mostly the karyotype is normal. In 2 cases, anomalies in chromosome 9-and in a single case, chromosome 3–12 translocation – have been detected . A varieWe considered our patient as having partial arhinia because there were some remnants of the external nose and a cul-de-sac was present. This was accompanied by central nervous system and ear anomalies, a submucosal cleft palate, and hypotelorism.Congenital arhinia is an extremely rare condition of unknown etiology. Facial anomalies and other concomitant distant anomalies may be present. These patients experience serious problems with regard to an open airway and feeding. Any accompanying anomalies should be detected and surgical correction should be planned.Written informed consent was obtained from the patient's parents for publication of this case report and any accompanying images. A copy of the written consent is available for review by the Editor-in-Chief of this journal.The author(s) declare that they have no competing interests.GA made substantial contributions with regard to the manuscript's conception and design and was involved in drafting the manuscript; the author will give final approval of the version to be published. BA made substantial contributions with regard to the manuscript's conception and design and was involved in drafting the manuscript; the author will give final approval of the version to be published. EA made substantial contributions with regard to the manuscript's conception and design and was involved in drafting the manuscript; the author will give final approval of the version to be published. MD made substantial contributions with regard to the manuscript's conception and design and was involved in drafting the embryologic part of the manuscript. LO was involved in revising the manuscript critically; the author will give final approval of the version to be published.


In vitro recordings of medial entorhinal layer II stellate cells have revealed subthreshold membrane potential oscillations (MPOs) whose temporal periods, and time constants of excitatory postsynaptic potentials (EPSPs), both increase along this axis. Slower (faster) subthreshold MPOs and slower (faster) EPSPs correlate with larger  grid spacings and field widths. A self-organizing map neural model explains how the anatomical gradient of grid spatial scales can be learned by cells that respond more slowly along the gradient to their inputs from stripe cells of multiple scales, which perform linear velocity path integration. The model cells also exhibit MPO frequencies that covary with their response rates. The gradient in intrinsic rhythmicity is thus not compelling evidence for oscillatory interference as a mechanism of grid cell firing. A response rate gradient combined with input stripe cells that have normalized receptive fields can reproduce all known spatial and temporal properties of grid cells along the MEC dorsoventral axis. This spatial gradient mechanism is homologous to a gradient mechanism for temporal learning in the lateral entorhinal cortex and its hippocampal projections. Spatial and temporal representations may hereby arise from homologous mechanisms, thereby embodying a mechanistic “neural relativity” that may clarify how episodic memories are learned.Place cells in the hippocampus of higher mammals are critical for spatial navigation. Recent modeling clarifies how this may be achieved by how grid cells in the medial entorhinal cortex (MEC) input to place cells. Grid cells exhibit hexagonal grid firing patterns across space in multiple spatial scales along the MEC dorsoventral axis. Signals from grid cells of multiple scales combine adaptively to activate place cells that represent much larger spaces than grid cells. But how do grid cells learn to fire at multiple positions that form a hexagonal grid, and with spatial scales that increase along the dorsoventral axis?  Spatial navigation is a critical competence of all higher mammals, and place cells in the hippocampus represent the large spaces in which they navigate. Recent modeling clarifies how this may occur via interactions between grid cells in the medial entorhinal cortex (MEC) and place cells. Grid cells exhibit hexagonal grid firing patterns across space and come in multiple spatial scales that increase along the dorsoventral axis of MEC. Signals from multiple scales of grid cells combine to activate place cells that represent much larger spaces than grid cells. This article shows how a gradient of cell response rates along the dorsoventral axis enables the learning of grid cells with the observed gradient of spatial scales as an animal navigates realistic trajectories. The observed gradient of grid cell membrane potential oscillation frequencies is shown to be a direct result of the gradient of response rates. This gradient mechanism for spatial learning is homologous to a gradient mechanism for temporal learning in the lateral entorhinal cortex and its hippocampal projections, thereby clarifying why both spatial and temporal representations are found in the entorhinal-hippocampal system. Navigating the world requires the brain to learn and maintain memory of spatial positions within various environments. Place cells in the hippocampal areas CA1 and CA3 demonstrate a neural code for position in large spaces that higher mammals inhabit In particular, Brun and colleagues The presence of multiple spatial scales on the dorsoventral axis of MEC has important implications for the development of the hippocampal place cells In vitro whole-cell patch clamp recordings Excitatory projections to the hippocampal formation from layer II of MEC are primarily from stellate cells This article develops a SOM neural model, called Spectral Spacing for reasons summarized below, to explain the above data. This model shows how a gradient of cell response rates along the dorsoventral axis of MEC can control the development of grid cells whose hexagonal grid firing fields exhibit a gradient of spatial scales and whose MPOs exhibit a gradient of frequencies. These results combine several conceptual and technical advances.First, these results are part of an emerging general entorhinal-hippocampal model architecture see also , which sSecond, these SOM laws have been proposed to control development and learning in many other parts of the brain, notably the visual cortex. Thus, both grid and place cells may develop using general SOM principles of brain map organization.Third, the linear velocity and angular velocity path integration inputs that drive model learning are derived from realistic trajectories of rats in spatial learning and memory experiments.Fourth, these linear velocity and angular velocity estimates can both be transformed into position codes by ring attractors.Fifth, the rate gradient mechanism for spatial learning in the MEC pathway and its hippocampal projections is homologous to a rate gradient mechanism that has been used to model temporal learning in the lateral entorhinal cortex (LEC) pathway and its hippocampal projections. Spatial and temporal representations in the medial and lateral processing streams may hereby arise from homologous mechanisms, thereby embodying a mechanistic “neural relativity” in the entorhinal-hippocampal system. This homology may clarify why spatial and temporal representations both occur in hippocampus, and provides new clues about how episodic memories may be learned.In summary, this model system exhibits parsimony and unity, both in its use of similar ring attractor mechanisms to code the linear and angular velocity path integration inputs that drive learning, and in its use of a rate gradient mechanism that can support the learning of both spatial and temporal codes.Even more striking is the fact that both grid cell and place cell receptive fields emerge by detecting, learning, and remembering the most frequent and energetic coactivations of their inputs. This co-occurrence property is different from the property of oscillatory interference that some other models have proposed e.g., . OscillaSimulation Settings). These simulations demonstrate that, at least among these variations, only a response rate gradient, combined with input cells that have normalized receptive fields, can explain all the data mentioned above.In order to better understand what aspects of the Spectral Spacing model are needed to explain how spatial and temporal properties of grid cell firing change along the dorsoventral extent of MEC, several model and input variations were simulated  and weight decreases (long-term depression). It hereby enables the weights to adapt to the Simultaneously active stripe cells are more likely to strongly activate map cells whose bottom-up weight patterns closely match their activity pattern. Adaptation of the weights to a map cell occurs only when its activity is above a threshold see  in . This poThe GRIDSmap model Until recently, SOM models of place cell learning used idealized or hand-crafted grid cells e.g., . Pilly aBoth the GRIDSmap and the GridPlaceMap models learn hexagonal grid firing fields whose spatial scale is derived from that of the input stripe cells. In particular, stripe cells with the same period were used to learn grid fields of a given spatial scale. Stripe cells of different spatial scales were assumed to activate different locations along the dorsoventral axis in layer II of MEC, thereby giving rise to grid cells with different spatial scales. But how is the selection of just one spatial scale of stripe cells realized for each grid cell scale? What would happen if stripe cells of multiple scales initially projected to the map layer before grid cell learning began, as in in vitro to a steady current input. Whereas a dorsoventral gradient in either response rate or habituation rate can explain the corresponding gradient in learned spatial scale and MPO frequency of grid cells, only a gradient in response rate was found to be consistent with data regarding the associated dorsoventral gradient in peak and mean firing rates of grid cells This article shows that the rates at which the category cells and their corresponding habituative transmitters respond, called the response rate . For a cell to respond with contrast-enhanced, or above-threshold, activity at any moment with the help of its self-excitatory feedback signal, its habituative transmitter needs to be at a sufficient high level. But each time the cell responds intensely, there is a collapse of the transmitter , which tThis property directly explains learned scale selectivity for the case of a rat running forward at a constant speed on a linear track. Then dorsal MEC cells in the model respond better to inputs at periodic positions with relatively smaller spacings, while ventral MEC cells respond better to those with relatively larger spacings. However, the situation is more complicated when the rat navigates along the type of two-dimensional real trajectory used in our simulations, for which the running speed of the rat through time varies between 0 cm/s and 146.6 cm/s with a mean of 14.03 cm/s, a standard deviation of 9.8 cm/s, and a mean length of piecewise linear segments of only 0.9 cm. How different response rates selectively learn different spatial scales in response to such realistic trajectories is discussed in the next subsection.Emergent properties of model simulations  emulate As noted earlier, for each input stripe scale considered separately, the most frequent and energetic activations of grid cells occur when sets of three stripe cells are coactivated whose preferred directions differ by 60° peak firing rate of MEC cells decreases from the dorsal to the ventral ends  exhibit lower gridness scores  and inte problem –7 is not problem  that wasells see . This reIn Case 8, unlike the data, dorsal cells learned hexagonal grid fields derived from large-scale stripe cells. This case imposes the same peak activity across both small-scale and large-scale stripe cells. Thus, the stripe cell receptive fields are not normalized across scales, and the large-scale stripe cells have a competitive advantage since they are sampled by map cells for a longer time. This advantage seems to be sufficient for them to win over small-scale stripe cells with the same peak activity, despite the lower frequency of their favored coactivations across space , in driving the learning of large-scale grid cells even for faster response rates. Thus, if stripe field widths increase with stripe spacing, similar to grid cells Normalized receptive fields occur in many other examples of multi-scale processing in the brain, and may be a general principle of brain design. The general design theme is how to achieve selective processing across multiple scales, so that the largest scales do not always win the competition to represent incoming data. Normalization ensures that the degree of brain commitment covaries with the amount of evidence for that choice gradient , and 7. gradient , in contin vitro studies have showed that layer II MEC stellate cells exhibit subthreshold MPOs, in response to steady current injection, whose temporal period increases from the dorsal to the ventral end of MEC  to convert a stripe cell population code that implicitly represents an animal's position using multiple small spatial scales into a place cell code in which a single place cell can explicitly represent spatial position in large environments. The intermediate stage of grid cells converts input stripe cell signals into a form conducive to the learning of such unimodal place cell spatial fields, which thereby significantly increase the scale of spatial representation compared to the inducing grid cells. Simulations in Can place cells be learned directly from stripe cells, without the intervention of hexagonal grid fields? The presence of the animal at a given spatial position strongly activates just one or few stripe cells in each directional ring attractor. So, a unimodal spatial field at that position could be learned, in principle, if a map cell could become tuned to the combination of all these coactive stripe cells across directions and scales. However, such input combinations are not favored by the self-organization process because they occur only at single positions in the environment, as opposed to the multiple positions at which the stripe cell combinations suitable for hexagonal grid fields are activated. As we mentioned above, map cell learning at both the grid cell and place cell levels is naturally sensitive to both the energy and frequency of input coactivations. How, then, are place cells learned, given they are activated only at single positions in the environment? If inputs to a SOM come from comprise grid cells of multiple spatial scales, then sets of co-active grid cells involving a greater number of scales are more likely to gain control of hippocampal map cells in vivo may be modulated by running speed, because of which the slope and intercept of the dorsal-ventral gradient in grid spacing may not be significantly altered in response to very fast or slow running speeds. It would be instructive to explicitly test this prediction.The spacings of grid fields in our model are adaptively selected based on cell response rate, which is inversely correlated with the minimum temporal duration between two episodes of intense activity. Therefore, it is important to discuss how the learned grid cells may respond if an adult animal were to run around an environment with a mean speed that is higher or lower than when the grid cells are learned during development. However, these extreme speed cases may be relevant only for theoretical purposes because of two reasons; namely, the distribution of running speeds in the realistic trajectory, used for our simulations, is relatively broadly tuned with a standard deviation of 9.8 cm/s, and existing relevant data indicates that the average running speed of rats increases by just ∼2 cm/s from P16 to adulthood . The nature of our model's incompleteness clarifies data about how and when deformations in grid cell receptive fields do occur In summary, our model here and in More work needs to be done to study how the response rate gradient and the habituative gating mechanism in our model relate to the HCN and leak potassium channels, which control the varied temporal integrative properties of MEC layer II stellate cells The model suggests several predictions regarding the development of grid cells at different anatomical locations along the dorsoventral axis of MEC as young animals begin to navigate for the first time. These predictions are tempered by the awareness that the model does not yet incorporate various known mechanisms, such as top-down matching and attentional mechanisms from hippocampus, that may influence model properties, notably their malleability after the predicted dynamical stabilization of grid field structures sets in due to attentive matching.Existing empirical studies on the development of grid cells The Spectral Spacing model illustrates how control by a single rate parameter can determine a gradient of grid cell spatial scales in response to inputs from multiple stripe cell spatial scales. Multiple small grid cell scales can then be adaptively combined in the hippocampus to generate place cell scales that are large enough to support spatial navigation In support of this prediction, MacDonald and coworkers Where cortical stream go through postrhinal cortex and medial entorhinal cortex on their way to hippocampal cortex, and object representations in the What cortical stream go through perirhinal cortex and lateral entorhinal cortex on their way to hippocampal cortex Thus, dorsoventral gradients in single rate parameters within the entorhinal-hippocampal system may create multiple small spatial and temporal scales that can be fused into larger spatial and temporal scales in the hippocampal cortex that are large enough to control adaptive behaviors. The mechanistic homology between these spatial and temporal mechanisms suggests why they may occur side-by-side in the medial and lateral streams through entorhinal cortex into the hippocampus. In particular, spatial representations in the This subsection highlights and justifies differences between the GRIDSmap model First, we introduced a threshold in the signal function that transforms membrane potentials of map cells into their output activities, which both govern the recurrent inhibitory interactions and gate the competitive adaptation of corresponding bottom-up weights .Increased stability of learned spatial fields as the bottom-up weights do not adapt in response to noisy or non-optimally activated map cells.Second, we initialized the pre-development synaptic weights of the connections from stripe cells to grid cells  in  using a Third, the inactivation and recovery dynamics of the habituative transmitter depend only on the self-excitatory feedback signal see  in the eFourth, the adaptive weights from stripe cells to category cells use a different version of the instar learning law (Equation 1.6) that more robustly enabled category cells to become tuned to coactivations of stripe cells In the GRIDSmap model, the stripe cells of different spacings were assumed to have the same maximal firing rate but different field widths (i.e., Several models exist to explain the generation of grid cells, but the Spectral Spacing model differentiates itself by providing for the first time a principled explanation of how grid cells learn not only their characteristic hexagonal grid firing patterns band cellsPrior grid cell models can be generally classified into two categories based on whether the linear velocity path integration happens before or at the level of grid cells. In addition to the SOM type of model, the former possibility has been modeled using mechanisms of oscillatory interference in vivo, result if this constraint is not met Oscillatory interference models The 2-D attractor models


We introduce a grid cell microcircuit hypothesis. We propose the ‘grid in the world’ (evident in grid cell discharges) is generated by a ‘grid in the cortex’. This cortical grid is formed by patches of calbindin-positive pyramidal neurons in layer 2 of medial entorhinal cortex (MEC). Our isomorphic mapping hypothesis assumes three types of isomorphism: (i) metric correspondence of neural space  and the external two-dimensional space within patches; (ii) isomorphism between cellular connectivity matrix and firing field; (iii) isomorphism between single cell and population activity. Each patch is a grid cell lattice arranged in a two-dimensional map of space with a neural : external scale of approximately 1 : 2000 in the dorsal part of rat MEC. The lattice behaves like an excitable medium with neighbouring grid cells exciting each other. Spatial scale is implemented as an intrinsic scaling factor for neural propagation speed. This factor varies along the dorsoventral cortical axis. A connectivity scheme of the grid system is described. Head direction input specifies the direction of activity propagation. We extend the theory to neurons between grid patches and predict a rare discharge pattern (inverted grid cells) and the relative location and proportion of grid cells and spatial band cells. These 2.(a)We first provide a minimal sketch of the operation of the grid cell system in layer 2 of the rat MEC. The isomorphic mapping hypothesis is inspired by the similarity between the pattern of patches in the MEC and grid cell discharge patterns in space . It is s(b)a shows the grid-like arrangement of these patches in layer 2 of the MEC. Calbindin patches have a diameter of about 150 µm and are arranged in a regular array According to our hypothesis, we assume three types of isomorphism: (i) a metric correspondence of neural space  and the external two-dimensional Cartesian space within calbindin grid cell patches; (ii) an isomorphism between the connectivity matrix of a grid cell and its firing field ; (iii) an isomorphism between single grid cell and population grid activity within a patch. All these assumptions are simplifications, and only assumption (i) is very strong and essential to the theory. Assumptions (ii) and (iii) greatly simplify the interpretation of entorhinal circuits, but they are less critical to the theory and can easily be relaxed.b). A layer 2 grid cell patch, seen in a top view, is a metric representation of repeating segments of real space (figure 1c). We show a patch in the shape of a hexagon (figure 1b), which makes it easy to illustrate how real space (figure 1c) is represented and tiled up by the grid patch. The hexagonal shape of a grid patch is not essential to our theory, however, and anatomically calbindin-patches look typically circular. While the hexagonal shape of a grid patch is not essential to the model, the arrangement of multiple grid patches in a hexagonal grid is of significance for the connectivity scheme proposed later and our ideas how spatial band cells might be generated from grid cell inputs (see below). Spatial direction applies in both the real and neural space. This is our first isomorphism assumption. Figure 1b illustrates, in a colour-coded population, firing rate in the patch at a set of positions a in real space, whereas figure 1c illustrates, in a colour-coded single neuron, firing rate of a grid cell, which has its peak firing rate at position A. The correspondence of single grid cell and population grid activity illustrated here is our third isomorphism assumption.figure 1a. It can be seen that different from conventional attractor models there is not one but many ‘bumps of activity’ [figure 2a, left), these bumps of activity move from left to right in the entorhinal cortex . Although these bumps of activity are at analogous positions and move at identical speed in laterally neighbouring patches, the bumps of activity move at different speeds in patches at different dorsoventral positions.figure 2ctivity’ –20. Spec(d)b) and in real space (figure 1c). A calbindin patch has a diameter of 150 µm [a,b). Specifically, we assume that there is an intrinsic difference in propagation speed of neural activity between dorsal and ventral patches (b). Note that the actual speed, with which activity travels through the patch, depends also on the animal's velocity in space. Spatial scale is therefore not interpreted as an absolute propagation speed of neural activity but rather as an intrinsic speed propagation factor to be multiplied with the animal's velocity. The idea that neural propagation of activity differs between dorsal and ventral entorhinal cortex is in line with experimental evidence on dorsal/ventral differences in intrinsic properties [figure 1f 150 µm  and in d patches b. Note toperties , synaptioperties –29.−1, a rat will cross a dorsal patch in 3 s and will cover 1.14 cm in one theta cycle. In external space, 1.14 cm corresponds to 5.7 µm in neural space. If one imagines the about 120 calbindin-positive pyramidal cells in one plane (the patch has an area of about 15 000 µm2), then they have a spacing of approximately 11 µm [These assumptions predict fast neural propagation in dorsal patches  and slow neural propagation in ventral patches . What do these numbers mean in neural terms? At a standard running speed of 10 cm sly 11 µm . Thus, w(e)c illustrates how head direction information drives the propagation of activity through the patch. We assume that the grid cell activity rests statically at a low level that is exactly self-sustaining in the absence of head direction input. While it seems fairly clear that head direction inputs control the direction of propagation activity, how this exactly occurs is not predicted by the theory. To give the reader a better idea how this may occur, we specify one possible implementation of head direction input to grid patches in figure 2c,d, but we emphasize that this scheme is not essential to our theory. As shown in figure 2c, directionally delayed inhibition activated by head direction inputs can be used to push the population activity forward (figure 2d). The local and long-range wrap-around connectivity between grid cell patches (see §2f) assures that the grid activity peak wraps around in time . The velocity signal could involve either the head direction cells or could be implemented through a higher grid cells activity with increasing velocity, which will also lead to faster propagation. Most likely, velocity is implemented in multiple ways in the grid/head direction circuit.The propagation of activity through a grid patch is what leads to grid cell firing, but several aspects of this process are not yet understood. Figure 2(f)The core of our hypothesis is to devise a connectivity scheme that generates grid cell discharges and relates spatial discharge in an unambiguous manner across spatial scales and grid cell patches. We assume that all patches have the same grid orientation. Patches at the same dorsoventral height of MEC are thought to have the same grid map. A set of six connectivity rules that connect grid cells within and across patches is illustrated in Is this connectivity scheme compatible with the experimental evidence? The mutual excitatory connectivity between layer 2 pyramidal cells postulated here differs sharply from the connectivity observed in paired recording studies of layer 2 stellate neurons, which show almost no mutual excitatory connections and which are coupled by strong disynaptic inhibition ,30. MorpThe fact that even same-sized grids can have deviations, such as slightly different tilts in their main axes , poses cGrids of different scales shift relative to each other in situations that cause place cell remapping . We thin(g)a. As we have seen in the description of the patchy anatomy, this is not the case, however. Instead, putative grid patches are displaced from each other as shown in figure 4b.This brief account of the isomorphic mapping hypothesis explains many features of grid cell activity, such as iterative firing and spatially coherent activity across scales. The most simple connectivity scheme to implement the entire theory outlined so far would be to arrange grid patches directly adjacent to each other, as depicted in figure 4b).We now extend our hypothesis to include this space between the grid patches in our theory. With two assumptions, one can also predict spatial firing patterns in this region. These assumptions are (i) cells in this region receive the same grid cell input as grid cell neighbours, but they do not return connections, and (ii) there is no recurrent connectivity in this region. These assumptions imply that the area outside of the patches is downstream from the grid system and that there is no metric spatial firing in between grid patches, because it is the symmetric connectivity of cells in grid patches which guarantees such a code b.c, where we show the connections of two grid cells in neighbouring patches in one dimension. The entire connectivity matrix would involve all grid cells, which make radial connections in two dimensions. Figure 4d,e shows schematically the resulting spatial discharge profiles at various regions in layer 2 of the MEC. Figure 4d also shows that the model predicts a gradual shift from grid-like to band-like firing patterns as one moves out from grid patches. In cells in the transition zone from grid-like to band-like firing, varying inputs from neighbouring grid and band cells might cause a flipping back and forth between such discharge patterns as observed recently [What then, are the firing fields of cells between the grid patches? We approach this issue in figure 4recently .(i)e, grid cells are observed in grid patches.As discussed before and shown in figure 4(ii)c), and the neural activity will be maximal and the same along a line connecting this position and the two neighbouring patches. Along the room axis orthogonal to this connecting line, activity falls off, because the distance of grid cells to this position increases along this axis.We predict that exactly between two neighbouring patches, one will observe cells that discharge in spatial bands. This band-like firing pattern comes about because the input from the two neighbouring patches will be the same at this point c, and tha. Because cells in the grey area between grid patches receive their inputs from grid patches and input strength from each grid cell falls off with distance, their activity will increase the closer they are to the peak of the activity bumps in grid patches. As the animal runs from left to right (figure 2a), the cells between two grid patches in the top row of figure 2a will see a constant input level. This constant input level comes about because inputs from the left-hand neighbouring grid patch increase as the activity bump approaches, but at the same time inputs from the right-hand neighbouring grid patch decrease as the activity bump moves away. If the animal runs from top to bottom, however, then input levels of the cells between two grid patches in the top row of figure 2a will vary. The cells will see maximum input when the peak of the activity bump in the neighbouring grid patches is exactly lateral from them, and a drop of inputs when the animal moves further up or down. Thus, these cells between two grid patches in the top row of figure 2a will fire in a spatial band extending from left to right.One can also understand how spatial band cells come about in our model, if one considers the dynamical scenario outlined in figure 2(iii)c, a novel discharge pattern—inverted grid cells . Again, one can also understand how inverted cells come about in our model if one considers the dynamical scenario outlined in figure 2a.Based on the connectivity considerations shown in figure 4see also )—can be Finally, we would like to add that the spatial firing profiles observed between grid patches depend fairly strongly on the connection parameters, such as radial spread of connections. This parameter dependence differs from the grid connectivity. Parameter dependence arises because we are dealing here with unilateral, asymmetric connections. For example, inverted grid cells come about only if not too long a radial connectivity is chosen; otherwise, one observes very little spatial modulation in this region of the entorhinal cortex. This is an important consideration because there is evidence for a large number of spatially non-modulated cells in the MEC , whereasAre the fractions of grid and band cells predicted by this model compatible with published results? There are discrepancies between the reported percentages of grid cells in superficial layer MEC in the literature. Older studies report large fractions of grid cells ,2, which(h)f may be modified by learning, such that they represent space more accurately , but we do not think that grid cells represent environment-specific landmark information. We find it difficult to imagine how individual grid cells could learn landmark information when grid scale is changing with experience, and hence the firing nodes of grid cell activity drift relative to landmark inputs [So far, we have not considered here how the grid is anchored to the world. According to our theory, the grid system is a purely metric representation of space, which needs to be aligned with the world, but which does not contain specific spatial memories. The synaptic connections specified in §2k inputs . The typk inputs . As we ak inputs  and why k inputs .In line with evidence from cognitive psychology  and the We have also neglected fine temporal dynamics. Such dynamics play a major role in dorsal MEC. We expect that phase precession of grid cells occurs, because the directed propagation of excitation through the grid patch accelerates grid cells relative to theta in the running direction. This issue needs further investigation, however.3.We have described a circuit model for grid cell activity. Based on three isomorphism assumptions , a connectivity scheme for layer 2 grid cells is derived. In this scheme, intrinsic propagation speed in the patch is the neural analogue to spatial scale and is thought to differ roughly 10-fold between dorsal and ventral entorhinal cortex. Head direction input is thought to specify the direction of propagation of activity, but the details of this process are not worked out. We extend the theory to neurons between grid patches and predict the location and discharge properties in these neurons.(a)We try to explain the anatomy and the spatial discharge properties in layer 2 of MEC. We did not consider temporal dynamics, the anchoring of the grids to the environment and spatial memory. This account is an explicitly neural/microcircuit hypothesis. The neural nature of the theory implies that it predicts in great detail experimental results. We predict that there is an isomorphism between neural and external two-dimensional space in grid patches. This prediction is in conflict with experimental observations on grid cells in extracellular recordings, which did not show a systematic progression of grid phase in recording tracks . We do n(b)The core idea of this paper is that the ‘grid in the world’ (evident in grid cell discharges) is generated by a ‘grid in the head’ . Isomorphic neural representations are evident in the retinotopy of the mammalian visual cortex and the eye-catching representations of body parts such as whiskers  or nose (c)In recordings from the MEC, grid spacing does not appear to increase continuously from dorsal to ventral entorhinal cortex, but it seems to increase in discrete steps ,5. We su(d)Grid cells are also found in other layers  of MEC  as well Third, we see multiple possibilities of how grids could be propagated to the pre- and parasubiculum. It may be the case that these structures also simply inherit grid responses from layer 2 of MEC, but again it is unclear whether the connectivity to accomplish such a transfer of grid properties is present  and the (e)The proposed isoposition connectivity between patches is very similar in phenomenology to the patchy iso-orientation connectivity in visual cortex . The micRecent intracellular recording ,48 studiThe fact that we approach grid cell discharge from an anatomical perspective differentiates our hypothesis from previous models . Thus, b


Grid cells in the medial entorhinal cortex (mEC) and place cells in the hippocampus are paradigms for population coding of spatial information . Both thIn order to understand the relative contributions of grid cells and non-spatial inputs in determining place field size and stability, we propose a computational model of the hippocampal-entorhinal network that includes a modular organization of grid cell inputs arranged in order of increasing spatial scale, as is seen experimentally in the mEC. Our underlying place cell model is a firing-rate based model inspired by previous work , in whicOur main findings suggest that:1.) For a wide range of parameters, the relative amounts of spatial and non-spatial inputs to place cells plays a more important role in determining place field size and stability than the spatial scale of grid cell inputs. This implies that the dorso-ventral gradient in place field size may reflect a dorso-ventral gradient in non-spatial inputs, rather than grid scale, and is agreement with prior suggestions of a functional distinction between the dorsal and ventral regions of the hippocampus .2.) In our model, place fields are sensitive to changes in the firing rates of the grid vertices of individual grid cells, emphasizing the potential implications of this grid field heterogeneity for place field formation and stability.


Medial entorhinal grid cells and hippocampal place cells provide neural correlates of spatial representation in the brain. A place cell typically fires whenever an animal is present in one or more spatial regions, or places, of an environment. A grid cell typically fires in multiple spatial regions that form a regular hexagonal grid structure extending throughout the environment. Different grid and place cells prefer spatially offset regions, with their firing fields increasing in size along the dorsoventral axes of the medial entorhinal cortex and hippocampus. The spacing between neighboring fields for a grid cell also increases along the dorsoventral axis. This article presents a neural model whose spiking neurons operate in a hierarchy of self-organizing maps, each obeying the same laws. This spiking GridPlaceMap model simulates how grid cells and place cells may develop. It responds to realistic rat navigational trajectories by learning grid cells with hexagonal grid firing fields of multiple spatial scales and place cells with one or more firing fields that match neurophysiological data about these cells and their development in juvenile rats. The place cells represent much larger spaces than the grid cells, which enable them to support navigational behaviors. Both self-organizing maps amplify and learn to categorize the most frequent and energetic co-occurrences of their inputs. The current results build upon a previous rate-based model of grid and place cell learning, and thus illustrate a general method for converting rate-based adaptive neural models, without the loss of any of their analog properties, into models whose cells obey spiking dynamics. New properties of the spiking GridPlaceMap model include the appearance of theta band modulation. The spiking model also opens a path for implementation in brain-emulating nanochips comprised of networks of noisy spiking neurons with multiple-level adaptive weights for controlling autonomous adaptive robots capable of spatial navigation. How our brains acquire stable cognitive maps of the spatial environments that we explore is not only an outstanding scientific question, but also one with immense potential for technological applications. For example, this knowledge can be applied in designing autonomous agents that are capable of spatial cognition and navigation in a GPS signal-impoverished environment without the need for human teleoperation.Lesion and pharmacological studies have revealed that hippocampus (HC) and medial entorhinal cortex (MEC) are critical brain areas for spatial learning, memory, and behavior Since the time of the proposal of Sensory context includes properties of the following kind: The primary determinants of grid cell firing are, however, path integration-based inputs Along similar lines, Discussion section reviews how hexagonal grid structures can be learned in the brain even when stripe cells of multiple spacings converge initially on entorhinal cells Why do grid cells learn to fire at hexagonally-located positions as an animal navigates in an open field? Most computational models focused on learning of either hippocampal place cells The original GridPlaceMap model uses neurons that interact using rate coding; that is, they interact via signals based on spiking frequency, rather than in terms of their individual spike trains. The goals of the current model are threefold; namely, to test whether the insights gained from the rate-based GridPlaceMap model can be applied and extended to simulating and explaining the development of spiking grid and place cells, as an instantiation of a general method for converting rate-based adaptive neural models, without the loss of any of their analog properties, into models whose cells obey spiking dynamics; to develop a neural system that makes it possible to address, for the first time, known temporal coding properties of hippocampal place cells and medial entorhinal layer II grid cells, such as theta band modulation Additional extensions of the GridPlaceMap and sGridPlaceMap models will be needed to achieve a general-purpose neural architecture for spatial navigation. It has, for example, been proposed how top-down attentive matching processes from hippocampal to entorhinal cortex may facilitate fast learning and dynamic self-stabilization of learned spatial memories, provide a pathway whereby environmental cues may modulate properties of grid and place cells that arise through path integration, and may help to explain a wide range of additional data about modular grid orientations, grid realignment, place remapping, and gamma and beta oscillations e.g., .A receptors, and whose synaptic plasticity is governed by a spike timing-dependent variant of the competitive instar learning law The spiking GridPlaceMap model, called sGridPlaceMap see , employs+ channels and delayed rectifier K+ channels that underlie the generation of stereotypical spike waveforms in membrane potential dynamics, synaptic transmission delays, axonal conduction latencies, and refractory periods are not considered. GABAA-gated channel conductances are approximated by single exponentially decaying traces because their rise times are typically negligible  via voltage-dependent Ca2+ channels, and an EPSP mediated by NMDA receptors, respectively The adaptive weights, These variables are initialized to zero at the start of each trial. The weights are only initialized once, at the start of the first trial, by sampling from a uniform distribution between 0 and 0.1. The learning law in A channel conductance gate that is opened by the spiking of the The membrane potential A channel conductance gate For this stage too, all gates are initialized to zero, and all membrane potentials are initialized to The adaptive weights, These variables are also initialized to zero at the start of each trial. The pre-learning weights are sampled from a uniform distribution between 0 and 0.03. The initial weights of projections from stripe cells to MEC cells have a higher individual mean to compensate for the relatively lower number of input cells; see below.The parameter values used in the simulations were The model was run with 100 HC map cells receiving adaptive inputs from three distinct populations of 100 MEC map cells, each of which was driven by adaptive inputs from stripe cells of one of three spacings. Stripe cells were activated in response to linear velocity estimates derived from realistic rat trajectories of ∼10 min in a 100 cm×100 cm environment  from its adaptively smoothed firing rate map. Distinct fields were indentified from circular templates around local peaks based on the criteria that the maximal rate within a field is at least more than 50% of the overall peak rate 16 points) whose amplitude response is squared, and normalized to the maximal value, to yield the power spectrum between 0 Hz and 250 Hz.Temporal modulation in the spiking responses of cells was assessed by computing the power spectra of the corresponding spike trains, with a temporal resolution of 2 ms, using a standard procedure This section shows that all the results of the rate-based GridPlaceMap model ly Data: . Both arer Data: . While tsquare box of 100 cm×100 cm , each human brain consumes just about 20 W of power. This power budget contrasts dramatically with that required  to run the most advanced supercomputer in the world; namely, the Blue Gene/Q at the Lawrence Livermore National Laboratory in Livermore, CA. Moreover, such advanced supercomputers occupy a lot of physical space, and need to be explicitly programmed for each specific task that they are supposed to perform. Aggressive efforts are currently underway across the world to develop a fundamentally new class of computer chips that closely mirror biological brains to herald the arrival of a transformative new technological field of natural intelligence. With respect to sGridPlaceMap model computations, the spiking competitive instar learning law described in This form reveals a single inhibitory term  currents mentioned above, and the m-current IhIh is known to generate subthreshold MPOs The spiking grid and place model developed in this article makes it feasible to ascertain more directly the relative contributions of and interactions among different synaptic currents in setting up the spatial scale topography of grid cells, and to probe them further computationally; namely, the AHP, leak K


Drinking or local application of human or animal urine for medicinal purposes has been practiced all over the world for millennia. Documented prescriptions in Europe originate from ancient Egypt, Greece and Rome. While many of the advances of antique medicine were forgotten after the fall of the Roman Empire, the use of urine and other excrements enjoyed continued popularity in mediaeval times. Ancient Indian yogic texts and ancient Chinese documents describe benefits of drinking one’s own urine, and it can be assumed that people in Africa, the Americas and other parts of the world have traditionally used urine for various medical indications for a very long time, too.The almost 100,000 hits of the search for “urine therapy” on Google, and the over 150 videos on the subject on YouTube are an indicator that drinking “waters out of thine own cistern” is still, or again, rather popular today.th century application of cow’s urine in the Apozème Suisse for oedema or ascites. Urine is sterile where it is produced in the kidney, but once it has left the body, it is usually contaminated. It is not toxic per se. There may be rare situations where urine is the cleanest liquid at hand to pour over a dirty wound, or the only liquid to drink when buried under a collapsed building or lost at sea for days, but most of the time there are better or tastier ways to improve one’s health.The supposed indications for urine therapy, ancient or contemporary, are too numerous to recite. There is, it seems, virtually nothing urine won’t cure. Modern proponents use pseudoscience to explain the benefits of the various, mostly exaggerated, components of urine. Some hint at a conspiracy by the medical establishment and the pharmaceutical industry to keep the knowledge of the many fantastic healing properties of cheaply available urine a secret. There is no money to be made from urine, well, unless one was to write a book about its many virtues. But, seriously, what do we really know? Urine is mostly water, lots of urea (25g/d) and uric acid (1g/d), creatinine (1.5g), various electrolytes , phosphate and organic acids (3g/d), only trace amounts of proteins , varying traces of (not necessarily active) hormones, glucose and water-soluble vitamins. Urea has a known potent diuretic effect which is at the base of the 19This said, the situation described in the paper by Ogunshe, Fawole and Ajayi in this journal , is quit


This paper proposes an interactive method of model clipping for computer-assisted surgical planning. The model is separated by a data filter that is defined by the implicit function of the clipping path. Being interactive to surgeons, the clipping path that is composed of the plane widgets can be manually repositioned along the desirable presurgical path, which means that surgeons can produce any accurate shape of the clipped model. The implicit function is acquired through a recursive algorithm based on the Boolean combinations (including Boolean union and Boolean intersection) of a series of plane widgets’ implicit functions. The algorithm is evaluated as highly efficient because the best time performance of the algorithm is linear, which applies to most of the cases in the computer-assisted surgical planning. Based on the above stated algorithm, a user-friendly module named SmartModelClip is developed on the basis of Slicer platform and VTK. A number of arbitrary clipping paths have been tested. Experimental results of presurgical planning for three types of Le Fort fractures and for tumor removal demonstrate the high reliability and efficiency of our recursive algorithm and robustness of the module. Computer-assisted surgical planning, a technique integrating the computer technology in presurgical planning and navigation, has been used to improve the safety and accuracy of the surgery , 2]. Or. Or2]. OMany softwares have been developed for computer-assisted surgical planning. For example, Magic Rapid Prototyping(or Magic RP), used for 3D imaging reconstruction , 6], is, is6], iOur research is related to the work of both mesh segmentation and polygon clipping algorithms in computer graphics. Firstly, a number of algorithms for mesh segmentation have been proposed to separate the semantically meaningful regions, such as the method of K-mean clustering , spectraSecondly, several polygon clipping algorithms are developed to visualize the model inside the clipping window. Such efforts include Sutherland-Hodgman algorithm, Weiler-Atherton clipping algorithm, Vatti clipping algorithm and so on. Sutherland-Hodgman algorithm obtains the intersection of the subject polygon and clip polygon  19] by  by 19] bIn this paper, an interactive method is presented for surgeons to make computer-assisted surgical planning. A module named SmartModelClip is developed on the basis of 3D-slicer , leveragThis paper is organized as follows: In section 2,we describe the recursive algorithm for resolving the implicit function and provide three different types of examples to implement the algorithm. In section 3, we develop a module named SmartModelClip and analyze the robustness of the algorithm. In section 4, we discuss the time performance of the clipping algorithm and compare it with some other polygon clipping algorithms. Our mesh segmentation method is then compared with other methods in terms of segmentation time. And finally conclusions are made in the last section.Our approach to the acquirement of the clipping path’s implicit function is on the basis of a recursive algorithm. The recursive algorithm decomposes the problem into three subproblems according to the relative poses of plane widgets. The subproblems are combined through Boolean operations. The kind of Boolean operations is determined by the pose of each line segment of polyline on the transection plane of clipping path.F = 0, which separates the 3D space into three regions: on the plane = 0), the region inside the plane<0) and the region outside the plane>0). A plane widget has the handle of normal axis fixed on the center of the plane widget, such as the handle AB in Some notions about implicit function is first introduced. A plane widget is on the plane defined by the implicit equation of AB and vector OMi:Seeing from Comparing with α1 ∪ α2 means the Boolean union of the implicit function of plane widget α1 and that of α2, while α1∩α2 means the Boolean intersection of the implicit function of plane widget α1 and that of α2. α1∪α2 and α1∩α2. In α1, while region ② and region ③ are inside α2. After the Boolean operation of α1∪α2, all the colored regions are inside α1α2. In α1, while region ① and region ② are inside α2. The Boolean operation of α1∩α2 makes the region ③ inside the α1α2.Two kinds of Boolean operations are used in the recursive algorithm to combine the implicit functions: Boolean union and Boolean intersection. Boolean union takes the minimum value of all implicit functions while Boolean intersection takes the maximum value of all implicit functions. For the concision of expression, Boolean union and Boolean intersection are denoted respectively by the commonly used notation of ∪ and ∩ in the set theory. To be specific, It is noticed that the result of the Boolean operations of two implicit function is still an implicit function that the Boolean operations can still be applied to.The problem of splitting 3D space by clipping path can be de degraded into the equivalent problem of splitting 2D space by a polyline on the transection plane(perpendicular to the first and second plane widget) of the clipping path. Thus the algorithm of acquiring the clipping path’s implicit function is implemented by judging the relative pose of the line segments. These line segments determine what kind of Boolean operations applies to the implicit function of each plane widget.β, forming a polyline PiPi+1Pi+2Pi+3 that’s on the edge of αiαi+1αi+2. The transection plane is specified to be perpendicular to the plane widget α1 and α21: ifi = = jthen2:  αi3:   return else if (j − i) == 1 then4:  ifPj+1 inside αithen5:   αi⋂αj6:    return else7:   αi⋃αj8:    return end if9:   else10:  k ← j11:   whileLoop Conditionline26th)16:    αk, αj)17:          ⋂ CombImpFuns19:    αk, αj)20:          ⋃ CombImpFuns(end if21:   CombinedFun22:   return end if23:  end function24: 25:Loop Condition:26: linePkPk+1 and polyline Pk−127: i + 2 ⩽ k ⩽ j)28: intersect(for orrayPk−1Pk and polyline 29: i + 2 ⩽ k ⩽ j − 2)30: intersect  is extended to infinitive on the both directions. k is initialized with the value of j. For the while-loop at line 12th, we check respectively whether line PkPk+1(i + 2 ⩽ k ⩽ j) and polyline Pk−1 intersect or the ray Pk−1Pk(i + 2 ⩽ k ⩽ j − 2) and polyline k is declined by 1. The while-loop is broken if any one of the conditions is satisfied: (1)k < i + 2, (2)line PkPk+1 and polyline Pk−1Pk and polyline αk−1 and αk. If point Pk+1 on the plane αk is inside the plane widget αk−1, we apply the Boolean operation of αiαi+1 ⋯ αk−1 ∩ αkαk+1 ⋯ αj. Or else we apply the Boolean operation of αiαi+1 ⋯ αk−1 ∪ αkαk+1 ⋯ αj. The same algorithm applies to the implicit function of αiαi+1 ⋯ αk−1 and that of αkαk+1 ⋯ αj. In this way, the problem has been decomposed and will finally be simplified into the subproblem1 and subproblem2.Subproblem3 first extends respectively line segment of Three different types of examples that implement the algorithm are given below to have a better understanding of the whole algorithm. Specifically, the while loop condition is explained in detail. Although all the examples contain 4 plane widgets, the poses of the plane widgets are slightly distinct and the implementation process is totally different.α1 and plane widget α2, leaving the polyline P1P2P3P4P5 on the edge. The shaded space in α1α2α3α4.For the first example in α1α2α3α4, line segment P2P1 in P2P1 and it becomes the ray P4P5 is extended to infinite on both directions. Since line P4P5 and polyline Q1, the value of k(3 = i + 2 ⩽ k ⩽ j = 4) should be decreased to 3. Then we check whether line P3P4 and polyline Q2. Therefore, the value of k should be decreased again to be 2. As the required minimum value of k is 3, seeing from line 28th of the algorithm, the loop condition is not satisfied after this decrement of k. P3 is outside plane widget α1, so it’s easy to get the implicit function of α1α2α3α4 on condition that the implicit function of α1 and that of α2α3α4 are known respectively because the implicit function of α1α2α3α4 is the result of α1 ∪ α2α3α4. Region ① and region ② are inside the implicit function of α1, while region ② and region ③ are inside the implicit function of α2α3α4 in α1 ∪ α2α3α4 makes the regions of ①, ② and ③ inside the implicit function of α1α2α3α4.To get the implicit function of α1 is the solution of subprolem1, the task is to get the implicit function of α2α3α4. As P4P5 and extended polyline Q3 in k(4 = i + 2 ⩽ k ⩽ j = 4) is decreased to 3. So it comes out of the loop for k = 3. As P4 is outside plane widget α2, the implicit function of α2α3α4 is available through the Boolean operation of α2 ∪ α3α4. Region ① and region ② are inside the implicit function of α2, while region ② and region ③ are inside the implicit function of α3α4 in α2 ∪ α3α4 makes the regions of ①,② and ③ inside the implicit function of α2α3α4.Now that the implicit function of α2 is the solution of subproblem1 and the implicit function of α3α4 is the solution of subproblem2, so the whole problem is solved by the Boolean combinations of implicit functions of all the plane widgets. In conclusion, the implicit function of α1α2α3α4 is available through the following equation:The implicit function of α4 to that of α3, from which we’ll see that different poses of plane widgets lead to different expressions of the implicit function yet using the same recursive algorithm.For the second example in P4P5 and polyline Q1; line P3P4 and polyline Q2 in α1α2α3α4 is to apply the Boolean operation of α1 ∪ α2α3α4 as is illustrated in Line P4P5 and polyline α2α3α4 is to apply the Boolean operation of α2α3 ∩ α4 because point P5 is inside α3. Region ① and region ② are inside the implicit function of α2α3, while region ② and region ③ are inside the implicit function of α4. Boolean operation of α2α3 ∩ α4 makes the region of ② inside the implicit function of α2α3α4. The implicit function of α2α3 is the solution of subproblem2, while the implicit function of α4 is the solution of subproblem1, making it possible to solve the whole problem.Seeing that line In conclusion, the solution to the implicit function of this series of plane widgets can be expressed as follows:P4P5 and polyline Q1 intersect. Then we decrease the value of k and find that line P3P4 and ray P2P3 and polyline Q2, which satisfies the loop condition at line 29th in our algorithm. Thus, the value of k should still be decreased again and the implicit function of α1α2α3α4 equals to α1 ∪ α2α3α4. The implicit function of α2α3α4 is the same with that in example 2. So the implicit function of α1α2α3α4 is expressed as:For the last example in These three simple examples illustrate the implementation of algorithm. In fact, the correctness of this algorithm has been widely tested, not merely the clipping path with four connected plane widgets, which will be discussed in the next section.A module named SmartModelClip, served as computer-assisted surgical planning, is developed to clip the model on the basis of 3D Slicer and VTK using our recursive algorithm. The results of model clipping demonstrate the robustness of the algorithm.3D Slicer(or Slicer) is a open-source and cross-platform software that processes the analysis and visualization of biomedical images . The funOur module is developed on the platform of 3D-Slicer, making use of VTK and Qt as visualization and GUI toolkits. Le Fort fractures are common in facial trauma. The clipping path can be more complex than the previous situation. In The arrows in The clipping processes fit the reality in computer-assisted surgical planning because the yellow, green and red part of the model are clipped in sequence on a clipping path. What’s more, the boundary of the clipping path in The thickness plane allows surgeons to clip model with certain thickness. Limitation in constructing the clipping path. Our recursive algorithm proves to be pretty reliable and robust for most experimental cases except for Moebius clipping path, however, the limitation may still exist in the construction of the clipping path. It’s noticed that our clipping algorithm is restrained to a series of plane widgets connected from the beginning to the end because the polyline referred in the section ‘Conversion of 3D Problem to 2D Problem’ is the judgment of the type of Boolean operations applied to each plane widget. The algorithm is able to obtain the implicit function of three connected plane widgets in Another limitation in the construction of the clipping path, is the case of complex clipping path in For another case that our clipping algorithm may not work is the clipping path tangent to Moebius strip because Moebius strip is a surface with only one side. However, this kind of case may not appear in the computer-assisted surgical planning. So our algorithm still meets the need of the most clinical applications.Limitation of thickness plane. As the back side of the tumor is unobservable, we only consider using one thickness planewidget to cut the tumor. But the curved surface of the back of the tumor to be clipped sometimes could be very complex, for example, it can be seen in the The time performance of the clipping algorithm for each case is first discussed and then the algorithm is evaluated based on the model clipping experiments, which demonstrates the time performance analysis of the clipping algorithm.The time performance of the recursive algorithm depends on the number of plane widgets and their relative poses. The best case runs as fast as the linear algorithm, while the worst case runs as slowly as the sub-quadratic algorithm. But the average case has the logarithmic time performance. For most cases of the computer-assisted surgical planning to remove the tumor, our clipping algorithm has the best time performance or the average-case performance.αj ∪ αiαi+1⋯αj−1 or αj ∩ αiαi+1⋯αj−1 in each recursion. Any case of the convex clipping path will have the best-case performance. α1α2α3α4 can be expressed respectively byα1α2α3α4.The best case happens when the condition of the while-loop (from line 27th to line 30th) in our algorithm is not satisfied all through the stage of recursion. In that case, the recursive function will only apply Boolean operation of T(N) is the time of acquiring the implicit function of N plane series, T(N − 1) is the time of solving the subproblem3. Since the recursion just returns when N equals to 1 or 2, T(2)=T(1) = 1. So the above recurrence has the solution ofAs the problem is only decomposed into the subproblem1 and subproblem3 each time and the loop condition is never satisfied, the time complexity can be calculated by the sum of the time of solving subproblem1, subproblem3 and the time of the Boolean operation of the result of subproblem1 and subproblem3, which can be expressed in the following equation:j − i − 1 times in each recursive call. The worst case behavior occurs in the situation that the condition inside the loop (at line 15) of the pseudo code is satisfied and the loop is executed at N plane widgets, the time complexity can be calculated by the sum of three parts. The first part is the time to go through the loop for N − 1 − 1 times, the second part is the time to solve the subproblem3 and the last part is to take Boolean operations of the implicit functions. Thus the time complexity can be expressed in the equations ofT(1) = T(2) = 1.For the implicit function of This recurrence has the solutionN plane widgets is the average time to execute the two recursive functions plus the time to do the loop and the time to apply the Boolean operations of the two recursive functions. The implicit function of α1α2⋯αN is decomposed into the Boolean operation of the implicit function of α1α2 ⋯ αi and that of αi+1αi+2⋯αN. And the times to do the loop is N − (i + 1). So the time complexity of the average-case performance can be expressed as:T(1) = T(2) = 1.As to the case of average time performance, we can consider the every possible looping times in the subproblem3 and take their average. If the possibility of the times that the loop will be executed is equal, then the average time to split the space with The solution of this recurrence isO(N log N).Consequently, the average time complexity of this algorithm is O(M*N), in which M stands for the number of edges in the clipping polygon and N stands for the number of vertex pairs in the subject polygon. And Vatti clipping algorithm is twice as fast as Sutherland-Hodgman Algorithm and substantially faster if the algorithm is used for both clipping and filling [O(N2), the average time complexity of the algorithm is O(N log N) and the best time complexity of O(N). In fact, as the clipping path is defined manually according to the boundary of something like tumor, the number of the clipping path widgets would not be very large(let’s say 15 or 30). In that case, the time complexity of our algorithm is approximately linear, making it possible to separate the 3D space along the clipping path in a very short time.In comparison with other polygon clipping algorithms, such as Sutherland-Hodgman Algorithm and Vatti clipping algorithm, our clipping algorithm has better time complexity in the computer-assisted surgical planning. For the general case, Sutherland-Hodgman Algorithm has the time complexity of  filling . AlthougThe time to clip the model is actually composed of two parts: the first part is the time of acquiring the implicit function of the clipping path by our algorithm and the second part is the time to clip the triangles of the model by the class of ‘vtkClipPolydata’ in VTK. These two parts of time are tested respectively. The execution time of the recursive algorithm is tested using different clipping paths that have different number of plane widgets, while the second part of time performance is tested by clipping the models that have different number of triangles using the same clipping path. The experiments are tested on the laptop with the Intel CPU T1400 @1.73GHz and 2GB DDR2 RAM.We evaluate the time performance by clipping the tumor in In terms of the segmentation time, our method has an advantage over Random Walks Algorithm and fitting primitives Algorithm. Since the time to separate the model relates both to the computation speed of CPU and the complexity of the model, we only make the rough comparison of each method’s average segmentation speed, in which the CPU speed is almost the same with each other.The timing performance of various models mentioned in TABLE 1 of  shows thIn this paper, a recursive algorithm is developed for clipping models based on the Boolean combination of the implicit functions of the series of the connected plane widgets. The problem of splitting 3D space by clipping path is degraded into the equivalent problem of splitting 2D space by a polyline. The recursive algorithm then decomposes the problem of splitting 2D space into three subproblems that makes the whole problem easy to solve. Three examples of four connected plane widgets illustrates the implementation process of the algorithm.A module named SmartModelClip is developed for computer-assisted surgical planning using our recursive algorithm. Three experiments of model clipping for presurgical planning has successfully demonstrated the robustness of the module: (1)presurgical planning with simple clipping path for clipping Le Forts fractures, (2)presurgical planning with complex clipping path that can be twisted from one view to another for clipping the model into several parts, (3)presurgical planning with thickness plane for tumor removal. The limitations in the construction of the clipping path and thickness plane widget are discussed in details.O(N) and the time complexity of the worst-case performance is O(N2), while the time complexity of the average-case performance is O(N log N). The algorithm tends to have linear time performance when the removed part is enclosed by the clipping path. Rough comparison with other polygon clipping algorithms shows the efficiency of our algorithm in the presurgical planning.After that, the time performance of the algorithm is analyzed. The time performance is relevant to the poses of the series of plane widgets. The time complexity of the best-case performance is The time performance of model clipping and the execution time of our algorithm to acquire the implicit function of clipping path are both evaluated highly efficient in two tests: (1)the test of time to clip different models that have different number of triangles with the same clipping path, (2)the test of time to clip the same model with the clipping paths that have different number of plane widgets. Compared with other segmentation methods, our method has an advantage over these methods in terms of the average segmentation time.From O(N log N).We want to prove that the N − 1) on both sides of the N with N − 1, we obtain the recurrence ofN(N − 1), we obtainMultiply (1N-2T(i).Subtract1N-1T(i).Substitu


This study assessed the microbial quality of clay samples sold on two of the major Ghanaian markets.The study was a cross-sectional assessing the evaluation of processed clay and effects it has on the nutrition of the consumers in the political capital town of Ghana. The items for the examination was processed clay soil samples.Staphylococcus spp and fecal coliforms including Klebsiella, Escherichia, and Shigella and Enterobacterspp were isolated from the clay samples. Samples from the Kaneshie market in Accra recorded the highest total viable counts 6.5 Log cfu/g and Staphylococcal count 5.8 Log cfu/g. For fecal coliforms, Madina market samples had the highest count 6.5 Log cfu/g and also recorded the highest levels of yeast and mould. For Koforidua, total viable count was highest in the samples from the Zongo market 6.3 Log cfu/g. Central market samples had the highest count of fecal coliforms 4.6 Log cfu/g and yeasts and moulds 6.5 Log cfu/g. “Small” market recorded the highest staphylococcal count 6.2 Log cfu/g. The water activity of the clay samples were low, and ranged between 0.65±0.01 and 0.66±0.00 for samples collected from Koforidua and Accra respectively.The clay samples were found to contain Klebsiella spp. Escherichia, Enterobacter, Shigella spp. staphylococcus spp., yeast and mould. These have health implications when consumed. The consumption of nonfood materials is a common practice in many cultures in different parts of the world. Pica is the broad term that describes this habit and it has been defined as the pathological act of eating nonfood items . A commoTraditionally, clay is used for making drinking and cooking pots with the belief that it has the ability to bind toxins. In Ghana, edible clay is called “eshire” by the Akan tribes, “farankese” by some northern tribes, “fefe” by the Ewes while the Gas call it “ayilo”. The clay soil is dug from the earth and prepared by milling the large particles and blocks to help reduce the particle size. The fine powder is then mixed with water and stirred into a uniform paste. Afterwards, the paste is molded into a sausage- like shape and then baked to reduce the moisture content and retain the shape. In some cultures the molded paste is baked and smoked to give it an appealing aroma. Both types are found commonly on the Ghanaian market. Geophagia is very prevalent among pregnant woman who are believed to develop craving for clay as a remedy for morning sickness and vomiting . This trSample collection: This study involved laboratory analysis of clay samples. Also, a survey was done in the selected markets to identify the types of clay that are sold. Microbiological analysis was carried out on the clay samples that were purchased from the selected major markets in Accra and Koforidua. The markets selected in Accra were; Madina, Makola and Kaneshie markets. For Koforidua, Zongo market, Dwakesiem  and Dwaketoam  were selected. These represented the Greater Accra and Eastern Regions of Ghana. In each of the markets, the samples were bought from different retailers. A quick market survey was done at the selected major markets in Accra  to identify the different types of clay samples sold on the market. The various types of clay samples available on the markets were examined by looking at the differences in the shapes, colors, sizes and their purpose. The simple random sampling method was used for both the survey and laboratory analysis. The clay samples were collected with stomacher bags and sealed with cello tape and properly labeled with the market it was bought from. Samples were handled with care and treated under aseptic conditions to avoid any contamination. At each market, 20 pieces of the clay were purchased randomly from different retailers and packaged into sterile stomacher bags. The stomacher bags were then carefully sealed with cello tape and labeled appropriately by the names of the specific markets they were bought from.Microbiological analyses: The bacteria yeast and molds present in the samples were enumerated using specific nutrient media. Series of morphological examinations and biochemical tests were conducted to help characterize and identify the specific microorganisms that were growing on the clay samples. Details of these procedures are described as follows; dehydrated media  were used in the analysis. The media used were: Nutrient Agar (NA) (to help in the acquisition of a pure culture), Levine Eosin Methylene Blue (EMB) (to determine the presence of coliforms), Mannitol Salt Agar (MSA) (to detect staphylococcus species), Plate Count Agar (PCA) , Yeast Extract Agar (YEA) (for detection of yeasts and molds), and Peptone Water (used as a diluent). They were prepared according to the manufacturer's instructions. For enumeration and characterization of microorganisms, samples from each market were aseptically homogenized by crushing and about 10 grams aseptically transferred into new stomacher bags and blended with 90 ml of sterile diluent (maximum recovery diluents), in a Seward stomacher blender 80  for 1 minute. Aliquots of the homogenates were serially diluted and plated for the determination of total plate count and the detection of coliforms, staphylococcal spp. and fungi, using standard procedures. For the identification and characterization of the microorganisms in the samples, some morphological and biochemical examinations were done as described by Prescott, Harley and Klein [Water holding capacity: An amount of 5 grams of the powdered sample was weighed into a centrifuge tube. 30 ml of water was added and mixed using the vortex mixer for 30 seconds. The mixture was made to stand for 30 minutes and stirred after every 10 minutes until the 30 minutes was due. This was followed by centrifugation using a bench top centrifuge set at 3000 rpm for 15 minutes. The supernatant was decanted and the weight of the residue was recorded. The amount of water absorbed, expressed as a percentage of the dry sample is equivalent to the gram of water per gram of sample that it could hold. Triplicate measurement was taken for each sample.Percent water holding capacity = weight of hydrated sample-weight of sample X 100 weight of dry sampleWater activity: The water activity of the clay samples was determined using the Retronic hycropalm water activity meter . An amount of 2 grams of the powdered sample was weighed into clean dishes. The dish was then placed into the panel of the water activity meter. The probe was then used to cover the dish containing the sample. The start button of the meter was pressed and the water activity reading was recorded after about 2 minutes when the reading had stabilized. This was done in triplicates for each sample and the average was taken.Moisture content: The air-oven method was used for this determination. The temperature of the oven was adjusted to 105 °C0 C to precondition the moisture cans for about 15 minutes. Afterwards, the moisture cans were removed and made to cool in a desiccator. Approximately 2 g of the powdered clay samples was weighed into the cans and were transferred into the oven. The sample was left overnight to allow the moisture in it to be evaporated. After 24 hours the final weight of the sample was taken and the moisture content of the sample was calculated on dry mater basis. This was repeated thrice for each of the samples average was taken.Percent Moisture content = weight of hydrated sample-weight of sample X 100 weight of dry sampleData processing and analysis: For microbiological analysis, the data was entered into Microsoft Excel (2010). Means and standard deviations were computed and graphical representations were used where appropriate.The clay samples from both Accra and Koforidua had lower moisture content thus 1.46±0.30% for the Accra samples and 1.86±0.24% for the Koforidua samples. The water activity of the samples were also low thus 0.66±0.00 and 0.65±0.01 for the Accra and Koforidua samples respectively. In another study, the moisture content of clay samples was observed to be between 0.18-0.19% when the air oven method was used . The valAnother genus of bacteria that were detected was staphylococcus spp. The detection of Staphylococcus spp also pointed to the unhygienic handling practices. Species of Staphylococcus are known food toxicants. Their presence could therefore implicate that there was some cross- contamination of the clay samples. Most spoilage bacteria, yeasts and molds require a minimum water activity of 0.9, 0.88 and 0.8 respectively . The yeaHence, pica patients consume a less varied diet and are at risk of iron deficiency anemia. Additionally, kaolin which is a common ingredient in medications to treat diarrhea, may cause constipation in high amounts. Geophagia may cause hyperkalemia because clay can bind to potassium in the intestine and lead to increased excretion. The most dangerous of the complications of Geophagia is probably lead poisoning . Lead exThe clay samples were found to contain Klebsiella spp. Escherichia, Enterobacter, Shigella spp. staphylococcus spp. yeast and mould. These findings calls for health promotion educational campaigns to halt consumptions of ayilo and related materials as this habit is detrimental to the health of particularly pregnant women and their unborn babies.The prevalence of geophagia has been studied largely and has been found to be highest among pregnant women than the non-pregnant women and men;Studies have shown that these clay samples consumed could lead to heavy metal contamination and lead to micronutrient deficiencies;The microbiology of these processed clay soil samples have been done in some markets in Accra, Ghana.This study further validated the microbiological quality of the clay samples in Greater Accra region and extended it to the Eastern region of Ghana;The different types  of clay samples available on the market was surveyed as well as the uses of these different types;Also the weight of the clay samples were determined in the Accra market to have an idea of how much clay can be consumed.


Disruption of riverine connectivity by artificial structures, such as culverts, can obstruct critical fish movements. We investigated the effectiveness of replacing smooth substrates with rough, naturalistic substrates (i.e. river stones) in improving fish swimming capacity and modelled fish passage through culverts. Ucrit) of two small-bodied fish, purple-spotted gudgeon  and crimson-spotted rainbowfish  were examined. Swimming trials were conducted in a hydraulic flume fitted with either a smooth acrylic substrate (control) or a rough substrate with fixed river stones. Swimming performance was improved on the rough compared to the smooth substrate, with Mo. adspersa  and Me. duboulayi  both experiencing a 26% increase in relative Ucrit. Traversable water velocity models predicted maximum water speeds allowing successful upstream passage of both species to substantially increase following roughening remediation. Together these findings suggest culvert roughening may be a solution which allows hydraulic efficiency goals to be met, without compromising fish passage.Worldwide declines in riverine fish abundance and diversity have been linked to the fragmentation of aquatic habitats through the installation of instream structures . Restoring riverine connectivity can be achieved by remediating structures impeding fish movements by, for example, replacing smooth substrates of pipe culverts with naturalistic substrates . However, empirical evaluations of the efficacy of such remediation efforts are often lacking despite the high economic cost. We assessed the effectiveness of substrate roughening in improving fish swimming performance and linked this to estimates of upstream passage success. Critical swimming speeds ( Disruption of riverine connectivity is one of the leading threats to the persistence of riverine fishes ; whereasFish passes have been developed to facilitate fish movements around instream barriers, but a comprehensive set of conditions conducive to optimizing passage  is unavailable for many species. Research in this area has predominantly focused on enabling fish to bypass large obstructions such as dams and weirs  on fish movement is estimated to be greater than that of large dams due to their high numbers  and wereAcipenser brevirostrum , and crimson-spotted rainbowfish, Melanotaenia duboulayi . These species are sympatric and endemic to Australia, with populations spread along coastal catchments in south-east Queensland and northern New South Wales (1) swimming performance would be markedly improved over rough compared to smooth substrates, and (H2) culvert remediation models would show roughening to be an effective approach, exemplified by higher maximum water velocities allowing successful upstream passage of fish.Remediation approaches can be economically costly, with finite funds directed towards waterway restoration, deeming it imperative to ensure restoration efforts benefit target species. The aim of this study was twofold: (i) to determine if fish swimming performance is improved above rough compared to smooth substrates, and (ii) to model and evaluate the effectiveness of substrate roughening as a remediation strategy. Two small-bodied , freshwater species were used to address these aims: purple-spotted gudgeon, th Wales . Both spMelanotaenia duboulayi)  and purple-spotted gudgeon (Mogurnda adspersa)  were obtained from a commercial hatchery . Fish were housed in 45 L glass aquaria  at a stocking density of approximately 3 g (body mass) L−1 (Mo. adspersa) and 1 g L−1 (Me. duboulayi). Aquaria contained Brisbane city tap water conditioned with water primer , maintained at a constant temperature (25 ± 1°C). Water chemistry  was monitored weekly to ensure water quality. Fish were fed commercially supplied food pellets  daily to satiation. The photoperiod was set to a 12-h light: 12-h dark cycle.Crimson-spotted rainbowfish . Each swimming trial incorporated one substrate treatment, either a smooth acrylic panel or a custom-made, roughened substrate with fixed river stones  and crimson-spotted rainbowfish (Me. duboulayi), respectively. The top surface area (SA) of each stone was measured using the particle analysis function in ImageJ (50) = 4.99 cm2; median diameter (D50) = 2.52 cm) (see L × W × H) and the swim chamber walls were made of smooth acrylic in both treatments. The substrates were detachable so treatment order could be randomized.Swimming trials were conducted in a 185 L flow-controlled hydraulic flume (Loligo cm) see . The sub®, Unanderra, AUS) and custom-built air-water manometer set to a 30° angled incline. A 5 × 5 cross-section in the centre of the swim chamber was measured for each water velocity increment (revolutions s−1) on the smooth and roughened substrates. A time-averaged water velocity (m s−1) calibration curve was determined for each substrate. The calibration curve for the smooth and roughened substrates were described by the following equations:−1) for the swim chamber containing smooth and rough substrates, respectively, and −1). The rough substrate consistently derived lower water velocities for a given propeller speed compared to the smooth substrate between 6–18 revolutions s−1, and water velocity converged for both substrates at 21 revolutions s−1 , and fish were tested once to avoid training effects . Body sizes of fish were similar between rough and smooth substrate treatment groups for both species . Fish were allowed 30 min to adjust to conditions in the swimming flume with water velocity switched off (i.e. 0.00 m s−1), after which water velocity was increased every 5 min at increments of 0.05 m s−1, starting at 0.05 m s−1, until the fish fatigued. Fatigue was defined as the fish resting on the back wall of the swim chamber for ≥ 3 s. Total swimming time until fatigue and water velocity at fatigue were recorded to calculate critical swimming speed (Ucrit), using −1), −1), Tf is the time (s) swum in the final increment and Ucrit tests provided a measurement of the maximum velocity at which a fish can sustainably swim without fatiguing (Ucrit = 0.3 m s−1 × 300 s = 90 m)—a distance far exceeding the length of most culverts (−1), and both absolute (i.e. m s−1) and relative critical swimming speeds (BL s−1) are reported. The swim chamber was constantly aerated and water temperature was maintained at 25 ± 0.5°C using a submersible heater . Swimming gait was observed and classified as either direct, body-caudal fin (BCF) (−1), blot-dried and photographed. ImageJ  was used to measure BL for each fish. Cross-sectional body area of all fish was less than 10% of the cross-sectional area of the swimming flume chamber; therefore, corrections for the solid-blocking effect . Substrate treatment was randomly assigned to fish using a random number generator ( effects  (n = 30 atiguing  and wereculverts . Criticain (BCF)  or statiin (BCF)  where peg effect  were not−1) within a culvert, Ucrit), d is the length of the culvert (m) and Vs (s). The endurance was 5 min, as that was the period of time in which the fish swam for before the velocity was increased in the swimming performance trials. Equation , with either smooth or roughened substrates, using Vf=Vs– was determined using a one-way analysis of covariance (ANCOVA), with body size (BL) and holding tank number included as a covariates. Minimal adequate models were determined using stepwise simplification, and separate models were run for each species. P-values < 0.05 were considered statistically significant and all data are presented as mean ± s.e.Data analyses were performed using R Studio  of both Me. duboulayi  and Mo. adspersa   and Mo. adspersa , respectively  and tank numbers were excluded from minimal adequate models. Mo. adspersa employed a combination of both station-holding and direct, BCF gaits in trials. Me. duboulayi employed direct, BCF gait in all trials but did not station-hold.Substrate treatment had a significant effect on critical swimming speeds (01) Fig. . SwimminMe. duboulayi through ‘small’ (2 m), ‘medium’ (<20 m) and ‘large’ (20 ≤ 60 m) culverts with a smooth substrate, water velocities would need to be ≤0.46, 0.40 and 0.26 m s−1, respectively  if culvert interiors were roughened . This heightened performance translated into the traversable water velocity models, with maximal allowable water speeds being higher in roughened compared to smooth culverts, suggesting roughening may be an effective remediation approach to improve fish passage.Roughened culverts are often assumed to improve fish swimming performance and upstream passage (Oncorhynchus kisutch), for instance, have been observed to actively exploit reduced-velocity pathways during upstream movement through a culvert test bed  and the size and number of eddies generated . For exatest bed . The nexMo. adspersa and Me. duboulayi were similarly affected by the rough substrate, with both species experiencing a ~26% increase in swimming performance, despite different gaits employed during swim trials. Mo. adspersa employed station-holding behaviour in all trials, whereas Me. duboulayi did not station-hold and instead, employed a BCF swimming mode. Species utilizing bottom-swimming behaviours (e.g. station-holding and substratum-skimming) are expected to derive a greater net benefit from substrate roughening than fishes reliant on BCF modes, as these energy-saving behaviours are largely ineffective on smooth surfaces  limit water velocities through culverts to a maximum of 0.3 m s−1. At this velocity maximum culvert transit is likely to be unrestricted for Me. duboulayi for culverts up to 50 m in length, but compromised for Mo. adspersa in culverts with smooth substrates. Passage is predicted to be restricted for Mo. adspersa in culverts 2–15 m in length with a smooth substrate , but roughening remediation increases allowable water velocities to levels exceeding current guidelines (i.e. 0.31–0.35 m s−1). Transit through very long culverts (>20 m) is likely to be restricted for Mo. adspersa even with roughening remediation, and these structures may require additional restoration efforts, such as the installation of rest areas (−1).Successful passage through culverts is critically important as population declines of both barriers . In agreMugil cephalus]; Galaxias maculatus]; Hypseleotris compressa]). Similar to Mo. adspersa, many small-bodied species have been identified as weak-swimmers, requiring very low water velocities for upstream movements (ranging 0.05–0.20 m s−1). Reducing water velocity to this extent can be challenging, but culvert roughening may be a solution that allows hydraulic efficiency goals to be met without compromising fish access. Examining the effect of substrate roughening on the swimming performance of a greater number of species, with variations in morphology and swimming gaits, will allow us to gauge the potential benefit and wider application of roughening fish passes. In contrast to our findings, previous research has found roughening to provide no benefit to fish swimming performance (Cyprinus carpio); but this study differed to ours with respect to the position  and type (i.e. corrugated inserts compared to river stones) of substrate. Roughening the walls of culverts/experimental swim chambers, compared to the bottom, likely differentially affects hydraulic conditions (e.g. level of TKE). Wall roughening has been suggested to generate detrimental levels of turbulence, where the energetic expense of swimming is increased and fish become disorientated/unbalanced (Outputs from our traversable water velocity models were similar to other small-bodied species (e.g. formance . Newboldbalanced . Wall roAlthough the culvert remediation model presented here can be a powerful tool for decision making, the limitations of this model need to be considered. Estimates of swimming performance derived from non-volitional, laboratory studies can underestimate true ability, as fish often attain greater swimming speeds in open-channel, volitional trials . The swiSupplementary DataClick here for additional data file.Supplementary DataClick here for additional data file.Supplementary DataClick here for additional data file.