Static and kinetic friction play a fundamental role in sea-ice mechanics. The coefficient of static friction increases with hold time under normal load and is modelled in terms of creep and fracture of asperities in contact. The coefficient of kinetic friction exhibits velocity strengthening at lower speeds and velocity weakening at intermediate speeds. Strengthening is modelled in terms of asperity creep and hardness; weakening is modelled in terms of a progressive increase in the true area of contact wetted by meltwater produced through frictional heating. The concept is introduced of contact size distribution in which the smallest contacts melt first, leading to the onset of weakening; the largest melt last, leading to a third regime of kinetic friction and again to strengthening where hydrodynamics governs. Neither the static nor the kinetic coefficient is significantly affected by the presence of sea water. The paper closes with a few implications for sea-ice mechanics. The paper is based largely upon a critical review of the literature, but includes a more quantitative, physics-based analysis of velocity strengthening and a new analysis of velocity weakening that incorporates parameters that describe the (proposed) fractal character of the sliding interface.This article is part of the theme issue ‘Modelling of sea-ice phenomena’. Presumably, sliding (at a velocity not known) was initiated when the shear stress became large enough to overcome resistance offered by static friction and then continued while the shear stress remained large enough to overcome resistance offered by dynamic or kinetic friction. Sliding, we imagine, induced tensile stresses at opposite ends of the ‘parent’ crack: when large enough, tension was relieved through the initiation and stable growth of the out-of-plane extensions/leads, in the manner of wing-like cracks [To set the scene, consider e cracks –11. The One can imagine other scenes where wind-driven stresses activate friction-based processes that culminate in both local and global fracture. In every case, as already noted, a question is: how large are the coefficients of static and kinetic friction and what is their nature?2.3. That difference, however, is attributed not to friction, but to the presence of thermal cracks and other stress concentrators that are larger by up to a factor of approximately 106.Before proceeding, we note that many of the results to be described were obtained from measurements made in the laboratory. There, unlike the field, different factors can be explored systematically, thereby allowing the dominant ones to be identified. The question is whether the properties so measured and the interpretation that follows are reflective of the larger scale. One wonders because the brittle compressive strength of sea ice, for instance, to which friction contributes ,12, is cper se, two observations—albeit indirect—are relevant. The first is the slope of the low-confinement branch of the brittle compressive failure envelope, dσ1/dσ3 . Whether derived from measurements made in the laboratory on ice harvested from the winter sea-ice cover or from measurements made in the field of the stresses within the winter cover, the slope is essentially the same [μk, through the relationship [3, as mentioned above.) Also, 2 and σ22 instead of σ3, because S2 ice deforms in plane-strain when loaded across the columns. The other relevant observation is the included angle of intersection, 2θ, of conjugate Coulombic shear faults. Again, whether measured from deformation features generated in the laboratory or from satellite images of intersecting sets of linear kinematic features/faults, 2θ is almost the same .FigureWe proceed, therefore, with the view that friction in the laboratory, if not exactly the same as in the field, is close enough to offer a reasonable depiction of the larger scene, at least in terms of the physical factors and fundamental mechanisms that govern behaviour.3.μs, increases with time under load. This is evident from experiments performed in three different arenas: in the laboratory [The coefficient of static friction, boratory –19, in aboratory  and in tboratory ,21.−3 at −10°C, salinity 4–5 p.p.t., surface roughness Ra = 0.76 ± 0.51 × 10−6 m) and, for comparison, results from freshwater granular ice . The sea ice had been harvested from the 2009 winter ice cover on the Beaufort Sea, at 71° N, 156° W and then, to simulate nature, was slid in such a manner that the long/vertical faces of the columnar grains rubbed against each other in a direction perpendicular to the long axes of the grains. The experiments were performed at −10°C using a using a double-shear device attached to a servohydraulic actuator. For both materials, the interface was held under a low normal stress of σn = 60 kPa, reflective of the natural loading of the arctic sea-ice cover [Vs = 10−6, 10−5 or 10−4 m s−1. The coefficient of static friction, just before sliding was reinitiated, was defined as the ratio of the friction shear force Ff divided by the normal load Fn ; i.e. τ and σn denote the global shear stress and global normal stress, respectively, acting on the interface and Aa denotes the apparent contact area. The results from these and other SHS tests revealed the following characteristics:Δμs, termed static strengthening, is detected once hold time exceeds a threshold period, tt; the threshold decreases with increasing velocity, from (under the conditions of the laboratory tests) tt = 30 s at Vs = 10−6 m s−1 to tt = 3 s at Vs = 10−5 m s−1 to tt ∼ 3 s at Vs = 10−4 m s−1.(i) An increase in the coefficient of static friction, th, scaling logarithmically as Δμs = μs −μos = βlog10th for tt < th < tu where μos denotes a reference coefficient, tu denotes the upper limit on time for logarithmic dependence at which point the coefficient appears to level off; β = 0.30 ± 0.03 under the laboratory test conditions. Alternatively, the strengthening may be described by the power-law Δμs = mAt, where m = 0.5 ± 0.1 for tt < th < tu and A is a constant.(ii) Static strengthening increases with hold time, (iii) The shear stress required to continue sliding is little affected by holding, implying that the coefficient of kinetic friction (more below) is independent of holding under load.(iv) Within scatter in the data, the coefficient of static friction of sea ice is essentially indistinguishable from that of freshwater ice.(v) The presence of sea water at the interface (at least of metre-sized specimens) has little detectable effect on the coefficient of static friction .To illustrate this point, figures ce cover . Hold tiet al. [et al. suggest, to consolidation. Unfortunately, neither normal load nor measurements for rubble-free behaviour were reported. Hence the question about rubble and its role in the coefficient of static friction remains unanswered.On interfacial rubble  and to β ∼ 0 at both −100°C (Th = 0.63) and −175°C (Th = 0.36).In exhibiting static strengthening, ice on ice is not unique. Similar behaviour has been detected when ice is pressed against steel, against PMMA and against rubber at temperatures from −13.5°C to −3.5°C ,24. Statwarm ice , falling4.Bowden & Taylor ,30 propo(a)l the rate of creep is given by so denotes effective stress at time zero. Upon introducing through conservation of volume l = lo(ao/a)2 and then integrating, the ratio of final to initial contact area, (a/ao)2, for hold time th, is given by the following relationship [n denotes the stress exponent in the power-law relationship for creep rate  and ϕ is the factor that accounts for stress relaxation during holding, 0 ≤ ϕ ≤ 1.To quantify this explanation, Schulson & Fortt  invoked tionship :4.1whe(b)N denotes the number of contacts; τy is the magnitude of the local shear stress at the point of rupture and is given byσt denotes the tensile strength of the asperity. Thus, from equations (4.1)–(4.3), the coefficient of static friction versus hold time may be given by the relationshipH has a negative value in the first term, to reflect the stress tensor, but a positive value in the second term to reflect creep stress.) Upon inserting appropriate parametric values for ice at −10°C, albeit for bulk material and not specifically for asperities , and upon assuming ϕ = 0.5, equation (4.4) reduces toϕ is the only ‘free’ parameter in the model and is a relatively unimportant one, for if reduced to zero (no relaxation) or increased to unity (complete relaxation) the computed coefficient differs only by about 10%.The coefficient of static friction may then be given by the relationships et al. : n = 3,  = 6 MPa ; μk = 0.Vs = 10−5 m s−1. For hold time th ≤ 1000 s, the model captures the experimental measurements reasonably well. It is consistent, also, with the observation that sea water at the interface has either little or no detectable effect, for there is no evidence that ice creeps more rapidly when wet.  However, the model overestimates (underestimates) by about 20–40% the lowest (highest) velocity SHS tests (at 10−6 and 10−4 s−1), discrepancies that could be attributed to the n-value and to whether a single value applies over the entire period of holding. Also, the model does not include the trend towards saturation. Missing, too, is log-time dependence.  is shown as the dashed curve in & Estrin , requireSea ice creeps more rapidly than freshwater ice, by about an order of magnitude ,35. Why,5.σ1 = σ2 =σh of 60–750 kPa and then holding for a period from th = 3 s to 18 h at temperatures from −30°C to −3°C. Subsequently, the unconfined, brittle compressive strength of the faulted and partially healed material was measured.An implication of the above analysis is that strength can be restored to damaged sea ice upon holding under load. To explore that point, experiments were performed  on a labσr, scales linearly with confining stress and increases with both increasing time and temperature [σ*, the restored strength can be described by the relationshipk′ and n′ are constants for a given set of conditions and th again denotes time under load. Modelling, again in terms of creep-driven increase in the real area of contact, leads to the short-time limit [C is a materials constant, Q′ is an apparent activation energy, k is Boltzmann's constant and T is the absolute temperature. Strengthening/healing kinetics yielded parametric values C = 0.027 MPa sn1/′, n′ = 3.4 and Q′ = 50 ± 6 kJ mol−1. The values derived for n′ and Q′ are similar to those derived by Barnes et al. [The restored strength of the damaged ice, perature . When nome limit 5.2whers et al.  from creRa ≤ 0.26 mm. This suggests that, like the case for ductile metals [Interestingly, healing kinetics exhibit little dependence upon interface roughness, at least over the relatively small range 0.06≤ e metals –39, therThe restoration of strength implies that molecular bonds form at points of contact. In turn, this implies that the cohesive strength in Coulomb's Law (§6b) is increased upon healing, but that the coefficient of kinetic friction is not affected. This is consistent with the observation  that the6.et al. [Once static friction has been overcome, sliding ensues. In the discussion below, velocity is taken to be constant at the macroscopic level. The discussion is limited to lower-speed sliding. Higher-speed sliding of the kind related to winter sports has been reviewed by Kietzig et al. .(a)Ra ∼ 1 µm) and rough (Ra ∼ 1 mm) [Within the low-speed regime  ,44–46. WStick slip is a dynamic instability. It is characteristic not only of ice, but of the compliance of the loading system. Its nature, however, has not been studied systematically and hence meaningful discussion must await future work. Stick slip implies that, while the overall velocity imposed by the loading system (e.g. the actuator of a servohydraulic system used in the laboratory) may be constant, the relative displacement across the interface varies somewhat as elastic energy is stored and then released.Although stick slip imposes temporally variable shear stresses, in the description below we use a temporal average over many cycles to quantify this parameter, in keeping with common practice.(b)τs, versus the global normal stress, σn. The conditions include both sub-metre and metre-sized specimens of first-year sea ice and freshwater ice, both wet and dry, sliding in the laboratory and in the field at temperatures around −10°C and −40°C at velocities of Vs = 8 × 10−4 m s−1 and 8 × 10−2 m s−1 [Vs = 5 × 10−8 to 1 × 10−3 m s−1 [σn = 2 kPa to 2 MPa . This ieratures .There are instances in which non-linear behaviour has been reported, where  ,49,50. I(c)Ra = 0.4 − 1200 µm) and under the conditions explored , the coefficient scales as Ra ∼ 1 mm) Coulombic faults and smooth/milled (Ra ∼ 1 µm) surfaces (more below). Furthermore, a cover of snow increases μk by about a factor of 1.3, at least when sliding over warm arctic sea ice at about Vs = 1.5 × 10−2 m s−1 [−1 [The primary factor that affects the coefficient of kinetic friction under terrestrial conditions is sliding velocity; its effects are described below. On other factors, temperature is important: it leads to an increase of about 20 to 40% upon dropping from −10 to −40°C ,47 and t s−1 [−1 . On the  s−1 [−1 ,45,52 ha s−1 [−1 , reminis s−1 [−1 . Similar(d)Th ≥ 0.8) exhibits two kinds of behaviour. At the lowest speeds, i.e. at velocities lower than about Vs = 10−5 to 10−4m s−1, depending upon temperature, the coefficient of kinetic friction increases with increasing velocity. This behaviour we term velocity strengthening. At higher speeds, the coefficient decreases with increasing velocity, termed velocity weakening. Ra ∼ 1 mm) at −40°C and at −10°C. Low-speed sliding of warm ice (homologous temperature of around Ra ∼ 1 µm) and rough (Ra ∼ 1 mm) surfaces on both saltwater and freshwater ice plus data obtained in the field and in the laboratory. As well as the trends of strengthening and weakening noted above, the data indicate that the rather moderate et al. [Vs > 10−4 m s−1), ice/steel, ice/concrete, ice/rubber and ice/nylon [In exhibiting dual behaviour, again ice on ice is not unique. The material systems ice/glass and ice/granite  behave iet al. . In addice/nylon ,53,61 al(e)To the unaided ear, sliding within the upper range of the velocity weakening regime is rather noisy . It is a7.(a)Vt1, affected possibly by the liquid-like layer that forms on the surface of ice near its melting point [Vt2, greater than the first, a layer of water, we think, covers all original contacts. At that point, hydrodynamics governs [In exhibiting two regimes of behaviour—indeed three regimes when high-speed sliding is included —kinetic ng point , insuffi governs –65 and s governs ,63,64. F(b)(i)μk = γ/Hλ where γ denotes surface energy, H, the hardness and λ, a nanoscale contact length. Although data for λ are not available, it was assumed that this parameter is equal to the size of the smallest cluster of H2O molecules that is stable in the solid state. Based upon calculations by Pradzynski et al. [λ = 2 nm. Taking this value and taking the values γ = 75 mJ m−2 and H = 60 MPa (where 60 MPa corresponds to hardness [−4 s (which is about the amount of contact time t = λ/Vs to be expected for sliding at Vs ∼ 10−5 m s−1 across an asperity approx. 2 nm in diameter)), the thermodynamic model dictates μk = 0.6 for dry sliding at −10°C. This value agrees reasonably well with measurement μk = τy/H. At low rates of loading both the shear strength, τc, and the hardness of ice increase with increasing strain rate, t. The rate-dependencies are expressed as [B, C, n and n′′ are material constants, and Q and Q′′ are activation energies for creep and hardness, respectively. Upon substituting h is the thickness of the near-surface inelastic zone, and t = 2a/Vs, where again 2a denotes contact diameter (assumed not to increase significantly at the low velocities of dry sliding), it follows thatA different explanation-cum-model is based upon creep of asperities and of underlying material ,45,69. Aessed as   and  wh2a. A value can be obtained from static strengthening experiments (§3), as follows [tt, described in §3a that marks the beginning of detectable static strengthening also helps to define asperity size. The relationship is 2a = ttVs = 30 × 10−6 = 3 × 10−5 = 0.3 × 10−4 = 30 µm. , an average diameter of 30 µm is within the range 1–100 µm found for ductile metals [To evaluate the model requires asperity size,  follows : given te metals ,70 and fe metals , lendinga = 30 × 10−6 m and using creep data of Treverrow et al. [et al. [Q = 0.81 eV and Q′′ = 0.75 eV [h = 100 µm [n = 3.5 [γ = 2a/h approx. 0.3 is well beyond the transition to tertiary creep) and taking the value n′′ = 4.4 [B = 2.5 × 1015 Mpa−3.5 s−1 and C = 0.027 MPa sn1/. Equation (7.1) then gives for the coefficient of dry kinetic friction at −10°C the relationship−1.Thus, setting 2w et al.  and hard [et al. , taking  0.75 eV , setting= 100 µm  and usin[n = 3.5  for tert′′ = 4.4 , we deriEquation (7.2) is shown as the dashed line on The fact that the kinetic friction of sea ice and freshwater ice is practically indistinguishable, even though bulk sea ice creeps more rapidly, is again taken to mean that asperities and the underlying deformation zone are too small to contain creep-enhancing pockets of brine.(c)−3–10−2 m s−1 . This speed is within the weakening regime. Solid-state healing of the kind discussed in §5 could not have been at play, for the contact time was too short. Instead, cohesion probably originated in the freezing of frictional-produced meltwater upon uploading the ice.As already mentioned, fundamental to velocity weakening is frictional heating and the creation of meltwater. Yet, direct observations of water at the sliding interface are lacking. There is, however, indirect evidence, deduced from Coulombic shear faults. When introduced into relatively warm (at −10°C) specimens of both sea ice and freshwater ice through proportional, biaxial loading, the faults did not come apart . They ha(i). The issue, however, concerns quantitative agreement. To conform to measurement, the contact size was set [a = 1 mm without linking this size to any underlying physics.Current models that incorporate meltwater are based on the concept of thermal balance at contacts and the conduction of frictional heat into the ice ,64,66,67et al. [Vs ∼ 10−2 m s−1 at −5°C, compared with the observed onset at velocities two to three orders of magnitude lower, albeit at −10°C; and, that the value of the coefficient approaches zero at velocities lower than approximately 4 × 10−3 m s−1 compared with measured values at that speed that range from µk ∼ 0.1 (smooth interface) to 0.6 (rough interface).A better model, we think, incorporates asperities of a range of sizes. Hatton et al.  envisage(ii)Vs = Vt1, the largest when Vs = Vt2. To remain wet, the time for asperities to slide over each other, ts, must be shorter than the time to transfer the latent heat of fusion via conduction into the adjacent ice, tc. The passage time is given by ts = 2a/Vs. The conduction time from a one-dimensional model of heat transfer may be estimated from the relationshipLv denotes the latent heat of fusion per unit volume, δ the thickness of the meltwater layer, κ the thermal conductivity of ice, ρ the density of ice, Cp the specific heat of ice and ΔT the difference in temperature between the melted contact (0°C) and the adjacent ice; the factor of 2 takes into account heat transfer into both sides of the contact. Upon setting ts = tc, it follows that the velocity Vsc that marks the contribution to weakening from meltwater that forms on contacts of diameter 2a scales as Vsc ∝ aΔT2 and is given by the relationshipRather than a range of asperity heights, we propose that velocity weakening is related to a variation in contact area. The smallest contacts, we imagine, melt when Vs = 10−3 m s−1 at temperatures of −100°C and lower [Tcold/ΔTwarm)2 = (100/10)2 = 100 or greater.Incidentally, although cold ice is beyond the scope of this discussion, we note that velocity weakening is not detected in freshwater ice sliding against itself at velocities up to nd lower . The abs(iii)μkd is the kinetic coefficient for dry contacts, given by equation (7.1), μw is the kinetic coefficient of water on ice and η denotes the fraction of the real area of contact covered by a layer of meltwater.Returning to warm ice and to the presence of meltwater, the coefficient of kinetic friction may be described by a rule of mixturesη, we imagine that the sliding interface has fractal character. Accordingly, and in keeping with the description of natural and prepared surfaces in other materials (e.g. see [N(A) is the total number of contacts of area equal to or smaller than A, where S and D are constants. The fraction of contact area that is wet may then be given by the relationshipAro denotes the initial real contact area and Amin denotes the smallest contact area in the distribution. Square-shaped contacts of area A = (2a)2 are related through equation (7.3) to the corresponding critical velocities through the equality K is a constant and a function of a number of thermal parameters, K = f(κρCpΔTLvδ).To quantify e.g. see  and refeK′ is another constant related to K and Vscmin denotes the velocity at which the meltwater remains on the smallest contact.Thus, from equations (7.4) and (7.6), and assuming that meltwater offers insignificant resistance to sliding within the velocity-weakening regime, the coefficient of kinetic friction versus velocity may then be expressed by the relationshipD ≠ 2). But how well does the relationship account quantitatively for the actual kinetic coefficient? We do not know, because at this juncture values of the distribution parameters S and D are not known. However, if it is assumed that the above depiction-cum-mechanism is close to the truth, then the data plotted in D. Accordingly, by rewriting equation (7.7) and then taking logarithmsμkd, a double-log plot of the velocity-weakening data in D = 1.90 ± 0.05 for both the smoother and rougher interfaces. While we caution against putting too fine a point on this value, we note that closely similar D-values have been obtained for quartz, calcite, glass and acrylic plastic from direct observations of contact areas on prepared and roughened surfaces [Equation (7.7) captures velocity weakening How much creep-driven healing—i.e. the recovery of strength in the absence of ‘welding’ through the freezing of sea water—is expected within the arctic sea ice cover during periods of dormancy? If one assumes that the underlying physics is scale-independent and that parametric values are similar to those derived in the laboratory, then the analysis given in §5 suggests that only about 5% of the original strength may be recovered upon holding damaged ice at −10°C for a period of 10 days under a low confining stress of 20 kPa, comparable to the stress measured  within t(b)−1 is reached, decreases as the velocity increases further to around 10 km d−1. Based upon the response of the unaided ear during experiments in the laboratory [T2. A difference in temperature of say 20°C from the upper/colder layers to the lower/warmer layers of a floating ice sheet is then expected to spread the transition velocity by two to three orders of magnitude, such that under some conditions the upper part of the sheet, of greater ΔT, may slide within the strengthening regime while the lower part of smaller ΔT may slide within the weakening regime. The temperature difference per se is expected also to lead to different values of the coefficient of kinetic friction across the thickness of the sheet. Over the sliding speeds that have been measured within the sea-ice cover—i.e. from almost nothing to up to around 10 km d−1—the coefficient of kinetic friction is expected to vary by as much as an order of magnitude. In other words, sliding is expected to be initially relatively quiet and stable, but then to become noisier and unstable. Episodes of sudden slip are thus expected as wind-driven stresses build-up. Superimposed on this stable-to-unstable transition is stick slip where sliding is expected to occur in a jerky manner, possibly on both the macroscopic and microscopic spatial scales. Whether the interface is submerged is expected not to affect significantly the resistance to sliding. Rubble of the kind evident in Returning to boratory , increas(c)From the perspective of the arctic sea-ice cover, sliding is expected to occur on all scales, from the sub-millimetre, as related to transgranular cracks and brittle compressive failure, to tens of metres and greater, as related to linear kinematic features/Coulombic faults. There is little to suggest that the coefficient of kinetic friction changes significantly upon sliding a few millimetres. Whether sliding over a much greater distance has an effect is an open question.9.Following a short threshold period during which no increase is detected, the coefficient of static friction increases with hold time under normal load, scaling with the logarithm of time. The coefficient can be understood in terms of the creep and fracture of asperities in contact, leading to a model that agrees closely with measurement.−1) kinetic friction exhibits velocity strengthening at lower speeds (below approx. 10−5 to 10−4 m s−1) and velocity weakening at higher speeds. Velocity strengthening can be understood in terms of asperity creep and then quantitatively modelled in terms of that process. Velocity weakening can be understood in terms of a progressive increase in the true area of contact that is wetted by meltwater produced through frictional heating. The smallest contacts melt first which leads to the onset of weakening; weakening ends when the largest contacts melt, leading to a third regime of kinetic friction where hydrodynamics governs.The coefficient of low-speed  fractal character of the sliding interface, the friction of sea ice may be summarized as follows.


Multicentre randomised trials provide some of the key evidence underpinning healthcare practice around the world. They are also hard work and generally expensive. Some of this work and expense are devoted to sites that fail to recruit as many participants as expected. Methods to identify sites that will recruit to target would be helpful.We asked trial managers at the Centre for Healthcare Randomised Trials (CHaRT), University of Aberdeen to predict whether a site would recruit to target. Predictions were made after a site initiation visit and were collected on a form comprising a simple ‘Yes/No’ prediction and a reason for the prediction. We did not provide guidance as to what trial managers might want to think about when making predictions.After a minimum of eight months of recruitment at each site for which a prediction had been made, all trial mangers in CHaRT were invited to a group discussion where predictions were presented together with sites’ actual recruitment performance over that period. Individual trial managers reflected on their predictions and there was a general discussion about predicting site recruitment. The prediction reasons from the forms and the content of the group discussion were used to identify features linked to correct predictions of recruitment failure.Ten trial managers made predictions for 56 site visits recruiting to eight trials. Trial managers’ sensitivity was 82% and their specificity was 32%, correctly identifying 65% of sites that would hit their recruitment target and 54% of those that did not. Eight ‘red flags’ for recruitment failure were identified: previous poor site performance; slow approvals process; strong staff/patient preferences; the site recruitment target; the trial protocol and its implementation at the site; lack of staff engagement; lack of research experience among site staff; and busy site staff. We used these red flags to develop a guided prediction form.Trial managers’ unguided recruitment predictions were not bad but were not good enough for decision-making. We have developed a modified prediction form that includes eight flags to consider before making a prediction. We encourage anyone interested in contributing to its evaluation to contact us.The online version of this article (10.1186/s13063-019-3287-6) contains supplementary material, which is available to authorized users. Multicentre randomised trials provide some of the key evidence underpinning healthcare practice around the world. They are also hard work and generally expensive.Some of this work and expense is devoted to sites that fail to recruit as many participants as expected or simply fail to recruit at all. This contributes to the well-known fact that many trials struggle to recruit participants. For the UK National Institute of Health Research Health Technology Assessment programme, a major public funder of multicentre trials in the UK, around half of all trials fail to recruit to target –3. In thSite selection for multicentre trials is done in a variety of ways but often relies on the investigators’ networks of colleagues or hospitals (often through NHS Research and Development departments in the UK) putting themselves forward. Not all of these sites will be suited to the trial’s recruitment task. The problem is distinguishing those that are from those that are not. There are some formal questionnaire-based methods , 11 but Minimise form-filling. We want to make use of the knowledge, experience and instincts of those tasked with setting-up sites: trial managers (TM).Quantify the veracity of TMs’ knowledge, experience and instincts.Suggest ways in which TMs’ knowledge, experience and instincts could be guided so as to improve predictions.The Estimating Site Performance (ESP) project aims to see if it is possible to predict which sites will and will not meet their recruitment targets. Its approach has three components:In short, can TMs predict which sites are worth investing energy in and should the trial team believe them?All TMs employed in the Centre for Healthcare Randomised Trials (CHaRT), Aberdeen, Scotland, UK, who were conducting site initiation visits (SIV) between 2014 and 2015, were invited to take part.Participating TMs all received an information sheet about ESP and signed a consent form. Each TM was asked to predict the recruitment success of each site they opened after having completed the SIV. Predictions were collected on a form that comprised a ‘Yes/No’ answer to whether the site would recruit to target and a reason for the prediction , as well as sensitivity and specificity, were calculated for all TMs, TMs with < 2 years of experience and TMs with ≥ 2 years or more experience. Two years of experience was chosen arbitrarily as a reasonable cut-off point for the time it takes a TM to have acquired a range of relevant experience that allows him or her to judge when sites may struggle with recruitment. For our recruitment predictions, PPV and NPV, sensitivity and specificity can be defined as:Both the reasons for predictions given on the prediction forms themselves as well as the transcript of the group discussion were included in our qualitative analysis although this analysis focused more on the prediction forms than the group discussion. The reasons for the prediction provided on the prediction form were sorted into types of predictions according to the prediction made (Yes/No) and the accuracy of the prediction (Correct/Incorrect).Will this site recruit to target?’ question or questions: in other words, ‘Will this site recruit to target? Think about x, y and z when making your prediction’. Our starting point was that TMs’ unguided predictions might be adequate but that guided ones may be better; the problem was what guidance  to give. The way this guidance would be operationalised led us to think about what might undermine recruitment success; in other words, ‘red flags’ that, if present, raise doubts about a site’s ability to recruit. Conventional content analysis , that a lot of patients favour [treatment A over treatment B]…’ .‘One TM was told by a principal investigator (PI) that patients had a clear preference for a certain treatment. If most patients do not want to be randomised, this significantly limits the pool of potential participants available to a trial at that site.… and we were about halfway into it [the site visit], it was just obvious he was not in equipoise, he was not going to recruit to this trial… and lo and behold they closed down about six months later.’ .‘4.Target for recruitmentReflections from the post-results group discussion also highlighted a lack of equipoise as a possible reason for recruitment failure:Research nurse…was doubtful about target recruitment.’ .‘How recruitment targets are decided varies between trials and sites. Although a recruitment target is just a number, that number has significance in that it will affect motivation to recruit, especially if it is set unrealistically high at the outset. Here, the research nurse expressed doubt that the recruitment target for the site was realistic. This realisation is demoralising to the research nurse and likely to impact on their efforts to recruit.Well with hindsight looking at the targets that are set they were too low.’ .‘they done really well but did promise a lot more. So their target was small, they did promise that they had, you know, hundreds of people that they could contact. So I mean although they did meet prediction and they did recruit really, really well I still would’ve expected more from them.’ .‘[…site name…] Reflections from the post-results group discussion also highlighted that recruitment targets do not get much attention at the early stages, are set artificially and for some trials appear to have been set too low across sites.…and [site] had an incentive because we said if they got to 20 we’d pay for them to have a [name of equipment], which is a piece of equipment they needed for the study and we said if they didn’t reach 20 they’d have to make a contribution to that piece of equipment…And they reached 20 and stopped.’ .‘5.Problems with the trial protocol and/or its implementationCombining an incentive with the ‘per patient’ payment is, however, one way of encouraging sites to reach their target.RN [research nurse] did recognise it was a difficult trial to recruit to…’ .‘The nature of a trial can make it more or less difficult to recruit to across sites. Issues with the trial protocol are likely to be generic across all sites, although the degree to which it affects recruitment may vary depending on a site’s capacity to work around the challenges the protocol presents, particularly if this is at odds with the local patient pathways. Here, a research nurse recognised the trial as difficult to recruit to at the SIV. Trials that are more difficult to recruit to place a higher demand on the site team and this influences their engagement with the trial.And they just said “This is just not how things work here, it’s just impossible to execute.” So you find out these little gems when the PI isn’t around.’ .‘Reflections from the post-results group discussion also highlighted that a mismatch between the local care pathway and the trial design can cause an issue:Split site, two pharmacists involved.’ .‘Sometimes, the way a site is physically set up, e.g. split so that there are several hospitals that can recruit to the trial but they are only counted as one site, can cause delays in set-up, especially if the TM is only made aware of this at the SIV. Here, the TM notes that due to the site being split there would be two pharmacies involved, which has implications for the set-up process and can cause delays to recruitment start.Like in [name of site]…, it turned out that they have a massive [name of treatment] centre and they have no surgery spaces for [name of alternative treatment] so how can then they offer ... how can they randomise to have the treatments and say that they’ll get the treatments in eight weeks ... because they don’t have a surgery space.’ .‘Reflections from the post-results discussion also highlighted site facilities as a reason for recruitment failure:Lots of confused discussion between team about how best to identify patients and recruit them.’ .‘Lots of problems and difficulties thrown at me at SIV teleconference.’ .‘An important function of the SIV is often that the whole team is gathered and there is an opportunity to plan how recruitment will work and distribute responsibility for all the tasks involved. However, this process works best if the parties involved have come prepared. If the way recruitment is going to be done cannot be agreed early, it is likely to cause delays to recruitment start.Thought about recruitment in advance and have identified PICs [Participant Identification Centres] …’ .‘…The site have proactively pushed for teleconference to arrange site training – were very engaged at the teleconference and asked questions about approaching patients. They had identified potential participants at PI clinic before the teleconference…’ .‘6.Lack of engagement of site teamWhereas for successfully recruiting sites, this was one of the main purposes of the SIV.PI only engaged in the study at a very late stage.’ .‘The level of engagement of a site with the trial will be evident to the TM throughout the set-up process and usually before the SIV. Often the PI is essential to recruitment of a trial as he or she will often be the person who first introduces patients to the trial, which may not happen if the PI is not fully engaged with the trial.[Site name], although recently the research nurse has advised that she’s having issues with engagement from the PI there. Again, looking back at the site initiation as well there were some signs there, but again, initially when they started recruiting they were recruiting quite well and it’s just been the last few months that it’s sort of slowed down… Lack of engagement with the PI, he’s just stopped… there’s lack of communication with the research nurse.’ .‘Reflections from the post-result group discussion also highlighted that a PI’s engagement can vary over the lifespan of the trial, highlighting that this is an issue throughout.PI listened but seemed keen to have SIV over.’ .‘The level of engagement of site team members can be picked up in many ways, even body language at the SIV.…PI has sent emails to R&D on her own initiative to chase up R&D approval to allow site opening. …’ .‘Very engaged team who have discussed early staffing issues with us up-front and plan to work around this in the long term.’ .‘Conversely, good engagement supports recruitment:The PI was quite awkward I felt ... how did it work if he saw a patient out in rural hospital, would they have to come into the main hospital, how would the research nurses know he’s seen a patient ... just lots and lots of ifs and buts that I kind of should’ve flagged up more warning signs at the start I think.’ .‘Reflections from the post-results discussion also illustrate how this kind of lack of engagement is a warning sign of likely recruitment failure:Other named consultant who will recruit did not attend SIV.’ .‘There is a lot of background work involved in planning and conducting SIVs; often many site team members will be invited as it is important that they receive the training relevant to the trial. If a member of the site team does not attend the SIV, alternative arrangements will have to be made, which again is likely to slow down the trial progress at the site.They’re just… I mean I said lots of problem… at their site initiation visit there was just… the PI never came to the investigator meeting, the site visit was a nightmare to even organise.’ .‘…but just half an hour of dedicated time for the meeting would be good. … I don’t know whether really it’s made any difference me coming here because I don’t think they were listening. … And then when people don’t attend, that’s the worst as well.’ .‘Reflections from the post-results discussion also highlighted lack of engagement with the SIV as a factor affecting recruitment success:Email communication is slow.’ .‘Throughout the lifespan of a trial there needs to be a flow of communication between the trial office and sites and if this flow is poor, then this will slow down the trial at that site.[name of site] were always, they were just a bit cagey.’ .‘Reflections from the post-results discussion also highlighted this as a reason for recruitment failure:And I know in [trial] you know, the sites that have probably done really, really good are the ones that we’ve had that great communication with, the ones that picked up the phone whenever there’s been the slightest query and we’ve been able to deal with that really quickly and then away we’ve went again… But I think yeah, to keep that communication going is probably one of the key things.’ .'7.Lack of research experience of site staff and staff changesConversely, good communication supports recruitment:PI first time as PI; two other consultants relatively research naïve.’ .‘The site is about to lose an experienced research nurse and promote a nurse with no research experience.’ .‘The lack of research experience of staff, for both PIs and research nurses, was noted as a problem. Having site staff without research experience is also likely to slow down the trial at a site due to research-naïve staff having to become familiar with the research process in general as well as with the procedures involved for specific trials.[name of site] I think have had quite a few changes of staff so that might explain quite a lot of those issues.’ .‘Reflections from the post-results discussion also highlighted turnover of site staff as a factor affecting recruitment success:Well yeah, there’s almost a complete turnover in people at [name of site], but they have been good. The new guy’s brilliant … Well yeah, they’ve got, well everyone who was at the site visit is no longer there.’ .‘8.Busy site staffAlthough the discussion also highlighted that there are exceptions.[name of research nurse] but she has many studies to deal with.’ .‘Often a site is involved in multiple trials and if a TM knows the site staff have a high team workload there is an anticipation that their trial may not be prioritised by busy site staff.Well looking back now at the site initiation the PI was very busy, she had just come off a nightshift and she was wanting to do everything. So thinking back now in retrospect there was warning signs then.’ .‘…and they have no support currently at all. They’re taking the projects on because they want them on the books, but they have absolutely no network to support it…’ .‘Reflections from the post-results group discussion also highlighted this as a reason for recruitment failure:Trials are hard work and we want that work to be worth it. Investing substantial amounts of work in trial sites that fail to recruit is something to try and avoid because there is plenty of work to do elsewhere in the trial. Some work has been done with site selection questionnaires , 11 but We asked TMs to make simple Yes/No predictions and we gave no guidance as to what they should think about when making their predictions. Ten TMs made 56 predictions across eight trials and it turns out that TMs are pretty good at making these predictions. If we think of TMs as a diagnostic test of recruitment, our TMs had a sensitivity of 82% and a specificity of 32%, correctly identifying 65% of sites that would hit their recruitment target and 54% of those that did not.The 32% specificity is the critical feature of this diagnostic test and it is very low. As mentioned in the ‘We expected this and the work described here was always intended as the first stage of our ESP work. This is why we also looked for ‘red flags’—factors associated with recruitment failure—because we suggest that these can be used to provide guidance to TMs when thinking about their recruitment predictions. Table It is clear that there is some overlap between flags and that some may well be more important than others. We do not think the flags should be a simple box-ticking exercise but be used as a prompt for discussion when doing a SIV. Discussion of the flags after the visit by the central trial team could support decisions about which sites will need more support, which less and whether poor recruitment expectations at some sites means new sites are needed. Some could also be considered before the SIV. In particular, previous poor performance, the recruitment target, problems with the trial protocol and/or its implementation and lack of research experience of site staff/staff changes could be considered early on in site identification and set-up. We did not use the predictions to influence trial conduct in the work described in this paper, but it is easy to imagine that ticks against these red flags well before the SIV could give the central trial team reason to reconsider whether the site was worth pursuing. At the very least, the site might fall down the order list of which sites are brought on-stream or perhaps a member of the team could do a ‘site selection visit’ (in person or by teleconference) to confirm whether it was worth moving ahead with the site’s involvement in the trial. We also think that the flags might be a helpful training tool for new or inexperienced TMs to provide a basic structure to the discussions they have with sites during set-up and at SIVs.By the end of the SIV, it may be possible to make a judgement about recruitment success by looking across the red flags, combined with any other relevant information the TM has. The TM could create a matrix listing all sites and their red flags, which would give an overview of high-risk sites, those not expected to recruit to target. The aim, of course, would be to have mostly low-risk sites, with very high-risk sites having been excluded before the SIV or put on hold until one or more red flags change. That matrix will give the central trial team an indication of where their limited resources should be targeted when thinking of sites and recruitment.There are a number of limitations. First, the work was done at a single trials unit and involved a relatively small number of TMs. While clearly a limitation, this work was the first stage of the project so its limited scale seems appropriate. Moreover, we expected predictions to be improved by having red flags and identifying those flags was part of this work. Now we have those red flags, further work needs to be on a bigger scale and involve more trials units and teams. To this end, we have developed a revised prediction form . We are confident that any TM looking at the list in Table We think the study also has some strengths. It set out with the intention of tapping into the experience of TMs rather than to ask site or trial staff to do substantial amounts of form-filling. Even in the complete absence of guidance, a very simple form and TMs’ experience led to a reasonable first shot at predictions. The qualitative work, especially the post-results reflection, leave us reassured that the red flags have face-validity. For example, they map nicely onto the issues listed on the Clinical Trials Toolkit, a tool to provide practical advice to researchers in designing and conducting publicly funded clinical trials in the UK Additional file 2:Modified prediction form. The modified prediction form for the evaluation of ‘red flags’ identified in the current study. (DOCX 29 kb)Additional file 3:Quantitative data. (XLS 18 kb)


Drosophila. Mixtures of odors with opposing valences elicit strong inhibition in certain attractant-responsive input channels. This inhibition correlates with reduced behavioral attraction. We demonstrate that defined subsets of GABAergic interneurons provide the neuronal substrate of this computation at pre- and postsynaptic loci via GABAB- and GABAA receptors, respectively. Intriguingly, manipulation of single input channels by silencing and optogenetic activation unveils a glomerulus-specific crosstalk between the attractant- and repellent-responsive circuits. This inhibitory interaction biases the behavioral output. Such a form of selective lateral inhibition represents a crucial neuronal mechanism in the processing of conflicting sensory information.Evaluating odor blends in sensory processing is a crucial step for signal recognition and execution of behavioral decisions. Using behavioral assays and 2-photon imaging, we have characterized the neural and behavioral correlates of mixture perception in the olfactory system of  Fruit flies need to appropriately respond to mixtures of attractive and repellent odors in their natural environment. Here, the authors propose that lateral inhibition between glomeruli activated by attractants or repulsive odors mediates the appropriate response. However, the origin and the underlying neuronal mechanisms and its correlate to the behavioral output are still unresolved.An important role of an animal’s brain is to encode, integrate, and interpret olfactory information from the surrounding environment in order to translate this sensory input into a relevant behavioral output. However, most, if not all, odors encountered are not single molecular compounds, but rather complex blends that vary in both valence and ratio of their individual components. Mixture processing has been well studied in vertebratesDrosophila melanogaster makes it a favorable model to study mixture processing. The vinegar fly detects odors with olfactory receptor neurons (ORNs) housed in olfactory sensilla on the antennae and the maxillary palps6. Most of the ~50 ORN types express one (or two) odorant receptors (ORs) together with the co-receptor (Orco)7. All ORNs expressing the same OR innervate the same glomerulus in the antennal lobes (ALs)9, where they synapse onto projection neurons (PNs)10. Glomeruli are interconnected by local interneurons (LNs) which are mainly GABAergic and synapse onto both ORNs and PNs12. The multiglomerular innervation of most LNs supports the idea of their role in global inhibition, thereby ensuring gain control16. Nonetheless, some of the inhibitory LNs were shown to connect defined subsets of glomeruli16 and might contribute to mixture processing.The simplicity of the olfactory system of 17. Recent behavioral studies in flies showed that the attraction of a mixture can be predicted by the behavioral responses towards the individual mixture constituents19. Another study in mice showed that aversive odors can neutralize attractive odors in a blend, or even turn the mixture into a repellent20. However, it still remains elusive how the olfactory circuitry accomplishes mixture processing and where the neuronal correlate to the fly’s decision is located along the olfactory pathway.Mixture interactions are influenced by the composition and concentration of each component within an odor blendIn order to dissect mixture processing in the fly, we used binary blends of odors having opposing valences and established a mixture ratio at which the repellent odor starts to significantly reduce the attraction towards the mixture. We demonstrate that certain glomeruli contribute differentially to the mixture processing through inter-glomerular crosstalk mediated by GABAergic inhibition, which bias the behavioral output.21, a behavioral bioassay monitoring odor-guided walking behavior of individual flies  as an attractive odor18, benzaldehyde (10−1) as an aversive odor22 and their binary mixture. In this experiment, flies showed the same attraction to the mixture as to ethyl acetate alone  of ethyl acetate. Although ethyl acetate on its own was still highly attractive, the attraction towards the binary mixture was significantly reduced . Ethyl acetate and benzaldehyde evoke activity in, mostly, non-overlapping glomeruli25. We first focused on the AL output to analyze whether any mixture processing in form of lateral excitation26 and/or lateral inhibition28 was taking place. We expressed GCaMP6s29 in PNs under control of GH146-Gal4, which labels most of the uniglomerular PNs31. Using two-photon imaging we monitored odor-evoked signals in PNs applying the same odor delivery system and odor concentrations as used in our behavioral experiments , benzaldehyde (10−1) and their mixture (MIX( + )), we observed that the mixture was linearly represented , we noticed a strong inhibition in four out of the 34 glomeruli compared to their activity to the single odor component  and MIX(−) in the FlyWalk by reducing the concentrations 10-fold and measured responses in PNs Fig. . In lineDrosophila repellent22 and activates only glomerulus DL1 at the used concentration of 10−3  and MIX(−). In this odor combination, behavioral attraction towards high concentration of ethyl acetate (10−2) was not affected, while the attraction of the lower concentrated attractant (10−3) was significantly reduced by the repellent in the mixture  for this odor combination  did not result in any inhibition in flies bearing one of the two mutant backgrounds , MIX(−), and the individual odors benzaldehyde and ethyl acetate in flies where DL1 or DL5 were individually silenced using a mutant background of Or10a or Or7a, respectively Fig. . Both muw1118 as a control line, we observed a robust repulsion to benzaldehyde (10−1), while both concentrations of ethyl acetate (10−2 and 10−3) were highly attractive . Notably, when the input to DL1 was silenced, mutant flies were attracted to MIX(−), while they were still repelled by benzaldehyde alone. As the inhibition of the attractant-responsive glomeruli DM1 and DM4 was subsequently abolished, the modified behavioral output indicates the importance of these glomeruli for behavioral attraction. Indeed, silencing the input to these two glomeruli by expressing UAS-Kir2.135, odor attraction towards ethyl acetate as well to both mixtures was abolished and even shifted to aversion for MIX(+) and MIX(−) . As predicted, a lower concentration of ethyl acetate (10−3) combined with artificial activation led to significantly reduced attraction and mimicked the behavioral response to MIX(−). Notably, photoactivation of DL1, DL5, or both resulted in aversive behavior compared to the control flies  could be mimicked by replacing the aversive odor with optogenetic activation of the two repellent-responsive glomeruli. CsChrimson was expressed in Or10a- or Or7a-expressing ORNs and the behavioral response to ethyl acetate was monitored in the T-maze Fig. . Artificies Fig. . This suDrosophila AL are mediated by the inhibitory neurotransmitters GABA, which binds to GABAA and GABAB receptors28, and glutamate, which opens glutamate-gated chloride channels (GluClα)39.We next turned our attention to the neuronal mechanism. Most of the odor-induced inhibitions in the B receptors and picrotoxin (100 µM) to block the Rdl subunit of the GABAA receptor and the GluClα. We simultaneously monitored the odor-induced calcium signals in PNs. By blocking GABAB receptors, we noticed a reduction in the MIX(−)-induced inhibition in the four attractant-responsive glomeruli compared to the saline or wash-out situation  -induced inhibition was totally abolished in all four attractant-responsive glomeruli  to silence GABAion Fig. . To quanion Fig. . As expe(−) Fig. . Picrotouli Fig. , while wA and GluClα receptors. Second, the antagonists act on the pre- and postsynaptic sites and do not allow pinpointing where the inhibition takes place. To overcome these issues we used RNA interference to target either GABAergic or glutamatergic receptors selectively at the pre- and postsynaptic sites of AL input and output neurons. We employed UAS-Rdl RNAi against the Rdl subunit of GABAA40, UAS-GBi against the GABABR2 subunit28, UAS-gluclα RNAi against the GluClα39 and UAS-empty-RNAi as a control. We confirmed the efficiency of the RNAi lines by RT-PCR  in two out of four attractant-responsive glomeruli (DM1 and DM4)  in any of the attractant-responsive glomeruli. The repellent-responsive glomeruli revealed a linear mixture response independent of the RNAi line expressed -specific inhibition at the postsynaptic site in two out of four attractant-responsive glomeruli, which is well in line with our results deriving from the pharmacological treatment with picrotoxin  Fig. . Neitherxin Fig. .Fig. 7MiB receptors significantly reduced the MIX(−)-induced inhibition in all four attractant-responsive glomeruli . Notably, the MIX(−)-induced inhibition was abolished in DM2 and DM3 and strongly suppressed in DM1 and DM4 when GABAB receptors in ORNs were silenced  inhibits the other two attractant-responsive glomeruli (DM2 and DM3) predominantly on the pre-synaptic site via GABAB receptors.Although our results show that pharmacological blocking of GABAuli Fig. , geneticion Fig. . We therced Fig. . We did ced Fig. . In sum,B receptors in ORNs, we next verified that the MIX(−)-induced inhibition occurs at the sensory level and performed calcium imaging in ORNs. As expected, stimulation with MIX(−) induced a strong and significant inhibition in the attractant-responsive glomeruli, while MIX(+) was linearly represented  and benzaldehyde (i.e. Or10a/DL1) are housed in the same basiconic sensillum ab1B receptor, while DM1 and DM4 are inhibited at their pre- and postsynaptic terminals via GABAB and GABAA receptors, respectively. To the best of our knowledge, our results are novel in revealing that specific glomeruli are inhibited at two different synaptic sites, while other glomeruli are inhibited solely at the presynaptic locus.In conclusion, our results demonstrate that GABA mediates the observed mixture inhibition in the attractant-responsive glomeruli. Glomeruli DM2 and DM3 are inhibited at the presynaptic locus through the GABA13. To selectively silence LN-mediated inhibition we expressed an RNAi construct against glutamic acid decarboxylase (Gad) in conjunction with UAS-Dicer2 to knock-down GABA synthesis41 in each of the different LN subsets, while we monitored calcium responses to MIX(−) and the individual odors in PNs. We confirmed the reduction in GABA production via immunostaining 42. We illuminated PA-GFP in single somata to selectively label individual LNs, reconstructed and annotated their innervation in our glomeruli of interest , while presynapses are stronger pronounced in the attractant-responsive glomeruli  in the olfactory bulb of vertebrates44. As an alternative scenario it is also conceivable that instead of inhibiting the attractant-coding pathway to shift the behavior towards aversion, the response of the repellent-responsive glomeruli could be boosted via lateral excitation45. Lateral excitation has been described to drive synergistic interaction between the binary mixture of cis-vaccenyl acetate and vinegar26. Although odors representing sex and food are mutually reinforcing26, a binary mixture of odors with opposing valences means a conflicting input. We therefore postulate that, in contrast to reinforcing input, conflicting sensory input is processed via lateral inhibition in the fly AL. An assumption that would be intriguing to be tested in the future.In this study, we analyzed the integration of binary odor mixtures of opposing hedonic valences and demonstrate how glomerular-specific inhibition and crosstalk results in an appropriate behavioral output. We show that glomeruli that strongly respond to the attractive odor are inhibited by the repellent odor in the mixture, which is mediated by defined subsets of GABAergic LNs Fig. . Heterog5.We did not observe any inhibition of the attractant-responsive glomeruli when we stimulated with MIX(+). This lack of inhibition is probably due to the strong ORN input leading to high presynaptic firing rates in the attractant-responsive glomeruli. Consequently, lateral inhibition deriving from the aversive circuit has only a low impact and does not decrease the excitation of the attractant-responsive glomeruli34 or to the activity of single processing channels47. The latter argument is supported by the finding that only very few, special glomeruli seem to be valence-specific and induce clear attraction or aversion behavior upon artificial activation.Obviously not all glomeruli that are activated by an attractive odor are inhibited by a repellent in a mixture and might not contribute to the attractiveness of an odor. This observation makes sense in the light of accumulating evidence suggesting that the innate behavioral output is correlated either to the summed weights of specific activated glomeruli24. However, an attractive odorant may indeed activate some aversive input channels beside their main activation of the attractive circuitry (or the other way around). What actually matters is the behavioral output that is consequently elicited when a specific glomerulus becomes activated. For example, ORNs that respond to CO2 are also activated by ethyl benzoate and E2-hexenal48. However, the CO2 circuit has been clearly demonstrated to mediate behavioral aversion49. Following this argument, artificial activation of glomeruli DL1 and/or DL5 leads to aversive behavior, while silencing DM1 and DM4 abolished attraction to the attractant. These experiments provide evidence that activation of the repellent- and attractant-responsive glomeruli causes a valence-specific behavior, and can therefore be defined as attractive or aversive input channels, respectively.It is important to mention that our subset of repellent-responsive glomeruli does also respond to non-aversive and even partly attractive odors, such as E2-hexenal and ethyl benzoate250. Glomeruli processing these ecologically labeled lines differ from broadly tuned glomeruli with regard to their neuronal composition51. Hence, it is conceivable that the narrowly tuned geosmin-responsive glomerulus does not exhibit strong interglomerular interactions and has therefore a different impact on the attractant-responsive glomeruli. Mixture interactions between geosmin and attractive odors might be implemented in higher processing centers which contain circuit elements mediating interactions between odors52.Interestingly, we observed one exception in our data set: although the repellent odor geosmin reduced the attraction to balsamic vinegar in the mixture, we did not observe any mixture inhibition. The detection of geosmin is one of the rare cases, where an odor is detected by only one receptor type and consequently activates only one glomerulus. Similar specialized pathways have been described for the detection of sex pheromones and CO55. In mice, dense center-surround inhibition refines mitral cell representation of a glomerular map56, while other evidence showed that lateral inhibition can be rather selective and biased between different mitral cells57. In accordance with the olfactory bulb, the AL exhibits broad, selective or even both forms of lateral inhibition58, whereby certain glomeruli can show different sensitivities towards an inhibitory input14. Lateral inhibition in the Drosophila AL is largely mediated through GABA58. Most of the GABAergic inhibition in the Drosophila AL has been shown to take place predominantly on the presynaptic site mediated through GABAA and GABAB receptors28. In addition, PNs also receive GABAergic inhibition via GABAA and/or GABAB receptors from LNs12. Notably, we found that two out of four attractant-responsive glomeruli are inhibited on the pre- and postsynaptic levels (via GABAB- or GABAA-receptors), while the other two glomeruli are inhibited only presynaptically through GABAB-type receptors. Previous results have so far shown that GABAA-type receptors contribute weakly to lateral inhibition and shape the early phase of odor responses12. However, our data demonstrate that GABAA-type receptors largely mediate mixture-induced inhibition during the full period of the odor presentation which is reminiscent to tonic inhibition in the mammalian system59.Lateral inhibition, which is believed to enhance contrast and to facilitate discrimination of similar stimuli, is an important motif throughout the nervous systemGH298-Gal4 and H24-Gal4), that globally release GABA, might be involved in gain control16.We show that mixture-induced lateral inhibition of the attractant-responsive glomeruli was abolished when we silenced GABA synthesis in mostly patchy LNs. Hence our data suggest, in consistency with previous studies, that LNs with more selective innervations mediate glomerulus-specific interactions and rather contribute to mixture processing, while pan-glomerular LNs  or the receiver (i.e. glomerulus) side. First, since glomeruli vary dramatically in their GABA sensitivity and consequently their sensitivity to LN activation24, which correlates with spontaneous fluctuations in the membrane potential of the postsynaptic PNs61. Consequently, inhibitory responses (i.e. hyperpolarizations) are difficult to capture with calcium imaging.In contrast to our expectation, sole photoactivation of DL1 or DL5 or stimulation with the repellent alone did not induce inhibition in the attractant-responsive glomeruli. This might be due to the low spontaneous activity of ORNs innervating the attractant-responsive glomeruli62 or the rat visual cortex63. Also for the Drosophila AL, previous studies suggested that glomerular subgroups are connected via inhibitory LNs58. However, these studies could neither pinpoint the precise connections nor their significance for behavioral perception. Our data provide evidence for a specific inhibitory crosstalk between identified glomeruli and substantiate the existence of selective lateral inhibition in the fly AL. Our postulated network circuits offer insights into the principle of sensory integration. It will be intriguing to see whether neuron-specific crosstalk represents a general phenomenon to integrate multiple and rather conflicting input channels in other sensory modalities.In other sensory systems, lateral inhibitory connections of neuronal subsets involved in sensory processing have been elucidated in great detail, such as in the retina of mice°C . All experiments were performed on adult females. Genotypes used in each figure are listed in Supplementary Table GH146-Gal431 , 20XUAS-IVS-GCaMP6s (attP40)29 (Ilona Kadow’s lab.), 20XUAS-IVS-GCaMP6f (VK00005) (Bloomington Drosophila Stock Center (BDSC) 52869), w1118 (BDSC 3605), GH298-Gal431 (Reini Stocker’s lab.), NP3056-Gal4 (Kyoto DGGR 113080), HB4-93-Gal4 (liqun luo’s lab), H24-Gal4 (BDSC 51632), Or10a mutant (Or10a[f03694])64 , Or7a mutant (Or7a−/−)65 (gift from Christopher Potter), UAS-Rdli-G and UAS-GBi66 (both are gifts from Mani Ramaswami), UAS-dicer2 (BDSC 24644), Or42b-Gal4 (BDSC 9972), Or59b-Gal4 (BDSC 23898), UAS-Kir2.1 (P(w[ + mC] = UAS-Hsap/KCNJ2.EGFP)1 BDSC 6596), UAS-Syt::HA; UAS-mCD8-GFP (gift from Hiromu Tanimoto), UAS:homer-GCaMP3.0 , GH146-QF,QUAS-mtdTomato (BDSC 30037), UAS-C3PA (gift from Sandeep Datta), nsyb-Gal4 (BDSC 51635), tubP-GAL80[ts] (BDSC 7018), UAS-GluClα RNAi39 (BDSC 53356), Or10a-Gal4 (BDSC 9944), Or7a-Gal4 (BDSC 23908), UAS-empty RNAi (attp40) (BDSC 36304), 20XUAS-CsChrimson-mCherry-trafficked (VK00005) and 20XUAS-CsChrimson-mCherry-trafficked (in su(Hw)attP5) (both are gifts from Vivek Jayaraman), Orco-Gal4 , Orco-Gal4 on (II) (BDSC 26818), UAS-GAD RNAi (BDSC 51794), and GH146-QF,QUAS-GCaMP3.034 (gift from Hokto Kazama’s lab.).Flies were reared on conventional cornmeal agar medium under 12 h/12 h light/dark cycle at 25 67. In brief, we tested 15 starved (24 h) female flies in 15 parallel glass tubes (inner diameter 0.8 cm). The flies were continuously exposed to a humidified airstream with a velocity of 20 cm s−1 . All experiments were performed under red light background conditions (λ = 630 nm) generated by a LED cluster. Flies were monitored during the whole experiment using an overhead camera . Odor stimulation was done using a multicomponent stimulus device, where flies were repeatedly presented 1 s pulses of single odors or mixtures with an interstimulus interval of 90 s. Despite the well-controlled production of the odor stimulus, this stimulus temporarily broadens while moving through the tubing system of the setup. At the same time the stimulus has been shown to decrease in concentration only by <10% along its travel through the system21 resulting in a ca. Two seconds stimulus of well-defined concentration arriving at the tested flies. At the same time the flies’ XY-position was recorded for each pulse. The stimulus protocol consisted of 2 single odors, their mixture, a negative control  (Carl Roth) and clean air pulses, which were presented for 50 times each in a pseudorandomized sequence.FlyWalk experiments were performed adapting previous protocolsOdors were presented to the mixing chamber made of polyetheretherketone (PEEK) via ball-stop check valves. Basically there are two airflows – continuous and odor. The continuous airflow stands for the “no odor”-condition, where the clean airflow passes through empty and clean odor vials. While presenting an odor, the clean airflow is redirected through the vial containing the odor dilution, picking up the saturated headspace. Thus, odors are presented to the flies with minimal disturbances in the total airflow.https://www.r-project.org/). A second custom-written script was used to calculate the response of the flies towards an odor. On one hand we calculated the mean movement speed of the flies from 1 s before the odor until 7 s after the odor pulse . The head was glued to the stage using Protemp II (3 M ESPE) and the antennae were pulled forward by a fine metal wire. A small plastic plate with a hole that is covered with polyethylene foil was placed on the fly’s head. A small cut in the foil was made to expose the head; the foil was sealed to the cuticle using two-component silicon (World Precision Instruments) to prevent the leaking of the Ringer’s solution onto the antennae. We added Ringer’s solution : 5 mM) to the exposed head, and the head cuticle was removed. Care was taken while removing all fat, trachea, and air sacs to reduce light scattering.All calcium imaging experiments were performed on starved (24 h) female flies aged 4–6 days post-eclosion unless otherwise mentioned. Flies were briefly cold-anesthetized on ice and fixed with the neck onto a custom-made Plexiglas mounting stage with copper plate  and a needle before the head to stabilize the proboscis  equipped with an infrared Chameleon UltraTM diode-pumped laser  and a ×40 water immersion objective lens (W Plan-Apochromat 40×/1.0 DIC M27). The microscope and the laser were placed on a smart table UT2 . The fluorophore of GCaMP was excited with 925 nm. Fluorescence was collected with an internal GaAsP detector. For each individual measurement, a series of 40 frames acquired at a resolution of 256 × 256 pixels was taken with a frequency of 4 Hz. To cover the whole AL, we imaged from 5–6 imaging planes (depending on the preparation) which cover the dorsal-ventral axis of the AL at ~25–30 µm intervals in Figs For pharmacological experiments, antagonists were prepared in concentrated stock solutions in Dimethyl sulfoxide . Just prior to the experiments, we diluted the stock solution into 500 µl Ringer’s saline to obtain the final concentration. Picrotoxin (PTX) (Sigma) was prepared as 4.2 mM stock solution in DMSO, while 3-[[1-ethyl]amino]−2-hydroxypropyl](cyclohexylmethyl) phosphinic acid (CGP54626) (Tocris) was prepared as 5.6 mM stock solution in DMSO.2 anesthesia within one day of eclosion. Five males and five females were housed in with food supplemented with all trans-Retinal  for 8–9 days. Flies were starved for 24 h on a wet tissue with 5 ml of water supplemented with all trans-Retinal prior imaging. A 619-nm one high power LED collimated with an optic fiber of diameter (4.5 mm) to activate CsChrimson. The optic fiber was placed in front of the antennae on top of the nozzle for the odor delivery. Light powers were measured below the objective (i.e. at the fly’s antennae position) using a power-meter (Coherent). Intensity measures ranged from 0.04 to 0.4 mW/mm2. The imaging protocol was the same as described above except we replaced the odor stimulation with 2 s of continuous red light. During imaging, a bandpass emission filter (BP470-550) was used to prevent the 619 nm LED light from interfering with the GCaMP signal and to protect the GaAsP detector. The light onset and offset were triggered using LabVIEW software  which was connected to the ZEN software (Zeiss) and to an LED controller. For “Odor + Light” conditions, light onset was delayed by 0.25 s from the odor onset to account for the delay in odor delivery.For optogentic imaging experiment, flies were reared in dark on a standard cornmeal agar food, genotyped under CO−1 was monitored by a flowmeter. A peek tube guided the airflow to the fly’s antennae. For mixtures, the headspaces of the two odors (0.5 l min−1 each) were passing through a mixing peek chamber to mix the two headspaces before they were delivered through a common Teflon tube (1 mm diameter) to the fly’s antennae resulting in a velocity that with ~20–30 cm s−1 was comparable to the airflow in the FlyWalk. In case of single odor, the airflow was compensated by replacing the other odor with clean air (0.5 l min−1). Odors were applied during frames 8–15 (i.e. after 2 s from the start of recording for 2 s). 1.5–2 min of clean air was applied between odors, in order to flush any residues of odors and to let the neurons go back to its resting phase.We have developed a computer-controlled odor delivery system that is similar to the one used in the FlyWalk to a great extent. Pure compounds were diluted in mineral oil and in water in case of balsamic vinegar. Two millilitre of the diluted odors were added to glass bottle , with two sealed openings for the in-and-out of the air flow Fig. . For odoThe flow of the individual odors and the mixtures were monitored by a photo ionization detector (Aurora Scientific) which was placed at the opening of the odor delivery tube. To check whether there are interactions between the two headspaces of the two odors when they were mixed, we performed SPME GC-MS. The SPME fiber was placed in the nozzle of the odor delivery system. Ten times of 2 s odor stimulation was applied on the SPME fiber with 3 s intervals immediately before injection into the GC-MS.69. In short, brains were dissected in phosphate-buffered saline (PBS)  in PBS for 30 min at 25 °C. Afterwards, the brains were washed 3–4 times for 1.5–2 h in total in PBS-T (PBS + 0.3% Triton X-100) and blocked for 1 h in PBS-T + 4% normal goat serum (NGS) at 25 °C before incubation in primary antibody diluted in PBS-T + 4% NGS for 48 h at 4 °C. After the incubation period with primary Ab, brains were washed 3–4 times in PBS-T at 25 °C before incubation in secondary antibody for 24 h at 4 °C. After secondary antibodies, brains were washed for 3–4 times for 1.5–2 h at 25 °C in PBS-T before mounted in VectaShield (Vector Labs) on glass slides with bridging coverslips. Stained brains were acquired with Zeiss LSM 880 with a ×40 water immersion objective lens. The following primary antibodies were applied: chicken anti-GFP , mouse anti-HA , rat anti-Cadherin , rabbit anti-GABA , and mouse mAb anti-bruchpilot ; secondary antibodies are Alexa Fluor 488 goat anti-chicken , Alexa Fluor 568 goat anti-rabbit , Alexa Fluor 633 goat anti-mouse .Whole-mount immunofluorescence staining was performed following standard procedures68, (code available upon request). Each imaging plane was analyzed separately. All recordings were manually corrected for movement. The raw fluorescence signals were converted to ΔF/F0, where F0 is the averaged baseline fluorescence values of 2 s before the odor onset (i.e. 0–7 frames). For the average ΔF/F0, average of frames 11–18 was calculated for each trail and averaged among trails. All images were compared with a published in vivo 3D atlas of the AL30. The glomeruli could be reliably identified from the baseline fluorescence of GCaMP6s, GCaMP6f, or GCaMP3.0.Functional imaging data were analyzed using a custom written IDL software  provided by Mathias DitzenF/F0 of the mixture from the single odor, and then data was normalized to the maximum value within a glomerulus.To access the strength of the inhibition, we calculated the “peak responses difference” by subtracting the Δ43) marker in this ROI was calculated.To analysis the presynaptic and postsynaptic signals, brains were dissected and went through the immunostaining procedures. Glomeruli of interest were labeled with a fixed area of region of interest (ROI) using ImageJ and the florescence of either the presynaptic (synaptotagmin-hemagglutinin (Syt-HA)) or postsynaptic /T, where O is the number of flies in the odor arm, C is the number of flies in the control arm , and T is the total number of flies used in each trail (i.e. 20 flies). Each trail lasted for 20 min.T-maze experiments were carried out as shown in Fig. 2 was directed on one arm of the T-maze, while the other side was kept in the dark. The preference index was calculated as mentioned above.For the optogentic experiments, flies were raised in dark throughout their larval stage to adult. Two days after eclosion, flies were genotyped and transferred onto food supplemented with 1.5 mM all-trans retinal for 7 days. Flies were starved on wet kimwipe supplemented with 1.5 mM all-trans retinal for 24 h prior the experiment day. 619 nm LED with 0.3 mW/mmhttp://www.marzhauser.com/de/)) to measure the extracellular signals originating from the ORNs, while the reference electrode was inserted into the eye. Both electrodes were positioned under a microscope . Signals were amplified , sampled , and filtered (100–3000 Hz with 50/60-Hz suppression) via a USBIDAC connection to a computer (Syn-tech). Action potentials were visualized and analyzed using Syntech Auto Spike 32 software. Each measurement was for 10 s, starting 2 s before a stimulation period of 1 s. Responses from individual neurons were calculated as the increase/decrease in the action potential frequency (spikes/s) relative to the prestimulus frequency.Three-days-old female adult flies were immobilized in pipette tips, and the third antennal segment or palps were positioned onto a glass coverslip. The tungsten wire electrode (recording electrode) was inserted into extracellular into the base of a sensillum , was crossed with UAS-dicer2 and the corresponding UAS-RNAi. Flies were raised at 18 °C. Two-days-old flies were heat shocked at 30 °C for 3 days to relief Gal80 repression before they were moved to 25 °C prior dissection . The rest of the data was statistically analyzed using GraphPad Prism 7 (https://www.graphpad.com/scientific-software/prism/). Graphs were generated using GraphPad Prism 7, R-studio, Excel, or MetaboAnalyst  for the PCA. The statistical tests applied for each data set are specified in the corresponding figure legend.Sample size was determined based upon preliminary experiments. Statistics were computed using the statistics toolbox in R for the FlyWalk data (Further information on experimental design is available in the Supplementary InformationPeer Review FileReporting Summary


Xylan, a prominent component of cellulosic biomass, has a high potential for degradation into reducing sugars, and subsequent conversion into bioethanol. This process requires a range of xylanolytic enzymes. Among them, β-xylosidases are crucial, because they hydrolyze more glycosidic bonds than any of the other xylanolytic enzymes. They also enhance the efficiency of the process by degrading xylooligosaccharides, which are potent inhibitors of other hemicellulose-/xylan-converting enzymes. On the other hand, the β-xylosidase itself is also inhibited by monosaccharides that may be generated in high concentrations during the saccharification process. Structurally, β-xylosidases are diverse enzymes with different substrate specificities and enzyme mechanisms. Here, we review the structural diversity and catalytic mechanisms of β-xylosidases, and discuss their inhibition by monosaccharides. In turn, these side chains may be further esterified with acetic, ferulic, and p-coumaric acids  and lignin, a complex polymer of phenylpropane units ,2,3. Hemic acids . The typl-arabinofuranosidase (EC 3.2.1.55), α-d-glucuronidase (EC 3.2.1.139), acetylxylan esterase (EC 3.1.1.72), and p-coumaric acid and ferulic acid esterases (EC 3.1.1.73), which release the side chain substituents from the xylan backbone, and endo-β-1,4-xylanase (EC 3.2.1.8), which works synergistically with β-xylosidase (EC 3.2.1.37) to break down the xylan backbone. Endo-β-1,4-xylanase hydrolyses the internal β- linkages of the xylan backbone producing short xylooligosaccharides, while β-xylosidase removes xylose units from the non-reducing termini of these xylooligosaccharides [As a complex heteropolysaccharide, full degradation of xylan into its monosaccharide constituents requires the concerted action of various hydrolytic xylan-degrading enzymes with different specificities . These ease EC 3..1.55, α-EC 3.1.1.2, and p-EC 3.2.1., which wcharides . In natuEC 3.1.1.2, and p-charides ,12,13,14Xylan-degrading enzymes have found application as environmentally friendly agents in a wide range of industrial processes, such as bleaching of paper pulp, deinking of recycled paper, enhancing the digestibility and nutritional properties of animal feed, degumming of plant fiber sources, manufacturing of beer and wine, clarification of fruit juices and maceration of fruits and vegetables, preparation of high-fiber baked goods, and the extraction of coffee ,7,15,16.endo-β-1,4-xylanases and cellulases, the activity of β-xylosidase can improve the efficiency of the saccharification process by degrading the xylooligosaccharides and thus alleviating inhibition of those enzymes [In xylan saccharification, β-xylosidase is a crucial enzyme since, of all the xylanolytic enzymes, it cleaves the greatest number of glycosidic bonds. ,18,19. I enzymes ,21,22,23 enzymes ,25,26,27In this review, the structural diversity, catalytic mechanisms and inhibition by monosaccharides of β-xylosidases are discussed.β-Xylosidases are a group of structurally diverse enzymes with varying specificities, in line with the diversity of the organisms that produce them and the heterogeneity of their substrates . Howeverhttp://www.cazy.org/), which groups the enzymes into families based on their amino acid sequence similarities [GHs are classified in the Carbohydrate-Active Enzymes database  [R. santonensis De Feytaud (GenPept ADT62000) [Trichoderma koningii G-39  [d-xylopyranoside derivatives. However, to our knowledge there is no evidence that these enzymes are able to release xylose from natural substrates. Similarly, a bifunctional aryl β-glucosidase/β-xylosidase from the hyperthermophilic archaeon Saccharolobus solfataricus P2  in GH116 is called so based on its activity on aryl β-glucosides and β-xylosides, but the enzyme likely does not hydrolyze xylooligosaccharides [A search using the enzyme classification number for β-xylosidase EC 3.2.1.37) in the CAZy database  revealedDK12988)  and R. sDT62000)  in GH1, AA81024)  in GH54  in the C8 catalytic domain, also known as triose-phosphate isomerase (TIM) barrel domain [GH families 1, 5, 30, 39 and 51 are part of clan GH-A, the largest clan in the CAZy database with currently 23 GH families. Enzymes in this clan all have a (β/α)Thermoanaerobacterium saccharolyticum B6A-RI  [Caulobacter crescentus NA1000  [8-barrel catalytic domain, sequentially followed by a β-sandwich and an α-helical accessory domain. Their structures are very similar. Superposition of the structures of isolated proteins gave an overall root mean squared deviation (RMSD) of 1.6 Å for 462 amino acid residues. However, while CcXynB2 exists as a monomer in solution [Of clan GH-A, structural data for β-xylosidases are currently only available for GH39, i.e., β-xylosidases from DB 2BS9)  and CaulDB 4EKJ) . These esolution , TsXynB solution ,41. The solution . Additiosolution .Phanerochaete chrysosporium BKM-F-1767 5 inidases . Sometiminidases . Althouginidases ,49. Morecosidase .Erwinia chrysanthemi D1  [A total of 103 enzymes with β-xylosidase annotation are currently found in GH3, making it the largest β-xylosidase-containing GH family. A protein domain search using the program InterProScan 5  revealedAA80156)  has two AA80156)  has fourTrichoderma reesei RutC-30  [8 TIM-barrel, a (α/β)6-sandwich, and a FnIII domain, but at different positions in the primary structure. As observed for the majority of GH3 structures [3D Structures of GH3 β-xylosidases are available for a β-xylosidase from the fungus AA93248)  and a β-AA93248) . Both structures . Despitel-arabinofuranosidase, arabinanase, xylanase, galactan 1,3-β-galactosidase, α-1,2-l-arabinofuranosidase, exo-α-1,5-l-arabinofuranosidase, exo-α-1,5-l-arabinanase, or β-1,3-xylosidase activities. As observed for the GH3 members, several enzymes in this family are bi/multifunctional.GH43 is the second largest β-xylosidase-containing GH family with currently 96 members annotated as β-xylosidase. In addition to β-xylosidases, this family also contains enzymes with (putative) α-G. thermoleovorans IT-08 (GbtXyl43B), for example, the extension is at the N-terminus [Together with GH62, GH43 is grouped into clan GH-F in the CAZy database with a structural characteristic of a 5-bladed β-propeller catalytic domain . Some ofterminus . Thus, GB. subtilis (PDB 1YIF) , Clostridium acetobutylicum ATCC 824  , B. halodurans (PDB 1YRZ) , G. stearothermophilus T-6  [B. pumilus IPO (PDB 5ZQJ) [Bacillus sp. HJ14 (PDB 6IFE) [G. thermoleovorans IT-08 (PDB 5Z5D) [C. acetobutylicum ATCC 824  , respectively.For detailed characterization, enzymes in GH43 have been divided into 37 subfamilies, GH41_1 to GH43_37 . In thisDB 3C2U) , B. pumiDB 5ZQJ) , and BacDB 6IFE) . AdditioDB 5Z5D)  and C. aThermobifida fusca YX [Enterobacter sp. [l-arabinofuranosidases [While β-xylosidases from GH43_1 and GH43_26 have only a single 5-bladed β-propeller catalytic domain ,65 and bfusca YX  and Entecter sp. . In factcter sp. ,67,68,70osidases , some doG. stearothermophilus 236 , which is composed of only 618 amino acid residues. The GH52 β-xylosidases are very similar to each other with amino acid sequence identities of around 41%–90%. In this family, crystal structures are available for β-xylosidases from Parageobacillus thermoglucosidasius TM242  . With a sequence identity of 86%, the two proteins fold into almost the same structures; they display two distinct domains, an N-terminal β-sandwich domain and a C-terminal (α/α)6-barrel domain. The catalytic residues of the GH52 enzymes, which are Glu-357 and Asp-517 in GT2_24_00240 [6-barrel domain. Protein homology modeling based on the structure of GT2_24_00240 suggested that the domains are conserved among the GH52 β-xylosidases, except for the C-terminal domain of GsXylA. Compared to other GH52 β-xylosidases, this latter domain lacks five α-helices of the C-terminal domain, such that it displays an open half-barrel structure.Currently, GH52 contains 112 entries, of which 11 enzymes are annotated as β-xylosidases. These β-xylosidases have comparable amino acid sequence lengths of about 700 amino acid residues, with the exception of a β-xylosidase from 24_00240 , are locl-arabinofuranosidases. However, two sequences in this family are annotated as β-xylosidase. TkAbf from T. koningii G-39 was characterized as a bifunctional α-l-arabinofuranosidase/β-xylosidase due to its activity on synthetic nitrophenyl derivatives of α-l-arabinofuranoside and β-d-xylopyranoside with comparable catk/mK values [l-arabinofuranosidase from A. kawachii IFO4308  [Most of the characterized enzymes in GH54 are annotated as α-m values . A threeDB 1WD3) . The priDB 1WD3) . The catDB 1WD3) ,38.p-nitrophenyl- and methylumbelliferyl-linked β-d-xylopyranosides [T. xylanolyticum LX-11 . This protein folds into an N-terminal β-sandwich domain and a C-terminal (α/α)6 solenoid catalytic domain [6-barrel domain  [Bifidobacterium adolescentis LMG10502  [Of the 176 sequences that are currently available in the CAZy database for the GH120 family, two enzymes were characterized and identified as β-xylosidases, i.e., enzymes from BM68042)  and BifiAF39080) . While TAF39080) , BaXylB AF39080) . The thrAF39080) . BaXylB exo-acting enzymes [Despite the diversity of their three-dimensional folds, all structurally characterized β-xylosidases display a typical pocket-shaped active site  that is  enzymes . The poc enzymes ,67. The Furthermore, comparison of active site structures of several β-xylosidase-ligand complexes suggests that there are similar interactions between the enzymes and their ligands . The ligWith respect to their catalytic mechanism most GHs can generally be classified into retaining and inverting enzymes ,78. The The retaining enzymes use a two-step double-displacement mechanism, in which enzyme glycosylation is followed by deglycosylation. In the glycosylation step, the nucleophile attacks the anomeric carbon to form a glycosyl–enzyme intermediate with the inverted configuration at the anomeric carbon. Concomitantly, the (protonated) acid/base residue transfers its proton to the glycosidic oxygen atom, to cleave the scissile glycosidic bond. Departure of the aglycone creates space allowing a catalytic water molecule to come closer to the anomeric center. In the deglycosylation step, the incoming catalytic water molecule, which is activated by the now negatively charged acid/base, attacks the anomeric carbon to release the glycone product from the intermediate. The attack re-inverts the inverted configuration of the anomeric carbon and hence the released glycone has the same stereochemistry as it had in the substrate. In contrast, the inverting enzymes follow a single-displacement mechanism to hydrolyze the glycosidic bond. A catalytic water molecule, which is deprotonated by the general base, does a nucleophilic attack on the anomeric carbon in concert with the general acid protonating the glycosidic oxygen. This cleaves the scissile glycosidic bond and frees the glycone with the inverted stereochemistry of its anomeric carbon.Except for the enzymes from family GH43, all β-xylosidases in the CAZy database are predicted to have a retaining mechanism . Among tG. stearothermophilus T-6, for example, a distance of ~7.9 Å between the carboxylate groups of its catalytic residues has been observed [β-Xylosidases from GH43 are inverting enzymes . They usobserved .d-xylose  of less than 10 mM  for d-xylose and, therefore, they are susceptible to product inhibition by this sugar.Most characterized β-xylosidases have significant affinity for an 10 mM . To our s 1.3 mM , suggestmilus 12 , G. therns IT-08 , and uncns IT-08  with Ki’onas uda  is the βd-xylose and their organismal origin or GH family. Many β-xylosidases from different bacteria and fungi suffer from such product inhibition. Likewise, product inhibition is observed for β-xylosidases belonging to different GH families. This is reasonable because all β-xylosidases bind the same substrate (d-xylose oligomers), necessitating affinity for xylosyl residues and some commonality in their active site structure (see above).From d-xylose, particularly at low concentration. The β-xylosidase from Thermotoga thermarum DSM 5069 was stimulated by d-xylose concentrations of up to 500 mM; its maximum activity was observed in the presence of 200 mM d-xylose, which was ~20% higher than in the absence of the sugar [l-arabinofuranosidase from Phanerochaete chrysosporium BKM-F-1767 [Dictyoglomus thermophilum DSM 3960 [Interestingly, the activity of several β-xylosidases is stimulated by he sugar . A similM-F-1767  and the DSM 3960 ,158. Howl-arabinose is one of the monosaccharides produced during enzymatic saccharification of cellulosic biomass [l-arabinofuranosidases, and, during the saccharification process, its concentration may sufficiently increase to inhibit the activity of hemicellulolytic enzymes [l-arabinose has been identified as an inhibitor of various β-xylosidases  -glucose . Activatrabinose . Althougd-xylose and l-arabinose, d-glucose is another monosaccharide that has been frequently reported to affect β-xylosidase activity. d-glucose inhibits the β-xylosidase activity of the β-xylosidases from S. ruminantium GA192 (Ki 44 mM) [B. pumilus 12 (9% inhibition at 50 mM) [T. harzianum (3% inhibition at 5 mM) [Thermomonospora fusca [A. niger 90196 [A. oryzae [N. crassa ST A [2+-activated β-xylosidase RS223-BX could even withstand much higher d-glucose concentrations displaying a Ki value of 1270 mM on the substrate p-nitrophenyl-α-l-arabinofuranoside (pNPA) [d-glucose varies considerably among β-xylosidases.Apart from i 44 mM) , B. pumit 50 mM) , T. harzat 5 mM) , and theat 5 mM) . On the ra fusca , A. nigeer 90196 , A. oryz. oryzae , and N. ssa ST A  at conced-xylose, l-arabinose, and d-glucose, also other monosaccharides have been reported to inhibit β-xylosidases, including -d-arabinose [d--erythrose [d-fructose [d-galactose [d-ribose, and -l-xylose [Finally, besides rabinose ,113, -d-rythrose , -d-frucfructose ,138, d-galactose ,113,120,l-xylose ,113. Agad-xylose acts as a competitive inhibitor when using p-nitrophenyl-β-d-xyloside (pNPX) as substrate [N. crassa ST A showed non-competitive inhibition by this sugar [d-xylose inhibition of the β-xylosidase from S. ruminantium GA192, which also displays α-l-arabinofuranosidase activity, followed non-competitive kinetics for its β-xylosidase activity on pNPX as substrate, but competitive kinetics for its α-l-arabinofuranosidase activity on pNPA as substrate [A. carbonarius KLU-93 β-xylosidase, for which d-xylose was a competitive inhibitor for the conversion of both substrates [l-arabinose acts as a competitive inhibitor for the hydrolysis of pNPA by the RS223-BX β-xylosidase [S. ruminantium GA192 β-xylosidase hydrolyzing pNPX or pNPA [d-arabinose, d-glucose, and -d-ribose are competitive inhibitors of S. ruminantium GA192 β-xylosidase, whereas -d--erythrose and -l-xylose are non-competitive [S. ruminantium GA192 β-xylosidase, d-glucose inhibition of RS223-BX was competitive when using pNPA as substrate [d-fructose for the activity of A. oryzae β-xylosidase on pNPX [d-xylose is observed. Other monosaccharides can display both competitive and non-competitive inhibition, and in one case, -d-fructose, un-competitive inhibition takes place. The exact mechanism and the structural details of non-competitive and un-competitive inhibition remain unknown.The inhibition of β-xylosidases by monosaccharides follows competitive, non-competitive, or un-competitive inhibition kinetics. For most β-xylosidases, ubstrate ,120,134.is sugar . Furtherubstrate . This dibstrates . Similar or pNPA . With repetitive . As also on pNPX . Thus, cS. ruminantium GA192 β-xylosidase, the monosaccharides -d-arabinose, l-arabinose, -d--erythrose, and -d-ribose can bind in both subsites –1 and +1, but d-xylose and -l-xylose bind only in subsite –1 [d-glucose binds in one subsite only. Its binding position was speculated to be in subsite –1, with partial occupancy of subsite +1, because glucose is too large to fit in subsite –1 only. Alternatively, it could bind in such a way that it excludes binding of a second sugar [As discussed above, the active site pockets of β-xylosidases contain two substrate-binding subsites, subsites –1 and +1, on either side of the scissile bond. In the active site of bsite –1 . Similarnd sugar .G. thermoleovorans IT-08 β-xylosidase shows rather dissimilar binding properties for l-arabinose and d-xylose compared to S. ruminantium GA192 β-xylosidase. Crystal structures of G. thermoleovorans IT-08 β-xylosidase revealed that l-arabinose binds exclusively in subsite –1, while d-xylose prefers subsite +1 [In contrast, bsite +1 . Thus, dd-xylose, l-arabinose, and d-glucose, may reach concentrations that are high enough to inhibit β-xylosidase activity [S. ruminantium GA192 β-xylosidase, resulting in a variant with a 3-fold lower affinity for d-xylose and a 2-fold lower affinity for d-glucose [d-xylose, but not that of subsite -1, where catalysis occurs, suggesting a strategy for reducing inhibition by monosaccharides by mutating residues of subsite +1 [G. thermoleovorans IT-08 β-xylosidase, d-xylose binds in subsite +1 interacting, among others, with Asp-198, which is not present in most other β-xylosidases [G. thermoleovorans IT-08 β-xylosidase variants with lower affinity for d-xylose.During saccharification of cellulosic biomass, monosaccharides such as activity ,27. Ther-glucose . Subjectosidases . Therefo8 TIM-barrel structure, those from families 43, 52 and 116, 54, and 120 have as their main structural feature a 5-bladed β-propeller, a (α/α)6-barrel, a β-sandwich, and a right-handed parallel β-helix, respectively. Likewise, the catalytic mechanism of β-xylosidases is also varied. Although generally β-xylosidases hydrolyze their substrates with retention of the substrate’s anomeric carbon configuration, the enzymes from GH43 invert the anomeric configuration. However, despite their diversity in overall folds, all structurally characterized β-xylosidases have a typical pocket-shaped active site with two carbohydrate-binding subsites, which bind a xylobiosyl moiety of the xylooligosaccharide substrates. Unfortunately, the active sites of many β-xylosidases also possess a relatively high affinity for monosaccharides, such as xylose, arabinose, and erythrose, that competitively inhibit the enzymes’ activity. Moreover, some β-xylosidases are also non-competitively or un-competitively inhibited by monosaccharides. Such product inhibition limits the application of β-xylosidases in xylan saccharification, since high monosaccharide concentrations may easily be generated during the process. Therefore, β-xylosidases with low monosaccharide affinity are highly desirable for various applications. A random mutagenesis approach has already shown success in reducing the affinity of a β-xylosidase for d-xylose. Furthermore, with 3D structures available for a variety of β-xylosidases, rational site-directed mutagenesis may also be a good approach to render the enzymes less prone to product inhibition.β-Xylosidases are highly diverse in their amino acid sequence. Currently, enzymes with β-xylosidase activity can be found in 11 different glycoside hydrolase families in the CAZy database, i.e., in GH families 1, 3, 5, 30, 39, 43, 51, 52, 54, 116, and 120. They fold into several distinct three-dimensional structures. While the enzymes from GH families 1, 3, 5, 30, 39, and 51 all show a (β/α)