Lipoxygenases (LOXs) catalyze the (per) oxidation of fatty acids that serve as important mediators for cell signaling and inflammation. These reactions are initiated by a C-H activation step that is allosterically regulated in plant and animal enzymes. LOXs from higher eukaryotes are equipped with an N-terminal PLAT  domain that has been implicated to bind to small molecule allosteric effectors, which in turn modulate substrate specificity and the rate-limiting steps of catalysis. Herein, the kinetic and structural evidence that describes the allosteric regulation of plant and animal lipoxygenase chemistry by fatty acids and their derivatives are summarized. Lipoxygenases (LOXs) are widely represented in plants, animals, fungi, and select bacteria . These eS-hydroperoxyoctadecadienoic acid (13S-HpOD) in both a regiospecific and stereospecific manner ,382+-dep2+ ions bound in the PLAT domain  step  59]. Th. Th59]. 2+, Mn2+, Co2+) renders the enzyme virtually inactive . T. TDkcat,state, ‡ B; in thiDkcat/KM drops . T. TDkcat/cleavage ,99. Simicleavage ,97, suggDkcat/KM is significantly smaller than Dkcat for the reaction of LA with SLO  [At 5 °C, addition of OS was found to have a modest decrease (~15%) on otium LA . Importantration A. A diss0.05 μM) .Dkcat/KM ≅ Dkcat) supports the hypothesis that substrate binding is no longer partial rate-limiting in the presence of OS at lower than room temperatures. Based on the description of the Dkcat/KM from the microscopic rate constant analysis, the off rate of substrate binding was found to be five times faster than its on rate [The observation of matching isotope effects for the first- and second-order rate constants AA/(kcat/KM)LA = 1.8 [50 [While AA is not a physiological substrate in plants, SLO-1 exhibits a kinetic preference for AA over LA based on the second-order rate constant ratio,  is favored at physiologically relevant pH and AA is favored at slightly elevated pH values  at low allosteric effector concentrations [Human LOXs can also react with their own oxylipin products, producing multiple oxygenated molecules ,106,107.maresins ,109,110.epoxyDHA . Both h1S-HODE to h15-LOX-2 reactions implicate a specific binding site. To begin to resolve the allosteric site without co-crystal structures available, the PLAT domain was biochemically removed, generating a stable, though slightly less active , “No PLAT” h15-LOX-2 variant [kcat/KM)AA/(kcat/KM)GLA = 1.3, even in the absence of the effector  and G. graminis (GgLOX); structures of both enzymes have been solved [Two fungal LOXs have emerged as model enzymes, including that from n solved ,114. Besn solved ,115.Dkcat/KM for the reaction of dideuterated  LA were equivalent to Dkcat values (60–70) within the accessible temperature range explored (10–30 °C). This kinetic behavior is exclusive to fungal LOXs and there is no evidence for allosteric regulation of MoLOX by fatty acids. Extensive kinetic analysis determined that first- and second-order rate constants (and their isotope effects) for the MoLOX reaction with LA are completely independent of pH . At pH 9kcat/KM)AA/(kcat/KM)LA, for h15-LOX-2 was found to be sensitive to pH, increasing from 1.4 ± 0.3 at pH 6.0 to 4.5 ± 0.5, at pH 10 [a of 7.7 ± 0.1. Addition of 13S-HODE (d ≈ 5 nM) [S-HODE upon protonation of the histidine. Using a competitive HPLC assay , the subat pH 10 . These d13S-HODE , an allo ≈ 5 nM) . At pH 8S-HODE. Of the eight solvent exposed histidine residues in h15-LOX-2, only three, positioned in the catalytic domain, lined the site in which 13S-HODE could be docked: H376, H394, and H627 , that alters the substrate regio-specificity of the enzyme . The AKBHydrogen-deuterium exchange coupled with mass spectrometry (HDX-MS) was employed to study the allosteric network in SLO-1 stemming from the interaction with OS . HDX-MS d = 0.6 ± 0.3 μM) [Peptic digests of SLO-1 resulted in approximately 300 reproducible peptides, providing 94% coverage of the entire primary sequence . EX-2 co 0.3 μM) . Examina 0.3 μM) . The behThe HDX-MS results provide a framework for the mechanistic implications of OS inferred from kinetic analysis cf. . The emeHDX experiments were also conducted at 20, 25, 30, and 40 °C . At room2+ binding at the PLAT domain and membrane association [R-LOX crystal structure, similar to that of h5-LOX [2+-dependent membrane binding (at the PLAT domain) is anticipated to trigger a conformational change in helix 2  to permit substrate access [R-LOX structure (PDB: 3O8Y), a conserved Trp was identified in the PLAT domain that lies at the interface of the catalytic domain  spatially adjacent to the tryptophan in the PLAT domain and positioned at the end of helix 2 in the catalytic domain. It has been proposed that this cation-π interaction may be essential to the long-range allosteric control of 11R- and h5-LOX reactivity by calcium [If the allosteric regulation of LOX is influenced by the PLAT domain, how are the dynamics communicated to the catalytic domain? Coral 11R-LOX is one of the most highly regulated lipoxygenases. Activity is completely contingent upon Caociation . Furtherf h5-LOX , shows ae access . From thc domain . For bot calcium ,125. Non calcium . How theR-LOX [2+ [Sequence alignment of several plant and animal LOXs indicated complete conservation of a tryptophan at the homologous position of W107 in 11R-LOX . The hom-LOX [2+ , yet an -LOX [2+ , this ca-LOX [2+ . The phy2+ and membrane association does not appear to be a significant determinant for h15-LOX-2 reactivity. Therefore, this conserved cation-π interaction could serve as a general allosteric control “switch” to regulate LOX catalysis. The access to more crystal structures coupled with functional analysis of strategic site-directed mutants using comparative kinetic isotope effects on the first- and second-order rate constants could help to resolve these open queries.Contrarily in the crystal structure of h15-LOX-2, the cationic residue, K175, is in a “flipped out” conformation and an aliphatic residue, L172, is positioned within van der Waals distance to W109 . This h1In higher eukaryotes, the initial and often rate-limiting C-H activation step of fatty acid oxidation by LOXs is allosterically regulated by fatty acids and their derivatives. These fatty acid derivatives may also influence substrate selectivity and/or product distribution. The accumulating data from kinetic analysis of substrate selectivity and comparative isotope effects, X-ray crystallography and hydrogen-deuterium exchange of the model plant (SLO-1) and human (h15-LOX-1/2) enzymes support the hypothesis that the allosteric effector binding originates at or near the N-terminal, regulatory PLAT domain. This allosteric interaction elicits altered conformations and/or enhanced structural plasticity of helix 2 in the catalytic domain via a conserved cation-π structural feature, with helix 2 expected to serve as a gatekeeper for substrate binding. As summarized herein, this combinatorial application of biophysical tools is an excellent approach to resolve the allosteric regulation of lipoxygenase C-H activation chemistry and could provide guidelines for the development of effective allosteric molecules to be leveraged as LOX isoform-selective drugs, which can tune substrate selectivity and/or product distribution of LOX to quell the inflammatory process.


Chondrostereum purpureum (Pers. Ex Fr.) Pouzar has been shown to be effective. Yet, heavy rain during spreading of this fungal inoculum on freshly cut stumps may affect the efficacy of the treatment, i.e., stump mortality during the following years. Thus, we performed an experiment where freshly cut birch stump surfaces (Betula pendula Roth and Betula pubescens Ehrh.) were treated with fungal inoculum under heavy irrigation and without it. Furthermore, two different adjuvants which aimed to fix the fungal inoculum to freshly cut stumps during irrigation and to protect against solar radiation were tested. Our results revealed that the artificial rainstorm treatment caused a delay in the efficacy of C. purpureum, but after three growing seasons, there was no significant difference in the mortality of birch stumps treated under irrigation or without it . Adjuvants did not improve the efficacy in stumps treated under irrigation nor in those treated without irrigation.In forest regeneration areas, alongside roads and railways, under electric power lines and above gas pipe lines, there is a need for regular sprout control. A biocontrol method against broadleaved sprouting with formulations including the decay fungus • Heavy rain delayed the sprout control efficacy of a fungus Chondrostereum purpureum.• Final efficacy of formulations was the same in wet and dry conditions.• No additional adjuvants are needed to improve formulations. In Finland, ca. 58 million euros are used annually for young stand management operations in forest regeneration areas  Pouzar is common  on freshly cut stumps . We also evaluated whether fixing the fungal inoculum on cut stump surfaces by adjuvants would increase the treatment efficacy since rain is one of the most detrimental issues for herbicide performance  H. Karst.) in Loviisa, Southern Finland . The first area was Myrtillus type forest  in 2013, and spruce seedlings 1–2 years old were planted in 2014. By 2017, a dense cover of naturally regenerated birch (Betula pendula Roth and Betula pubescens Ehrh.) saplings had regrown at both sites.The experiment was performed in two medium-fertile forest regeneration areas of Norway spruce (Cajander  on coarsB. pendula and B. pubescens) saplings were included in the study  per g, according to Gonzáles , (3) similar C. purpureum treatment with an adjuvant Polyhydra , and (4) the control . All inoculums were spread on potato dextrose agar Petri plates before and after the treatments to verify their viability .Altogether, 640 birch  and other treatments were performed in the same order as presented above. The same order of treatments was followed also in the other four blocks without artificial rain. A buffer zone of at least 4 m was used around the sample plots, and all of the saplings were cut between them. Stumps in treatments without artificial rain were covered by a plastic shelter that was supported by a wooden framework (forming a triangular tent open at both ends) for 5 days to ensure that natural rains did not affect the results. During these 5 days, i.e., 7th to 12th of June 2017, the mean maximum temperature was 19.8 °C  with a mean daily temperature of 16.2 °C . Furthermore, in 2017, 2018, and 2019, we counted the number of living stump sprouts, measured the height of the tallest stump sprout per stump (cm), and counted the number of fruiting bodies of C. purpureum per stump . Traces of hare or moose browsing were also recorded .Saplings were investigated in September 2017, 2018, and 2019. In 2017, one growing season after the treatments (3 months), we measured the diameter (mm) and the height of the stumps , and counted the number of other saplings and stumps around an investigated stump  on October 16–17 in 2017 to investigate whether C. purpureum strain R5 in treated stumps. Master mix, 25 μl, was prepared as follows: 20.4 μl DDW, 2.5 μl Dream Taq™ buffer , 0.5 μl dNTP (10 μM), 0.5 μl forward primer (25 μM), 0.5 μl reverse primer (25 μM), 0.1 μl DreamTaq DNA polymerase , and 0.5 μl of DNA sample. Four pairs of primers with increased specificity to C. purpureum strain R5 were used in PCR analyses  in order to verify the presence of C. purpureum strain R5 from them. In case a hypha of C. purpureum was growing out of the chip, a single hyphal tip was moved onto Potato Dextrose agar . After hyphal growth, a piece from the mycelial edge was moved onto PDA agar covered with cellophane agar. After the hypha was grown on a cellophane PDA, a piece of ca. 2 mm2 was moved to a tube for DNA extraction  was used in 20 μl PCR reactions as follows: 5 μl Master Mix , 14.5 μl DDW, 0.2 μl forward primer (25 μM), 0.2 μl reverse primer (25 μM), and 0.1 μl DNA sample. PCR conditions were modified from Hamberg et al.  95 lme4, using function glmer, and binomial distribution with logit link  in library nlme using function lme after the response was log transformed  C. purpureum with an adjuvant Silwet Gold, (3) C. purpureum with an adjuvant Polyhydra, and (4) control, were analyzed separately.Effects of the treatment conditions  on the mortality of birch stumps  were investigated with generalized linear mixed models (GLMMs) in the statistical program R, in library Each model included explanatory variables as follows: (1) interaction between condition  and year , (2) diameter of a stump (mm), (3) height of a stump (cm), (4) the number of other stumps and saplings around an investigated stump, and (5) a factor with two levels indicating whether a wood sample had been taken from a stump . Furthermore, a factor indicating whether hare or moose browsing was visible  was added to the maximum height of stump sprout models. Explanatory variables 2–4 (and browsing for the maximum height models) were included in the models because these may have an effect on the mortality, on the number of, or on the maximum height of stump sprouts  stumps but never from the control stumps , verifying Koch’s postulates. Almost all fungal strains grown from wooden samples were proven to be C. purpureum strain R5 used for inoculations, showing that the fungus had successfully penetrated the stumps, both cut and treated  than in dry condition  so that by the end of the experiment (2019) there was no statistically significant difference between treatments conducted under artificial rain and in dry conditions. In the C. purpureum treatments with adjuvants (both Silwet Gold and Polyhydra) performed under artificial rain, the mortality increased from 4–6% up to 58–67% in 2017–2019 and stayed at lower levels than in the treatments performed in dry weather , stump mortality increased from the first inventory in 2017 to the last in 2019 similarly both in artificial rain (from 3 to 19%) and dry conditions .One growing season after the treatments , mortality of birch stumps was lower in stumps treated with  p ≤ 0.00, Table 1p ≥ 0.068), except in the C. purpureum treatment with the adjuvant Silwet Gold .In all C. purpureum treatments . However, in the control treatment (cutting only), living larger stumps yielded more sprouts (p < 0.001), and in the C. purpureum treatment with the adjuvant Silwet Gold and the control, they yielded taller stump sprouts  than smaller stumps. In the control, stump mortality was lower (p = 0.003) and the number of stump sprouts per stump was higher (p = 0.042) in taller than shorter stumps. In the C. purpureum treatment with the adjuvant Polyhydra, the number of stump sprouts decreased with increasing number of other saplings and stumps around an investigated stump (p = 0.050). Drilling did not affect stump mortality. In the C. purpureum treatment with the adjuvant Silwet Gold, the maximum height of stump sprouts was higher in drilled than in non-drilled stumps (p = 0.024) but in the C. purpureum treatment with the adjuvant Polyhydra, the maximum height of stump sprouts was lower in drilled than in non-drilled stumps (p = 0.035). Browsing did not affect the maximum height of birch stump sprouts in different treatments.Larger stumps (in terms of stump diameter) died faster than smaller stumps in all C. purpureum treatments, fruiting bodies were most frequent after the second growing season (Table In on Table . It seemC. purpureum treatment against sprouting of birch (B. pendula and B. pubescens). However, in the pure C. purpureum treatment, this effect was only temporary since three growing seasons after the treatment there was no significant difference in stump mortalities when stumps were treated under heavy irrigation or without it. This agrees with previous studies, where natural (slight) rain has caused only temporal  saplings  Penz & Sacc. against sicklepod, Senna obtusifolia (L.) Irwin & Barnaby, which is a serious weed pest in the southeastern US (Boyette Unfortunately, adjuvants which aimed to help the spray adhere better seemed inefficient, since mortalities in the treatments under artificial rain remained at a lower level than in the treatments in dry conditions. This was disappointing as in fruit plants, the use of adjuvants has been proven to improve cell adherence and persistence of biological control agents on plant tissues (Calvo-Garrido et al.  Boyette . The adjC. purpureum treatments, but the general feature was that the number of stump sprouts decreased whereas the maximum height of stump sprouts increased from year 2017 to 2019 (see also Laine et al. The number and maximum height of stump sprouts did not differ between the In this study, stump mortality was higher in stumps with larger diameters than in smaller stumps (similarly as in Hamberg et al. C. purpureum treatments since C. purpureum hyphae grew only from two control stumps but none of the hyphae belonged to the applied fungal strain R5. Furthermore, ether is relatively easy to use in the field since it evaporates quickly so that there is no need for long waiting times between different samples. Yet, more research is needed to verify that ether can be used in sampling of environmental DNA, where cross-contamination may occur even from dead tissues, although in this, study direct DNA extraction from wood chips yielded highly similar results as the more time-consuming cultivation of fungal hyphae from wood chips followed by isolations of single hyphae and DNA extraction from a mycelial culture.In our experiment, diethyl ether was used to sterilize drill bits and other equipment between the samplings. Based on our observations and results, it seemed that this method is relatively good: no cross-contamination was observed between the control and the


Khazar variety) and its flour. Determination coefficients of calibration models to flour samples of AC, PC, BDV, and SBV generated by the partial least‐squares (PLS) regression were obtained as R2cal ≥ .85 and R2pre ≥ .80. Root mean square error of calibration (RMSEC) was calculated as 0.393, 0.07, 2.55, and 1.33, respectively. Similarly to grain samples, were obtained as R2cal ≥ .88 and R2pre ≥ .71 for calibration and prediction. RMSEC was measured as 0.303, 0.27, 2.59, and 3.11, respectively. NIRS has the potential to be used as a quick technique for predicting the quality attributes of kernel specimens.The measurement of different quality properties requires particular tools and chemical materials, most of which are time‐using. The present research was accomplished to survey the possibility of using NIRS (870–2450 nm) to predict the amylose content (AC), protein content (PC), breakdown (BDV), and setback viscosity (SBV) of white rice  was calculated as 0.393, 0.07, 2.55, and 1.33, respectively, and to grain samples, was obtained as R2cal ≥ .88 and R2pre ≥ .71 for calibration and prediction. RMSEC was measured as 0.303, 0.27, 2.59, and 3.11, respectively.Determination coefficients of calibration models to flour samples of AC, PC, BDV, and SBV generated by the PLS regression were obtained as  NIRS has been reported to be used for determining the quality attributes and assessing its quality.The commercial value of rice is largely determined by the quality characteristics of the milled rice . Bao et al. (R2 < .92).Chen et al.  studied o et al.  studied Khazar variety).The quality indices of white rice are useful for the consumers in terms of marketing of this product. Quality analysis of rice, which is usually used in food industry, is time‐consuming and highly expensive due to requiring special testing instruments. This study was conducted to use NIR spectroscopy a quick and nondestructive techniques to determine the quality of rice, through focusing on the chemical and physicochemical properties. As mentioned earlier, the present study was conducted to expand NIRS calibration models for predicting the AC, PC, BDV, and SBV of rice and its flour specimens on the common Iranian rice  into a laboratory oven with a temperature of 130°C for 24 hr  detector, and inflammatory lamps (incandescent lamps) were used as a light source. The samples were subjected to the spectrometer by contacting the inner part of a cylindrical probe with the lamps̓ radiation, so that the light was reflected through the probe by an optical fiber. Reflected light from the surface of the samples was displayed by the Ocean Optics Spectra Suite software in different measurement modes. Reflectance (R) readings at 6.5‐nm increments  in the Laboratory of Optic Physics located in the Research Institute of Color Science and Technology. A rotating cup was used for all of samples, that is, rice grains and its flour. Spectrometer is equipped with the InGaA . To avoid enhancing the noise, spectra were first smoothed, which was done by using the Savitzky–Golay (SG) algorithm  were recognized by the principal component analysis (PCA). The outliers limited to the excess of outliers were removed. The validation and calibration processes accomplished for rice kernel and its flour spectrums then are called the rice and its flour set, respectively. All sample spectrums were separated into two subsets including calibration and external validation Table . PreprocR2cal) quantified between measured values from each observation and predicted values from cross‐validation, as well as calculating the RMSECV. Results of prediction were appraised by computing the RMSEC, determination coefficient (R2pre) between reference and predicted amounts, and residual predictive deviation (RPD)  to RMSECV) was used to confirm the calibration model precision. The model with the highest RPD value is the admissible model for analysis of quantitative  and via coefficient of determination . Regarding the RPD, PLS models furnished amounts greater than 2.2, in particular for AC, PC, BDV, and SBV . Among them, higher RPD values (4.97 and 3.27) were mostly obtained for SBV of the rice and its flour samples, respectively, which are consistent with the previous reports by Siriphollakul et al. (Khazar variety) skin is not transparent. Thus, the rice grain is not much different from its flour. R2pre for AC using white rice was poorer than those using their corresponding flour samples, while R2pre for BDV using flour (.86) was poorer than those using their corresponding white rice (.89) samples. Starch quality indexes, such as AC, PC, and SBV, could be predicted with enough precision using NIRS based on flour spectrum, whereas only BDV could be predicted with enough precision based on grain spectrum.As shown in Table l et al. . NIRS cal et al. . For AC,l et al. . In thisR2 of .95% and .65%, respectively.In this research, the absorption peaks at wavelengths of 1,566 and 2,100 can be attributed to the SBV. These findings are in line with the findings reported by Osborne  and SiriFigure 4R2 ≥ .80 and R2 ≥ .71, respectively. The results of the present research demonstrated that the use of NIRS is appropriate for predicting the quality attributes of rice and its flour.Considering that the conventional methods of rice's quality assessment usually used in food industry laboratories are time‐consuming and expensive due to requiring special testing instruments, nondestructive measurement of chemical and physicochemical properties of rice flour and grain samples is possible using the NIRS in different varieties with considerable accuracy. The results of this study showed that the suggested technique had satisfactory performance in predicting the AC, PC, BDV, and SBV, while the validity of the calibration models was statistically tested. Prediction accuracy of quality parameters for rice and its flour samples was obtained as Data S1Click here for additional data file.Data S2Click here for additional data file.


Nephrops norvegicus), based directly on burrow counting using the survey assumption of “one animal = one burrow”. However, stock size may be uncertain depending on true rates of burrow occupation. For the first time, 3055 video transects carried out in several Functional Units (FUs) around Ireland were used to investigate this uncertainty. This paper deals with the discrimination of burrow emergence and door-keeping diel behaviour in Nephrops norvegicus, which is one of the most commercially important fisheries in Europe. Comparisons of burrow densities with densities of visible animals engaged in door-keeping  behaviour and animals in full emergence, were analysed at time windows of expected maximum population emergence. Timing of maximum emergence was determined using wave-form analysis and GAM modelling. The results showed an average level of 1 visible Nephrops individual per 10 burrow systems, depending on sampling time and depth. This calls into question the current burrow occupancy assumption which may not hold true in all FUs. This is discussed in relation to limitations of sampling methodologies and new autonomous robotic technological solutions for monitoring.Underwater Television (UWTV) surveys provide fishery-independent stock size estimations of the Norway lobster ( Nephrops norvegicus (L.), is one of the most commercially important fisheries in Ireland and also Europe1. The 2019 EU Total Allowable Catch (TAC) for Nephrops2 for the north east Atlantic Functional Units (FU) was close to 44,000 tonnes, and valued at approximately 360 million EUR in 20163. Traditional fishery-dependent sampling methods such as commercial trawling provide indirect biomass estimates of exploited stocks, by means of abundance indices derived from surface density data .The Norway lobster, 7 and are not available for trawl capture when hiding in the substrate9. The burrow emergence rhythmicity of populations causes marked fluctuations in catch rates over the 24-h10. Peaks in trawl Catch Per Unit Effort (CPUE) shift in timing with increasing fishing depth13: from full night to dusk- dawn transitions, going from upper to middle-lower shelves, to be finally fully diurnal (i.e. at midday) on upper and middle slopes. This indicates that the species sets its timing of burrow emergence upon a maximum illumination threshold that varies on the depth axis, based on the differential penetration of light as the sun progresses through its diurnal trajectory14.However, animals construct and inhabit burrow systems used for shelter and for territorial control15 : Full emergence, full retraction and an intermediate period in which individuals wait at the burrow entrance (i.e. door-keeping16). To date, the proportion of animals not emerging from their burrows on a daily basis is still largely undetermined, although acoustic tagging of individuals of a philetically closely related species has offered some insight17. In addition to environmental light, other ecological reasons seem to modulate the predisposition of individuals toward emergence or retraction. For example, crustaceans are at intermediate levels of the marine food webs and their feeding activity (coinciding with burrow emergence in the case of Nephrops) is the product of a mortality risk ratio between hunger state and chances to meet visual predators19.The diel rhythm of burrow emergence is more complex than previously thought and it can be subdivided in three different phases21. Those video-based surveys are carried out in several European countries and are coordinated through the International Council for the Exploitation of the Sea Working Group on Nephrops Surveys (WGNEPS)23. This more direct (i.e. image-based) method of assessment counts burrow systems, based on their characteristic structural features 27. This method is independent from the time of the day and season. The burrow system counts can be used as a relative or absolute index for determination of Nephrops’ stock status and together with catch data can provide a Harvest Rate 29.Alternative fishery-independent assessment methods as Underwater Television (UWTV) surveys using towed camera-sledges, have been developed to estimate stock abundanceNephrops Functional Unit (FU) have been documented and the cumulative correction factor considers edge effect, burrow identification, burrow detection and burrow occupancy29.To use UWTV burrow abundance to calculate catch and landings derived from an acceptable Harvest Rate (HR = catch in numbers/UWTV abundance) it is necessary to adjust using agreed correction factor which takes into account the bias associated with UWTV surveys. The key bias contributions for individual 30, which is the current assumption for Nephrops stock assessment29. The burrow system acts as the center of a strong territorial rhythmic behavior32 and two adult lobsters are rarely found in the same shelter33. No spatial segregation occurs between juveniles and adults34 and the majority of juveniles build adult-juvenile burrow complexes, which become separated as juveniles grow, and each individual develops its own section35. However, burrows systems could also be inhabited by other benthic fish and crustacean species or may remain empty and intact for an unknown period of time after the animals’ death . These factors still pose uncertainties about the true numbers of animals occupying video-counted burrow systems, representing a problem when using UWTV data in stock assessment models30.Given the strong territorial behavior of the species, burrow counting seems to be a good proxy for local population abundance, assuming the condition “one burrow system, one animal”11. The sum of animals engaged in both behaviours could then be compared with counted burrows at phases of maximum population emergence. Unfortunately, temporally scheduled UWTV operations have never been systematically performed and the analysis of rhythmic fluctuations in video-observed animals performing full emergence and door-keeping is not yet available.To improve knowledge of the stock assessment assumption “one burrow system, one animal” a precise temporal description of burrow emergence rhythmicity could be provided by temporally distributed transects within UWTV surveys, similar to that explored with trawl dataHere, we used UWTV survey data reporting densities of full emergence and door-keeping animals and burrow systems from more than three thousand video transects, conducted in the past decade around Ireland, to temporally define both behaviours and their reciprocal relationship over the 24-h. Results on estimated densities of animals engaged in full emergence and door-keeping were then compared with burrow system density estimates, to provide a comparison to the stock assessment assumption “1 burrow system, one animal”.29. For FU 16, 10 min were counted for all years, due to the lower densities observed and the relative scale of variation between minutes was higher than typically found in other areas. All considered data were collected in spring–summer surveys (from May to September) . The final perimeter has been established using an adaptive approach where stations were located beyond the previously known perimeter of the ground, until the burrow system densities were zero or very close to zero. Once established, the survey area was not changed between years.Sampling design followed either a randomised isometric grid with a station spacing dependent on the individual survey area or a random stratified design37 Table . The ini29.At each station, the UWTV sledge was deployed and once stable on the seabed, a 10 min’ tow was recorded onto DVD. The field of view of the camera (Kongsberg OE14-366) at the bottom of the screen with the sledge flat on the seabed (i.e. no sinking), was validated at 75 cm by two parallel spot lasers. Vessel position  and sledge position  were recorded every 2 to 5 s. USBL navigational data were used to calculate the video transect distance over the ground, as required for surface density estimates (see next section). The navigational data were quality controlled using an “R” script developed by the Marine Institute40. All scientists were trained prior to counting following the ICES recommendations29, in such a way the counters can be quite consistent in the recognition of a N. norvegicus burrow as compared to other species. Final burrow densities were based on an average from the two independent counts after passing quality control processes such as screening for outliers and use of Lin’s concordance CCC to evaluate counter performance20. The quality assured burrow density values were then corrected for stock specific survey cumulative bias as described in ICES29.The same footage was viewed and counted by two scientists independently of each other and burrows were identified based on key structural features from an established set of classification keys15. As with burrow systems, densities of animals in door-keeping and full emergence were obtained for each transect by dividing respective counts by the video-swept surface.Video-counts of door-keeping animals, defined as those with partial cephalothorax or claws were visible across the burrow mouth entrance, and animals in full emergence which were entirely visible were available42 nominally as: 15–50, 51–100, 101–160 and 340–570 m. No data were available for 161–339 m depth. Data for the bathymetric range 340–570 m were only available in FU 16 for the years 2012 and 2013, and this inclusion was necessary to characterize behavioural rhythms in the deepest range for comparison with shallow shelf observations (as previously done with trawling11).Each density estimate for burrow systems and animals in both behavioural categories was associated to a time stamp, represented by the time at mid transect length. All density data were grouped per depth ranges within the upper and lower shelf, based on the previous knowledge from trawl catch patternsNephrops burrow emergence is an adaptive life trait under strong selection which can be temporally described as different on upper and lower shelves as well as slopes11. Moreover, the burrow emergence rhythm manifests itself similarly in all its geographic range, with coincident nocturnal, crepuscular or diurnal timings according to the depth light driven peaks not blurred by the tidal status43. This behaviour is constant through years, subjected only to a seasonal reproduction and growth pattern , the effects of which were eliminated here by selecting only summer data . Waveforms plots describe the phase of a rhythm as an averaged peak into a time series of density data for both behavioural categories. Waveform computing procedure was as follows47). MESOR is a re-average of all waveform values to be represented onto waveform plots as a horizontal threshold line. All waveform values above the threshold delimit the duration of the peak (i.e. activity peak duration46). Waveforms for full emergence and door-keeping density estimates were plotted together to highlight their temporal relationship.The resulting set of averaged density estimates were then represented over the 24-h with their standard deviations, plus the Midline Estimated Statistic of a Rhythm , from zero to 23 h, was the covariate used to characterize behavioural rhythms. The day-length and the average location of the transects (latitude and longitude) were adopted as spatiotemporal covariates in the full model, following the form:E(NEP) is the Expected value of Nephrops full emergence or door-keeping, conditionally distributed according to the Gamma distribution family. g is the log link function, s is the smoothing function with the term bs = "cc" specifying the 24 h’ knot based (k = 24) cyclic cubic regression spline. The day-length was estimated as the difference between the sunrise and sunset times. te is the tensor smooth function for the interaction among transect locations (i.e. latitude and longitude) accounting for spatial dependence on diel activity rhythms affecting NEP. Alternatively to the te effect, the potential effect of the station locations per FU, te, and the interaction between the station locations with the year survey ti, were also tested in the models. The ti tensor product spline tested the significance of the space-year interaction effect. The 2-dimensional space and the 1-dimensional year factor were specified with the argument d = c.Then, we fitted Generalised Additive Models (GAM) onto full emergence and door-keeping data for spring–summer at the established depth ranges (see above), to achieve statistic formalization of observed emergence patterns beyond data variability Appendix . The pac50. The range of AIC values of models within depth ranges was generally narrow and not assumed to be critical for the final model choice. The selected final model followed the form (see Appendix f(Cov) represented the term te for the emergence and door keeping behaviours in the upper depth range (15–50 m). In the case of the emergence behaviour at the depth ranges between 51 and 160 m and door keeping at 101-160 m, f(Cov) represented the term s(Daylength). Because of the indistinguishable effect of the terms te or s(Daylength), on the NEP behavioural pattern , and other significant covariates substantially improving the model variance were selected as the final models  in relation to burrow counting, hence obtaining a best estimate of burrow occupancy, a temporally integrated chart of all waveform and GAM results phasesNephrops full emergence varied from nocturnal toward midday hours with increasing depth of sampling. This pattern is particularly evident, when comparing the two extremes of the sampling depth range: upper shelf (15–50 m depth) with two peaks  versus middle slope (340–570 m depth) with single peak . At intermediate sampling depths of the lower shelf (51–100 m depth) and shelf-break (101–160 m depth), a less clear crepuscular (dusk and dawn oriented) pattern was reported, with less distinct peaks merging toward daytime. In contrast, door-keeping behaviour had some defined pattern with crepuscular peaks coinciding with full emergence only on the upper shelf (15–50 m depth) and the shelf-break (101–160 m depth). No defined rhythms were discernible at the other depth zones.Waveform analysis Fig.  indicateThe statistical model results by GAM Fig. , revealeThe temporal comparison between the waveform analysis see Fig.  and GAM The same comparison for door-keeping behaviour see Fig.  showed a2, respectively, in the FU 15, West Irish Sea), coinciding with the maximum number of burrows per area (0.908 burrows/m2) , and one order of magnitude lower on the slope (340–570 m depth) with densities between 0.0064 and 0.009 Ind./m2.The timing of the maximum number of animals in full emergence varied between depth strata Fig. , Table 32 over the continental shelf, and from 0,012 to 0,013 Ind./m2 over the slope. The fraction of door-keeping animals  is slightly lower on the continental shelf than on the slope   at peak timing as a proxy of total population densities Fig. , the folNephrops in terms of burrow emergence and door-keeping, based on observations in more than three thousand UWTV transects. Populations emergence patterns varied their timing from the shelf to the slope with a timing shift, which is consistent with previous observations based on trawl catch temporal rates . In contrast, the description of the temporal variation in door-keeping behaviour is an entirely new finding for Nephrops, since individuals at the entrance of their tunnel systems are unlikely to be catchable in trawling operations53. Here, we provide evidence of arrhythmic fluctuations in counts of animals expressing this behaviour, with relevant counts sparse over the whole 24-h cycle. This points out that the arrhythmia of observations of door-keeping animals could be due to: a behaviour which is in fact arrhythmic in some individual, or that animals retract into the burrows because they perceive approaching sleds. An unknown part of the population may therefore avoid haul capture by a quick withdrawal of individuals into their burrows when trawls approach54. Consequently, the number of counted “door-keeping animals” could be dependent upon sledge towing speed , as well as the presence of the sledge light system and overall noise. In any case, for trawl gear to efficiently catch Norway lobsters, they have to be fully outside of the burrows.The present work describes for the first time the diel behavioural rhythms of 20. Present results suggest a visible individuals’ ratio of around 1 visible Nephrops to 10 counted burrows. This result is a general estimate considering all the FUs, with results closer to the 1:1 assumption in some areas (i.e. FU 15). Taken together, our results indicate that there may well be variations in the burrow occupancy across different FUs. In fact, this difference appears to actually be related to the latitude/longitude position rather than the FU as the GAM showed FU to be irrelevant, while sample location (or day length) was influential in the model  and the absence of potential predators or sympatric competitors. Furthermore, laboratory tests on large numbers of individuals indicate that shelf animals may exhibit a differently phased dusk or dawn emergence, possibly to reduce interspecific competing pressure13. This matches present results where the temporal patterns in emergence observed in Nephrops UWTV transects generally follow those described by trawl catches on the shelf. The environmental factors such as the lunar cycle, tides and bottom currents, whose strength could vary according to the different local topography in different FUs, could also impact on burrow emergence16. Depending upon the future spatiotemporal availability of environmental data (to date missing) new variables could also be modelled, to improve the model correction approach.This burrow occupancy assumption has been identified a major uncertainty in UWTV bases assessment approach, particularly when using the UWTV index as an absolute measure of stock abundanceNephrops burrow identification, the accuracy of burrow identification may vary across FUs. In some areas sympatric fish and other decapod species occupy or even construct burrows with morphology similar to those of Nephrops30. It is also possible, in environmentally stable lightly trawled grounds, that unoccupied burrows may persist, and appear to be active (clearly inactive systems are not counted). However, most of the grounds in our study are heavily trawled with swept area ratios > 529. The number of burrow entrances per counted burrow system may also be variable in different habitat types. It is unlikely that all these factors can fully account for the discrepancy in the animals to burrow count ratios observed here between areas.It is possible that the number of burrow systems are over estimated, for a variety of reasons. Despite the training systems in place to ensure consistency in Nephrops abundance. A visible Nephrops index does provide a minimum population estimate of those emerging on a diel basis, but may not account for those concealed. The Irish Sea (FU 15) is a very dynamic system with strong bottom currents and highly populated sea bed29 so the 1:1 assumption could likely hold in that area. At the same time, this assumption could be quite different in FU 20–21, which is less fished and has lower densities of burrow systems. The HR for FU 15 has for long periods been around 20% and that observation clearly invalidates any possibility that our ratio of 0.1 Ind./burrow can be close to the true value . Still that doesn’t mean that the ratio 1:1 is true for all other FUs.Considering our results and the previously accounted sources of uncertainty for the UWTV-based stock assessment equation, our estimation of the general value of “1 Ind./10 burrow correction factor” does call into question the use of UWTV surveys as an absolute index on 51. UWTV surveying has distinct advantages over trawling, being more ecologically sensitive, causing minimal physical damage to seabed habitats and allowing better behavioural characterization. However, considerable work remains to be done in order test the key assumptions used in the assessments based on UWTV survey programmes.Trawling is a traditional sampling approach for the scientific monitoring of demersal resources but it does not provide data on the behaviour of the target species nor how such a behaviour can influence catchabilityNephrops behavior is usually highly variable55. In fact, the fitted GAMs suggested that there are no significant effects of the survey year  on the emergence or door keeping patterns. The models also suggested that the variability across FUs is not relevant (i.e. significant) in explaining the behavioral patterns.We chose to group the data for depth ranges and across years rather than keeping at a higher level of granularity, since 57, and are unavailable at the high spatiotemporal frequency of UWTV transects for all the FU areas considered.Additional data sub-grouping based on light data (not available) could have been performed. However, the estimation of any environmental illumination index based on transect timing, geographic position and depth would result in a mere modelling exercise. Factors such as cloud cover, water column primary productivity and turbidity have a significant impact on light scattering and absorbance (i.e. extinction) coefficientsNephrops rhythmicity is part of burrowing behavioral life-styles under natural selection in crustacean decapods, which was shown to be expressed independently from contingent variations in background light intensity58. The species possesses a biological clock that would ensure a temporally averaged burrow emergence pattern. The biological clock activates the locomotor activity (at the base of burrow emergence), every 12-h or 24-h, depending from the shelf or slope depth stratum considered59. This rhythmicity is self-sustained since it keeps its period and phase based on an environmental memory of previously experienced environmental light conditions when animals are transferred to laboratory constant conditions (i.e. entrainment upon intensity and photophase duration)60.Moreover, 39. In the case of the Mediterranean Nephrops stocks, fishery overexploitation is not reducing the number of captured animals but the biomass 61. To date, there is no evidence of a similar finding for the Irish Sea. The local stocks are not experiencing declines due to excessive fishing mortality 62. It is feasible that a behavioral mechanism modulating emergence is preserving the populations from the fishery exploitation .Another source of data variation may be the underlying dynamics of the populations due to recruitment variations, fishing and natural mortality53. To improve stock assessments, more intensive data collection efforts are needed to collect data for improved models. Data collection may include optoacoustic by multi-beam cameras that should be used in combination with High-Density (HD) imaging from Autonomous Underwater Vehicles (AUVs)63 and Internet Operated Vehicles (IOVs) such as crawlers64. A complete photomosaic of the targeted parcel describing burrow systems and their reciprocal positioning, should be undertaken as a first step. Only after that, hourly scheduled AUV and IOV’ acoustic sweeps should be continuously performed during consecutive day-night cycles, replicated in different seasons, to picture emerging and door-keeping Nephrops and associated predator and prey species under silent and non-light conditions.Towing the UWTV sledge could bias counts of emerged individuals causing them to flee outside the field of view and cause door-keepers to retract inside their burrowshttps://www.obsea.es and https://www.smartbay.ie/), could be used to picture burrow emergence modulation (for an insight on monitoring network geometry and characteristics see66). The different observational points could be synchronously used to account for the control of oceanographic and ecological drivers on the burrowing behavior of the species67. With such a multidisciplinary demographic, behavioral and environmental approach one may finally derive more accurate stock assessment models, predicting the density of animals that could be sampled with different fishery-dependent and independent tools67.In addition, different stand-alone or cabled observatories, holding several seabed and water column sensors for environmental monitoring  and bimodal with a nocturnal increase between 100 and 160 m. During the hours of maximum peak abundance of visible individuals , we have observed that on average there is about 1 visible individual per 10 burrows, at most. This represents the average peak, although there were higher peaks within individual transects. In general, considering all areas together, our ratio is well below that assumed in current stock assessments , suggesting that a high proportion of the population remains cryptic even during periods of peak emergence. This bias should be carefully considered since an undefined number of animals may avoid the sledge at its approach. Further technological development toward optoacoustic technologies and additional effort for calibration and modelling to integrate observations of visible individuals may further improve the utility of UWTV surveys for stock assessment. Four lines for technological based calibration should be foreseen in future stocks monitoring actions: burrow identification, as other sympatric fish and decapod species occupy or even construct burrows with morphology similar to those of Nephrops; intraspecific aggressive relationships and hierarchy, where dominant individuals may occupy different burrow systems nearby; emergence enhancement and inhibition depending on hunger state, due to predators’ presence and the quantity of emerging conspecifics at the “rush hour timing”; and finally burrow persistence after an animal’s death, depending on the density of burrow-dwelling species, local hydrographic and fishing pressure conditions.Our results highlight that Supplementary Information.


Agrobacterium tumefaciens.Ros/MucR is a widespread family of bacterial zinc-finger (ZF) containing proteins that integrate multiple functions such as virulence, symbiosis and/or cell cycle transcription. NMR solution structure of Ros DNA-binding domain  has been solved by our group and shows that the prokaryotic ZF domain shows interesting structural and functional features that differentiate it from its eukaryotic counterpart as it folds in a significantly larger zinc-binding globular domain. We have recently proposed a novel functional model for this family of proteins suggesting that they may act as H-NS-‘like’ gene silencers. Indeed, the N-terminal region of this family of proteins appears to be responsible for the formation of functional oligomers. No structural characterization of the Ros N-terminal domain (region 1–55) is available to date, mainly because of serious solubility problems of the full-length protein. Here we report the first structural characterization of the N-terminal domain of the prokaryotic ZF family examining by means of MD and NMR the structural preferences of the full-length Ros protein from  Agrobacterium tumefaciens3, RosR from Rhizobium etli4, RosR from Rhizobium leguminosarum6, MucR from Brucella spp9, MucR from Sinorhizobium meliloti11, MucR from Sinorhizobium fredii12; they all regulate genes required for the interaction of these bacteria with plants or animals20. Furthermore, MucR1 and MucR2 in Caulobacter crescentus have been recently shown to participate in the coordination of the systematic expression of the cell cycle genes22.Ros/MucR is an important family of prokaryotic proteins that includes Ros from 25 they act as H-NS-‘like’ gene silencers27 by binding low consensus AT rich regions in DNA rather than functioning like their eukaryotic counterparts that mainly act as DNA sequence-specific transcriptional regulators. We have also demonstrated how MucR oligomerization is a key feature for its proper gene regulatory function in Brucella.We have recently proposed a novel functional model for the prokaryotic zinc-finger (ZF) proteins belonging to the Ros/MucR family:2His2 ZF domain has evolved29. Structural and functional similarities and differences between the prokaryotic and the eukaryotic ZF have been largely documented37: the two domains are similar in the tetrahedral coordination of a structural zinc ion38 and in the existence of a ββα topology surrounding it while they differ in the fact that the prokaryotic ZF shows a second α-helix and a larger hydrophobic core. Moreover, this latter domain can bind the structural metal ion with a variable set of coordinating residues or even overcome the structural metal ion requirement to properly fold and function43. NMR solution structure of Ros DNA-binding domain  has been solved more than a decade ago30. Ros87 globular domain presents a βββαα topology, where the zinc ion is fully coordinated and supports the structural organization of the protein fold. No structural characterization of the Ros N-terminal domain (region 1–55) is available to date, mainly because of serious solubility problems of the full-length protein. Nonetheless, as formerly reported, the N-terminal domain of this family of proteins appears to be responsible for the formation of higher-order oligomers25. Structural predictions of the Brucella MucR based on the structural propensity of the amino acid sequence, report two α-helices as characterizing the N-terminal region of this family of proteins24. A number of deletions and single residue mutants have been designed to prove the involvement of these helices in the formation of the quaternary structure necessary for MucR function. Here, we report the first structural characterization of the N-terminal oligomerization domain of the prokaryotic ZF family investigating the structural preferences of the full-length Ros protein from Agrobacterium tumefaciens by means of Molecular Dynamics studies and Nuclear Magnetic Resonance experiments.From an evolutionary standpoint, the prokaryotic ZF has been proposed as the ancestral domain from which the eukaryotic Cys44 and by evaluating amino acid intrinsic secondary structure propensities45. In the first approach, we produced homology models of protein domains obtained by detecting PDB homologs  used as templates46 to build native-like structures using Rosetta algorithm47. In the second, secondary structure propensities of the N-term amino acids were used to manually model and refine N-domain (see Methods). Such domain was then joined to the NMR solution structure of the C-terminal domain (Ros87 - 2JSP) using Maestro software package48. Next, we evaluated and selected a very extended 2-helices N-term domain structure  present 2 long α-helices characterizing the N-terminal domain: namely α′ (Tyr6-Ala23), α′′ (Asn27-Thr45) for M1 and α′ (Tyr6-Asn27), α′′ (Glu34-Gly48) for M3 linked to Ros87. The same domain for the M2 model is made by 3 shorter helices, namely α′ (Gln10-Ser26), α′′  and α′′′ (Gly37-Glu48).To further evaluate the intrinsic folding propensities of the N-terminal domain (amino acids 1–55) and its effect on the structural rearrangement of the full-length Ros, we carried out additional molecular dynamics simulations of the isolated N-domain Figs.  and SI1bIt is worth underlining that the use of extensive MD simulations and several starting models are planned to overcome initial approximations due to the lack of structural data.MD simulations on the obtained models were run as described in the materials and methods section and the conformational sampling of the full-length Ros was investigated in terms of cluster analysis. The integrity of the simulations and the stability of the modelled structures were evaluated by means of atomic position deviations Figure . The fulClustering studies underline the high conformational variability of the full-length protein that converges in a preferential structural arrangement: conformations where the N-term and the C-term domains come together turns out to be more stable/visited Fig. . Indeed,On the other side, only a small population is made by a very disordered and extended N-domain, namely accounting for ~20%  and ~10%  of the entire trajectory in the table reported in Fig. In the clustering obtained by fitting onto the N-terminal domain, more than 83% of the conformational ensemble coincides with a protein structure characterized by a highly structured N-terminal domain made by α′ (Tyr6-Asn27) and α′′ (Glu34-Gly48) helices, closely resembling M3 model in Fig. Importantly, both representative Ros structures of the first clusters Fig.  exhibit In this framework, comparison with the simulations of the N-domain fragment (amino acids 1–55) is useful to underscore the key role played by the C-term domain (Ros87) in stabilizing the 2-helices of the N-terminal domain. In Fig. 24.In this framework, it is worth underlining that in the full-length Ros  appears to have a key role in stabilizing the 2-helices (α′ and α′′) of the N-terminal domain. For this reason, we have focused our attention on the interactions at the interface between the two domains. The number of hydrogen bonds between the two subunits (amino acids 1–55 and 56–142) ranges from 1 to 7 along the entire meta-trajectory and key salt bridges are established. Among these, interesting ionic interactions take place occasionally between the α′′-helix residues (Glu34 and Asp41) and amino acids from the loop region (amino acids 61–70) preceding the C-term domain βββ structure . Full-length Ros globular state might be primed and favoured by stable bonds occurring between the two domains and stabilizing their reciprocal orientation. In Fig. 49.Notably, from Fig. t) of 1.16 (±0.11) *10−10 m2sec−1, which is quite comparable to the same value obtained for Ros8730 and demonstrates the monomeric form of this mutant also at the NMR concentration used51. LS experiment confirmed a monomeric structure for del29-Ros . The NMR characterization commenced with the analysis of the DOSY experiment recorded for this deletion mutant. This experiment gave a diffusion coefficient .While it is quite plausible that the removal of del29-Ros N-terminal region to obtain Ros87 perturbs the chemical environment of the contiguous 56–64 portion, the structural perturbation of the C-terminal domain requests a supplementary molecular elucidation. The CSI analysis has shown no significant differences in secondary structure content and distribution of the two proteins. According to the simulation data, a transient interaction between the hydrophobic N-terminal part of del29-Ros and His96 could support this observation, allowing del29-Ros to populate, even at a smaller extent, a more compact structure.24.The ability to form oligomers is emerging as a key structural feature for the regulatory function played by the Ros/MucR family of prokaryotic ZF containing proteins. We have previously demonstrated that the deletion of the first 55 amino acids  gives soluble monomeric protein capable of binding DNA, while the full-length protein has been showed capable of forming high order oligomersWe have also demonstrated that a monomeric MucR mutant obtained by mutating to alanine three hydrophobic residues in the N-terminal domain cannot control the transcription of target genes.Agrobacterium tumefaciens, no structural studies concerning the N-terminal region (the first 55 amino acids) were available to date.This has led the speculation of a different role played by the N-terminus rather than a direct participation in DNA-binding mechanism: a possible oligomerization domain located in the N-terminal portion of this family of proteins. While Ros87 NMR structural characterization has elucidated the peculiar structural features of the C-terminal portion of Ros protein from The conformational dynamics displayed by the N-domain and the full-length Ros protein concur to suggest a direct role of the C-domain onto the stabilization of the N-term folding. MD simulations analyses return a preferential structural rearrangement of the full-length Ros, whereby the N-domain folds in a two-antiparallel α-helices (namely α′ -Tyr6-Asn27- and α′′ -Glu34-Gly48-) connected to the C-term domain (Ros87), as described above. The N-term domain helices are kept in place and stabilized by hydrophobic interactions at their interfacing surfaces. Significantly, this structural organization is observed in largely more than half of the total full-length Ros simulation time, underscoring the ability of the N-term domain to fold into the two packed antiparallel helices regardless of its initial structuration. On the other hand, in the absence of the C-term domain, MD simulations show that the N-term domain is not capable of adopting a comparable helical rearrangement, yet a smaller 3-helices domain is preferred. Accordingly, the structural characterization of del29-Ros shows how the deletion of the α′ helix (Tyr6-Asn27) disrupts the helical character of the α′′ (Glu34-Gly48) portion. As a result, del29-Ros forms only unstable oligomers Figs. –SI5, undFurthermore, in the full-length Ros simulation, the N-term and C-term domains can rearrange in a more compact globular shape by establishing electrostatic interactions. Nevertheless, the transient character of such contacts (occurrence < 10% of the simulation time), also confirmed by our NMR data, substantiates the hypothesis of a bi-lobal conformation of Ros protein, where the C-term-domain serves/aids the stabilization of the two-helices N-term domain rather than directly lock onto it.56. Although it would be preferable to investigate the dynamical features of the modelled proteins described here with different force-fields, it is important to underline that our calculations allowed to capture significant differentiated behaviours of the modelled systems with or without the C-domain. Indeed, our simulations were corroborated by independent experimental evidences endorsing differential roles for the two domains.It is worth remarking that the simulations and analyses discussed here have been based on the use of one specific version of the Amber force field53. Truncations and site-directed mutations proved the importance of the N-terminal domain and of its helical secondary structure to form high order structures also in the case of H-NS54. H-NS is made by two functional domains separated by a flexible linker: the N-terminal domain is responsible for the formation of high order structures that bind DNA via the C-terminal domain.Ros shows interesting structural analogies with the Histone-like Nucleoid-Structuring (H-NS) protein that plays a role in the formation of nucleoid structure25 suggesting that the prokaryotic zinc-finger proteins control genes expression by adopting a mechanism similar to that used by the H-NS proteins found in many other Gram-negative bacteria instead of working as classical eukaryotic transcription factors.Under this light, the two Ros domains might work as independent units with specific functions: C-term domain harbouring the zinc-finger adopts a conformation that is consistently prone to DNA-binding, regardless of the presence/orientation/interaction of the N-term domain along the entire simulation time whereas N-domain itself might control oligomerization mechanism. Our here reported results convincingly support and integrate our previous findings44 or by estimating amino acid intrinsic secondary structure propensities45.The full-length Ros protein structural models were produced either by means of comparative modellinghttp://robetta.bakerlab.org). The integrative modeling protocol includes different sequential operations: first, alignments to one or more template structures, by using external programs; then, models based on the template structure are refined by rebuilding the missing parts (loop modelling) and subjected to a full-atom refinement of using the Rosetta full-atom energy function. The final step is a selection of the models using clustering. Sequence alignments and the list of the template proteins used for the modeling are given in Supporting Material  was done using the fully automated server Robetta by Ginzu domain prediction  were built by joining and energy minimizing the so-obtained N-terminal domains with the high-resolution NMR C-terminal domain (Ros87 – 2JSP) using Maestro software48 Fig. .55 with the Amber99sb-ildn force field56. All proteins were centered in triclinic boxes allowing a 0.9 nm distance from each box edge and solvated by explicit water molecules (TIP3P model)57. Counter-ions were randomly added to neutralize the simulated systems58. In details: M1 was solvated by 15111 water molecules; M2 by 7375 water molecules, M3 by 7872 water molecules and all full-length systems were neutralized by 1 Cl-. On the other hand, the N-terminal domain (amino acids 1–55) was solvated by 6383 water molecules and neutralized by 6 Na+ counter-ions.MD simulations were performed using Gromacs package (v.4.5.5)2)59. Each system was first energy minimized using the steepest descent approach, followed by an equilibration phase where water molecules and protein heavy atoms were position restrained. A short NVT (500 ps) and NPT (500 ns) simulations were run at 300 K previous to the production run. Then the unrestrained systems were kept in an NPT ensemble, at constant temperature of 300 K by the velocity rescaling thermostat60 and at a pressure of 1 bar by the Berendsen barostat61. Electrostatic interactions were evaluated using the particle mesh Ewald method62 and Lennard-Jones forces by a cut-off radius of 0.9 nm. Bond lengths involving hydrogen were restrained by the LINCS algorithm63. The time step was set to 2 fs and periodic boundary conditions were applied in all three dimensions. To enhance sampling two independent replicas with different initial velocities were run for each system, totally collecting 0.8 µs of simulation time.Because zinc ion is crucial for protein structure stability, Zn(II) was introduced, tetrahedrally coordinated by Cys79, Cys82, His92 and His97, and distance restraints were applied with a force constant of 1000 kJ/. Clustering is computed applying the method developed by Daura and collaboratorsros gene encoding for the deletion del29-Ros lacking the first 28 amino acids of wild-type Ros, was amplified by PCR using the primer 1 (5′-ACATGCCATGGTCGTTCCGGTAACTGAG-3′), the primer 2 (5′-CGGAATTCTCAACGGTTCGCCTTGC-3′) and the genomic DNA purified from A. tumefaciens as a template. The obtained fragment was digested with NcoI and EcoRI (New England Biolabs) and cloned into a pET-11d vector digested with same restriction enzymes. The sequence of the obtained clone del29-Ros-pET-11d was checked by Sanger method sequencing. del29-Ros-pET-11d was used to transform E. coli BL21(DE3) strain to express the protein del29-Ros in M9 salt medium containing 15N and 13C with the addition of BME vitamin solution (SIGMA). Cell growth was followed by absorbance at 600 nm. The expression was induced with 1 mM IPTG when the bacterial culture reached 0.5 OD600nm and carried out at 28 °C until the culture absorbance at 600 nm reached 1 OD. Then, bacterial cells were harvested by centrifugation and lysed by sonication on ice in presence of cOmplete EDTA-free Protease Inhibitor Cocktail (Roche). The lysate was clarified by centrifugation at 29000 rcf for 30 minutes and total soluble protein fraction was used to purify del29-Ros. The purification was carried out by cation exchange chromatography using a Mono S HR 5/5 column (GE) equilibrated with 20 mM Na2HPO4 (pH 6.8) and by a following step using a HiLoad 26/60 Superdex 75 gel filtration chromatography column (GE) equilibrated with a buffer composed by 20 mM Na2HPO4 (pH 6.8) and 0.2 M NaCl. It should be here noted that, after the first step of purification, we were not able to obtain good quality spectra as the solution obtained appeared as a mixture of different oligomeric species (data not shown); only the second step (gel filtration) allowed the separation of a monomeric soluble form of del29-Ros.The region of the For molecular weight measurements, a MiniDAWN Treos spectrometer (Wyatt Instrument Technology Corp.) was connected on-line to a size-exclusion chromatography. A sample of 1 mg was loaded on a Superdex 75  equilibrated in the same buffer used for purification procedures and connected to a triple-angle light scattering detector equipped with a QELS (Quasi- Elastic Light Scattering) module. A constant flow rate of 0.5 ml/min was applied. Elution profiles were detected by a Shodex interferometric refractometer. Data were analyzed by using Astra 5.3.4.14 software (Wyatt Technology).15N-13C double-labelled del29-Ros protein in 20 mM phosphate buffer, 0.2 M NaCl, pH = 6.8, with 10% of D2O. The experiments were acquired at 298 K on the Bruker Avance III HD 600 MHz equipped with cryoprobe at the Environmental, Biological and Pharmaceutical Sciences and Technologies Department of the University of Campania “Luigi Vanvitelli” . To allow sequence-specific backbone and Cβ resonances assignment, bidimensional 1H-15N HSQC and a standard set of triple-resonance NMR experiments were acquired NH65). The translational diffusion coefficient (Dt) was obtained by pulsed field gradient spin-echo DOSY experiments68. All the spectra were processed using the software TOPSPIN 4.0 (Bruker) and analyzed with the software CARA69. 1H − 15N HSQC J-18 spectra were acquired using as transfer delays a τm [1/(4 J)] of 13.8 ms, to obtain the coherence transfer from the Hε1 and Hδ2 histidine side chain protons to Nε2 and Nδ1 through the 2JHN coupling constant73.All NMR samples contained 200 µM of 15N chemical shift perturbations were estimated by using the following equation:7430 and del29-Ros.HN and 100 μM of Ros87 or del29-Ros were incubated with dimethyl adipimidate (DMA) in NaP 20 mM at room temperature for 0,5 and 1 hour in a molar ratio of 1:10 and 1:20. The reactions were stopped with Tris/HCl 50 mM pH 8.0. The reactions were analyzed on 15% SDS-PAGE.Supplementary information.


The prepared potato steamed bread has good sensory and texture properties, with natural potato flavor.The present study aimed to study the preparation and quality evaluation of potato steamed bread by using potato flour, wheat flour, and gluten at the presence of yeast and inorganic additives. As the rheological properties of the potato–wheat formulated flour negatively related to the potato flour, the potato–wheat formulated flour with 35% potato flour was set as the basic flour (100%). The effects of wheat gluten on the rheological properties of the dough were also evaluated, and gluten addition amount was set at 6.5%. The effects of yeast, sodium bicarbonate, citric acid, and monocalcium phosphate addition on steamed bread properties have been studied and optimized by orthogonal test. The obtained potato steamed bread formula was 100% basic flour (potato/wheat mass ratio of 35:65), 6.5% wheat gluten, 1.1% yeast, 1.4% NaHCO Potato steamed bread. Traditional steamed bread formula was wheat flour, water, and yeast or sourdough. Nowadays, wholegrain and multigrain foods have become a new trend to improve the nutritional value of food  is the fourth major food crop in the world after wheat, rice, and corn. With cultivation area 19.3 million ha, worldwide production of potato was estimated to be 388.2 million tons in 2017 2 has been developed.In this study, the preparation and quality evaluation of potato steamed bread with wheat gluten have been studied. The influence of potato flour ratio on the rheological properties of the dough has been studied. With a fixed potato/wheat mass ratio, the effect of wheat gluten on rheological properties of the dough was evaluated. The influences of yeast, sodium bicarbonate, citric acid, and monocalcium phosphate addition on steamed bread properties have been studied and optimized by orthogonal test. At last, a new potato steamed bread formula by using potato flour, wheat flour, and gluten at the presence of yeast, NaHCO22.13, citric acid, and Ca(H2PO4)2 were of food grade, and other chemical reagents were of analytical grade.Commercial wheat flour  and potato flour  were purchased from Jinyuan Co., Ltd and Fuguang Co., Ltd, respectively. Wheat gluten was supplied by Zhengte Co., Ltd. Yeast, NaHCO2.2The potato/wheat mass ratio was determined as 0:100, 5:95, 10:90, 15:85, 20:80, 25:75, 30:70, 35:65, and 40:60, referred as S0, S5, S10, S15, S20, S25, S30, S35, and S40, respectively. Potato flour and wheat flour were, respectively, weighed according to the proportion and then thoroughly incorporated in the mixer and sealed preserved at 4°C.The formulated flour of S35 with a potato mass ratio of 35% was selected to be the basic flour. Gluten was added to the basic flour at levels of 6.0%, 6.5%, and 7.0%, referred as S35+W6.0, S35+W6.5, and S35+W7.0, respectively. The basic flour and gluten were, respectively, weighted according to the proportion and then thoroughly incorporated in the mixer and sealed preserved at 4°C.2.3Before steamed bread preparation, chemical composition and rheological properties of the potato–wheat formulated flour samples  and basic flour–gluten formulated flour samples  were tested. Moisture, ash, protein, and gluten index were determined according to the AACC methods 44‐15A, 08‐21.01, 46‐12.01, and 38‐12.02, respectively AACC, . Farinog2.42.4.13, 0.6 g of citric acid, and 0.3 g of Ca(H2PO4)2 were mixed homogeneously. Second, 1.0 g of dried yeast was dispersed in 50 g distilled water (40°C) for 10 min before mixing with the flour. Then, the dough was prepared by mixing the ingredients in a Swanson mixer  at a speed of 105 rpm for 5 min. The dough was sheeted for ten times on a noodle machine, hand‐kneaded for 1 min, and shaped. The proofed dough was fermented at 37°C and 55% relative humidity for 40 min in a controlled fermentation cabinet , then steamed in a steaming chamber for 20 min, and cooled at room temperature for 0.5 hr before evaluation.The production of steamed bread followed a previous method with some modification Zhu, . The for2.4.23, citric acid, and Ca(H2PO4)2 addition.With fixed basic flour amount of 100% and gluten addition amount of 6.5%, the effects of inorganic additives on steamed bread properties were studied. As Table 2.4.3As Table 2.52.5.1The cooled steamed bread was weighed, and its volume was measured by the millet replacement method. The specific volume of the steamed bread was the result of volume divided by weight.2.5.2Sensory evaluation of the steamed bread was carried out by ten trained panelists from Henan University of Technology according to the Chinese standard method SB/T 10139‐1993 with some modification . Texture profile analysis (TPA) was set as: pre‐ and post‐test speed of 2.0 mm/s, test speed of 1.0 mm/s, trigger force of 5 g, compression distance of 25 mm, and pause between two compression of 1 s. Hardness, stickiness, springness, cohesiveness, chewiness, and resilience were measured.2.6SD. The ANOVA procedure and Duncan's multiple range method were used to evaluate the significant differences between treatments (p < .05).All experiments were repeated in triplication, and results were expressed as mean ± 33.1Chemical composition of the formulated flour was characterized in our study Table . As FiguCompared with the control (2.83 min), the formation time of the formulated flour with 5% (2.43 min) or 10% potato flour (2.43 min) significantly decreased. But further increase of the potato ratio raised the formation time of the formulated flour. For instance, the formation time of the formulated flour with 35% potato flour was 4.26 min. The literature revealed that blending potato flour with wheat flour needed more time for the dough to reach the maximum consistency , and the gluten addition amount was set at 6.5%. The effects of yeast, sodium bicarbonate, citric acid, and monocalcium phosphate additions on steamed bread properties have been studied.3.3.12 produced by yeast fermentation made the dough to be looser, resulting in increase of the specific volume. However, excessive CO2 would lead to formation of so many irregular holes, broken of the wheat gluten network, even collapse or deformation of the steamed bread , the color of the steamed bread became yellower, unpleasant alkali aroma could be detected, and the aroma of the potato flour and the steamed bread fermentation could not be detected. NaHCO3 can react with organic acid produced by lactic acid bacteria during proofing and steaming, at the same time producing more gas  on sensory properties of steamed bread was tested. The results showed that there was no significant difference in specific volume and appearance score between steamed breads with different addition amounts of citric acid. The aroma score firstly increased and then decreased as the citric acid amount increased. With addition of 0.8% or 0.9%, the steamed bread had an unpleasant sour flavor. It has been reported that citric acid in steamed bread could not only enhance the taste property of the steamed bread, but also prolong the retention period of the steamed bread with good antioxidant ability 2 addition increased from 0.1% to 0.3%, the specific volume increased gradually. But further increase of the addition did not raise the specific volume value. With Ca(H2PO4)2 addition of 0.5%, both shape and smoothness scores reached the maximum value. Phosphate has three hydrogen bond positions , producing stronger bonding through the negatively charged oxygen to prevent CO2 escape. Phosphate has been reported to be able to enhance both the specific volume and the texture parameters of the bread 2 addition of 0.4%. Therefore, the appropriate Ca(H2PO4)2 addition amount was 0.4%.As Ca(H3.43 (B) > yeast (A) > citric acid (C) > Ca(H2PO4)2 (D), and the optimal combination was A2B3C3D3. If sensory score was taken as the evaluation index, the effects sequence was: NaHCO3 (B) > Ca(H2PO4)2 (D) > citric acid (C) > yeast (A), and optimal combination was A3B2C3D3.Optimization of the steamed bread preparation was obtained by the L9(34) orthogonal design method, as Table 3.52B3C3D3 was 2.18 ml/g, and sensory score was 90.6. Specific volume of the steamed bread at experiment A3B2C3D3 was 2.21 ml/g, and sensory score was 92.7. Therefore, the optimal steamed bread preparation additions was 1.1% yeast, 1.4% NaHCO3, 0.75% citric acid, and 0.50% Ca(H2PO4)2. The prepared steamed bread had good sensory and texture properties. As Figure Afterward, the model validation was evaluated. As Table 43, 0.75% citric acid, and 0.50% Ca(H2PO4)2. Therefore, the new potato steamed bread formula was 100% basic flour (potato/wheat mass ratio of 35:65), 6.5% wheat gluten, 1.1% yeast, 1.4% NaHCO3, 0.75% citric acid, and 0.50% Ca(H2PO4)2. The prepared steamed bread had good sensory and texture properties.As the rheological properties of the dough negatively related to the potato flour, the potato–wheat formulated flour with potato mass ratio of 35% was set as the basic flour. The addition of 6.5% wheat gluten could effectively improve rheological properties of the formulated flour. The L9(34) orthogonal design results showed that the optimal steamed bread preparation addition was 1.1% yeast, 1.4% NaHCOAll authors declare no conflicts of interest related to this article.There was no ethical issue related to his manuscript.Written informed consent was obtained from all study participants.Table S1Click here for additional data file.


Continuous monitoring of the herd status is important but routine assessments on farm can be challenging. In this study, a remote herd assessment tool was developed to help producers and advisors detect herd management issues related to herd welfare and health. This tool was based on pre-recorded indicators from routinely collected on-farm records. Indicators were aggregated to a composite index to benchmark the overall herd status, with a large composite index indicating an overall high herd status and a small composite index indicating an overall low herd status. Robustness of the composite index was evaluated and indicated little fluctuation for herds with a low or high status. These results suggest that herds in need of support could be prioritized and effectively monitored over time, limiting the need for time-consuming farm visits. The benchmarking tool allows evaluating herds relative to their peers, highlights areas with opportunities to improve, may be further suitable for certification systems, and may be applied to studies to benchmark multidimensional aspects of livestock farming such as environmental and socio-economic studies.Continuous assessment of the herd status is important in order to monitor and adjust to changes in the welfare and health status but can be time consuming and expensive. In this study, herd status indicators from routinely collected dairy herd improvement (DHI) records were used to develop a remote herd assessment tool with the aim to help producers and advisors benchmark the herd status and identify herd management issues affecting welfare and health. Thirteen DHI indicators were selected from an initial set of 72 potential indicators collected on 4324 dairy herds in Eastern Canada. Data were normalized to percentile ranks and aggregated to a composite herd status index (HSI) with equal weights among indicators. Robustness analyses indicated little fluctuation for herds with a small HSI (low status) or large HSI (high status), suggesting that herds in need of support could be prioritized and effectively monitored over time, limiting the need for time-consuming farm visits. This tool allows evaluating herds relative to their peers through the composite index and highlighting specific areas with opportunities for improvements through the individual indicators. This procedure could be applied to similar multidimensional livestock farming issues, such as environmental and socio-economic studies. Assessing the overall status of a dairy herd can be challenging as it affects various important aspects of dairy farming such as welfare, health, reproduction and production performance, and profitability. Continuous assessments on farm are necessary to monitor the herd status but collecting animal-based measures is time consuming and expensive ,2. TherePrevious initiatives have further looked at a multicriteria evaluation approach by aggregating potential indicators to evaluate complex issues such as on-farm herd welfare ,5,7,8, aThe objective of this study was to develop a tool to assess, monitor, and benchmark the overall herd management, status covering key aspects of welfare and health such as longevity, reproduction, nutrition, production, young stock management, and profitability. We propose an approach based on a composite herd status index (HSI) composed of individual indicators linked to key aspects of herd welfare and health. As DHI records can differ among various countries, this paper does not aim at identifying an absolute set of indicators for defining an acceptable or good herd status, but to propose a dynamic approach that can be generalized and applied to other farm records. Records for 4324 dairy herds in Eastern Canada (the Atlantic Provinces and Quebec) from September 2016 to September 2017 were extracted from the DHI database at Lactanet . All data were expressed at the herd level as an average over the study period. Herd records with less than three available test dates over the study period and herds with less than 10 cows were removed. Cows were housed in tie stalls (83%) and free stalls (17%), the latter equipped with either a milking parlour (10%) or an automatic milking system (7%). The predominant breed was Holstein Friesian (91%), followed by Ayrshire and Jersey (4% each). Average herd size was 60 ± 28 (mean ± SD) cows for tie stalls, and 111 ± 83 cows for free stalls. Average milk production per year was 9364 ± 1504 kg for tie stalls, and 9717 ± 1569 kg for free stalls. Initially, 72 pre-recorded variables assembled in a DHI database were assessed to be part of the benchmarking tool . Data inA composite index was calculated for each herd using the 13 individual indicators described above. As distribution among indicators varied, the individual indicators were normalized by transforming values to percentile ranks. Indicators for which a lower prevalence is desirable  were normalized by transforming values to the reciprocal of the percentile rank. This approach prevented extreme values from distorting the HSI and allowed for the comparison of indicators expressed in different units within and among herds. Moreover, transformation to percentile ranks enabled assessing the performance of a herd relative to its peers. Other common methods of transformation such as transforming to standard scores and rescaling to range are discussed in this study as modifications in the computation of the HSI may affect the herd ranking and interpretation of the HSI. rank denotes the percentile ranks for each of the 13 individual indicators, and N denotes the number of individual indicators. Transformed values of the 13 individual indicators were aggregated to a unique HSI based on a linear additive aggregation with equal weights for each indicator: The HSI was then transformed to percentile ranks, resulting in a normal distribution of HSI across the population . Herds wMulticollinearity analysis in combination with bivariate correlation analysis and a principal component analysis were conducted in R 3.5.0 to assess a potential collinearity among the final set of individual indicators. A strong interrelation among indicators may distort the HSI, as those indicators will have a stronger weight on the composite index. Associations among the HSI and the individual indicators were estimated through Kendall rank correlations to handle the asymmetrical distribution and extreme values for some indicators in the dataset. Size effects of individual indicators on the HSI were evaluated based on differences between the means of the top 25% and bottom 25% herds using Cohen’s d parameter and proposed thresholds ,14. A robustness analysis of the composite index was conducted to verify whether herds might be assigned different ranks depending on the underlying indexing method and, thus, to assess whether future modifications to the composite index might render it unstable . Robustness was assessed as recommended by the Organisation for Economic Co-operation and Development (OECD)  by analyIn addition, robustness of the composite index over time was assessed by comparing the median absolute difference of the original HSI (4321 herds from September 2016 to September 2017) with the HSI generated from indicators covering the following period (4003 herds from September 2017 to September 2018). A multidimensional approach was used to calculate a composite index based on 13 indicators selected to cover various aspects of herd management affecting herd welfare and health, extracted from DHI records for all registered herds. These indicators were not strongly correlated to each other as suggested by a tolerance test of minimum 0.99 and a variance inflation factor of maximum 5.4 ). A prinThe strength of association between the individual indicators and HSI differed . AssociaThe tool proposed in this study may likely undergo modifications, mainly with regard to the choice of indicators, as DHI records are continuously updated and grow in terms of volume and diversity. Future modifications in the composite index computation might render the HSI unstable . TherefoSome uncertainty related to the indexing method was observed ; howeverIn-depth analysis revealed that the variation in HSI originated at an equal proportion from the three indexing methods texted in the present study , with a coefficient of variation (CV) of 0.18‒0.19. Overall, herds with the smallest (<p10) variation in HSI showed considerably less fluctuation across the 13 individual indicators  as compared to herds with the largest (>p90) variation in HSI (mean CV of 0.92). These results suggest that stability of HSI was mainly influenced by the presence of extreme values for the individual indicators. Careful removal of extreme values might improve the overall robustness of the HSI to some extent. The concerned herds can be identified from A comparison of the HSI computed for two subsequent years (2016–2017 versus 2017–2018 period) revealed that the ranking of herds fluctuated to some extent between the 2 years. The median absolute difference in HSI across all herds between the 2 years was 0.11; however, the low-ranking (<p10) and high-ranking herds (>p90) in the first year fluctuated considerably less  than the average herds (median absolute difference in HSI of 0.14 for herds ranking between p25 and p75). These results suggest that herds with a low and high status are relatively stable over time. Although certain indicators may fluctuate to some extent, large variations are not to be expected for these herds from one year to another. Herds with a high status are evidently classified as such because of their high performance across all indicators. Herds with a low status will however require extensive changes in overall herd management. These findings suggest that herds with a low welfare and health herd status can be targeted using the proposed DHI variables, and advisory service effectively allocated. Comparison with other studies is not possible at this stage as, to our knowledge, similar sensitivity tests have not been reported previously. It should however be noted that these fluctuations could reflect to some extent potential changes in management or housing made by farmers over the 2 years that are not accounted for in the DHI records. Aggregating scores into an overall assessment offers the opportunity to provide a broad assessment of herd status. However, the usefulness of such an approach depends heavily on the integration of on-farm records and standardization of measurements. On-going collaborative research efforts into standardizing welfare indicators and scoring methods such as those for lameness, body injuries, or cleanliness  are expeAggregation of individual indicators may mask the relative contribution of weaker yet potentially relevant indicators on the overall herd status . In the Our results suggest that herds with a low and high status can be well distinguished, with most indicators having a distinct effect on the observed difference between herds in the top 25% and bottom 25% . The effThe multicriteria evaluation approach described in the present study can be used routinely to assess the herd status on a large number of herds and can be thus an interesting tool to improve continuously herd welfare and the herd health status on large scale. By ranking and comparing herds, the individual herd status can be continuously improved, even if the overall herd status or specific indicators such as the overall herd welfare level gradually increases . As this approach does not allow assessing issues at the animal level such as individual animal welfare or health, the individual cow status might not be necessarily adequate despite an overall inconspicuous status at the herd level. Moreover, a comprehensive assessment of individual key aspects is not possible with this approach; for instance, for a comprehensive welfare assessment, further indicators that are typically not routinely measured and assembled in current DHI databases such as natural living and affective states of the animals  may haveThe selected set of indicators originated from pre-collected routine data and is therefore not a comprehensive list. However, additional indicators may be integrated in the future such as routine measurements of the affective states of animals using kinematics , and actAlthough a monitoring instrument based on routinely collected DHI records might be considered a valid alternative to a standardized herd check , concernOur results suggest that the method is repeatable, but care should be taken for herds with extreme values for some indicators, as those herds might be more prone to an unstable composite index. Nonetheless, this effect on the composite index can serve as an incentive to improve quality of data collection and raise awareness among stakeholders on the importance of accurate data collection. From an advisory service point of view, the evaluation tool can be explained relatively easily to stakeholders as it focuses on a set of important herd welfare and health indicators. It can encourage participation as it allows producers benchmarking the herd performance relative to their peers and allows developing improvement strategies specific to the individual herd . This apThe remote assessment tool proposed in the present study allows benchmarking the herd management status in dairy herds kept under stall conditions based on a predetermined set of routinely collected indicators. The herd status can be continuously assessed and monitored without the need for time-consuming and expensive individual farm visits. This tool allows highlighting individual herd strengths and weaknesses related to 13 potential indicators, covering key aspects of herd welfare and health such as longevity, reproduction, nutrition, production, young stock management, and profitability. The tool further provides stakeholders with an overall herd evaluation compared to their peers. Individual herds of interest can be identified, such as low-performing herds or herds experiencing sudden and large changes to help producers adopt tailored strategies. Likewise, high-performing herds can be identified to evaluate their strengths and management strategies in more detail. The multicriteria evaluation approach described in the present study might be used with other national herd databases and might be generalized for other benchmark studies based on a multidimensional approach, such as environmental and socio-economical aspects of livestock farming.