Mullus surmuletus) in the Mediterranean Sea. Univariate multiple regressions were used to test the influence of environment , geographic isolation, and larval dispersal on single nucleotide polymorphism (SNP) allele frequencies. We used Moran's eigenvector maps (db‐MEMs) and asymmetric eigenvector maps (AEMs) to decompose geographic and dispersal distances in predictors representing different spatial scales. We found that salinity and temperature had only a weak effect on the variation in allele frequencies. Our results revealed the predominance of geographic isolation to explain variation in allele frequencies at large spatial scale , while larval dispersal was the major predictor at smaller spatial scale . Our findings stress the importance of including spatial scales to understand the drivers of spatial genetic variation. We suggest that larval dispersal allows to maintain gene flows at small to intermediate scale, while at broad scale, genetic variation may be mostly shaped by adult mobility, demographic history, or multigenerational stepping‐stone dispersal. These findings bring out important spatial scale considerations to account for in the design of a protected area network that would efficiently enhance protection and persistence capacity of marine species.Genetic variation, as a basis of evolutionary change, allows species to adapt and persist in different climates and environments. Yet, a comprehensive assessment of the drivers of genetic variation at different spatial scales is still missing in marine ecosystems. Here, we investigated the influence of environment, geographic isolation, and larval dispersal on the variation in allele frequencies, using an extensive spatial sampling (47 locations) of the striped red mullet ( The striped red mullet is a demersal fish species inhabiting coastal shelf ecosystems from 0 to ~100 m depth. This species is widely distributed in the eastern North Atlantic Ocean, from the British Isles in the North to Senegal in the South, including the Mediterranean and Black Sea , and heterogeneous sea currents   Figure . The meaSea surface temperature (SST) and sea surface salinity (SSS) were computed in each site from the oceanographic model NEMOMED8  according to the manufacturer's protocol. DNA quality was assessed by running 3 μl of each DNA sample on 1% agarose gels. DNA concentration was determined using NanoDrop 8000. A total of 558 DNA samples that fulfilled the quality and quantity criteria  were then sequenced. Individuals were genotyped using genotyping‐by‐sequencing technique (GBS) based on the use of restriction enzyme digestion to sequence a reduced portion of the genome . Sequences shorter than 60 bp were discarded from the dataset. For each sequenced library, the number of raw reads and filtered data are provided in Table To remove the potential confounding effect of SNPs under selection, we eliminated candidate SNPs . The sites‐by‐edges matrix E was filled with 0s and 1s representing the absence or presence of the various edges linking each site to a hypothetical site at the root of the connection diagram .To account for the directionality of larval dispersal, we applied asymmetric eigenvector maps (AEMs) modeling  was then computed on the column centered The db‐MEMs and AEMs associated with the largest eigenvalues represent the large‐scale spatial structure in the data, while those associated with smaller eigenvalues reflect finer‐scale variations . As we have a high number of SNP, we used the loading scores of the 47 sites on the first axis of a PCA computed on all SNP allele frequencies as a response variable. The spatial predictors were selected using both forward selection and backward selection based on the Bayesian information criterion (BIC) using the MASS package version 7.3.45 . The relative importance of each predictor variable at explaining allele frequencies of each SNP was estimated by summing the AICw values across all models that included that variable and averaged over the 1123 SNPs to obtain their global contribution (ω). Analyses were conducted using the R package MuMIn version 1.15.6  for each SNP with at least one significant explanatory variable (693 SNPs). The effect of environmental variables was not tested in these models as SST and SSS showed low contribution to the variation in allele frequencies. We computed the adjusted R² of the model with AICmin for each SNP and averaged it across SNPs.To determine whether geographic isolation or larval dispersal had a larger influence on spatial distribution of allele frequencies, we used the likelihood ratio (LR) tests. This test compares the full model (with both db‐MEMs and AEMs) to nested models that consider only geographic isolation (five db‐MEMs) or only larval dispersal (five AEMs). The LR test was performed only on the best models  and all the other sites. The Alboran Sea  also displays slightly stronger FST values with the rest of the Mediterranean Sea. The Mantel test between pairwise FST and marine geographic least‐cost distances was significant , suggesting a pattern of isolation by distance in the data  and three db‐MEMs  had ω values significantly higher than the remaining predictors  had statistically significantly smaller effect , on average larval dispersal and geographic isolation explain, respectively, 11% and 9.66% of the variation in allele frequencies, with no significant difference between the two types of predictors  and fine resolution (~100 km) sampling covering the entire Mediterranean Sea. We show that larval dispersal is the major variable influencing allele frequency variation  at small and intermediate spatial scales , while geographic isolation is the main driver  at larger scale . Environmental variables  have significantly weaker effects on the variation in allele frequencies  was influenced by sea surface temperature (SST) variability and geography, most likely due to dispersal at the larval stage. A recent study based on SNPs genotyping of the greenlip abalone  showed that its adaptive genetic structure along southern Australian coast has been influenced by environmental heterogeneity. This pattern probably results from adaptation to minimum sea surface temperature and oxygen concentration ‐based analyses  and asymmetric (AEMs) spatial variables. At our broadest spatial scale—about 4,500 km—the first db‐MEM (db‐MEM 1) has the highest contribution to allele frequency variation (ω), whereas no broad‐scale AEM was selected by the stepwise variable selection procedure. It suggests that geographic isolation is the main driver of variation in allele frequencies at this scale. As no large‐scale AEM was detected as significant, genetic variation at broad scale cannot be explained by passive larval dispersal. At such scale, genetic connectivity could result from processes such as adult mobility, demographic history, or multigenerational stepping stone larval dispersal , as the possible distance traveled by larvae increases with PLD , which has a similar PLD as M. surmuletus (30 days), disperse over distances of about 90 km on average , used to build the AEMs. E was a binary matrix of connection , which ignored the strength of the connections. This representation is required for the computation of AEMs, but it provides coarse information for larval dispersal models, in which dispersal probabilities vary between connections . Thus, we cannot focus our work on the relative importance of environmental versus geographic or dispersal variables, but more on determining which are the spatial scales where geographic isolation and larval dispersal influence the variation in allele frequencies.MEM and AEM analyses are recognized as a relevant way to capture the spatial structure in data (Legendre, Borcard, & Peres‐Neto, 5Given that genetic variation is closely related to the adaptive and resilience potential of populations (Kokko et al., M. surmuletus, and more generally in populations of demersal fishes, particular attention should be given to the spacing of MPAs. Indeed, a network of reserves that can be connected by larval dispersal would efficiently protect gene flow between protected areas. Such a network is thus likely to conserve the genetic diversity and adaptive potential of species, and to support renewal of fishery stocks (Olds et al., We show that larval dispersal influences genetic variation at small and intermediate scales, but not at broad scale where simple geographic distances primarily explain observed allele frequency variation, which may result from other processes such as adult mobility, demographic history, or multigenerational stepping‐stone dispersal. In order to maintain genetic variations in populations of https://doi.org/10.5061/dryad.31tk592.Data for this study are available at the Dryad Digital Repository: None declared. Click here for additional data file.


Biomineralisation processes invariably occur in the presence of multiple organic additives, which act in combination to give exceptional control over structures and properties. However, few synthetic studies have investigated the cooperative effects of soluble additives. This work addresses this challenge and focuses on the combined effects of amino acids and coloured dye molecules. The experiments demonstrate that strongly coloured calcite crystals only form in the presence of Brilliant Blue R (BBR) and four of the seventeen soluble amino acids, as compared with almost colourless crystals using the dye alone. The active amino acids are identified as those which themselves effectively occlude in calcite, suggesting a mechanism where they can act as chaperones for individual molecules or even aggregates of dyes molecules. These results provide new insight into crystal–additive interactions and suggest a novel strategy for generating materials with target properties. With their versatility and ease of application, soluble additives are widely used to control crystallisation processes. There is no better demonstration of the power of this approach than the process of biomineralisation, in which organisms achieve exceptional control over mineral formation to generate structures such as bones and seashells.While the focus of these studies has principally been on the activities of individual additives, crystals in biology typically grow under the influence of a suite of additives. This has the potential for greater control, where individual additives could be deployed at different time‐points in the reaction,This article describes an investigation into the crystallisation of calcium carbonate in the presence of mixtures of organic additives, where we focus on the combined effects of amino acids and coloured dye molecules. The selection of a coloured dye as one of our additives is crucial to our strategy, where it allows us to go beyond basic properties such as morphology and use optical microscopy to determine how the additives cooperate to drive occlusion within the crystal lattice.m solutions of CaCl2 and NaHCO3, and adding aliquots of amino acids and BBR at concentrations that yielded single crystals of calcite. Crystallisation was then allowed to proceed for two days. In all cases the polymorphs generated were confirmed by using powder XRD and Raman microscopy . The synergistic effects of the amino acids and dyes can be immediately seen at typical reaction conditions of [Ca2+]:[amino acid]:[dye]=250:25:1 , glycine (Gly), glutamic acid (Glu), and asparagine (Asn) . Calcite crystals precipitated in the presence of Asp alone comprised 0.26 wt % Asp and exhibited a peak shift towards lower angles due to lattice distortions of Δc/c=3.05×10−4 and Δa/a=5.21×10−5, whereas calcite containing Asp and BBR displayed smaller lattice distortions of Δc/c=1.86×10−4 and Δa/a=1.40×10−5, but broader peaks. This anisotropic lattice distortion is consistent with the elastic anisotropy of calcite.2+]:[amino acid]:[dye]=250:250:1) exhibited greater lattice distortions, but no further peak broadening . These data show that the peak shifts were exclusively due to the incorporated Asp, but that BBR contributes to peak broadening. Similar effects have been observed for calcite containing a range of nanoparticles.Having demonstrated that Asp effectively chaperones BBR into calcite, we investigated the effects on the crystal lattice using synchrotron high‐resolution powder X‐ray diffraction (HR‐PXRD). The data were analysed using Rietveld analysis Figure . Calcium2+]=1.2 mm and the concentrations of Asp and BBR were set at [Ca2+]:[amino acid]: [dye]=100:1:1. Calcite grows via a step‐growth mechanism under these conditions, and steps originate from screw dislocations present on the {104} faces  using a liquid cell Figure , where t441‾and 448‾steps, while most of the obtuse steps preserved their well‐defined shapes  simulations. These were performed at 300 K and utilised the AMBER force field to model the dye and amino acid molecules.We are also able to rule out the second scenario, where no visible increase in BBR occlusion was obtained on reduction of the calcium concentration (and thus supersaturation). In addition, analysis of the quantities of Asp and BBR occluded showed that the amount of BBR occluded correlates with the amount of Asp occluded, rather than the amount of Asp in solution Figure , where tConsidering then the third scenario, the binding of amino acids to calcite has previously been studied using simulations and experiments. A complex picture emerges which shows that occlusion efficiency cannot be predicted based on binding strength to the crystal surface. Under the experimental conditions employed here, the amino acids are zwitterionic and preferentially bind through the carbonyl oxygen and the amide hydrogen of the peptide functionality.second hydration layer rather than binding directly to the mineral surface—from where it cannot be occluded.Simulations provide an explanation for these effects. Asp binds directly to the calcite surface, stabilised by interactions of the side‐chain carboxy group with local water molecules.Finally, it is interesting to consider the generality of this mechanism. That the amino acids can chaperone aggregates of BBR into the crystal lattice likely contributes to the strong colouration observed. We therefore performed additional experiments with Asp and the highly soluble fluorescent dye HPTS  to confirm that Asp is equally effective in driving the occlusion of individual molecules in calcite. Confocal fluorescence microscopy of the product crystals confirmed that the dye is uniquely located in the zone occupied by Asp Figure —that is,This work demonstrates that greater control over the growth and properties of crystals can be achieved if organic additives are employed in combination, rather than individually. Focusing on the attractive test‐system of a coloured dye (BBR) and amino acids, we show that four amino acids—those that are themselves effectively occluded in calcite—drive the occlusion of BBR at levels that vastly exceed those achieved with the dye alone. Importantly, our work also shows that these cooperative effects are not restricted to the BBR/Asp partnership. These results therefore suggest a novel strategy for generating materials with target properties, where combinatorial methodsThe authors declare no conflict of interest.As a service to our authors and readers, this journal provides supporting information supplied by the authors. Such materials are peer reviewed and may be re‐organized for online delivery, but are not copy‐edited or typeset. Technical support issues arising from supporting information (other than missing files) should be addressed to the authors.SupplementaryClick here for additional data file.


Hydrophobically modified acrylamide copolymers dewater oil sands tailings more effectively than anionic polyacrylamide, but the root causes for this enhanced performance have not been investigated systematically. Polyacrylamide‐poly(ethylene oxide methyl ether methacrylate) copolymers with different comonomer compositions, hydrophobic chain lengths, and molecular weights to map out these effects systematically are synthetized. Through a statistical design of experiments, it is found out that all three variables above significantly affected flocculation performance and that certain combinations achieve optimal results. The effect of centrifugation on the flocculation and dewatering performance of these polymers is also investigated. One of the main contributors to this stigma is the generation of large volumes of waste stored in tailings ponds. Tailings ponds are composed of water, residual bitumen, fine mineral particles, and organic compounds, covering an area more than 175 kmThe industry has practiced several technologies to dewater and consolidate tailings. Thin lift and freeze‐thaw drying, centrifugation, and composite tailing are examples of some of these practices.−1) anionic polyacrylamide (PAM), but the sediments from PAM‐induced flocculation are loosely packed and have a gel‐like structure because they trap water in their matrix via hydrogen bonding among polymer chains and water molecules.The industry standard flocculant is ultrahigh molecular weight (>10 million g molg‐PPO) copolymer, but only compared the data points in terms of flocculation performance, and did not inquire into the reason for differing results at different copolymer compositions.−1 or 1000 g mol−1 macromonomers, but with only two different values it is not possible to determine a correlation between graft size and flocculation/dewatering performance. Their results did show that the PPO graft with the lowest molecular weight (300 g mol−1) achieved better results, such as lower turbidity and faster settling rate, which was a unique outcome, since most tests ascertain the opposite: higher molecular weight polymers usually performs better than lower molecular weight ones.g‐PPO flocculant. Ren et al. flocculated kaolin suspensions with PAM, poly (PDADMAC), and 3‐acrylamido‐2‐hydroxypropyltrialkylammonium chloride copolymers, where the alkyl group was ethyl, butyl, or octyl.Though some research groups have tested acrylamide copolymers as flocculants for oil sands tailings, to the best of our knowledge, there are only few studies that have performed a systematic study of the causes for improved dewatering and consolidation of oil sands tailings in the open literature.−1, sought in gravity thickeners, may not be as important if dewatering is done by postflocculation centrifugation or by thin lift drying. The Alberta Energy Regulator, however, has approved Directive 085 for fluid tailings management for oil sands mining projects, where it states some minimum acceptable criteria for treated tailings before they are ready to be reclaimed , which is not helpful for flocculation performance characterization.Laboratory scale screening of polymer flocculants for oil sands tailings is still controversial, mainly because of the lack of clear procedures and accepted values to quantify flocculation and dewatering, as well as different performance metrics required by different dewatering technologies. For example, a high settling rate of 20 m hIn this study, we used a central composite rotatable design of experiment to analyze our results and evaluated the performance of our novel copolymers through CST, supernatant turbidity, initial settling rate (ISR), and solid content of sediments. To observe the effect of centrifugation on flocculation performance, we also tested the turbidity, solid content, and CST after centrifugation. By studying the cause of this increased flocculation performance, we will be able to understand the reason for improvement and tailor polymers for optimal performance during oil sand tailing flocculation and dewatering. It is worth noting that the intention of this study was not to optimize the proposed flocculant composition for what are arbitrarily selected conditions or a particular MFT concentration, but rather to study how certain polymer microstructure properties  affected their performance in small‐scale tests under controlled conditions.22.1TableA Dean Stark apparatus was used to determine the amount of solids, water, and bitumen in the tailings we used for testing. The Dean Stark apparatus comprises of a trap, round bottom flask, and reflux condenser.2.2FiguresAll normal and residual plots are displayed in the L) and quadratic (Q), and their two factor interaction effects, where the notation used is linear effect by linear effect  and PEOMA wt% (2).In the ANOVA tables, we compared the three main variables, both linear , and the results are displayed in Table I], significantly affected the molecular weight of all PAM‐g‐PEOMA copolymers, as it is known that the molecular weight of a polymer is controlled by the initiator concentration, and according to Shawki and Hamielecr¯n represents the number‐average chain length of the copolymer, f is the fractional initiator efficiency, ktrm, kp, kI, and kt are the reaction rate constants for transfer to monomer, propagation, initiator decomposition, and termination, respectively, and [M] is the monomer concentration.The molecular weight distribution of all PAM‐g‐PEOMA. Figureg‐PEOMA increases as the PEOMA wt% increases, irrespective of the PEOMA length. The effect of initiator concentration on PAM‐g‐PEOMA molecular weight is observed in Figure I] increases PAM‐g‐PEOMA molecular weight, as expected for solution free‐radical polymerization. Figure I] and PEOMA wt%, and shows that polymerization with low [I] and high PEOMA wt% will produce PAM‐g‐PEOMA with the highest molecular weights.Interestingly, PEOMA wt% in the copolymer also affected the molecular weight of PAM‐g‐PEOMA molecular weight as the length of the PEOMA grafts increases, as shown in Figure g‐PEOMA copolymers with higher molecular weights; it only means that when all other variables are kept constant—namely initiator concentration, monomer/macromonomer concentration, and temperature—the molecular weight of PAM‐g‐PEOMA will decrease as the length of the hydrophobic PEOMA macromonomer increases. One could, evidently, change the polymerization conditions (such as decreasing [I]) to compensate for this effect.There seems to be a trend indicating a slight decrease in PAM‐g‐PEOMA copolymers with molecular weights ranging from 12 000 to 1 473 000 g mol−1. This wide molecular weight range will help show the effect of PAM‐g‐PEOMA molecular weight on flocculation/dewatering performance. Compared with most flocculants, the molecular weights of our PAM‐g‐PEOMA flocculants are relatively low, as it is generally accepted that high molecular weight polymers are more effective flocculants ,As seen in Table 2.4TableI], are significant factors, and the residual plots indicate a normal distribution.Figureg‐PEOMA performs best (high ISR) when the PEOMA length and wt% are high (upper right quadrant), or when the PEOMA lengths and PEOMA wt% are low (lower left quadrant). Combining this finding with the results shown in Figure g‐PEOMA copolymers lead to higher ISR when their molecular weights are higher. This agrees with our knowledge of the flocculation mechanism with these polymers, as higher molecular weight PAM‐g‐PEOMA can create larger and heavier flocs that settle faster via the bridging mechanism. Since these polymer flocculants are neutral, charge patch and charge neutralization cannot be the underlying flocculation mechanism.Once the insignificant factors are eliminated, we can create surface plots on which the effects of the manipulated variables on ISR are depicted as a function of two variables. g‐PEOMA with low PEOMA length and low PEOMA% is almost twice as high as for the PAM‐g‐PEOMA with high PEOMA length and high PEOMA%. Steric hindrance of the chains may explain these results: High molecular weight PAM‐g‐PEOMA with long hydrophobic PEOMA chains may become very large in solution, interact with one another, and hinder the entrapment of clay particles in the MFT suspension.Analyzing the numbers associated with the colors in Figure g‐PEOMA is high, and it contains high PEOMA%. The high PAM‐g‐PEOMA molecular weight and PEOMA wt% allow the flocculant to form strong bridges with multiple points of contact among the suspended particles, forming large, strong flocs that quickly settle.Figure g‐PEOMA molecular weight and ISR is stronger than for the longer PEO unit discussed in Figure I] is lowered below 0.001 mol L−1. On the other hand, the weight percent of PEOMA is not as significant, as one may achieve a high ISR for any PEOMA% when the molecular weight of PAM‐g‐PEOMA is high , although lower PEOMA% seems to be produce slightly higher ISR values.Figure 2.5TableSimilar to the ISR, the analysis of variance for turbidity values found out that all variables were significant, but we had to take the natural logarithm of the turbidity values in order to obtain a correlation with accurate prediction power. The ANOVA table shown in Figureg‐PEOMA had a high PEOMA wt% with high length (right upper corner) may be due to steric hindrance among the long hydrophobic PEOMA grafts that lower the performance of flocculation when there is a high amount of PEOMA in the copolymer. These chains may interfere with each other and are unable to trap the finest particles. On the other hand, PAM‐g‐PEOMA with shorter PEOMA grafts requires a higher PEOMA% on the copolymer to trap all of the fine particles because the PEOMA chains are shorter. Having more frequent, shorter PEOMA side chains increase the surface area of PAM‐g‐PEOMA and create more sites onto which the flocculant may adsorb to the surface of the suspended MFT particles.I] and PEOMA wt% for a constant PEOMA length of 40 and confirms our conclusions above: PAM‐g‐PEOMA with long PEOMA grafts produces supernatant with low turbidity when a low PEOMA% is present in the polymer so that there is no steric hindrance between the hydrophobic groups. Lowering the PAM‐g‐PEOMA molecular weight for a given PEOMA wt% also helps decrease the turbidity for the same reasons.Figure g‐PEOMA had high PEOMA wt% and high PEOMA length, while the turbidity analysis shows that high PEOMA length and low PEOMA% are ideal. We may suggest an explanation for this observation using the proposed flocculation mechanism for these polymers: When PAM‐g‐PEOMA polymers trap particles to form flocs, the larger flocs are heavier, and thus settle faster, but these polymers may not be able to trap all the fine particles because the larger hydrophobic PEOMA side groups interact with each other, hindering the ability to adsorb on to the suspended particles. Such copolymers would yield high ISR, but would also produce supernatants with higher turbidity. Notice that this is a discussion relative to the other PAM‐g‐PEOMA tested in this investigation. The highest turbidity measured with all copolymers was 26 NTU, which is still an acceptable value, even for the poorest performance among the PAM‐g‐PEOMA listed in Table These conclusions are different from those observed for ISR, as ISR was the highest when the PAM‐2.6TableCST data were transformed by taking the natural logarithm of the numerical data to achieve a normal residual plot  when PAM‐g‐PEOMA had a high molecular weight and PEOMA wt%. These results are easy to explain because the higher the PEOMA%, the higher the hydrophobicity of the copolymer; since CST measures the dewatering ability of flocs, a more hydrophobic flocculant is expected to retain less water inside the flocs. By the same token, a higher molecular weight PAM‐g‐PEOMA flocculant makes larger, more compact flocs that retain less water, and yield a lower CST. Although the effect of copolymer molecular weight on CST is not very significant, it contradicts the findings of our recent work where the CST of flocs produced by cationic copolymers of acrylamide and DADMAC did not depend on molecular weight.Based on the results in Table Figure 2.7Figure g‐PEOMA with long PEOMA side chains requires low PEOMA% to capture all of the fine particles, and that PAM‐g‐PEOMA with shorter PEOMA grafts needs higher PEOMA% in the copolymer to produce clear supernatants and follows the same pattern for the turbidity without centrifugation—the copolymers with a large percentage of long PEOMA grafts may hinder the entrapment of smaller particles, while the copolymers with a low percentage of short PEOMA grafts are unable to adsorb to all of the particles as there are fewer sites to form a proper bridge. Although we could obtain a strong correlation among these variables, it should be noted that the highest turbidity after centrifugation was only 16.4 NTU, which still corresponds to a very clear supernatant. For all practical purposes, after centrifugation, all PAM‐g‐PEOMA flocculants produced supernatants that were essentially free of fine particles. All polymers produced supernatants with very low turbidity. Although the differences are statistically significant and indicate that the polymer microstructure affects turbidity, these differences may have little practical significance, since all of them satisfy the condition of less than 0.5% solid residues in the supernatant indicated by Directive 085.The supernatant turbidity following centrifugation yields very similar results to the turbidity after 24 h of settling. 2.8TableAn interesting observation obtained through this study was the effect of centrifugation on the solids content of the sediments, compared that obtained in settling over a single day. By dividing the solid content of the centrifuged solids by that of the 24 h settled solids, we were able to compare the increase in solid content by centrifugation. Figureg‐PEOMA, the greater the increase in solids content. The centrifuge increases the g‐force on the sample, forcing the flocs to settle to the bottom of the centrifuge tubes, where the high hydrophobic PEOMA groups decrease the amount of water trapped inside of the sediments. Equally important, the lower the [I], the higher the molecular weight of PAM‐g‐PEOMA, and the greater the effect of centrifugation on the solids content. The impact of PAM‐g‐PEOMA molecular weight is shown most clearly in Figure I] values and high PEOMA%. As shown by Figure The red areas in 2.9g‐PPO copolymer, where the shorter length hydrophobic grafts combined with a high percentage of grafts performed the best.−1 macromonomer) was when they used only 5 wt% PPO. The combination of a long hydrophobic chain length and low wt% was required in our model so there was no steric hindrance. The only copolymer with the longer PPO length that performed better in some tests was a 35 wt% PPO, but it performed better in CST, where our model states that the additional hydrophobic groups would expel water faster, producing a lower value. The alignment between their results and our conclusions confirms that our model is applicable in other polymer systems where a hydrophobic chain is added to acrylamide.As mentioned in the introduction, Reis et al. explored the addition of hydrophobic groups to an acrylamide backbone. They found similar results with the PAM‐Shang et al. also added hydrophobic groups to acrylamide, making poly(acrylamide‐methacryloxyethyl trimethyl ammonium chloride‐methacryloxypropyltrymethoxy silane) (P(AM‐DMC‐MAPMS)).3As displayed through the different optimal variables for each one of the tests, there is no “perfect” polymer for treating MFT; however, it is possible to tailor a polymer for specific flocculation purposes. Depending on the required characteristics of flocculation, one is able to synthesize a polymer based on the criteria. This study allowed us to delve deeper into the reasons behind enhanced flocculation performance gained by the addition of hydrophobic chains to an acrylamide backbone. Through the analysis of the effects of initiator concentration, PEOMA length, and PEOMA wt%, we determined the effect of hydrophobic chain addition to acrylamide polymers. Adding many hydrophobic chains (>30 wt%) can effectively help in the dewatering performance of the flocculant, shown in the ISR and CST tests, but may not capture all of the fine particles, leading to a higher turbidity. A lower initiator concentration (higher molecular weight) was also shown to improve flocculation performance in most categories. The length of hydrophobic chains was observed to be of mild importance—the shorter chains required a larger percentage of hydrophobic groups to achieve the same effects as the larger chains, whereas the longer hydrophobic chains needed a low weight percent of PEOMA to prevent steric hindrance.g‐PEOMA copolymer molecular weights studied were low (<200 000 g mol−1), we still observed very good flocculation performances from all polymers tested. This contradicts the most common assumptions about flocculants, but it actually has some advantages as well. There are benefits to having low molecular weights, as high molecular weight flocculants will lead to an increase in viscosity and may cause problems such as nonuniform mixing. Reis et al. also noted that having a lower molecular weight flocculant allows for a wider window of mixing environments in which the flocculant may be used.Although most PAM‐4Materials: Monomers  methyl ether methacrylate (PEOMA)), initiator  dihydrochloride (V‐50)), and organic solvents  were purchased from Sigma Aldrich. Coanda Research and Development supplied the MFT used in this investigation.Polymerization: The copolymers were synthesized by free‐radical polymerization at 40 °C in a batch reactor. The reagents were added to the reactor in the following order: deionized water, acrylamide, and then PEOMA. Once in the reactor, the solution was purged with nitrogen for 1 h to ensure a complete nitrogen atmosphere. After that, the initiator, also purged with nitrogen, was added to begin the polymerization. The polymerization was left to run for 24 h to obtain high monomer conversion for all experiments. The total monomer concentration was kept constant at 0.1 mol L−1 for all polymerizations. Once the reaction had finished, the polymer, whose structure is shown in FigureMolecular Weight Measurement: The molecular weights of all copolymers were measured by gel permeation chromatography using an Agilent 1260 Infinity GPC and PL aquagel‐OH Mixed‐H 8 µm columns. PEO standard samples with narrow molecular weight were used for the column calibration. The concentration of all samples was 3.6 mg mL−1, diluted in an aqueous solution containing 0.2 m NaNO3, and injected at a volume of 100 µL. The analysis was performed at a flow rate of 1 mL min−1 and at a temperature of 30 °C.Experimental Design: Utilizing a central composite rotatable design allows us to compare the effects of macromonomer size, copolymer composition, and molecular weight as a function of flocculation performance. Macromonomer refers to a macromolecule with a functional group to allow further polymerization. As per the design, there were five different coded values for each variable, where 15 unique combinations and an additional three repetitions of the center point to ensure reproducibility were tested, allowing us to create a surface to map the effects of the design variables shown in TableI], was varied to obtain a range of copolymer molecular weights. All data through Statistica 13 (StatSoft Inc.) were analyzed. In Table Although Table Test Methods: In order to create the MFT slurry for testing, the MFT sample was diluted to 5 wt% solids in 160 g of slurry, using deionized water. The concentration of solids in the suspension to obtain as uniform mixing as possible when testing different polymers, and to maximize the ability to observe performance differences upon addition of polymers with different microstructures was diluted. The slurry was mixed at 610 rpm for 30 s to obtain a homogeneous distribution of solids in the slurry. At that point, 1000 ppm of a Ca2+ solution was added and mixing continued for an additional minute. The addition of calcium is necessary, as the neutral polymer flocculants here used are unable to form bridges with the suspended particles without the charge neutralization effect obtained by calcium ions. The polymer dosage was kept constant at 10 000 ppm (mg polymer per kg of dry solid) for all experiments, and the polymer mixed with the slurry at 610 rpm. The dosage of 10 000 ppm in this study was chosen based on preliminary tests with the polymers (from the highest MW to the lowest MW), and it was found out that this dosage was not too far from the working window of the polymers. It is true that 10 000 ppm would be considered a high dosage in practical application ;Once flocs formed, the mixing speed was reduced to 300 rpm and stirring continued for another 2 min. After flocculation was done, while the flocculated suspension was still under gentle mixing (to achieve uniform solid concentration upon sampling), a large pipet (0.5 cm diameter) was used and separated the flocculated suspension into three different portions: (1) A volume of 10 mL was used for testing CST with a Triton Electronics Meter (Type 319 multi‐CST) with Triton filter paper (7 cm by 9 cm); (2) An aliquot of 50 mL was transferred to a Corning centrifuge tube for centrifugation purposes; and (3) The remaining 100 mL of sample was poured into a graduated cylinder so the change in mudline level could be recorded as a function of time.After the slurry had settled in the graduated cylinder for 24 h, the supernatant was extracted for turbidity measurement in a Hach 2100 AN Turbidmeter, and additional CST measurements were performed on the solids after settling. The 5 mL of settled solids were also taken, and the solids content was calculated by weighing the sample before and after 24 h drying in an oven at 60 °C. The solids content is calculated by the following formula, where weight was measured in gramsThe authors declare no conflict of interest.


Wheat blast was first reported in Brazil in 1985. It spread rapidly across the wheat cropping areas of Brazil to become the most important biotic constraint on wheat production in the region. The alarming appearance of wheat blast in Bangladesh in 2016 greatly increased the urgency to understand this disease, including its causes and consequences. Here, we summarize the current state of knowledge of wheat blast and aim to identify the most important gaps in our understanding of the disease. We also propose a research agenda that aims to improve the management of wheat blast and limit its threat to global wheat production. The correct name for the Pyricularia lineage(s) causing wheat blast is currently under debate , following Castroagudín et al.  and 59 strains of other Pyricularia species, could not determine whether the wheat blast pathogen in Brazil had a single or multiple origins. The absence of strict host specialization amongst the major Pygt subclades indicates that the capacity to infect wheat may have originated multiple times , millet , oats (Avenasativa), perennial ryegrass (Lolium perenne), wheat (Triticum aestivum) and more than 50 other grass species have been classified under the P. oryzae species complex , the Lolium pathotype was pathogenic on perennial ryegrass and the Triticum pathotype was pathogenic on wheat , signalgrass (Urochloa brizantha) and more than 10 other grass species  will clarify the association between host range and the possibility of recent speciation.From the time at which the rice blast pathogen was first named Pyricularia and Pyricularia‐like species, leading to substantial changes in the order Magnaporthales , which were both independent of the Oryza pathotype. The assignment of the new Pygt species was challenged by Gladieux et al.  lineage which was placed into a separate clade when only 10 housekeeping genes were used   the hypothesis of grass‐specific populations for the overall P. oryzae species complex is falsified; and (3) P. oryzae may not provide a suitable model for understanding the biology of Pygt.A more recent study including 95 genomic sequences of the wheat blast fungus from Brazil and other Pygt as a distinct species has important implications for quarantine regulations worldwide  activity and a reduced RuBisCO activation state  correlates negatively with CO2 internal concentration (Ci), and A is negatively correlated with blast severity  and the light saturating point (LSP), coupled with an increase in dark respiration (Rd), indicating plant photoinactivation, have been reported in infected leaves , capture efficiency of excitation energy by the open PSII reaction centres (Fv′/Fm′), photochemical quenching coefficient (qP) and electron transfer rate (ETR).Infection of plant leaves and spikes by Pygt show lower fructose, glucose and sucrose concentrations and reduced sucrose phosphate synthase (SPS) activity  and superoxide anion radical (O2•), and a reduction in lipid peroxidation, are essential for leaf and spike resistance to blast, especially on cultivars with a high level of partial resistance , glutathione reductases (GRs), peroxidases (POXs) and superoxide dismutases (SODs) have been associated with the high level of basal resistance of some cultivars  contained high genetic and virulence diversity, including 198 multilocus microsatellite genotypes (MLMGs) and 25 virulence groups , indicating that it is a promising candidate for the observation of the teleomorph in the field.Contemporary populations of Pygt isolates are capable of infecting wheat and other grasses, and can move back and forth between hosts, we hypothesize that sexual recombination may occur preferentially on non‐wheat hosts with the resulting ascospores and/or conidia infecting nearby wheat fields, giving rise to the highly diverse Pygt population detected on wheat. This scenario is supported by previous reports of gene flow amongst wheat‐ and other Poaceae‐adapted populations of Pygt   strategies will be needed to minimize crop losses without impacting environmental sustainability Mehta, . The impPygt inoculum  on crop residues between wheat cropping seasons  cultivation could be an option for crop rotation  difenoconazole. The eradicant fungicide dimethyldithiocarbamate thiram, which is labelled for the control of other seed‐borne wheat pathogens, is also effective against et al., Sisalert (Plant Disease Epidemic Risk Prediction System), was developed to calculate the risk of an epidemic , coupled with high temperatures (25–30 °C)   have been identified in common wheat (T. aestivum) and in tetraploid emmer wheat (T. dicoccum) , KU120 (St24) and KU122 (St25), carriers of Rmg7  in its pedigree. In the 2017 screening of Embrapa wheat genotypes in a wheat blast hotspot in Uberaba, Minas Gerais (MG), Lagoa Vermelha showed high levels of blast resistance during an epidemic year. However, in Patos de Minas, MG, under more tropical conditions, cultivar Thatcher showed high susceptibility to blast . Interestingly, the et al., Progress in breeding for wheat blast resistance will probably be facilitated through the development of uniform sets of host and pathogen differentials that can be used as controls to enable comparisons of results and better coordination of breeding efforts distributed across several locations. Screening of germplasm for wheat blast resistance should include a representative set of the contemporary virulence diversity existing in the wheat blast population across Brazil, including the HVG A super‐race. An optimal field screening for resistance would include several locations across different agroecosystems, including Mato Grosso Sul State, which is a hotspot for the pathogen’s virulence diversity, with the highest number of SVGs (11) and HVGs (6). For countries interested in pre‐emptive breeding for wheat blast resistance, germplasm should be screened for blast resistance in Brazil, where the pathogen is genetically diverse, virulent, endemic and distributed across a temperature cline ranging from the colder regions of Rio Grande do Sul to the warmer Cerrado areas of Goiás Fig. . Three ret al., et al., et al., et al., Pygt inoculum produced on the lower wheat leaves, with a subsequent reduction in ear infections   and triazole (tebuconazole and epoxiconazole) fungicides was found to be pervasive in populations of the pathogen across the major wheat cropping areas from central western to southern Brazil  values at least 30–200 times higher than the wild‐type isolates  and triazoles   combined with the QoI pyraclostrobin and/or the DMI epoxiconazole , it suggests that much remains to be learned about wheat seed infection.There are many important gaps in our knowledge of wheat blast that will need to be filled in order to develop long‐term management strategies. Although population genetic and genomic studies have provided important insights into the possible origins of Pygt population in Brazil, it is not obvious what sources of germplasm should be used in the case of a newly emergent disease that did not co‐evolve with its main economic host (wheat), especially given the broad host range of Pygt. If non‐wheat hosts, such as Urochloa, are shown to be the primary host for the pathogen, with blast disease on wheat mainly collateral damage occurring as a result of inoculum spillover from an epidemic on the non‐wheat host under particular environmental conditions, it may be more appropriate to breed for resistance in the non‐wheat host.Equally important will be to identify new sources of wheat blast resistance that are effective and stable. These efforts could be made more efficient by agreeing on a uniform set of host and pathogen differentials to include in screening programmes. Although it seems obvious that the screening for resistance should take place under selection by the highly diverse Pygt, management strategies may need to focus on the control of the infection in the non‐wheat host in order to prevent spillover into nearby wheat fields. For example, biological control agents or fungicides may be more effective if applied to the primary, non‐wheat host instead of the wheat field. A crop rotation designed to remove Pygt inoculum from wheat stubble of previous crops is unlikely to be effective if the primary source of inoculum is coming from the perennial Urochloa pastures growing near the wheat field. It is becoming clear that we need to know much more about the biology of Pygt on its non‐wheat hosts.Similarly, if a non‐wheat grass is the primary host of


SD) by sensory stimulation. However, stimulation‐induced SD can be stressful and can cause non‐specific side effects. An emerging alternative method is “genetic SD”, which removes sleep using genetics or optogenetics. Sleep requires sleep‐active neurons and their regulators. Thus, genetic impairment of sleep circuits might lead to more specific and comprehensive sleep loss. Here, I discuss the advantages and limits of genetic SD in key genetic sleep model animals: rodents, zebrafish, fruit flies and roundworms, and how the study of genetic SD alters our view of sleep functions. Genetic SD typically causes less severe phenotypes compared with stimulation‐induced SD, suggesting that sensory stimulation‐induced SD may have overestimated the role of sleep, calling for a re‐investigation of sleep functions.Sleep is a fundamental conserved physiological state in animals and humans. It may serve multiple functions, ranging from energy conservation to higher brain operation. Understanding sleep functions and the underlying mechanisms requires the study of sleeplessness and its consequences. The traditional approach to remove sleep is sleep deprivation ( ALACaenorhabditis elegans interneuron/mechanosensory neuronname of a specific AMPAα‐amino‐3‐hydroxy‐5‐methyl‐4‐isoxazolepropionic acidCNOclozapine‐N‐oxidedFBdorsal fan‐shaped bodyEEGelectroencephalogramEGFepidermal growth factorGABAγ‐aminobutyric acidGPCRG protein‐coupled receptorHPAhypothalamic–pituitary–adrenal axisMSmedial septumPBparabrachial nucleiPIpars intercerebralisPZparafacial zoneREMrapid eye movementRISC. elegans interneuronring interneuron s, name of a specific ROSreactive oxygen speciesSDsleep deprivationSIK3salt‐inducible kinase 3VLPOventrolateral preoptic nucleusWe spend about one‐third of our lives asleep. Sleep curtailment is detrimental to human health and is associated with an increased risk of infection, depression, cardiovascular disease, obesity, type 2 diabetes, and cancer, illustrating the importance of this time investment. The high prevalence of insomnia and insufficient sleep quality in modern societies thus presents a massive unmet health problem Sleep research first focused on humans. Seminal work using EEG recordings showed that humans have two types of sleep, REM sleep and non‐REM sleep, which are also called active and quiet sleep, respectively. During REM sleep, the brain shows high asynchronous activity similar to wake, concomitant with paralysis of striated muscles, with a few exceptions including the musculature controlling eye movement or breathing. During non‐REM sleep, both muscles and neurons show reduced activity with highly synchronous brain activity Drosophila, and Caenorhabditis elegans, as these models facilitate a molecular and mechanistic dissection of sleep Behavioral quiescence has long been observed across species, including invertebrates. However, defining sleep using EEG correlates for REM and non‐REM sleep often is not possible due to brain anatomical differences. Nevertheless, quiescence can also be identified as sleep by using four key behavioral criteria Compelling evidence for the existence of a species that has a nervous system but never sleeps is lacking, but the amount of sleep is highly plastic and some animals can get away with little sleep. Environmental conditions impact sleep need, and the time spent in sleep differs substantially across species. Under extreme conditions, temporary sleep restriction or even complete loss appears to exist and confers a selective advantage. For example, migrating and mating birds appear to be able to suspend or reduce the need to sleep for at least several days The physiological state of sleep has been proposed to play multiple roles that can be coarsely sorted into three groups that are overlapping and not mutually exclusive. (i) The first group of sleep function theories posits that sleep plays a role in optimizing behavior and the conservation or allocation of energy. (ii) The second group states that sleep may regulate core molecular and cellular processes. (iii) And the third group suggests that sleep serves higher brain functions An adaptive value of sleep could be understood by viewing sleep as an inactive state. At times when wakefulness is not advantageous, the organism would enter an inactive state and thus save energy. A strong argument that energetic and ecological constraints play a role in determining sleep is the large variation in sleep amount and intensity seen across species sleepy causes excessive sleep duration and intensity It has been proposed that sleep becomes regenerative by allowing or facilitating key molecular and cellular housekeeping functions. This view has been supported by biochemical and transcriptomic studies that found that sleep is associated with an increase in the expression of genes required for biosynthesis and transport Seminal experiments showed that a good night's sleep is important for learning and memory. Memory formation requires synaptic and cellular changes across neural circuits and brain regions that encode this memory. Transcriptome analysis of sleeping brains has found that an increased expression of genes required for plasticity and protein synthesis during sleep is required for memory formation, suggesting that sleep serves the expression of plasticity genes to support learning The nervous system plays a crucial role in sleep induction. Research on the neural substrates of sleep control started with work on human patients who had suffered from sleep loss as a consequence of infection‐induced neural injury. Lesions in a particular brain area, the anterior hypothalamus, led to a reduction of sleep, demonstrating that dedicated centers exist in the mammalian brain that control sleep Most of the theories regarding the functions of sleep are based on observations of processes that correlate with sleep, and causality is established by studying the consequences of sleep deprivation. Sleep is under the control of wakefulness‐promoting and sleep‐promoting circuits, which oppose each other to generate discrete states An alternative strategy to SD by sensory stimulation is to render model animals sleepless by impairing the sleep‐inducing system. In this paradigm, the organism specifically lacks sleep induction, not requiring additional stimulation. The increase in arousal following sleep neuron inhibition should be attributable to a disinhibition of the wake‐promoting system Fig . How canSpecificity of the sleep mutant phenotype is essential to link sleep loss to its consequences. However, many mutations affect sleep indirectly. For example, circadian rhythms control global physiology, and their abrogation can also result in sleep loss Chronic sleep restriction in humans is associated with long‐term health consequences, and model animals that genetically reduce sleep will be important tools to study the mechanisms underlying chronic sleep restriction. For studying the functions of sleep in model organisms, it may be favorable if the degree of sleep removal is high, perhaps even complete. Homeostatic compensatory processes exist that can compensate for sleep loss. For example, reduction of sleep amount in experimental models can lead to increased sleep depth during the remaining sleep time, which, at least in part, ameliorates the consequences of sleep loss. Some animals can live with little sleep, suggesting that relatively small amounts of sleep can be sufficient to fulfill sleep's essential functions Dreamless, a dominant mutation in a gene that encodes an ion channel required to control neural excitability, leading to a strong reduction of REM sleep but also causing defects in other rhythmic processes Dreamless mutant and optogenetic induction of REM sleep present tools to investigate REM sleep functions, but such studies have not yet been published. Proving causality for REM sleep functions has been a challenge because manipulating REM sleep typically also affects non‐REM sleep Seminal discoveries on sleep were made using a variety of mammalian models including mice, rats, cats, and monkeys. These model animals have been pivotal in studying both non‐REM and REM sleep. The brain structures controlling sleep in mammals have turned out to be highly conserved. Its molecular amenability has made the mouse the most intensively used species for genetic sleep studies in mammals Mutants that specifically and completely remove non‐REM sleep in mammals have not yet been described, and the known mutants that show reduced sleep all display only partial sleep loss and often are not very specific but also confer additional phenotypes and are thus not ideal for genetic SD Restriction of sleep by VLPO lesion in rats has been used to study the role of sleep in metabolism. While ghrelin was increased and leptin was reduced, a decreased body weight and no signs of metabolic syndrome or obesity were found in this model Danio rerio presents an important vertebrate sleep model system between rodent and invertebrate models. Like humans and unlike rodents, zebrafish sleep mostly during the night. Zebrafish appear to have a quiet sleep state but evidence for a sleep state that resembles REM is lacking. While one study could not find evidence for rapid eye movement during sleep, this result does not exclude the possibility that other components of REM sleep are present in zebrafish The zebrafish hcrt or neuromedin U causes hyperactivity and suppresses sleep. The effects of transient overexpression are quite variable but can suppress about half of the sleep time hcrt neurons can be used to decrease or increase sleep, respectively kcnh4a potassium channel genes act in hcrt neurons to regulate their activity, with kcnh4a knockout resulting in a 15% sleep reduction npvf neuropeptide gene also causes hyperactivity and reduces sleep by 10% aanat2 in zebrafish reduces night sleep in the presence of light–dark cycles by about 50%. In free‐running conditions , the increase of sleep during the subjective night is almost completely eliminated. These results suggest that melatonin is the major factor for circadian regulation of sleep in zebrafish Through genetic screening multiple mutants with reduced sleep have been identified. For example, knockout of the sleep‐promoting neuropeptides QRFP and prokineticin 2 reduce sleep. However, these mutants produce only small effects because these factors control the relatively small amount of sleep that occurs during the day. Overexpression of wake‐promoting genes such as Drosophila melanogaster has emerged as a leading model system to study the molecular basis of sleep. Its main advantages are genetic amenability and a clear coupling of sleep to the circadian rhythm. Like humans and zebrafish, Drosophila sleep mostly during the dark phase and also have a period of behavioral inactivity during the middle of the light phase that is called a siesta. Thus, behavioral activity in fruit flies occurs mostly during both the morning and the evening hours. Drosophila has been instrumental in solving the molecular underpinnings of circadian rhythms and thus presents a prime system to study the control of sleep and its regulation by the circadian clock Drosophila can severely reduce sleep. For instance, mutation of the nicotinic acetylcholine receptor α subunit gene redeye, the potassium channel regulator hyperkinetic, or RNAi of cyclin A or its regulator reduced sleep by about half shaker potassium channel, the ubiquitin ligase adapter complex gene insomniac, and the dopamine transporter gene fumin reduced sleep by about two‐thirds sleepless mutation with about 80% of sleep reduction. sleepless encodes a neurotoxin that regulates shakercrossveinless‐c, a Rho GTPase‐activating gene, reduced sleep by about half. Importantly, mutation of crossveinless‐c decreases sleep without causing signs of hyperactivity Drosophila (Fig crossveinless‐c mutants present specific, albeit partial, genetic SD in Drosophila and should, along with other mutants, provide useful models for studying the effects of sleep restriction in fruit flies. Similar to mammals, several populations of sleep‐promoting neurons exist and the ablation of individual populations causes partial sleep loss. It is not well understood how the various sleep centers in Drosophila interact to cause sleep, but they likely act, at least in part, in parallel pathways. It might be possible to combine mutations that target different sleep‐promoting areas and test whether this would result in near‐complete sleep loss. This would not only shed light on how the different sleep centers interact but might also generate stronger models of genetic SD. It will be interesting to see whether near‐complete genetic SD will be possible and whether and how it would result in lethality.Fly brains possess several centers that contain wake‐promoting or sleep‐promoting neurons. Wake‐promoting centers are, for example, cyclin A‐expressing neurons of the pars lateralis hila Fig . Thus, sDrosophila, and is detrimental Drosophila sleep mutants. For instance, loss of the sleepless gene causes both a shortening of sleep and lifespan, while neuronal knockdown of insomniac leads to sleep reduction without a shortening of longevity fumin did not cause a shortening of lifespan but a reduction of brood size insomniac did not demonstrate a role for sleep in survival of infection or starvation. The short‐sleeping mutant did, however, exhibit a sensitivity to survive oxidative stress. Several other short‐sleeping mutants showed oxidative stress sensitivity as well, suggesting that the sensitivity was probably not conferred by pleiotropic side effects caused by the mutation but rather is broadly associated with sleep loss. Consistent with this finding, increasing sleep genetically or pharmacologically conferred greater resistance to oxidative stress Drosophila, but also illustrate how the use of multiple sleep mutants distills a sleep phenotype from potentially pleiotropic mutations.Sensory stimulation‐induced SD leads to hyperarousal, the activation of cellular stress responses in Caenorhabditis elegans is the genetic animal model with the smallest nervous system, as it has only about 0.3% the number of neurons of an adult Drosophila or zebrafish embryo brain. The connectome of the 302 neurons of the hermaphrodite has been mapped, providing an entry point for circuit studies C. elegans is attractive to study due to its genetic amenability and the invariant number of neurons allowing straightforward genetic SD. Caenorhabditis elegans shows sleeping behavior across many life stages and conditions. In the developing larva, sleep is linked to the molting cycle, and sleep bouts occur during a phase called lethargus prior to the molt lin‐42C. elegans sleep following severe cellular stress C. elegans; however, they do not present specific manipulations Caenorhabditis elegans possess two major individual neurons that have been implicated in the induction of sleep. Cellular stress causes the secretion of EGF, which activates EGF receptor signaling in a neuron called ALA ceh‐17 or ceh‐14 renders ALA dysfunctional and thus strongly impairs sleep following cellular stress As in other species, hyperactive mutations can reduce sleep in C. elegans is called RIS. This neuron is sleep‐active as it depolarizes at the onset of sleep bouts and its optogenetic depolarization acutely induces sleep aptf‐1 renders RIS inactive, because AP2 is required for the expression of sleep‐inducing neuropeptides Drosophila and humans aptf‐1 mutant worms show no severe hyperactivity during wake, indicating that they are not strongly hyperaroused following sleep loss and that sleep loss is likely not a consequence of increased arousal C. elegans presents both a virtually complete as well as a highly specific model for sleeplessness (Fig The second major known sleep‐promoting neuron of ness Fig . It has C. elegans are viable and display much less severe consequences compared with SD by sensory stimulation, which can even be lethal Caenorhabditis elegans lives a boom‐and‐bust lifestyle and alternates between short periods of superfluous food and long periods of starvation. Consistent with these ecological constraints, sleep becomes essential during larval starvation‐induced developmental arrest. It was shown that genetic SD shortens the survival of starved larvae by half. Rather than merely conserving energy, sleep appears to slow down the progression of aging phenotypes Caenorhabditis elegans has a short generation time and life span and may thus be uniquely able to survive sleep loss under ideal laboratory conditions. In summary, similar to Drosophila, essential sleep functions following genetic SD in C. elegans are most obvious under challenging conditions.Loss of ALA function is viable during physiological conditions but impairs survival upon cellular stress, demonstrating the importance of sleep in recuperating from cellular insult. The need to sleep after cellular stress is plastic and is reduced if the general stress resistance is increased, suggesting that sleep is part of a stress resistance program Drosophila and C. elegans produces smaller phenotypes compared with stimulation‐induced SD. Work from Drosophila and C. elegans suggests that sleep becomes especially important for survival during challenging conditions. Improving the genetic sleep loss models by increasing the degree and specificity of sleeplessness as well as fine‐tuning the amount and timing of lost or gained sleep will be important next steps in facilitating the study of sleep functions in animals. Analyzing phenotypes of genetic SD models will help define core functions of sleep and support our endeavor to understand how sleep becomes vital.Recent advances in the understanding of how sleep is regulated in genetically accessible model organisms have made it possible to genetically remove sleep to a high degree and specificity. Acute SD by sensory deprivation and chronic genetic SD are obviously different experiments and can lead to different conclusions as to the functions of sleep. In future studies, it will be important to understand the basis of these differences. It is as of now unclear whether SD by sensory stimulation overestimates the role of sleep because it causes non‐specific side effects or whether genetic SD underestimates the role of sleep because of compensation processes. Genetic SD models can be used to study the consequences of sleep restriction or loss. Until now, specific phenotypes from genetic SD are scarce. However, sleepless model animals are increasingly employed for studies aiming to understand the consequences of sleep loss and will likely be key to comprehend why animals and humans need to sleep. Initial results indicate that much of the phenotypes observed after SD may not be a direct consequence of the lost sleep. For instance, the metabolic consequences of sleep loss in humans have been challenged by more specific surgical or genetic SD in rodents. Similarly, genetic SD in The author declares that he has no conflict of interest.What are the vital functions of sleep?The functions of sleep have been studied for decades, mostly by either correlation or SD induced by sensory stimulation. Genetic SD is an emerging alternative to remove sleep but typically produces weaker phenotypes compared with stimulation‐induced SD. It could be that constitutive genetic SD leads to compensatory changes, whereas acute SD cannot be easily compensated for. However, the power of constitutive genetic SD lies in the potential accumulation of the consequences of sleep loss over time. Also, transgenerational effects of sleep loss should be studied for long‐term effects of sleep loss. Thus, a thorough analysis of the different SD methods and a re‐evaluation of the previously proposed roles of sleep will be necessary to understand sleep functions.Can sleep be removed specifically and completely using genetic SD?A prerequisite for genetic SD is specificity of the manipulation as well as a high degree of deprivation. However, it is yet unclear what level of specificity can be achieved. Genes and neurons that control sleep may have functions that overlap with other processes. Also, complete genetic SD likely is lethal in many systems such as mammals. Thus, partial or conditional genetic SD will be the methods of choice for studying sleep functions in this case.How did sleep evolve and how conserved are sleep functions?Molecular analysis has suggested that there is a high level of conservation of sleep regulation but it is less clear how conserved molecular sleep functions are. Also, it is not clear for which initial functions sleep has been selected for. Speculatively, sleep emerged in evolution to save energy or serve basic needs and was co‐opted later to also serve higher brain functions. Studying sleep functions across models should shed light on these questions. Evolutionary studies would be aided by studying sleep and sleeplessness in additional models beyond the widely used animals described herein.How does sleep exert its functions?While many ideas exist as to the potential functions of sleep, little is known about the underlying mechanisms. For example, it is not known what molecules are preserved, how resources are allocated, and how cellular processes are aided. It is unclear how basic molecular and cellular functions of sleep support a healthy physiology and how sleep is linked to aging. Also, how sleep aids higher brain functions is not clear. To answer these questions, the consequences of sleep loss need to be studied using multiple molecular and systems approaches across animals.


Many studies, including the discovery of the transcription factors autoimmune regulator (AIRE) and fasciculation and elongation protein zeta family zinc finger (FEZF2), have shown that a defect in thymus central self‐tolerance is the earliest event promoting autoimmunity. AIRE and FEZF2 control the level of transcription of many neuroendocrine self‐peptides and TRAs in the thymic epithelium. Furthermore, AIRE and FEZF2 mutations are associated with the development of autoimmunity in peripheral organs. The discovery of the intrathymic presentation of self‐peptides has revolutionized our knowledge of immunology and is opening novel avenues for prevention/treatment of autoimmunity.Confirming Burnet's early hypothesis, elimination of self‐reactive T cells in the thymus was demonstrated in the late 1980s, and an important question immediately arose about the nature of the self‐peptides expressed in the thymus. Many genes encoding neuroendocrine‐related and tissue‐restricted antigens (TRAs) are transcribed in thymic epithelial cells (TECs). They are then processed for presentation by proteins of the major histocompatibility complex (MHC) expressed by TECs and thymic dendritic cells. MHC presentation of self‐peptides in the thymus programs self‐tolerance by two complementary mechanisms: (1) negative selection of self‐reactive “forbidden” T cell clones starting already in fetal life, and (2) generation of self‐specific thymic regulatory T lymphocytes (tT reg cells. There is no doubt that thymus‐based novel strategies could alleviate in the future the weight of so many known and still unknown autoimmune diseases that remain the heavy tribute, mainly paid by mankind, for the performance and extreme diversity of its highly complex adaptive immunity.Presentation of self‐peptides in the thymus is the basic mechanism that underlies T cell differentiation, negative selection of self‐reactive T cell clones, and generation of tT Thymus cunila (savory or “sarriette” in French). Galen thought that the thymus had no other function than providing protection between the sternum and superior vena cava. Galen also observed that the size of the thymus was larger in young animals and decreased with aging.The word “thymus” first appeared in the manuscripts of Claudius Galen (129−around 216 A.D.) and was described as an excrescence having some morphological analogy with the leaf of the plant Over many centuries, the anatomy of the thymus was the essential focus of the studies on this organ until the Scottish embryologist John Beard 1858−1924), a pioneer of today's theory of cancer stem cells, wrote that he considered the thymus to be the source of some white blood cells in the body., a pioneIn 1890, the German anatomist Wilhelm Waldeyer 1836−1921) noticed that in elderly people islets of normal thymic tissue could be observed even after adipose involution of this organ. Jan‐August Hammar  also showed that, although thymic development is maximal at puberty, normal thymic tissue persists until advanced age. He observed that animal castration before puberty prevents thymic involution with age and that thymic hypoplasia is associated with pregnancy, undernourishment, and some infectious diseases. He showed that, conversely, thymic hyperplasia is associated with autoimmune Graves’ thyroid disease, Addison's adrenal insufficiency, myasthenia, and acromegaly.−1921 notThe first immunological function of the thymus was discovered when French‐born Australian immunologist Jacques F.A.P. Miller showed that thymectomy performed in mice immediately after birth rendered them highly susceptible to infections and provoked their premature death. He also observed a marked lymphopenia in blood, spleen, and lymph nodes of these mice. These animals were also unable to reject a foreign skin graft, an essential hallmark of the immune response at that time. Miller concluded that the thymus was the organ responsible for the development of immunocompetent cells that constitute a specific cell population, thymus‐dependent (T) lymphocytes.In the perspective of hematopoietic growth factors identified at that time, Miller advanced the hypothesis of one or several soluble thymic factors that would be responsible for driving T cell differentiation.tolerance to characterize one of the cardinal properties of the adaptive immune system with diversity and memory. In marked contrast with Medawar's statement, during a conference at the University of London in 1962, Burnet prophesized: “If, as I think, the thymus is the site where occurs proliferation of lymphocytes in clones with precise immunological functions, we have also to consider another function: elimination or inhibition of clones with reactivity to self.”Since the foundation of immunology at the end of the 19th century, prediction of autotoxicity/autoimmunity has been intimately associated with the discovery of antitoxins/antibodies. As soon as in 1900, Paul Ehrlich  proposed the aphorism “horror autotoxicus” to claim the impossibility that one organism could be attacked under normal conditions by its own cells in charge for its defenses. Ehrlich thought then that either structures or mechanisms should exist to avoid autotoxicity, and this should be of the highest importance for individual health and species survival.30 cumulated TCR and BCR combinations, the majority of which are able to recognize self‐antigens. In normal conditions, however, the adaptive immune system does not aggress self and, for a long time, immunology was defined as the science of self−nonself discrimination. Burnet was actually speaking about immunology as the science of self and nonself recognition. Indeed, without “training,” lymphocytes are not able to discriminate between self‐ and nonself‐peptides. In 1987 and 1988, the research groups of Douarin,before the acquisition of T cell immunocompetence. Therefore, the thymus is not only responsible for the generation of diversity of the TCR repertoire, but is also primarily a cemetery for early T cells expressing a TCR specific for self‐antigens that are presented to differentiating T cells by proteins of the major histocompatibility complex (MHC) expressed by TECs and thymic dendritic cells (DCs).Afterward, the molecular mechanisms responsible for the stochastic recombination of gene segments encoding the variable domains of the immunoglobulin B cell receptor for the antigen (BCR)reg cells that are able to inhibit in the peripheral self‐reactive T cells having escaped negative selection in the thymus. The Foxp3 transcription factor, through its expression in Treg cells, is essential for the prevention of autoimmunity as shown in the human immune dysregulation, polyendocrinopathy, enteropathy, X‐linked (IPEX) syndrome, as well as in scurfy and −/−Foxp3 mice.reg cells also depends on the presentation of self‐antigens by thymic MHC proteins. How the same mechanism of MHC‐mediated self‐peptide presentation promotes CD4+ fates that are so distinct during thymic T cell differentiation (negative selection of self‐reactive T cells and generation of self‐specific tTreg cells) is still a matter of active exploration.In 1972, Richard Gershon (1932−1983) from Yale identified immunosuppressive cells regulating competent lymphocytes.(OXT) transcription coincides with its expression in the hypothalamus.Following these seminal studies about the powerful tolerogenic mechanisms occurring inside the thymus, the real nature of self‐peptides that are presented by thymic MHC proteins was then scrutinized. Previously, it was largely assumed that proteins circulating in the blood were captured somewhat “passively” in the thymus  and then presented to differentiating T lymphocytes during their transitory residence in this organ. In 1978, immunoreactive neurotensin (NT) and somatostatin were identified in the chicken thymus epithelium,Most importantly, in the thymus, the processing of OT precursor does not lead to classic neurosecretion as established a long time ago for the hypothalamo−neurohypophysial axis. Thymic OT is not located in classic secretory granules, but is diffuse within TEC/TNC cytosol, in large clear vacuoles, and around perimembranous space.The hypothesis that a thymic neuropeptide actually behaves as a self‐peptide was further investigated with NT, a 13‐amino acid linear neuropeptide. Intrathymic MHC‐I presentation of NT was demonstrated according to the biochemical rules of MHC‐I presentation as established by Hans‐Georg Rammensee's laboratory in Tübingen.In the early 2000s, Bruno Kyewski, Jens Derbinski, and Ludger Klein in Heidelberg further proposed that promiscuous gene expression of tissue‐restricted antigens (TRAs) in the thymus was a novel key to understand immune self‐tolerance and autoimmunity.IGF2 transcription in TECs,The epigenetic regulation of promiscuous gene expression in the thymus is currently under intense investigation.While the unique physiological function of the thymus in programming central self‐tolerance is now well established, more and more experimental observations point to a disturbance of thymus‐dependent tolerance as a key event in the early development of organ‐specific autoimmune diseases Fig. . In 1973reg cells. If this hypothesis was verified, it would be clear that a defect in thymic tolerogenic function might determine early the development of organ‐specific autoimmune diseases. Our laboratory investigated this question more particularly for the pathogenesis of autoimmune insulin‐dependent T1D. Concerning the members of the insulin gene family, IGF2 is highly expressed in TECs in the cortex and medulla of the thymus from different species. IGF1 transcripts are detected in all TECs as well as in thymic macrophages, while INS is transitorily transcribed in rare subsets of mTECs.Igf2 transcription is detected in the thymus of BB‐DR rats but is absent in the thymus of ±85% of BB‐DP rats, in close coincidence with the incidence of autoimmune diabetes in BB‐DP rats.reg cells), as well as defective programming of central tolerance to insulin‐secreting islet β cells.Several groups then addressed the fundamental question whether inhibition or a significant decrease in intrathymic self‐presentation could result in a continuous release of “forbidden” self‐reactive T cells accumulating in the peripheral repertoire, as well as to a decrease in the generation of self‐reactive tTIns1 and Ins2. Ins2 transcription predominates in murine mTECs, while Ins1 is mostly expressed in islet β cells. These contrasted profiles explain why insulitis and autoimmune diabetes are accelerated in Ins2−/− NOD mice,Ins1−/− NOD mice.Ins2 transcription in the thymus also modulate insulin‐specific T cell tolerance.Ins2 transcription is independent of glycemia and is markedly enhanced by an anti‐lymphotoxin‐β monoclonal antibody.Mafa also promotes thymic Ins2 transcription and Mafa inactivation was shown to reduce thymic Ins2 expression and to stimulate in parallel the generation of autoantibodies against anti‐islet β cells.INS transcripts are quantified at a lower level in the thymus of human fetuses with the genetic marker insulin‐dependent diabetes mellitus 2 (IDDM2) of susceptibility to T1D.A specific topography and hierarchy also exist in the expression of the two mouse insulin genes, AIRE), a member of the zinc‐finger gene family, played a major role in further evidencing the central role played by a thymus dysfunction in the pathogenesis of organ‐specific autoimmunity.AIRE mutations determine a rare recessive congenital syndrome called autoimmune polyglandular syndrome type 1 or autoimmune polyendocrinopathy‐candidiasis‐ectodermal dystrophy syndrome.Aire transcription is maximal in mTECs, and −/−Aire mice develop several autoimmune processes in parallel with a marked decrease in the intrathymic expression of numerous neuroendocrine self‐peptides  and many TRAs.IDDM2 and AIRE transcription determine the level of INS transcription in the human thymus.Aire‐expressing mTECs is regulated by RANK signals from thymic CD4+CD3− lymphoid tissue inducer cells.Aire‐expressing cells were recently identified as distinct bone marrow−derived tolerogenic cells that could anergize in secondary lymphoid organs effector self‐reactive CD4+ T cells having escaped thymic negative selection.The identification of the autoimmune regulator gene .More recently, it has been shown that the gene TSHR transcripts than carriers of the protective allele.With regard to the most frequent autoimmune endocrine disease, all major thyroid‐specific autoantigens, such as thyroperoxydase, thyroglobulin, and thyrotropin receptor (TSHR), are also transcribed in human TECs in normal conditions.H1 effector response toward peripheral nerves.A defect in α‐myosin expression in TECs exerts a major role in the physiopathology of autoimmune myocarditis,Rag1 and Rag2 in jawed fishes was responsible for the appearance of a novel and highly complex system of immune defenses, the adaptive immunity. The subsequent development of the combinatorial immune system has been sometimes regarded as the immunological “Big Bang.” Because of its high risk of autotoxicity inherent to its diversity, the emergence of adaptive immunity exerted an evolutionary pressure so strong that, according to Ehrlich's prediction of horror autotoxicus, novel structures and mechanisms appeared with the specific role of orchestrating immune self‐tolerance. The first unique thymus also appeared in sharks and rays but it was preceded by thymoid lymphoepithelial structures located in the gill baskets of lamprey larvae.Foxn4), the paralog of Foxn1, which is responsible for the differentiation of the thymic epithelium in most vertebrates. Thus, Foxn4/Foxn1 determined the emergence of the thymic epithelium, which is an absolute requirement for T cell differentiation and programming of central immune self‐tolerance.The innate immune system evolves in parallel with the neuroendocrine system in all living species without any sign of autotoxicity/autoimmunity Fig. . Toll‐liThe hierarchy observed in the organization of the thymic repertoire of neuroendocrine self‐peptides has also evolutive implications. Neuroendocrine functions having been installed before adaptive immunity, they had to be protected against autoimmunity. OT is implicated at different steps of the reproductive process and is therefore fundamental for the preservation of animal and human species. Because of its predominance in the thymus, OT is much more tolerated than VP, its neurohypophysial homolog, which mainly regulates water homeostasis and vascular pressure. This lower immunological tolerance of VP may explain why rare cases of autoimmune central diabetes insipidus have been repeatedly observed.−/−Igf2 mice.In the insulin family, insulin is the primary T1D autoantigen and no autoimmunity has been reported against IGF‐2, which is fundamental for fetal growth and development. Nevertheless, because of their homology, thymic neuroendocrine self‐peptides program immune cross‐tolerance to their whole family, and tolerance of insulin is indeed decreased in Ins2 is expressed at very low levels in rare mTEC subsets, proinsulin per se does not exert any tolerogenic properties that could be used to reprogram immunological tolerance toward islet β cells. With the exception of only two studies (that were not confirmed until now),INS transcription in mTEC subsets. The risk of hypersensitivity or anaphylaxis following administration of an autoantigen was also reported.Until now, the development of anti‐infective vaccines was essentially based on immunogenic and memory properties of the adaptive immune system. Future vaccines against autoimmune diseases could be developed on the basis of the initial property of self‐tolerance programmed for this system and recent knowledge about the potent tolerogenic properties of the thymus, which have not been exploited until now. As reported above, although Igf2 is a dominant member of the insulin family expressed in the thymus.Igf2 transcription is defective in the thymus of BB‐DP rats.11−25 and insulin B9−23 (InsB9−23) compete for binding to DQ2 and DQ8 , the MHC‐II alleles conferring the highest genetic susceptibility to T1D.IGF‐2 B9−23, IGF‐2 B11−25 presentation by peripheral blood mononuclear cells isolated from DQ8+ diabetic patients induces a tolerogenic cytokine profile with marked IL‐10 induction.Contrary to InsBIGF‐2 mediates significant cross‐tolerance to insulin.Igf2 transcription and IGF‐2 production.Infection of a murine TEC line with the diabetogenic coxsackievirus B4‐E2 inhibits reg and Breg cells.IGF‐2 induces the activation of TIGF‐2 could provide a more efficient basis than insulin for developing a specific “reverse/tolerogenic self‐vaccination”This promising concept of reverse tolerogenic self‐vaccination is under current development with the active support of Wallonia DGO6 Win2Wal THYDIA Project.reg cells. There is no doubt that thymus‐based novel strategies could alleviate in the future the weight of so many known and still unknown autoimmune diseases that remain the heavy tribute, mainly paid by mankind, for the performance and extreme diversity of its highly complex adaptive immunity.Far from being a useless “vestigial” organ, numerous studies performed worldwide have definitively demonstrated that the thymus is crucial for ensuring the global homeostasis of the adaptive immune system and is unique in programming central immune self‐tolerance. Presentation of self‐peptides in the thymus is the basic mechanism that underlies T cell differentiation, negative selection of self‐reactive T cell clones, and generation of tTThe authors declare no competing interests.


The aim of this study is to explore advanced practice nurses’ perceptions of their leadership capabilities.A qualitative descriptive methodology informed by a well‐established leadership framework was used to explore advanced practice nurses’ perceptions of their leadership.NVIVO 10 software and content analysis.Purposive sampling of advanced practice nurses working in tertiary acute care facilities in Eastern Canada was employed. Data sources included face‐to‐face interviews and document analysis. Fourteen advanced practice nurses participated in two audio‐taped semi‐structured interviews from March 2013–January 2014. Data were transcribed and analysed using Two main themes were identified: “Patient‐focused leadership” and “organization and system‐focused leadership”. These two themes are further described through leadership domains and capabilities that clearly articulate advanced practice nursing leadership and its contribution to improving the care environment for patients and families, nurses and other healthcare providers, organizations and the healthcare system. This unpredictable and dynamic environment requires healthcare professionals to demonstrate effective leadership.Registered nurses working in advanced practice nursing (APN) roles have completed graduate education, have an expert level of knowledge and complex decision‐making skills and clinical competencies for expanded practice specific to the context in which they are credentialed to work  identify they have limited knowledge about the value‐added elements of these roles . LEADS is particularly relevant to this study because it was developed and is used by Canadian healthcare organizations, providing a common and coordinated approach to developing high quality healthcare leaders  managing patient‐centred care; 2) coaching and educating; 3) advocating and 4) initiating meaningful communication. “Organization and system‐focused leadership” included the following seven leadership capability domains: 1) improving the quality of care provided; 2) enhancing professional nursing practice; 3) being an expert clinician; 4) communicating effectively; 5) mentoring and coaching; 6) providing leadership on internal and external committees and 7) facilitating collaboration. Each capability domain has associated leadership capabilities. Leadership capabilities are the skills and abilities advanced practice nurses described as necessary for demonstrating leadership. Table 4The APN Leadership Capabilities Model Figure  and assoLeadership is often referred to as a competency in APN literature CNA, . The accThe capabilities described in the APN Leadership Capabilities Model Figure  & 3 are Exemplary Leadership . Both models have layers of competencies or capabilities arranged in a circular manner, emphasizing patient care as the central feature of APN. Hamric, et al. (2009) denotes clinical and professional leadership as a core competency of APN practice. The clinical and professional leadership skills they describe are similar to some of the capabilities described in patient‐focused  are integral to implementing and sustaining APN roles. Therefore, the more consistent and evidence‐based the information that decision‐makers receive about APN roles, the more likely they are to understand and support these roles (Carter et al., APN leadership needs to be articulated clearly, so that the impact of this leadership can be understood and measured (Kilpatrick, Kaasalainen, et al., 4.1The study focused on describing leadership from the perspective of a sample of advanced practice nurses in two hospitals in one Canadian province, which may minimize the transferability of these results, given the variation in advance practice nurse roles across the globe. Although the study was open to both female and male advanced practice nurses in the two study sites, only females chose to participate. There may be gender differences in how leadership capabilities are defined and enacted that were not captured in this study. Future studies are required to expand on these findings with a larger sample spanning other practice settings, for example, primary health care.5This qualitative descriptive study provides evidence to better understand and use leadership capabilities as a lesser known dimension of APN roles. Having language that describes APN leadership can enable advanced practice nurses to recognize the leadership capabilities they demonstrate as part of their daily practice. The ability to recognize and then describe their leadership will enable advanced practice nurses to clearly articulate their leadership to decision‐makers, other healthcare professionals and the public. This may enhance understanding of the APN role and contribute to optimization of APN leadership capabilities. The current state of health care is one of continuous changes where leaders at all levels are needed to lead changes to create positive results. This study describes APN leadership and the contributions APN leadership make to patient, organizational and system goals.None declared.All authors have agreed to the final version.