The adaptive immune system arose around 500 million years ago in jawed fish, and, since then, it has mediated the immune defense against pathogens in all vertebrates. Antibodies play a central role in the immune reaction, recognizing and attacking external invaders. During the evolutionary process, several immunoglobulin isotypes emerged, each having a characteristic structural organization and dedicated function. In this work, we investigate the evolution of the immunoglobulin isotypes, in order to highlight the relevant features that were preserved over time and the parts that, instead, mutated. The residues that are coupled in the evolution process are often involved in intra- or interdomain interactions, meaning that they are fundamental to maintaining the immunoglobulin fold and to ensuring interactions with other domains. The explosive growth of available sequences allows us to point out the evolutionary conserved residues and compare the biophysical properties among different animal classes and isotypes. Our study offers a general overview of the evolution of immunoglobulin isotypes and advances the knowledge of their characteristic biophysical properties, as a first step in guiding protein design from evolution. Antibodies or immunoglobulins (Igs) are specialized molecules that can recognize and bind foreign invaders. The antibody molecule is a Y-shaped glycoprotein, that can be present free in solution or bound to membranes. Immunoglobulins are formed by two identical light chains, including two domains each, and two identical heavy chains, usually with four or five domains each . The N-tThe domains of the immunoglobulin chains share the common Ig-fold. They consist of approximately 70–110 amino acids, which form 7 β-strands in the constant domains or 9 in the variable ones, connected via loops and distributed in 2 antiparallel sheets ,7. In thImmunoglobulins exist in different isotypes, that differ mainly in the number of subunit domains and consequently in their function in the immune response. Only in mammals do five isotypes exist: IgA, IgM, IgG, IgE and IgD. They are distinguished by the types of heavy chain; γ, μ, α, δ and ε heavy chains characterize, respectively, IgG, IgM, IgA, IgD and IgE. The heavy chain sequences differ between isotypes in the number and position of disulfide bridges, in the number and types of oligosaccharides and constant domains and in their hinge region lengths . In factFrom an evolutionary point of view, IgM is the most ancient antibody isotype , originaIn the last decades, evolutionary studies have become increasingly important in many fields, including 3D structure prediction of proteins or nucleic acids. By comparing the sequences of related proteins across different species, evolutionary relationships and functional constraints can be identified. In protein structures, for example, coevolving residues often correspond to intra- or interdomain contacts and this information can consequently be used to predict the structures of monomers or complexes, or even to predict the effects of mutations ,44,45,46value) was set to 0.01 to select a significant alignment. The remaining parameters were set to default values. The sequences with 100% identity and the same length as the reference were excluded. After the BLAST search, the complete sequences were downloaded.The protein sequences for each immunoglobulin isotype have been collected via protein BLAST  search . A separH1, CH3 and CL domain output of the IMGT webserver [All the sequences obtained from the BLAST search were merged and aligned using Clustal Omega  implemenebserver ,54,55,56ebserver .Starting from the multiple sequence alignment of the complete constant domains sequences of IgM molecules, we generated the phylogenetic tree using the software IQ-TREE 2 , which uH3 domain of a human IgG was used as reference structure (PDB: 3AVE [H3 domain of a human IgG (PDB: 3AVE). The residues were considered in contact if their minimum atom distance in the reference structure is lower than or equal to 5 Å. The same distance criterion was also used to evaluate the interdomain contacts and, in addition, the GetContacts software provided the interaction type information [For the analysis of the evolutionary coupled residues, we used the EVcouplings webserver . The CH3DB: 3AVE ). The seDB: 3AVE  to find DB: 3AVE . We downDB: 3AVE . The conormation . In ordeThe protein sequences were illustrated via sequence logos, which gave an idea of the occurrence of each residue in each position and, consequently, of its biochemical properties. The sequence logos were created with the python package Logomaker . We usedThe PCA analysis was used to lower the dimensionality of the sequence space and visualize separations between different animal classes and/or immunoglobulin isotypes. The analysis was performed in Python 3 . First, H1, CH3 and CL domains belonging to different isotypes and animal classes, highlighting differences in biophysical properties that explain the observed separation in the PCA space. In this work, we analyzed the biophysical properties of immunoglobulin isotypes and how they evolved in different animal classes. We focused on the constant domains of immunoglobulins because of their highly conserved structure and their critical role in the immune response . First, H3 reference domain (PDB: 3AVE). We chose the CH3 domain as reference because of its presence in every isotype, its relevant role for the production of bispecific antibodies [H3 domain is the only domain that can homodimerize in common IgGs, therefore also interdomain interactions can be evaluated. A total of 82% of the evolutionary coupled residues form an intradomain contact (green dots), meaning that these residues are relevant to maintain the correct fold. However, some other pairs are separated in the structure with a minimum atom distance higher than our cutoff (5 Å), and therefore are not considered to make intradomain interactions in our reference structure  is shown in We systematically compared the biophysical properties of sequences originating from different isotypes and animal classes. For the analysis of the Ct domain . It has 2 of IgM . We refeH3 domain sequences colored according to isotypes and animal classes, respectively. The first 4 PCs can only explain 28.6% of the total variance in the dataset , meaning that our sequences cover a noisy and multidimensional space that is hard to completely describe with this technique alone. However, already the first four PCs can separate various isotypes: PC1 separates the mammalian IgG, while PC2 can distinguish between IgM and IgE. PC4 distinguishes the mammalian IgA from the other sequences. To understand which biochemical properties can drive this separation, we plotted the normalized charge, hydrophobicity and van der Waals volume values on the same PCA space. In order to normalize the properties, we assigned a value representing the considered property to each residue of the sequence, summed up the values in the sequence, and divided by the number of positions that are not gaps. For the CH3 domains, both charges and hydrophobicity play a role in the isotypes‘ separation in the PCA space. In fact, IgG and IgE seem to have higher normalized charge values compared to the remaining isotypes  , and CD, DE and EF-turns . Furthermore, in this case, the additional charges in the E-strand and DE-turn could interact with the facing domain, making additional salt bridges. However, the first part of the sequence of the CH1 domains of bony fishes is characterized by a loss of charge , and a consequent increase in hydrophobicity and van der Waals volume in the C strand, for the recurrent F or Y residues in position 43 (IMGT: 39). The charged residue count appears to be the primary distinguishing factor, as depicted in the heatmap in The animal classes show major differences in the biophysical properties of the Cnd 85.5) c, as in Antibody light chains can exist as four isotypes: lambda (λ), kappa (κ), sigma and sigma-cart. However, the sigma isotype is only present in ectothermic vertebrates and sigma-cart in sharks only . In thisThere are only few charged residues that distinguish the two isotypes, and they are mainly located in the BC and DE-turn and in the G strand . Igκ is More differences are present between the animal classes and, also in this case, they mainly concern charges. The strands stay again more conserved compared to the loops, even if the hydrophobicity of strands C and F in sharks is lowered by the presence of some charges . This difference in charge can also be spotted in the CD (position 52) and DE-turn . The few other species that show a higher charge in the loops are highlighted in In this study, we systematically investigated the amino acid composition of the immunoglobulin isotypes in different animal classes, to gather insights into the substitutions that have naturally occurred within these proteins. Immunoglobulins are key components of the jawed vertebrate adaptive immune response. Since their origin over 500 million years ago, they have widely differentiated during vertebrate evolution resulting in a diverse repertoire of Ig isotypes ,36. The H3 domain structure (PDB: 3AVE), which is in line with the established knowledge that evolution constrains the amino acid sequence variations to maintain the protein structure and function [Evolutionary studies play a crucial role in understanding how individual amino acids contribute to the structure and function of a protein of interest ,84. The function ,85,86. Ffunction ,92. We cfunction . Our dataset, obtained via pBLAST search, groups the various isotypes in all the vertebrate classes. Therefore, we combined the coevolutionary information obtained from the EVcouplings webserver with biophysical property information derived from the amino acid sequence. While some residue pairs remain fixed in their properties, allowing the same type of interaction between them, others exhibit greater variability, such as the coupled residue 392–398 , both situated in the D strand. Notably, the charged–apolar residue combination is the most prevalent; however, their placement is not consistent, resulting in the possibility of the charge being in position 392 (IMGT: 79) and the apolar residue in position 398 (IMGT: 84.1), or vice versa. This interchange in biophysical properties serves as a clear indication of coevolution between the residues. H3 dimer is much lower than that of the oxidized state [A conservation analysis based on the sequence alignment shows that, as expected, the most conserved residues are the cysteines in the B and F strands and the tryptophane in the C strand in close proximity . The inted state . We findH3 domains of IgNARs contain positively charged residues in the A strand, which usually has a hydrophobic character, and positive ones in the C strand and the DE-turn. Furthermore, the CH1 domains of the IgNARs follow the same trend, showing predominant charged residues in various loops and strands. These characteristics contribute to the peculiar properties of the IgNAR molecules. In fact, contrary to the IgG CH1 domain which is unfolded in isolation [H1 and CH3 form a dimer both in crystal structure and in solution, with CH1 dimerization driving the formation of the CH3 dimer [H1 dimer also differs from the others because of the wide angle between the two domains, which is fundamental to ensuring an appropriate spatial separation of the variable domains [H1 domains of sharks and hold true for the CL domains. As vertebrate evolution proceeds, the prevalence of charge decreases. In the CH1 domains of bony fishes, there are still many charged residues , however the B strand and BC-loop reveal a lower number of charges and show instead an increase in hydrophobicity. No major charge changes can be found in the other domains of bony fishes. Although some point mutations may be present, there are no significant differences observable in tetrapods. Furthermore, a comprehensive investigation on the biophysical properties of individual residues was conducted, to determine the distinctions between various isotypes and animal classes. In general, sharks, which are the first animal class that developed an adaptive immune response, have more charges throughout the overall sequence of each considered constant domain. In fact, the Csolation , IgNAR CH3 dimer . Moreove domains ,97. The H3 domains of IgG and IgE, especially in the AB and DE-turn and in the C and E strand. On the other side, few charged residues are present in the G strand of IgG CH1 domains, but no other modification is noticeable. In fact, as literature confirms, while the IgG CH3–CH3 interface is mainly characterized by salt bridges, the CH1–CL interface is mostly hydrophobic with the only salt bridge present between the G strand and the AB-turn [As has already been said, the IgNAR is the one that differentiates the most between the different isotypes, exhibiting many charged residues. Although they are evolutionarily distant, the increase in charges is also significant in the C AB-turn ,99. The H3 domains and it makes van der Waals interactions with residues 16, 18 and 19 in the opposite domain ; in particular, 18 and 19 (IMGT: 12 and 13) are conserved negatively charged residues. In the IgNAR Y11 (IMGT: Y5) is substituted by a R, which can, if the interdomain orientation is preserved, make stronger salt bridges with the same partner residues . Another example is the T60 (IMGT: T81) in the D strand of IgG that, in IgA and IgNAR, is substituted by a bulky F or W. This residue normally interacts with itself in the opposite domain making van der Waals interactions. The presence of a hydrophobic bulky residue in the same position allows a pi stacking interaction which may further stabilize the interdomain interface. Residue 79 (IMGT: 88) represents a case in which evolution inserted a highly favorable residue in order to obtain the strongest interactions. In fact, IgG and IgE, the most recent Ig isotypes, present in this position high occurrent positively charged residues (R or K), whereas other isotypes are mainly characterized by apolar residues (L) or low occurrent charges. Residue 79 (IMGT: 88) can make a salt bridge with the negatively charged residue in position 65 (IMGT: 84.2) and an additional pi stacking with the conserved F75 (IMGT: 85.1). Position 65 and 75 (IMGT: 84.2 and 85.1) already showed a negatively charged residue and the aromatic F in the other isotypes, but the negatively charged residue in position 79 (IMGT: 88) only became predominant in the more recent IgG and IgE. Some of the changes in biophysical properties between classes and/or isotypes can have a strong impact in the intra- and interdomain contacts. For example, residue 11 in strand A is a conserved tyrosine in the IgG CIn conclusion, the study of biophysical properties across different isotypes and animal classes has provided valuable insights into the preserved and variable sites within the primary structure of immunoglobulins. Coevolving residue analysis has shown that, on the one hand, intradomain contacts are essential for proper Ig monomer folding and, on the other, interdomain interactions are crucial for the proper assembly and therefore functionality of the Ig molecule. The conservation score has revealed that the most conserved strands are those forming the structural core, and that solvent-pointing residue positions are more likely to undergo mutation. Our systematic analysis of biophysical properties has identified significant differences in sequences between ancient sharks and bony fishes compared to those of tetrapods, with the first ones presenting more charged residues that are instead less frequent in the higher vertebrates. Moreover, the analysis of other isotype sequences revealed mutations that contribute to strengthen the interface contacts. Overall, this research adds to the existing knowledge in the field of immunology and paves the way for future studies to learn from the first designer: evolution.


The lowest glycemic index (32 +/− 17) was found when using sorghum flour with an intermediate particle size (167 +/− 4 μm). Muffins made using brown rice had the next lowest glycemic index at 37 +/− 17. All GI values calculated had large standard deviations, which is common for these types of studies. These results can assist in the product development process to advance the quality of healthy, gluten-free sorghum-based foods for consumers. Further research should investigate if these results can be duplicated and the possible reason for the lower GI of intermediate particle size sorghum flour.The unique properties of sorghum are increasingly being studied for potential health benefits, with one area of emphasis being the impact of sorghum consumption on mitigating type 2 diabetes. The glycemic index (GI) of muffins made from whole grain sorghum flour ground to three different particle sizes  was tested on eight healthy volunteers (ages 18–40) and compared to the glycemic index of whole grain corn, wheat, and rice flours produced using a similar product formula. Sorghum flour ground through a 0.5 mm screen (“fine”) had an overall similar particle size to that of the brown rice flour ground using a 0.5 mm screen. The range of GI values was 32 to 56, with only the GI of intermediate milled sorghum flour being lower than that of corn, rice, or wheat ( Cereal grains are important food staples around the world and a major source of calories and nutrients for a significant part of the world population . In manyGrains, especially whole grains, contain a wide range of beneficial compounds for human health ,5,6. ConThe impact of consumption of whole grains has been related to preventing and managing type 2 diabetes, which may at least be partially related to the rate of digestion of carbohydrates and subsequent effects on insulin and blood sugar levels ,16,17. TAlthough sorghum has been grown for years in Africa and many parts of Asia, consumption of sorghum-based food products in the United-States has recently received increased attention, primarily due to its status as a gluten-free cereal ,21 and dWhile several factors can influence the GI of foods, flour particle size is one factor that may influence the digestibility and subsequent GI of grain-based foods ,33,34,35Sorghum bicolor; white-grained variety Fontanelle 1000), hard red winter wheat , yellow corn , a commercial sample of brown rice , and commercial all-purpose wheat flour  were used for this study. Whole grains were ground into flour using a UDY Cyclone Lab Sample Mill . Sorghum grain was milled into three samples using a 0.5 mm screen (fine), a 1 mm screen (intermediate), and a 2 mm screen (coarse). Wheat, brown rice, and corn grains were ground using a 0.5 mm screen. All seven flour samples were placed in re-sealable bags  and stored in a commercial freezer at approximately −3 °C until they were used.Whole sorghum grain  to determine the particle size distribution of the milled flours. The flour was placed into the load cell until it was approximately 2/3 full. The cell was then loaded into a Tornado™ Dry Powder Dispersing attachment for the instrument, and measurements were taken. Starch damage was determined using a Megazyme Starch Damage Assay Procedure, K-SDAM 02/2008 with a Limit of Detection of 0.5 g/100 g , following AACC-approved methods 76-31.All seven muffin formulations were composed of 300 g of flour, 4.5 g of baking powder , 3.5 g of salt , and tap water. Water levels varied on account of flour type and particle size and were based on test bakes to determine optimal water levels that resulted in muffins deemed unanimously acceptable in terms of texture by the 8 human subjects who were recruited to consume them. Amounts of water used per formula are listed in Dry ingredients  were measured and mixed by hand for 30 s. Water was added to the dry mixture and blended by hand for 1 min. Sixty grams of batter was scaled out into each cup of a twelve-cup muffin tray. No oil was applied to muffin trays before baking. Muffins were baked in a pre-heated oven at 177 °C. Muffins made from all-purpose flour, whole wheat flour, corn flour, and rice flour were baked for 30–35 min, whereas those made from sorghum flour were baked for 15 min . Muffins were cooled in the tray for ~10 min, and then removed and placed on a cooling rack for an additional 2 h, or until muffins reached room temperature. Muffins were placed in re-sealable bags , labeled, and stored in a commercial freezer at approximately −3 °C until human trials.The moisture contents of the flours were measured using the Association of Official Analytical Chemists (AOAC)-approved method 930.15. The protein content of each flour was measured using AOAC-approved method 990.03, with a nitrogen to protein conversion factor of 6.25. The crude fat content of each flour was measured using AOAC-approved method 920.39. The crude fiber content of each flour was measured using the Ankom Method, based on AOAC 962.09, and ash contents were measured using AOAC-approved method 942.05 on an “as is” basis. Total carbohydrate for each muffin type was calculated by difference using results from the above proximate analysis. Muffins were then formulated to contain 20 g of available carbohydrate using the following calculation:The final serving size for each muffin is shown in The seven treatments included muffins made from all-purpose flour, whole wheat flour, brown rice flour, corn flour, and sorghum flours with three different particle sizes . Treatments were labeled with codes 101–107.Eight healthy volunteers (aged 18–40) participated in this study. Volunteers were recruited among Kansas State University students. All subjects gave their signed consent and were aware of all considerations surrounding the study. Approval for this study was obtained from the Institutional Review Board (IRB# 3941.2). Subjects were asked to maintain their regular lifestyle throughout the entirety of the study, avoiding any extreme behaviors. Each subject selected two weekly time slots for their testing. Subjects received two treatments per week in random order with at least a 48 h period in between. Each treatment was given twice as well as one dose of a 20 g dextrose drink (reference food) over an eight-week period. Subjects were required to fast for ten hours prior to each visit but were permitted to consume unlimited drinking water throughout testing. A weighed portion of muffin containing 20 g of available carbohydrate that had been thawed to room temperature overnight was consumed at each testing session. Two finger prick samples were taken from volunteers for capillary blood analysis by a lab assistant at 0 (fasting), 30, 45, 60, 90, and 120 min after consumption, and then averaged. A YSI 2300 Glucose Oxidase Analyzer  was used to measure blood glucose concentrations.The incremental area under the glycemic response curve (iAUC) was constructed using the trapezoid model, with fasting levels as the baseline, using GraphPad Prism 5 software . Area below the baseline was excluded. The GI values were calculated by dividing the iAUC for the test food (muffin) by that for the standard (20 g dextrose drink) and multiplied by 100. The GI for each treatment was calculated as the mean from the respective average GIs of the eight volunteers. Glycemic index was calculated using the following formula: p-values were used to differentiate treatment means. A level of significance was reported at p < 0.05.Two replicates of each treatment were used in a randomized block design. GI values were analyzed using SAS, Software Release 9.3 . The differences in response between grains, flour particle size, and the proximate analysis composition of muffins were analyzed for significance. When treatment effects were found to be significantly different, the least square means with Dunnett’s Regression analysis was performed to find out whether a correlation between starch damage and glycemic response exists, using Excel .The particle size of flour represents the degree of volume of particles and the total exposed surface area of particles dispersed throughout the flour . For theTo provide additional information on the GI responses among the flours tested in this study, proximate analysis was conducted, and the properties of the flours were characterized. The proximate composition for the flours is shown in When grains are ground or milled into flour, intact starch granules become damaged, leaving starch granules that have been fractured, shattered, or chopped . Damagedp < 0.05). Values for the glycemic index of sorghum and sorghum products reported in the literature vary greatly. This could be due to sorghum grain variety, milling method, processing conditions, or issues with GI testing itself. The GI of sorghum is reported to lie between 72 and 70 by the Diabetes Council [Among the muffins made using whole-grain sorghum flour, the flour with the intermediate particle size had the lowest GI . The ran Council . Sydney  Council  reported Council  tested tMoraes et al.  studied Wolter et al.  investigAs particle size decreases, more surface area is exposed to digestive enzymes, and this may lead to a higher glycemic response as a result. Holt and Miller  showed aIn our study on human subjects, all GI values calculated had large standard deviations, about 50% of the mean, which is common for these types of studies. Flavel et al. [As mentioned in the Introduction, several factors can influence the GI of a given food. As these muffins were all made from the same sorghum flour, the differences in the GI could not have resulted from differences in phenolic content, protein, etc. Food structure has been found to influence the GI of foods, and the differences found here among the sorghum muffins may indicate differences in the microstructure of these muffins. Further research investigating relationships between the physical properties of sorghum flour , flour composition, food structure, and GI are warranted. If differences in the GIs of the sorghum flour muffins are related to differences in food structure, this may be a unique way to control the GI in sorghum-based baked foods. This would be especially interesting when using sorghum lines known to have high levels of phenolic compounds.Comparing all the flours ground using the 0.5 mm screen, the muffins made from brown rice had the lowest GI , which wThe greater the starch damage in flour, the more susceptible the damaged granules to enzymatic attack . Starch Muffins made from the whole-grain sorghum flour used in this study fell in the GI range of low to intermediate, depending on the particle size of flour used. Intermediately milled sorghum flour had in the lowest glycemic response when comparing the three particle sizes of sorghum flour used in this study. This may reflect differences in food structure when baked into a muffin. When compared to other grains tested, sorghum products from intermediately ground flour exhibited a significantly lower glycemic response, with the exception of brown rice flour. This points to the possible advantage of using sorghum flour in gluten-free foods for delivering products with a lower GI than corn or wheat. No correlation was found between the percentage of starch damage in flour and the GI of the baked product tested. These results can assist in the product development process to advance the quality of healthy, gluten-free, sorghum-based foods for consumers. Further research should investigate if these results can be duplicated and the possible reason for the lower GI of intermediate-particle-size sorghum flour.


The core of a Gigahertz Spin Rotation (GSR) sensor, a compact and highly sensitive magnetic sensor, is composed of Co–Fe-based amorphous magnetic wire with a diameter of 10 μm. Observations of the magnetic domain structure showed that this magnetic wire has unusual magnetic noise characteristics. Bamboo-shaped magnetic domains a few hundred micrometers in width were observed to form inside the wire, and smaller domains a few micrometers across were observed to form inside these larger domains. The magnetic domain pattern changed abruptly when an external magnetic field was applied to the wire. Herein is shown how these changes may be a source of magnetic noise in the wire. Magnetic sensors are used in various automotive, medical, and data-storage applications, and their performance is expected to be improved. The magnetic core of a Gigahertz Spin Rotation (GSR) sensor is composed of amorphous magnetic wire, on which a detection coil is wound ,2,3. AmoThe wire used for our observations was an as-cast Co–Fe–Si–B amorphous magnetic wire that had a diameter of 10 µm. A vibrating sample magnetometer was used to measure hysteresis loops, and a Kerr effect microscope was used to observe the magnetic domains. To polish the sample to have a mirror-like surface, sections of wire approximately 5 mm long were embedded in epoxy resin, mechanically polished using a diamond paste, then chemically polished using alumina and colloidal silica. An external magnetic field was applied using a Helmholtz coil; the changes that then occurred in the magnetic domain structure were observed. The magnetic domain contrast was enhanced by differential processing of magnetic domain images .The measured hysteresis loop of the Co–Fe–Si–B amorphous magnetic wire is shown in H, that was applied in the axial direction was varied from +15 to −15 Oe. The bright and dark areas in H = 0), the magnetization direction alternates between left and right in the radial direction of the wire. When H reaches either −15 or +15 Oe, the original bamboo-like domain structure, which is characteristic of wires containing Co [ining Co ,17, disaH = +15 Oe. When the magnetic field is reduced to +6 Oe, the magnetization rotates so that it is directed to the left and right of the wire radius and magnetic domain walls appear, thus forming a striped magnetic domain structure with the magnetization facing the wire radius in the remanent magnetization state. When the external magnetic field is subsequently increased in the negative direction, new magnetic domains are nucleated; in addition, continuous rotation of the magnetization and movement of the magnetic domain walls occurs. Thus, a discontinuous magnetic domain change occurs inside the wire during the magnetization process in addition to the rotation of the magnetization and magnetic wall movement. These sudden changes in the magnetic domain structure can produce noise in magnetic sensors. We consider that the nucleation of magnetic domains occurs when a magnetic field is applied in the axial direction of the wire because the easy axis of magnetization is inclined from the radial direction of the wire.Based on these results, a model showing the structure of the magnetic domains in the wire was constructed and is shown in To investigate the magnetization process for this fine magnetic domain structure, we observed the change in the magnetic domain structure when a magnetic field directed along the wire axis was applied. H = 0 Oe, the direction of the magnetization is to the left.H = 0 Oe. The magnetic domain contrast becomes weaker as the magnetic field increases and disappears when magnetic saturation is reached. In addition, when the magnetic field direction changes from positive to negative, the magnetic domain pattern reverses; in other words, when the magnetic field changes from 0 to −1 Oe, a sharp reversal of the magnetization component in the direction of the wire axis occurs. This abrupt change in the magnetic domain pattern is a possible source of noise in the GSR sensor element.In contrast, θ, wasThe region where this rapid change in the magnetization direction occurs is shown in blue in Observations were made of the magnetic domain structure on the polished surface of a Co–Fe-based amorphous magnetic wire that had a diameter of 10 µm. Bamboo-shaped magnetic domains a few hundred micrometers in width formed inside the wire. This macroscopic domain structure causes rotation of the magnetization and a magnetic wall shift during reversal of the magnetization; in addition, the nucleation of multiple magnetic domains causes discontinuous and abrupt magnetization changes. These changes in the domain structure can produce noise in magnetic sensors. Inside the bamboo-shaped magnetic domains, there is an additional, finer domain structure consisting of domains a few micrometers across. Within the micrometer-scale zigzag-shaped region observed in the center of the wire, large changes in the direction of the magnetization occur. Such a fine magnetic domain structure inside the bamboo-shaped magnetic domain was not observed in the 100 µm diameter magnetic wires observed in the past. These changes in direction and the magnetic domain structure are another source of noise in magnetic sensors.In this study, observations of magnetic domains were performed at the polished surface of an amorphous magnetic wire. Further investigations, such as three-dimensional magnetic domain observations ,34,35,36


Protein domains can be viewed as building blocks, essential for understanding structure–function relationships in proteins. However, each domain database classifies protein domains using its own methodology. Thus, in many cases, domain models and boundaries differ from one domain database to the other, raising the question of domain definition and enumeration of true domain instances.We propose an automated iterative workflow to assess protein domain classification by cross-mapping domain structural instances between domain databases and by evaluating structural alignments. CroMaSt  will classify all experimental structural instances of a given domain type into four different categories . CroMast is developed in Common Workflow Language and takes advantage of two well-known domain databases with wide coverage: Pfam and CATH. It uses the Kpax structural alignment tool with expert-adjusted parameters. CroMaSt was tested with the RNA Recognition Motif domain type and identifies 962 ‘True’ and 541 ‘Domain-like’ structural instances for this domain type. This method solves a crucial issue in domain-centric research and can generate essential information that could be used for synthetic biology and machine-learning approaches of protein domain engineering.10.48546/workflowhub.workflow.390.2).The workflow and the Results archive for the CroMaSt runs presented in this article are available from WorkflowHub (doi: Bioinformatics Advances online. There are several resources available that provide information about the protein domains and their classification  may be different.Such difficulties are particularly deleterious for domain-centric investigation in the frame of synthetic biology and protein design. Indeed, such projects require a very precise knowledge about existing instances of a given domain type associated with a specific function, in order to be able to engineer synthetic versions of this domain type without losing the function. In practice, the exhaustive enumeration of all true domain instances of a certain domain type is a complex problem and cannot be solved by querying a single domain database. To solve these issues, we propose here a generic iterative approach aiming to clarify the assignment of domains to a given domain type using their membership to domain families from various databases and the evaluation of their structural alignment with a typical representative 3D structure.As a use-case to develop and test our approach, we use the RNA Recognition Motif (RRM) domain type, with the goal to integrate all existing information about it, including all available experimentally determined 3D structures. RRM domains are ∼90 amino acids long, with a typical ectively . All theInconsistencies regarding RRM domain families appear with a very simple search using keyword ‘RRM’. There are 16 Pfam families and two CATH superfamilies with RRM in their name (for a search performed on Pfam version 35.0 and CATH version 4.3.0). Only 13/16 Pfam families are listed in the Pfam RRM clan. In CATH, several domains matching keyword ‘RRM’ were retrieved from outside the two ‘RRM’ superfamilies . These structures are hereafter named structural instances (StIs) and a given protein domain sequence may have more than one StIs. We first describe our approach including our cross-mapping concept. Then, Section 3 details the implementation of the CroMaSt generic workflow and is followed by Section 4 with RRM domain type as a use-case. Finally, we will discuss the possible usage of the CroMaSt workflow.Our objective is to clarify the assignment of protein domains to a given domain type of interest, based on their 3D structure. The CroMaSt workflow is currently designed to be used with the domain classifications from two well-known and widely used domain databases, Pfam (primarily sequence-based) and CATH (primarily structure-based). Our generic approach assumes that there exists a consensus basic definition of the considered domain type, with a few known 3D structures qualified by experts as representative StIs (see Section 3.2) for this domain type. Then, our first hypothesis is that it is possible to verify that a given domain StI belongs to the considered domain type by running structural alignment with these representative StIs. The result can be either manually inspected or automatically filtered using appropriate thresholds defined by the experts of this domain type. However, this task can become tedious in view of the ever-increasing number of StIs. Therefore, one needs to rely on existing domain classifications, in particular those with wide coverage, such as CATH and Pfam. Let CATH-rep1 and Pfam-rep1 be two most representative domain families in CATH and Pfam databases, respectively. Our second hypothesis is that if a domain StI belongs to both CATH-rep1 and Pfam-rep1, then it is likely to be a ‘True’ StI. If a domain StI is only present in CATH-rep1 (respectively in Pfam-rep1), it can be relevant to ‘cross-map’  it to another domain family in Pfam database (respectively in CATH database), and to check all the StIs of this new domain family that may become a new representative family of the considered domain type.At the end of the process, four categories of domain StIs are produced : the ‘CoMore precisely our CroMaSt workflow is described in The files used in this study are as (i) the CATH domain description file—v4.3.0, (ii) from Pfam: the file with Pfam-A matches for each PDB chain—v35.0, (iii) from PDB . Thus, the current implementation of CromaSt does not handle discontinuous domains.This unified format facilitates identification of shared StIs between the two databases (see Section 3.3). Moreover, this format allows to save the start and end positions from the PDB chain in order to extract the 3D coordinates of the domain StIs for computing structural average and for structural alignment purpose. However, this format does not take into consideration discontinuous domains, which are handled by CATH but not Pfam  or UniProt sequence (for StIs derived from CATH). The SIFTS resource is used for this residue-mapping step. The workflow also keeps track of the domain StIs for which the residue-mapping cannot be done  residues for the same domain instance. By default, we use a maximum difference of 30 residues, but users can change this setting as needed. This step of the workflow results in three different sets: Common domain StIs, Domain StIs unique to Pfam and Domain StIs unique to CATH.At this stage, the CroMaSt workflow compares two sets of domain StIs  using the unified StI representation and more precisely the The common domain StIs obtained at the first iteration of the workflow are the ‘Core’ domains and are used to compute the core average structure of the domain type (Step 3.5.2). The domain StIs unique to each database must be ‘cross-mapped’ to the other database.Each domain StI unique to one database (CATH or Pfam) is ‘cross-mapped’ to the other database . To do so, the PDB and chain IDs are used to query the domain description files downloaded from the data sources. When a hit is found, the start and end positions  are checked with the same tolerated difference as in Step 3.3. If the cross-mapping is successful, the corresponding cross-mapped StI is created in order to be tested for the ‘True’ domain category by structural alignment (Steps 3.5.2 and 3.6). If the cross-mapping is not successful, the corresponding StI is also created in view of structural alignment and is stored as un-mapped. Some of these un-mapped StIs may result from the fact that the two domain databases rely on different releases of sequence databases. Nevertheless, un-mapped StIs will also undergo a structural alignment test (see Section 3.6.2) to avoid missing any instance of the domain type of interest.In summary, this step provides three different sets of domain StIs as follows: Pfam StIs cross-mapped to new families in CATH, CATH StIs cross-mapped to new families in Pfam and Un-mapped StIs from both databases.The coordinates for average structure are computed using an in-house python script after aligning a set of 3D coordinates (extracted from PDB entries) using the Kpax program . The res‘Core’ domain StIs are the common domain StIs (from Step 3.3) retrieved during the first iteration. Core average structure is computed using these ‘Core’ domain StIs as an average of ‘UniProt-instance-level’ averages, as described in Step 3.5.1. It should be noted here that the core average structure of a given domain type is dependent on the families selected for the first iteration of the workflow.An average structure is computed for each cross-mapped (newly found) family from all the unique domain StIs that are mapped to this family (Step 3.4). These average structures are computed at the ‘family-level’ as described in Step 3.5.1.Average structures are computed at the ‘UniProt-instance level’ for each un-mapped domain StI produced at Step 3.4.csv file with all the scores provided by Kpax for each target structure. By default, the CroMaSt workflow uses ‘M-score’ as the alignment score and 0.6 as the score threshold to evaluate the alignments. Users can choose to evaluate the alignments using any other alignment score provided by Kpax  with different thresholds. Depending on the domain type studied, users can run some tests and visualize some of the ‘True’ and ‘Failed’ StIs in order to make an educated guess of which threshold to use. For RRM domains, which are ∼90 amino acids long, we used the M-score with 0.6 threshold. It also worked nicely for one other domain type shown in The core average structure of the domain obtained at Step 3.5.2 plays a crucial role in the structural alignments aimed at assigning the cross-mapped or un-mapped StIs to the studied domain type. The workflow considers the core average structure as the prototype of the studied domain type. For all structural alignments, the CroMaSt workflow uses Kpax as it provides a Gaussian-based Multiple Structural Alignments quality measure called ‘M-score’ , which cEach average structure for cross-mapped families from Pfam and CATH (Step 3.5.3) is aligned against the core average structure using Kpax. If the average structure for a cross-mapped family passes the given threshold , the family identifier is added to the list of families for the next iteration of the workflow. In parallel, the StIs that were mapped to this new family are also kept for the next iteration in order to be recognized as ‘True’ domain StIs when comparing lists from the two domain databases. If the average structure for a cross-mapped family does not pass the threshold, this family and the corresponding cross-mapped StIs are labeled as ‘Failed’.The un-mapped average structures (from Step 3.5.4) are also aligned the same way as in Step 3.6.1. The StIs corresponding to average structures passing the threshold (for given score) are included in the list of ‘Domain-like’ StIs. The StIs corresponding to average structures failing to pass the threshold are considered as false positives and labeled as ‘Failed’.CroMaSt uses the Common Workflow Language (CWL)  from Pfam and superfamily 3.30.70.330 (RRM domain) from CATH. The average structures were computed at the ‘UniProt-instance-level’ for all un-mapped StIs from Pfam (450) and CATH (78). After structural alignment against the core average structure, only seven StIs from Pfam failed to pass the threshold. Thus, all remaining StIs  are qualified as ‘Domain-like’ StIs.In summary, the first iteration resulted in a total of 883 ‘Core’, 521 ‘Domain-like’ and 171 ‘Failed’ domain StIs, with 14 Pfam families and 79 CATH StIs ready for the next iteration.The second iteration started with the 14 Pfam families and 79 StIs from CATH. A total of 100 StIs were extracted from the 14 Pfam families with no inconsistent or obsolete entry. The two sets shared 79 StIs (‘True’ domain StIs) and the other 21 StIs from Pfam remained un-mapped in CATH. Nearly all of them (20/21) passed the alignment threshold leading to 20 ‘Domain-like’ StIs and 1 ‘Failed’ domain StI. Thus, at the end of the second iteration, no new family was found, hindering any further iteration of the workflow.The CroMaSt workflow keeps track of all obsolete and inconsistent domain StIs, which are detected mostly at the residue-mapping step based on SIFTS. To illustrate that, we listed the inconsistent StIs in In summary, the CroMaSt workflow, initialized with Pfam PF00076 and CATH 3.30.70.330 domain families, identified 962 ‘True’ domain StIs (among which 883 are ‘Core’), 541 ‘Domain-like’ and 172 ‘Failed’ domain StIs with respect to the RRM domain type. In terms of domain families, the CroMaSt workflow explored a total of 19 families  and 16 of them (15 from Pfam and 1 from CATH) qualified for the RRM domain type . InteresIn total, this run of CroMast workflow lasted 68.9 min on a machine equipped with eight 2.40 GHz Intel(R) Xeon(R) Silver 4214R processor without any prior downloads of PDB and SIFTS entry files.The multiplicity of biological databases and the lack of systematic cross-references between them lead to important issues in data integration and consistency. Protein domain databases are no exception. Here, we show the way to increase interoperability between protein domain databases using a cross-mapping approach. Our CroMaSt method constitutes a systematic, reproducible and automated solution to retrieve domain StIs corresponding to a certain domain type of interest for the user. Along with this, CroMaSt also points out some irregularities in the databases.It should be noted that the results returned by CroMaSt are determined by the starting domain families used as input. In fact, the set of ‘Core’ domain StIs is dependent on the starting domain families. This influences the computation of the ‘core average structure’ of the domain type and in turn all other results. This strong dependency of CroMaSt on input domain families has at least two practical consequences. Firstly, it is recommended to start the workflow with the two most populated domain families in order to get the most exhaustive results. It is also possible to start with more than one family from one or both databases. Second, this feature can be used to explore particular domain families within a given domain type in order to characterize possible sub-type core average structures.In the setting described in this article, we found only 15 from the 33 Pfam families currently grouped in the Pfam RRM clan CL0221 . There aThe structures predicted by AlphaFold2  and highlight StIs wrongly assigned to a domain family or domain type. Two examples illustrate this. In Pfam, we observed that one ‘Failed’ domain StI from RRM_1 domain family (PF00076), derived from the 5OPT_h PDB entry, has been assigned by Pfam erroneous domain boundaries resulting in the loss of step see . In geneCurrently, CroMaSt takes Pfam and CATH as sources for domain databases. However, CroMaSt can be expanded to include other domain databases as well. As the cross-mapping step can lead to many un-mapped StIs because the compared databases do not use the same version of UniProtKB, CroMaSt should in future take advantage of the integrated InterPro domain family resource, which runs all the domain models on the same version of UniProtKB.CroMaSt is a fully automated workflow that assesses the assignment of protein domains to a given domain type thanks to cross-mapping between CATH and Pfam domain databases and to 3D structural alignment. The multiple structural alignments provided by CroMaSt can provide new information about conserved and variable residues, loops or SSEs, for evolutionary studies of a given domain type. In the case of RRM domain type, such information was exploited recently to decipher the RNA recognition code for the canonical binding mode to RRM domains (vbad081_Supplementary_DataClick here for additional data file.


Appraising the quality of narratives used in assessment is challenging for educators and administrators. Although some quality indicators for writing narratives exist in the literature, they remain context specific and not always sufficiently operational to be easily used. Creating a tool that gathers applicable quality indicators and ensuring its standardized use would equip assessors to appraise the quality of narratives.We used DeVellis’ framework to develop a checklist of evidence-informed indicators for quality narratives. Two team members independently piloted the checklist using four series of narratives coming from three different sources. After each series, team members documented their agreement and achieved a consensus. We calculated frequencies of occurrence for each quality indicator as well as the interrater agreement to assess the standardized application of the checklist.We identified seven quality indicators and applied them on narratives. Frequencies of quality indicators ranged from 0% to 100%. Interrater agreement ranged from 88.7% to 100% for the four series.Although we were able to achieve a standardized application of a list of quality indicators for narratives used in health sciences education, it does not exclude the fact that users would need training to be able to write good quality narratives. We also noted that some quality indicators were less frequent than others and we suggested a few reflections on this. Along with the implementation of competency-based medical education (CBME), there has been a recent call for the qualitative appreciation of students’ performance 2. As suc3332389Unfortunately, narratives often seem to lack quality 711. For 76117Some quality indicators for writing narratives exist in the literature, but they remain context specific and not always sufficiently operational to be easily used. In the context of clinical competency evaluation, Dudek and colleagues  developeOur aim was to create and pilot a list of evidence-based quality indicators comprised of quality indicators previously identified through a rigorous scoping review . This toWe used DeVellis’s  8-step f3define the quality of narratives) and 2 (generate items) were completed in a recent scoping review [Steps 1 (g review . A main step 3: determine the format of measurement) would be a user-friendly approach for this tool [We determined that a checklist format  we solicited input on item pool of indicators from three potential users and three researchers in HSE. They were asked to assess the clarity and the pertinence of each indicator using a 10-point Likert scale. Furthermore, they were asked to provide suggestions for reformulation and improvement when applicable. After receiving their feedback, the core team members (MC & KO & VRD & CS-O) met, discussed, and applied minor changes.In Step 4  was not carried out in this study. Step 5 suggests the inclusion of additional items in studies where there is a sensitive construct or a vulnerable population which is not applicable to this study.Steps 5 of DeVellis  (inclusistep 6: administer items to development sample).Once the checklist created, we piloted it with a sample of narratives , a clinical clerkship in UGME (source 2), and a course to engage students in a reflective process to increase their awareness about their professional identity development (source 3). We favored different sources of narratives to increase the generalisability of our findings. The assessors that provided the narratives did not receive specific training on how to write good quality narratives, this is true for the three different data sources. Some assessments include scores and narratives, but only narratives were provided to us following ethical approval by our Local Institutional Review Board (2017–1494/St-Onge).Procedures. Two team members (MC & VRD) independently applied the checklist (tool) to four series of narratives . These two team members met after each series to discuss document agreement, achieve a consensus on the presence/absence of each indicator and refine their understanding of the indicators. Interrater agreement (IA) was calculated after each of the four narrative rating rounds, using percentage of agreement. IA was calculated on the team members initial appraisal, that is, before consensus.analysis), we calculated frequencies of occurrence for each quality indicator, per rater. We subsequently calculated IA  for each quality indicator to establish whether the team members arrived at the same decision regarding the presence or absence of a quality indicator for each narrative. Analyses were undertaken in SPSS (version 24).For Step 7  are presented per indicator in In IA ranged from 88.7% to 97.3% in the first series of narratives, from 94.7% to 100% in the second series, from 98% to 100% in the third series and from 95.3% to 100% in the fourth series of narratives. IA increased for indicator 4 “Describe performance with a focus on specific elements ”, indicator 6 “Use language that is clear and easily understood” and indicator 7 “Use a non-judgmental style” from the first series of narratives to the last series of narratives which suggests an improvement in the standardization through its progression. IA mean per indicator ranged from 94.2% to 99.3%. Raters achieved the highest standardization for indicators: 4 (mean 97.5%), 6 (mean 99.0%) and 7 (mean 99.3%).Good quality narratives are essential to contribute to students’ learning and development, and to inform committees’ decisions about learners’ progression toward competence . Given tRegarding the frequencies, we noted that some quality indicators were more frequent than others, such as “Use a language that is clear and easily understood” and “Use a non-judgemental style”. We assume that, in some ways, these two are probably the most intuitive indicators. On the other hand, some indicators were less frequently present in our sample of narratives. We noticed that assessors do not always link their comments to the required level of achievement relating to standards/expectations in their narratives. One plausible reason is that assessors may presume that the expected level of performance is already known by learners. Another important key point to consider is the live discussion that occurs (or not) between the assessor and the learner. A written comment may be formulated differently if the assessor has been able to discuss it with the learner. Likewise, assessors seem reluctant to provide recommendations to learners on how to improve their performance and to provide a balanced message between positive elements and elements needing improvement. This may be due to several issues such as the fear of damaging the relationship with students or lacking coaching abilities 1017. Ind1023Although our checklist seems suitable to appraise the quality of narratives, we must recognize that only six out of seven indicators were studied, as we did not have access to the scores associated with the sample of narratives. Empirical evidence regarding the application of the indicator 5 “Provide justification for the mark/score given” in a standardized way still needs to be addressed. Regarding this specific indicator, we recognize the usefulness of the “Quality of Assessment for Learning score” (QuAL score) , a tool Our checklist could be used as a tool by clinical supervisors during the process of writing narrative comments . Nevertheless, we also want to emphasize that a standardized use of our checklist does not exclude the fact that users  would need training to be able to write good quality narratives. We recognize that the checklist has only been tested by members of the team, and therefore, a future step would be to test it as tool to write good quality narratives with assessors or supervisors after received training. While Nichols et al.  were una21The views expressed herein are those of the authors and do not necessarily reflect those of the Société des Médecins de l’Université de Sherbrooke.


Insects evolved various modifications to their mouthparts, allowing for a broad exploration of feeding modes. In ants, workers perform non-reproductive tasks like excavation, food processing, and juvenile care, relying heavily on their mandibles. Given the importance of biting for ant workers and the significant mandible morphological diversity across species, it is essential to understand how mandible shape influences its mechanical responses to bite loading. We employed Finite Element Analysis to simulate biting scenarios on mandible volumetric models from 25 ant species classified in different feeding habits. We hypothesize that mandibles of predatory ants, especially trap-jaw ants, would perform better than mandibles of omnivorous species due to their necessity to subdue living prey. We defined simulations to allow only variation in mandible morphology between specimens. Our results demonstrated interspecific differences in mandible mechanical responses to biting loading. However, we found no evident differences in biting performance between the predatory and the remaining ants, and trap-jaw mandibles did not show lower stress levels than other mandibles under bite loading. These results suggest that ant feeding habit is not a robust predictor of mandible biting performance, a possible consequence of mandibles being employed as versatile tools to perform several tasks. Insect mouthparts are ectognathus, meaning they articulate externally with the head and are composed of the labrum, a hypopharynx, a pair of mandibles and maxillae, and a labium2. Morphological diversification of insect mouthparts is associated with many insect feeding habits, enabling them to feed on food items with variable mechanical demands3. One crucial aspect of insect feeding is the ability to capture prey and chew food items using mandibles4, which leads to considerable mandibular morphological divergence across species3, and whose relevance is highlighted by the size of the mandible adductor muscles2.Morphological variation is a central component of biological evolution. Among arthropods, insects are taxonomically diverse and show substantial morphological variation associated with several aspects of their life-histories, including their feeding habits5. Dicondylic mandibles articulate with the head through two ball-and-socket joints, reducing mandibular movement to a single axis of rotation so that abduction and adduction are the only possible movements, in contrast to the ancestral condition of insects that were also able to protract their mandibles5. Despite a simplification in mandibular movement, this condition increases the stability of mandibular movement5, and insects with dicondylic mandibles exhibit a vast diversity of feeding habits and mandible morphologies3. While mandibular morphological variation is often associated with functional aspects, such as feeding characteristics7, there is no compelling evidence that such an association is a frequent evolutionary pattern8. In many insect lineages, the mandibles perform several functions, which compromises specialization and may underline the lack of association between mandible morphology and functional aspects. This is particularly true for ants, especially the worker caste10.Many current insect lineages exhibit a dicondylic pattern of mandible articulation with the head12. To carry out their daily tasks workers rely heavily on their mandibles, which enable them to bite, transport objects, dig, process food, and perform other functions9. The significant reliance on mandibles for task performance has led to the crucial enlargement of the mandibular adductor muscle M. craniomandibularis internus (0md1) in workers, occupying most of the head’s internal volume. A much smaller pair of muscles, the M. craniomandibularis externus (0md3), is responsible for mandible abduction19. Due to mandible movement limitations, ant workers must perform various tasks by modulating the force and velocity of their bites, along with employing other mouthparts to further process food, for example. This modulation is accomplished through the versatility of the 0md1 muscle, composed of several sets of fibers that vary in their contractile characteristics21. Some muscle fibers have long sarcomeres, which generate slow but powerful contractions, whereas others consist of short sarcomeres that produce fast but weaker contractions, and the proportion of these distinct fiber types in the 0md1 also varies interspecifically13. In some specialized ant species, power amplification mechanisms have evolved in which subtle morphological modifications, mainly in the mandible articulations with the head, generate a mechanical system where the contraction of the 0md1 muscle stores potential energy that is suddenly released to generate high-speed strikes. This results in faster movements than possible under muscle contraction only28.Ants are eusocial insects that exhibit a reproductive division of labor. Winged members of the colony, namely queens and males, are responsible for reproduction, while the wingless individuals, or workers, perform the non-reproductive tasks essential for colony maintenance29, there is considerable interspecific variation in mandibular morphology9, which may reflect functional adaptations to specific feeding habits or other ecological roles30. Although some studies did not find a clear relationship between mandible shape and function31, there is evidence that particular ant species evolved specialized mandible shapes for specific tasks. For example, Melissotarsus ants have mandibles specialized for excavation32, while Pheidole spp. major workers have mandibles with a notable ability to exert pressure during a bite33. In leaf-cutting ants, the mandible shape of some worker types excels at cutting leaves35. Sub-major workers of Eciton spp. have mandibles that allow for a more powerful grip to carry large prey37. The falcate mandibles of Dorylus spp. males seem associated with a better capacity to grip the queen during copulation18. Ants with power amplification mechanisms, such as trap- and snap-jaw ants, have particularly distinctive mandibles that are longer and narrower than the remaining ants. Among many functional aspects, this may be an adaptation to withstand the mechanical demands of their powerful strikes39 and facilitate prey capture39. Additionally, the fossil record suggests that disparate mandible shapes were prevalent from the beginning of ant diversification40, and shovel-shaped mandible may not be the ancestral condition in ants41. Some authors argue that the ancestral ant may have relied on a falcate-shaped mandible18. Overall, understanding the functional implications of mandibular morphological variation in ants is a crucial area of research that can shed light on the ecology and evolution of these insects.Beyond muscular modulation, bite performance can potentially be influenced by mandible morphology. While most ant mandibles are shovel-shaped with a narrow base and a triangular blade43. In other cases, however, allometric scaling results in two or more recognizable discrete worker types  or even continuous variation along a gradient, representing different levels of worker polymorphism44. Worker polymorphism can improve colony division of labor and task efficiency by allowing different worker types to specialize in distinct colony tasks45. There is evidence that variation in the mandible morphology of polymorphic workers is associated with task activity in some ant species35.In addition to interspecific variation, mandible shape can vary intraspecifically between worker types in polymorphic ant species. In monomorphic ants, there is subtle morphological variation in the worker caste, mainly related to variation in body size50. In recent years, there has been a significant increase in the availability of high-quality morphological data for ants due to improved access to microtomography facilities (high-resolution desktop and synchrotron µCT)51. It has allowed for a broad application of biomechanical simulations and studies of functional morphology, and has been particularly useful in investigating the relationship between mandible morphology and bite performance53. However, in the case of ants, most studies have focused on a few species54.Biomechanical simulations are valuable tools to explore the relationship between mandible morphology and biting performance55. It is a mechanical response of the structure to external loading demands and is a valuable property to consider when comparing the responses of different organisms to similar external loading conditions57. We selected ants with different feeding habits, including generalist and specialist predators (trap-jaw ants), omnivorous ants, and one leaf-cutting species. The main focus of our study was to discuss how the variation in mandible morphology influences stress distribution and investigate whether predatory ants, which need to capture and subdue living prey, possess mandibles morphologically specialized in dealing with bite loading. To achieve this, we used Finite Element Analysis (FEA) to simulate biting behaviors in 3D mandible models of several ant species. Being widely applied in distinct engineering fields and more recently in the context of biological functional mechanics, FEA is a numerical technique used to approximate the mechanical responses of structures to the loading demands they need to withstand57. To be performed, FEA demands a digital representation of the structure of interest, knowledge of its material properties, and the loading conditions intended to be simulated57.In this context, our study aimed to investigate the role of mandible shape in bite performance across a wide range of ant species. By biting performance we considered the distribution of stress in solid models of ant mandibles under loading conditions that characterize distinct biting behaviors. Stress is the force that concentrates in a specific area59, whose mechanical demands need to be withstood by their mandibles. Additionally, we predicted that sturdier mandibles, such as those of major workers in polymorphic species like Pheidole spp., would perform better than slender mandibles in simulations of pressure biting, regardless of the main feeding habit of the species, due to their more robust constitution that allows for a better concentration of stresses around thicker regions of the mandibles33. By conducting this comparative study across a wide range of ant species, we aimed to improve our understanding of the relationship between mandible morphology and bite performance and shed light on the functional implications of mandible variation in ants.Our main hypothesis was that predatory species would show proportionally lower stress levels than non-predatory species in biting simulations, assuming that the need to capture and subdue living prey with the mandibles represents higher mechanical demands than dealing with dead organisms, plant tissues, or liquid exudates. We also expected that trap-jaw ants would perform better at strike biting than all other species since the power amplification mechanism of those ants allows for the generation of remarkably fast strikesAcantognathus brevicornis, the major worker of Carebara sp.01, Lophomyrmex sp.01, Platythyrea cribrinodis, and Parasyscia sp. had high-stress concentrations around the mandibular articulations, whereas Bothroponera fugax, the minor worker of Carebara sp.01, the media worker of Eciton burchellii, Ectatomma edentatum, Myrmica ruginodis, and Octostruma petiolata also showed high levels of stress along the mandible blade. Similarly, omnivorous species such as Cephalotes pusillus, Formica fusca, Heteroponera dentinodis, Lasius niger, Odontomachus chelifer, the major worker of Pheidole aper, and Wasmannia affinis concentrated high levels of stress around the mandible articulations with the head, while Azteca sp., Camponotus zenon, Dorymyrmex sp., the minor worker of Pheidole aper and Solenopsis sp.04 exhibited relatively high stresses along the mandible blade too. Finally, the leaf-cutting species Acromyrmex aspersus showed intermediate to high stress levels along the mandible blade  from species more associated with intervals 2–5 (such as Holcoponera striatula) , which were generally isolated from other species on the PCAs. These ants typically had a larger volume of their mandibles filled with intervals of intermediate to high stresses, contradicting our hypothesis that trap-jaw ants would show a better mechanical performance in strike biting. The more robust mandibles of Acromyrmex aspersus, Ectatomma edentatum, Octostruma petiolata, Holcoponera striatula, and the major worker of Eciton burchellii, there were only subtle differences in stress distribution between pressure and strike biting, indicating a lack of specialization towards a specific biting behavior.Although there were no evident differences in stress patterns when comparing the differential use of the masticatory margin , we observed differences between pressure and strike biting. Pressure biting tended to generate higher relative stress levels along a larger mandibular surface area than strike bite, suggesting that pressing with mandibles is more mechanically demanding than striking. However, pressure biting tends to generate lower values of non-normalized stress than striking bite. Therefore, the mechanical demands of pressure biting are more relevant in long-lasting biting, where tiny failures can accumulate along the cuticle than in an acute high-stress peak, which is characteristic of strike biting. In some species, such as 8 and beetles60. Regarding ants, the relationship between mandible morphology and feeding habit is supported by data from fossil61 and current lineages63. Studies focused on ant functional groups have found relationships between mandible morphology and species feeding habits66. However, not all studies have found clear correlations. A recent study using a geometric morphometric approach with 3D data of the diverse ant genus Pheidole did not find significant relationships between the mandible and head shape with feeding and nesting habits31. A phylogenetically broader assessment suggested that predatory ants tend to have longer and straighter mandibles, with predatory and omnivorous species differing in general morphological traits, although this effect was not statistically significant30. Also, the mandible length of more than 400 ant species showed no correlation with trophic position67. Despite such reports, studies investigating the effects of mandible morphology on biting behavior in ants are still limited. Although there is evidence that mandible shape has an impact on the responses of these structures to bite-loading demands, these studies have often relied on small sets of species38. In this context, our study stands out by comparing a higher number of species, and indicate that the main feeding habits, such as omnivory versus predation, are not good predictors of stress patterns in ant worker mandibles when considering only the variation in mandible shape and thickness.The form-function relationship of mandibles has been explored with FEA in other insect groups, such as OdonataCephalotes pusillus and majors of Carebara sp.01 and Pheidole aper, are among the ones exhibiting relatively lower stress levels in pressure biting, as predicted. Interestingly, however, these stouter mandibles also showed relatively lower stresses in strike biting, as confirmed when considering normalized and non-normalized stress levels. Those species had mandibles with a broader blade and a subtle constriction near the mandibular base, a more convex curvature on the mandibular external face, and either short teeth or the absence of such structures. The lack of teeth means that the stresses generated in the masticatory margin do not concentrate heavily on smaller regions, as when teeth are present, but spread more evenly along the masticatory margin. A prominent mandibular curvature hampers the stress dissipation towards the delicate mandible blade, generating stress concentrations on the more robust masticatory margin and mandible base. Pheidole major workers are task specialists, often recruited for food processing (e.g. crushing seeds), resource retrieval (e.g. chopping and carrying pieces of large dead arthropods), and colony defense70, behaviors that require strong bite forces and generate higher mechanical demands than other activities. Pheidole aper is not a granivorous species71, but there seems to be no strong morphological differences between granivorous and non-granivorous species31. A better biting performance of Pheidole major worker mandibles was previously highlighted72, mainly in pressure biting33, but not in comparison with other ant genera. Majors of Carebara spp. perform similar specialized tasks as those described for Pheidole74, and their mandibular mechanical responses to bite-loading demands were correspondingly similar. Finally, species of Cephalotes spp. are known by their thicker exoskeleton, culminating in the morphological specialization of major heads to protect nest entrances76, but the mechanical demands of bite in worker mandibles were never tested before.Among the species studied, those with stouter mandibles, such as Ectatomma edentatum and Holcoponera striatula, have similar mandibular morphology but show contrasting stress patterns in all biting scenarios tested, with Holcoponera striatula exhibiting relatively higher stress levels than Ectatomma edentatum. Regarding their positioning in the PCAs, both mandibles are more separated along the PC1 in simulations with the masticatory margin, suggesting that this mandibular region could represent an important source of mechanical distinction between the species. In fact, the masticatory margin in Ectatomma edentatum is wider (~ 0.9 mm) than in Holcoponera striatula (~ 0.3 mm), resulting in the bite load being applied along a broader area and hence dissipating over a larger area. However, a deeper investigation is needed to unveil the possible morphological aspects that explain the observed disparity in mechanical performance.Two closely related species, Acantognathus brevicornis, Odontomachus chelifer, and Strumigenys denticulata, along with the major worker of Eciton burchellii, possess specialized mandibles, which in general are long, slender, with well-developed teeth, although the major worker of Eciton burchellii differed from the trap-jaw ants in having a hook-shaped mandible with a single tooth. Despite sharing these mandibular characteristics, those four mandibles displayed distinct stress patterns. Strumigenys denticulata and the major worker of Eciton burchellii showed proportionally higher stress levels throughout the mandibles than Acantognathus brevicornis and Odontomachus chelifer. However, when considering the non-normalized stress intervals, all these mandibles were highlighted on the PCAs by showing a proportionally larger volume filled with higher stress intervals, particularly in strike biting. These results do not support the initial hypothesis that trap-jaw ants would show relatively lower stress levels in strike biting. This partial contrast between the results of color maps and stress intervals provides intriguing suggestions about how trap-jaws deal with biting mechanical demands. In general, the long and slender mandibular blade withstands much lower stresses, reducing the risk of failure, but different from what was suggested for the snap-jaw ant Mystrium camillae, its general morphology and cross-section geometry does not indicate that such mandibles are prone to deform and withstand stress26, but this requires further investigation. Evidence from Odontomachus monticola highlights the importance of mandibular hollowness in withstanding the stresses generated by their powerful strike bites38. The natural hollowness of this species appears to optimize the trade-off between power generation and impact resistance, compared to mandibles with higher indices of hollowness or completely solid mandibles38. Accordingly, for all species tested in our study, their natural hollowness and cuticle thickness was kept for the simulations.The trap-jaw ants, including 77, which varies across different body regions. This material gradient proved to be functionally significant in some studies80 and is particularly relevant to ant mandibles, which can accumulate heavy metals along their masticatory margin, leading to increased cuticular stiffness in those regions85. However, information about the variation in material properties of ant mandibles is still limited, with only a few studies restricted to a few leaf-cutting ants86. Moreover, incorporating material property variation in FEA increases the simulation complexity and can lead to confounding effects on structure shape and material properties56. Therefore, it is essential to consider the influence of material property variation under specific hypotheses and with appropriate data on the species in question. Further efforts are needed to measure the heterogeneity of cuticle material properties in ant mandibles to provide a better picture of their mechanical behavior.Although this study has shown some effects of mandible shape variation on stress distribution, it is important to note that the approach here presented does not fully capture the complexity of the mandible cuticle’s material properties. Insects have a gradient of material properties along the layers of their cuticle54. Our effort builds on previous studies that found weak associations between mandible morphology and feeding ecology in ants31. Therefore, mandible morphology could reflect the necessity to perform multiple tasks54 or to excel in other activities, such as excavation32, hypotheses that demand further studies to be properly tested.Biomechanical approaches such as FEA are interesting tools for testing hypotheses about the role of mandible morphology in bite mechanics. Our results suggest that more robust mandibles exhibited lower stress levels under both pressure and strike biting, whereas mandibles of trap-jaw ants did not show signs of adaptation to strike biting in relation to the remaining species. Also, the main feeding habits of the ant species tested do not anticipate the general stress patterns observed in their mandibles under bite loading, being that the interspecific morphological disparity seems more relevant to biting performance among the ants tested. Ant mandibles are essential for food capture and processing but also play a crucial role in other colony tasks performed by workers41, and although the shovel-shaped mandible is considered the Bauplan of extant ant mandibles29, many ant lineages exhibit significant deviations from this general morphology . Our sampling set covers a significant sample of this variation and relevant functional differences were observed. Further studies may explore the effects of variation in cuticle material properties, particularly considering the interspecific variation in heavy metal accumulation85. It would also be meaningful to investigate more widely the relationship between mandible morphology and bite force in ants88.The evolution of ant mandibles has been the subject of intense study in recent years89) and literature sources91. In cases where we did not find the species’ main feeding habit or were able to identify the species only at the genus level (morphospecies), we considered the main feeding habit of the genus. We classify as predators the ant species known to actively hunt other animals (mainly arthropods) because of the mechanical demands of subduing a prey, which is assumed to differ from simply feeding on dead animals. For the predatory category, we included species with specialized mandible morphology and mechanics, the trap-jaw ants. Due to their specialized mandibles, these species were classified here as specialized predators, although not necessarily feeding exclusively on live prey. Omnivorous species do not actively predate other organisms as their primary feeding habit, usually relying on many different food sources without subduing living prey. We also added a leaf-cutting ant (Acromyrmex aspersus), which exhibits a unique biting behavior for cutting leaves. We aimed to broadly categorize species based on their primary differences in feeding mechanical demand, not to describe in detail their natural diets, since many species are known to opportunistically feed on a wide range of trophic sources71. In cases where different species displayed similar mandible morphology, we relied on the quality of the µCT scans (see below) to decide which species would enter our final dataset. Species identifications were confirmed with assistance from taxonomists (see Acknowledgments). All scanned specimens are deposited in the Formicidae collection of the “Coleção Entomológica Mítia Heusi Silveira”, Universidade Federal de Santa Catarina, Brazil  and omnivorous (10), along with a leaf-cutter ant —and KIT Light Source (Eggenstein-Leopoldshafen). As a result of the tomographic reconstruction the scans were assembled as Tiff image series for subsequent segmentation, with scanning parameters varying depending on the ant species (Table 95 using Matlab (Math-Works) and the Astra Toolbox98. Volumetric CT data of ant worker mandibles were then pre-segmented using the software Amira 5.4 . A set of slices was manually segmented using the Magic Wand tool, at intervals ranging from 5 to 15 slices, depending on the complexity of the mandible and the quality of the scans. Automatic interpolation between the pre-segmented slices was done using the online platform Biomedisa99. To ensure that the reconstructions accurately represented the morphology of ant mandibles, the outputs from Biomedisa were imported back into Amira 5.4, where we corrected inaccuracies and reduced the complexity of the reconstructed morphology, taking into account the presence of hairs, sharp edges, and holes, among other factors. Cuticle thickness can vary substantially along the ant mandible, and the degree of mandibular hollowness can also differ between species, both being potentially relevant to its bite mechanics38. As such, the natural hollowness and cuticle thickness of each mandible were represented in our mandible digital representations.Ant specimens were scanned using synchrotron radiation X-ray tomography at two German facilities, Imaging Beamline P05 (IBL)es Table . TomograBlender 2.93 (https://www.blender.org/) to position them in a common orientation. We then used FUSION 360 (AUTODESK) to decrease mesh density. By conducting mesh convergence simulations, we determined a minimum mesh density for each mandible that approximates the 3D mandible morphology and provides a lower computational demand to solve the finite element equations100. To emulate actual simulation parameters, we generated a simplified version of the final simulation setup, sampling three to six elements from specific mandibular regions to check for convergence (less than 5% of error) on von Mises stress values101. We exported meshes from FUSION 360 as .stl files and imported them into FEBio102, where we conducted convergence tests and the final FEA simulations. The number of elements for the definitive meshes is available in Table The mandible surface models were imported into 0md1 contraction. We also restricted the mandible articulations with the head and the masticatory margin or apical tooth to zero nodal displacements in all directions. To ensure consistency, we applied the same material properties to each simulation. The Young's modulus was defined as 2.75 GPa based on measurements from the mandibles of Atta laevigata available in the literature86, and the Poisson ratio was set at 0.3, as commonly considered for ant cuticle54. To account for differences in mandible size, we applied a 1 N load to the largest mandible and adjusted the load for the remaining mandibles based on surface area differences103 104. This approach allows for a direct comparison of stress distribution between the mandibles in a more quantitative way than the color maps, where normalized stress values are being compared. In comparing the proportion of mandibular volume filled by each stress interval, we can observe which structures are submitted to higher or lower non-normalized stresses, and how relevant those stresses are for the structure as a whole, which is informative when a size-corrected load is considered for the simulations, as was done here.The Intervals Method is an approach developed to quantify the area (2D) or volume (3D) of structures covered by a specific range of stress102. Then we removed elements that corresponded to the 2% higher stress values in each simulation, as these values often represent artificially high-stress values105. Even after the removal of such potentially artificial high-stressed elements, a few mandibles showed substantially higher stress values than the remaining ones, achieving values up to one order of magnitude higher , and we retrieved the coefficient of determination (R2) to analyze the convergence of PC scores. The stop of increase in R2 defined the final number of intervals104. For all biting scenarios, convergence occurred with 15 intervals, so we used this number for the PCAs (File 106 using the FactoMineR version 2.4107 and factoextra version 1.0.7.999108 packages.We applied the method independently to each biting scenario. We extracted data on von Mises stress and volume from the elements of each simulation from FEBioher Fig. . To accoher Fig. , we log-her Fig. . To defiCAs File . We cond104. Color maps were graphical outputs generated by FEA software displaying stress distribution along the mandibles. To compare results between species and biting scenario stress values were normalized using a reference model. The resulting color variation on the maps represents the proportion of stress based on the maximum normalized stress value for each simulation (Fig. 104. These proportions served as input variables for the PCAs used to address differences in stress distribution among ant mandibles across the 15 stress intervals (Fig. To summarize, we provide our FEA results in two ways: color maps and the intervals methodals Fig. .Supplementary Figure S1.Supplementary Figure S2.Supplementary Figure S3.Supplementary Information 4.