OBA and OB had a significantly dense crumb pattern made up of more cells less than one millimeter squared. Quantitative–descriptive analysis (QDA) and consumer acceptance testing was conducted 24 hr after baking and mechanical endpoints were evaluated 24, 72, and 120 hr after baking. AF and AOB breads were preferred over OBA and OB breads in QDA evaluation and consumer acceptance scores. Greater percentages of oat bran fiber resulted in a bread that was less moist, firmer in texture, and chewier with trained panelists. In both sensory evaluations, higher amounts of almond flour resulted in higher values in eggy flavor while increased amounts of oat bran fiber correlated with higher values in earthy flavor. Mechanical testing identified higher percentages of almond flour resulted in bread that was less firm and less chewy. Over time, all variations with almond flour became softer and less chewy, while the OB bread increased in firmness. Sensory cohesiveness did not correlate with the mechanical equivalent, identifying a need to re‐evaluate the parameters used to calculate this objective endpoint.Ketogenic, gluten‐free breads comprised of almond flour, oat bran fiber, or combinations of both were compared. The textural properties, sensory attributes, and consumer acceptance were analyzed on each bread containing 100% almond flour (AF), 66.7% almond flour with 33.3% oat bran fiber (AOB), 66.7% oat bran fiber with 33.3% almond flour (OBA), and 100% oat bran fiber (OB). AF and AOB breads had a more open crumb structure composed of cells between 1–4 mm Textural properties, sensory attributes, and consumer acceptance were analyzed for ketogenic, gluten‐free breads comprised of almond flour, oat bran fiber, or combinations of both. Higher ratios of almond flour to oat bran fiber resulted in a bread with better structure, flavor and texture which was preferred by trained panelist and consumers. However, traditional breads are not acceptable to consume for individuals on a low carbohydrate diet as they typically average 15–20 g of carbohydrates per serving. Low carbohydrate options are becoming more popular, particularly for those following a ketogenic diet. Ketogenic diets are a growing trend for both weight loss and as a means of treating diabetes , flours made from nuts, seeds, soy, and some legumes are ideally suited to satisfy consumer needs. However, not all of these flours are acceptable. For example, cashew flour contains excessive carbohydrates to meet ketogenic requirements. Additionally, the flavors in coconut, chickpea, flaxseed, and soy flours were identified as overwhelmingly strong during early exploration. In investigational studies, other potential flours such as flax and sesame meal produced inferior crumb structures (unpublished data). Almond flour was identified as the most suitable candidate for these breads due to its nutritional and sensory attributes. Oat bran fiber was also identified as a strong candidate and showed more promise as a primary ingredient for its ability to provide an improved structure while adhering to the low net carbohydrate requirements. Psyllium husk also met the gluten‐free and ketogenic criteria, providing unique characteristics to early prototypes. However, as a primary ingredient psyllium husk alone resulted in denser, heavy bread, and functioned better as an adjuvant for almond flour and oat bran fiber (data are not shown).As such, the purpose of this study was to evaluate ketogenic, gluten‐free breads formulated with varying levels of almond flour and oat bran fiber through sensory and mechanical means.22.1Breads tested were developed in the Brigham Young University Bread and Dairy Laboratory following extensive optimization of multiple formulas and preliminary sensory preference evaluations. Formulations for tested breads are given in Table 2.2Loaf volume was conducted by the Rapeseed Displacement method 10–05.01 and moisture content was conducted by the two‐stage bread moisture method 44–15.02 as described by the American Association of Cereal Chemistry AACC, .2.3Crumb quality was evaluated through digital image analysis (DIA) using ImageJ . Samples were cut as described above. Images were scanned using a flatbed scanner , analyzed according to the procedure described by Rosales‐Juarez et al. , and pho2.4Bread crumb firmness, cohesion, adhesion, springiness, and chewiness were measured on a TA‐XT2 texture analyzer (Texture Technologies Corp.) according to AACC method 74–09 modified with a 10 N load cell and a probe speed of 1.7 mm/sec, set 10 mm from the bread surface using a with a TA‐4 acrylic cylinder  probe. Applications software  and system macros were applied without modification. Texture measurement  was performed using three center samples from four loaves per treatment. Two bread slices were combined to make a 25‐mm thick sample for compression to 60% deformation. After initial deformation, samples rested for 5 s, followed by a second compression cycle. Firmness was a measurement of the maximum force of the first compression. Cohesiveness was the positive area of work of the second compression divided by that of the first compression. Springiness was expressed as the ratio of the distance of the detected height during the second compression and the original compression distance. Adhesiveness was measured as the negative area of compression one. Chewiness is expressed as firmness multiplied by cohesiveness and springiness. Samples were tested 24, 72, and 120 hr after baking.2.5Twelve trained panelists, all regular consumers of bread, met weekly for a twelve‐week period to establish descriptor attributes and terminology around bread attributes. Reference standards and performance evaluations were conducted as described by Ahlborn and colleagues  were divided into two groups, with one being informed they were evaluating a ketogenic, gluten‐free bread. The second group was just informed they would be evaluating bread. Following their evaluation, participants of the second group were informed the breads were ketogenic and gluten‐free and asked to re‐evaluate their impression of breads. Every panelist received samples by means of a pass‐through compartment following a monadic sequential order. Sample were served on separate foam plates with distilled water and crackers for pallet cleansing between samples. Attributes selected for evaluation were: appearance, aroma, flavor, texture, aftertaste, moistness, chewiness, color, yeasty flavor, earthy flavor, and eggy flavor. Questions were delivered on a computer screen using Compusense Cloud®  software instructing panelists to evaluate samples one at a time. Sensory evaluation was approved through the Brigham Young University Institutional Review Board and panelists provided informed consent prior to participation.2.7p‐value < .05.Data were analyzed using JMP® Pro 15.0 . Treatment effects were compared through Least Square Means with Tukey‐Kramer grouping to differentiate treatment effects and significant differences determined at α ≤ .05. Multiple linear regression was conducted to determine significant of the interaction between variables and Pearson's correlation coefficients were applied to determine if positive or negative correlations existed between the different terms analyzed. Consumer acceptance data were analyzed using analysis of variance with Tukey's HSD, except for ranking data, which were analyzed using Friedman Analysis of Ranking. Significance was set at a 33.1Loaf volume was greatest in AF Figure  and no d2. Thus, almond flour contributes to a larger, softer crumb while oat bran fiber contributes to a finer, denser crumb.Scanned and photographed images were analyzed with all methods described. Photographs Figure  were of 3.2Results of texture and moisture analysis are presented in Table Higher percentages of almond flour correlated with increased cohesion scores, and AF was significantly more cohesive than AOB, which was higher than OBA, with OB being the least cohesive. Cohesion values increased in all varieties at 72 hr, and then no significant changes were observed at 120 hr. All breads exhibited extraordinary springiness and there were no significant difference between variations and time points. Typically, springiness is associated with positive interactions between gelatinized starches and gluten Tegge, . In the At 24 hr, adhesion was significantly higher in AF, and at 72 hr, all breads showed slight increases in adhesion values, though no significant differences were observed. It was also apparent all breads appeared stickier visually and through tactile evaluation. However, after 120 hr, AF and OB were not different from each other while hybrids AOB and OBA were significantly more adhesive than homogeneous loaves. It should be noted, early texture profile analysis (TPA) described adhesiveness as a primary TPA parameter. However, more recently it is deemed as a secondary parameter and the value and validity of measuring adhesiveness via TPA is not considered accurate .4Almond flour and oat bran fiber are suitable ingredients for ketogenic, gluten‐free breads. Higher ratios of almond flour to oat bran fiber resulted in a bread with better structure, flavor, and texture that was preferred by trained panelists and consumers. Higher levels of oat bran fiber resulted in a bread that was denser, more firm, and preferred less. Additional work is required to correlate sensory endpoints such as adhesiveness and cohesiveness, which involve mastication processing, with Texture Profile Analysis methods.The authors of this article certify that they have NO affiliations or involvement with any organization or entity related to the subject matter or materials discussed in this manuscript.Rachel Gillespie: Conceptualization ; Data curation ; Formal analysis ; Investigation ; Methodology ; Writing‐original draft ; Writing‐review & editing . Gene Ahlborn: Conceptualization ; Data curation ; Formal analysis ; Funding acquisition (lead); Investigation ; Methodology ; Supervision (lead); Validation ; Writing‐original draft ; Writing‐review & editing .


During the various stages of shock loading, many transient modes of deformation can activate and deactivate to affect the final state of a material. In order to fundamentally understand and optimize a shock response, researchers seek the ability to probe these modes in real-time and measure the microstructural evolutions with nanoscale resolution. Neither post-mortem analysis on recovered samples nor continuum-based methods during shock testing meet both requirements. High-speed diffraction offers a solution, but the interpretation of diffractograms suffers numerous debates and uncertainties. By atomistically simulating the shock, X-ray diffraction, and electron diffraction of three representative BCC and FCC metallic systems, we systematically isolated the characteristic fingerprints of salient deformation modes, such as dislocation slip (stacking faults), deformation twinning, and phase transformation as observed in experimental diffractograms. This study demonstrates how to use simulated diffractograms to connect the contributions from concurrent deformation modes to the evolutions of both 1D line profiles and 2D patterns for diffractograms from single crystals. Harnessing these fingerprints alongside information on local pressures and plasticity contributions facilitate the interpretation of shock experiments with cutting-edge resolution in both space and time. In contrast, when shocking Fe along [110], the compressive wave induces an α (BCC) → ϵ (HCP) phase transformation, and then the release wave reverses this transformation and induces twins13. Finally, when shocking Cu along [111], stacking faults rather than twins or phase transformations dominate the deformation response17.Understanding and optimizing a shock response requires characterization not only of the final state but also of the transient microstructures that form during the propagation of the compression wave, the release wave, and their interactions leading to that final state. Over only a few tens of picoseconds, several deformation modes can activate and deactivate (even simultaneously) to significantly alter the material down to the nanoscale. Moreover, the relative contributions and timings of these deformation modes can differ dramatically depending on the material. In this manuscript, we examined three representative metallic systems in order to feature three of the most important deformation modes for shocked FCC and BCC microstructures: dislocation slip (stacking faults), deformation twining, and phase transformation. When shocking Ta along [110], twins form during the compressive wave and are annihilated by the release wave20. However, post-mortem analysis can merely infer the complex mechanistic history. Such deduction is particularly difficult when multiple mechanisms activate and deactivate over such small scales. Simulations have provided valuable insight into this process21 but cannot replace in situ experimentation. Most common in situ techniques, such as laser interferometry, lack the spatial resolution necessary for detailing transient microstructures. However, researchers have just begun to harness high-speed X-ray diffraction to detail an experimental shock response in real-time23. Over just the last five years, this approach has resolved a long-standing controversy over the phase transformations of shocked graphite24, connected stacking faults to the plastic deformation of shocked Au25, tracked the twins and slip in shocked Mg26, demonstrated the pressure dependence of slip and twining for Ta8, and elucidated the phase transformation of BCC Fe27. Moreover, researchers have just begun to harness high-speed electron diffraction for even finer spatial resolution31.Because these deformation modes operate at the picosecond time scale and the nanometer length scale, traditional shock characterization has been largely limited to post-mortem techniques, such as electron microscopy33. For example, numerous methods for characterizing defects from the widths of diffractogram peaks often yield conflicting results36. To resolve such uncertainties, researchers have recently generated virtual diffractograms from atomistic simulations38. By comparing the ground truths of the atomistic simulations to interpretations of the corresponding virtual diffractograms, they evaluated the efficacies of the interpretative methods themselves. These relatively inexpensive simulations are statistically robust, avoid measurement-induced damage, and can even match the time scale of a shock experiment39. In fact, several recent studies have accurately simulated diffractograms of shocked materials43.While high-speed diffraction can theoretically track nanoscale deformations in real-time, the traditional methods for interpreting diffractograms are unfortunately lackingClassical molecular dynamics simulations provide the ability to directly correlate atomic microstructures with their diffraction patterns during the various stages of shock loading. For the current work, we simulated the shock responses for single crystals of [110] Ta, [110] Fe, and [111] Cu during various stages of loading. These systems were chosen to feature three prominent modes of deformation in FCC and BCC metals: dislocation slip (stacking faults), deformation twining, and phase transformation. To facilitate the experimental, real-time tracking of these modes, we then produced virtual diffractograms from selected volumes of each of the atomistic simulations. Specifically, we performed both powder X-ray diffraction (XRD) and selected area electron diffraction (SAED). The powder XRD effectively represented a large-wavelength, 1D line profile, and the SAED represented a small-wavelength, 2D diffraction pattern. Fingerprints of both types of diffractogram were identified and then correlated with the operating deformation modes. Especially when harnessed in concert with knowledge of the local pressures and plasticity contributors provided by atomistic simulations, these fingerprints have the potential to supercharge the quantitative interpretation of experimental diffractograms from shocked crystals.When subjected to shock loading, Ta predominantly twins due to the compressive wave and then quickly detwins due to the release wave. Hence, twinning in Ta represents a transient deformation mode that can only be experimentally observed via a high-speed, in situ method. For comparison with such experiments, we simulated shock via molecular dynamics for a 300-nm bar of Ta oriented along 110]. Figure 10. FigurFrom these atomistic simulations, we selected the 40-nm regions depicted by blue rectangles in Fig. in-situ Laue XRD of Ta8. For the 1D XRD line profile, the shock compression induced an upward peak shift from the dotted black line to the dotted red line  → ϵ (HCP) phase transformation, and the release wave quickly reverses this transformation to generate twins in the BCC microstructure. Having first understood the effect of twins on the diffractograms of shocked Ta, we isolated the effects of phase transformations in shocked Fe. Figure As depicted by the blue rectangles in Fig. The SAED/XRD for the initial BCC Fe microstructure Fig. a had sha27.The release wave not only reversed the phase transformation but left behind a significant twin fraction e.g., 46%) in the 42-ps microstructure. Although these twins were caused by a release wave in Fe rather than by a compressive wave in Ta, their diffraction signatures were similar. The twins again split preexisting spots in the SAED, produced (112) mirror-plane spots in the SAED, and broadened the XRD peak . Therefore, the combination of a heterogeneous shift and a broadening of the XRD peaks could be used to fingerprint stacking faults in 1D diffractograms. The aforementioned methods for decoupling the effects of various broadening sources in XRD could be used to quantify the relative contributions if the model parameters were optimized.As in the cases of Ta and Fe, the initial microstructure of Cu was 100% crystalline Fig. a and thuBy 50 ps, the release wave had removed the pressure from the analyzed slice, healed some Shockley partial dislocations, and reduced the stacking-fault volume fraction from 8 to 6% Fig. c. HoweveWe systematically linked salient deformation modes for shocked metals to two types of diffractograms by atomistically simulating shocked microstructures, 1D XRD line profiles (large-wavelength), and 2D SAED diffraction patterns . For the Ta system, the results demonstrated that deformation twins induce broadening of peaks in 1D diffractograms and the splitting of spots in 2D diffractograms. Because peak broadening can be induced by numerous types of defects, further knowledge, such as the distribution of local pressures and plasticity contributors, must be incorporated in order to quantify the contributions from dislocations and twins in line profiles. In contrast, the spot splitting in 2D diffractograms serves as a twinning fingerprint. For the Fe system, the results demonstrated that phase transformations manifest in both 1D and 2D diffractograms as new peaks and spots, respectively. The results also demonstrated that the simultaneous activation of multiple deformation modes can be identified via diffractograms. In this case, twins nucleated and affected the diffractograms in similar fashion as with the aforementioned Ta despite differences in loading style. For the Cu system, the results demonstrated that stacking faults also affect both types of diffractograms. The HCP structure of the stacking faults induced the formation of new spots in the 2D diffractogram and the broadening and shift of the peak in the 1D diffraction pattern.Overall, this study demonstrates how generating virtual diffractograms from atomistic datasets can facilitate the fingerprinting of deformation modes that operate during the complex wave propagations and interactions during shock loading. Specifically, these results detail how transient modes of deformation, such as dislocation slip, deformation twinning, and phase transformation, induce the generations, shifts, splitting, and broadening of peaks/spots in 1D/2D diffractograms. This capability will facilitate the understanding and optimization of shock responses by empowering the real-time, nanoscale characterization of shocked materials via high-speed diffraction. With such methods, experimentalists will be able to observe and understand the complex microstructural evolutions inaccessible to post-mortem characterizations and continuum measurements.45 by Sandia National Laboratories. For each material, we constructed a representative volume (30 nm × 30 nm × 300 nm) oriented such that a shock wave would travel along [110] for a BCC system  or along [111] for the FCC system .We performed atomistic shock simulations for Ta, Fe, and Cu via the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS)46, Fe47, and Cu48 represented the interatomic interactions. These potentials reproduced the Hugoniot response observed in experimentation51. The Ta potential reproduced the twinning predicted by density functional theory51. The Fe potential reproduced the BCC-HCP phase transformation close to the experimental pressure of ∼ 13 GPa54. The Cu potential has been used to study the shock and spall response for the range of microstructure and loading conditions57.Embedded-atom-method (EAM) potentials for Ta62. Twins were identified by performing polyhedral template matching (PTM)63 on atoms with a similar structure type to identify Euler angles corresponding to mismatches greater than 10° angle.We used a Nośe-Hoover ensemble (NPT) to equilibrate the microstructures at 300 K and 0 GPa. We shocked each system by driving a 3-nm piston into the metal sample at a constant velocity of 1 km/s for a pulse of 20 ps. For each simulation, we computed the temporal evolution of pressure via spatial binning along the shock direction. We also characterized the phase compositions, dislocation densities, twin densities, and stacking-fault densities65. This package first constructed diffraction vectors corresponding to each point on a mesh in a selected volume of reciprocal space. We choose a mesh spacing of 0.0015 Å−1 for XRD and 0.0010 Å−1 for SAED. We selected the volume by setting the maximum diffraction vector to 1.25 Å−1 for SAED and limiting 2θ from 0° to 60° for XRD. The kinematic diffraction intensity was computed for each of these vectors via the irradiation wavelengths, the atomic scattering factors, and a polarization factor (for XRD only). For the wavelengths, we selected 0.0251 Å to model 200-keV electrons for SAED and 1.54 Å to simulate Cu Kα for conventional powder XRD. Each of the scattering factors was parameterized specifically for the simulated material and the type of diffraction. Finally, the diffraction patterns were generated from the grid of diffraction intensities. For SAED, the grid was sliced according to an Ewald sphere with a radius inverse of the wavelength, a position corresponding to desired zone axis, and a thickness of 0.005 Å−1. Then, the intensities within that slice were projected in the direction of the zone axis to create a SAED pattern within Paraview66. Alternatively, for powder XRD, an Ewald sphere was rotated around the origin of reciprocal space. The line profile was constructed by binning the resulting intensities according to their diffraction angle and then smoothed by fitting with pseudo-Voigt curves67 via SciPy68. Note For select, 40-nm regions of the shocked microstructures, we non-periodically simulated both 2D SAED diffraction patterns and 1D powder XRD line profiles via the LAMMPS user-diffraction packageSupplementary Information.


Dynamic allosterism allows the propagation of signal throughout a protein. The PDZ (PSD-95/Dlg1/ZO-1) family has been named as a classic example of dynamic allostery in small modular domains. While the PDZ family consists of more than 200 domains, previous efforts have primarily focused on a few well-studied PDZ domains, including PTP-BL PDZ2, PSD-95 PDZ3, and Par6 PDZ. Taken together, experimental and computational studies have identified regions of these domains that are dynamically coupled to ligand binding. These regions include the αA helix, the αB lower-loop, and the αC helix. In this review, we summarize the specific residues on the αA helix, the αB lower-loop, and the αC helix of PTP-BL PDZ2, PSD-95 PDZ3, and Par6 PDZ that have been identified as participants in dynamic allostery by either experimental or computational approaches. This review can serve as an index for researchers to look back on the previously identified allostery in the PDZ family. Interestingly, our summary of previous work reveals clear consistencies between the domains. While the PDZ family has a low sequence identity, we show that some of the most consistently identified allosteric residues within PTP-BL PDZ2 and PSD-95 PDZ3 domains are evolutionarily conserved. These residues include A46/A347, V61/V362, and L66/L367 on PTP-BL PDZ2 and PSD-95 PDZ3, respectively. Finally, we expose a need for future work to explore dynamic allostery within (1) PDZ domains with multiple binding partners and (2) multidomain constructs containing a PDZ domain. Allosterism is a phenomenon where communication exists within a biological macromolecule between the ligand-binding site and a distal region. While allosteric effects were initially attributed only to structural changes, Cooper and Dryden  made a cThe PDZ family comprises 268 domains in 151 unique human proteins . The sheAs key players in regulating signaling pathways, the PDZ family propagates signals via dynamic allosterism. Various groups have revealed how highly conserved protein-protein interactions in the PDZ binding pocket propagate allosteric effects through the PDZ domain ,31,32,3315N/13C [1,2H-methyl [2 and τe) that can describe protein dynamics in the picosecond to nanosecond timescale. The S2 parameter describes the amplitude of a bond’s fluctuations, where a value of 1 corresponds to complete rigidity and a value of 0 corresponds to complete flexibility. The τe parameter describes the timescale of these motions. Several groups have wonderfully summarized allostery in the PDZ family [In 1999, Lockless and Ranganathan brought allostery in the PDZ family to attention. Suspecting that allosteric networks are evolutionarily conserved, they proposed that such networks could be statistically predicted using multiple sequence alignment (MSA) . Lockles15N/13C ,35,36,37H-methyl ,35,36,37Z family ,42,43,44Lockless and Ranganathan’s original study identified the αA helix in the network of residues propagating dynamic allostery through PDZ. Since this initial hypothesis, more than a dozen publications have pointed to the αA helix as a key player in dynamic allostery. These studies have recognized αA allostery by utilizing experimental and computational approaches, including nuclear magnetic resonance (NMR) ,28,30,45NMR experiments have identified αA allostery triggered by ligand binding in PTP-BL PDZ2. In 2002, Walma et al. noted chemical shifts at the αA helix of PTP-BL PDZ2 upon binding to human Fas receptor and RIL C-terminal peptides . Two yeaComputational techniques have been able to reproduce the experimentally observed αA allostery in PTP-BL PDZ2 via MD ,30,31, MIn addition to equilibrium MD, Cilia et al. used Monte-Carlo sampling  to identify a change in dynamics at the αA helix , without commenting on whether fluctuations increased or decreased . FurtherWhile computational techniques have pointed to ligand-induced αA allostery in PSD-95 PDZ3, these results have not been consistently confirmed experimentally. As the first PDZ domain to have its structure characterized by x-ray crystallography ,17, PSD-Ligand-induced αA allostery in the PSD-95 PDZ3 domain has been identified using equilibrium MD ,31, noneNext, we will review the studies of three groups that used MD simulations to identify ligand-induced αA allostery in PSD-95 PDZ3. As described above, Morra et al. performed equilibrium MD simulations on both the PTP-BL PDZ2 domain and the PSD-95 PDZ3 domain . While tWhile most efforts have focused on ligand binding as the triggering event in PDZ allosterism, the Par6 PDZ domain has been a unique case. Allostery at the αA helix of the Par6 PDZ domain has been studied in two directions: (1) ligand binding provoking dynamics allosterism at the αA helix and (2) protein–protein interactions at the αA helix altering PDZ binding affinity. In a more traditional understanding of allosterism in the PDZ family, Ho et al. applied rotamerically induced perturbations (RIP) to show that perturbations at the binding site increase fluctuations at the αA helix . More inPar6 contains a CRIB domain directly adjacent to its PDZ domain. Cdc42 is a protein that regulates the Par6 PDZ domain by binding to the CRIB domain and increasing PDZ binding affinity by ~13 fold . PetersoIn summary, the αA helix is the most recognized region of the PDZ domain that exhibits dynamic allostery. A previous study has primarily explored three PDZ domains: PTP-BL PDZ2, PSD-95 PDZ3, and Par6 PDZ. While the PTP-BL PDZ2 domain has had αA allostery identified by both experimental and computational efforts, the PSD-95 PDZ3 domain lacks consistent experimental results to support αA allostery noted by computational efforts. This may be partially due to the key focus on the αC helix in PSD-95 PDZ3. Studies focusing on the Par6 PDZ domain have been unique to other PDZ domains. Rather than exploring how ligand binding induces dynamic changes, most studies have focused on how protein–protein interactions with distal regions, such as the αA helix, affect binding affinity. Most interestingly, this points towards a symmetrical communication between the αA helix and the binding pocket in the PDZ family. While ligand binding affects the dynamics of the distal αA helix, interactions at the αA helix also regulates binding affinity of ligands.Following the αA helix, the N-terminal region preceding the αB helix has been most frequently identified as a region of dynamic allostery in the PDZ domain. Here, we refer to this region as the αB lower-loop. A previous study has recognized dynamic allostery at the αB lower-loop through various techniques, including NMR, Monte-Carlo sampling, and PRS. Taken together, the various approaches have identified dynamic allostery in PTP-BL PDZ2 and PSD-95 PDZ3.0 and P−2 are primarily responsible for interactions with the PDZ domain, a previous study has revealed that positions P−4, P−5 and P−6 can also directly interact with the PDZ domain at the region of the αB lower-loop [Taken together, experimental and computational techniques have pointed to the dynamic allostery at the αB lower-loop of PTP-BL PDZ2. In 2004, Fuentes et al. used NMR experiments to show that residues V61, V64, L66, A69, T81, and V85 of the αB lower-loop have increased flexibility upon ligand binding . In a cower-loop . This poAs described above, Dhulesia et al. , Kong etIn addition to traditional simulations, a variety of other computational approaches have been used to explore dynamic allosterism at αB lower-loop in PTP-BL PDZ2. Gerek et al. applied PRS to show that applied forces to specific residues in PTP-BL PDZ2 resulted in a relative displacement of the αB lower-loop  . They noWhile computational results pointing to ligand-induced dynamic allosterism in the αB lower-loop of PTP-BL PDZ2 have been validated by NMR experiments, computational results pointing to ligand-induced dynamic allosterism in the αB lower-loop of PSD-95 PDZ3 have not been so consistent. As described above, original experimental work on the PSD-95 PDZ3 domain specifically noted the lack of conformational changes to the protein backbone upon ligand binding . Since tNormal mode analysis (NMA) of the PSD-95 PDZ3 domain showed a shift of the αB helix that more widely opened the binding pocket to permit ligand binding . AdditioWhile the PDZ family has a highly conserved fold, the PSD-95 PDZ3 domain has a unique αC helix at the C-terminal of the domain. Due to the fact that this added helix is not a part of the traditional PDZ fold, it drew attention for researchers to explore its functional role. Taken together, experimental and computational techniques have identified this unique helix as a key player in allosteric regulation of ligand binding. In 2009, Petit et al. made the first effort to explore the role of the αC helix by comparing the wild-type PSD-95 PDZ3 to an αC-truncated PSD-95 PDZ3 using NMR . While tComputational efforts also have supported the allosteric role of the αC helix in ligand binding. Morra et al. performed equilibrium MD simulations to identify a significant correlation between fluctuations and core energy at the αC helix (E401 and A402) . Rigid-bWhile 268 PDZ domains have been identified in the human proteome , effortsFrom the above, we have listed residues consistently identified as key players in dynamic allosterism in each PDZ domain. For PTP-BL PDZ2, these residues include V40, A45, A46, V61, L66, A69, L78, and T81. For PSD-95 PDZ3, these residues include A347, V362, L367, H372, and Y397. Interestingly, some of the key residues involved in dynamic allosterism have structural agreement between the two domains. The structural alignment between PTP-BL PDZ2 (pink) and PSD-95 (blue) is shown in In Lockless and Ranganathan’s original study , they prWhile some consistently identified allosteric residues within PTP-BL PDZ2 and PSD-95 PDZ3 are in good agreement between the two domains, this is not the case for all key residues. For example, experimental and computational efforts consistently point to V40 and A45 of the αA helix as key allosteric residues in the PTP-BL PDZ2 domain while the structurally equivalent I341 and P346 of PSD-95 PDZ3 are not. Similarly, A69, L78, and T81 of the αB lower-loop have been identified as key allosteric residues in the PTP-BL PDZ2 domain but their structural equivalents in PSD-95 PDZ3 are not. H372 of the αB lower-loop in PSD-95 PDZ3 is a key allosteric residue. While two computational studies identify the structurally equivalent His71 of PTP-BL PDZ2 as a key residue, it has not been consistently identified over all previous studies. These results suggest that while pieces of the allosteric network are conserved in the PDZ family, the dynamic allostery within each PDZ domain remains unique. Our previous study explored ligand-induced dynamic allosterism within the PICK1 PDZ domain . This stHaving identified dynamic allosterism in the PDZ family, we are left with an apparent question: How does the PDZ domain utilize dynamic allosterism in its biological function? Various efforts have begun to address this mystery. Specifically, in the Par6 PDZ domain, interactions at the αA helix have shown to alter the binding affinity of the PDZ domain through allosteric communications . Additio


As an important basis of navigation safety decisions, ship domains have always been a pilot concern. In the past, model parameters were usually obtained from statistics of massive historical cumulative data, but the results were mostly historical analysis and static data, which obviously could not meet the needs of pilots who wish to master the ship domain in real time. To obtain and update the ship domain parameter online in time and meet the real-time needs of maritime applications, this paper obtains CRI as the weight coefficient-based PSO-LSSVM method and proposes to use short-term AIS data accumulation through the risk-weighted least squares method online rolling identification method, which can filter nonhazardous targets and improve the identification accuracy and real-time performance of nonlinear models in the ship domain. The experimental examples show that the method can generate the ship domain dynamically in real time. At the same time, the method can be used to study the dynamic evolution characteristics of the ship domain over the course of navigation, which provides a reference for navigation safety decisions and the analysis of ship navigation behavior. The ship domain is an important concept of water transportation, which was defined in 1975 by Goodwin . ResearcThe utilization of AIS information has received increasing attention, and many scholars have begun to use massive AIS data to analyze and obtain ship domain models ,11. RefeThe above research results provide ideas and references for obtaining ship domain models using AIS data, but most of them are statistical analyses of diachronic data, lacking real-time dynamic research. With the need for real-time information navigation applications, it is necessary to propose a more dynamic and real-time ship domain model acquisition method. Different from traditional statistical methods, the method in this paper makes AIS data serve navigation safety better, enables ships to quickly perceive the dynamics of surrounding ships, identifies the actual parameters of the ship domain, and generates the boundary of the ship domain so that pilots can grasp the dynamics of their own ship domain in time and provide a basis for their navigation safety decisions more quickly. The main work of this paper is as follows:The idea of generating ship domains online is proposed to provide a reference for the dynamic application of AIS data in navigation safety decision making. Short-term accumulated AIS data are adopted. When the minimum requirements for identification data are met, the collision risk index, which is obtained by the PSO-LSSVM method, is used as the control method of identification error filtering, combined with the weighted least square method, to quickly generate the ship domain and carry out dynamic rolling updates. In this paper, starting from a period of the navigation process, the change in the ship domain brought by the change in ship speed and navigation water area in the dynamic navigation process is observed to discover the evolution law of the ship domain, grasp its dynamic change in time, and provide a reference for an in-depth and detailed study of its change.The rest of this paper is organized as follows: Section 2 introduces the ship domain model to be identified, considers the data required for model identification using AIS, and describes the corresponding ship-encounter parameter calculation formula. Then, Section 3 proposes a schematic diagram of online identification, including single online identification and real-time online rolling identification. In Section 4, the PSO-LSSVM method for collision risk index estimation is introduced, and the identification method of collision risk weighting is established. Then, in Section 5, an experimental example combined with the application of this method is described, and the results are analysed. In Section 6, based on the experimental results, some dynamic evolution laws of the ship domain in navigation are summarized. Finally, Section 7 summarizes the main findings and applications of the methods described in this paper, as well as suggestions for future work.x0, y0) are the main parameters to be identified. According to the equation-solving requirements, at least four or more target ship data are required to identify model parameters. To meet the identification accuracy requirement, the position of the target ship should be distributed in four quadrants of the coordinate system around the ship, not concentrated in one or a few quadrants. Considering that the identification model is nonlinear, to improve the accuracy and efficiency of identification, this paper adopts the collision risk weighted least square method to carry out online identification.In terms of ship domain shapes, ship domain models usually include circular, fan-shaped, elliptical, quasi-elliptical, polygonal, etc. Davis  proposedAIS data contain dynamic information required by ship-encounter situations, which is the basis for identifying ship domain models. Only by calculating the ship encounter parameters can the superposition of the target ships around the ship be dynamically grasped, the situation distribution of the ship encounter be obtained, the required data for solving the domain model equation be obtained, and then the model parameters can be identified. The parameters are calculated as follows :xO, yO); the speed is vO; the course is CO, and the corresponding coordinates of the longitude and latitude of the target ship are ; its speed is vT, and its course is CT.Vr is the relative speed using Eq (Cr is relative course using Eq (Suppose that the corresponding coordinates of the longitude and latitude of the ship are (xOyT−yO)K=VTVOxOyT−yO)K=VTVOK=VTVOVr=VO2−CT)VOVTCr=arcO−CT)Vr)DCPA=D(Cr−q−π)TCPA=Dr−q−π)VrConsideAs the parameters are identified online and need to be updated dynamically, only a small amount of data can be relied on to obtain the final results. This is significantly different from the results obtained from massive data mining. Therefore, it is necessary to solve the problem of reliability of the method and meet the requirement of the minimum dataset required for identification. Based on the above considerations, this paper proposes the following identification schematic.The core idea is to achieve the required dataset identification through short-term data accumulation. The dataset is updated by rolling to meet the requirement of real-time results. Differential data processing can be realized through collision risk to improve the identification progress. At the same time, the average collision risk and the number of identification data points were used as references for the reliability of the conclusions. Combined with the average collision risk and dynamic identification results, the pilot can better grasp the real-time dynamics of the ship domain. Compared with the traditional model, the pilot can also understand the safety margin of the compressible space in the ship domain. The results are a research method of the dynamic evolution of the ship domain and a reference for navigation safety decision-making. The schematic diagram includes single identification and dynamic rolling identification.Through the VTS center of the maritime administration of the jurisdiction, AIS data can be obtained. Then, according to the AIS data, the meeting situation between the target ship in the surrounding waters and the ship can be obtained through dynamic and real-time calculations. Considering the general range of the ship domain, the boundary is 3 nautical miles for open waters and 1.5 nautical miles for narrow waters.DCPA, TCPA, relative distance D, relative bearing Q, velocity ratio K and other parameters are calculated dynamically. The ship collision risk CRI is obtained by the PSO-LSSVM method, and the risk of the target ship is graded.With a unit time interval, ships that meet the requirements of the encounter range are identified as target ships and alternatives that can participate in the identification data of the ship domain model.When the number of identified target ships reaches the required number of ships for model identification, online parameter identification can be carried out quickly; the current ship domain model parameters can be dynamically updated, and the ship domain boundary curve can be drawn.To avoid the identification error caused by the small amount of data, ship collision risk is adopted as the method of filtering nonhazardous targets. As the error weight coefficient of identification, CRI can control the influence of nonhazardous targets on the results so that the dangerous targets are reflected in the identification results, and the meaning of the ship domain as the scope of ship navigation safety is better reflected.According to the above process, when the ship domain elliptic equation can be identified and the error accuracy requirements are met, the ship domain identification results can be dynamically output. The identification schematic diagram is shown in AIS data are received in real time, and when the accumulated data requirements are met, the encounter situation of ships is superimposed, and the ship domain is identified according to According to the schematic method in CRI, namely, the collision risk index, is a parameter used to measure the degree of ship collision risk. Usually, the value range can be . The higher the value, the more dangerous it is. The DCPA, TCPA, relative distance D, relative bearing Q and ship speed ratio K can be used to measure the relevant indicators of ship collision risk. The indicator value and weight of CRI are related to the judgment of expert experience, and this paper refers to the fuzzy calculation result of referring to the survey results of experts in References ,28 as a pbest and global extremum gbest are constantly updated, and the particles are constantly updated accordingly. The i particle in the n dimension is represented as xi = , the speed is , the corresponding individual extremum is pbest = , and the global extremum of the particle swarm is pbest = . The velocity and position of particles are constantly updated with the extreme value, which are calculated as Eq ; and r1、r2 is a random number between .Particle swarm optimization (PSO) is a fast iterative algorithm to find the optimal particle in the search area and the search for the optimal particle as the solution of the optimization problem . In the ed as Eq , and finxi, yi)|i = 1,2,⋯,n}, xi∈Rm, yi∈R, xiis input vector, yi is the output value, and n is the number of training samples. High-dimensional nonlinear mapping is φ: RN→H, where H is a high-dimensional characteristic space, in which the sample set is as follows:w is the weight vector and b is the value of the offset.The least squares support vector machine (LSSVM) model is an improvement on the standard SVM. LSSVM uses the equality constraint and linear solution to replace the inequality constraint and nonlinear solution in SVM, which has higher accuracy and efficiency. To train sample set {16), the If the CRI is acquired by the LSSVM, its model diagram is shown in σ is the kernel function parameter. The smaller the value is, the easier it is to underfit, and the larger the value is, the easier it is to overfit. Because of its simple structure and strong generalization ability, the RBF function can satisfy the rapid optimization of parameters.The kernel function of the LSSVM method in this paper is the Gaussian radial basis function (RBF), and the equation is as follows :K; the coordinate of point P on the boundary of the identified ellipse domain model is , and the distances from to the coordinate  of the ellipse center point O are di and d, respectively.Considering that the elliptical ship domain is adopted as the identification object in this paper, as shown in The geometric distance between AIS data points P’ and P on the boundary of the identified elliptic ship domain model is as follows:xi = x0 and x = x0 are substituted into Eq 22), the According to the above method, the geometric distance between the identified elliptic domain and the identified AIS data can be calculated , which cCRIi is the ship collision risk corresponding to AIS data points, which is used as the weight of identification error and obtained by the PSO-LSSVM method.Eqs  and 26)26) are sBy substituting AIS data into the ship domain model equation and iterating parameters, the objective function in Eq  is minimTo verify the feasibility of the research method, the dynamic changes in the ship domain during ship navigation are analysed, and the differences in the evolution characteristics of the ship domain in different waters are compared. Therefore, navigation process experiments are carried out in ship routing and other waters. The Yangtze Estuary area, which requires a long voyage in and out of the port, was chosen to allow a sufficiently long time to observe changes. Busy Yangtze River estuary waters can obtain more data to meet the update frequency requirements. In the selection of ship type, the mainstream ship size and ordinary cargo ship type are selected. To better reflect the general situation, dangerous ships with special characteristics should be avoided.In experiments, AIS data are accessed, decoded, and preprocessed; abnormal data are deleted; data within the research scope are screened, and the dynamic overlay is calculated to generate the encounter situation. Before using the PSO-LSSVM method, the data need to be normalized. Then, identification is carried out according to the method presented in this paper. Through the test, it is found that selecting approximately 1 hour of data accumulation can meet the requirements of data volume and data distribution, and approximately 10 minutes of dynamic update can observe significant changes, which basically meets the requirements of dynamics. Two ordinary cargo ships of the same size are selected for the experiment. Periods close to the same day are selected to eliminate weather and other disturbances as much as possible and enhance comparability. For example, a general cargo ship (with a length of 190 m and a width of 32 m) exits the port through the South Channel of the Yangtze River Estuary and enters the port through the north deep-water channel. The AIS track is shown in c1 and c2 are 1.5 and 1.7 respectively, the momentum coefficient ω is 1, the maximum number of evolution is 200, and the size of the particle population is 20. The parameters that need to be optimized include the penalty factor γ and the kernel parameter σ. After PSO optimization, the optimized parameter γ is 100 and σ2 is 0.1062.Some training samples are shown in The previous AIS data of the test area are used to obtain the CRI training set by fuzzy mathematics, and then these data needs to be normalized. After PSO-LSSVM training and testing, the CRI model based on PSO-LSSVM is established to obtain the CRI parameters of the surrounding ships required for ship domain identification. The PSO parameters are set as follows: the learning factors In (T6)According to the AIS data of the ship’s outbound voyage in the South Channel, real-time online rolling identification of ship domain parameters is performed; the dynamic change curves of the ship domain are drawn, and the results are analyzed as follows:The results of the outbound process are analyzed as follows:From In Compared with the classic Fujii model, the ship domain presented in this paper is dynamic and correlated with CRI. According to the Fujii model, in narrow waterways, the length of the domain is six times that of the own ship, and the corresponding long axis of the domain is 0.31 nautical miles. In open waters, the length of the domain is eight times that of the own ship, and the corresponding long axis of the domain is 0.41 nautical miles. All of these are obviously smaller than the results in this paper. The Fujii model can be considered as a ship domain with CRI of 1, and then the long-axis results of the two models will sometimes be relatively close. For example, in According to In (T6)According to the AIS data of the ship’s inbound navigation in the North Channel, real-time online rolling identification of ship domain parameters is performed; the dynamic change curves of the ship domain are drawn, and the results are analyzed as follows:The results of the inbound process are analyzed as follows:Comparing From Based on the online identification experiment and result analysis of navigation in the South Channel and North Channel of the Yangtze Estuary, it can be summarized as follows:In a water area where the ship’s routing is not implemented, the long axis of the ship domain changes obviously with ship speed, while the short axis is basically unaffected. This shows that with increasing ship speed, the pilot tends to maintain a greater safety distance, and therefore, the boundary of the ship domain is greater. In comparison, in ship-routing waters, the ship domain is more affected by the regulations of navigation, and the domain boundary is more stable. In other words, the fluctuation characteristics of the ship domain are obviously influenced by the traffic environment of sailing waters.Through the overlying diagram of the ship domain, it is found that the ship domain has been changing dynamically in the actual navigation process, with the change in the ship’s motion parameters, the surrounding environment of the navigation waters, and the encounter situation of the own ship and other ships, showing certain correlation, volatility, and randomness.The PSO-LSSVM method is used to obtain CRI, and the collision risk weighting method proposed in this paper can effectively filter nonhazardous targets and improve the impact of dangerous targets on the results, which is consistent with the concept of the ship domain, and its identified ship domain is more consistent with the actual situation of navigation. Through dynamic online rolling identification in the experiment, ship domain parameters can be quickly calculated; the dynamic evolution process of the ship domain in the navigation process can be better presented, and its dynamic fluctuation characteristics and change rules can be found.The online identification method in this paper introduces risk weighting, which will make the ship domain obtained contain some subjectivity, because the collision risk concept has some subjectivity. In addition, the experiment was carried out in the specific water area, which has a certain representativeness. The corresponding results reflect the evolution characteristics of the ship domain during ship navigation. However, the ship domain is affected by many factors, such as navigation rules, the natural environment and traffic conditions, etc. Different waters will differ in these respects. Therefore, the characteristics summarized in experiments can be used as a reference for other similar waters, but the characteristics of the ship domain in specific waters need to be analyzed through specific experiments.Based on real-time AIS data and through the short data accumulated, an online identification method can dynamically generate the ship model by calculating ship-encounter parameters and acquiring the risk of collision as identification data sources to ensure identification accuracy and real-time requirements. By selecting representative waters, the characteristics of the ship domain in ship-routing waters and unimplemented ship-routing waters are analyzed experimentally. It is found that the ship domain changes dynamically in the sailing process, and to some extent with the waters environment and ship motion parameters, which can provide a reference for the study of similar waters.The method in this paper enables AIS data to be applied to navigation safety decisions in a more real-time manner, explores the dynamic evolution law of the ship domain, and promotes the better application of relevant theories in the ship domain to intelligent navigation. In this paper, dynamic, real-time and procedural research methods are proposed to lay a foundation for research on dynamic characteristics in the ship domain. In the future, it will be possible to study ship domains in complex scenes, such as analyzing the change characteristics of ship domains with ship navigation environments by considering weather conditions, visibility, wind direction, sea state, water depth, and tide, etc.S1 FileThe training and testing data in the file can be used to train the PSO-LSSVM to obtain CRI and plot the results as shown in Figs (XLSX)Click here for additional data file.S2 FileThe own ship’s trajectory can be plotted using the position data, as shown in (XLSX)Click here for additional data file.


The research presented in this paper is part of a larger project concerning deformation behavior, microstructure and mechanical properties of high-manganese steels with different chemical compositions and processed under various conditions. The current investigation deals with the development of microstructure and crystallographic texture of Fe-21.2Mn-2.73Al-2.99Si steel deformed in tension until fracture at ambient temperature. The deformation process of the examined steel turned out to be complex and included not only dislocation slip and twinning but also strain induced phase transformations (γ → ε) and (γ → α′). The formation of ε-martensite with hexagonal structure was observed within the microstructure of the steel starting from the range of lower strains. With increasing deformation degree, the α′-martensite showing a cubic structure gradually began to form. Attempts have been made to explain the circumstances or conditions for the occurrence of the deformation mechanisms mentioned above and their impact on the mechanical properties. The obtained results indicate that the strength and plastic properties of the steel substantially exceed those of plain carbon steels. Since both, mechanical twinning and the strain-induced phase transformations took place during deformation, it seems that both types of deformation mechanisms contributed to an increase in the mechanical properties of the examined manganese steel. It was reported that both the TRIP and the TWIP effects may occur if SFE value is within the range 18–25 mJ/m2 [2, the dominating mechanism of deformation is twinning. Some authors suggest, that if the value of stacking fault energy of the γ-austenite increases, the overlapping of stacking faults becomes more irregular and nucleation of α’-martensite becomes more difficult . Some oe.g., [2 ,15. Moree.g., [2 ,28,29, te.g., [2 ,31). A ve.g., [2 ,33. The In addition to the basic alloying element, the composition of the mentioned above group of high-manganese steels also contains aluminum and silicon. Aluminum in Fe-Mn alloys increases the value of SFE, however silicon decreases it. In these steels the contents of Si do not usually exceed 4 wt.% and the contents of Al may reach even 12 wt.%, depending upon the steel grade ,34. The 2, subjected to uniaxial tensile deformation at ambient temperature.The aim of this work was to analyze and explain deformation behavior and microstructure development leading to an increase in mechanical properties of high manganese steel with an estimated SFE value of 14 mJ/mThe material for the present research was obtained by laboratory casting carried out in the type of vacuum induction furnace VSG-100PVA TePla AG  in argon atmosphere. The basic metallic charge was Armco iron, and the alloying additions were pure metals Mn, Si and Al. The chemical analysis was performed by ICP (Inductively Coupled Plasma) optical emission spectroscopy using the Ultima 2 Jobin-Yvon spectrometer . The chemical composition of the steel is presented in After casting, the ingot was homogenized at 1150 °C and then hot rolled in 6 passes, with the starting temperature of 1000 °C. The initial and the final thickness were 60 and 16 mm, respectively. The last step of the preliminary treatment was annealing in argon atmosphere at the temperature 1150 °C for 1 h, followed by water quenching.−3 s−1. Subsequently the steel was investigated in its initial condition (0%), following the tensile deformation of 10, 20, 30, 40, 50%, and after the fracture. An additional examination of the mechanical properties was the microhardness measurements carried out for the starting material and after successive degrees of deformation. The Vickers INNOVA TEST microhardness tester  with 1 Newton of the load was applied, each time on electrolytically-polished surfaces of examined specimens.Steel samples suitable for tensile tests were cut from the material after the applied pretreatment and then subjected to tension using an MTS-810 universal testing machine . Tensile testing was carried out at ambient temperature and the strain rate 10X-ray diffraction investigations were carried out with Simens D500 and D5005 diffractometers  using CuKα and MoKα radiation for both, phase analysis and texture measurements. The specimens were previously ground with abrasive paper, polished with diamond paste and electropolished to remove the deformed layer formed during grinding. X-ray diffraction analysis was carried out in the middle layers of the specimens in sections parallel to the tensile direction. The texture measurements were performed by registering the incomplete pole figures of crystallographic planes required for individual phases. In the case of the γ-austenite—the pole figures: {111}γ, {200}γ, {220}γ, {311}γ. For the ε-martensite—the pole figures: {100}ε, {002}ε, {101}ε, {102}ε and {110}ε  and microstructure analysis was performed using TecnaiG2 Twin20 transmission electron microscope  as well as the FEI QUANTA 3D FEG-SEM scanning electron microscope  applying the EBSD method.The stress-strain diagram of the steel under examination, recorded during the tensile test at ambient temperature until fracture, is shown in The diffraction pattern of the initial material  shows lines originating from the austenite planes (111)γ, (200)γ, (220)γ, (311)γ and (222)γ. The intensity of the diffraction peaks from planes (111)γ and (200)γ remained high throughout the entire range of deformations. In addition, with increasing deformation degree, the intensity of (220)γ lines also increased substantially. It turned out, however, that austenite of Fe-21.2Mn-2.73Al-2.99 steel is an unstable phase under the conditions of plastic deformation. During the tensile test, peaks coming from ε-martensite with hexagonal structure and α′-martensite with cubic structure occurred additionally and showed quite pronounced intensities.The X-ray phase analysis caused some problems because the diffraction lines originating from the γ, ε and α′ phases overlap for certain values of the 2θ angle. The most striking example is the overlapping of (111)γ and (0002)ε lines as well as (220)γ and  t,44 t2¯0)eduction  found ineduction  obtainededuction ,16,28,31As mentioned in the previous section, the analysis of crystallographic texture was based on the calculated pole figures . In the When analyzing the pole figures calculated for the {110} austenite planes, it is possible to observe, that following 20% of strain, the texture of austenite showed a fibrous-like character. Additionally, a pronounced {110}<111> orientation component occurred within the deformation texture. Starting from about 40% of strain a partial disappearance of that fiber was locally observed. Nevertheless, the {110}<111> orientation remained the component of the austenite texture up to fracture .For all examined degrees of deformation, there was a strong component corresponding to the  observations revealed almost equiaxed and sizable grains of austenite (above 100 μm) with numerous annealing twins and a very small amount of ferrite precipitates . The volSFE~14 mJ/m2) both, mechanical twinning and especially the strain-induced phase transformations (γ → ε) and (γ → ε → α′) turned out to be as important deformation mechanisms as a dislocation slip. Microstructure analysis carried out by means of TEM confirms the occurrence of phase transformations during deformation and indicates their significant role and gradually increasing contribution into the process of plastic deformation.Due to the low SFE value of austenite in the examined high-manganese steel , using the method of electron backscatter diffraction (EBSD), seems to indicate that a significant amount of the α′-martensite was formed in the regions showing the occurrence of ε-martensite. As seen from the comparison of phase maps and microstructures by SEM , these wSuch an observation would indicate that in the studied steel, the deformation-induced transformation of unstable austenite could take place not only through the formation of the intermediate ε-phase (γ → ε → α′) but also in a direct way, i.e., (γ → α′). The amount of newly formed phases after 30% of deformation was 10.1% and 19.9% for the ε- and α′-martensite, respectively. Similar amounts of deformation induced ε- and α′-phases were found in a number of austenitic steels, both high-manganese Fe-Mn-Al-Si as well as chromium-nickel Fe-Cr-Ni steels  and (γ → ε → α′) in addition to the usual mechanism of dislocation slip. Therefore, from the point of view of the operating mechanisms, the plastic deformation process of the examined steel displays the features of both TWIP and TRIP steels.2) is definitely within the range of lower values. This low SFE value additionally allowed for the simultaneous occurrence of strain-induced phase transformations during deformation. According to Olson and Cohen [Taking into account the stability of austenite, the fundamental difference between TRIP and TWIP steels is that the austenite in the first case is not stable under mechanical load, i.e., phase transformations (γ → ε) and/or (γ → α′) take place when the material is loaded. On the contrary, in the case of TWIP steels the strain induced phase transformation of austenite does not proceed during deformation, however the orientation of part of austenite is changed due to mechanical twinning. The different deformation behavior of the austenitic phase is first of all attributed to different values of stacking fault energy (SFE). It is simply the excess energy per unit area of the crystal associated with the existence of stacking faults, which are the regions where the regular stacking sequence of close-packed atomic planes is disturbed. The SFE value is the primary factor that controls the distance over which Shockley partials are separated, i.e., the width of faults between the dislocation lines. As the SFE value decreases, the faults get wider and cross-slip of dissociated (extended) dislocations becomes more difficult. In such a case the process of mechanical twinning may prove to be the dominant deformation mechanism. It should be emphasized that the SFE value of the steel under examination . In The process of plastic deformation of the examined high-manganese steel is not only complex but also non-uniform. This means that certain areas of the microstructure or some grains of austenite are observed, showing different dislocation density and thus unequal intensity and strain rate. The different course of the deformation process in individual areas (grains) undoubtedly results from their different orientation in relation to the applied external stresses. However, the analysis and comparison of the microstructures by SEM and EBSD phase maps  does notIn general, Olson and Cohen ,22 foundIn the case of the investigated steel, a characteristic aspect of the formation of strain-induced α′-martensite is that, in some areas, it is not only formed at the intersections of non-parallel bands, but additionally in individual bands from a single deformation system. Obviously, the first of these mechanisms is the most frequently proposed and already classic mechanism for the formation of the α′-martensite in the course of the deformation process. A very convincing explanation of the α′-martensite formation, in an area with a single operating deformation system, was suggested by Fujita and Katayama . This reWith increasing deformation degree, the microhardness and strength of the examined steel considerably increased, which obviously resulted from changes in microstructure. Increasing dislocation activation and their gradual dissociation due to low SFE value of the austenitic structure, resulted in activation of mechanical twinning and formation of twin bands. Another operating deformation mechanism was the strain-induced phase transformation (γ → ε) and the formation of the bands of ε-martensite. As mentioned above, these are similar processes from the viewpoint of the movement of extended dislocations. Mechanical twins cause a strengthening of the material because constitute strong barriers to the dislocation slip. In turn, the ε-martensite is an intermediate phase in the formation of the α′-martensite, which causes fragmentation and refinement of the microstructure, leading to substantial hardening. It is the α′-martensite, usually observed in the microstructure inside the bands of the ε-phase, which is principally responsible for TRIP steel strengthening. According to some hitherto studies, the amount and morphology of the indirectly formed α′-martensite depends upon the morphology of the intermediate ε-phase and gives a larger steel strengthening in comparison with α′-martensite developed through direct transition (γ → α′) ,52. In g2, turned out to be unstable during tensile deformation, starting from the range of relatively small deformations.The austenitic structure of the examined high-manganese Fe-Mn-Al-Si steel, with the estimated SFE value of 14 mJ/mLow stability of the parent γ-austenite was manifested by the occurrence of strain induced martensitic transformations (γ → ε → α′) as well as (γ → α′). Apart from dislocation slip, the mechanical twinning was additionally the active deformation mechanism. Thus, the examined steel revealed behavior typical for TRIP and TWIP steels, respectively.In the studied Fe-Mn-Si-Al steel, the formation of strain-induced cubic α’-martensite proceeded both, directly from the parent austenite (γ → α′) and through the intermediate hexagonal ε-phase (γ → ε → α′). The EBSD analysis indicates that indirect mechanism of the α’-martensite formation prevailed during tensile deformation.The occurrence of strain induced transformations resulted in the appearance of preferential crystallographic relations between the parent γ-austenite and the product ε- and α′-martensite. The crystallographic relations of Shoji-Nishiyama (S-N)—{111}γ‖{0001}ε and <110>γ‖<1120>ε as well as Kurdjumov-Sachs (K-S)—{111}γ‖{101}α′ and <101>γ‖<111>α′ were detected during tensile deformation.It was possible to recognize two different mechanisms of the α′-martensite formation, namely: the classic and frequently observed mechanism of the intersecting sets of deformation bands from the two non-parallel systems and the second one, based on the pile-ups of partial dislocations within a set of parallel bands from a single deformation system.The formation of strain-induced ε- and α′-martensite proceeded in a non-uniform way and in general occurred the most effective in austenite grains showing a high density of deformation bands. Therefore, it seems very likely that the formation of strain-induced martensitic phases depends on the crystallographic orientation of austenite grains to the extent that this orientation affects the local dislocation activation and further the efficient formation of banded deformation structure.The strength and plasticity of the examined Fe-Mn-Al-Si steel exceeded those of plain carbon steels. The explanation of this advantageous combination of mechanical properties seems to result from the course of the deformation process involving both mechanical twinning and strain-induced martensitic transformations.


The blood glucose levels of both Riceberry waffle and wheat flour were not significantly different. The glycemic index of Riceberry waffle and wheat flour waffle were 94.73 ± 7.60 and 91.96 ± 6.93, respectively. Therefore, Riceberry waffle could be used as an alternative gluten-free product for celiac patients, but not for diabetic patients. Gluten-free products have been developed due to increasing consumer demand. The improvement of the sensory quality and nutritional value of these products may support functional food development and provide health benefits. The purpose of this study was to develop a gluten-free waffle formulation with Riceberry rice flour by replacing the sucrose with maltitol and palm sugar powder. Evaluations of the sensory acceptability of these products and the blood glucose levels of healthy volunteers after consuming Riceberry and wheat flour waffles were carried out. The glycemic responses of the volunteers to the Riceberry and wheat flour waffles at 0, 15, 30, 45, 60, 90, 120, 150, and 180 min were monitored. In addition, the glycemic index of the products was calculated. The finding revealed that replacing sugar with 50% ( Celiac disease (CD) is known to be a cause of severe malnutrition because it causes the inflammation of the small intestine after gluten consumption in some people. The prevalence of celiac disease has been reported to be approximately 1% worldwide and is expected to become higher . AdditioWaffles are a type of wheat-based baked product and are popularly consumed as breakfast or dessert items across the world. Waffles are usually made using a moderate to high content of table sugar . Sugar nAfter the consumption of carbohydrates, blood glucose levels increase based on the glycemic index (GI) of the food. GI is defined as an incremental area under the curve (iAUC) of the blood glucose after the consumption of food that contains 50 g of carbohydrates and is expressed as a percentage of the iAUC of 50 g of glucose in the same participant . GI can Oryza sativa L.) is a type of dark purple rice that was created through crossbreeding between Khao Hom Nin Rice and Khao Hom Mali 105. The GI of Riceberry rice is 62 and it has recently been used as a functional ingredient in several food products, such as noodles, bread, pudding, and yogurt [Riceberry rice  waffle on postprandial glycemia was investigated and compared to that of a waffle made from wheat flour (WF), followed by the GI calculation of RB and WF waffles. The developed non-sucrose gluten-free and low-GI waffle could have reduced post-prandial glycemic effects when consumed; thus, they are suitable for celiac and diabetic patients as well as other health-conscious consumers.Riceberry grain, rice flour, glutinous rice flour, tapioca flour, caster sugar, palm sugar powder, and salt were purchased from a local supermarket . Baking powder, xanthan gum, and maltitol were purchased from the Gerbera house shop . Riceberry flour was made by grinding grain for 5 min at setting 5 . The dry ingredients were weighted according to the formulation shown in Nine-point hedonic sensory evaluations were performed with 50 untrained volunteer panels; volunteers were both male and female and were recruited from the university staff and undergraduate students. Briefly, the waffles were cooked in the manner described in 2, fasting blood sugar level of <100 mg/dL, and normal waist circumference ≤90 cm for males and ≤80 cm for females. Subjects with chronic diseases , gastrointestinal problems, currently took medications, had allergies or sensitivity to the product’s ingredients, had donated blood in the prior 2 months, pregnant and/or breastfeeding were excluded from this study. The sample size of 10 subjects was determined according to Brouns et al. [The digestibility of the acceptable Riceberry waffle and wheat flour waffle  was tests et al. . The purThis study was a single-blind controlled study. The study consisted of three experimental trials  carried out on three different occasions in accordance with the literature ,30,31,32® Performa . Blood sugar levels after the consumption of the glucose solution was used to calculate the iAUC and GI of waffles. The study design is described in The day before each visit, subjects were told to avoid consuming food with high levels of dietary fiber, food with a high polyphenol content , alcohol, caffeinated beverages , smoking, and exercise. Additionally, subjects were asked to fast for 10–12 h. In the morning of each visit, subjects arrived at the study center and their capillary blood glucose was taken at the fasting state (0 min) before the consumption of food samples and 15, 30, 45, 60, 90, 120, 150, and 180 min after a test meal. Capillary blood glucose was acquired through the puncture of the ventral finger skin and measured with the ACCU-CHEKThe results are presented as means ± standard deviations. The differences between averages were determined through analysis of variance (ANOVA) tests followed by Duncan’s Multiple Range Test (DMRT) at a 95% confidence interval. A Randomized Complete Block Design (RCBD) was used for the sensory analysis. t-test. All statistical analyses were completed with SPSS version 25 . Statistical significance was achieved at a 95% confidence interval (p ≤ 0.05).The iAUC was calculated based on the trapezoidal rule, where the area of all glucose responses that were collected for three hours were added together after the area beneath the baseline was ignored using Microsoft Excel 2013. Average comparisons of blood sugar levels before and after the consumption of Riceberry waffle, wheat flour waffle, and glucose at different time points were made using the analysis of variance (ANOVA) test. The glycemic index (GI) was calculated by Alongi et al. . The aveCereal grains are generally used as major raw materials in waffles, as they impart an elastic or chewy texture . TherefoRiceberry flour was used as an alternative ingredient in waffle formulation and adapted by replacing the sucrose with maltitol and palm sugar powder. Four formulations with 100% sucrose (as control), 100% maltitol, 50% sucrose to 50% maltitol, and 50% palm sugar powder to 50% maltitol were prepared according to p ≤ 0.05). The waffle with 50% palm sugar powder and 50% maltitol was the most acceptable and received the highest acceptability score in terms of overall acceptability and texture. Moreover, the score of all attributes of this formulation was not significantly different from that of the Riceberry waffle made with 100% sucrose (control). On the other hand, the aroma, taste, and overall acceptability scores of waffles made with 100% maltitol were significantly lowered than those of the control samples (p ≤ 0.05).There was no significant difference between each treatment in terms of appearance and color, indicating that maltitol and palm sugar did not decrease the acceptability of the waffle in terms of appearance and color. However, in terms of aroma and taste, 100% maltitol contributed to a significant decrease compared with the control and other recipes. The texture score of the 100% maltitol waffle was lower than that of the waffle with 50% palm sugar powder and 50% maltitol  and wheat flour waffles (white) were prepared and are shown in Descriptive characteristics of subjects are summarized in The blood levels of participants after the consumption of glucose solution and Riceberry waffle peaked at 30 min. However, the highest level was observed at 45 min for the waffle made from wheat flour. Subsequently, the blood glucose levels of the participants slowly decreased until 180 min in all treatments A. No sigThe incremental area under the curve (iAUC) from 0 to 180 min was 9824.92 ± 946.82, 9536.92 ± 678.63, and 10,220.92 ± 744.72 mg/dL min for the RB waffle, WF waffle, and glucose solution, respectively, as represented in The sensory evaluations were performed based on the appearance, color, aroma, taste, and texture acceptability of the RB and WF waffles. The given highest score of each measuring total indicated which waffle had the highest acceptability. Based on sensory analysis, the acceptability of the waffles in terms of appearance and color did not differ between the formulations. This is possibly due to the similarity of the melting point 145 °C) of maltitol and sucrose. Additionally, after exposure to high heat, maltitol liquifies, caramelizes, and turns brown in color similarly to sucrose . In addi5 °C of mIn a literature review, the sensory quality of noodles made by substituting wheat flour with Riceberry flour was studied and it was found that noodles made with a 30% substitution were not significantly different from noodles made with 100% wheat flour in terms of flavor, taste, softness, stickiness, and overall acceptability . In addiThis is the first study to develop a gluten-free Riceberry waffle and investigate the glycemic response to its ingestion in humans. The dark brown appearance of the Riceberry waffles in this study was due to a polyphenol called anthocyanin, which are present in high amounts in Riceberry flour ,18. The In this regard, a human study was planned to explore the effect of the consumption of 100 g of RB waffle on postprandial glucose in this research. This human study revealed that wheat flour was absorbed more slowly into the blood than Riceberry flour or glucose. This may be due to the food matrix of the waffle, making it harder to digest and absorb than the free form of glucose . FurtherIn this study, the iAUC and cMax are consistent with the blood glucose level of each treatment. The blood glucose level of free sugar was reduced to baseline within 2 h, while that of waffle samples was clear within 3 h. This may be because of their dietary fiber content. The digestibility of waffles may be increased by the use of different types of starch and may be affected by the duration of glycemic response . AdditioAlthough the glycemic index and glycemic load of Riceberry waffle are still high and it may not appropriate for controlling the blood glucose level, it is still a functional product used for celiac patients. In addition, compared to other gluten-free waffle products, as presented in The waffle made from Riceberry flour with the substitution of palm sugar and maltitol for sugar was found to be acceptable to consumers in terms of taste and texture. The waffle also contained a higher fiber and mineral content than waffles made with wheat flour. The postprandial glycemic responses of participants to the consumption of the Riceberry flour waffle and the wheat flour waffle did not significantly differ. The developed product may be a gluten-free alternative for CD patients but it still not suitable for consumption by diabetic patients. Overall, the present research indicates that gluten-free flour made from Riceberry may be an alternative valuable source for use in functional food products and could be recommended for celiac disease patients. It is also important to further explore the role of gluten-free diets and the use of antioxidant-rich gluten-free flours in diabetic people.


Apoptosis is crucial for the correct development of the nervous system. In adulthood, the same protein machinery involved in programmed cell death can control neuronal adaptiveness through modulation of synaptic pruning and synaptic plasticity processes. Caspases are the main executioners in these molecular pathways, and their strict regulation is essential to perform neuronal remodeling preserving cell survival. FAIM-L and SIVA-1 are regulators of caspase activation. In this review we will focus on FAIM-L and SIVA-1 as two functional antagonists that modulate non-apoptotic caspase activity in neurons. Their participation in long-term depression and neurite pruning will be described in base of the latest studies performed. In addition, the association of FAIM-L non-apoptotic functions with the neurodegeneration process will be reviewed. Neuronal apoptotic pathways play crucial roles during the nervous system development  as selecDuring the last 2 decades, however, accumulating evidence has been supporting a non-apoptotic and non-inflammatory function of caspases in neurons, essential in processes that occur during the whole life of the organism, as dendritic pruning and synaptic plasticity. Overall, caspases emerge as decisive in every structural change of the nervous system beyond apoptosis, starting from development, remodeling, and finally degeneration .In the non-apoptotic processes as pruning it has been shown that while active caspases are found in degenerating axons, the cell bodies remain viable . To alloHere we will review FAIM-L and SIVA-1, two proteins involved in the complex molecular interplay that regulates caspase activation which our group has shown to be functional antagonists on caspase-activation and consequent functions in neurons.The first Fas apoptotic Inhibitory Molecule gene product, FAIM-short (FAIM-S), was identified as a death receptor (DR) inhibitor in immune cells . Few yeaFAIM-L blocks DR-induced cell death by two main mechanisms: direct binding to non-stimulated Fas receptor, therefore impairing caspase-8 recruitment to the DISC complex , and actAs part of FAIM-L characterization we were able to identify SIVA-1 as an interacting partner . SIVA-1 Many forms of learning and memory require experience-dependent synaptic adjustments in the hippocampus . The besDuring recent years several modulators of apoptosis have been implicated in LTD regulation. Our group has shown that both FAIM-L and SIVA-1 are able to play a role in AMPAR internalization, but with opposite roles . In our SIVA-1 on the other hand has the opposite effect, as its overexpression is sufficient to decrease GluA2 levels, a component of AMPAR, in a caspase-dependent manner. FAIM-L restores AMPAR levels changes induced by SIVA-1 in hippocampal neurons, indicating that both proteins act on the same pathway.Another non-apoptotic mechanism in which apoptotic proteins are essential is neurite pruning, essential to sculpt neuronal connections, as it removes excessive or inaccurate projections without resulting in the death of the cell. The mechanism can involve small-scale pruning of axon terminals, or large-scale removal of branches .Pruning is characterized by cytoskeletal destabilization, microtubules disassemble, neurofilament fragmentation and degradation of axonal components. The portion of the axon destined for removal is finally engulfed and digested by surrounding glia .In vitro culture of peripheral sensory neurons is a convenient system to study this process, and has been widely used as a model for developmental disease  and neurite pruning . As otheOur group has found a correlation between FAIM-L levels and Alzheimer’s disease (AD) progression . AD is tOver-activation of caspases would afterwards exacerbate the hallmarks of AD, being first extreme plasticity processes in neurons, and finally neuronal degeneration . Hundredin vitro. Therefore, FAIM-L decrease in AD could also be pathologically linked to a more rapid, aggressive Aβ aggregation. In a Faim-KO mice model  is responsible for the observed effects. Currently available  in 2009 . These m in 2009 . The obe’s group , and in ’s group . SeveralIn conclusion, we have reviewed the role of FAIM-L and SIVA-1 as novel regulatory proteins that modulate the activity of caspase-3, involved in a wide range of remodeling processes that are essential for a correct neuronal function. Given the relevance of this fine-tune regulation, any alteration in regulatory proteins may be a possible cause of pathology, and may constitute therefore, a plausible therapeutic target. A better understanding of the mechanisms involved in synapses remodeling will help the development of new therapeutic strategies for neurodegenerative diseases.


In order to screen for a proper baking condition to improve flavor, in this experiment, we analyzed the effect of baking on the flavor of defatted tiger nut flour by electronic tongue (E-tongue), electronic nose (E-nose) and headspace solid-phase microextraction gas chromatography-mass spectrometry (HS-SPME-GC-MS). According to E-tongue and E-nose radar plots and principal component analysis (PCA), baking can effectively change the taste and odor of defatted tiger nut flour, and the odors of samples with a baking time of >8 min were significantly different from the original odor of unbaked flour. Moreover, bitterness and astringency increased with longer baking times, and sweetness decreased. HS-SPME-GC-MS detected a total of 68 volatile organic compounds (VOCs) in defatted tiger nut flour at different baking levels, and most VOCs were detected at 8 min of baking. Combined with the relative odor activity value (ROAV) and heat map analysis, the types and contents of key flavor compounds were determined to be most abundant at 8 min of baking; 3-methyl butyraldehyde (fruity and sweet), valeraldehyde , hexanal (grassy and fatty), and 1-dodecanol, were the key flavor compounds. 2,5-dimethyl pyrazine, and pyrazine, 2-ethylalkyl-3,5-dimethyl- added nutty aromas, and 1-nonanal, 2-heptanone, octanoic acid, bicyclo [3.1.1]hept-3-en-2-ol,4,6,6-trimethyl-, and 2-pentylfuran added special floral and fruity aromas. Cyperus esculentus L.), also known as wild chestnut, underground walnut, etc., is a highly efficient and high-quality edible oil crop that is drought-resistant [Tiger nut  screen and was added to a tank for supercritical COThe heat of the oven was adjusted to 150 °C and preheated for 10 min. Defatted tiger nut flour was spread 3 mm thick on a baking sheet, placed it the oven, and baked for 0, 4, 8, 12, 16, or 20 min.Defatted tiger nut flour (0.8 g) was added to a headspace bottle, which was immediately sealed with a latex cap and equilibrated at 50 °C for 30 min. The SPME fiber  was inserted and pushed out, absorbed for 30 min, slowly retracted, and then immediately inserted in the gas chromatograph inlet, slowly pushed out the fiber head, and desorbed at 250 °C for 5 min. GC-MS analysis was then performed.m/z range of 35 to 500. The VOCs were qualitatively analyzed by NIST mass spectrometry database. Results were accepted when matching and reverse matching values were above 800. Quantitative analysis was based on the area normalization method, removing the erosion of SPME fiber and the capillary column and unidentified peaks. Only the identified peaks were used for normalization.The VOCs were separated in an INNOW-WAX capillary column . The temperature conditions were as follows: initial temperature of 40 °C was held for 3 min; raised to 70 °C at a rate of 3 °C/min, held for 2 min; raised to 170 °C at a rate of 5 °C/min, held for 2 min; and raised to 230 °C at a rate of 8 °C/min, then held for 5 min. The carrier gas was helium (99.99%), the flow rate was 1 mL/min, the pressure was 112.0 kPa, the split ratio was 10:1, and the gas chromatograph inlet temperature was 250 °C. The ion source was EI at an energy of 70 eV, the MS transfer line temperature was 230 °C, the ion source temperature was 240 °C, a solvent delay of 3 min was used, and a full scan mode was adopted across the AC and AT are the relative content and threshold of compound A, respectively. stan, TstanC are the relative content and threshold of the compound that contributed the most to the main odor of the sample.With reference to Yi C.P. , the ROADefatted tiger nut flour (1 g) was placed into a 20 mL headspace bottle and balanced at (25 ± 1) °C for 0.5 h. The PNE3 electronic nose  was used for detection. g for 20 min at 20 °C, then the supernatant was withdrawn and filtered. The filtrate was used for E-tongue analysis after calibration and diagnosis of the sensor at (25 ± 1) °C. Each sample was tested for six tastes.With reference to Cai W.C. , analysiE-nose and E-tongue principal component analysis (PCA) and the radar chart were calculated using Origin 2018. Analysis of variance (ANOVA) was used to analyze the differences among samples at a significance level of 0.05 by SPSS 25 . The heat map clustering analysis was performed using the TB tools. All experiments were repeated three times.Reasonable baking can effectively improve the flavor of defatted tiger nut flour. Some studies have shown that the Maillard reaction temperature is around 100 °C and the caramelization reaction temperature is around 120 °C . If the Aldehydes are mainly produced by lipid oxidation, decomposition, and Strecker degradation of amino acid, and their odor threshold is low ,24. TheyAlcohols, mainly derived from the thermal oxidation of lipids and degradation of carbohydrates , with veKetones, mainly from the thermal degradation of amino acids or thermal oxidation of polyunsaturated fatty acids , have a Esters are typically generated by the dehydration of hydroxy fatty acids , with a As one of the critical products of the Maillard reaction, pyrazines are formed by Strecker degradation of leucine, isoleucine, and glycine , and havAcids are produced by the further oxidation of aldehydes; they have a high odor threshold and have little effect on the odor . They haIn summary, reasonable baking conditions can improve the richness of VOCs of defatted tiger nut flour and achieve the purpose of improving aroma. When baked for 8 min, the relative content of valeraldehyde, hexanal, and 1-hexanol in defatted tiger nut flour decreased, and thus, grassy odors decreased. Meanwhile, 3-methylbutyraldehyde, 2-heptanone, 2-nonanone, γ-undecanolide, (-)-limonene and other fruit aroma compounds as well as baking aroma compounds, such as pyrazine, increased. However, excessive baking will destroy the prominent odor and produce a burnt odor and harmful substances. When the baking time exceeded 12 min, pyrazine significantly increased, and the samples had caramel and burnt aromas and bitterness.As shown in In the heat map, the difference between the content of various VOCs and the average content is indicated by different shades of color, providing a more visual indication of the differences between the samples . The relThe sample’s odor was determined by both the relative content of each VOC and its threshold value; when the content was certain and the threshold value was smaller, the greater the contribution to odor. The ROAV method was used to determine the contribution of each volatile compound to the main odor. The greater the ROAV value, the greater the contribution to the main odor, and the compounds with ROAV > 1 were key volatile compounds.It can be seen from It can be seen from The PCA was performed to further analyze the odor differences of defatted tiger nut flour with different baking times. It can be seen from It can be seen from The radar plots of defatted tiger nut flour baked for 4 and 8 min were closer, indicating similar tastes. Their plots were far away from that for the unbaked defatted tiger nut flour, indicating that the taste was significantly different after baking. However, overbaking will cause the defatted tiger nut flour to produce unfavorable tastes such as astringency and bitterness, and produce coffee, burnt and other odors. When samples were baked for 12, 16, and 20 min, the respective defatted tiger nut flour radar plots were far away from that for the unbaked defatted tiger nut flour, and the samples began to develop bitterness; the sweetness decreased, and the overall taste worsened. According to the above, the VOCs, E-nose and E-tongue changed, the taste and odor of the defatted tiger nut flour were greatly changed at these baking times—its original flavor was lost, changing into a caramel and burnt flavor, indicating that its flavor decreased.Tiger nut, as a high-quality oil-making raw material, will produce a large amount of defatted tiger nut flour after oil extraction . Due to With the increase in baking level, the Maillard reaction, Strecker degradation, and caramelization consumed the sugar and amino acids in defatted tiger nut flour, resulting in a caramel taste or bitterness, while at the same time producing VOCs such as alcohols, aldehydes, pyrazines, etc. When the baking time exceeded 12 min, the main odor changed to coffee and burnt odor, the color of the flour was too dark, and the quality was obviously reduced. When the flour was baked for 8 min, the VOCs and key flavor compounds were the richest. The grass-like VOCs such as hexanal and 1-hexanol were reduced, and the VOCs of fruit, roasted peanuts, nuts, chocolate, milk, and sweet aromas such as γ-undecanolide, 1-dodecanol, (-)-limonene, 2-nonane, 2-tridecone, hexanoic acid, and pyrazines were increased. This indicates that a reasonable baking condition can improve the taste, aroma and flavor richness of defatted tiger nut flour.This experiment showed that 8 min of baking can enhance the flavor of defatted tiger nut flour to compensate for the loss of flavor after oil extraction. Properly baked defatted tiger nut flour becomes a higher-quality food raw material. As a product that meets the needs of today’s market, defatted tiger nut flour can be used to produce a wide variety of low-fat foods and help achieve the complete and high-value utilization of tiger nut resources.