The relationship between stress and strain rate and deformation temperature was analyzed. The constitutive equation of alloy high-temperature deformation was constructed by the Zener–Hollomon method, and the hot working diagram with the true strain of 0.2 and 0.5 was constructed according to the dynamic material model. The research results show that flow stress has a positive correlation with strain rate and a negative correlation with temperature. The steady flow stress during deformation can be described by a hyperbolic sinusoidal constitutive equation. Adding Er and Zr into Al−Mg alloy can not only refine grains and strengthen precipitation but also form a core–shell Al3 phase. In the deformation process, Al3 precipitates can pin dislocations and inhibit dynamic recrystallization (DRX). Dynamic recovery (DRV) is dominant during hot deformation. The mechanism of dynamic recovery is dislocation motion. At high temperatures, Al3 can also inhibit grain coarsening. The average hot deformation activation energy of the alloy is 203.7 kJ/mol. This high activation energy can be due to the pinning effect of Er and Zr precipitates. The processing map of the alloy was analyzed and combined with the observation of microstructure, the hot deformation instability zone of the alloy was determined, and the suitable process parameters for hot deformation were obtained, which were 450~480 °C, and the strain rate is 0.01~0.09 s−1.The hot compression experiment of homogenized Al−5.2Mg−0.6Mn−0.29Zn−0.16Er–0.12Zr alloy was carried out by the Gleeble-3500 thermal simulation testing system. The deformation behavior in temperatures of 350~500 ℃ and deformation rates of 0.01~10 s How3Mg2) phase to the τ 49) phase, which can effectively reduce the enrichment of the β phase at grain boundaries, thereby enhancing the corrosion performance of the alloy  − 1000/T as shown in the Find the partial differential of Equation (7) and sort it out:−1, which is a higher activation energy than other alloys with similar compositions. This is due to the solid solution of Zr and Er atoms in the matrix.In Equation (8), R is the gas constant, n is the slope of linear lnAccording to the law in Take the logarithm of both sides of Equation (9):32.62942.Substitute the Q value and deformation parameters into Equation (9) to calculate the Z value and obtain the value of lnZ, then combining the value of ln[sin h(ασ)], the lnZ−ln[sin h(ασ)] is plotted on the diagram. According to Finally, with the relevant parameters, N, α, A, and Q, by substituting them all into Formula (1), the flow stress equation of the alloy during hot deformation can be derived: Its Z parameter can be expressed as:At present, the commonly used processing map theory is based on the dynamic material model (DMM) proposed by Prasad. On the basis of DMM theory, the thermal deformation of alloy can be regarded as the process of power dissipation, and the total energy P consumed can be expressed by the following formula :(14)P=G+The strain rate sensitivity index m of the material under a given stress condition can be expressed as :(15)m=dJWhen the strain and deformation temperature are constant, the stress can be written as :(16)σ=KεThen the dissipative covariant J can be expressed as :(17)J=∫0max = η=JJη and its value varying with deformation temperature and rate can form a basic power dissipation diagram. The different regions of the power dissipation diagram are related to the specific deformation structure of the alloy after deformation. Based on the extreme value principle of irreversible thermodynamics, the instability criterion is used to identify the rheological instability region in large-scale plastic rheology. The instability conditions of plastic rheology are as follows:Parameter ξ (Using dimensionless parameters erion is :(20)ξε˙=Through (−1). The greater the strain, the more the instability region of the alloy. At 0.2 true strain, the best safe working range of the alloy was 450~500 °C, 0.01 s−1~0.09 s−1; when the ε was 0.5, the suitable processing range of the alloy was 380~480 °C, 0.01 s−1~0.1 s−1. The instability of the material was mainly due to the shear band and local flow. By superposing the power dissipation diagram and instability diagram, the processing maps of 0.2 and 0.5 true strain shown in 3 phase dispersed in the collective can be seen in the homogenized alloy in −1 and 10 s−1, and dislocation entanglements occur in some regions. At the same time, a small number of sub-grains were generated. Due to the long deformation time at a low strain rate, the dislocation slip can be fully carried out, which reduces the dislocation density. In the figure, it can be seen that the circular Al3 phase and the long strip Al6Mn phase pin dislocation, which hinders the grain boundary migration. This makes the grains not obviously coarsened even with the increase in temperature. When the shear deformation occurs in the region with high-density dislocation entanglement, it is easier to make the material unstable. At this time, the pinning effect of Mn and Al3 particles can delay the occurrence of instability. At a high strain rate, when the material deforms rapidly, these dislocation structures will store energy, which is not enough to be released through dynamic recrystallization, so it will eventually lead to instability.3 precipitates, grain coarsening can be effectively inhibited [Owing to the softening effect of DRX and DRV, stable deformation generally occurs at low strain rates and high temperatures. With the increase in temperature and the decrease in strain rate, the recrystallization fraction increases. These grains coarsen easily at high temperatures. However, due to the blocking effect of Alnhibited .−1, the grains are observed with a flat shape. At 350 °C and 400 °C, local flow instability microstructure and serrated grain boundary appear in the grains. Instability decreases with increasing temperature. According to the recrystallization fraction diagram in −1, so the deformation rate should be less than 0.1 s−1, which corresponds to the results obtained by the hot processing map.3 phase plays a role in pinning dislocations and hindering grain boundary migration during deformation.3 particles pinned some dislocations. The Er particles formed during deformation can inhibit the DRX, and the cross slip of dislocation is a deformation mechanism [3 in the deformation process. At high temperatures and low strain rates, Al3 hinders the migration of grain boundaries. At low temperatures and high strain rates, dislocations can be effectively pinned, and dislocation tangles can be accelerated [echanism . With thelerated . This ph−1 rate was carried out. According to the analysis of the hot processing map and microstructure, the following conclusions were summarized:In this paper, the thermal compression test of Al−5.2Mg−0.6Mn−0.29Zn−0.16Er–0.12Zr alloy at 350~500 °C/0.01~10 s3 particles. At high temperatures, due to dynamic recrystallization, the appearance of DRX can consume lattice energy storage, reduce dislocation density, and stabilize material deformation. Al3 can inhibit the coarsening of recrystallized grains at high temperatures. When deformed at low temperatures and high strain rates, it can effectively pin dislocations and accelerate the generation of dislocation tangles. High-density dislocation entanglement leads to unstable flow.1. The deformation behavior of the alloy will be affected by Al−1, calculated by fitting the correlation line.2. The peak stress during isothermal compression is negatively correlated with temperature and positively correlated with strain rate. The dynamic softening of the alloy is mainly attributed to dynamic recovery and dynamic recrystallization. During hot deformation, dynamic recovery dominates. The hot deformation average activation energy of the alloy is 203.7 kJ·mol−1.3. According to the hot processing map and microstructure evolution, the unstable region of the alloy is mainly concentrated at low temperatures and high strain rates, and the power dissipation value increases with the increase in temperature. The optimized hot processing range of the novel Al−5.2Mg−0.6Mn−0.29Zn−0.16Er−0.12Zr alloy is temperature 450~480 °C, strain rate 0.01~0.09 s


Simulation is an essential technique for generating biomolecular data with a ‘known’ history for use in validating phylogenetic inference and other evolutionary methods. On longer time scales, simulation supports investigations of equilibrium behavior and provides a formal framework for testing competing evolutionary hypotheses. Twenty years of molecular evolution research have produced a rich repertoire of simulation methods. However, current models do not capture the stringent constraints acting on the domain insertions, duplications, and deletions by which multidomain architectures evolve. Although these processes have the potential to generate any combination of domains, only a tiny fraction of possible domain combinations are observed in nature. Modeling these stringent constraints on domain order and co-occurrence is a fundamental challenge in domain architecture simulation that does not arise with sequence and gene family simulation.Here, we introduce a stochastic model of domain architecture evolution to simulate evolutionary trajectories that reflect the constraints on domain order and co-occurrence observed in nature. This framework is implemented in a novel domain architecture simulator, DomArchov, using the Metropolis–Hastings algorithm with data-driven transition probabilities. The use of a data-driven event module enables quick and easy redeployment of the simulator for use in different taxonomic and protein function contexts. Using empirical evaluation with metazoan datasets, we demonstrate that domain architectures simulated by DomArchov recapitulate properties of genuine domain architectures that reflect the constraints on domain order and adjacency seen in nature. This work expands the realm of evolutionary processes that are amenable to simulation.http://www.cs.cmu.edu/~durand/DomArchov. The data underlying this article are available via the same link.DomArchov is written in Python 3 and is available at Bioinformatics online. Simulation is an essential technique for studying protein evolution, where most processes of interest act on the time scales that exceed a human lifetime. However, modeling the evolution of multidomain proteins poses special challenges.domains. Domains act as independent units that may be found in many, otherwise unrelated proteins. Modular domains are able to fold correctly in multiple sequence contexts.Multidomain proteins are mosaics of sequence segments that encode structural or functional modules , called domain architecture representation, the amino acid sequence of the domain instances is ignored, as is the sequence of the linker regions separating the domains.The domain content of a protein sequence  can be dThe domain content of a gene evolves via domain gain and loss .Juxtaposing gene and domain events on a species tree provides a description of the temporal relationships between events on multiple levels of biological organization. As before, gene trees are generated by simulating gene events on a species tree. Simulating domain events  on a gene tree gives rise to genes represented as mosaics of domain segments, as well as domain trees, one for each domain family encoded in the gene family.This results in mosaics of segments that have different evolutionary histories. On the sequence evolution level, these segments correspond to sequences that are evolving at different rates.This model can be made more realistic by modeling each domain-encoding segment as an instance of a genuine domain family. This imposes two additional requirements:3. Functional and structural constraints acting on the evolution of each domain sequence preserve its characteristic sequence motifs, e.g. the propensity for alanine at Position 2, aromatic amino acids at Position 6, and tryptophan at Position 32 in SH3 .4. Changes in domain architecture resulting from domain gains or losses must reflect the constraints on domain order and co-occurrence that are observed in nature, such as the absence of genes encoding both a Zinc Finger domain and an Immunoglobulin domain in the same protein.A comprehensive multidomain protein simulator that supports all four goals has yet to be developed. Several systems have been developed that realize one or more of these goals. The simulator indel-Seq-Gen  simulateREvolver . The problem of modeling the constraints on domain order and adjacency during domain architecture evolution does not arise in SagePhy, because sequence segments in SagePhy do not correspond to genuine domains. Sequence segments in REvolver do correspond to genuine domains, but REvolver does not model explicit domain events that modify domain architectures.Here, we present a domain architecture simulator specifically designed to generate domain architectures that mimic the constraints on domain order and co-occurrence observed in nature . DomArchov is a Python implementation of this design. To our knowledge, no algorithms or software that address this problem are currently in existence.In our model, each state corresponds to a domain architecture (DA), i.e., to an ordered list of domains in N- to C-terminal order. Two states are adjacent if they differ by a single domain . State tThe birth-death formalism, widely used to simulate gene events, is not sufficiently expressive to capture constraints acting on domain order and adjacency. States in the birth-death model correspond to family size. Each state is adjacent to two neighboring states corresponding to a birth event, which increments family size, and a death event, which decrements family size. State transition probabilities and the distribution of waiting times between events are fully determined by the birth and death rates, which are explicit state-independent parameters, and a single state parameter, the family size. Thus, birth-death is a single-dimensional model and benefits from various properties that facilitate efficient simulation. In particular, the history of birth and death events can be simulated by repeatedly sampling from the waiting time distribution using the Gillespie algorithm.In contrast, in DomArchov, states of the system are defined by a sequence of domains. The probability of gaining or losing a domain depends on the specific location of the proposed change in the current DA. For this model, a different simulation strategy is required to account for high dimensionality and transition probabilities cannot be expressed as a simple function of state-independent event rates. To address this problem, we developed a novel stochastic model, described in detail in Section 2, that simulates the evolutionary trajectory of a DA using the Metropolis–Hastings algorithm.DomArchov can be applied at multiple time scales. In the stationary regime, DomArchov can be used to sample DAs according to an (unknown) equilibrium distribution to investigate evolutionary processes, test competing hypotheses or probe steady-state behavior. On much shorter, non-equilibrium timescales, DomArchov can be applied to generate evolutionary trajectories, producing datasets with ‘known’ histories for testing, validation, and comparison of evolutionary algorithms and software e.g. .DomArchov provides an important contribution for studies that use the DA abstraction as the primary data structure. The DA abstraction is widely used to probe questions of protein evolution, including the co-occurrence and variation in domain repertoire across taxonomic lineages , plasticDomArchov is a significant step towards the development of a comprehensive simulator for multidomain protein evolution. By modeling constraints on domain architectures that are not captured by any other software, DomArchov complements a rich body of work for simulating protein evolution on other levels of biological organization. DomArchov can be combined with simulators that model gene tree topologies and amino acid sequence evolution to create hierarchical models of multidomain protein evolution that reflect the multiple scales on which these families evolve. For example, following generation of a gene tree using a birth-death simulation of gene events on a species tree, DomArchov is applied to simulate the evolution of a domain architecture along the branches of that tree. In this scenario, an artificial DA is instantiated at the root of the gene tree. Next, an amino acid sequence is instantiated for each domain based on the HMM model for that domain. Linker sequences inserted between domains are based on the background distributions of sequence composition and linker lengths. DomArchov is then applied to evolve the DAs along each gene tree branch, progressing from the root to the leaves. Finally, domain sequences are evolved using a simulator that constrains evolution based on the domain model. Linker sequences are modeled using a standard sequence evolution simulator.Transitions between states in Metropolis–Hastings depend on the ratio of state probabilities. Because there is no general theory of ‘modular collaboration’ that would allow us to calculate state probabilities directly from domain architectures, we estimated state probabilities using a first-order approximation wherein the probability of observing a domain depends only on the identity of the domain that immediately precedes it. A key question is whether an algorithm that makes decisions based only on the immediate genetic neighborhood can nevertheless recapitulate longer range domain architecture properties. To investigate this question, we simulated DAs using four training sets derived from four disparate metazoan lineages and, in each case, compared the simulated DAs with genuine architectures taken from the same lineage as the training data.We examined properties including those that are not explicitly reflected in domain pair statistics used as training data: domain architecture probabilities, the number of copies in tandem arrays of repeated domains, domain promiscuity, and the propensity of domain pairs to co-occur in the same protein. Remarkably, although state transitions are based only on local information about domain order and proximity, DomArchov is able to elicit global properties of domain architectures.DN domain superfamilies in the training data and dD be an instance of domain superfamily n is denoted by Algorithm 1:DA simulator (DomArchov) pseudocodeInput: DA0, initial DA; T, maximum iterations1DA = DA02 for t to T do3  length(DA)r  // Choose new event uniformly at random4 if r then // Insert5    i RandomNumber6   d RandomNumber7   DAinsert8  else // Delete9   i RandomNumber10   DAdelete11  if  then next; // Prevent extinction12 else with probability min. Each state is a DA, where each domain instance is represented by its superfamily ID. Let tes e.g. . GeneralDN, the probability of selecting a gain event is set to be DN times greater than a loss event to ensure that all states transitions are considered with equal probability. Once the event type has been determined, a state is selected uniformly at random among the adjacent states corresponding to that event.At each iteration, a new state is proposed, uniformly at random, from all states that are adjacent to the current state. DomArchov allows the user to modify the ratio of gain to loss state proposals; however, deviation from uniformly distributed state proposals will disrupt the convergence properties of the model. First, an event type (gain or loss) is selected. Since the number of gain states exceeds the number of loss states by a factor of roughly s to an adjacent state t is:sp and tp are the probabilities of the DAs associated with states s and t, respectively. The probability of observing a given DA is estimated from domain pair frequencies using a first-order approximation, in which the probability of observing a domain is only dependent on the domain that immediately precedes it. For example, the probability of observing state Proposed states are accepted or rejected in accordance with the Metropolis–Hastings algorithm. The probability of a transition from state t be the DA that results from the gain of s:Let state t and s can be approximated by the ratio of two first-order approximations. Most of the terms in the numerator and the denominator of that ratio cancel, yielding:Then, the ratio of the probabilities of i is:Similarly, the transition probability for deletion of the domain at position The conditional probability k is added to the count of each bigram. To offset the fact that the number of pseudocounts added per domain is The matrix of domain bigram frequencies is very sparse. A pseudocount is required to account for bigrams that have not been previously observed. This also ensures that the state space is connected and that the Markov chain is therefore irreducible. Based on the Add-k smoothing method , a smallDomArchov is written in Python 3 and is composed of three modules:Raw data pre-processing of genomes downloaded from SUPERFAMILY, with domain architectures as the output;Pre-calculation of domain statistics used when calculating transition probabilities; andThe simulator.The pre-processing module extracts domain annotations to calculate the domain architectures as well as the domain alphabet, bigrams, and trigrams, and their associated counts. The pre-calculation module then calculates information that will be used repeatedly during simulations, such as the domains that are observed between domain pairs, the number of domains observed following a given domain, domains that are never observed in a multidomain context, etc. These two modules are used to set up the suitable environment for the simulator and are the basis for calculating transitions. They only need to be run once for a given set of genomes.k. A batch of simulations can also be run over a defined range for a given variable. Output includes the final DA simulated for each replicate; these data can be used to calculate the same statistics as the input genomes. Summary statistics over all replicates are also written to file, and options exist to output the evolutionary trajectory (states in the Markov chain) for each replicate.The simulation module takes as input a JSON file that includes the user-defined parameters for the simulation, including the starting DA, chain length, number of replicates, and pseudocount Our goal is to build a domain architecture simulator that can generate artificial DAs with properties that are comparable to those of genuine DAs. We evaluated DomArchov empirically using four training datasets derived from four metazoan lineages: 7 primate genomes, 11 fish genomes, 13 Drosophila genomes, and 4 cnidarian genomes see . We focuT is the number of states proposed by the Metropolis–Hastings algorithm (see Algorithm 1). Each experiment consisted of a suite of replicates equal in number to the number of unique DAs in the corresponding training set . Indeed, our pseudocount model is based on smoothing methods used in NLP . HoweverAlthough it may seem that simulation techniques for sequence evolution could be adapted to modeling domain architecture evolution, the entities (biomolecular sequences and domain architectures) are governed by very different sets of mutational processes, selective constraints, and data size. In nucleic and amino acid sequences, substitutions at a single site are the fundamental unit of change. Although more recent models have been expanded to account for indels and site-specific interactions, the core process focuses on replacement of a residue with a different residue at a single site.In contrast, domain architectures change through domain gain and domain loss, but not domain replacement. In nature, replacement of one sequence segment by another is mediated by homologous recombination, which requires that the sequences be related. This mechanism can, and likely does in some cases, act to replace an instance of a domain superfamily with a different instance of the same superfamily. Such events would indeed change the sequence, but not its DA representation. Domain replacement in the DA model would necessitate the replacement of a sequence segment encoding one domain superfamily with a segment encoding an unrelated superfamily. It is not clear what molecular mechanism would result in a such a replacement in a single event.Combining domain architecture simulation with simulation of other aspects of protein evolution is an important next step. There are also important new directions to explore within the restricted context of domain architecture evolution. Currently, a single event is used to model all gains in DomArchov; there is no distinction, e.g. between domain insertion and domain duplication. Expanding DomArchov to distinguish between various mechanisms that result in the addition of a domain is a fruitful area of future research. Modeling the gain or loss of several domains in a single event is another important future direction. This is particularly relevant for realistic simulation of tandem repeat formation, where duplication of several adjacent copies is a frequent occurrence (From an empirical standpoint, the studies presented here have focused on metazoan lineages. Whether DomArchov’s data-driven functions perform as well in other taxonomic lineages must be examined. This study has focused on establishing convergence and characterizing the steady-state behavior of the simulator. We look forward to probing its behavior on shorter time scales, as well.btac242_Supplementary_DataClick here for additional data file.


The samples EF5.0 and EF7.5 showed higher diameter, expansion and thermal factor, while the EF0 and EF2.5 had higher thickness (p < 0.05). The flour addition significantly increased the hardness, Water activity (Aw), Titratable Acidity (TA) and Soluble Solids (SS) in the cookie, however, L* a* and b*, pH and SS/TA ratio values were reduced (p < 0.05). Increased levels of ash, dietary fibers, ascorbic acid, anthocyanins, total phenolic compounds and antioxidant activity were verified on the cookie after eggplant flour addition. Meanwhile, there was a reduction in energy and carbohydrate values. It is concluded that eggplant flour addition up to 5% in cookie maintains the sensory acceptability similar to the standard product when evaluated by children. In addition, it can be considered a viable alternative to improve most of the physicochemical and nutritional characteristics of the product.This research aimed to evaluate the effect of adding different levels of eggplant flour in cookie on the physicochemical and nutritional characteristics and to verify the sensory acceptability among children. Four eggplant flour cookie formulations were prepared: EF0 (or standard), EF2.5, EF5.0, and EF7.5 . The sensory acceptability, physicochemical and nutritional composition were evaluated. The eggplant flour addition of 7.5% to cookie reduced the acceptability ( Solanum melongena L., is a non-tuberous species of great economic and agronomic importance for Solanaceae family [The eggplant, botanically classified as e family ,2. Its ce family . In Braze family .Although different varieties of eggplants exist, the most commonly cultivated has an oblong format, shining deep-purple color and green stem. The vegetable has characteristics of berries and it contains many edible soft seeds ,5. Its cThe per capita consumption of eggplant is small and it is 0.88 lb/yea . The lowSome technological alternatives have been used to introduce the eggplant into the usual diet, as an example the use of eggplant flour  in cookiPanification products such as biscuits, bread, cakes and pasta products are widely accepted by the world population. In Brazil, biscuits represent the most profitable category for the market, with a per capita consumption of 7.2 kg/year. Currently, there are several options being sold. Biscuit- type cookies stand out due to the constant increase in production and sales. It is estimated that 29,000 tons were produced in 2019, with sales of R$ 967,000 . CookiesThe study was conducted in accordance with the Declaration of Helsinki and the ethical approval was conceded by the Ethics Committee of Midwest State University, protocol number 3,089,447/2018. This research included children aged 7–10, enrolled between 2nd and 5th grade in 10 public schools in the urban area of Guarapuava, PR, Brazil. All children declared their consent and had informed consent from the legal guardian to participate in the research.In order to evaluate the food acceptance a questionnaire containing food designs belonging all food groups was elaborated: cereals; fruits; vegetables; dairy; meats and eggs; beans and oilseeds; oils and fats; sugar and sweets . The fooSolanum melongena L.), with better visual appearance, smooth surface without imperfections and bright deep-purple color, were purchased (20 kg) from a local market in Guarapuava. The fruits were washed in potable running water and sanitized in sodium hypochlorite solution (200 ppm) for 15 min and rinsed under running water again [®, SEDi-C 40L model, Rio de Janeiro, RJ, Brazil) at 65 °C for 24 h. After they remained at room temperature (22 °C) up to total cooling, they were milled in a mill  and passed through a sieve with 32 mesh/Tyler opening , obtaining a final yield flour of 1.5 kg.The eggplants (er again . Then weFour eggplant flour cookie formulations were prepared: EF0 (or standard), EF2.5, EF5.0 and EF7.5  . These p®, SX34 model, Itaqui, SP, Brazil). Then, the eggs were added and mixed until homogenization. Afterwards, the wheat and eggplant flour were incorporated to dough. The chemical yeast and chocolate drops were added and mixed manually at the end of preparing. The cookies were molded in circular format (approx. 4 cm of diameter) and baked at 180 °C for 12 min in electric oven  preheated. After that, they rested until reach room temperature (22 °C) and were packed in hermetically sealed plastic containers until the analysis.For the cookies’ elaboration, the butter, brown sugar and vanilla essence were mixed using a domestic blender  were chosen randomly. The mass parameters, diameter and thickness were determined according to the precepts describe by the American Association of Cereal Chemists [®, 530-104BR model, Kawasaki, OL, Japan). The expansion factor was obtained by the ratio diameter/thickness of the cookie. The thermal factor was obtained by the ratio between the pre-bake and after baking mass values. Each unit of cookie was considered as an experimental replay.To the physical evaluation cookies from the same batch , interconnected with the Exponent Lite Software  installed in a computer. The cookie (approx. 1 cm of thickness and 4 cm of diameter) was compressed up to break, the strength spent was defined as cutting force. The measurement conditions were maintained as the pre-test speed at 1 mm/s, test speed at 3 mm/s, post-test speed at 10 mm/s and trigger force of 0,1 N.The hardness was measured with five replicates in all formulations within 24 h after baking. A texture Analyzer with a probe HDP/BSK (a knife simulator) was used  using CIELAB  color systems according to Commission Internationale de l’Écleirage. L* defines Lightness , a* indicates red (positive a*) or green value (negative a*) and b* indicates yellow (positive b*) or blue value (negative b*) [The color was analyzed in five replicates for eggplant flour and cookies. Chromatic parameters were obtained in a colorimeter (Konica Minoltative b*) .®, Labswift model, Lachen, SZ, Switzerland) at 22 °C; pH, measured by a bench pH meter , calibrated with pH 4.0 and 7.0 buffers; Titratable Acidity (TA) evaluated according to the Association of Official Analytical Chemistry (AOAC) [®, Z567132 model, Essex, CT, USA), containing sodium hydroxide solution (0.1 mol L−1) until reaching pH 8.1. The results expressed as g of citric acid 100 g−1; Soluble Solids (SS) were measured according to AOAC [m/v) at room temperature (22 °C). Two to three drops of the sample filtrate were used and the reading was performed on a bench-top refractometer . Values were expressed by °Brix ; SS/TA ratio which was obtained by dividing SS and TA values.The chemical determinations were performed in triplicate on the eggplant flour and all the cookie formulations. The water activity (Aw) was determined by an Aw analyzer (Novasinay (AOAC) . Initial to AOAC  with sol−1); ash (g 100 g−1) analyzed in a muffle furnace at a temperature of 550 °C; protein (g 100 g−1) analyzed through to the Kjeldahl method. The factor 6.25 was used for the nitrogen conversion into crude protein [−1) by the hot extraction method with Soxhlet extractor and petroleum ether [−1) by difference method (% carbohydrate = 100 − (% moisture + % ash + % protein + % lipid + % dietary fiber); total, soluble and insoluble dietary fiber were determined according to the AOAC 991.43 method. Total fiber and insoluble dietary fiber by enzymatic method and the soluble dietary fiber were calculated by the difference of the total and insoluble dietary fiber results [−1), calculated by the values recommended for lipid , protein , carbohydrate  and fiber  [−1.For the nutritional composition analysis, the samples were evaluated in triplicate according to the following measurements: moisture determined in a drying oven at 105 °C until weight constant  . The Daical g−1) , resultiThe bioactive compounds and antioxidant activity were evaluated in triplicate in the eggplant flour and cookies.m/v) and titrated against 2,6-dichlorophenol-indophenol (DCFI) dye. The results were expressed in mg ascorbic acid 100 g−1.Ascorbic acid was estimated by the titrimetric method of AOAC (2016) modified by Benassi and Antunes . Samplesg rpm for 10 min (22 °C) and the supernatant was recovered. Then the supernatants were pooled and the volume standardized to 25 mL.The samples submitted to the analysis of anthocyanins, total phenolic compounds and antioxidant activity were extracted using the solvent ethanol. 2.5 g of sample plus 15 mL of 80% ethanol solvent were used. The samples were vortexed (1 min) at room temperature (22 °C) in low light and placed in a water bath at 40 °C for 10 min . The homogenate was centrifuged at 5000× ®, Cary 60 UV model, Santa Clara, CA, USA) at 520 and 700 nm. The reading at 700 nm was performed to discount the turbidity of the sample. The results were reported as mg of cyanidin-3-glucoside (C3GE) 100 g−1.The quantification of anthocyanins was made by the differential pH method . Two buf2CO3 (4%) was added. The contents were well mixed and left at room temperature (22 °C) without light for 2 h. Absorbance was measured on a spectrophotometer  at 765 nm. The results were expressed as mg of Gallic Acid Equivalent (GAE) 100 g−1.The Total Phenolic Compounds (TPC) was determined based on the Folin-Ciocalteau method described by Woisky and Salatino , using g•+ radical was given by homogenizing equal parts of ABTS (7 mM) and potassium persulfate (140 mM) with storage for 16 h at room temperature (22 °C) in the dark. The solution was then diluted in phosphate buffered saline (PBS) (5 mM–pH 7.4) until the absorbance of 0.70 nm ± 0.05 nm at 734 nm. This solution was used to determine the standard curve (with Trolox up to 100–2000 μM) and samples. In a test tube, 0.5 mL of the ethanolic extract was added with 0.2 mL of ABTS•+ solution. The contents were mixed well and remained at room temperature (22 °C) for 2 min. Spectrophotometer  reading was performed, absorbance at 734 nm and the results were expressed as µmol Trolox equivalents 100 g−1.The Antioxidant Activity (AA) was evaluated using the 2,2′-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid (ABTS) radical method, as proposed by Re et al.  with mod®, version 3.5.3, Alpharetta, GA, USA) was used in this study [p < 0.05).The software R (Lucent Technologiesis study . The datn = 173), chard , watercress , radish  and chayote . Hanson et al. [The foods that children did not like more often were eggplant  among children. Similar results were verified by Brasil et al. [−1) [In l et al.  evaluatial. [−1)  promote al. [−1) , which cal. [−1)  explain al. [−1) .p > 0.05), as expressed in p > 0.05). Cookies prepared with eggplant flour have high fiber content, which increases water absorption and, consequently yield, once it shows a lower shrinkage [The samples EF5.0 and EF7.5 showed higher diameter, expansion and thermal factor, while the EF0 and EF2.5 had higher thickness  [−1) [The eggplant flour addition reduced 2 ± 1.5)  if compa.5) [−1)  also red.5) [−1) . Thus, tp > 0.05). According to the scientific literature the presence of phenolics in the eggplant flour, such as chlorogenic acid (1.73 mg g−1), caffeic acid (0.19 mg g−1) and ferulic acid (0.04 mg g−1) [The chemical characteristics results of eggplant flour and cookie are described in c acid 1.3 mg g−1,c acid 1.3 mg g−1, mg g−1) , which m mg g−1) .In p > 0.05). According to Uthumporn et al. [The moisture content of eggplant flour is according to Brazilian legislation 15%) , and oth% , and on et al.  conseque−1), magnesium (158.1 mg 100 g−1), calcium (130.9 mg 100 g−1), sodium (68.1 mg 100 g−1), iron (2.9 mg 100 g−1), manganese (2.5 mg 100 g−1), zinc (2.1 mg 100 g−1) and copper (1.0 mg 100 g−1). During school-age, a higher consumption of these micronutrients can be beneficial, as it is associated to health growing and appropriate physical, cognitive and emotional development of children [The ash content observed in the eggplant flour impacted the increased ash level in cookie formulations. It is known that ash reflects the concentration of minerals in its composition. A study by Rodriguez-Jimenez  on the mchildren .−1 and 1.7 g 100 g−1, respectively) [p > 0.05). The opposite was verified in levels of carbohydrate and energy, as eggplant flour presents lower amount when comparing to wheat flour . With it, there was a reduction in carbohydrate and energy levels in EF5.0 and EF7.5 formulations.The protein and lipid amounts in eggplant flour are similar to wheat flour (12.0 g 100 gctively) . Therefo−1), corroborating with literature [There was an increase on total fiber content to EF2.5 (57.1%), EF5.0 (114.3%) and EF7.5 (171.4%) with regard to EF0, as eggplant flour has a high fiber level (34.5 g 100 gterature . The 5% terature . That waterature .p < 0.05). A similar effect was related by Uthumporn et al. [The eggplant flour addition in cookie increased proportionality the ascorbic acid, anthocyanins, total phenolic compounds and antioxidant activity p < 0.0. A similn et al. , which cn et al. . Gürbüz n et al.  showed tThe eggplant flour is a viable alternative as cookie ingredient. The 5% level addition of this flour in product is well accepted by children at school-age, obtaining similar acceptability to the standard product and good commercialization expectation. However, eggplant flour harms some of the physicochemical characteristics in the cookie, such as water activity, acidity, hardness, thickness and instrumental color. On the other hand, higher contents of ash, dietary fiber, ascorbic acid, anthocyanins, total phenolic compounds and antioxidant activity are observed after eggplant flour addition, which improves the nutritional profile of the product. Thus, an added product of eggplant flour is a possible alternative to promote healthier feeding among school-age children.For further studies, it is suggested to include other vegetable flours and their by-products in cookies and similar products. The development of flour blends is also encouraged, ones that contribute to the nutritional aspects of the food and may be an innovative option for the industry and the consumer.


Whether and how these distinct functions contribute to TLR signaling is unknown. Here we show divergent signaling requirements for dSARM in injury-induced axon degeneration and TLR-mediated developmental glial phagocytosis through analysis of new knock-in domain and point mutations. We demonstrate intragenic complementation between reciprocal pairs of domain mutants during development, providing evidence for separability of dSARM functional domains in TLR signaling. Surprisingly, dSARM’s NAD+ hydrolase activity is strictly required for both degenerative and developmental signaling, demonstrating that TLR signal transduction requires dSARM’s enzymatic activity. In contrast, while SAM domain-mediated dSARM multimerization is important for axon degeneration, it is dispensable for TLR signaling. Finally, dSARM functions in a linear genetic pathway with the MAP3K Ask1 during development but not in degenerating axons. Thus, we propose that dSARM exists in distinct signaling states in developmental and pathological contexts.Elucidating signal transduction mechanisms of innate immune pathways is essential to defining how they elicit distinct cellular responses. Toll-like receptors (TLR) signal through their cytoplasmic TIR domains which bind other TIR domain-containing adaptors. dSARM/SARM1 is one such TIR domain adaptor best known for its role as the central axon degeneration trigger after injury. In degeneration, SARM1’s domains have been assigned unique functions: the ARM domain is auto-inhibitory, SAM-SAM domain interactions mediate multimerization, and the TIR domain has intrinsic NAD +, leading to energetic collapse in the axon. This was surprising since dSARM/SARM1 is a TIR-domain containing protein, and as such, was thought to act exclusively as an adaptor in Toll receptor-mediated pathways. We recently uncovered a developmental role for dSARM in glia where it promotes engulfment of dying neurons. To understand the relationship between this function for dSARM and its better-known function in axon degeneration, we set out to (1) compare the enzymatic requirement of dSARM in both settings, and (2) determine if signaling mechanisms are the same. To address these questions, we used CRISPR to generate new dSARM knock-in alleles. We find that the NAD+ hydrolase activity of dSARM is absolutely required for both its axon degeneration function and its signaling function. Pointing to differences in dSARM-mediated pathways in the two contexts, we provide evidence that Ask1 kinase is required for dSARM signaling in glia but not for axon degeneration. Thus, we propose that the NAD+ hydrolase activity of dSARM leads to Ask1 activation and subsequent signal transduction.Following injury, severed axons are actively destroyed by a protein called SARM1, or dSARM in Drosophila. It was recently shown that dSARM/SARM1 accomplishes this feat by degrading the key coenzyme NAD Brain homeostasis is maintained by cell-intrinsic and cell-extrinsic surveillance mechanisms. During normal development, a commonly cited estimate is that 50% of neurons die, and injury can precipitate the death of even more. The prevalence of neuron and neurite death during development highlights the importance of defining underlying molecular mechanisms as well as those active in phagocytic glia that engulf and dispose of neuronal corpses.S), exhibits markedly delayed axon degeneration [S mutation is a tandem triplication of the NAD+ synthetic enzyme Nicotinamide mononucleotide adenlyl transferase 1 (Nmnat1) and Ubiquitination factor e4b (Ube4b) [+ levels normally plummet following injury [+ depletion is blocked by WldS [+ in the decision to degenerate across evolution [Wallerian degeneration is a specific type of axon degeneration in which the axon distal to an axotomy degenerates . A spontneration ,3. This g injury , NAD+ de by WldS –9, hintivolution .+, SARM1 drives axonal death via intrinsic NAD+ hydrolase activity that is proposed to culminate in metabolic catastrophe [SARM1 encodes a protein with an N-terminal ARM domain, two tandem SAM domains, and a C-terminal TIR domain. Biochemical and genetic studies indicate that the TIR domain contains NAD+ hydrolase activity, the SAM domains are responsible for multimerization, and the ARM domain mediates auto-inhibition [+ once released by the ARM domain in response to an increase in the NMN/NAD+ ratio [The discovery that Wallerian degeneration is an active destructive process prompted forward genetic screens for loss-of-function (LOF) mutants in which axons are protected following injury. Drosophila SARM1 was identified in such a screen as its loss confers robust protection of distal axons following axotomy . Mice laastrophe ,14. SARMhibition –16. Recehibition –19. In ihibition –22. The D+ ratio .+ hydrolase activity of dSARM necessary for TLR-dependent signaling? And are dSARM’s downstream signaling mechanisms conserved in development and degeneration?SARM1 was first identified as an innate immune adaptor protein ,25 and rin vitro assays and in vivo overexpression paradigms [dSARM knockout allele by replacing exons containing the ARM, SAM, and TIR domains with an attP recombination target. We then recombined in a series of domain mutants as well as a catalytically inactive point mutant (E1170A) and assessed the contributions of each domain to injury-induced degeneration and developmental phagocytosis. We find that the E1170A allele exhibits long-lived protection of axons following injury, demonstrating that dSARM’s NAD+ hydrolase activity accounts for its pro-degenerative function in vivo. Unexpectedly, the TIR-only allele can drive spontaneous axon degeneration over the course of days, indicating that SAM-mediated multimerization is not essential for dSARM activity in the absence of ARM domain-mediated inhibition. We next analyzed these new dSARM alleles in glial TLR signaling and find that dSARM’s NAD+ hydrolase activity is essential for this role. In contrast, the SAM domain is dispensable for signaling in glia. Finally, we explored signaling downstream of dSARM in glia and present evidence that dSARM functions through the MAP3K Ask1 in glia but not in degenerating axons. These findings argue that dSARM has distinct signaling modes in degenerative and non-degenerative signaling pathways.To date, functions of individual SARM1 domains have been assigned largely via aradigms –15,17–22in vivo and so undertook a CRISPR/Cas9-mediated genome engineering approach. Using CRISPR/Cas9, we precisely deleted the ARM, SAM, and TIR domain-encoding exons of dSARM and replaced them with an attP site to create a founder knock-out allele  present in the allele . The lethality of dSARME1170A animals indicates that the NAD+ hydrolase activity serves an essential developmental function. Interestingly, the lethal phase of dSARMTIR homozygotes is at the first-instar larval stage (L1), suggesting that this allele has gain-of-function (GOF) activity. We also generated a SARMRescue line by recombining back in wild-type sequences  axotomy model, the same paradigm in which dSARM’s role in axon degeneration was first uncovered [We employed these ncovered  In this dSARMRescue mutant clones display normal axon degeneration following injury  and assessed axon protection 7 days post injury (DPI). To test if the knock-out/knock-in strategy itself interfered with wild-type dSARM function, we assayed whether homozygous g injury . We findSARM896; ; Fig 2C,dSARM knock-in mutants enable critical in vivo tests of the function of dSARM’s catalytic activity and protein domains that to date have been largely assigned via in vitro assays and in vivo overexpression paradigms. We interrogated requirements of the TIR domain in general as well as the NAD+ hydrolase activity in particular [dSARMARM-SAM, in which the entire TIR domain is deleted, and dSARME1170A, in which a key glutamic acid residue in the active site is mutated, provide perfect protection of distal axons at 7 DPI indicating a strict requirement for dSARM’s NADase activity in axon degeneration  relative to ARM-SAM domains or TIR domain enzymatic activity (dSARME1170A). Interestingly, while dSARME1170A heterozygotes degenerate as rapidly as controls, axon degeneration in dSARMARM-SAM heterozygotes is incomplete at 1 DPI  to what extent is signaling downstream of dSARM conserved in development versus degeneration? We previously demonstrated that RNAi-mediated knockdown of dSARM in cortex glia results in an identical increase in Dcp-1 debris as observed in dSARM nulls, while pan-neuronal dSARM knockdown does not affect corpse clearance [dSARM alleles can be attributed solely to dSARM’s function in cortex glia.We recently uncovered a requirement for a glial dSARM-mediated TLR pathway in clearing neuronal debris during development. In loss-of-function (LOF) mutants of Toll-6, FoxO or dSARM, levels of Dcp-1-positive apoptotic debris are increased in the L3 brain . We demolearance . Thus, tdSARM alleles. For all alleles except dSARMTIR (see below), we conducted this analysis at early L3 before the lethal phase of these animals. We developed an Imaris imaging pipeline to automate and standardize debris quantification, where to account for differences in brain size, Dcp-1 puncta count is normalized to individual brain lobe volume  The enzymatic activity of dSARM is essential for signaling, thus extending the known roles of the NADase activity of dSARM to signal transduction.We continued by interrogating the contributions of individual dSARM domains to developmental signaling. We find excessive neuronal debris in both ozygotes , indicat1 debris , indicat mutants , indicatis stage . Given t alleles , demonstdSARMARM-SAM/dSARMTIR and dSARMARM-TIR/dSARMSAM heteroallelic animals to test whether the domains can complement each other and restore wild-type dSARM function. We find that heteroallelic combinations of both pairs of reciprocal mutants are viable until the mid-pupal stage, while all homozygous mutants die as wandering third-instar larvae. The finding that ARM-SAM suppresses the early lethality observed in TIR-only homozygotes argues that ARM-SAM inhibits the GOF activity observed in this allele in trans. To look more specifically at the function of these reciprocal pairs of mutants in signaling, we quantified Dcp-1 apoptotic debris. We find that Dcp-1 counts are at wild-type levels in both heteroallelic combinations  activates Nsy-1 (the worm homolog of Ask1) to specify asymmetric odorant receptor expression [The discovery that dSARM’s NADase activity is required for pro-phagocytic signaling begs the question of how information is transmitted by dSARM’s enzymatic activity. As a first step toward answering this question, we sought to identify downstream signaling components. We hypothesized that dSARM activation engages a MAPK cascade in cortex glia, a model initially based on Toll-6 signal transduction in motorneurons, where Toll-6 and dSARM drive JNK activation . We lookpression . More repression .ask1 null allele did not exist, we employed CRISPR to generate a 6 KB deletion of the ask1 locus to enable a genetic analysis  and is maintained until the late third instar larvae [ase-FLP activity in the wing imaginal disc [ase initiates in sensory organ precursors, which develop in third instar larvae and the first 10 h after puparium formation [dSARMTIR observation is that ORN clones are generated significantly earlier relative to wing sensory neuron clones, which might result in relatively higher levels of dSARMTIR in adult neuron clones.The generation of MARCM clones relies on enhancer-driven Flippase (FLP) activity that recombines Flippase Recombination Targets (FRTs) on a chromosomal arm . ORN clor larvae . In contnal disc . The actormation . Thus, adSARM allele lacking only the SAM domains and found that it behaves as a null in axon degeneration. Thus, SAM domain-mediated dSARM multimerization is essential for axon degeneration in vivo. In contrast, dSARMARM-TIR homozygotes display normal glial TLR signaling, demonstrating that the SAM domains are not required in this context. We propose that in TLR signal transduction, dSARM’s TIR domains heterodimerize with TIR domains on TLR receptors. In this model, TIR-TIR interactions between TLRs and dSARM support the NADase activity of dSARM, which is consistent with the finding that catalytically inactive dSARM variants retain the TIR-TIR interactions leading to NADase activation , vasa>cas9 , MKRS, hs-FLP / TM6B, Cre, Tb , ey-flp, UAS-mCD8::GFP , OR22a-G896 , FRT2A, 896 , Wrapper896 ), dSARM dSARM attP KO was designed following published methods [dSARM gene were replaced with an attP and loxP site through homology-directed repair using a donor template. The attP site facilitates the knock-in of dSARM domain alleles. The pHD-DsRed-w+  was used to generate the donor template for homology-directed repair. Briefly, ~1 kb targeting homology arms flanking the domain-encoding exons of the dSARM gene, the pHD-DsRed-w+ backbone, and attP-loxP-DsRed-loxP insert were generated by PCR. These four products were then assembled using NEB HiFi DNA assembly . A dSARM targeting chimeric RNA (chiRNA) was cloned into the pU6-BbsI-chiRNA  vector using site-directed mutagenesis for the following target sequence: 5’- AAGGTTGTAAGGGTCCCCAGGGG -3’ . Both the chiRNA and the pHD-DsRed-w+ plasmids were injected (BestGene) into vasa>Cas9 embryos  to produce dSARM deletions. Successful events were screened for by the presence of DsRed positive eyes. The allele was named dSARM attP KO. Genomic PCR bands corresponding to primers flanking the targeted exons confirmed the deletion of the domain encoding exons of dSARM attP KO. The DsRed selection marker was removed by crossing to flies expressing Cre recombinase  .The  methods ,54. NucldSARM attP KO embryos and integrated at the dSARM locus through phiC31-mediated recombination. Successful events were screened for by the presence of red eyes, and the red (w+) containing cassette was removed by Cre recombinase. The resulting dSARM rescue flies are homozygous-viable, fertile, and display no apparent phenotypes. To generate dSARM E1170A, we mutagenized the dSARM rescue plasmid, converting the NADase catalytic glutamic acid residue (GAA) to an alanine (GCC) (nucleotides 3L:8105127–8105129). To generate dSARM ARM-SAM, we mutagenized the dSARM rescue plasmid to remove the nucleotides 3L:8104756–8105455 corresponding to the TIR domain. To generate dSARM TIR, we mutagenized the dSARM rescue plasmid to remove the nucleotides 3L:8103445–8104722 corresponding to the ARM and SAM domains. To generate dSARM ARM-TIR, we mutagenized the dSARM rescue plasmid to remove the nucleotides 3L:8104303–8104722 corresponding to the SAM domains. To generate dSARM SAM, we mutagenized the dSARM ARM-SAM plasmid to remove the nucleotides 3L:8103445–8104299 corresponding to the ARM domain.As a control, an integration plasmid (pGE-attB-GMR)  containiThe Ask1Δ6 mutant was designed following published methods ,54. The We induced antennal injury using a modification of a previously described protocol ,55. AnimWe performed wing injuries using a modification of a previously described protocol . One winAdult heads were fixed with 4% formaldehyde  in 1x PBS and 0.1% Triton X-100 (PTX) for 30 min and washed 5 times, 2 minutes each with PTX before dissection. Brains were dissected in PTX. Dissected brains were subsequently fixed in 4% formaldehyde in PTX for 10 min and washed 5 times, 2 minutes each with PTX. Primary antibody anti-chicken GFP  was used at 1:500 in PTX and rocked overnight at 4°C. Brains were washed 3 times, 10 minutes each with PTX. Alexa Fluor 488 goat anti-chicken  was used at 1:100 in PTX and rocked overnight at 4°C. Following antibody incubation brains were washed 3 times, 10 minutes each with PTX. Brains were mounted in ProLong Gold with DAPI .Fluorescent 16-bit images were acquired on an upright Zeiss LSM 800 (Carl Zeiss) using Zen software (Carl Zeiss). The following objectives were used: 20x Plan-Apochromat (0.8 NA) air objective  or 40x Fluar (1.3 NA) oil immersion objective (adult experiments).Maximum intensity Z-projections were analyzed in Image J/Fiji. Axons were isolated from the image  one side at a time. A threshold using the default settings was applied to highlight the axons. This threshold was then used to create a selection. From this selection, the average intensity was measured. For axonal debris, we started with a maximum intensity Z-projection in Image J/Fiji. We generated a region of interest on the injured side by applying a threshold to detect the fluorescent signal and generated a selection. From this selection, we used the analyze particles function to get total fluorescent area.T comparison protocol to quantify relative changes in gene expression, all in technical triplicate. No-reverse transcriptase controls were conducted to confirm the purity of each cDNA sample, and lack of gDNA contamination in the qRT-PCR. We performed 4 biological replicates per mRNA and 3 technical replicates per biological replicate.Fifty first or second instar larvae of each genotype were collected in an 0.6mL Eppendorf tube in nuclease-free water on ice. The larvae were homogenized in the 0.6 mL tube with a pestle. RNA was isolated using the RNeasy plus kit (Qiagen). cDNA was synthesized from 1 mg total RNA using the iScript kit (BioRad) and the cDNA was diluted 1:5. qRT-PCR was performed using Taqman probes from the Life Technologies database: Rpll140 (Dm02134594_g1) and dSARM (Dm01840803_m1). These were utilized in a StepOnePlus Real-Time PCR system (Applied Scientific/ Thermo Scientific). Rpll140 was used as the reference gene for the DDCrd instar larval (L3) CNS. Extracted brains were fixed in 4% paraformaldehyde (PFA) in PBS for 20 minutes. Fixed brains were washed 3x 5min in PBS and blocked in PTN  for 30 minutes and incubated in primary antibody for two overnights at 4°C. Following incubation in primary antibody, brain samples were washed 3x 5min in PTN and incubated in secondary antibody for 2 hours at room temperature. After incubation in secondary antibody, samples were washed 3x 5min in PBS and mounted in Prolong Gold antifade reagent with DAPI (Thermo Fisher). Mounted samples were solidified at room temperature overnight before image acquisition. The following primary and secondary antibodies were used: rabbit anti-cleaved Drosophila Dcp-1 (Asp216) , goat anti-rabbit IgG (Alexa-conjugated fluor 568) , and mouse anti-Draper .Rapid dissections were performed in PBS to expose 3rd instar larval (L3) CNS were analyzed using Imaris Microscopy Image Analysis Software (Oxford Instruments). To reduce background noise, a background subtraction filter (2.5 μm in width) under Image Processing function was first applied to the image. After image processing, contour of both brain lobes was traced across top to bottom slices using the surface function to define a region of interest. Each region of interest recapitulates the entirety of an individual brain lobe and measures its volume. Within each region of interest, Spots Detection function  was deployed to automatically label individual Dcp-1 puncta based on a local contrast screening of relative intensity and to generate a total count in each brain lobe. Total Dcp-1 puncta count is then divided by individual brain lobe volume to yield normalized Dcp-1 puncta count (#/μm3).Acquired confocal images of 3Larval brains were imaged and processed as previously described . BrieflyAll statistical analyses were performed and graphs generated using Prism 9 (GraphPad Software). In all box and whisker plots, the whiskers represent minimum and maximum data points, and the line within the box depicts median values. In all bar graphs, error bars are mean ± SEM. All pairwise sample comparisons were performed using a Mann-Whitney test. For data in a group of three or more, a Kruskal-Wallis test was used to compare each sample with other samples, and a Dunn’s multiple comparison test was subsequently performed. A two-way ANOVA followed by a Dunnett’s multiple comparisons test was performed on adult ORN axon degeneration data Figs  and 4J. S1 FigTIR: 0.56 (n = 22). Representative z-projections of OR22a ORNs of the indicated genotypes labeled with anti-GFP at 12 Hours post injury (HPI). (C) Normalized mean axon intensity at 12 HPI: dSARM rescue: 0.80 (n = 24), dSARM(PDF)Click here for additional data file.S2 Fig Representative z-projections of OR22a ORNs of the indicated genotypes labeled with anti-GFP at 7 DPE, 1 DPI. (C) Normalized mean axon intensity at 1 DPI: wild type (FRT2A): 0.91, Ask1Δ6: 0.86, (D-F) Representative z-projections of OR22a ORNs of the indicated genotypes labeled with anti-GFP at 7 DPE, 1 DPI. (G) Normalized mean axon intensity at 1 DPI: wild type (FRT2A): 0.85 (n = 35); OR22a-Gal4, UAS-Ask1-Flag: 0.76 (n = 24); OR22a-Gal4, UAS-Ask1ΔN: 0.61 (n = 26). n.s., not significant.(PDF)Click here for additional data file.S3 FigRescue: 1.56x10-4, dSARMKO: 1.45x10-4, dSARMARM-SAM: 1.53x10-4, dSARMARM-TIR: 1.36x10-4, dSARME1170A: 1.72x10-4, dSARMSAM: 1.60x10-4, and dSARMTIR: 1.89x10-4. n values can be found on the graph. n.s., not significant.(A-G) Representative z-projections of brain lobes of the indicated genotypes labeled with anti-Dcp-1. (H) Quantification of the number of Dcp-1 puncta normalized to brain lobe volume. Mean number of Dcp-1 puncta/brain lobe volume: dSARM(PDF)Click here for additional data file.S1 Data(PDF)Click here for additional data file.


Cold plasma (CP) technology has emerged as a novel non-thermal technology with the potential to improve food quality or impart functionality to ingredients. Our previous studies on wheat flour demonstrated how the structure and functionality of wheat flour might be modified using CP to provide an alternative to chemical additives . However, understanding of the further effects of plasma functionalized ingredients in existing or new product formulation is limited. This study investigated the effects of CP treatment of wheat flour on traditional white pan bread development. The bread was formulated using plasma functionalized flour (PFF), and critical product characteristic responses were analyzed. Plasma treatment of flour positively affected the bread's expansion ratio, crust color, and water activity. Farinograph analysis suggests improvement in water absorption capacity, dough development time, and dough stability. X-Ray Microtomography (XRMT) analysis was conducted to understand how plasma functionalising the flour impacted the microstructure of bread. The 3D scans suggested no macro-change in the bread matrix compared to control; however, the porosity decreased in line with the increasing plasma treatment duration of the flour. The texture profile analysis showed an improvement in the gluten network developed in the dough developed from PFF. Sensory analysis results showed overall acceptance for bread formulated with PFF compared with a commercial sample. Overall, CP treatment of the flour improved the functionality in relation to dough and bread preparation and can thus provide an alternative to chemical additives in bread making. The CP processes may be modulated to deliver tailored effects for bread product development.  •The expansion ratio of bread improved using plasma functionalized flour.•The porosity distribution ratio within the bread loaf increased with the flour treatment time.•XRMT images indicated the increasing porosity distribution in bread with increasing flour treatment duration.•Sensory analysis showed acceptance of bread developed from plasma functionalized flour using a consumer acceptance test. Plasma is the fourth state of matter containing quasi-neutral ionized gas, electrons, ions, atoms, UV photons, and charged particles . DielectIn the bread-making process, apart from using main ingredients like wheat flour, fat, yeast, salt, and water, there is a common practice to use food additives and processing aids such as oxidants, reductants, enzymes, dough conditioners, etc. These substances are added to improve different properties of bread-like texture, volume, flavor enhancement or moisture retention, etc. . HoweverSubstitution of chemical additives with clean alternatives has been previously explored. For example, the addition of lemon juice as an alternative to chemical analogs due to its acidification properties was studied by . Though The application of a non-thermal technology to improve the properties of bread was proposed previously. The effect of irradiation doses  on wheat grain and, later, French bread developed from the irradiated flour was investigated. A decrease in falling number was associated with increasing irradiation dose, which was attributed to a change in starch gelatinization properties. However, no change in dough texture, dough development, or dough's gas retention capacity was observed following irradiation . Misra eDespite the increased interest in alternative approaches to improve functional properties of grain flours, there is a need to understand the effects of CP treatments on the functional properties and the subsequent behaviour in standard product formulations. Other non-thermal technologies including high-pressure processing, ultrasound, and ozone have been evaluated for use with cereal-based products like bread, cookie dough, or pregelatinized corn flour bread .To the best of our knowledge, there has been no scientific study reported to date on the application of cold plasma for cereal-based product development and their quality factors assessment. Among cereal-based products, bread has been a subject of many scientific studies due to its frequent consumption and essential role in daily diets. Our previous studies on wheat grains and flour identified CP processes for cereal grains decontamination, enhancement of quality factors such as seed germination , 2019, aThe plasma process development conducted on wheat flour and grain showed an increase in the water hydration properties and the whiteness index of flour, accompanied by a change in starch crystallinity observed after CP treatment . The phy22.1A commercial hard wheat flour  was used in this study. Salt and compressed baker's fresh yeast were obtained from local retailers . All experimental chemicals and reagents used were of analytical grade and were procured from Merck Dublin, Ireland.2.22.2.1.The schematic of the contained high voltage atmospheric air plasma reactor based on a dielectric barrier discharge (DBD) is shown in 2.2.2The production of the control bread was based on a straight dough system, as explained in After the doughs were mixed to the correct temperature and consistency, they were covered to ferment at ambient conditions for 60 min, and the dough was punched down after 45 min. The fermented dough (497 g batches) was then knuckled out into a pad, the ends of the dough were folded to form a rectangle, the dough was rolled up and placed into the rectangular moulds (9cm × 10cm × 17 cm) and fermented again in the proving chamber  for 45 min at a temperature of 31 °C and 70% RH. Before the dough was baked, it was scored down the middle and placed into a pre-heated deck oven  of 230 °C, that dropped to 210 °C with the heat settings at 6 (front heat), 4 (top heat), and 2 (bottom heat) for 35 min with 10 s of steam to develop the caramelization of the crust. Finally, the bread was removed from the oven then cooled at ambient bakery conditions. Henceforth, the bread formulated from plasma functionalized flour for 10, 20, and 30 min will be termed PFFB-10, PFFB-20, and PFFB-30. The control was bread formed from untreated flour.2.2.3The control and plasma-treated flour moisture content were determined by the oven-drying method at 105 °C, and ash content was estimated in a muffle furnace at 500 °C. The protein content of flour was determined by the Kjeldahl method and was calculated by multiplying nitrogen content with a protein conversion factor of 6.25. The fat content was determined by Soxhlet extraction with hexane for 6 h. Finally, carbohydrate content was calculated by the difference method as Total carbohydrate = (100-protein-ash-fat-moisture) .2.2.4Color has been determined by CIELAB hunter lab colorimeter  at room temperature (21 ± 1 °C). The observation angle was 10°, equal to the perception of a human observer, and illuminant D65 was used (daylight source), following the CIE recommendations. For calibration, a white  and black reference standard were used before the measurements. A 2.5-inch glass sample measuring cup was filled with crushed bread crumb/crust, and the color was measured. Color parameters obtained were L* (L* = 0 [black] and L* = 100 [white]), a* (–a* = greenness and +a* = redness) and b* (–b* = blueness and +b* = yellowness).2.2.5The expansion ratio is a measure of dough's rise during the fermentation period. The expansion ratio is calculated for dough after the mixing stage as per the method described by . Equal p2.2.62O, which gives 0.760 value at 25 °C. Approximately 2 g of the sample was placed in the water activity container and the water activity meter chamber.Water activity is a measure of the ability of the sample to retain water and is a measure of the product's shelf life. Water activity indicates how tightly water is bound structurally or chemically with a given substance. It is the ratio of the sample's vapor pressure to the water's vapor pressure at a given temperature and relative humidity. The water activity of bread was measured using Aqua Lab Series 3 . The instrument was standardized using 6.00 mol/kg of NaCl in H2.2.7The dough mixing properties were carried out with Brabender Farinograph  according to the method of AACC approved method 54-21 . The 3002.2.8X-ray microtomography (XRMT) has been applied to study food microstructure such as air bubbles or cells, starch granules, protein assemblies, and food biopolymer matrices . XRMT exhttps://www.volumegraphics.com/en/products/myvgl.html). All images were reduced to 16-bit unsigned size with two intensity ranges. Approximately 300 scans were segmented to obtain similar-sized rectangular (73.115 × 36.55 × 73.115 mm) regions of interest from the center part of the sample to provide uniformity. The reconstructed microtomographic images were viewed at nominal resolution 73 μm, 2.7x magnification, and 6000 resolution along each edge of the voxels in the 3D arrays—voxel size for all images was 0.0731891 × 0.0732624 × 0.0731891 mm. Porosity was calculated as the percentage of voxels designated as pores divided by the total volume of interest  with 5 kg load cell. The middle bread slices of 25 mm thickness underwent a compression cycle up to 40% deformation of their original height with a 35 mm flat end cylindrical aluminum probe (P/35). The compression test was performed using the selected settings of 1 mm s−1 pre-test, 1.70 mm s−1 test speed, and 10 mm s−1 post-test speed with 5 g trigger load and 40% strain. Five replicates were analyzed for each sample, and hardness in force (N) was estimated.Texture analysis of bread was conducted according to AACC method 74.09  of fresh2.2.10The sensory analysis test was conducted using the 9-point hedonic scale on plasma-treated, control, and commercial samples. The samples were served to 20 semi-trained panelists from a population of staff and students at Technological University Dublin ranging from age 20–35 years of age. A 9-point hedonic scale was estimated to measure participant's preference of the samples. The samples were coded randomly, presented in identical containers, and served to panelists for evaluation. The panelists were given categories converted to numerical scores ranging from 1 to 9, with 1 representing dislike extremely and 9 as like extremely . The sen2.2.11Statistical analysis was performed using SPSS software . The results were analyzed using one-way ANOVA. The significance among the samples was compared at p ≤ 0.05 by Tukey HSD test post-hoc comparison. All the tests were performed in triplicate, and the average of the tests are presented.33.1The bread was formulated from each of the control and plasma functionalized flours. The plasma-treated flour behaved similarly to the control during the whole bread development process, as shown in The increase in expansion ratio can be related to the improvement in the gluten structure of flour and hydration properties. Many authors noted the improvement in flour hydration and hydration capacity due to plasma treatment , 2017. T2 and promote bread volume rise   . In cont2, 5%N2) . A decreThe protein content of the samples was in the range of 6.53–7.2%, while the ash content was in the range of 0.806–0.975%, and the fat value was in the range of 0.976–0.983%. No change in carbohydrate, protein, fat, and ash content of both control and plasma-treated wheat flour was observed. Thus, this plasma treatment did not affect the macro-nutritional properties of wheat flour.3.3The color of the bread crust treated with plasma treatment after 5 min also showed a change in color, suggesting the oxidation of reducing sugars by the reactive oxygen species, thus preventing the Maillard reaction between glucose and amino acids which causes browning of bread. A similar increase in the lightness value of bread was reported for ozonated flour bread . Wheat f3.4The water activity value for both crust and crumb of bread samples are shown in Based on the color analysis, control bread was darker in color than plasma-treated bread, suggesting the possibility of the Maillard reaction. The Maillard reaction occurs in three stages; in the first stage, the carbonyl group on a sugar reacts with a protein or amino acids producing glycosylamine and water, the glycosylamine compound further isomerises to give ketosamine by amodari rearrangement. In the third and final stage, the ketosamine reacts in different steps giving a brown-colored complex compound . In the 3.5The farinograph is used to assess the baking quality and performance in plasticity and mobility of wheat doughs and records the behaviour of dough against the mixing action of blades over time and at a specific constant speed and temperature . The resThe dough development time is the time required for the top of the curve to reach 500 FU line. It refers to the initial water addition and the point at which the dough seems to have the best elastic and viscous qualities for gas retention  and is aThe dough stability represents the time during which the maximum dough consistency does not change significantly, and it is an indication of flour's tolerance to mixing . The douThe degree of softening measures the dough resistance to mechanical stirring. The cold plasma treatments did not significantly affect the degree of softening of the dough. A high water absorption capacity with a lower degree of softening indicates good quality flour . A decreFarinograph Quality Number was also significantly (p < 0.05) improved with the application of plasma treatment. Overall, plasma treatment improved farinograph dough quality by affecting the behaviour of protein and starch fractions in wheat flour.3.6Food microstructure is defined as the study of the spatial arrangement of its structural arrangement and their interactions . Food miThe microstructure of bread depends on physical and chemical changes occurring on flour components  and different processing stages. Three main processing stages that affect bread's microstructure are mixing, fermentation, and baking. Mixing is crucial in creating and retaining small cylindrical air cells inside the food matrix, which defines its cellular structure. The dough oxidation is enhanced by air incorporated in bread, which aids in forming disulfide bonds and ultimately forming a gluten network. During fermentation, the released carbon dioxide moves towards gas nuclei leading to dough expansion and volume increase in the dough. Finally, the dough is subjected to high temperature during baking, leading to gas expansion and stretching of dough. Expansion of dough during baking is dependent on protein coagulation and starch gelatinization. Water availability plays an essential factor for protein coagulation during baking . Hence, 3, and with increasing treatment time, air void volume has declined. The air void volume followed the same trend when the bounding box was removed. The porosity of control bread was 54.44%, and for PFFB-30 was 45.2%. 3.7The textural properties of bread were calculated from the force-time graph using texture profile analysis. Significant variations in the hardness value for control bread (6.794 ± 0.45 N) and PFFB-30 bread (7.85 ± 0.3 N) were noted. The hardness value of pan bread with traditional strain was 736 g . Though 3.8While the porosity of the bread decreased with the application of cold plasma treatment compared to control, texture (by touch) did not show a significant difference in the PFFB-30, control, and market sample 7.00 ± 1.18, 7.55 ± 0.86, and 6.40 ± 1.59. Visually, the variation in the porosity did not affect the panelist's perception of the bread crumb texture. The improvement in the water activity of bread crumb can indicate that the panelist did not find the bread to be denser. The PFFB-30 rated lowest on the aroma of crumb (4.8 ± 1.96) compared to others; very few panelists preferred the smell of bread, while others did not favor it. The possible reason for PFFB-30 aroma can be the smell of Vitamin B3 (Niacin) which was a part of the composition of flour. Niacin is a derivative of pyridine, which has an unpleasant fish-like smell . A 20% iThe literature on sensory analysis of non-thermally treated flour bread is scarce. The sensory evaluation of bread formed from the HPP treated dough (50–250 MPa) had consumer acceptance up for 50 and 100 MPa treated dough only. The HPP treatment altered the textural properties of bread, giving a croissant-like texture . Singer 4Plasma functionalized ingredients can be exploited to form cereal-based products. In our study, pan bread was developed from plasma functionalized flour treated for 5–30 min at 80 kV air plasma. The expansion ratio of PFFB was greater than that of control, making the bread rise higher during baking. The results further revealed that the water activity of PFFB crumb was higher and color analysis suggested a decrease in L* value compared to control. It should be noted that PFFB scored well among different sensory attributes with no significant difference in overall acceptability when compared to the control and retail samples. The farinograph characteristics of plasma-treated wheat flour dough provided useful information on modifying the effect of plasma treatment on the behaviour of the dough, indicating an improvement in dough properties like water absorption capacity, dough development time, and stability time.XRMT is a non-destructive analysis of the 3D microstructure of food matrix and their interaction with processing parameters in such a way as to allow not only a fundamental understanding of the process but also a process design resulting in specific desired microstructures. In general, the XRMT images of PFFB demonstrated no change in the gluten network and an increased porosity by comparison with control. Thus, cold plasma treatment did not adversely affect the dough structure formation. Cold plasma has been studied previously to enhance the functionality of a different variety of wheat flour; our present work further confirms that a new product with improved properties can be developed from plasma functionalized flour. Thus, cold plasma can be exploited as a more sustainable alternative to current FDA-approved additives to improve dough properties and, subsequent to thorough sensory testing, can prove a useful tool in new product development and new ingredient formulations.Sonal Chaple: conceived the work, Formal analysis, Writing – review & editing, wrote the manuscript, reviewed and edited the manuscript. Chaitanya Sarangapani: Formal analysis, Writing – review & editing, conceived the work, analyzed data, wrote the manuscript, reviewed and edited the manuscript. Shannon Dickson: Formal analysis, Writing – review & editing, conceived the work, analyzed data, wrote the manuscript, reviewed and edited the manuscript. Paula Bourke: Writing – review & editing, conceived the work, wrote the manuscript, reviewed and edited the manuscript.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.