Aeruginosins, a family of nonribosomal linear tetrapeptides discovered from cyanobacteria and sponges, exhibit in vitro inhibitory activity on various types of serine proteases. This family is characterized by the existence of the 2-carboxy-6-hydroxy-octahydroindole (Choi) moiety occupied at the central position of the tetrapeptide. Aeruginosins have attracted much attention due to their special structures and unique bioactivities. Although many studies on aeruginosins have been published, there has not yet been a comprehensive review that summarizes the diverse research ranging from biogenesis, structural characterization and biosynthesis to bioactivity. In this review, we provide an overview of the source, chemical structure as well as spectrum of bioactivities of aeruginosins. Furthermore, possible opportunities for future research and development of aeruginosins were discussed. Water eutrophication is a phenomenon of water pollution caused by the enrichment of nutrients containing phosphorus and nitrogen in water, which is manifested by the abnormal reproduction and growth of algae and other plankton, the reduction of dissolved oxygen in water and the death of a large number of aquatic organisms . Due to Microcystis ..MicrocysMicrocystina aeruginosa collected from a fish pond in Kibbutz Geva, Israel, with 70% methanol. Five novel natural products, aeruginosin GE686, GE766, GE730, GE810 and GE642 (14)   5], as , as Micr642 (14)  [4], KY6642 (14)  [7] and 642 (14)  8], wer, werMicrPlanktothrix agardhii (NIES-205) from cyanobacteria collected from Lake Kasumigaura, Japan. Extracts with significant inhibitory activities on trypsin and thrombin were obtained by mass cultivation, and two new types of aeruginosins, aeruginosin 205A and 205B  , an, an27], The second position of the N-terminus of aeruginosin is occupied by variable hydrophobic amino acids, relatively abundantly by leucine (Leu) and isoleucine (Ile), followed by phenylalanine (Phe), tyrosine (Tyr) and homotyrosine , while vChoi is one of the characteristics that distinguishes aeruginosin from other peptide compounds . It has The C-terminal site is occupied by a variable arginine residue that presents a variety of types: the first is an argininol reside generated by the reduction of the carboxyl group of arginine to the hydroxyl group (aeruginosa 298-A)  18]; th; th18]; Aeruginosin 205A and 205B  are the The distribution and functions of nonribosomal peptide synthases (NRPSs) and polyketide synthases (PKSs) have been extensively studied. They are two similar types of assembly machinery composed of multi-functional megaenzymes that are responsible for the synthesis of nonribosomal peptide (NRP) and polyketide (PK), respectively, in plants, bacteria and even fungi ,39,40,41PKs have a wide range of bioactivities due to their structural and functional diversity, but their biosynthetic mechanisms are similar. The generation of their core structures is catalyzed by PKS. PKS can be classified into three types based on their compositions, namely type I (modular), type II (iterative) and type III  . Type I The backbone of NRP is synthesized by an assembly line consisting of multiple modules. A typical NRPS is composed of several modules in a certain order, typically 4 to 10 modules and some even 50 modules. Different domains with various enzymatic activities are responsible for assembling specific monomers to the nascent peptide chain. A standard module consists of three core domains, adenylation (A) domain, thiolation (T) domain/peptidyl carrier protein (PCP) domain and condensation (C) domain ,44,45. IThe iterative reaction process for the extension of the peptide chain of NRP mainly is: (1) the pantoyl–thioglyamine (Ppant) arm is tethered in the T/PCP domain A; (2) thBoth NRPS and PKS belong to a megasynthetase assembly line composed of multiple modules, using similar strategies for synthesis. Recently, some secondary microbial metabolites have been shown to require both NRPS and PKS domains to participate in the synthesis . With thIn the initial step of aeruginosin biosynthesis, the modules responsible for the addition of α-keto acid include adenylation (A), ketoreductase (KR) and the peptidyl carrier protein (PCP) domains. The A domain uses a hitherto not fully revealed mechanism to specifically select α-keto acids, distinguishing them from α-amino acids and α-hydroxy acids: aspartic acid in contact with the α-amino acid in the amino acid selective A domain is substituted with a hydrophobic residue in the α-keto acid selective A domain . The α-kaerM gene, AerM is responsible for the C-terminal extension of aeruginosin, and the R domain of AerM is responsible for the formation of the C-terminal structure of aeruginosin and the release of the final product [aerM gene is synthesized by AerH to generate Aeap residue [aerO, aerP and aerQ genes have so far only been found in the varlaxin biosynthesis gene cluster [AerB, AerG, AerD, AerE, and AerF are found to be present in the biosynthesis pathway of all the aeruginosins discovered and perform similar functions, being responsible for peptide intermediate assembly and Choi precursor synthesis . In the  product ,61  of aeruginosin 865 was inferred from the BGC of nostopeptolide A1 to contain homologs of eral BGC   is not only affected by external environmental factors but is also closely related to metabolism status . The coaFew low molecular-weight natural product is a selective inhibitor of thrombin at present. Because of the strong inhibition of coagulation factors exhibited by aeruginosin, it has become a key candidate in the development of anticoagulants. To date, nearly 100 compounds of this family have been isolated, many of which are thrombin inhibitors . The binTrypsin is an enzyme that plays a major role in the digestion of food but also has important functions beyond that in the digestive system . Cancer Nodularia spumigena HKVV in 1997, has been reported to inhibit human trypsin at low micromolar concentrations  and identify important structural motifs for various biological activities.During a long evolutionary process, microorganisms have relied on linear combinations of various types of domains to obtain thousands of NRPSs, thus creating a diversity of structures and functions of NRP natural products. To date, even though many members of the aeruginosin family have been isolated, more compounds are still needed to deepen the understanding of the NRPS-PKS synthetic pathway . The modThe synthesis of aeruginosin has greatly facilitated the creation of novel analogs applied to the healthcare of humans, which is the focus of future research. As a serine protease inhibitor, future perspectives of aeruginosin studies should focus more on finding better activity as well as solving the structural and pharmacological aspects of these compounds to enable better efficacy. At the same time, the sustainable production or engineering application of aeruginosin drugs through the heterologous expression of genes involved in the aeruginosin biosynthesis pathway will be a promising alternative for future chemical de novo synthesis. It is hoped that safe and effective drugs can be synthesized in this field in the future to treat common life-threatening diseases such as thrombosis and cancer.Altogether, we have provided a detailed and comprehensive overview of the studies of aeruginosins in terms of biogenesis, structural diversity, biosynthesis and multiple bioactivities, which pave the way for the preclinical trials of these highly diverse nonribosomal linear tetrapeptides probably carried out in the future.


The results indicate that the addition of Mn contributes to the precipitation of Al-Mn-RE intermetallics and the refinement of α-Mg matrices, thereby improving the tensile strength of the 1.0 Mn alloy at 190 MPa. The fracture mechanism of Mn-containing alloys transforms from a cleavage fracture to a ductile fracture as the Mn content increases from 0.3 to 1.0 wt.%. The presence of intermetallic particles in the dimples confirms the hindrance effect of Al10Mn2  on dislocation slips. The novel technology of in-grain misorientation axes (IGMAs) is used to identify activated slip modes and deformation twins. It can be concluded that the activated pyramidal <a> slip during tensile deformation significantly promotes the ductility of the 1.0 Mn alloy with an elongation rate of 9.8%. It is worth noting that reducing the coarse The effects of Mn addition on the room temperature tensile strength and deformation mechanisms of as-cast Mg-8Al-1Nd-1.5Gd- The add<a> slip, which can significantly improve the plastic deformation ability of the alloy. It is of great significance for improving the ductility of heat-resistant Mg-Al-RE alloys. In this work, the influence of Mn modification on the mechanical properties of Mg-Al-RE alloys was discussed. The addition of Mn promotes the precipitation of various intermetallic compounds while promoting the activation of prismatic (1)β-Mg17Al12 into dispersed granular divorced eutectic particles and promotes the precipitation of Al10Mn2  and Al8 Mn4.The addition of Mn transforms continuous island-shaped eutectic (2)10Mn2  particles can effectively hinder the slip of intragranular dislocations and alleviate stress concentration at grain boundaries. The dimples in the tensile fracture confirm the transition of the fracture mode from cleavage fracture to ductile fracture.The Al(3)<a> slip and deformation twinning enhances the plastic deformation ability of the Mg-Al-RE alloy.The combination of activated prismatic


Relative density clouds employ k-nearest neighbor density estimates to provide information about group differences throughout the entire distribution of the variables. The method can also be used to decompose overall group differences into the specific contributions of differences in location, scale, and covariation. Existing relative distribution methods offer a flexible toolkit for the analysis of univariate differences; relative density clouds bring some of the same advantages to fruition in the context of multivariate research. They can assist in the exploration of complex patterns of group differences, and help break them down into simpler, more interpretable effects. An easy-to-use R function is provided to make this visualization method widely accessible to researchers.This paper introduces  Traditional approaches to group comparisons focus on differences in central tendency  and, less often, dispersion . Important as they are, these summary indices only convey limited information about the shape of the distributions. Differences in central tendency, dispersion, and other aspects of the distributions  often combine to yield complex patterns of group differences, with different groups being over- or under-represented at different locations. For example, researchers are sometimes interested in directly comparing the representation of two groups at the distribution tails, which can be done with specialized indices such as l ratios , 2 or th[S-index . As distrelative density plots such as those in The general task is to devise ways to effectively compare the shape of two distributions across the entire span of the relevant variables, and (if possible) identify the specific contributions of differences in means, variances, and other features to the overall pattern. In univariate contexts, relative distribution methods –6 offer location component that reflects the effect of shifts in location  and a residual shape component adjusted for both location and scale. In principle, this approach can be extended to yield finer-grained decompositions involving higher moments of the distributions .Relative distribution methods can be used to perform various kinds of statistical inference, and to decompose the overall pattern of differences  into a lribution . The shacentroids  can be quite informative in many scientific contexts. Drawing from my own interests, researchers who study psychological sex differences are increasingly tackling multivariate questions—for example about the overall distance between the sexes in multivariate space, or the proportion of overlap between the male and female distributions  method to estimate the relative density at each probe; and (3) plot the probes in paired bivariate scatterplots, using color and transparency to visualize local patterns of relative density.In this paper, I make a contribution in this direction. I introduce a simple yet powerful method to visualize the relative density of two groups in multivariate space, and explore the specific contributions of differences in location, scale, and covariation among variables. The procedure consists of three steps: (1) sample the multivariate distribution at a large number of locations, or “probes”; (2) use a relative density cloud, which can be explored through its two-dimensional projections in the bivariate scatterplots, as exemplified in The result is a multivariate rdclouds), which is available at https://doi.org/10.6084/m9.figshare.21743537.To make this new visualization tool widely accessible, I provide an easy-to-use R function . Each probe is a point in d-dimensional space, where d is the number of variables in the dataset.The first step of the method is to “probe” the multivariate distribution of the data at a large number of locations. The rdclouds function uses a multivariate normal distribution for jitter, with a default standard deviation of 0.2 times the SD of each original variable. Users can change the amount of jitter by specifying a different multiplier or eliminate it altogether if preferred.The most straightforward way to generate probes is to sample with replacement from the empirical dataset (both groups combined); especially when sample size is comparatively small, coverage can be improved by adding a small amount of multivariate jitter to each point. Jittering helps probe the sparser regions of the distribution more thoroughly, and yields smoother and more readable clouds. At the same time, it tends to “blur” the distribution to some extent, and may partially mask certain features such as floor/ceiling effects, sharp discontinuities, or quantization of the variables. The latter effect is visible in some of the scatterplots of ks [k-NN density estimates. Specifically, the rdclouds function employs a k-NN estimator discussed in [k is the number of nearest neighbors, N is the sample size, d is the number of dimensions, Vd is the volume of the d-dimensional unit ball, and ‖xj(x) − x‖ is the Euclidean distance between the point x at which density is evaluated and its j-th nearest neighbor xj(x). Nearest-neighbor distances are obtained with the RANN package [rdclouds equalizes the group Ns before density estimation, by sampling (with replacement) from the smaller group the number of cases needed to reach the size of the larger one. To estimate the relative density at each probe, the function simply computes two k-NN estimates separately for the two groups and takes their ratio . With equal Ns in the two groups, the formula to calculate the relative density of groups A and B at probe x becomes:Estimating the density of multivariate data is a computational challenge for standard methods such as kernel density estimation (KDE). With more than a few dimensions, the increase in computational cost makes these methods highly inefficient if not prohibitive. For this reason, most available R packages are limited to one or two dimensions, with a maximum of 6 dimensions in package ks . To overussed in :f^x=1N package . In prack) is a problem that lacks a simple, unambiguous solution. Two common rules of thumb found in the applied literature on k-NN methods are (a) the square root of the sample size and (b) 1–2% of the sample size [rdclouds function uses the square root of N as the default number of neighbors, but users can choose a different rule (1% of N) or directly specify their preferred k. Especially when sample size is comparatively small, increasing k above the suggested value can “smooth out” the estimates, reducing sampling noise and hence yielding cleaner, more readable clouds. However, larger neighborhoods also become less local , and excessive values of k may end up obscuring local patterns of variation. For a simple illustration, consider the following scenario . If datncreases –22. Alsok-NN estimates, especially in low-density regions of the distributions. As I explain below, relative density clouds use transparency to reduce the visual influence of ratios close to one, and emphasize more extreme ratios that are less likely to arise by chance. Still, it can be useful to perform a diagnostic check by comparing the distribution of estimated relative densities with a “null” distribution produced only by stochastic processes. By default, the rclouds function draws diagnostic plots like the one shown in k, especially when sample size is comparatively small (see above).Even if the two groups being compared come from the same distribution, local relative densities can fluctuate away from one owing to sampling error and noise in the rdclouds, the alpha value of each point is inversely proportional to the logarithm of the corresponding density ratio, so that a density ratio of one corresponds to alpha = 0 (maximum transparency) and the most extreme observed ratio—in either direction—corresponds to alpha = 1 (maximum opacity). The most extreme ratio can be identified globally  or locally (within each plot). Using the global maximum as a reference makes it easier to visually compare the relative strength of different effects within a given decomposition sequence (see below). The logarithmic scaling of transparency acts as a “compression filter” that limits the potential influence of isolated outliers, further contributing to reduce noise in the plots.Once relative densities are estimated, the probes are plotted at their respective coordinates, with different colors to indicate whether density is higher in one or the other group, and transparency inversely related to the extremity of the ratio. In rdclouds include the numeric range of the relative densities that are displayed graphically in the clouds  background information about the absolute density of the distribution , and (b) information about the relative density of the two groups (from the color/shade of probes). Decoding this information is made easier by judicious graphical choices about the colors used for plotting, the size/number of the points in each plot, and so forth. To further assist interpretation, the plots generated by rdclouds offers three decomposition sequences, labeled LS , LSS , and LSCS .One of the most attractive features of relative distribution methods  is the aIn Figs rdclouds replicate the basic decomposition procedure described in [The first two sequences available in ribed in , 5. In tAs expected, the location component displayed in and variances?” The LSS decomposition helps separate the specific contribution of differences in scale from that of other features such as skewness, kurtosis, etc. It is particularly useful when there are reasons to anticipate the existence of systematic differences in variability between the two groups .Inspection of covariation component and a location-, scale-, and covariation-adjusted residual shape component . The LSCS sequence extends the approach presented in  to isolaomponent . This isNB × d data matrix XB into a matrix NA × d data matrix XA. The first step is to whitenXB with an appropriate whitening matrix WB. Function rdclouds employs the ZCA-cor whitening transformation, which maximizes correlations between the original and transformed variables [whitening [XB isR is a correlation matrix and V is a diagonal matrix of variances. Whitening XB yields a whitened data matrix ZB, in which the variables are all orthogonal with unit variance:More formally, the goal is to transform the centered ariables  (performhitening ). The ZCcoloring matrix. The ZCA-cor coloring matrix for XA is ZB into The inverse of a whitening matrix is known as a XA.Matrix The covariation component of the relative density can now be computed as the ratio between the mean-shifted, rescaled, and colored distribution of group B and its mean-shifted and rescaled version . Then, tIn the personality example, the effect of covariation shifts is rather weak, as is apparent from the plots in rclouds include only one shape component as the last step in the sequence, following the approach outlined in [The default decomposition sequences in lined in . HoweverRelative density clouds are a flexible visualization tool for the study of group differences. Just like other powerful exploratory methods, they require careful interpretation; researchers must be aware of potential pitfalls to avoid drawing incorrect or unwarranted inferences.d-dimensional point that appears—with the same color and transparency—in all the two-dimensional projections within a plot . Stated differently, bivariate scatterplots show two-dimensional projections of a single cloud of multivariate relative densities calculated simultaneously over the entire set of variables, not bivariate relative densities calculated on two variables at a time. If one intends to visualize bivariate densities, one must run the procedure on subsets of two variables each.The first thing to keep in mind is that relative density clouds are multivariate objects; each probe is a To illustrate, d-dimensional cloud in cannot be visualized in its entirety; but it can be inferred—in partial “chunks”—by triangulating among multiple two-dimensional views of the probes. This point applies in full when one is trying to locate and characterize a specific region of the distribution. Sometimes, there are clusters of probes that remain clearly identifiable throughout multiple plots, as for example the dark blue probes with high Agreeableness, Conscientiousness, Extraversion, and low Neuroticism in compatible with certain patterns in the data, rather than dispositive; investigators should then use other sources of information  to substantiate their exploratory findings.The example discussed in the previous section illustrates the more general point that individual scatterplots should not be interpreted in isolation. When there are more than three variables, the shape of the all the rectangles and hence satisfy the investigators’ criterion of “low scores across the board”.For a different perspective on the same problem, consider some of the projections of the hyperrectangle—like the vast majority of the female-biased probes—does not actually lie within the hyperrectangle itself.  Hence, A closer inspection of tail region, where density is comparatively low. That is, the majority of the points move far away from the centroid, along a progressively thinner “shell” that envelopes a mostly empty interior [distance concentration and contributes to limit the usefulness of k-NN methods in high-dimensional problems [effective dimensionality of the dataset equals the number of variables. As correlations among variables increase, the effective dimensionality shrinks, with the result that (Euclidean) distances grow less steeply and concentrate at a slower pace [Whenever one is dealing with more than a handful of dimensions, it is important to remember that our geometric intuitions—which are based on the experience of a three-dimensional world—can become seriously misleading when they are applied to high-dimensional spaces. For an illustration of the counter-intuitive phenomena that take place in high dimensions, consider a normal distribution of points. In the familiar uni- and bivariate cases, the mass of the distribution clusters around the mean/centroid, and only a small proportion of points are located in the tails. But as dimensionality increases, a larger proportion of the probability mass becomes concentrated in the interior . While tproblems , 26, 27.wer pace , 28.High-dimensional phenomena are not merely statistical curiosities, and affect multivariate data analysis in all sorts of ways , 27. ThiThe issue of measurement artifacts is not unique to relative density clouds, and is just as relevant to the interpretation of univariate relative density plots such as the one in Earlier in the paper, I noted that Agreeableness scores show a marked ceiling effect, which likely contributes to inflate the apparent size of sex differences in scale see Figs –6. NaturThe study of multivariate group differences presents a host of challenges, but also many opportunities for methodological innovation. Relative density methods offer a powerful, flexible toolkit for the analysis of univariate differences . In this paper I introduced a novel visualization method that brings some of the same advantages to fruition in the context of multivariate research. Relative density clouds can assist in the exploration of complex patterns of group differences, and help break them down into simpler, more interpretable effects. They should prove useful to researchers in disparate fields who are looking for new sources of insight into their data.S1 File(PDF)Click here for additional data file.


For GF bread, increasing white sorghum flour levels in the blends primarily affected specific volume, color, flavor and texture characteristics, leading to decreases (p ≤ 0.05) in specific volume, cohesiveness, springiness, chewiness and moistness, but increases (p ≤ 0.05) in color intensity, brown and nutty flavors, graininess and roughness. White sorghum flour could be used in the blends at the maximum level of 25% to get a good bread volume without sacrificing texture quality.This research studied the effects of white sorghum flour levels at 0, 10, 25, 40, 70, 85 and 100% in the matrix of rice and tapioca flours and corn starch on the properties of flour blends and the qualities of gluten-free (GF) bread. Single and composite flours were analyzed for moisture content, color and pasting properties. GF bread samples prepared from composite flours were analyzed for specific volume, moisture content, water activity, crumb color and instrumental texture. Sensory profiles of the breads were determined by nine trained descriptive panelists. The results show that increasing the sorghum flour content increased ( Bread is one of the most widely consumed foods worldwide due to its nutritional quality, convenience and palatability . Wheat fSorghum bicolor L. Moench) has recently received increasing attention for its GF nature [Studies have been conducted to promote the utilization of GF flours or starches from locally grown crops such as sweet potato , corn 66,7,8, caF nature . SorghumF nature . MoreoveF nature . In addiF nature . SorghumF nature . As a woF nature ,9,10,16.Manihot esculenta Crantz) to facilitate the development of a cohesive crumb network that traps gas bubbles and prevents crust collapse [Hager and Arendt  and Siddcollapse . Akin ancollapse  observedcollapse , GF breacollapse .Depending on the color of grain pericarp and endosperm, sorghum can be classified as white, yellow, brown, red or black. White sorghum flour is considered a favorable substitute for wheat flour in breadmaking due to its neutral color and flavor, as well as its highly resistant starch content ,21,22,23The current research used white sorghum flour in combination with rice and tapioca flours and corn starch for the preparation of GF bread. Based on the authors’ knowledge, such a flour/starch combination has not been studied before. The specific objective of this research was to determine the effects of sorghum flour levels in the blends of rice and tapioca flours and corn starch on the physicochemical properties of flour blends, as well as their effects on the physicochemical and sensory properties of GF bread. The findings from this research should offer new GF flour formulations, and should play an important role in promoting the utilization of sorghum flour in GF products.White sorghum grains of KU 804 cultivar were provided by the National Corn and Sorghum Research Center, Kasetsart University, Nakhon Ratchasima, Thailand. The grains were sorted, washed, dried in a tray dryer  at 80 °C for 4 h, and then ground using a stainless-steel blender . The obtained whole sorghum grain flour was sieved through a stainless steel 80-mesh screen to get a particle size of 180 µm or less. According to the available literature, the KU804 white sorghum flour contained 9.47% protein, 0.32% lipid, 0.39% ash and 0.33% fiber . Rice flour , tapioca flour  and corn starch  were purchased from supermarkets. Pure refined sugar , salt , instant dried yeast , baking powder , xanthan gum , calcium propionate , unsalted butter  and eggs were acquired from supermarkets or bakery supply stores.Sorghum, rice and tapioca flours and corn starch were mixed at various proportions to form six GF composite flour treatments (T1–T6), as shown in Flour samples, both single and composite flours, were analyzed for moisture content, as well as color and pasting properties. Moisture content, an indicator of flour stability during storage, was determined according to the AOAC method 10.1.2 . Color vSix composite flour samples (T1–T6) and pure sorghum flour (T7) as shown in 3) was determined by the rapeseed displacement method according to the AACC method 10–05.01 [3) divided by loaf weight (g). Loaf volume (cm10–05.01  with a sw) were determined using the AOAC method 10.1.2 [Bread crumb samples taken from the center of the bread loaf were analyzed for moisture content (%), water activity and color. Moisture content and water activity  was performed to determine crumb hardness (N), cohesiveness, gumminess (N), chewiness (N), springiness and resilience using a texture analyzer  equipped with a P/36R stainless steel probe. The bread loaf was cut into slices (2 cm thickness) and three slices taken from the center of the loaf were cut into cubes (2 × 2 × 2 cm) . The meaBread crumb samples for sensory testing were prepared similarly as described for instrumental texture analysis. A traditional descriptive test, according to a profile method adapted from Keane  and usedThe procedure of the descriptive test involved using a trained panel to develop a sensory lexicon, which took three 3 h sessions in this study. Panelists individually evaluated bread crumb samples and identified terms for describing the flavor, texture and aftertaste characteristics of the samples. Then, they discussed possible terms, and compiled a list of attributes for testing. The panel also discussed and reached agreement on attribute definitions, references, reference intensities and evaluation procedures. Subsequently, the process continued with two 3 h training sessions in which the panelists practiced scoring each attribute on a 15 cm line scale, with 0 meaning “none” and 15 meaning “extremely high”. Thereafter, product testing was performed in five 3 h sessions. Two replications were evaluated for each of the seven bread crumb samples and serving order was randomized within each replication. Each panelist received 4 pieces (2 cm cube) of each sample in a plastic cup covered with a lid and labeled with a three-digit blinding code. After tasting the sample, the panelists rated the intensities of all attributes on the 15 cm line scales. References were provided during evaluations to anchor values on the scales. Reverse osmosis deionized water was provided for panelists to cleanse their palates between samples. The panelists had at least a 10 min break after each sample evaluation. All evaluations were conducted in the sensory facility at KUSCR. The rooms were air conditioned (25 °C), had appropriate lighting and had no extraneous odors. The testing room was separated from the sample preparation room.p > 0.05) were excluded from the data set before performing PCA and HCA. The statistical software used for ANOVA, DMRT and correlation analysis was IBM SPSS Statistics version 28.0 , and that for PCA and HCA was XLSTAT version 19.6 .Analysis of variance (ANOVA) was performed to determine differences among samples. For physical and chemical data, the effects of sample and replication were included in the ANOVA model. For sensory data, the effects of sample, replication and panelist, and an interaction effect between sample and panelist, were included in the ANOVA model. Duncan’s multiple range test (DMRT) was then used in the mean comparisons. Correlation analysis was conducted to investigate relationships among the quality parameters of flour and bread samples. A significance level of 0.05 was adopted in this research. Principal component analysis (PCA) with varimax rotation was performed to uncover relationships between sensory attributes and bread samples. Hierarchical cluster analysis (HCA) was conducted using Ward’s minimum variance method to classify bread samples into subgroups with similar sensory characteristics. Non-significant attributes (p ≤ 0.05) moisture contents than tapioca flour and corn starch (The sorghum and rice flours had slightly lower (n starch . As a rep ≤ 0.05), while a* and b* values increased (p ≤ 0.05), with each increase in sorghum flour level. Sorghum flour had a darker color than other single flours/starch. It had the lowest L* (lightness) value and the highest a* (redness) and b* (yellowness) values . Althougp ≤ 0.05) for rice flour, followed by sorghum flour, corn starch and tapioca flour, respectively (p ≤ 0.05) for sorghum flour, followed by rice flour, corn starch and tapioca flour, respectively. According to Akin and Miller [p ≤ 0.05) for sorghum flour as compared to other single flours/starch. This indicates that sorghum flour paste was the most stable under heat and high shear conditions [p ≤ 0.05) for sorghum and rice flours than for tapioca flour and corn starch. The higher final and setback viscosities indicate higher starch retrogradation and syneresis rates, which could adversely affect the texture, especially the softness, of bread after baking and during storage [For pasting properties, the results show that pasting temperature and peak time were the highest  depending on the proportions of sorghum flour in the blends (p ≤ 0.05) for flours with sorghum (T2–T7) than without (T1). The pasting temperature increased (p ≤ 0.05), while peak and breakdown viscosities decreased (p ≤ 0.05), with increased sorghum flour. Setback viscosity varied slightly among composite flours with 0–40% sorghum flour (T1–T4). As the amount of sorghum flour in the blends increased to 70% (T5) and 85% (T6), the setback viscosity of the blends increased dramatically (p ≤ 0.05). Composite flours T5 and T6 even had higher setback viscosities than pure sorghum flour (T7), and the results could be attributed to the interaction of sorghum flour with other flours in the blends, especially rice flour.The pasting properties of flours T1–T7 used for breadmaking varied (e blends . The peap ≤ 0.05) with increased sorghum flour, particularly at 70% levels and onwards (p ≤ 0.05), but positively correlated with peak viscosity . Additionally, sorghum proteins, specifically kafirin, were hydrophobic and lacked the ability to form a cohesive, gas-holding viscoelastic dough, because their polypeptide chains were too short and too tightly cross-linked together [The loaf specific volume decreased  lightness (L*) and yellowness (b*), while increasing (p ≤ 0.05) redness (a*), in the bread crumb (p ≤ 0.05) between color values of flour blends and those of bread crumb samples . Consumers in certain countries such as Germany and Eastern Europe associated dark crumb color with healthiness [Regarding color, increasing the sorghum flour levels in the blends reduced  of all GF bread samples varied in a narrow range of 43.00–44.65% and 0.880–0.894, respectively (p > 0.05) effect on moisture content and aw of breads. The moisture content and water activity (aectively . Statistp ≤ 0.05) were found for almost all parameters except gumminess. Hardness tended to increase with increased sorghum flour, particularly at 40% levels onwards. The increase in hardness corresponded to the decrease in bread specific volume (p ≤ 0.05). Other studies [c volume  and the c volume . Crumb h studies ,49 also p ≤ 0.05) as the proportions of sorghum flour increased (p ≤ 0.05). According to De Alcântara et al. [Unlike hardness, cohesiveness and cutting force decreased  among the samples. Large differences across the samples were detected for cohesiveness, springiness, moistness and graininess, suggesting the higher impact of sorghum flour on texture than on the flavor characteristics of GF breads.The mean intensities of all 40 sensory attributes of GF breads with varying sorghum flour levels in flour blends are given in The results from PCA indicate that significant attributes divided into two key dimensions explained 86.08% of total variability , as shown in p ≤ 0.05) in terms of dairy product flavor, cohesiveness, toothpull, and moistness, but lower (p ≤ 0.05) in terms of brown, dry and roughness than the rest of the samples  were rated higher ( samples . In addip ≤ 0.05) intensities in brown, dry and roughness, but lower (p ≤ 0.05) intensities in dairy product, vanilla, cohesiveness, toothpull and moistness (The bread with 40% sorghum flour (T4) was differentiated from the samples in cluster 1 by higher (oistness . AdditioAs the proportions of sorghum flour increased from 40% (T4) to 70–100% , further increases in brown, dry, nutty, bitter, graininess, brown aftertaste and dry aftertaste, along with decreases in cohesiveness, springiness and moistness of breads, were observed . The levThe effects of sorghum flour on flavor characteristics—specifically musty, nutty and bitterness—have been documented in the literature. The increase in a musty note with increased sorghum flour levels could be linked to the volatile compounds present in sorghum grains, such as geosmin, 1,2-dimethoxybenzene, 3-octanone and 1,2,4-trimethoxybenzene , while tThe decreases in cohesiveness, springiness and moistness and the increase in graininess of breads with increased sorghum flour levels could have adverse effects on consumer acceptance, because the consumers generally preferred sorghum bread with moist crumbs, while they disliked the grainy texture . Other rp ≤ 0.05), while sensory springiness was positively correlated with instrumental springiness  and resilience . Sensory firmness and cohesiveness were in alignment with instrumental cutting force . Therefore, instrumental analysis could be used to evaluate these texture attributes in place of sensory analysis when expert panelists are not available. However, inconsistent results were observed between sensory and instrumental hardness values. A lack of relationship between the two data could be attributed to several reasons, one of which was the misleading similarity in language used by panelists and instrumentalists. They may use the same words but measure different properties [The current study also found significant relationships between texture properties that were evaluated by trained panelists and by a texture analyzer. For instance, sensory cohesiveness was positively correlated with instrumental cohesiveness  of white sorghum flour can be useful for exploring its application in other GF products. The sensory lexicon developed in this research can be beneficial to future research on the sensory characteristics of GF bread.


The underlying atomistic mechanism of deformation is a central problem in mechanics and materials science. Whereas deformation of crystalline metals is fundamentally understood, the understanding of deformation of amorphous metals lacks behind, particularly identifying the involved temporal and spatial scales. Here, we reveal that at small scales the size-dependent deformation behavior of amorphous metals significantly deviates from homogeneous flow, exhibiting increasing deformation rate with reducing size and gradually shifted composition. This transition suggests the deformation mechanism changes from collective atomic transport by viscous flow to individual atomic transport through interface diffusion. The critical length scale of the transition is temperature dependent, exhibiting a maximum at the glass transition. While viscous flow does not discriminate among alloy constituents, diffusion does and the constituent element with higher diffusivity deforms faster. Our findings yield insights into nano-mechanics and glass physics and may suggest alternative processing methods to epitaxially grow metallic glasses. How matter deform is a central question in mechanics of materials science. Here, the authors reveal a size-dependent deformation in amorphous metals changing at ~100 nanometer sample size from collective homogeneous flow to interface diffusion realized through individual atomic transport. At low temperature and high strain rates, deformation is highly localized in narrow shear bands. During such highly heterogenous shear banding controlled deformation, the vast majority of the sample’s atoms do not undergo any plastic strain and essentially all plastic strain is carried by the minute fraction of atoms4. At high temperature and low strain rates, deformation is essentially homogenous. Such dramatic transition in the deformation mechanism within amorphous metals has been explained by a crossover of the relaxation time, the internal time scale enabling structural rearrangements, and the external time scale, set by the applied deformation rate. The relaxation time exhibits a strong temperature dependence and is commonly described by the Vogel-Fulcher-Tamman (VFT) equation5. Microscopically, such homogenous viscous flow has been envisioned through a collective movement of atoms, which has been suggested to occur within flow units, also called shear transformation zones (STZs)9, although recent direct experimental evidence suggests atomic homogenous flow10.Deformation, the response to external stress through a change in configuration, has been a focus of metallurgy for almost 100 years. Plastic deformation in crystalline materials typically utilizes dislocations which slide in slip planes. For amorphous metals, the plastic deformation changes dramatically as a function of temperature and strain rate11. A correlation between viscosity (η), length scale (d), strain rate (v/d), and applied shear stress (τs), 12. This relation reveals that v decreases with decreasing d for constant applied stress. Even though molding of amorphous metals has been successfully realized down to small sizes ~10 nm14, this fundamental relation suggests the viscous flow mechanism to become less effective with decreasing length scales. Such predicted behavior points potentially to a change in deformation mechanism for amorphous metals on a small scale.Homogeneous flow enables thermoplastic forming, a unique method in metal processing17. For example, the size dependence of the deformation of amorphous metals at low temperatures and/or high strain rates has revealed a transition from highly localized and heterogenous deformation to a homogenous mechanism20. This transition is explained by a Griffith-like criterium18 and has provided valuable insights into energetics of shear band formations21. However, the study of the effect of size confinement on the deformation under processing conditions of homogenous deformation is still elusive and is the focus of the here presented research.To advance fabrication and use of amorphous metals, a fundamental and complete understanding of their size- and temperature- dependent deformation is requiredIn this study, we employ nanomolding techniques at elevated temperature region Fig.  to invesp) and temperature (T)23. The deformation in nanomolding is confined in a well-controlled nano-geometry defined by the mold, offering a powerful tool to investigate the deformation on the nanoscale as a function of size and temperature. Specifically, as practical examples for amorphous metal molding, we use three distinct glass forming systems: Ni45Pd35P16B4 bulk metallic glass (Ni-BMG), Zr46Cu46Al8 (Zr-BMG), and Pd43Ni10Cu27P20 (Pd-BMG), which we mold into cylindrical nanopore arrays with diameters (d) ranging from 10 nm to 250 nm. For the fabrication of nanowires, we consider a range of molding temperatures above and below the calorimetric glass transition temperature  can be determined by a modified Hagen–Poiseuille equation12:t is the molding time, γ is the surface tension of the BMG, and θ is the contact angle of the nanowire-mold interface. At high pressure, the effect of capillary force can be neglected  and the molding diameter (d) of various BMG systems at different temperatures  within the fixed molding time (t), with the length scale (d). Within the description of homogeneous flow   Fig. . A clear nm Fig.  and Pd-B nm Fig. .18. At large scales, it agrees with the widely accepted homogeneous flow-dominated deformation. In contrast, at small scales, we show here that deformation is no longer solely governed by homogeneous viscous flow.The existence of this transition in the here considered different BMG systems highlights a size-dependent mechanism in the deformation of amorphous metals, which challenges the previously accepted deformation categorization into the homogeneous deformation region45Pd35P16B4 samples formed through homogeneous flow region  and outside the homogeneous flow region . The EDS data of the nanowire formed through homogeneous flow exhibits a uniform element distribution from the feedstock and throughout the nanowire  and transmission electron microscopy (TEM) equipped with energy dispersive X-ray spectroscopy (EDS) to measure the composition profile of Ni-BMG nanowires. We examined Niire Fig. , EDS mapAlong with the contrast in the composition profile between nanowires formed in and outside of the homogeneous flow region is the contrast in their atomic structures Fig. . We obseL vs. d, with an increasing deformation rate with decreasing length scale  can be several orders of magnitude higher than the bulk diffusivity30. This interface process causes L to increase with decreasing d  is maximum at the known BMG composition and decreases with changing composition. If τcry falls below the experimental time scale (texp) due to changing composition, crystallization occurs  in Fig. τcry decreases. For the structure to remain amorphous, some compositional changes can occur as long as τcry > texp (blue (ii) in Fig. τcry < texp, where the nanowire starts to crystallize (blue (iii) in Fig. τcry), ensuring τcry > texp. As a result, the formed nanowire remains in its amorphous structure , the inize , collective atomic transport (viscous flow) dominates the deformation at all length scales ≥ 10 nm, while for T ≤ Tg, to Tg – 50 °C, a transition length scale (dc) between collective (red) and individual atomic transport regions (blue) is suggested  indicates the glass transition present under the applied nanomolding conditions 37. At temperatures above Tg – 30 °C, η increases rapidly following the Vogel-Fulcher-Tammann (VFT) equation of η = η0D*VFT is a fragility parameter and T0 is the temperature at which the relaxation time diverges. At T ≤ 287 °C, the supercooled liquid freezes into a glass, evidenced by a change of fragility, DI reveals a smooth function of temperature , Eqs. , Eqs.  and 2)  dc origidc is proportional to (η/(1/ DI))1/3, showing a similar non-monotonic behavior at 287 °C.Equation  reveals dc over a wide temperature range and summarize the results in a deformation mechanism map . At low temperatures ≤100 °C, the map suggests that individual atomic transport through interface diffusion can still dominate deformations at scales below 10 nm29.We use Eq.  to predimap Fig. . Within 25. Nevertheless, such diffusion dominated deformation has not been observed in metallic glasses where homogenous deformation is envisioned as viscous flow. We argue that the suggested transition of deformation mechanism from collective (viscous flow) to individual atomic transport (diffusion) is due to i) the increasing effectiveness of interface diffusion when the surface ratio is increased by reducing sample size, and ii) the decreasing effectiveness of viscous flow when decreasing size due to boundary conditions which require higher and higher shear rates. The later factor is more pronounced in the here presented compression scenario. For the tension case, other boundary conditions such as the surface tension working against the sample elongation will replace the here present non-sliding boundary condition in the compression case. As a result, a similar mechanism transition will be present in tension, however at a different critical dimension than in the compression case.Deformation describes the transformation of a body from a reference configuration to the current configuration in response to an external stress. In this study, metallic glasses are plastically deformed into nanomolds by an applied compression pressure. The nanofeatures form from an originally flat surface. Plastic deformation dominated by atomic diffusion has been recognized in crystalline materials for high temperatures  or ultra-small sample sizes39. At high temperatures in the liquid state, viscosity and bulk diffusivity (DB) are quantitively correlated through the Stokes-Einstein relation, R is the effective radius of the diffusing particle and c is a constant. This relation decouples when the temperature approaches Tg, and the change of η and DB with temperature are independent. A deviation between the temperature dependent curves of DB and the diffusivity from SER (TSER) can be revealed by measuring the temperature dependent viscosity and bulk diffusivity. To extract the bulk diffusivity from nanomolding, we consider the random first-order transition theory and set the activation energy for bulk diffusion QB = 2QI = 3.16 eV40. Here, the activation energy for the interface diffusion (QI) of the Ni-BMG is measured through nanomolding SER Fig. . Therefo37. However, a similar kink does not exist for interface diffusivity measured through individual atomic transport  equals the experimental time scale (texp), τ = texp. In amorphous metals, higher atomic mobility on the surface/interface results in shorter relaxation time than in bulk and, consequently, a lower glass transition temperature43. Using the relaxation time determined by differential scanning calorimetry at Tg of τg = 76 s, we achieved a VFT fitting of the bulk relaxation time (τB) by τI) by texp = 36000 s, these relaxation times result in a glass transition at the interface at 254 °C and in the bulk at 288 °C. The latter is very similar to the kink in the temperature dependent viscosity at 287 °C. The interface glass transition temperature is ~30 °C lower than that of the bulk, comparable to values determined through electron correlation microscopy and simulation results of other metallic glass systems44.Under collective transport, the temperature dependent viscosity exhibits a kink at 287 °C, which we consider the bulk glass transition in nanomoldingort Fig. . This in47. Here, they are templated on the amorphous structure and assume a position in an epitaxial-manner. This mechanism results in packing motifs of forming nanowire only occurring on the 2D surface, different from the typical volume process where 3D packing motifs dominate (such as widely used melt-quenching methods)48. Such different packing may result in different glass structures. As discussed above  was prepared by induction melting of high purity raw materials of Ni, Pd, Ni50P50 alloy, Cu, and B (>99.95 %) in an evacuated quartz tube (with vacuum level <10 mTorr). We then fluxed the alloy with dehydrated boron trioxide (B2O3) at 1150 °C for 30 min. Melt-quenching was then applied by re-melting the fluxed alloy at 1100 °C for 2 min in vacuum and subsequent 1 min in Argon atmosphere (positive pressure of ~5 × 105 mTorr) before vitrified into a 3 mm rod by water quenching. Zr46Cu46Al8 BMG was fabricated through suction casting. A master alloy (~5 g) was mixed by arc melting from high purity constituent  and in positive Argon pressure. The alloy was melted for at least six times and turned over between melting to ensure fully mixing. This master alloy was then remelted in positive Argon pressure and suction casted into a water-cooled cylinder copper mold with 2 mm as the inner diameter. The fabricated BMG rods were sliced into discs of thickness ~ 0.8 mm with surfaces polished down to ~ 1 µm for nanomolding. The amorphous nature of the prepared BMG feedstocks was confirmed by X-ray diffraction (XRD) and differential scanning calorimetry (DSC)  at 337 °C for Ni-BMG  with a linearly increasing load from 0 to 350 MPa in 30 s and hold for 3 s before releasing, 470 °C for Zr-BMG  with a linearly increasing load from 0 to 280 MPa in 30 s and hold for 30 s before releasing, and 330 °C for Pd-BMG  with a linearly increasing load from 0 to 560 MPa in 30 s and hold for 3 s before releasing. These conditions were carefully calibrated to sufficiently remove the surface roughness of the feedstock and result in only small prints from the mold with aspect ratio smaller than 0.5  and molded with controlled pressure (p) and time (t) without demolding. We adjusted the molding conditions of p and t for different molding temperatures to get practical length of nanowires for length measurement. These conditions are: for Ni-BMG p = 1 GPa, t = 300 s for 327 °C and 317 °C; p = 2 GPa, t = 900 s for 307 °C; p = 1.25 GPa, t = 36000 s for 297 °C; and p = 2 GPa, t = 36,000 s for 287 °C, 277 °C, and 267 °C, for Zr-BMG p = 1.25 GPa, t = 900 s for 420 °C, and for Pd-BMG p = 2 GPa, t = 3600 s for 290 °C. Upon nanomolding, the AAO mold was removed by wet etching in 3 mol/L potassium hydroxide (KOH) solution at room temperature for 10 hours followed by rinsing in distilled water for 10 hours. We then characterized the length of molded nanowires by scanning electron microscopy (SEM).Ni-based, Zr-based, and Pd-based BMG feedstocks were deformed by nanomolding with various molding conditions. Finely polished feedstock discs were first prepressed against the mold  of desired diameter (d = 20 nm and d = 120 nm). To prevent the lamella bending which results in decoupling of the BMG feedstock and the AAO template, only the upper part of the sample was further milled leaving the lower part to support the lamella rigidly  equipped with a Super-X energy-dispersive X-ray spectrometer (EDS). It is essential to prepare the TEM samples including the BMG feedstock and the deformed nanorods altogether to see the change of chemical composition and crystal structure. Thus, the thin lamellas for TEM were carefully prepared in focused ion beam  keeping the original structure. Lamellas were taken from inside of the mold combinations after mechanical polishing the cross-section edge of the combinations followed by an additional 100 μm milling by Ga+ ion in FIB (Supplementary Fig. Supplementary InformationPeer Review FileSource Data


Microstructure analysis demonstrates that the primary dynamic softening mechanism of Zn-2.0Cu-0.15Ti alloy after hot compression is significantly influenced by temperature and strain rate. At low temperature (423 K) and low strain rate (0.1 s−1), the interaction of dislocations is the primary mechanism for the softening Zn-2.0Cu-0.15Ti alloys. At a strain rate of 1 s−1, the primary mechanism changes to continuous dynamic recrystallization (CDRX). Discontinuous dynamic recrystallization (DDRX) occurs when Zn-2.0Cu-0.15Ti alloy is deformed under the conditions of 523 K/0.1 s−1, while twinning dynamic recrystallization (TDRX) and CDRX are observed when the strain rate is 10 s−1.Through isothermal hot compression experiments at various strain rates and temperatures, the thermal deformation behavior of Zn-2.0Cu-0.15Ti alloy is investigated. The Arrhenius-type model is utilized to forecast flow stress behavior. Results show that the Arrhenius-type model accurately reflects the flow behavior in the entire processing region. The dynamic material model (DMM) reveals that the optimal processing region for the hot processing of Zn-2.0Cu-0.15Ti alloy has a maximum efficiency of about 35%, in the temperatures range (493–543 K) and a strain rate range (0.01–0.1 s Zinc-based alloys are highly sought after due to their exceptional mix of qualities, such as excellent ductility, weldability, outstanding corrosion resistance , and surThe constitutive equations and the processing maps are important methods to study the hot workability characteristics and the deformation mechanisms of zinc-based alloys ,13. UnfoThe aim of this work is to investigate the thermal deformation behavior of Zn-2.0Cu-0.15Ti alloy under various strains, strain rates, and deformation temperatures. The 3D processing maps of Zn-2.0Cu-0.15Ti alloy are created to optimize the hot deformation parameters and find out the unstable deformation factors. The effects of thermal deformation parameters on the DRX mechanism are analyzed by the electron backscatter diffraction pattern (EBSD) and transmission electron microscopy (TEM), which provides scientific guidance for the thermal deformation behavior and dynamic softening mechanism of Zn-Cu-Ti alloy.−1, 0.1 s−1, 1 s−1, and 10 s−1, and the height of each sample was compressed by 60%. All compression tests were carried out in a vacuum, and in order to achieve a homogeneous temperature ahead of deformation, all samples were maintained at the necessary temperature for 200 s. Additionally, after the compression test, each sample was promptly water-quenched so that the microstructures could be preserved. Samples for electron backscatter diffraction pattern (EBSD) analysis were prepared into round pieces with a thickness of 100 μm and a diameter of 3 mm. The round pieces were mechanically and vibrationally polished for 0.5 h using a Buehler VibroMet2 Vibratory Polisher , and then the Angle was reduced using a plasma thinning instrument. SEM with a 200 Sirion field emission gun was used for the EBSD studies. OIM7.3 software was used to analyze the EBSD data. Transmission electron microscope (TEM) images were collected using a Tecnai G220 transmission  electron microscope at 200 kV. The shape of TEM samples was similar to EBSD samples, and twin-jet thinning was applied in a mixed solution of HNO3:CH3OH = 1:3. The experimental parameters were 30 V and −243 K, respectively.−1 and deformation temperatures 423, 473, 523, and 573 K. The evolution of flow stress curves is roughly divided into three stages. In the first stage, flow stress increases with increasing strain resulting from an increase in dislocation density (work hardening). In the second stage, the increasing rate of flow stress slows down and reaches its peak value, indicating the occurrence of dynamic recovery (DRV) and DRX. In the third stage, flow stress gradually drops until reaching the final steady value. This phenomenon in hot working is usually caused by dynamic recrystallization (DRX) -lnασ)]-1000/T .The value of both parameters  are known to be functions of the strain and can be described using polynomial functions [The material constants , and relative error parameters are used to evaluate the reliability. The formula can be written as:iA and iB indicate the measured data and the predicted data, respectively. iA and iB, respectively.R is used to reflect the closeness of the correlation between variables [R is to one, the more accurate the prediction of the model. Furthermore, the MAPE is typically used to reflect the practical prediction errors. The smaller the value of MAPE, the better performance for the predicted model. It can be observed from R was calculated at 0.986. Moreover, the MAPE was 6.48%, and the relative error percentage was used to investigate the predictability performance of the model. One can therefore obtain the following:Generally, ariables . In our From P) of the input system is made up of two components: dissipated co-content J and dissipated content G [σ and At present, Prased proposed hot processing maps by studying the theory of DMM ,29 and content G , which aη) is used to assess an alloy’s capacity for power dissipation, which is as follows [Typically, the power dissipation efficiency ( follows :(16)η=JJmaxJ). A 3D contour map of power dissipation can express the changes in η under various deformation temperatures, strain, and strain rates. Generally, the higher the power consumption efficiency is, the better the performance of alloy processing can be [When m ≤ 0, there is no power dissipation in the entire system. When 0 < m < 1, it is considered to be in a steady-state flow regime. When m = 1, the value of J is equal to its peak value . The colored grids of the power dissipation map represent η, and the shaded regions of the flow instability map can be used to identify the unstable area. The results suggest that the power dissipation efficiency (η) and the flow instability zone change significantly under different deformation conditions. With the increase in strain (from 0.1 to 0.7), the value of η declines (−1) and the temperature range (493–543 K)  b,c.−1) (−1).From −1) b,c. To e−1 and the deformation temperature of 423–573 K. In In From −1. The softening of the alloy was mainly achieved through dislocation slip, cross-slip, climb, and recombination inside the grain [−1, a polygonal structure appeared clearly inside the grain due to the segmentation effect of dislocation grids or walls  and CDRX are observed (−1), due to the highly effective interface velocity [he grain . When T or walls b, indicaal ratio ,43, whicatio [−1 c, an obvatio [−1 c. Howeveobserved d. This ivelocity  of the tvelocity ,44.−1 and deformation temperatures 423, 473, 523, and 573 K, using the Arrhenius model and 3D processing maps. The following are the primary conclusions:The Arrhenius-type model is utilized to forecast flow stress behavior. The results show the Arrhenius model can accurately predict the flow stress behavior of Zn-2.0Cu-0.15Ti alloy.−1.Three-dimensional processing maps are generated at different strains based on DDM theory. The ideal processing domain for Zn-2.0Cu-0.15Ti alloy is the temperature range from 493 to 543 K and strain rate range from 0.01 to 0.1 s−1. CDRX is activated at low deformation temperature (423 K) and high strain rate (1 s−1) and is inhibited with increasing deformation temperature and decreasing strain rate. However, when the deformation temperature increases (523 K) and the strain rate declines (0.1 s−1), DDRX becomes the primary softening mechanism, and is weakened with increasing strain rate. CDRX and TDRX become the main softening mechanisms when the strain rate is 10 s−1.The softening mechanism of Zn-2.0Cu-0.15Ti alloy has diversification, including CDRX, DDRX, and TDRX, and is activated at T = 423–573 K and The hot deformation behavior of Zn-2.0Cu-0.15Ti alloy was investigated at strain rates 0.01, 0.1, 1, and 10 s


Persistent left superior vena cava (PLSVC) is a common congenital anomaly, with an incidence of 0.5%.The patient, a 70‐year‐old man, had visited our outpatient clinic 8 years prior, after experiencing dyspnea. A 12‐lead electrocardiogram (ECG) showed complete atrioventricular block with an escape rhythm of 43 bpm. Transthoracic echocardiography (TTE) showed a left ventricular ejection fraction (LVEF) of 58%. We attempted to insert a temporary pacing lead from the right internal jugular vein; however, the guidewire did not progress directly to the heart. Venography revealed PLSVC and ARSVC Figure . A tempoEight years after PM implantation, he was admitted to our hospital for battery exchange. Since he still felt dyspnea while walking, we decided to perform a CRT device upgrade concurrent with the battery exchange. Preoperative three‐dimensional images of the PLSVC and coronary sinus (CS) were obtained using ECG‐gated contrast‐enhanced computed tomography (CECT) Figure , and theThe subclavian vein was punctured and a 0.035 inch hydrophilic guidewire was advanced into the right atrium through the PLSVC. A 9Fr introducer sheath (MediKit) was inserted into the PLSVC. A delivery catheter with 130° curve  was advanced to the junction between the PLSVC and the CS and contrast agent was injected, revealing the posterolateral vein Figure . A 0.014In patients with PLSVC and ARSVC, it is difficult to identify all branches of the CS during the CRT device upgrade procedure because retrograde venography using a balloon occlusion catheter is usually impossible. Because 90% of the PLSVC drains into the CS, the LV leads pass through in the following order: PLSVC, junction between PLSVC and CS, main body of the CS, and branch of the CS.Sufficient LV lead slack is usually left in the right atrium to prevent LV lead dislodgement during CRT device implantation. However, patients with PLSVC and ARSVC have insufficient space to leave the LV lead because it does not pass through the right atrium. The active fixation lead can be fixed using side helix, decreasing the risk of LV lead dislodgement, even in patients with PLSVC and ARSVC.None.The authors declared that no competing interests exist.The patient provided consent for publication.


However, it significantly reduced (p < .05) the bulk density and swelling power of flour samples. As the germination period increased from 0 to 72 h, the viscosity of the porridge decreased significantly (p < .05). At 24 h after germination, the sensory analysis revealed no significant difference in color, taste, aroma, mouth feel, or overall acceptability samples when compared to the ungerminated sample. Germination improved the functional properties of finger millet flours as well as the sensory aspects of porridge. Hence, 24‐h germinated finger millet flour is best in all aspects compared to ungerminated, 48‐ and 72‐h germinated flours to prepare porridge. The 24‐h germinated finger millet‐based porridge is recommended for infants, pregnant mothers, and breastfeeding mothers.Finger millet is a stable and nutritious cereal crop, mostly grown in the semiarid tropics of the world. Processing is important for improving the nutritional value of finger millets. The aim of the research was to evaluate the effect of the germination period on the functional properties of flours and the sensorial quality of finger millet porridge. Four finger millet varieties were collected, cleaned, and soaked for 24 h, then germinated at room temperature (20–25°C) for 24, 48, and 72 h. The germinated samples were oven‐dried at 60°C for 6 h and milled into flour at the size of 1 mm using a cyclomiller. Unsoaked and ungerminated finger millet grains are also milled into flour and used as control. Porridge was prepared with a flour‐to‐water ratio of 1:12 (weight/volume), and sensory analysis was done by semitrained panelists. Germination enhanced the water absorption capacity, solubility, and oil absorption capacity of flour samples significantly ( Germination enhanced the water absorption capacity, solubility, and oil absorption capacity of flour samples but reduced the bulk density and swelling power of flour samples. Twenty‐four‐hour germinated finger millet flour is best in all aspects compared to ungerminated, 48‐ and 72‐h germinated flours to prepare porridge. The 24‐h germinated finger millet‐based porridge is recommended for infants, pregnant mothers, and breastfeeding mothers. Finger millet yield constitutes 11% of all millets produced worldwide  in a seed‐to‐water (1:3) ratio, as described by Derbew and Moges , with sl2.2Porridge was developed from germinated finger millet flour as indicated by Onyango and Wanjala . Fifty gThe porridge samples that were prepared from ungerminated and germinated finger millet were tasted by 15 semitrained panelists using a 9‐point hedonic scale from extremely like up to extremely dislike  and added into 25‐ml centrifuge tube. Then, it was stirred for 2 min and centrifuged for 20 min at 4000 rpm. The separate oil formed as supernatant was measured in 10‐ml cylinder (v2). The oil absorption capacity was calculated as follows.v1 is the initial volume of oil; v2 is the volume of oil formed as supernatant.Oil absorption capacity was determined using the method reported by Edema et al. . The wei2.3.5w1) and 10‐ml distilled water were added into the weighed centrifuge tube (w2). The sample was stirred occasionally by heating for 30 min at 90°C. After stirring, it was cooled and centrifuged at 5000 rpm for 10 min. The supernatant was decanted into the Petri plate and weighed (w4). The weight of the Petri plate was taken after drying in oven at 105°C and also the weight of the centrifuge tube (w3) was recorded after removing the supernatant. The swelling power and solubility were calculated as follows.w1 is the weight of sample; w2 is the weight of centrifuge tube; w3 is the weight of left/viscous flour.w1 is the weight of sample; w2 is the weight of centrifuge tube; w4 is the weight of dried supernatant; VE is the volume of distilled water.Swelling power and solubility were analyzed as described by Adeyeye and Akingbala . One gra2.4The viscosity of porridge was determined by Brookfield viscometer as cited by Derbew and Moges . The por2.5p < .05 significantly different. All measurements were performed in triplicate and the results were recorded as mean ± standard deviation (SD).The variation between the mean levels of all treatments was analyzed by two‐way ANOVA using SPSS version 23.0 (SPSS Inc.). Sample treatment comparisons were analyzed statistically using Duncan's multiple range post‐hoc test with a probability 33.13.1.13 at 72 and 0 h, respectively (Table p < .05) at 24 h of germination as compared to 48 and 72 h of germination but showed no significant difference to ungerminated flour. At all germination times, Tadesse and Tessema varieties showed no significant difference, whereas Axum and Meba varieties showed a significant (p > .05) decrease in bulk density as compared to ungerminated flour. The reduction of the bulk density during germination might be due to the breakdown of complex compounds like protein and starch into smaller constituents  as malting time increased. The Mungbean malt flour had lower bulk density than unmalted flour as indicated by Onwurafor et al. (p < .05) at 48‐ and 72‐h germination in finger millet and it ranged from 0.75 to 0.82 g/cm3  at 48‐ and 72‐h germination as compared to ungerminated and 24‐h germinated finger millet flour. Similar results were reported by Mankambou et al.  in the water absorption capacity of Tessema variety at 48‐ and 72‐h germination and also Tadesse variety at 72‐h germination relative to ungerminated flour and also there was a significant difference (p < .05) between 24‐ and 48‐h germination of Axum variety. These results are in line with the report of Kumar et al.  and the lowest (1.20 g/g) water absorption capacity values were obtained from Tessema variety at 72 h of germination and at 0‐h germination, respectively. There was no significant difference in the water absorption capacity of Meba variety at all germination periods. There was a significant increase  at 48‐ and 72‐h germination as compared to ungerminated Axum variety flour. Basically oil absorption capacity increased as germination period increased but not significantly. This is due to the hydrolysis of starch during germination because the hydrolyzed starch absorbs more water and oil  as germination period increased. The highest swelling power (3.70%) result was obtained from ungerminated Meba variety flour but the lowest value (0.76%) was recorded at 72‐h germination of Tadesse variety. The result of swelling power of germinated finger millet flour decreased significantly (p < .05) as compared to ungerminated flour. The decrease in swelling power of finger millet flour is in line with the report of Nefale and Mashau  at all germination periods as compared to ungerminated finger millet flour. This might be due to the starch hydrolyzed and increased sugar level during germination  at 48‐ and 72‐h germinated flours compared to ungerminated sample. There was a significant difference (p < .05) in the color of porridge made from 48‐ and 72‐h germinated Tessema flour as compared to ungeminated flour.The color of porridge developed from ungerminated and 24‐h germinated Tessema flour was the most preferred (moderately like) as rated by panelists, while a porridge prepared from 48‐ and 72‐h germinated Tessema flour was least preferred (dislike slightly) by panelists. The maximum color value was recorded in ungerminated flour, whereas the minimum value was obtained at 72‐h germination. There was no significant difference in the color of porridge samples that were prepared from 24‐h germinated Tessema flour but there was a significant difference (3.2.2p < .05) at 48‐ and 72‐h germination relative to 0‐h germination.According to panelists' ratings, ungerminated flour had the highest porridge flavor, whereas 72‐h germinated flour had the lowest. The most preferable taste was recorded at ungerminated and 24‐h germinated finger millet porridge but the least selected taste was found at 72‐h germinated finger millet porridge. The porridge sample developed from ungerminated flour was the most preferred (like moderately), porridge sample developed from 24‐h germinated flour was moderately preferred (like slightly), and the porridge developed from 48 and 72 h was leased preferred (neither like nor dislike and slightly dislike) by panelists. There was no significant difference in the taste of porridge between ungerminated and 24‐h germinated samples, but there was a significant difference (3.2.3p < .05) at 48‐ and 72‐h germination but there was no significant difference at 24‐h germination as compared to ungerminated finger millet flour.The aroma of porridge made from ungerminated and 24‐h germinated flours was most preferred (liked moderately and liked slightly). Hence, the highest score of aroma was observed both in germinated and ungerminated finger millet porridges. However, the porridge developed from 48‐h germinated flour was selected neither liked nor disliked scales, while the aroma of porridge made from 72‐h germinated flour was least preferred (slightly disliked) by panelists. The aroma value of porridge decreased significantly (3.2.4p < .05) at 48‐ and 72‐h germination as compared to ungerminated sample but there was no significant difference between ungerminated and 24‐h germinated porridge samples.Mouth feel for 24‐h germinated and ungerminated flours of porridge was the most preferred (slightly liked and moderately liked) but the porridge made from 48‐h and 72‐h germinated flours was least preferred (neither liked nor disliked and slightly disliked) as rated by panelists. The greatest mouth feel value was obtained in ungerminated and 24‐h germinated flours of porridge while the lowest value was recorded at 48‐ and 72‐h germinated porridge samples. The mouth feel value of porridge decreased as germination time increased and the values decreased significantly (3.2.5p < .05).The overall acceptability of porridge made from 24‐h germinated and ungerminated flours was most accepted (moderately liked and slightly liked) but the porridge samples that developed from 48‐ and 72‐h germinated flours were least accepted (neither liked nor disliked and slightly disliked) as scored by panelists. Ungerminated and 24‐h germinated porridge samples had the highest overall acceptability values, whereas 72‐h germination had the lowest. At 24 h after germination, there was no significant difference in the overall acceptability of porridge compared to ungerminated flour. However, at 48 and 72 h after germination, there was a significant difference (p < .05) compared to ungerminated millet flour but no significant difference in aroma, taste, and overall acceptability of the porridge. The color, odor, taste, and texture of malted millet fura decreased significantly (p < .05) at 24 h compared with unmalted millet fura as reported by Auta et al. (p < .05) in the color, aroma, taste, and overall acceptability of finger millet porridge at 72‐h germination but there was no significant difference in the mouth feel of the porridge to the rest of porridge samples based on the report of Nefale and Mashau (p < .05) in the color, aroma, taste, and overall acceptability of finger millet porridge at 72‐h germination but there was no significant difference in the mouth feel of the porridge to the rest of porridge samples  relative to ungerminated finger millet porridge. The reason for the reduction of viscosity of porridge prepared from germinated finger millet grain flour is that the activity of α‐amylase increased during germination  for nutritionally vulnerable infants and pregnant and breastfeeding women.The authors have declared no conflict of interest.


Protein tyrosine kinases (PTKs) are a large enzyme family that regulates many cellular processes. The key to their broad role in signaling is their tunable substrate specificity and regulatory mechanisms that allow each to respond to appropriate regulatory signals and phosphorylate the correct physiological protein substrates. Thus, in addition to the general PTK catalytic platform, each PTK acquires unique structural motifs that confer a unique combination of catalytic and regulatory properties. Understanding the structural basis for these properties is essential for understanding and manipulating the PTK-based signaling networks in normal and cancer cells. C-terminal Src kinase (Csk) and its homolog, Csk-homologous kinase (Chk), phosphorylate Src family kinases on a C-terminal Tyr residue and negatively regulate their kinase activity. While this regulatory function is biologically essential, Csk and Chk have also been excellent model PTKs for dissecting the structural basis of PTK catalysis and regulation. In this article, we review the structure-function studies of Csk and Chk that shed light on the regulatory and catalytic mechanisms of protein tyrosine kinases in general. This article reviews the studies contributing to our current understanding of PTK biochemistry.The function of Csk was understood long before the Csk gene or protein was identified. In the late 1970s, viral Src (v-Src) was established as the causative agent of oncogenic transformation in the Rous Sarcoma Virus , while iSince Csk was identified first, the enzyme attracted most of the research attention and became the Csk family representative by default. It is comprised of 450 amino acids organized into several distinct domains conserved in many PTKs and other signaling proteins. Csk shares a 54% sequence identity with Chk and 40% with Src. All three enzymes have a similar SH3-SH2-catalytic domain organization . HoweverThe crystal structures of the Csk  and Src Despite many structural similarities, Csk and Src family kinases have highly distinct substrate specificity and regulatory characteristics. Csk has an unusually stringent substrate specificity, while Src is known to phosphorylate many protein substrates . Csk andThe contrast between the regulatory mechanisms for Src and Csk is also quite striking. The Src kinase domain alone is intrinsically active. This activity is suppressed by the interactions between the kinase and the regulatory domains . The binThe contrasting substrate specificity and regulatory strategies embedded in the two highly homologous kinase structures of Csk and Src make these enzymes particularly attractive for structure-function studies. Csk is also a unique PTK because it can be readily expressed in bacteria without causing significant cellular toxicity . This isCsk has served as a model for studying the catalytic mechanisms of PTKs. Cole and others have extensively probed Csk’s catalytic mechanism using a combination of enzyme mutagenesis , protein2+ or Mg2+ ion could support Csk catalytic activity, with a Mn2+ ion resulting in a more favorable Kms for ATP and peptide substrates, while a Mg2+ ion results in a more favorable kcat  in Csk. The conclusion was also extended to other PTKs, such as Src and FGFR1 , but the catalytic affinity is much stronger (much lower Km). The low Km is due to the fast phosphoryl transfer that acts as a kinetic clamp facilitating substrate recognition , which recruits Csk to the IRF signaling complex and activates Csk activity. They further identified the SPOP-binding motifs in the Csk activation loop. It is unclear if and how the adaptor protein may alter the physical and catalytic interaction between Csk and Lyn in this process besides bringing them to the same complex. Although SFKs are the most well recognized substrates for Csk, other protein substrates have been identified for Csk. D’Arco et al. identified ATP-activated P2X3 receptor as a Csk substrate   and immuin vitro  and in Jin vitro , and thein vitro . These sThe Csk SH2 domain binds to a pTyr residue in the Csk binding protein, CBP, which serves two roles: recruiting Csk to the lipid rafts where SFKs are located  and direThe Csk SH2 domain activates Csk activity constitutively and also in a ligand-binding-dependent manner. To understand the structural basis of the SH2-mediated regulation, Lin et al. dissected the functional roles of the residues in the SH2 and catalytic domain interface by site-specific mutagenesis and kinetic analyses . They idThese studies suggest a two-tiered regulatory model of Csk by the regulatory domains. First, the catalytic domain is intrinsically inactive and activated by interactions with the SH3 domain (such as the cation-π interaction between Arg68 and Trp188) and the SH2 domains (SH2 domain residues Met150 and Asn148). Second, some SH2 domain residues (Glu154 and Glu258) also suppress the catalytic activity, keeping the catalytic activity suboptimal. This suppression is removed by the SH2 domain binding to a ligand. Several residues involved in the SH2 ligand-stimulated activation are D227, T229, Y133, and E147. Critical residues and motifs for Csk domain-domain interactions are shown in At the heart of the divergent regulatory strategies in Csk and Src kinases is this question: why is the Csk catalytic domain intrinsically inactive while the Src catalytic domain is intrinsically active? The answer to this question may also shed light on the regulatory strategies of eukaryotic protein kinases in general. Some kinases are like Csk and have an intrinsically inactive catalytic domain that is activated by the regulatory domain or subunits (such as cyclin-dependent protein kinases ), while in silico, they modeled reversible transitions between active and inactive forms as fluctuations between two energetic basins. Their analysis revealed that a conformational change in the α-helix C is required for a complete conformational transition. Restrictions in the α-helix C region resulted in global conformational changes. Introduction of restrictions in some α-helix C dihedral angles, deletions of SH3 and SH2−SH3 domains, and construction of an inhibitor mimetic potential negatively impacted the conformational transitions and, in some cases, vanishing them. These results are consistent with a previous mutagenic study that identified the α-helix C as a key motif of the “on switch” in both Csk and Src  on Tyr18 , even though the effect of PKA phosphorylation on Csk is controversial. One study reported that PKA phosphorylates Csk activity , while oactivity . The phoactivity . The stractivity .in vitro and in vivo (Csk is also post-translationally modified by small ubiquitin-like modifier, referred to as SUMOylation, on Lys53 both  in vivo , which iRedox regulation of PTK activity and protein Tyr phosphorylation-based signaling is widely documented . ReactivKemble and Sun found that reducing condition has only a minor effect on Csk activity, but it stimulates Src kinase activity approximately 13-fold . Ala scain vitro results of redox regulation (in vitro, it is much more challenging to assess the redox state of an enzyme in the cell under different redox conditions that may induce oxidative stress. This is likely the root cause of conflicting reports of how Src family kinases respond to redox regulation (While the above studies reveal the potential redox sensing mechanisms embedded in the PTK architecture, caution must be exercised in interpreting gulation . The redgulation .Protein tyrosine kinases are a large family of enzymes sharing a common catalytic core architecture. Each PTK contains unique structural domains, motifs, or residues that render each PTK unique in its catalytic or regulatory properties. These catalytic and regulatory properties enable each PTK to fit into various regulatory pathways that collectively become the cellular regulatory network. Dissecting the structure-function relationships leads to a better understanding of how PTKs work as individual enzymes and how they carry out their regulatory functions in the cells. Such understanding leads to ways of manipulating cell signaling for therapeutic or investigative purposes. Csk/Chk and the Src family kinases they regulate constitute an important signaling module in mammalian cell regulation and an excellent model system for dissecting regulatory and catalytic structure-function relationships for PTKs. These studies made an important contribution to our current understanding of Tyr phosphorylation as a global regulatory mechanism and a key target for anti-cancer therapeutic development.