Pleckstrin-2 is a member of pleckstrin family with well-defined structural features that was first identified in 1999. Over the past 20 years, our understanding of PLEK2 biology has been limited to cell spreading. Recently, increasing evidences support that PLEK2 plays important roles in other cellular events beyond cell spreading, such as erythropoiesis, tumorigenesis and metastasis. It serves as a potential diagnostic and prognostic biomarker as well as an attractive target for the treatment of cancers. Herein, we summary the protein structure and molecular interactions of pleckstrin-2, with an emphasis on its regulatory roles in tumorigenesis. Pleckstrin was first initially described as a prominent substrate of protein kinase C (PKC) in hematopoietic cells. In its naming, “PLEC” is derived from platelet and leukocyte C kinase substrate, and “KSTR” is derived from the amino acid sequence KSTR. Pleckstrin protein sequences are highly conserved across human and mouse, which is about 97% homologous to each other. Accordingly, the protein structures and functions of these two different species are also almost same. Pleckstrin-1 (PLEK1), specifically expressed in hematopoietic cells, is composed of two pleckstrin homology (PH) domains at the amino- and carboxyl-terminal and a central disheveled-Egl-10-pleckstrin (DEP) domain . As a maPleckstrin-2 (PLEK2) is another member of pleckstrin family, which is 65% homologous and 39% identical to paralog PLEK1 . SimilarBoth isoforms can induce the formation of large lamellipodia and peripheral ruffle of cells, thereby facilitating cell spreading, in which PLEK1 is entirely regulated by its phosphorylation by PKC . In contThree-dimensional (3D) protein structures have contributed enormously to our understanding of atomic and molecular structure, and help delineate complex macromolecules and their functions, as well as how they operate in the real world. Although the amino sequence of PLEK2 has been characterized with well-defined domains, its protein architecture is poorly investigated. Only the solution structures of C-terminal PH domain and DEP domain of PLEK2 had been reported, respectively . Due to The structures of PH domain and DEP domain had been well characterized, respectively, however, full-length structures of PLEK1 and PLEK2 are still largely unknown. Improvements in protein production, crystallization, as well as structure solution and refinement methods have brought the field to the verge of rapid protein structure determination. The major bottle neck to this process remains protein production and crystallization. It has been suggested that DEP interacts intramolecularly with the N-terminal PH domain to mediate membrane anchoring of PLEK1 . Such inExamining the crude 3D model built with the sequence for mouse PLEK2, two putative small molecule binding grooves appear to exist near K13 and R14 residues (N-PH domain) and at K256 residues (C-PH domain), respectively. Consistently, these sites have been suggested to be response for the phospholipid binding domains . FurtherDue to the highly similarity of protein structural features, it is not surprise that both of these two pleckstrin proteins regulate cell protrusions such as lamellipodia and filopodia, which are important for cellular shape change and spreading . MeanwhiPLEKs are best known for containing two PH domains at the amino- and carboxyl-terminal and a central DEP domain. PH domain is a functional domain consisting of approximately 120 amino acids, which has been found in a variety of proteins involved in cellular signaling or cytoskeletal functions. It plays an important role in cellular signal transduction by recruiting host proteins to cell membrane via phospholipid binding . DEP, a As a prominent substrate of PKC, the biological function of PLEK1 largely depends on its phosphorylation. There are three sites  between DEP domain and the N-terminal PH domain, which can be phosphorylated by PKC . N-termiUnlike PLEK1, PLEK2 is a poor substrate of PKC and regulated by the local generation of phosphatidylinositol 3-kinase (PI3K)-phosphorylated phospholipids . Both PHPLEKs was firstly found to be involved in cytoskeletal rearrangement. Expression of PLEK1 results in cell spreading and rearrangement of cytoskeleton characterized by morphologic change such as formation of peripheral membrane ruffles or dorsal projection, which is dependent upon the Rac activity and integrin binding . PLEK2 eIt has been well documented that rearrangement of cytoskeleton is fundamental to the immune synapse formation in lymphocytes . PLEK2 fGiven that PLEK1 is exclusively expressed in monocytes, macrophages, lymphocytes, and granulocytes, it is acceptable for the roles of PLEK1 in inflammation. In addition to the critical effects on platelet aggregation and secretion that was mediated by PKC, PI3K and Actin assembly . PLEK1 hPLEK2 has shown high expression level in erythroid cells and a critical role in erythropoiesis that starts with erythroid progenitors committed from hematopoietic stem cells to mature red blood cells . BindingDuring terminal erythropoiesis, the nucleus is gradually condensed, and the highly condensed nucleus in the orthochromatic erythroblast is extruded out from the erythroblast . The eryV617F mutation is the leading cause of the Philadelphia-chromosome-negative MPNs. Recently, PLEK2 has been identified to be a downstream effector for JAK2/STAT5 signaling in hematopoietic cells upon the stimulation of corresponding cytokines  are a group of bone marrow diseases with excessive production of blood cells, increasing risk of arterial or venous thrombosis, and a propensity to transform into acute myeloid leukemia. JAK2ytokines . ImportaIn addition, a recent bioinformatics study has also suggested that PLEK2 is significantly associated with survival of patients, as well as a novel therapeutic target for multiple myeloma .Increasing evidences suggest that PLEK2 plays a cancer-promoting role in tumorigenesis and metastasis . PLEK2 eThe epithelial-to-mesenchymal transition (EMT) process is a crucial mechanism in the progression of tumor metastasis . RegulatDespite conclusive evidence supporting PLEK2 is involved in cell migration and invasion, its regulatory roles in tumor cell invasion and metastasis remains largely limited. It has been demonstrated that PLEK2 interacts with epidermal growth factor receptor (EGFR) and suppresses EGFR ubiquitination mediated by c-CBL, leading to downstream CCL2 transcriptional overexpression and EMT process activation in gallbladder cancer . AdditioInteraction partners are essential for the study of molecular mechanism of proteins in cell biology. Our understanding of PLEK2 function is limited mainly due to the poorly identification of interaction partners . As mentSHIP2, well known to negatively regulate the signaling via dephosphorylation of the 3-position of PIP3 to generate PIP2, is one of the two recently identified interaction partners of PLEK2. PLEK2 mediates degradation of SHIP2 in a ubiquitin-dependent manner, which is further activated PI3K/AKT signaling to promote lung cancer metastasis and vascular invasion . The othAs our understanding of the biological functions of PLEK2 increases, it is becoming clear that PLEK2 plays important roles in other cellular events beyond cell spreading, such as inflammation, erythropoiesis, tumorigenesis and metastasis . HoweverThe role of PLEK2 in tumor development is gradually being recognized, and it serves as a potential diagnostic and prognostic biomarker as well as an attractive target for the treatment of cancers. However, there is no reported small molecule inhibitor for PLEK1 or PLEK2. Therefore, the development of small molecules that modulate the function of PLEK2 is not only beneficial for the treatment of diverse cancers, but also provides extremely useful tools for studying PLEK2 functions as a powerful complementary method to the genetic methods.Importantly, the three-dimensional protein structure is essential for designing and generating small molecules targeting PLEK2. Although there is no reported full-length structure of PLEK2, the prediction of the 3D structure of PLEK2 based on the solution structures of fragments through homology modeling methods , could pGW, QZ, and BZ conceived the manuscript. GW and BZ wrote the manuscript. GW and YX contributed to crafting figures. GW, YX, and BZ reviewed and edited the manuscript. All authors listed have made a substantial, direct and intellectual contribution to work, and approved it for publication.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Dear Editor, Huntington's disease (HD) is an autosomal dominant, inherited neurodegenerative disease. Clinical symptoms may include such as chorea, depression, and cognitive decline, begin at age of 40 years and the disease becomes severe up to the age of approximately 65 years . The esNormal Htt has 35 CAG repeats, whereas mutant htt having >40 sCAG repeats, patients with 36-39 CAG repeat expansion are at risk of HD . The fiAt present, there is no effective treatment available for the management of HD. Currently, only symptomatic therapeutic strategies are available for the treatment of HD, which focuses on neurological and psychiatric symptoms that aim to improve the quality of life . Tetrabin vitro , phosphal., 2009) as wellal., 2009). HuntinBased on the above discussion we hypothesized that N17 domain expression in Htt, may reduce the accumulation and increase the nuclear clearance of mHtt. Mutant Huntingtin accumulated in both the nucleus and cytoplasm. A study in invertebrates (zebrafish) revealed that zebrafish without the N17 domain showed an early onset of symptoms, whereas zebrafish with the N17 domain showed a delayed onset of symptoms. Therefore, the authors hypothesize that the expression of N17 domain in mHtt reduces the accumulation of mHtt and its toxicity in both nucleus and cytoplasm . MitochHence it is suggested that Htt protein having N17 domain in zebrafish, shows the reduction in disease progression. We have critically reviewed the literature and discussed the possible mechanism underlying the N17 domain lacking mHtt mediated mitochondrial dysfunction and neuronal cell death in HD. The N17 domain could be a potential therapeutic target for the development of the new therapeutic strategy for the treatment of Huntington disease.Authors are thankful to the Department of Pharmacology, ISF College of Pharmacy, Moga for providing support.The authors declare that there is no conflict of interest.


Forecasting regional economic activity is a progressively significant element of regional economic research. Regional economic prediction can directly assist local, national, and subnational policymakers. Regional economic activity forecast can be employed for defining macroeconomic forces, such as prediction of stock market and cyclicality of national labor market movement. The recent advances of machine learning (ML) models can be employed to solve the time series prediction problem. Since the parameters involved in the ML model considerably influence the performance, the parameter tuning process also becomes essential. With this motivation, this study develops a quasioppositional cuckoo search algorithm (QOCSA) with a nonlinear support vector machine (SVM)-based prediction model, called QOCSO-NLSVM for regional economic prediction. The goal of the QOCSO-NLSVM technique is to identify the present regional economic status. The QOCSO-NLSVM technique has different stages such as clustering, preprocessing, prediction, and optimization. Besides, the QOCSO-NLSVM technique employs the density-based clustering algorithm (DBSCAN) to determine identical states depending upon the per capita NSDP growth trends and socio-economic-demographic features in a state. Moreover, the NLSVM model is employed for the time series prediction process and the parameters involved in it are optimally tuned by the use of the QOCSO algorithm. To showcase the effective performance of the QOCSO-NLSVM technique, a wide range of simulations take place using regional economic data. To determine the current economic situation in a region, the QOCSO-NLSVM technique is used. The simulation results reported the better performance of the QOCSO-NLSVM technique over recent approaches. The QOCSO-NLSVM technique generated effective results with a minimal mean square error of 70.548 or greater. Astonishingly good results were obtained using the QOCSO-NLSVM approach, which had the lowest root mean square error (RMSE) of 8.399. The forecasting method predicts future value based on a provided time series data set by making assumptions on future trends and estimating historical data. This is employed for several regions of the decision-making process, like industrial process control, risk management, operations management, demography, and economics . ForecasForecasting regional economic activity is an essential component of regional economic study. The regional economic prediction could directly assist business executives, local, subnational, and national policymakers. These two business executives and policymakers require precise prediction of key economic aggregates, namely, employment, output, and income for medium-long term planning purposes . RegionaThe forecasting method connected to economic problems is utilized for predicting economic variables in several countries. The industry volatility prediction, critical to several important problems in business , and theThis study designs a quasioppositional cuckoo search algorithm (QOCSA) with a nonlinear support vector machine (SVM)-based prediction model, called QOCSO-NLSVM for regional economic prediction. The QOCSO-NLSVM technique involves the design of the density-based clustering algorithm (DBSCAN) to determine the identical states depending upon the per capita NSDP growth trends and socioeconomic-demographic features in a state. Besides, the NLSVM model is elected for the time series prediction process and the parameters involved in it are optimally tuned by the use of the QOCSO algorithm. The experimental validation of the QOCSO-NLSVM technique and the results are examined in various aspects.The rest of the research work is organized as follows. Mishra and Ayyub  introducZhu et al.  designedChatzis et al.  integratIn this study, an effective QOCSO-NLSVM technique has been developed for regional economic prediction. The QOCSO-NLSVM technique encompasses several subprocesses, namely, DTW-based preprocessing, DBSCAN-based clustering, NLSVM-based prediction, and QOCSO-based parameter optimization. One of the primary methods used to capture similarities among two regions, or among pairs of factors within a provided region according to time-series data is named dynamic time warping (DTW). DTW is an effective method utilized for learning similarity based on distance between two sequences that might differ in speed and quantifying time-based similarities among any two pairs. Generally, DTW is an ML method which estimates an optimum match between two provided sequences with some restrictions. The sequence is “warped” nonlinearly in the time dimension to define measures of their similarity, independent of nonlinear variation in the time dimension. The Euclidean distance uses the distance among every pair of the time series and compares it with the Euclidean distance. Simultaneously, the DTW searches for optimal alignments among the two-time series. Furthermore, all the points are utilized for comparing the points to make the best possible alignments among the two-time series according to their distance matrix.p, where it denotes the neighbourhood's maximum radius from p. To construct a dense zone, MinPts is the minimum number of points required. When MinPts is in the distance, p denotes a core point. When p is a core point, all points in its vicinity are grouped together. DBSCAN detects each density-reachable point in the cluster and adds it to a comparable cluster. When a point q is densely approachable from other core points but its neighbourhood is less than MinPts, it is a border point. An outlier or noisy point is one that is not accessible from other locations. DBSCAN achieves clustering by extracting clusters consecutively. Rep until no more density-reachable points are identified, and the final cluster is reached. DBSCAN divides a set of points into low-noise border points and high-density. The purpose of DBSCAN is to identify identical states based on a state's per capita NSDP growth trends and socioeconomic-demographic characteristics. DBSCAN was capable of detecting a variety of clusters based on the density distribution that was assessed. The DBSCAN methodology permits the calculation of identical states based on per capita income.DBSCAN might find distinct clusters based on the assessed density distribution. It could recognise structured groupings without knowing their numbers. The following illustrates DBSCAN's basic premise: DBSCAN finds each point in the neighbourhood of a random unvisited point x and y, d represent the similarities among them, Γε (x) denotes the ε-neighbourhood of x, in which Γε (x) = {y  ∈  V|d  ≤  ε} · ρ(x) = |Γε(x)|  indicates the density value of x:Assume two points xk, yk}k=1N with input data xk ∈ ℝn and respective binary class label yk ∈ {−1,  +1}, the SVM classification initiates from the subsequent assumption:During the prediction process, the NLSVM model receives the clustered data as input to predict the output. Assume a trained set {That is equal toφ(·) : ℝn⟶ℝnh maps the input space to a high-dimensional feature space. It is noteworthy that the nh dimension of this space is determined in an implicit manner (it is an infinite dimension). The b represent a bias as follows:Now, the nonlinear function But, at the same time, it is never evaluated in this form. One determines the optimization issue:w‖2 corresponds to a maximalization of the margin among the two classes. c indicates a positive real constant and must be taken into account as a tuning parameter. The Lagrangian can be expressed as follows  , its opposite number χ0 is represented as follows:The OBL method was proposed with the aim of decreasing the computation time and improving the ability of various EAs . TherefoHowever, the opposite point for multidimensional searching space (dimension) is determined by the following equation:xqo of arbitrary no. χ ∈  is represented as follows [The quasiopposite no.  follows :(17)xqo=d dimension) is determined by the following equation:Likewise, the quasiopposite point for multidimensional searching space  parts to train the model and to test the model. This process gets iterated for S times by varying the lasting parts, and the generalization efficiency can be determined by the use of MSE (mean squared error) over every test result.Gs indicates the s-th part for the testing process and  θs signifies the solution vector attained at the time of training process.For obtaining an objective function that could generalize the SVM outcome with no utilization of testing data, the cross validation approach is utilized. The cross validation process partitions the training dataset The performance validation of the QOCSO-NLSVM technique using the economic data from the Niti Aayog website and the Reserve Bank of India were inspected. The data includes several features such as fiscal deficits, revenue deficits, interest payments, capital expenditure, nominal NSDP series, social sector expenditure, electricity generation, infrastructure projects, per capita NSDP at factor cost (at constant prices), per capita NSDP, number of factories, state-wise fixed capital, sectoral growth rate, and pattern of land use. For instance, with an actual value of 16855.712, the QOCSO-NLSVM technique has attained predicted values of 16854.72, 16784.71, 16716.72, 16799.72, and 16851.73 under runs 1–5, respectively. At the same time, with the actual values of 18176.991, the QOCSO-NLSVM system has accomplished forecasted values of 18110.02, 18125.98, 18285.98, 18211.01, and 18294.03 under runs 1–5 correspondingly. Furthermore, with the actual values of 21138.478, the QOCSO-NLSVM method has achieved forecasted values of 21028.50, 21282.49, 21097.47, 21184.48, and 21098.48 under runs 1–5 correspondingly. Moreover, with the actual values of 22231.951, the QOCSO-NLSVM algorithm has reached predicted values of 22310.96, 22177.95, 22379.96, 22330.96, and 22374.97 under runs 1–5 correspondingly.A brief MSE analysis of the QOCSO-NLSVM technique under various runs and years is provided in A brief RMSE analysis of the QOCSO-NLSVM method over many years and runs has been demonstrated in From the abovementioned figures, it is ensured that the QOCSO-NLSVM model is an effective regional economic prediction method over the other existing techniques.In this research, a proposed QOCSO-NLSVM technique has been developed for regional economic prediction. The QOCSO-NLSVM technique encompasses several subprocesses, namely, DTW based preprocessing, DBSCAN-based clustering, NLSVM-based prediction, and QOCSO-based parameter optimization. The use of the DBSCAN model enables the computation of identical states depending upon the per capita NSDP growth trends and socioeconomic-demographic features in a state. In addition, the application of the QOCSO algorithm helps to properly select the parameter values and thereby reaches the maximum predictive outcomes. The QOCSO-NLSVM technique is used to discover identical states based on per capita NSDP growth trends and socioeconomic-demographic characteristics in a state. QOCSO-NLSVM is used to run a variety of simulations on regional economic data and is also used to assess a region's present economic position. The experimental validation of the QOCSO-NLSVM technique and the results are examined in various aspects. The comparative analysis revealed the enhanced outcomes of the QOCSO-NLSVM technique over the recent approaches. With a minimum MSE of 70.548, the QOCSO-NLSVM approach produced effective results. The QOCSO-NLSVM technique had remarkable results, achieving the lowest root mean square error (RMSE) of 8.399. In the future, advanced DL models can be used to improve the overall prediction outcomes.


Protein synthesis involves initiation, elongation, and termination. A number of studies show that translation elongation is highly regulated and is the most energy-intensive process, which controls the efficiency and accuracy of protein synthesis. In the elongation process, eukaryotic elongation factor 2 (eEF2) binds to the ribosome to induce its conformation change and promotes the translocation of the tRNAs by hydrolyzing guanosine triphosphate, which then allows the next AA-tRNA to enter the A site for translation elongation. The activity of eEF2 is negatively regulated by its kinase eEF2K that phosphorylates eEF2 at threonine-56 (Thr56). Dysregulation of elongation is the core of the pathogenesis of tumorigenesis and neurodegeneration. Thus, eEF2/eEF2K as a potential therapeutic target has attracted more and more attention . In a rePQBP1 gene  caused by mutations in the human BP1 gene . PQBP prBP1 gene . HoweverBP1 gene . Our stuBP1 gene . We elucW domain . AlthougW domain . PQBP1-YW domain . It is sW domain . Our datThe WW domain, similar to the SH3 domain, specifically recognizes proline-rich ligands and has been found in many signaling proteins. Unlike eEF2 that generally exists in both prokaryotes and eukaryotes, PQBP1 only exists in higher eukaryotes. We suspect that the regulatory mechanism of PQBP1/eEF2/eEF2K is specifically evolved for higher eukaryotes and provides a finer regulatory network for translation to meet various functional requirements of higher eukaryotes. How the PQBP1‒eEF2 interaction responds to different signals and coordinates the growth and development needs further study.2+ , the Natural Science Foundation of Jiangsu Province (BK20170080 to Z.C.Z.), and Guangdong Key Project (2018B030335001 to J.H.).]


Medullary thyroid cancer (MTC) is a neuroendocrine tumor that arises from the parafollicular C-cells, which produces the hormone calcitonin. RET is a transmembrane receptor protein-tyrosine kinase, which is highly expressed in MTC. Our previous studies reported that cyclin-dependent kinase 5 (CDK5) plays a crucial role in cancer progression, including MTC. However, the role of CDK5 in GDNF-induced RET signaling in medullary thyroid cancer proliferation remains unknown. Here, we investigated RET activation and its biochemically interaction with CDK5 in GDNF-induced medullary thyroid cancer proliferation. Our results demonstrated that GDNF stimulated RET phosphorylation and thus subsequently resulted in CDK5 activation by its phosphorylation. Activated CDK5 further caused STAT3 activation by its specific phosphorylation at Ser727. Moreover, we also found that GDNF treatment enhanced ERK1/2 and EGR1 activity, which is involved in p35 activation. Interestingly, we identified for the first time that CDK5 physically interacted with RET protein in MTC. Overall, our results provide a new mechanism for medullary thyroid cancer cell proliferation, suggesting that targeting CDK5 may be a promising therapeutic candidate for human medullary thyroid cancer in the near future. Medullary thyroid cancer (MTC) is a neuroendocrine tumor that arises from the parafollicular C-cells, which produces hormone calcitonin . CalcitoIn addition to RET mutations in MTC, the ligands of RET receptor protein  are the glial cells line-derived neurotrophic factor family . Glial cCyclin-dependent kinase 5 (CDK5) is a proline-directed serine/threonine kinase family. Its regulatory proteins, mainly p35, activate CDK5 and its activation is highly demanded in the development of the central nervous system (CNS) . Our prel-glutamine and 1% penicillin and streptomycin. Cells were maintained at 37 °C with 5% CO2 in a humidified atmosphere. TT cells were treated with GDNF (200 ng/mL) in a serum-free medium. The serum-free medium is not supplemented with FBS while supplemented with the same concentration of components mentioned above.Human thyroid cancer cell lines “anaplastic thyroid cancer ARO, follicular thyroid cancer WRO, papillary thyroid cancer Cg3 and medullary thyroid cancer cells TT ,35 were 4. Treated cells with or without GDNF were counted by using hemocytometer glass each day for 6 days. The growth curve and proliferation ability of TT cells were calculated and quantified.Cell viability and proliferation assay was performed as described previously . BrieflyImmunoblotting was performed as described previously ,37,38. BMixed Protein G Mag Sepharose Xtra beads  with the specific primary antibody in a ratio of 10 μL:1 μg to make beads/antibody precipitating complex. Cellular proteins were extracted through the Cell protein extraction procedure mentioned above and incubated with beads/antibody precipitating complex at 4 °C for about 12~16 h. Using magnetic force to collect beads and analyze interacting protein of precipitated protein by western blot .4. After the treatment period finished, cells were fixed with fixation buffer 4%paraformaldehydee  for 15 min at room temperature. Followed by washing three times with PBS and blocking with 5% BSA in PBS, cells were incubated and co-stained with specific primary antibodies overnight. After washing three times with PBS, secondary antibodies conjugated with Alexa 488 and Alexa 546 were incubated at room temperature for 1 h. Cell nuclei were stained by DAPI fluorescent dye (Sigma-Aldrich). After further washing with PBS, slides were mounted for observation by confocal microscopy  [Cells were seeded on coverslips with a density of 2 × 10, Japan) .siRNA transfection was performed as previously described ,39. siRNt-test was used to calculate the p-value. Statistical significance was marked as * p < 0.05, ** p < 0.01, and *** p < 0.0001. No significance was marked as n.s.Data were presented as the mean ± S.E.M. (Standard error of the mean) and paired Student’s We first investigated the expression level of CDK5 and RET proteins in anaplastic thyroid cancer ARO, follicular thyroid cancer WRO, papillary thyroid cancer Cg3, and medullary thyroid cancer cells TT. Our results showed that the RET protein expression level was higher in TT cells than other types of thyroid cancer cells A. RegardNext, we sought to determine whether CDK5 gets involved in the regulation of GDNF-activated RET on TT cell proliferation. Thus, we evaluated the role of CDK5 in GDNF-induced TT cell proliferation. Knock-down of CDK5 by its small interfering RNA (siRNA) transfection remarkably decreased GDNF-induced TT cell viability C. NonspeIt has been well established that RET, as a transmembrane protein, interacts with several tyrosine kinase proteins, and their intracellular interaction has been associated with various type of cancer cell proliferation ,41,42. SWe first confirmed that GDNF treatment increased RET activation in 30 min by its phosphorylation. However, CDK5 activity was not increased by short term GDNF treatment in both immunoprecipitated RET and CDK5 group C. To obsThese data suggested that GDNF-activated RET protein recruited CDK5 protein and consequently induced its phosphorylation and activation. Since we found that GDNF induced physical interaction between RET and CDK5 protein, which resulted in a CDK5 activation by its phosphorylation, we wondered whether intracellular localization of CDK5 was affected by time-dependent GDNF treatment. Our results showed that GDNF treatment altered the intracellular localization of CDK5. We found that GDNF induced intracellular localization of CDK5 mainly at the cell membrane , suggestOur current data demonstrated that CDK5 and its subunit p35 play an essential role in GDNF-activated RET signal transduction. Based on these findings, we next investigated the downstream of CDK5 in this signal transduction. Since we have previously reported that CDK5 phosphorylates STAT3 at Ser727 site in prostate and medullary thyroid cancer cells ,28,33, tOur previous findings demonstrated that nerve growth factor (NGF), a neurotrophic factor, induced p35/CDK5 activity through ERK-Egr1 activation during neuronal differentiation . Since oRET is a transmembrane protein consist of the intracellular tyrosine kinase domain, and therefore it is classified in the receptor tyrosine kinase (RTK) family. Classic RTK activation occurs when the specific ligands bind to its receptor . As obseIn addition to the role of CDK5 in the CNS, CDK5 has been reported to play essential roles in cancer progression. Our previous studies indicated that CDK5 promotes prostate cancer cell growth in vitro, in vivo, and prostate cancer patients ,23,53. SNext, we asked whether CDK5 plays a regulatory role in GDNF-induced MTC proliferation. To determine the function of CDK5, we performed CDK5 inhibition by using siRNA or its inhibitor, roscovitine. Our data demonstrate that CDK5 inhibition significantly prevents GDNF-induced MTC proliferation and cell viability. This data confirmed that CDK5 plays an important role in GDNF-induced MTC proliferation. Next, we asked whether CDK5 gets involved in RET signaling after GDNF stimulation. For this purpose, we performed immunoprecipitation to evaluate protein-protein interaction in TT cells. Interestingly, we identified for the first time that CDK5 physically interacts with RET protein in GDNF-induced medullary thyroid cancer cell proliferation. Our results reveal that CDK5 can bind intracellular kinase domain of RET protein in TT cells. Moreover, our results demonstrate that CDK5 protein co-localizes with RET protein mainly at the cell membrane. These data suggest that RET activation may recruit CDK5, and it may activate CDK5 to stimulate CDK5-mediated downstream signaling cascade. To observe the activation status of RET and CDK5 protein, we performed time-dependent GDNF stimulation. Our results show that short term GDNF stimulation can only increase phosphorylation of RET protein. Besides, in this short-term treatment, GDNF stimulation induces the protein-protein interaction between CDK5 and RET, although GDNF could not induce the phosphorylation of CDK5. Therefore, to observe the alteration of CDK5 phosphorylation, we performed GDNF stimulation for long time. GDNF stimulation increases CDK5 phosphorylation on Y15 after 1.5 h of treatment. Next, we asked whether time-dependent GDNF stimulation alters the colocalization between CDK5 and RET in TT cells. Our data showed that GDNF treatment enhanced the colocalization between CDK5 and RET protein mainly at the cell membrane. Overall, our data reveal that CDK5 activation occurs after RET activation by GDNF stimulation, suggesting that CDK5 may be a potential downstream of RET protein in GDNF-induced human medullary thyroid cancer cell proliferation. After understanding the physical interaction between CDK5 and RET, the next question is how this interaction passes their signal into the nucleus in GDNF-induced MTC proliferation. We first investigated that GDNF treatment enhanced nuclear localization of p35 protein in time dependent-manner, suggesting that CDK5 and p35 may localize in the nucleus and result in an enhanced MTC proliferation. Direct nuclear localization of p35 may be one explanation of how RET signaling goes to the nucleus in MTC proliferation. Besides, our previous study demonstrated that CDK5 phosphorylates STAT3 in prostate and medullary thyroid cancer cells ,28,33. SIn conclusion, our study reveals that CDK5 plays an essential role in regulating GDNF-induced human medullary thyroid cancer cell proliferation. CDK5 inhibition remarkably reduces cell viability and cell proliferation in human medullary thyroid cancer. In this study, we identified for the first time that CDK5 physically interacts with RET transmembrane proto-oncogene protein. This interaction results in the subsequent STAT3 activation to promote human medullary cancer cell growth . Conside


Alsin is a protein of 1,657 amino acids known for its crucial role in vesicular trafficking in neurons thanks to its ability to interact with two guanosine triphosphatases, Rac1 and Rab5. Evidence suggests that Rac1 can bind Alsin central region, composed by a Dbl Homology (DH) domain followed by a Pleckstrin Homology (PH) domain, leading to Alsin relocalization. However, Alsin three-dimensional structure and its relationship with known biological functions of this protein are still unknown. In this work, a homology model of the Alsin DH/PH domain was developed and studied through molecular dynamics both in the presence and in the absence of its binding partner, Rac1. Due to different conformations of DH domain, the presence of Rac1 seems to stabilize an open state of the protein, while the absence of its binding partner results in closed conformations. Furthermore, Rac1 interaction was able to reduce the fluctuations in the second conserved region of DH motif, which may be involved in the formation of a homodimer. Moreover, the dynamics of DH/PH was described through a Markov State Model to study the pathways linking the open and closed states. In conclusion, this work provided an all-atom model for the DH/PH domain of Alsin protein; moreover, molecular dynamics investigations suggested underlying molecular mechanisms in the signal transduction between Rac1 and Alsin, providing the basis for a deeper understanding of the whole structure–function relationship for Alsin protein. Alsin is a protein of 1,657 amino acids encoded by the amyotrophic lateral sclerosis type 2 (ALS2) gene. Its functions are crucial for neuronal homeostasis; indeed, Alsin mutations have been correlated with neurodegenerative disorders such as infantile-onset ascending hereditary spastic paralysis (IAHSP) . The seqUnBnd) and the DH/PH-Rac1 molecular complex (AlsinBnd).Alsin DH/PH domain structure was obtained through homology modeling since no experimental structure is available. The homology model was used to study the effect of the interaction with Rac1 on Alsin dynamics. To this purpose, two systems were simulated with classical MD: Alsin DH/PH domain (AlsinThe amino acid sequence of human Alsin has been retrieved from UniProt database (id: Q96Q42), and the residues corresponding to the predicted DH/PH domain  were extracted and numbered according to that of the whole Alsin. Then, the homology model was built giving the above-mentioned sequence as input to the I-Tasser suite . Models UnBnd initial configuration was obtained by adjusting the protonation state of the homology model according to a physiological pH of 7.4. The atomic coordinates of human Rac1 were obtained from Protein Data Bank  to LARG and RhoA  using the modified Berendsen thermostat   was usedBnd and AlsinUnbnd), the structural stability throughout the MD simulation was evaluated through the root-mean-square deviation (RMSD) from the initial configuration of C-alphas atomic positions during the trajectory. Since previously it has been observed that the essential dynamics of the DH/PH domain that are expressed in other proteins is characterized by a collective motion of PH domain and α6 helix  has been computed through Boltzmann inversion as:The position of PH domain with respect to DH region in the bound and unbound states has been investigated to understand the effect of Rac1 in the conformations of Alsin DH/PH domain. In the analysis of PH–DH relative position, the short replicas were considered in order to obtain a wider sampling of the state space. The potential mean force (PMF) of AlsinkB is the Boltzmann’s constant, T is the temperature, and p is the probability distribution obtained from the histogram of MD data, where bins of 1° and 0.1 nm were used to discretize the state space along αxy and PHz directions, respectively.where xz was analyzed as done previously , such that positive values indicate α3 being over DH center of mass. The second one is the distance between the centers of mass of α3-5 (residues 796–816) and PH domain.As mentioned before, helix α6 motion characterizes the essential dynamics of homologous domains. Moreover, interesting regions were helix α3 and the following coiled region (α3-5) due to their observed dynamics and interaction with Rac1. Therefore, to evaluate the effect of the interaction between Rac1 and helix α6 (residues 865–895) on its straightness, the curvature of α6 axis on plane eviously  , describing the state space in terms of DH–PH relative position through the previous mentioned coordinates, which were computed every 10 ps for both long and short replicas  algorithm  is characterized by higher fluctuations, reduced by the presence of Rac1. Finally, in the bound state, the fluctuations between β3 and β4 were reduced with respect to the unbound state.The regions that characterize Alsin dynamics have been investigated through RMSF computed on C-alphas . The grexy and PHz was analyzed to characterize the DH–PH relative position in AlsinUnBnd and AlsinBnd  center of mass and the segment linking the latter to PH domain  center of mass. Since the motion of last helix in DH region is involved in PH dynamics in other proteins, it was not considered when computing the center of mass to avoid possible changes in its position due to PH fluctuations . Notably, this is the only region of the energy profile in common between the two systems. The overall ability to explore different DH-PH relative positions is higher in AlsinUnBnd, where the minima are well connected. Instead, in the presence of Rac1, the minima are narrower and two low connected regions, characterized by positive and negative PHz, are explored. According to the definition of the two coordinates, the PMF showed that, in the absence of Rac1, more closed conformations were explored by the domain, i.e., the positions assumed by PH domain tended to move Alsin C-terminus closer to the N-terminus. On the other side, the presence of Rac1 seemed to stabilize a more linear and open conformation of the domain.The PMF along αAlsinBnd . To thistuations . Then, t surface . It was UnBnd (1.06 ± 0.34) than in AlsinBnd (0.22 ± 0.17) meaning that the presence of Rac1 stabilized a straighter conformation of α6 , while it was positive in AlsinBnd (0.19 ± 0.06 nm). The result of such Rac1-induced displacement has been described through the distance between α3-5 and PH domain (Finally, the effect of Rac1 in the position of helix α3 has been described in terms of relative position along the  of mass . The aveH domain . It was H domain .xy and PHz. From the analysis of the largest implied timescales at increasing lag times, the lag time to build the MSM was set to 9 ns. The number of states determined through PCCA+ algorithm was set to five according to the values of the first 10 implied timescales at 9 ns. The same lag time was used to build the MSM from the 5-state discretization (UnBnd and AlsinBnd were partially overlapped. It was characterized by a high probability to jump in state 1, while transitions from and to state 2 are less probable. State 1 was quite stable and communicated with all other states, with higher probabilities of jumping to state 2 or 0. In the former, the protein was closed on the side of DH domain, while the latter was the least stable state with a high transition probability toward state 4. The last state was quite stable and characterized by the protein being closed from the bottom of DH domain (The dynamics of free Alsin was characterized through a MSM built describing the state space in terms of αtization . The modtization . State 3H domain . From thH domain .The availability of 3D atomistic models of Alsin is crucial to understand the molecular mechanisms at the basis of its biological functions. Indeed, the first step toward the treatment of ALS2-related pathologies such as IAHSP is a proper comprehension of the protein physiological behavior. In this work, we focused on Alsin DH/PH domain and developed its all-atom model through I-Tasser suite using 16 templates from RCSB database. The quality of the model was confirmed both by the confidence scores predicted by the employed software, especially the TM-score, and the analysis of the Ramachandran plot . The ali1 recently released on the AlphaFold protein structure database (Moreover, the present DH/PH model is in close agreement with the DH/PH domain of the Alsin modeldatabase . In a grBnd. Since CR1 has been described as crucial for the transforming activity of Rho GEF family of proteins (Since the interaction between Alsin DH domain and Rac1 is known to trigger relocalization at membrane level and activation of Rab5 through the C-terminal VPS9 domain , the effproteins , these rproteins . The latproteins . Therefoproteins .Previously, it has been proved that Alsin is sequestered in the cytoplasm due to an interaction between RLD and its C-terminus . Rac1 siUnBnd was modeled through an MSM to describe the transitions between its different conformations. One open state was found in the region where the PMF of AlsinBnd and AlsinUnBnd partially overlapped, while two closed states were detected (UnBnd, the PH domain tends to move closer to a region that is involved in the homodimer formation in homologous proteins. In contrast, the Rac1-induced conformational change seems to stabilize the DH/PH domain toward a linear conformation. The closed and open conformations were connected mainly by one intermediate state, which allowed transitions both between the two closed states and from the open to the closed ones (The dynamics of Alsindetected . In one sed ones .UnBnd and AlsinBnd. One of them was helix α6, i.e., the one linked to PH domain, whose curvature was higher in the absence of Rac1 and, particularly, in the closed states (In particular, two main regions within the DH domain were characterized by different conformations in Alsind states . Since td states . The secd states . Hence, To summarize, this study highlights that free Alsin DH/PH domain exists mainly in a closed state, where the interaction between RLD and C-terminus may hide the putative dimerization surface. In these conformations, the helix α6 is bent such that its interaction with Rac1 might be unlikely. When, due to thermal energy, the PH domain moves to reach a more open state and helix α6 curvature decreases, the DH domain may be more prone to bind Rac1. Such interaction, together with the one between α3-5 and PH, seems to stabilize a linear conformation of DH/PH domain. At the same time, dimerization through DH domain may be favored by Rac1 interaction since it reduces the fluctuations within the residues that should mediate such processes.The understanding of molecular mechanisms underlying Alsin biological functions is still limited, and therefore, a clear comprehension of molecular mechanisms driving ALS2-related pathologies is difficult. In this view, investigating nanoscale phenomena involved in Alsin-mediated physiological pathways may shed light on the structure–function relationships and provide insight on essential mechanisms involved in neuronal homeostasis, whose impairment may lead to diseases such as IAHSP. However, the Alsin structure has not been resolved yet. In this framework, the first step is represented by the development of Alsin 3D atomistic model and the analysis of its dynamics. This work provides an all-atom, 3D structure for the DH/PH domain of Alsin, refined in explicit water, by molecular modeling techniques, available for the scientific community.in silico investigations and additional in vitro analysis will be needed to elucidate mechanisms and relationships between Rac1, DH/PH, and RLD.The analysis on Alsin DH/PH region, both with and without Rac1, revealed that its CR1 should not be involved in the interaction with its binding partner, unlike other Rho GEF proteins. This result suggests a possible molecular basis of its different biological functions. Moreover, the mechanism by which Rac1 may induce conformational changes that trigger the membrane relocalization has been investigated. While stabilizing a more open and linear conformation of the whole domain by changing the DH–PH relative position, the interaction with Rac1 caused a local increase in the mechanical properties within the putative dimerization site. As hypothesized from previous literature , Rac1 inThe original contributions presented in the study are included in the article/MM and MCn carried out molecular dynamics simulations and data analysis. MD was responsible for the research supervision. All authors have contributed to the conceptualization, data rationalization, manuscript writing, and read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Sorghum, cowpea, and cassava are underutilised gluten-free sources of flour that have the potential to be used in bread products in sub-Saharan Africa. Excessive wheat imports affect the economies of countries in sub-Saharan Africa, driving the search for wheat flour alternatives. To extend the use of sorghum, cowpea, and cassava flours toward bread production, it is vital that the sensory properties of these flours are better understood. A trained sensory panel evaluated and described the sensory properties of flatbread models prepared from red non-tannin sorghum, fractions (whole and dehulled) of two cowpea varieties, cassava starch, and designated flour composites. The composites were prepared using cassava starch and sorghum flour at 0%, 35%, and 70%, respectively, with 30% cowpea flour. The addition of sorghum intensified sorghum aroma in flatbread, while cowpea flours contributed a beany flavour. Flatbreads from cassava-cowpea composites had a chewier and rubberier mouthfeel, an intense fermented aroma and flavour, and a sour aftertaste compared to single flours, but were most similar to the wheat flatbread, with a residual beany flavour. Information from this study can guide food product developers toward developing new bread products from sorghum, cassava, and cowpea composite flours, thereby moving Africa towards a more sustainable food system. Further research on the effects of the sensory characteristics on consumer liking of the flatbreads is needed. Wheat-based flatbreads are very popular across sub-Saharan Africa (SSA). A modern chapatti-style flatbread made from wheat, known as East African chapatti, is widely consumed in Kenya, Uganda, Mozambique, Tanzania, and Burundi ,2. In GhSorghum, cowpea, and cassava are gluten-free climate-resilient crops that are widely cultivated in SSA ,7,8. CowSorghum flour is rich in starch (72.3–75.1%), minerals (1.6–1.7%), and polyphenolic compounds (3–43 mg/100 g) ,14. CowpDehulling of cowpea seeds is a common practice in the processing of cowpea flour  and may Hot and cold paste viscosity varied among 28 cowpea varieties as a result of differences in inherent physical and chemical properties, which may affect texture and other sensory properties of foods (such as baked goods) where the cowpea flour was used . In addiDescriptive sensory evaluation is a method used to describe the sensory profile of food products by using trained human assessors. Here a trained sensory panel described the sensory properties of flatbread-type food models prepared from different flours  and specific flour combinations. A secondary objective was to understand how the sorghum flour particle size affects the sensory properties of the flatbread. Moreover, the effects of cowpea variety and milling fraction  on the sensory profile of the flatbread were considered. Thus, we explored the potential of the flours to replace wheat flour in flatbread.w/v in deionised water in Ziploc bags, at room temperature (~25 °C) for 14 h. The soaked cowpea seeds were then dehulled manually and dried at 45 °C in a forced air convection oven  for 7 h to achieve a moisture content of 7–8%. Whole and dried dehulled cowpea seeds were milled into flour with the same hammer mill and a 500-µm sieve, vacuum-packed in polythene bags, and stored at −20 °C until analyses.Red, non-tannin, (King Korn super fine sorghum (mabele) meal (about 10% protein) and Snowflake white bread wheat flour were obtained from a local supermarket. White (Bechuana white variety) and red (Glenda variety) cowpea seeds were sourced from Agrinawar Agricol (Pty) Pretoria, South Africa, and cassava starch  from DADTCO . The initial moisture contents of the white and the red cowpea seeds were 11% and 12%, respectively. The sorghum meal denoted as FSorg was re-milled using a hammer mill  fitted with a 500-µm sieve to obtain a finer flour (XFSorg). Whole cowpea seeds were soaked in batches (250 g) at a ratio of 1:1 The particle size distribution of FSorg and XFSorg was determined in triplicate using a procedure described by . Flour aXFSorg was composited at 0%, 35%, and 70%, with 30% whole or dehulled cowpea flour and cassava starch  by mixing the required amounts for 20 min using a mixer . The speed was increased every 3–4 min. Composites with 70% cowpea flour or cassava and sorghum flours were not prepared due to the requirement for a nutritional balance of legume and sorghum or cassava flours to ensure a comparable protein content to wheat flour ,33. A 10Descriptive sensory evaluation is traditionally conducted in a laboratory where many factors are controlled during the assessment of product samples. The 2019 COVID-19 pandemic and lockdown restrictions changed work life. The necessity to avoid spreading the COVID-19 virus, to save lives, forced assessors to conduct descriptive sensory evaluations outside of laboratories, at the trained assessors’ own homes. A protocol was developed to control factors that may cause variations in samples during storage, preparation, and sensory evaluation at the assessor’s home.Written instructions on the preparation of the flatbreads and a demonstration video were developed for the panel . The panTwelve (12) panellists, students at the University of Pretoria who had been screened for basic sensory acuity were additionally screened using an online questionnaire to assess their understanding of good sensory evaluation practices, access to the internet at home, and willingness and availability to participate. The trained panel consisted of eight (8) females and four (4) males who were aged 18–45 years. A 2-h long introductory session was conducted to introduce the panel to the flatbread preparation and the nature of the sensory evaluation procedure. The written instructions on flatbread preparation, the video and items for preparing the flatbread  were supplied to the panel. Panellists prepared flatbreads from flours  and geneThe panellists collected flour samples prior to a week’s evaluation and an internet link to the evaluation ballot was emailed to them on the day of evaluation. In total, 15 sessions, 3 sessions per week, were conducted. Samples were evaluated in random balanced order following a Williams design. The panel prepared flatbreads from flours in A tristimulus colorimeter  was calibrated with a white tile as described by the manufacturer. The (L*) lightness, (a*) red–green, and (b*) blue–yellow colour values of the 7 single and 12 composite flours , and the® software was used for analysing the data.All the analyses were conducted in triplicate. One-way ANOVA based on 5% significance level was used to test (1) the effect of remilling the sorghum flour on the distribution of particle size and on the sensory properties of sorghum flatbreads; (2) the effects of sorghum or (3) cassava flour levels; or (4) cowpea type in the flatbread (with 30% cowpea flour) on sensory properties. For cowpea only flatbreads, a two-way ANOVA was performed to determine the main and interaction effects of cowpea variety and milling fraction on the sensory attributes of cowpea flatbread or L*, a*, and b* values of cowpea flours and flatbreads. Significant differences between means were calculated using Tukey’s honestly significant difference (HSD) test. The mean scores for sensory attributes of flatbreads made from composite flours were subjected to principal component analysis (PCA). PCA was applied on data standardised based on a correlation matrix. Factor 1 to 6 scores from the PCA were used for agglomerative hierarchical clustering (AHC) of the flatbreads. For AHC, the pairwise similarity and linkage function were measured using Pearson correlation coefficient and the unweighted pair-group average method, respectively. XLSTATp < 0.05). The % particles > 500 µm was reduced when sorghum flour was re-milled. Particles between 500 and 250 µm made up the largest portion of the flours. A significant increase from 4.9% to 8.6% (75% increase) in finer particles (<150 µm) was observed after re-milling the sorghum flour.p < 0.05). Flatbread prepared from FSorg with larger particles (>500 µm) had more residual particles, specks, a drier appearance and a grainier mouthfeel than XFSorg flatbread.p < 0.05). The intensity of colour, C* was higher for FSorg than XFSorg flour and flatbread. Even though the XFSorg flour looked lighter  than FSorg flour, when batters from the flours were baked into flatbreads, the XFSorg flatbread was darker than FSorg  was significantly lower (darker) than flour from the white cowpea variety (WC) . Moreovep < 0.05). Flatbreads with 0% sorghum was chewier, rubberier, and less grainy than those with 35% and 70% sorghum. The 70% sorghum flatbreads were less chewy with a drier mouthfeel compared to the 35% sorghum flatbread.The cassava-cowpea flatbreads were characterised by fermented aroma, fermented flavour, and sour aftertaste and had greener aroma compared to wheat flatbread and flatbreads with sorghum . Fermenty-axis on the dendrogram represents the Pearson correlation coefficient and measures the similarity of the flatbreads. Variance components were 11% for cluster 1, 36% for 2, and 23% for 3, based on ANOVA.Except for residual particles, C1 flatbreads had similar flavour/taste, aftertaste characteristics, and were comparable in thickness, dry appearance, and specks. Additionally, the C1 flatbreads had similar overall aroma strength compared to C2 and C3. The cassava-cowpea flatbreads were more separated in factor 3, 4, and 5 of the PCA, which was the basis of their separation into different clusters in The sorghum flatbread prepared with the finer flour (XFSorg) had less specks than FSorg. This is because finer particles are less visible to the eye than larger particles. The higher L* and lower a* of XFSorg flour compared to FSorg was because it had a greater amount of finer particles with a larger surface area to increase light reflection. This was in agreement with earlier reports by ,39, wherThe fineness of the flour also influenced other aspects of the bread surface appearance and thickness of the flatbread. Finer milling of sorghum flour contributed positively to the texture perception of the flatbread. It reduced the perception of grainy or gritty particles and dry mouthfeel, resulting from the smaller ratio of particles larger than 500 µm. Graininess or grittiness of sorghum products are perceived more readily with increased flour particle size and the amount of larger particles . FlatbreAs expected, photographs showed that the whole cowpea flatbreads were brown whereas the dehulled cowpea flatbreads were yellow–green. The white cowpea flatbread appeared brighter and less red (a*) than flatbread from the red cowpea variety. The difference in the colour of the cowpea varieties is due to the different seed coat colour. The red cowpea flatbread was browner due to more anthocyanins concentrated in the seed coat of the red seeds .The red cowpea flatbread smelled less beany compared to the white one. Beany aroma or beany flavour is a limiting factor in the acceptance of bread  and manyFlatbreads from dehulled cowpea flours were nuttier and sweeter compared to whole cowpea flour flatbread. Flatbread with nutty aroma may be desirable. Micronised and boiled cowpeas with nutty flavours were preferred by consumers ,50. NuttDehulled cowpea flatbreads appeared less thick than whole cowpea flatbreads. This could be attributed to the reduction in fibre content due to dehulling. A study by  found thThe sorghum-cowpea flatbreads were browner compared to flatbreads with cassava flour added. The flatbreads with a higher proportion of sorghum were browner due to the contribution of pigments in the sorghum pericarp ,59. CassThe lower brittleness in sorghum-cowpea flatbread compared to the sorghum flatbreads (Fsorg and XFSorg) may be due to an increase in protein content from cowpea . The sorThe aftertaste of cowpea flatbreads was more astringent than sorghum flatbreads. Single flours from cowpea may not be suitable for flatbread due to the intense beany aroma, flavour, and aftertaste. Likewise, the high brittleness, dry appearance, bitter aftertaste and dry mouthfeel make sorghum flour alone unsuitable for flatbread. The astringent aftertaste of the composited flour flatbreads did not differ probably because the cowpea proportion in the flatbreads was kept constant.While cowpea variety affected the beany aroma intensity in cowpea flour only flatbreads, there was no difference in beany aroma when the two cowpea varieties were composited with sorghum and cassava starch. This may be due to the lower proportion of cowpea flour in the flatbreads made from composite flours compared to flatbreads from only cowpea flours.The nutty aroma in the flatbreads was mainly contributed by sorghum and cowpea; it could have formed during baking by the Maillard reaction. Differences in the nutty aroma of flatbreads can be attributed to differences in protein content of the composites as a result of the flour combinations. The cassava starch has a small amount of protein (0.9%) and therefore cassava-dehulled cowpea flatbreads (8–9% protein in composite flour) were lower in nutty aroma compared to sorghum-cowpea and sorghum-cassava-cowpea (9–13% protein in composite flours). Nuttiness in cassava-cowpea flatbreads was similar to wheat flatbread.The cassava starch in the composites introduced fermented aroma and flavour in the flatbreads which were more pronounced in cassava-cowpea flatbreads. The fermented aroma and flavour were probably caused by volatile and non-volatile organic acids in cassava starch that may have been as a result of the wet extraction method for production of the starch. Wet extraction of cassava starch involves milling cassava with water, filtration, separating sediment from filtrate to obtain the wet starch and drying over a 24-h period at a low temperature . The sepThis study documents the sensory properties of sorghum, cowpea, cassava, and their composited flours when applied in a model flatbread type product. Flatbreads made from a composite of 70% cassava starch and 30% dehulled cowpea flour were most similar to the wheat flour flatbread, but with a residual beany flavour. Single flours from sorghum, cowpea, and cassava are less suitable for the manufacture of flatbread. Sorghum flour particle size affected some appearance, mouthfeel, and after swallowing characteristics. Finely milled sorghum flour will be an option to minimise dry appearance, graininess, and residual particles in flatbreads. The beany flavour differences between cowpea varieties observed in the 100% cowpea flatbreads were not apparent in the composites. Therefore, composites can be made from either cowpea variety. Dehulling cowpeas affected colour, in-mouth texture, taste, and residual particles. Dehulling cowpeas before compositing the flour with cassava starch is desirable to simulate wheat flatbread flour. Cassava starch is good for inclusion in composites flours owing to its functionality to contribute rubbery and chewy texture. Follow-up research is needed to upscale the formulations to commercial scale flatbread baking and to determine consumer acceptance. This study provides valuable information that food product developers could use to develop bread products using these climate-resilient gluten-free flours to increase their utilisation and move SSA countries towards a more sustainable food system.


Precision glass molding (PGM) technology is a cost-efficient process for the production of micro/nanostructured glass components with complex surface geometries. The stress distribution, surface profile, and reduced refractive index of the molded lens are based on the lens being fully formed. The process of the deformation of the glass preform is rarely discussed, especially in the case of multi-machining parameters in the experiment. The finite element method (FEM) was adopted to analyze the glass preform deformation. Due to the phenomenon of incomplete deformation of the glass preforms in the experiments, two groups of finite element simulations with different boundary conditions were carried out with MSC.Marc software, to reveal the relationship between the deformation progress and the parameters settings. Based on the simulation results, a glass preform deformation model was established. The error between the model result and the simulation result was less than 0.16. The establishment method of the glass preform deformation model and the established model can be used as a reference in efficiently optimizing PGM processing parameters when the designed lens has two different base radii of curvature. Glass optical components are widely used in the camera, computer, laser projection, biological, and consumer electronics fields because of their optical performance, mechanical stability, thermal stability, and high chemical resistance ,2,3,4. TPrecision glass molding (PGM) technology is a cost-efficient process for the production of micro/nanostructured glass components with complex surface geometries ,7,8. It dn/dTs of ChGs are lower than those of Ge, thus having advantages in weight and athermalization. In addition, ChGs have low dispersion, low cost, high optical uniformity, and excellent properties in achromatism [xSe1−x is a binary covalent glass that has been investigated extensively for its outstanding transparent property [Among optical glass, chalcogenide glasses (ChGs) are a group of materials that contain one or more of the chalcogen elements  and group IIIA-VA elements . ChG has a wide optical window that can be extended from the visible to far-infrared spectrum . Compareromatism ,14,15,16romatism ,17. Amonproperty . The lowproperty . Howeverproperty ,13,15.2S3 lens with the cooling rates of finite element method (FEM) to verify and modify the calculated stress relaxation function [40Se50 S10 lens and propose that the optical design should take the post-molding refractive index into consideration [Around the molding temperature, ChG is in a viscoelastic state . The finfunction . They alfunction . Zhang ederation . The proderation . Zhou etderation . FEM alsderation .The deformation of the glass preform was determined by the relative position of the upper and lower molds to a great extent. In a PGM experiment, the displacement of the mold was controlled by several setting parameters, including pressing force, pressing speed, press slope, and mold temperature. However, the effects of these parameters on the glass preform deformation are still scarcely addressed, especially for cases of molding with fixed die machines. The PGM machine usually has a unique execution program for a specific set of processing parameters, which is not known to the users. The unknown relationship between the designed parameters and the actual parameters will be a barrier for explaining the experimental phenomena and optimizing the processing parameters in the research and development cycle. Therefore, the interaction influence of multiple machining parameters on the glass preform deformations needs to be addressed.The remainder of this paper is organized as follows. Chalcogenide glass diffractive-refractive hybrid lenses are molded on a Toshiba precision glass molding machine , as shown in c1 is the curvature at the center of the profile, k1 is the conic constant, r1 is the radius of the lens, and A2, A4, A6, A8, and A10 are the even order coefficients. In Equation (2), c2 is the curvature at the center of the profile, k2 is the conic constant, r2 is the radius of the lens, n is the coefficient of refraction of the lens, and B2, B4, B6, B8, and B10 are the even order coefficients of the aspheric base. The material of the lens is Schott grade IRG26. The coefficients of the designed lens are listed in In Equation (1), The glass preform is a biconvex spherical lens with a curve radius of 20 mm, a thickness of 7 mm, and a diameter of 20 mm. The diagram of the designed lens and the glass preform is shown in Rapidly solidified aluminum RSA-905 was selected as the mold core material. Three PGM experiments were carried out with the processing parameters listed in A compact flash card can be inserted into the card slot of the PGM machine to store the data detected during automatic operation. The sampled data is saved in a file at each cycle. Sampling was performed from the start to the end of a cycle. The sampling period was set as 9000 s. One thousand data items were collected within the sampling period. That is, the sampling cycle is sampling period/1000 items, which is nine seconds. The waveforms of the force and displacement of the lower mold since the forming stage began are shown in t, the displacement is iy for the ith experiment. Based on the results, the line types of the curve consists of linear, conic, and cubic curves. The three kinds of curves are expressed as Equations (3)–(5).The deformation of the glass preform is related to the relative position of the upper and lower molds. The upper mold was installed on the fixed upper axis of the molding system, while the lower molds were installed on the mobile lower axis. Therefore, there is a need to describe the displacement of the lower axis with time. The displacement data are processed with regression analysis with IBM SPSS Statistics software . When the time is The fitting parameters are listed in ε(t)) goes on increasing with time (t), which is called creep. In creep, the deformation of glass is a superposition of instantaneous elastic deformation, delayed elastic deformation, and viscous flow, which can be described by viscoelastic mechanical models. When applying a constant stress σ0 at t = 0, the strain–time relationship can be obtained by Equations (6) and (7) according to Kelvin model [E and η are elastic modulus and viscosities of the viscoelastic material, respectively. τ is defined as the term “retardation time” to describe the creep rate in the viscoelastic deformation. When applying specified stress on glass at the molding temperature, strain (in model .(6)ε(t)The deformation of the glass includes two parts, which are the deformation of the upper half part and the lower half part of the glass preform. The respective deformation speeds of the upper half and the lower part are not known from the monitoring values in the experiment. FEM could be helpful for analyzing the deformation. The boundary condition could be displacement control and the element surface pressure control. The simulation model is firstly established, and then two groups of simulations with the two kinds of boundary conditions are carried out. To gain more information about the simulation result of the molded lens, three-dimensional simulation was carried out. If the geometries of the molds were the same as the actual molds, the computation workload would be too large. Thus, the profile of the mold core was the same as the design, but the height of the mold core was smaller than the actual mold core. As the simulation in this paper only included the molding stage of the PGM process, which does not involve heat transfer, the simplified geometry model would be acceptable.The three-dimensional geometric model is established in the finite element analysis software MSC.Marc , as shown in The PGM machine instruction book shows that the pressing force that is detected by the load cell is displayed in real-time. However, when the actual pressing forces that were applied to the glass were the same, the theoretical deformations should be the same, which were different from the actual deformations shown in According to the simulation results, the displacement in y direction of any node could be obtained. The displacement data of the three nodes were extracted: the center node of the top surface of the glass preform , and the actual press slope of the No. 1 experiment is assumed to be S1. When a force is applied to the glass preform with the spherical surface with a curve of 20 mm, the varying contact surface will lead to varying pressure.The PGM machine instruction book pointed that the pressing force that was detected by the load cell was displayed in real-time. However, when the actual press forces that were applied to the glass were the same, the deformation should be the same. Thus, the actual press force is a function dependent on the time (A(t), of the glass preform and the mold core can be calculated by the geometry relationship as shown in a(t), which can be expressed by Equation (9) when Vα1 is smaller than 0.02 mm/s. The actual pressure ασ1(t) applied to the upper surface of the glass can be expressed as Equation (10).In the progress of precision glass molding, the diffractive surface is upward and the aspherical surface is downward. The upper contact surface area, pR(t), of the lower contact surface shown in nF(t), applied to the lower contact surface can be expressed by Equation (12). The normal pressure applied to the lower surface is ασ2(t), expressed as Equation (13).The projection radius, αε1(t), of the upper surface can be concluded as Equation (14), according to Kelvin model. The strain of the lower surface is much smaller than the upper surface from α is a calculated constant. To know the property of the curve, like in Equation (6), when τ is assumed to be 81 s, the curves when the parameters are σ0/E, 0.95 × σ0/E, 0.6 × σ0/E are plotted as in The strain, t. The pressures in the No. 1, No. 2, and No. 3 simulations were 0.1 t MPa, 0.2 t MPa, and 0.3 t MPa, respectively. The personal computer parameters and the simulation operation times were listed in To obtain the deformation degree when the pressing force was different, a group of simulations were carried out. As the area of the bottom surface of the lower mold core was constant, the boundary condition in this section was the element surface distribution pressure. The pressure values that were applied to the element surface varied dependent on the time, S1. Therefore, the actual press slopes of the No. 2 and No. 3 experiments were assumed to be S2 and S3. Since the upper deformation was similarly linear, the strain calculation was similar to that in Vα1(t) of the upper half part of the glass in the first simulation can be calculated as Equations (15) and (16). Then, the ratio of the deformation velocities of the upper part of the glass in No. 1 experiment Vα1(t), No. 2 experiment Vβ1(t), and No. 3 experiment Vγ1(t) can be calculated by Equations (17) and (18). The actual simulation results are shown as Equation (19). The error between the model result and the simulation result was about 0.116 for the second simulation, while it was about 0.151 for the third simulation. The error may be caused by the approximation of the contact area in Equation (9). However, Equation (17) plays a certain role in setting the manufacturing parameters.The actual press force should be a function with the setting press speed. According to αVsimulation1_, βVsimulation1_, and Vγsimulation1_ are the deformation velocities of the upper part of the glass in No. 1, No. 2, and No. 3 simulations, respectively. Y(t) is a function that changes over time.2:0.0127592:0.0178522, immediately 1:2.92:5.72. The ratio does not have a linear relationship with the pressing speed. Therefore, the actual press force in the PGM experiments needs to be further studied.The ratio of the deformation rates of the glass preforms in the experiments was 0.007:0.012759:0.017852. Thus, the ratio of the rates of the applied force in the experiments was about 0.007In different experiments, the ratio of the deformations of the upper half part and the lower half part of the glass preform are approximately a constant when the pressing forces are big enough, but the pressing speeds are set differently. When the pressing force is set linearly over time and the primary pressing force is too small to achieve the setting pressing speed, after a period of time, the deformation speed of the glass preform starts to decrease significantly.FEM can be used to quickly establish the glass preform deformation model in the time when the glass is in a viscoelastic state. The error of the obtained glass preform deformation model is less than 0.16.The established model can be used as a reference to efficiently optimize PGM processing parameters when the designed lens has two different base radii of curvature.At present, there is little discussion on the deformation of glass preforms under multi-machining parameters of PGM technology, but more discussion on the properties of the molded lens. This paper investigated the glass preform deformation under multi-machining parameters when fabricating ChG diffractive lens with PGM technology through experiments, FEM simulations, and theoretical analysis. The primary findings are as follows:


The main conclusion was that UNS S32750 SDSS steel can be safely deformed by upsetting between 1050–1275 °C, with an experimented total degree of deformation of 30%.The present paper analyzes UNS S32750 Super-Duplex Stainless Steel hot deformation behavior during processing by upsetting. The objective of this paper is to determine the optimum range of deformation temperatures, considering that both austenite and ferrite have different deformation behaviors due to their different morphology, physical, and mechanical properties. Because the capability of plastic deformation accommodation of ferrite is reduced when compared to austenite, side cracks and fissures can form during the hot deformation process. Consequently, it is important to find the optimum conditions of deformation of this type of stainless steel to establish the best processing parameters without deteriorating the material. The experimental program involved the application of hot deformation by the upsetting method on a series of samples between 1000 °C and 1275 °C, with a total degree of deformation of 30%. The resultant samples were examined by SEM-EBSD to establish and analyze the evolution of the phases present in the structure from several points of view: nature, distribution, morphology (size and shape), and their structural homogeneity. The GROD (Grain Reference Orientation Deviation) distribution map was also determined while taking into account the possible precipitation of the secondary austenite phase (γ The material considered in this experimental research is Super-Duplex Stainless Steel (SDSS), which shows a bi-phasic microstructure, composed of proportionally equal phases of, δ ferrite, and γ austenite, with a sufficient content of Cr, Mo, and N to deliver high corrosion resistance to pitting and to stress corrosion cracking ,2. In ad−1 strain rate . The deformation temperature range was selected between 1000 °C and 1275 °C, in 25 °C steps. For “freezing” the internal microstructure after the hot deformation processing, all samples were cooled in water. The inspection with penetrant liquid was used after cooling to again investigate the samples for fissures/microcracks. A detailed presentation of the thermomechanical processing equipment used is presented in an earlier paper [From the as-received (AR) UNS S32750 SDSS alloy , cylindrical-shaped samples with an h/d ratio of 1.5  were machined. The inspection with penetrant liquid was used to verify, porosity, presence of microcracks, laps (pre-existing defects), etc., on the lateral surface of all samples. Hot deformation of samples was conducted by upsetting, in axial compression, up to 30% total deformation degrees, by applying a 0.37 ser paper .The reference system of the samples is shown schematically in A TESCAN VEGA II—XMU  scanning electron microscope (SEM) was used for the microstructural analysis using the SEM-EBSD technique. This microscope is equipped with an EBSD detector—BRUKER Quantax eFlash . According to For the constituent phases, the weight fraction (proportion), the nature, morphology, distribution, grain size, structural homogeneity, and dynamic recrystallization, were analyzed in correlation with the considered hot upsetting temperature.The SEM-EDS technique was used to investigate the chemical composition of as-received (AR) UNS S32750 Super-Duplex Stainless Steel. Grain Reference Orientation Deviation (GROD) distribution map for both γ-phase and δ-phase, in the investigated field, is shown in 2-phase) precipitation.2-phase)—red color, and σ-phase—yellow color. At temperatures below 1050 °C, the presence of deleterious σ-phase can be observed, mainly at the δ/γ interface, the decreased quantity of σ-phase fraction, indicating that the dissolution of σ-phase is completed at 1050 °C. Another important observation can be made for temperatures above 1175 °C, where one can observe the presence of a secondary austenite phase (γ2-phase), mainly within the δ-phase at the δ/δ interface. This secondary austenite (γ2-phase) is generated during heating within an intensely deformed δ-phase matrix by heterogeneous nucleation, in sections where the supersaturation in N of the δ-phase is supporting the precipitation phenomenon. Furthermore, the microstructure inclusions are working as preferential nucleation sites for the γ2-phase, which can easily nucleate next to these inclusions [The analysis of microstructure evolution, from 1000 °C to 1275 °C, showed the presence of the following constituent phases: ferrite (δ-phase)—blue colorized, primary austenite (γ-phase), and secondary austenite  can be noticed, mainly within the δ-phase at the δ/δ interface, due to the δ → γ phase transition, showing an increased weight fraction with the increase in deformation temperature  grains as a function of deformation temperature. One can observe that, in the case of δ-phase, the weight fraction of recrystallized grains is continuously increasing from 1000 °C (when the recorded weight fraction was close to 5.8%), up to 1175 °C . Further increasing the upsetting temperature above 1200 °C leads to a decrease in the weight fraction of the δ-phase, with a value close to 4.4% being recorded at 1275 °C. Considering the case of γ2-phase, it can be observed that the weight fraction of newly precipitated γ2-phase grains is continuously increasing in the 1200–1275 °C range, from 1.8% to 4.9%, due to the δ → γ phase transition, which occurs mainly within the δ-phase at the δ/δ interface For the experimented temperature range (1000–1025 °C) applied for hot deforming the UNS S32750 Super-Duplex Stainless Steel by upsetting with a total degree of deformation of 30%, the microstructure of the studied material is composed of approximately equal ratios of γ-phase and δ-phase before and after the hot deforming process.(b)After all the applied variants of hot deforming, both δ and γ phases showed typical morphologies of strain-hardened structures. During all experimented variants, lateral fissures or cracks were not observed on the surface of UNS S32750 SDSS samples.(c)The microstructural analysis via SEM-EBSD showed the presence of σ-phase between 1000–1025 °C, at the δ/γ interface; at temperatures above 1050 °C, this deleterious phase was not present due to its complete dissolution.(d)2 at the δ/δ interface, as the temperature increased up to 1275 °C. This signaled precipitation process can be correlated with GROD analysis, which indicated a decrease in values for δ in this temperature range, from 49° to 21° i.e., a decreasing stress for δ grains to values that favor the precipitation of γ2 as well as intensifying dynamic recrystallization. The small size of the new δ recrystallized grains occurs due to the short duration of the hot deformation process. For the γ phase, no RX mechanism was observed.For the temperature range 1200–1275 °C, the SEM-EBDS analysis indicated the increasingly intense formation of the secondary phase-γ(e)Considering the experimented temperatures for hot deforming (1000–1275 °C) and the signalized presence of the deleterious σ-phase between 1000–1025 °C, it can be concluded that the UNS S32750 Super-Duplex Stainless Steel can be safely deformed by upsetting between 1050–1275 °C with an experimented total degree of deformation of 30%.The main results of this research can be summarized as follows: