M (CD11b) and integrin β2 (CD18) is highly expressed on monocytes and macrophages. In the current study, we demonstrate that the I domain of αM (αM-I domain) might present a potential cellular target for oleanolic acid. In vitro data show that oleanolic acid induces clustering of αM on macrophages and reduces their non-directional migration. In accordance with experimental data, molecular docking revealed that oleanolic acid binds to the αM-I domain in its extended-open form, the dominant conformation found in αM clusters. Molecular dynamics simulation revealed that oleanolic acid can increase the flexibility of the α7 helix and promote its movement away from the N-terminus, indicating that oleanolic acid may facilitate the conversion of the αM-I domain from the extended-closed to the extended-open conformation. As demonstrated by metadynamics simulation, oleanolic acid can destabilize the local minimum of the αM-I domain in the open conformation partially through disturbance of the interactions between α1 and α7 helices. In summary, we demonstrate that oleanolic acid might function as an allosteric agonist inducing clustering of αM on macrophages by shifting the balance from the closed to the extended-open conformation. The molecular target identified in this study might hold potential for a purposeful use of oleanolic acid to modulate chronic inflammatory responses.Oleanolic acid is a widely distributed natural product, which possesses promising antitumor, antiviral, antihyperlipidemic, and anti-inflammatory activities. A heterodimeric complex formed by integrin α Panax ginseng and Eleutherococcus senticosus  is highly expressed on myeloid cells such as macrophages, neutrophils, and dendritic cells. It pairs with integrin β2 (CD18) to form the functional heterodimer αMβ2  that controls processes necessary for immune responses, such as actin skeleton reorganization, cell adhesion, phagocytosis, and cell migration. Accordingly, integrin αMβ2 is also important for pathogen removal and antigen presentation . Macrophages were stimulated with 1 μM oleanolic acid (Sigma) or 50 μg/ml fibrinogen for 60 min. Thereafter, cells were washed with cold PBS, fixed, permeabilized with 0.1% Triton X-100, and stained with FITC-conjugated CD11b antibody (Miltenyi Biotec). Images were acquired with a Nikon eclipse Ti microscope using a ×40 objective and NIS-elements microscope imaging software . The fluorescence landscape was visualized with the Interactive 3D Surface Plot V2.4 plugin in ImageJ , and clustering was identified and quantified as described  was analyzed by differential scanning fluorimetry . Briefly, 2 µM of αMβ2 were mixed with 5x SYPRO Orange dye (Sigma), treated with either DMSO solvent or oleanolic acid, 10 µM, and subjected to thermal unfolding. Melting temperature (Tm) was determined as the maximum of the first derivative of fluorescence emission as a function of temperature.Binding of oleanolic acid to human recombinant αorimetry  by usingM and oleanolic acid. The protein structure of the αM-I domain in extended-closed (PDBID: 1IDO) form meaning that the protein is partially open due to Mg2+ binding and extended-open (PDBID: 4M76) form meaning that the αM-I domain is fully open, activated by the presence of both, a divalent cation and the ligand C3d, were processed with Protein Preparation Wizard in Schrödinger suit and parameterized with OPLS-AA force field (Molecular docking was employed to investigate the interaction between the domain of αce field . A positce field . Moleculce field . Top scoFive different systems were prepared for unbiased molecular dynamics (MD) simulations . In eachTo remove defects in structural models and to resolve the collision between atoms, a three-staged energy minimization protocol was employed. Firstly, all heavy atoms were position-restrained for 500 steps, and then the backbone atoms of protein and heavy atoms of ligands were position restrained for another 500 steps. Finally, the system was energy-minimized for another 1,000 steps without restriction. The energy-minimized system was gradually heated to 310 K in 100 ps with all heavy atoms restrained. Finally, the restraints were gradually removed in constant pressure environment (NPT) for a total of 2.5 ns. The production NPT run was performed at 310 K and 1 atm for at least 500 μs for every system.−6. The cut-off for the van der Waals interactions was set to 9 Å. The temperature and pressure were controlled using Langevin dynamics and the Langevin piston barostat method. The SHAKE method was used to restrain hydrogen atoms and the tolerance was set to 10−8. Atomic coordinates of all atoms were saved every 1 ps. To guarantee the reproducibility of key findings in MD simulations, five independent MD simulations were performed for each system using NAMD 2  method  with a gg NAMD 2 .The conformational shift of the α7 helix was sampled with Metadynamics simulation (MetaD) using the equilibrated structure from production NPT run as the starting structure. To trace conformational change, the center of mass (COM) distance between Cα atoms of the N-terminus (residues 132–142) and α7 helix (residues 301–310) was defined as collective variable (CV). The grid’s boundary was set to 14.0 Å (lower boundary) and 30.0 Å (upper boundary), and the grid width was set to 0.2 Å. The hill weight and hill width was set to 0.01 and 1.0, respectively. To guarantee the reproducibility of key findings in MetaD simulation, three independent MD simulations were performed for each system using NAMD 2 .Gromacs  and CARMThe analysis of the dynamic behavior of key regions identified by RMSF analysis (regions with higher RMSF) in different systems was performed by PyEMMA  as previResidue Interaction Network analysis was conducted according to references  by takinp < 0.05. The 3D coordinates of snapshots were visualized with PyMOL . Differences were considered significant at th PyMOL , and theth PyMOL  in Pythoth PyMOL .Eleutherococcus senticosus bark extract enriched in oleanolic acid decreases the migratory activity of macrophages  confirming the binding capacity of oleanolic acid to αM  and extended-open conformation (PDBID: 4M76), were extracted from the protein data bank and used to construct the initial model.With respect to ligand binding, integrins may exist in different confirmations, exposing either low- or high-affinity. In the low-affinity state, integrins adopt the so-called closed confirmation formed by the ligand-binding I and β-propeller domains of the αa ligand . TherefoM-I domain adopts different conformation in its extended-closed and extended-open conformations. In the extended-open conformation, the α7 helix is slightly moved towards its C-terminus, which results in formation of a slightly enlarged allosteric binding site in the αM-I domain. Accordingly, the allosteric site shows now increased preference for a ligand with increased size or rigid scaffold. To validate the reliability of the established molecular docking model, leukadherin-1, an allosteric agonist of integrin αM, was used as a positive control. As shown in M-I domain in the extended-closed form  compared to the extended-open form   . This fical/mol) . Differecal/mol) .M-I domain differ remarkably. As demonstrated by in vitro studies, leukadherin-1 functions as an allosteric agonist of the αM-I domain by stabilizing it in extended-closed form  were employed. MSMs identified five metastable states, which are characterized mainly by different orientation and conformation of the α7 helix . In state IV and state V, the COM distance between the N-terminus and the α7 helix are 20.6 Å and 19.9 Å, respectively.To validate, whether the ligands are able to affect the dynamic behavior, especially the long-time statistical conformational dynamics of the αα7 helix . AlthougM-I domain complexed with oleanolic acid. The increased distance and the slightly shifted α7 helix indicate that oleanolic acid may switch the moving direction of the α7 helix away from its N-terminus  of heavy atoms was calculated along with the simulation. Compared to the extended-closed form, the α7 helix in the extended-open form shows higher RMSD, indicating that the α7 helix in the open form may possess higher flexibility . InteresM-I domain showed two local minima (I and II), which represent the extended-closed and extended-open conformation, respectively , and the COM distance between the N-terminus and the α7 helix (16.9 Å) indicates that leukadherin-1 stabilizes the αM-I domain in its extended-closed conformation. Interestingly, only one local minimum was detected in the presence of oleanolic acid (IV), the relatively large distance between the N-terminus and the α7 helix indicates that the αM-I domain is stabilized in the extended-open conformation. Besides, we found that the free-energy requirement for inducing larger conformational shift of the α7 helix (COM distance larger than 22 Å) was lower in the presence of oleanolic acid indicating that oleanolic acid may facilitate the conformational change from the extended-closed to the extended-open conformation .To explore the free energy profile along with the conformational change of the α7 helix from extended-closed conformation to extended-open conformation, metadynamics simulation was employed, and the average free energy landscape for each simulation system was calculated from three independent metadynamic simulations . In the ectively . The lowormation .M-I domain in extended-open conformations. Comparing the results from metadynamics simulation and conventional molecular dynamics simulation, a remarkably decreased correlated movement between α1 and α7 is observed, and the conversion from correlated movement to anti-correlated movement occurs as illustrated in the right hand panels (Dynamics Cross Correlation Maps (DCCMs) were employed to investigate the long-range interactions of the αd panels . To furtM-I domain/oleanolic acid complex was analyzed using conventional molecular dynamics simulation and metadynamics simulation. Compared with the residue interaction network of the αM-I domain in its apo form, oleanolic acid significantly decreased the direct interaction between the N-terminus or the α1 helix and the α7 helix  dominant to the extended-open dominant conformation.Importantly, the membrane distribution of integrins is closely controlled by their conformation, and it has been proposed that formation of integrin clusters is a result of conformational transformation from the closed to the extended-open conformation . AlthougM-I domain of integrin αM recognizes the ligand and binds it to the aspartate in its motif with high affinity  within the αaffinity . The binegrin αM . Finally pathway .M, this motif was considered as the first candidate as oleanolic acid binding site. However, the crystal structure of the complex between αM-I domain and biomolecules demonstrated that besides the electrostatic selectivity, MIDAS imposes strict steric selectivity on its ligands. Thus, a carboxyl group connected to longer side chains, such as aspartate or glutamate, represents an ideal MIDAS ligand  to a fully extended open form (less stable α7 helix and shifted C-terminus)  . In accoen state .2 integrin also contains an I-like domain that is structurally similar to the αM-I domain with a confirmation that can be changed upon ligand binding, integrin ligands bind to the α-I domain and it is the α subunit of an integrin heterodimer that regulates binding affinity and specificity of different ligands (2-I-like domain act as αMβ2 antagonists blocking the αM-I domain in a low energy, inactive, and closed conformation (Mβ2 clustering supporting our hypothesis that it targets the αM-I domain.Though β ligands . Yet, liormation . In contM-I domain within integrin αM presents a potential target of oleanolic acid. Combining in silico simulation and in vitro experiments, we found that oleanolic acid is able to induce clustering of integrin αM partially through destabilizing the α7 helix in its closed form and stabilizing the α7 helix in its partially unfolded state. The conformational change is facilitated by lowering the free-energy barrier between the two states, and finally shifting the balance from closed to open form. Although we have found that oleanolic acid induces the clustering of integrin αM and decrease macrophage motility similar to the αM agonists leukadherins, the conformational alteration of αM caused by oleanolic acid may not be sufficient to induce full activation of integrin αMβ2 as described for its physiological ligand fibrinogen.In summary, the results from our study show that the allosteric pocket of α


The increasing demand for parts with a large specific surface area such as fuel panels has put forward higher requirements for the plasticity of foils. However, the deformation characteristics of foils is hard to be illustrated in-depth due to their very short deformation process. In this paper, the digital image correlation method was applied to investigate the influence of size effect on the elongation of Ti-2.5Al-1.5Mn foils. The results showed that the elongation of Ti-2.5Al-1.5Mn foils increased with the decrease in the ratio of thickness-to-grain diameter . Then, the macro deformation distribution of foils was analyzed, combined with their microstructure characteristics, and it was found that the increasing influence of individual grain heterogeneity leads to the earlier formation of a concentrated deformation zone, which changes the deformation mode of foils. The concentrated deformation increases with the decrease in t/d value, thus dominating the trend of the foil elongation. Furthermore, the homogeneous deformation and concentrated deformation can be divided into two different zones by a certain critical t/d value. These results provide a basis for understanding and further exploration of the deformation behavior of titanium foils. With the miniaturization trend of mechanical equipment, the requirements for micro-parts are increasing significantly ,2,3,4,5.The elongation is an important index to measure the plasticity of foils. In recent years, extensive studies have been devoted to the influence of size effects on the foil elongation. In general, they can be divided into two major subsets: the thickness effect and the grain size effect. The thickness effect on the elongation of CuZn20 foils has been studied via the tensile experiment, showing that the elongation decreases with the decrease in foil thickness . This reTo represent the interactive effects between the thickness effect and the grain size effect, the ratio of thickness-to-grain diameter  has been proposed and applied widely, because it can interpret the experimental phenomenon of smaller sample size results in weaker deformation behavior ,19,20. HTaken together, most previous studies focus on the influence mechanism of size effects on the foil elongation from the perspective of grain numbers or surface grains, but there is a relative lack of research on the foil macroscopic deformation characteristics. In fact, the explicit deformation mode of a certain foil depends on comprehensively analyzing its macro and micro deformation characteristics, contributing to the illustration of the influence mechanism of size effects on the foil elongation. However, the deformation process of foils is very short, making it difficult to investigate its macroscopic deformation characteristics. The digital image correlation (DIC) technology is an optical measurement based on the calibration information of digital images. DIC has the advantages of non-contact, real-time, full-field, and local strain measurement. Therefore, it is an effective and accurate technology for studying lightweight bio-inspired composite materials  and meas−3 Pa at different temperatures and times, so various grain sizes were obtained. Then, Ti-2.5Al-1.5Mn foils with specific t/d values . The cross-head velocity was 1 mm/min, and the strain rate was less than 1 × 10(1)First, a layer of white paint was sprayed on the specimen surface as the background color, and black speckles were evenly sprayed so achieve each 50% of black and white. Then, specimens were installed in the material testing machine.(2)At the same time, when the material testing machine started running, the CCD camera, which was controlled by the GOM Correlate software, collected specimen images at the frequency of 1 image per second.(3)The changes in marked speckle information were calculated via the GOM Correlate software, and then, the displacement and strain field information were obtained.As shown in First, the elongation at the break of Ti-2.5Al-1.5Mn foils was evaluated with respect to the t/d value referring to the current size effect analysis method. As shown in Due to the thickness of foil specimens being constant at 50 μm, the elongation of Ti-2.5Al-1.5Mn foils was evaluated with respect to the grain size. According to existing research results, for most of materials, the larger the grain size, the smaller the material elongation is ,15,16,17Furthermore, the scatter of the elongation between different Ti-2.5Al-1.5Mn foil specimens in the same experiment was investigated. In order to quantitatively study the scatter level of the foil elongation, the coefficient of variation (C.V) value of elongation was introduced. As shown in Compared with hard-oriented grains, soft-oriented grains tend to deform easily due to their crystal structure characteristics. It is known that the deformation performs in the manner of minimizing energy consumption. The specimen with fewer grains is prone to having a section with a large proportion of soft grains ,27. TherA typical equivalent strain profiles during the deformation process of foils were collected with the DIC method, as shown in Therefore, combining the formation of concentrated deformation zone and the collected strain distribution characteristics with the DIC method, the deformation process of foils is further subdivided into the homogeneous deformation stage and the concentrated deformation stage. The critical point is set to the beginning of strain partition in this research. Accordingly, the foil deformation amount can be divided into the homogeneous deformation amount and the concentrated deformation amount. As shown in Ti-2.5Al-1.5Mn foil is a close-packed hexagonal lattice metal, and there are only three slip systems that can be activated at room temperature, so its deformation behavior is very sensitive to the change in deformation state. For the foil specimen with a large t/d value, the concentrated deformation zone is not easy to form because there are numerous grains inside the deformation zone ,17. AfteFor the specimen with a small t/d value, the concentrated deformation zone is easy to form because there are a few grains in the deformation area . After tThe percentages of homogeneous deformation and concentrated deformation of Ti-2.5Al-1.5Mn foils with various t/d values were calculated and summarized in (1)The influence of individual grain heterogeneity increases during the deformation process, which results in the premature formation of concentrated deformation zones, contributing to the increased role of concentrated deformation in the foil elongation.(2)The proportion of homogeneous deformation amount and concentrated deformation amount can be divided into two different zones by a certain critical t/d value, which is near 5 for Ti-2.5Al-1.5Mn foils.(3)It is feasible to increase the plasticity of Ti-2.5Al-1.5Mn foils with a thickness of 50 μm by increasing the grain size, but foils with the t/d value in zone 1 should be avoided for forming parts that require high uniformity.In this work, the influence mechanism of size effect on the elongation of Ti-2.5Al-1.5Mn foil with a close-packed hexagonal crystal structure was studied with the DIC method, and the following conclusions can be drawn:


Dictyostelium discoideum is a model organism that is used to investigate many cellular processes including chemotaxis, cell motility, cell differentiation, and human disease pathogenesis. While many single-cellular model systems lack homologs of human disease genes, Dictyostelium’s genome encodes for many genes that are implicated in human diseases including neurodegenerative diseases. Due to its short doubling time along with the powerful genetic tools that enable rapid genetic screening, and the ease of creating knockout cell lines, Dictyostelium is an attractive model organism for both interrogating the normal function of genes implicated in neurodegeneration and for determining pathogenic mechanisms that cause disease. Here we review the literature involving the use of Dictyostelium to interrogate genes implicated in neurodegeneration and highlight key questions that can be addressed using Dictyostelium as a model organism.The social amoeba  As the human population ages, neurodegenerative diseases are becoming increasingly prevalent. Neurodegenerative diseases are characterized by neuronal dysfunction and degeneration. This dysfunction and degeneration can be caused by both sporadic and genetic causes, affect different subsets of neurons, and have varied pathological hallmarks. Neurodegenerative disease progression inevitably leads to disability and death, and for nearly all these diseases there is a lack of curative treatments. Therefore, understanding the molecular mechanisms that drive neurodegenerative diseases is important for developing therapies.One way to investigate the mechanisms that drive neurodegenerative diseases is by utilizing model organisms. Neurodegenerative diseases have been studied in a wide variety of model systems ranging from yeast to non-human primates. Model organisms like yeast, worms, flies, and zebrafish can be utilized to investigate the basic pathology of neurodegenerative diseases and are useful models that are inexpensive and easy to genetically manipulate . These mOne major unanswered question for many neurodegenerative diseases is: What is the normal function of the proteins that cause disease? This is an important question because one possibility is that neurodegenerative diseases are caused by either a loss of or a toxic gain of protein function. One way to delineate the normal function of proteins that cause neurodegenerative diseases is through utilizing non-mammalian model systems. Many non-mammalian model systems have numerous benefits including simpler genomes, decreased genetic redundancy, ease of genetic manipulation, shorter generation times, and scalability for high throughput genetic and pharmacological studies . These aSaccharomyces cerevisiae, Caenorhabditis elegans, and Drosophila melanogaster are popular non-mammalian systems for neurodegenerative disease research, Dictyostelium discoideum (Dictyostelium) is another simple organism that is useful as a model to investigate mechanisms that cause disease. Dictyostelium is a soil-dwelling amoeba found throughout the world. Dictyostelium consumes bacteria, however, when bacteria are depleted, Dictyostelium undergoes a developmental cycle transitioning from a unicellular amoeba to a multicellular fruiting body  is the most common neurodegenerative disease. The pathological hallmarks of AD are the accumulation of extracellular amyloid-β (Aβ) plaques and intracellular tau neurofibrillary tangles. Under normal conditions, the amyloid precursor protein (APP) is processed by β-secretases, such as β-secretase 1 (BACE1), and γ-secretases to yield APP C-terminal fragments of different lengths. BACE1 initially cleaves the N-terminal region of APP, and the γ-secretase complex cleaves the C-terminal region. Presenilin (PSEN) 1 or 2 serves as the catalytic subunit of γ-secretase and is involved in processing APP into Aβ peptides . Under nxt of AD . TherefoDictyostelium does not encode for a β-secretase or APP, it does express a divergent form of the γ-secretase complex. Homologous genes have been identified in Dictyostelium for all components of the γ-secretase complex including PSEN1 and PSEN2. To investigate the biological function of PSEN1 and PSEN2, knockout strains  were developed. ps2– cells had developmental defects, could not differentiate into pre-stalk cells during the slug stage, and were unable to sporulate, suggesting that PSEN2 γ-secretase complex activity is required for cell differentiation  . It was pathways .Dictyostelium biology, it was also found that wild-type Dictyostelium cells expressing human APP process APP in a similar manner as human cells, producing both Aβ40 and Aβ42 fragments. This process is dependent on the γ-secretase complex, as cells lacking components of the γ-secretase complex are unable to process APP  is the second-most prevalent neurodegenerative disease caused by the loss of dopaminergic neurons in the substantia nigra. This occurs, at least in part, due to the formation and accumulation of protein aggregates called Lewy bodies within the neuron’s cytoplasm. In PD, neurons have numerous problems including oxidative stress, mitochondrial dysfunction, synaptic dysfunction, and inhibition of protein quality control pathways . There aDictyostelium does not encode a homolog of the α-synuclein gene, one study was performed to determine the effect of exogenous expression of α-synuclein in Dictyostelium. Expression of either wild-type or mutant α-synuclein resulted in impaired phototaxis and thermotaxis, altered fruiting body morphology, and decreased the rate of phagocytosis, consistent with a potential role for α-synuclein in disrupting mitochondrial function in Dictyostelium  is a commonly mutated gene in both sporadic and inherited forms of PD. LRRK2 encodes a large protein with GTPase, kinase, and scaffolding domains  gene. This CAG expansion is then translated into a polyglutamine (polyQ) tract in the huntingtin protein. Long polyQ tracts (>35Q) result in the misfolding and aggregation of the huntingtin protein. This aggregation process is thought to be one of the mechanisms that lead to disease pathogenesis. In addition to aggregation, both loss of normal huntingtin function and toxic gain of function have been proposed to contribute to toxicity  is caused by the expansion of a CAG trinucleotide repeat in the coding region of the huntingtin  polyQ tract further along in its amino acid sequence that is composed of mostly CAA trinucleotide repeats rather than CAG , or Batten Disease, encompasses a growing class of debilitating neurodegenerative lysosomal storage diseases. NCLs are also the most common neurodegenerative diseases seen in children . These dCLN3 gene cause the most common subclass of NCLs, juvenile NCL (JNCL)  . The CLNembranes . While Cembranes .Dictyostelium’s genome has a homolog for CLN3 (Cln3), which was used as a model to understand its normal function  was also observed, in addition to a correlating increase in Tpp1 enzyme activity . Cln5 localizes to the ER, and both Dictyostelium Cln5 and exogenous human CLN5 have glycoside hydrolase activity in Dictyostelium cells. Immunoprecipitation and mass spectrometry identified numerous Cln5 interactors, many of which are implicated in NCLs. Some of these interactors include cathepsin D, tripeptidyl peptidase 1, and CDC48 , is caused primarily by mutations in tripeptidyl peptidase 1 , a lysosomal peptidase . TPP1 cl unknown . Studies unknown . While Tfunction .Dictyostelium does possess a homolog of TPP1, ddTpp1. Deletion of ddTpp1 results in a reduced, but not a complete loss of, the ability to cleave a synthetic substrate of human TPP1 , tpp1- cells have a highly impaired developmental cycle  complex inhibitor rapamycin or by knocking down the upstream activator of the TOR complex Ras homology enriched in brain (RHEB) results in the same phenotypes observed in tpp1- cells  and on bacterial lawns (phagocytosis), chronic activation of AMP kinase, shorter and thicker stalks due to increased cell differentiation into pre-stalk cells, and altered ability to transition from growth to development  are present in Dictyostelium but not S. cerevisiae. There are also four other AD-related genes, one other PD-related gene, and three other NCL-related genes expressed in Dictyostelium that have yet to be investigated in Dictyostelium. In addition, many other neurodegenerative diseases have the potential to be studied using Dictyostelium as a model organism. For example, Dictyostelium expresses genes implicated in ALS and some spinocerebellar ataxias. Dictyostelium also has homologous genes involved in other rare neurodegenerative diseases including Niemann-Pick, Refsum, and Tay-Sachs diseases, among others . Interestingly, many of these SSRs are present in protein-coding regions of genes, resulting in Dictyostelium encoding nearly 10,000 homopolymeric amino acid tracts. Surprisingly, there are homopolymeric tracts for every amino acid except tryptophan, with asparagine (N) and glutamine (Q) being the most abundant repeats  screen was utilized to identify genes that are necessary for suppressing polyQ aggregation in Dictyostelium. This screen identified one gene that encodes for serine-rich chaperone protein 1 (SRCP1). Interestingly, SRCP1 is both necessary for suppressing polyglutamine aggregation in Dictyostelium and sufficient to suppress polyQ aggregation in other organisms  also cause a class of nine neurodegenerative diseases called the polyQ diseases. In these diseases, polyQ tracts within the coding region of specific genes become expanded resulting in aggregation-prone proteins that are neurotoxic. This led to the question: Is utamines . This isutamines . To begirganisms . One cavostelium .Dictyostelium’s resistance to polyQ aggregation raises many questions about how protein quality control pathways are regulated in Dictyostelium. In the future, studies investigating Dictyostelium’s response to various stressors are warranted and may identify novel aspects that regulate protein quality control. Initial work has identified heat shock protein 101 (Hsp101) as a key suppressor of polyQ aggregation during heat stress  in human cells. Furthermore, SRCP1 was packaged in adeno-associated virus 9 (AAV9) and injected into the cortex of a mouse model of ALS. Expression of SOD1 in this mouse model resulted in decreased SOD1 aggregation in the cortex, however, it did not result in an increase in lifespan (Finally, understanding how lifespan . In the Dictyostelium discoideum provide a platform to investigate the normal function of these proteins and to perform genetic screens to identify genes that modulate their functions. Because Dictyostelium is a simple model organism with a single cellular stage and encodes for numerous proteins implicated in neurodegenerative diseases, it is useful for understanding the normal function of these proteins and for identifying pathways that are disrupted in disease states. For example, knockout screens can easily be employed to determine the functions of the many homologous neurodegenerative disease proteins expressed in Dictyostelium. Additionally, the introduction of normal and mutant neurodegenerative disease genes not encoded for in Dictyostelium’s genome could also serve to identify protein functions and toxicity as well as cellular pathways affected. Dictyostelium may also be used to identify novel protein interactors with neurodegenerative disease proteins. Another important observation that must be further studied is Dictyostelium’s resistance to protein aggregation. Elucidating Dictyostelium’s proteostatic pathways could allow us to observe how Dictyostelium evolutionarily overcame the obstacle of protein aggregation. In the future, Dictyostelium can continue to serve as a simple, yet powerful, model to investigate neurodegenerative diseases and expand our knowledge to treat these diseases.Neurodegenerative diseases are incurable diseases with few treatment options. In many neurodegenerative diseases, the proteins that are mutated or accumulate have unknown functions. Simple model organisms including HH wrote the initial draft. HH and KS conceptualized and edited the review, contributed to the article, and approved the submitted version.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.


To explore the relationship between postural control and pain-related clinical outcomes in patients with chronic nonspecific low back pain (cNLBP).  Participants with cNLBP and healthy individuals were recruited. Muscle activities were recorded during internal and external perturbation tasks. Postural control capacity was assessed by muscle onset time and integrals of electromyography (iEMGs) of postural muscles during the phases of anticipatory postural adjustments (APAs) and compensatory postural adjustments (CPAs). Correlation analysis was employed to investigate the relationship between postural control capacity, pain, and disability.  Twenty-seven patients with cNLBP and 27 healthy participants were recruited. Gastrocnemius (GA) muscle onset time was earlier in the cNLBP group than in the control group in the internal perturbation task. The onset time of GA and erector spinae (ES) of the cNLBP group was later than that of the controls in the external perturbation task. Disability level moderately correlated with the iEMGs of rectus abdominis (RA), GA, and external oblique (EO) during APAs. Pain score moderately correlated with the iEMGs of RA, EO, and ES during CPAs of perturbation tasks.  cNLBP participants had altered muscle activation strategy to maintain postural stability in response to perturbation. This study further discovered that pain-related disabilities of cNLBP participants were likely related to the APAs capacity, whereas the pain intensity may relate to the CPAs capacity. Pain and disability may therefore be related to the control process of the posture-related muscles. Chronic low back pain (cLBP) is a common health problem that causes heavy social and economic burden . Among 7Among numerous proposed theories, altered motor control is commonly reported as a contributing factor to the persistence of pain . AlteredThere is a body of literature that reported APAs dysfunction in patients with cNLBP , 8, 9. TClinical symptoms of pain intensity reported to be associated with the magnitude of postural sway , and a rThe sample population was recruited from the student population of the hosting institute and from the community. Advertisements were displayed around the university campus and circulated on social media platform. The diagnosis of LBP was made by a physiotherapist based on the clinical assessment protocol established by the American College of Physicians and American Pain Society . The incIn the present study, postural control tasks included external and internal perturbation tasks. During internal perturbation task , particiIn the external perturbation task , particiSince the latencies of the trunk muscles on the ipsilateral side of arm movement in cNLBP participants were reported to be longer than those on the contralateral side during flexion and abduction , four muData processing was conducted offline in the software MATLAB . sEMG signals were full-wave rectified and band-pass filtered using a 2nd order, zero-lag Butterworth filter between 10 Hz and 500 Hz. In the internal perturbation task, the accelerometer data were averaged with a moving average filter of 10 seconds. The onset time of the internal perturbation task was defined as the time point where the acceleration magnitude reached 5% of the maximum of the square sum of the angular displacements in the medial-lateral and anterior-posterior directions. In the external perturbation task, the onset time of interruption (T0) was calculated from the pressure sensor when the pressure magnitude reached 5% of the maximum pressure for 10 ms. Each trial was aligned by T0 as a common reference point for sEMG signals. The sEMG data recorded between −600 ms (600 ms prior to T0) and  + 1000 ms (1000 ms post T0) of each trial was included for analysis. The onset time of each muscle could not be detected by the conventional method where sEMG amplitude was greater than the mean plus two standard deviation of the baseline value for at least 50 ms due to the presence of background noise. The Teager–Kaiser energy (TKE) operation is a computation algorithm that simultaneously considers the amplitude and instantaneous frequency of the sEMG when detecting muscle onset. It is applied in situation where the signal-to-noise ratio of the sEMG signals is too low to detect muscle onset by visual inspection of the signal or by setting a predetermined amplitude threshold . The TKEa is the iEMGs for APAs and CPAs and b is the iEMGs at baseline.Integrals of the EMG activities (iEMGs) of two different epochs were calculated for APAs and CPAs. In order to explore the dynamic change in the early and late APAs and CPAs, the time windows of APAs and CPAs in the present study were further divided into two subepochs. The time window of the epoch for APAs was from −250 ms to +50 ms, which could be divided into two subepochs: from −250 ms to −100 ms (APAs1) and from −100 ms to +50 ms (APAs2) . The tima is the iEMGs for each epoch of APAs1, APAs2, CPAs1, and CPAs2 and b is the iEMGs at baseline.The formula adopted to calculate the muscle iEMGs during APAs1, APAs2, CPAs1, and CPAs2 wasX and Y axes were related to CPAs. The calculation details of COP displacement make reference to the previously published studies [The force platform used to assess the COP displacement made reference to the study by Luo et al. . The raw studies , 27, 28.Pain intensity was assessed by the 0–10 numerical rating scale (NRS) . Participants were asked to rate their pain intensity over the past 7 days prior to data collection. LBP-related disability was assessed by the Oswestry Disability Index (ODI) . It consDemographic data of age, height, weight, and body mass index (BMI) were recorded at the beginning of the data collection session, followed by the completion of clinical history questionnaire, NRS, and ODI. Then the external and internal perturbation tasks with signal recording of sEMG and COP were conducted. The order of the external and internal perturbation tasks was randomized for each participant.t test was performed to assess the difference in demographic characteristics except sex, muscle onset time, and muscle iEMGs between the two groups. The chi-square test was performed to assess the between-group difference in sex. The Pearson correlation analysis was performed to explore the relationship between the parameters of muscle onsets/iEMGs/COP and ODI scores. The Spearman correlation analysis was conducted to explore the relationship between the parameters of muscle onsets/iEMGs/COP and NRS ratings. Statistical analysis was performed in the software SPSS 20 . Statistical significance level was set at p < 0.05.Descriptive statistical analysis was conducted to describe the sample characteristics. The independent-sample Twenty-seven participants  with cNLBP were recruited. A further 27 healthy individuals  were recruited as controls. Twenty-five of the cNLBP participants had bilateral low back pain or had pain at the central part of the lumbosacral area. Two of the cNLBP participants had unilateral low back pain at the lumbosacral area. All recruited participants were right-handed, with normal or corrected-to-normal visual acuity and no history of neurological and cardiovascular disorders. The demographic characteristics of the sample populations are shown in p=0.024) and APAs2 (p=0.016), the iEMGs of ES during APAs and APAs1 , and the iEMGs of ES during CPAs (p = 0.047). However, the iEMGs of GA and EO during APAs1 and APAs2 did not show significant between-group differences   .p=0.029, p=0.046). However, the iEMGs of EO during APAs2 were larger in the cNLBP participants than controls (p=0.030)  .y direction during CPAs was observed in the cNLBP group than the control group (p=0.010)  .r = 0.425 p=0.027; during APAs2: r = 0.459, p=0.016) and GA  . The NRS scores of cNLBP participants were moderately related to iEMGs of RA , EO , and ES  (p=0.046). p>0.05).In the internal perturbation task, ODI scores of cNLBP participants were moderately correlated with the iEMGs of RA (during APAs: p=0.007) . ODI scop=0.013) . ODI wasThe present study investigated the relationship between postural control and clinical symptoms in the patients with cNLBP. The main findings showed that the activation of GA was earlier in the cNLBP group than the control group in the internal perturbation task. The activation of the muscles GA and ES of cNLBP participants were delayed when comparing to healthy controls in the external perturbation task. The activation of the trunk muscles during APAs and CPAs in the cNLBP group was stronger than those in the control group in the internal perturbation task. However, the muscle activation of the EO was stronger in the cNLBP group than the control group, whereas the muscle activation of the GA in the cNLBP group was weaker than those in the control group during the external perturbation task. The ODI scores of the cNLBP group were moderately correlated with the iEMGs of RA, GA, and EO during APAs1 or APAs2. However, the NRS scores of the cNLBP group were moderately related to the iEMGs of RA, EO, and ES during CPAs and CPAs2. These observed relationships are some of the novel findings of the present study, suggesting that pain-related disabilities of cNLBP participants were likely related to APAs capacity, whereas the pain intensity seemed to be related to CPAs capacity.. Most of the published studies reported a delay in the activation of transversus abdominis or transversus abdominis/internal oblique muscles, which are the major muscles to maintain postural stability, during rapid arm movement [GA was activated earlier in the cNLBP group than the control group in the internal perturbation task. The direct comparison with published studies on GA activation was not feasible as the majority of the published studies that concerned APAs/CPAs impairments recorded activity of other muscle groups , 8, 9. Mmovement . The sEMmovement , which rIn the external perturbation task, the onset times of GA and ES of cNLBP participants were delayed comparing to the healthy controls. These are consistent with the findings of previous studies where a delayed activation of trunk or lower limb muscles in response to external perturbation , 33 was The iEMGs of the trunk muscles RA and ES during APAs phase were larger in participants with cNLBP than healthy participants during the internal perturbation task. These findings suggested higher trunk muscle activation to stabilize the spine. These findings are consistent with those reported by Ringheim et al. . The finIn the external perturbation task, the iEMGs of the EO were larger in the cNLBP group than the control group, whereas the iEMGs of GA in the cNLBP group were smaller than those in the control group. These findings may indicate altered postural muscle coordination in patients with cNLBP. Several published studies previously reported altered muscle coordination of the trunk and lower limbs in the patients with cNLBP , 39. It Due to technical difficulties with the force platform, the COP data from 14 participants collapsed during data collection of the internal and external perturbation tasks. This is a possible reason that affect the robustness of the analysis of between-group difference and relationship in COP data, which would not be discussed in this study.All of the onset time of ipsilateral muscles relative to the arm movement was not significantly related to the pain rating and ODI. There were several studies to explore the relationship among muscle onset time, pain, and disability. One study reported a positive correlation between contralateral EO onset and ODI and physical scores of the Fear Avoidance Beliefs Questionnaire (FABQ) . HoweverODI was moderately correlated with the iEMGs of GA in the early phase of APAs (APAs1) and the iEMGs of RA and EO in the late phase of APAs (APAs2). These suggested that the iEMGs of GA, RA, and EO for APAs were related to the disability in our daily tasks. The GA is the agonist muscle for knee flexion which indirectly affected the hamstrings and limits the range of trunk forward bending , 40. It The NRS scores of cNLBP participants were moderately associated with the iEMGs of RA, EO, and ES during CPAs and CPAs2. This finding indicated that the patients with higher pain intensity demonstrated larger muscle activation of postural muscles during CPAs. Our findings were supported by the findings of another study , which aThis study found no significant correlations between ODI/NRS and muscle onset/iEMGs during APAs and CPAs in the external perturbation task. The potential reason was the chosen perturbation load of 1.5 kg might be insufficient to fully activate the muscle fibers during CPAs, as it was lighter than 5% of participants that was adopted in a previous study . AccordiThere are several limitations in the present study. First, the weight of the load in the external perturbation task was only 1.5 kg, which did not reach 5% of participant's weight used in the previous study and could not elicit strong muscle activation during CPAs. Thus, the between-group differences and the relationship between postural control and pain-related outcomes in the external perturbation task could not be found. In the future study, the weight of the external loading should reach 5% of participant's weight. Second, the age range of sample population is from 21 to 27. The findings in the present study may not be accurately generalized to other age ranges of population. Third, the present study did not consider the difference of muscle activation pattern between the painful and nonpainful sides or between the contralateral and ipsilateral sides of the arm movement, which may prevent further conclusion to be drawn on the muscle activation pattern of cNLBP patients. Fourth, even though the between-group differences were observed in the present study, the findings only could reflect the general capacity of postural control in the population assessed. Some of the included cNLBP participants had comparable postural control ability as some of the participants in the control group. The variability between the two groups was different in some outcome variables according to the standard deviations, which may reduce the generalizability of the data. Future study with larger sample size to enable subgroup analysis based on postural control impairment is recommended. Fifth, sample calculation was not conducted in the present study. The underpowered sample size might lead to Type-II error.People with cNLBP adopted an altered strategy by increasing the activation of the superficial trunk muscles and dorsal muscles in APAs to achieve postural stability in response to internal perturbation. Different muscle activation pattern in internal and external perturbation tasks of cNLBP group suggested that the muscle activation in APAs and CPAs was task dependent. Pain-related disabilities of cNLBP participants are likely related to the APAs capacity, whereas the pain intensity may relate to the CPAs capacity. Pain and disability may therefore be related to the APAs/CPAs control process of the posture-related muscles.


Cytoplasmic ribonucleoproteins called stress granules (SGs) are considered as one of the main cellular solutions against stress. Their temporary presence ends with stress relief. Any factor such as chronic stress or mutations in the structure of the components of SGs that lead to their permanent presence can affect their interactions with pathological aggregations and increase the degenerative effects. SGs involved in RNA mechanisms are important factors in the pathophysiology of neurodegenerative disorders such as amyotrophic lateral sclerosis (ALS), frontotemporal degeneration (FTD), and Alzheimer's diseases (AD). Although many studies have been performed in the field of SGs and neurodegenerative disorders, so far, no systematic studies have been executed in this field. The purpose of this study is to provide a comprehensive perspective of all studies about the role of SGs in the pathogenesis of neurodegenerative disorders with a focus on the protein ingredients of these granules. This scoping review is based on a six-stage methodology structure and the PRISMA guideline. A systematic search of seven databases for qualified articles was conducted until December 2020. Publications were screened independently by two reviewers and quantitative and qualitative analysis was performed on the extracted data. Bioinformatics analysis was used to plot the network and predict interprotein interactions. In addition, GO analysis was performed. A total of 48 articles were identified that comply the inclusion criteria. Most studies on neurodegenerative diseases have been conducted on ALS, AD, and FTD using human post mortem tissues. Human derived cell line studies have been used only in ALS. A total 29 genes of protein components of SGs have been studied, the most important of which are TDP-43, TIA-1, PABP-1. Bioinformatics studies have predicted 15 proteins to interact with the protein components of SGs, which may be the constituents of SGs. Understanding the interactions between SGs and pathological aggregations in neurodegenerative diseases can provide new targets for treatment of these disorders. Cell function is divided between the organelles sited inside the cell. Based on the presence of lipid membrane, organelles can be divided into main two groups. Nucleus, mitochondria, endoplasmic reticulum, and Golgi apparatus are the major membranous organelles. Membraneless organelles are ribosomes  have the ability to phosphorylate the α subunit in the eIF2 factor  used in search strategy if available. PubMed and Embase related search strategies are shown in Studies in neurodegenerative diseases in relation to SGs in humans or in human-derived cell lines were screened from publications obtained during the search process. All types of publications were reviewed, including journal articles, conference presentations, conference abstracts and reports. Non-English articles with English abstracts were also included. Screening was done in two stages. At first, both researchers  screened articles separately based on title and abstract, according to the inclusion criteria mentioned above. In the next step, full texts of the selected articles were investigated to measure its relevance to the research question. Finally, appropriate articles were selected based on the eligibility criteria. Any contradiction was resolved in agreement with the opinion of the third person.Two separate charts were designed for human samples and human-derived cell lines in Microsoft Excel to help extracting the data. Chart related to human sample articles included author's name, year of publication, diagnosed neurodegenerative disease (number of patients), age and country, sample and method of analysis, protein component of SGs and major findings. The chart related to human-derived cell lines articles included the author's name, year of publication, origin of cell lines, age at biopsy, country, sample, method of analysis, mutation, and major findings. Two researchers  separately extracted data from articles based on charts.Quantitative and qualitative analysis was performed on the data obtained from the publications. In the quantitative analysis section, a descriptive numerical summary of the extent, nature and distribution of the studies were reviewed. In the qualitative analysis section, based on the research question mentioned earlier, a narrative review was performed on the available information with affirmation on the broader context suggested by Levac et al. .p < 0.05.Two disease-protein interaction networks were designed using Cytoscape v3.8.0 software , and with progressive loss of upper MNs (UMN) in the motor cortex of the brain and lower MNs (LMN) in the brain stem and the spinal cord , product of the TARDBP gene, plays the largest role in the formation of pathological aggregations. Most of the changes observed in this protein in aggregations include phosphorylation  is present in pathological aggregations with a lower percentage than TDP-43 and its association and effects on SGs have been studied. The mechanism of FUS toxicity is not fully understood, but due to its cytoplasmic localization, loss of nuclear activity and acquisition of cytoplasmic function might be involved  is another common mutation in ALS that is associated with an increase in the number of G4C2 repeats in the C9orf72 gene. The number of repeats in normal people are between 20 and 30, but in people with mutations, the number of repeats increases to hundreds   (Mori et al., FTD is one of the most common types of dementia that affects people under the age of 65 (Bird et al., AD is a chronic neurodegenerative disease that is accompanied by death of neurons and loss of synapses in the cerebral cortex and certain subcortical regions of the brain (Tiraboschi et al., Tau can act as a negative regulator of protein translation by binding to ribosomes and reducing protein synthesis (Meier et al., MS is a demyelinating disease in which the cover of nerve cells in the brain and spinal cord is damaged (Noyes and Weinstock-Guttman, GO classifies relationships between genes by annotating and categorizing them into three levels: biological process, molecular function, and cellular component. Biological process describes the cellular or physiological role performed on a larger scale by a gene in relation to other genes. The molecular function describes the molecular activity of the desired gene, and the cellular component determines where the gene product executes its function (The Gene Ontology Consortium., Equilibrium is the most important point in SGs. SGs are in the nature of the cell and are considered as cell solutions to stress. They are temporary constructions and when the stress is relieved, they are disassembled and reduced in number, and the cell condition returns to normal. If under any circumstances the presence of SGs becomes permanent and leads to disequilibrium, in interaction with pathological aggregations such as TDP and FUS aggregations, they can lead to pathological conditions such as neuron degeneration. SGs have been studied in many neurodegenerative diseases in humans, including ALS, FTD, AD, and MS. These studies have indicated common features in SG biology among neurodegenerative diseases. At least 15 proteins have been predicted to interact with the protein components of SGs in mentioned neurodegenerative disorders. Therefore, it seems that SG biology is common between these disorders. Yet, some components of SGs might be specific to these disorders. Based on the rarity of comparative analysis between these disorders, it is not possible to male conclusive interpretations in this regard. We have tried to provide a comprehensive summary of these studies and an overview of SGs in neurodegenerative diseases. To conclude, more studies can be done in diseases such as AD and MS and the association of the Tau protein with other protein components of SGs or the association of inflammatory pathways with the formation of SGs could also be assessed.MT, SG-F, and MR wrote the manuscript and contributed in study design. MA, MS, HS, and AJ contributed in the data collection, designed the tables and figures. All authors approved the 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.


Hemp raw materials increased bread hardness and decreased elasticity. The colour of breads containing 1% hemp was also visibly darker than the reference bread. The addition of more hemp led to further darkening and the deterioration of the technological parameters of the products. Consequently, while various hemp materials have high nutritional value, a balance with sensory properties, e.g., textural and colour, has to be reached.Hemp and hemp seed press cake—a by-product of hemp oil production—are high-protein, gluten-free raw materials that are often used to enhance the nutritional value of breads. The addition of hemp materials, however, often negatively impacts the technological parameters of breads. Consequently, this study investigated whether and how much the addition of various by-products of hemp seed press cakes to wheat bread mixtures adversely affects the texture and colour profile. The texture profile and colour were determined using a texture analyser and tristimulus measurements. The particle size of raw materials was also measured. Principal component analysis was then used to visualise the correlation between all measured values as well as nutritional parameters. The results showed that the addition of only 1% of some hemp raw materials caused significant technological changes ( Bread is one of the oldest food products and a staple of the human diet in many countries ,2. In reA drawback of hemp, however, is that the lack of gluten greatly impacts the texture and baking properties of bread. Studies have shown that consumer acceptance of bread greatly depends on textural properties and that as little as 5% hemp flour alters the texture profile of breads ,11.Cannabis sativa) press cakes, which are a by-product of hemp oilseed production, beans and their by-products, vegetables and their by-products, mushrooms and groundnuts, as well as other seeds, such as flaxseed, are also often used to enhance the nutritional value of gluten-containing and gluten-free breads [In addition to hemp flour and hemp seed  No 1924/2006. One such claim is high protein, which indicates that at least 20% of the energy of the product is provided by protein .Therefore, the current study investigated breads with 1% of various hemp raw materials made from press cakes and hemp grit, as well as breads with higher amounts of hemp materials, to meet the requirements of the aforementioned health claim. Previous studies have focused on hemp flour or hemp seed press cake and did not investigate the impact of hemp seed grit or hemp press cake isolate on technological parameters. Consequently, this study will help to better understand how different hemp by-products can be used and implemented in breads.Therefore, small amounts of each hemp raw material were used to study whether and to what extent the technological properties of the finished product are impacted by minimal changes. A previous study showed that 5% hemp raw material has an effect on technological parameters . TherefoThe dry matter (DM) of the raw materials ranged from 92.6 to 96.8% (ΔDM = 4.2%) with a mean of 94.1 and a median of 93.8%. The standard deviation (SD) and relative standard deviation (RSD) were 1.7 and 1.8%, respectively. The water-binding capacity (WBC) ranged from 1.2 to 1.9% (ΔWBC = 0.7%) with a mean and median of 1.4%. SD and RSD were calculated to be 0.3 and 21%, respectively. The high RSD value stems from the fact that the WBC of HSG is quite high compared to all other raw materials. The least gelation concentration (LGC) varied between 14 and 22% (ΔLGC = 8%). The means of all mentioned parameters are summarised in p < 0.05). The LGCs of samples also differed, with the half-and-half mixture of wheat and whole wheat flour (WFM) and hemp press cake flour protein (46%) (HP46) yielding the lowest value.The means and medians of the presented parameters are similar, which indicates that the data are not highly skewed. All raw materials had a dry matter content of over 90%. The WBCs of all but one hemp raw material (HP54) differed significantly from that of the wheat flour mixture  showed the lightest colour, whereas HP46 had the darkest colour. HP46 was also the sample with the greatest ΔE* relative to the wheat mixture reference. HP54 had the highest a* and b* values. All L*, a*, b* and ΔE* values are listed in All calculated ΔE* values are over 20. As a rule of thumb, samples are perceived as a different colour if the ΔE* value is greater than 5 . ConsequA scaled and centred principal component analysis (PCA) was performed for explorative data analysis. p > 0.05).Doughs and breads were freshly prepared as outlined in Material and Methods, p < 0.05). HP46-14-D expanded the least (50 mL) and H54-10-D expanded the most (53 mL). HPF-18-D expanded by 52 mL.Doughs with higher amounts of hemp expanded significantly less than the reference dough .The results of texture analysis are listed in p < 0.05). The cohesion of HPF-18-B and HP46-14-B also significantly differed from that of WFM (p < 0.05). HP54-10 did not differ significantly from WFM (p > 0.05). HP54-10 had the highest cohesion but also the highest standard deviation. The median and mean cohesion of all breads had values of 0.94 and 0.96 with SD and RSD values of 0.07 and 7.1%, respectively. Elasticity ranged from 1.05 to 1.13 (ΔElasticity = 0.08) and had a median and mean of 1.07 and 1.08, respectively. SD and RSD were both quite low, with values of 0.03 and 2.6%. Gumminess and chewiness ranged from 5.99 to 14.44 (ΔGumminess = 8.45 N) and 6.28 to 15.91 N (ΔChewiness = 9.62 N), respectively. Mean and median gumminess values of all samples were 9.42 and 8.11 N, and SD and RSD were 3.30 N and 35.1%. The mean chewiness of all samples was 10.19 N, and the median value of the same parameter was 8.88 N. SD was calculated to be 3.67 N with an RSD value of 36.0%, which indicates a high variance between samples.For the texture analysis of breads, hardness was measured, and cohesion, elasticity, gumminess and chewiness were calculated see . Hardnesp < 0.05). Regarding elasticity, all samples except for HPF-18-B also differed significantly from the reference (p < 0.05). Consequently, even the addition of as little as 1% hemp raw materials caused changes in technological properties.The mean and median cohesion and elasticity over all samples of breadcrumbs were close. Regarding hardness, gumminess and chewiness, the means and medians were further apart, mostly because HP54-1-B, HPF-18-B and HP46-14-B exhibited higher values than those of the remaining samples. The cohesion of all breads containing hemp, except for HP54-10-B, differed significantly from that of the reference (p > 0.05). All other breads showed significant differences in these parameters compared to the reference bread (p < 0.05). Gumminess differed significantly from the reference bread for all samples (p < 0.05). In the present study, the data for dough and breads are in good agreement. The presented data also suggest that HP54-10-B had similar textural properties to samples with 1% hemp materials, which is interesting considering the large increase in hemp material.The hardness and chewiness of bread containing 1% HP46 did not differ significantly from those of the reference bread . This finding is in good agreement with other studies, which showed that dough expansion is significantly influenced by the addition of at least 5% hemp flour [Dough expansion is an indicator of the textural parameters of breads. In the presented study, doughs with higher amounts of hemp expanded significantly less than the reference dough , hemp press cake flour (HPF), hemp press cake flour protein (54%) (HP54) and hemp press cake flour protein (46%) (HP46). All products were organically grown in Austria and are certified with the EU organic logo . Hemp seWheat flour (WF) (Austrian flour type W700) and whole wheat flour (WWF) (Austrian flour type W1800) were purchased from Anton Rauch GmbH & Co KG . A half-and-half mixture (w%) was prepared and served as reference material. In WFM: Amount of WFM/g/100 g;mWFM: Protein content of WFM/g/100 g;PWFM: Energy of WFM/kJ;WH: Amount of hemp raw material/g/100 g;mH: Protein content of hemp raw material/g/100 g;PH: Energy of hemp raw material/kJ;EF: Factor to convert g protein to kJ (1 g = 17 kJ).Hemp raw materials were added to a standard bread dough mixture of wheat flour, whole wheat flour, salt, dry yeast and water. The ratio of wheat flour to whole wheat flour was 1:1 in all doughs, and the amount of water was adapted according to the water-binding capacities of the used hemp raw materials, which were experimentally determined before dough preparation . The comFor dough preparation, all raw materials were mixed for 5 min at 100 rpm in a conventional food processor  and then fermented for 1 h at 30 °C in containers greased with sunflower oil and sprinkled with wheat flour. Lastly, doughs were baked at 195 °C for 30 min in a standard kitchen oven. All hemp raw materials and the wheat flour mixture were analysed for their dry matter, particle size, colour, water-binding capacity and least gelation concentration. Dry matter (DM) was analysed according to the standard protocol .Particle size was determined with the Mastersizer 2000  according to a previously published method . Air preColours of raw materials were determined using a tristimulus colourimeter . The variables L* (luminosity), a* (red to green) and b* (yellow to blue) were measured, and the colour difference was calculated according to the following equation:Water-binding capacity (WBC) was determined in triplicate according to the method of Sathe et al. as modified by Ionescu et al. ,41. ApprLeast gelation concentration (LGC) was determined in triplicate using the method of Badar with slight modifications . SuspensDough expansion, stickiness and colour were analysed. The latter was measured using a tristimulus colourimeter , as described in the previous section.Dough stickiness was determined with a texture analyser  with a Chen-and-Hoseney cell based on a protocol from Grausgruber et al. . For douThe colours of breads were determined as previously described using a tristimulus colourimeter. Breadcrumb hardness was measured using a texture analyser . Cohesiveness, springiness, gumminess and chewiness were subsequently calculated. Then, cylindrical samples with 25 mm diameter and 20 mm height were prepared and analysed according to an adapted method by Kadan et al. . All meaMeans and standard deviations (SDs) were calculated for all parameters for repeat measurements as well as for the whole sample set. The ratio of SD to mean expressed in percent, also called relative standard deviation (RSD) or coefficient of variation, was calculated for the sample set in order to estimate sample dispersion. Principal component analysis (PCA) was performed in R Stats using the prcom function and was used for explorative data analysis. For all PCA computations, data were scaled and centred. The Suitability of PCA was evaluated using scree plots see . AdditioThe present study explored technological changes in breads with 1% of various hemp materials and breads that are high in protein, with 20% of the energy coming from protein. Colour differences between doughs with 1% hemp raw materials compared to the reference material were very small (ΔE* < 2.4); however, colour differences between the remaining samples and the reference were higher, with ΔE* < 20.1. After baking, the colour difference increased for all samples, which suggests that hemp raw materials are subject to more browning reactions. HPF-1-B and HP46-1-B showed a ΔE* > 4, which means that consumers will likely perceive these breads to be darker than the reference bread. HPF-18-B and HP46-14-B showed a ΔE* > 15, which indicates large colour differences, which are visible in The texture of most doughs containing hemp raw material differed significantly from the reference dough. As expected, high dough stickiness translated to a high elasticity in breads. Previous studies showed that consumer acceptance only decreased when replacing 10% of the flour or more with hemp . In the This study proved that even the addition of as little as 1% hemp leads to statistically significant technological changes in breads. The current study further revealed that by carefully selecting hemp raw materials , the protein content of breads can be significantly increased, while textural properties remain similar to the reference.


Titanium alloys are extensively employed in the fabrication of various aviation structural parts, of which the most crucial processing step is hot working. In order to study the high-temperature deformation behavior of the TC21 titanium alloy, high-temperature tensile tests were performed. The results reveal that the flow stress of the material gradually decreases with an increased strain rate, and the stress increases rapidly with an increase in strain during the deformation of the alloy. Following this, flow stress gradually decreases. Flow stress decreases sharply, and the sample fractures when the appearance of necking and microvoids is observed. The Arrhenius and Radial basis function (RBF) neural network constitutive models are established in order to accurately describe the high-temperature deformation behavior of the material. In the modified Arrhenius model, strain rate indexes are expressed as a function of deformation temperature and strain rates; furthermore, the high prediction ability of the model was obtained. For the Radial basis function, the network parameters were obtained using the trial-and-error method. The established models could better forecast the flow stress of materials, and highly accurate results are obtained using the radial basis function model. The relationships between the stress index and the deformation activation energy with strain indicate that the primary deformation mechanism involves grain boundary slip and viscous slip of dislocations. The process of dynamic recrystallization primarily promotes the softening of the material. Titanium alloys are the best choice in the fabrication of various aviation structural parts  as they are characterized by high specific strength, good mechanical properties, and excellent corrosion resistance ,2. TitanThe process parameters of titanium alloys dictate microstructure evolutions and mechanical property evolution characteristics ,6. Qu etu et al.  reportedu et al.  found thu et al.  reportedu et al.  studied u et al.  reportedg et al. ,14,15 stg et al.  investigrbagallo  reportedNumerous constitutive models have been developed to accurately express high-temperature deformation behaviors of alloys. Peng et al.  developeThe TC21 titanium alloy can be reinforced through heat treatment. Although researchers from various countries have widely studied the structure evolution of titanium alloys, they have rarely studied the thermal deformation behavior of TC21 alloy. To investigate the deformation behavior of TC21 titanium alloy, high-temperature tensile tests were conducted. We aimed to analyze the influence of deformation process parameters on the deformation behavior of alloys and establish an accurate constitutive model.The nominal composition of the TC21 alloy used in the experiment is (wt.%) Ti-6Al-2Zr-2Sn-2Mo-1.5Cr-2Nb, which is a typical Ti-Al-Sn-Zr-Mo-Cr-Nb(-Ni-Si) alloy. The β transition temperature of the TC21 alloy is approximately 975 °C. The cast TC21 alloy is forged at the beta region first and then deformed at the alpha + beta region. The forged alloy is used in this work. The initial microstructure of the alloy was characterized using Olympus DSX500 optical microscope. The initial microstructure  of the material is shown in −1). Subsequently, the sample was cooled to room temperature at a cooling rate of 50 °C/min. During the experiment, the temperature, force, and strain were collected simultaneously.The test is conducted using an Instron 3369 high-temperature testing machine. The temperature control accuracy of the testing machine is ±1 °C, the force control accuracy is ±0.1 MPa, and the displacement control accuracy is ±1 μm. At the beginning of the experiment, the sample was heated to the specified temperature  at an initial heating rate of 20 °C/min. The temperature was increased slowly to the given temperature when the temperature is close to the given value due to the PID control strategy of the equipment. The entire heating time is about 1 h. Then, the samples were cooled down to 20 °C in the heating furnace after the tests. This temperature was maintained for 5 min to guarantee the generation of a uniform temperature. Following this, the sample was stretched at a constant stretching speed , flow stress decreases, which is attributed to the thermal activation of dislocation. The ability of the dislocation to move increases as temperature increases. Dislocation slip or dislocation climb can potentially occur under these conditions, resulting in the plastic deformation of the material, and the nucleation rate and the grains growth rate significantly increase with increased temperature. Under these conditions, the extent of DRV realized increases, increasing the extent of the softening of the material. This renders the material susceptible to plastic deformation.An analysis of the flow stress curves generated at varying temperatures reveals that the The high-temperature tensile flow stress of TC21 titanium alloy is affected by ε˙ . When ε˙T increases and T(K), In general, the flow stress generated during the deformation process can be expressed as a function of F(σ):σ is flow stress (MPa), R is the ideal gas constant , flow stress can be expressed as a function of the parameter Q is independent of T, the following relations can be obtained.Taking the value of true strain as 0.1 (representative example), the method of obtaining the value of each parameter is illustrated. Under low stress (Logarithms of Equations (5) and (6) are taken to obtain Equations (7) and (8), respectively. The equations are expressed as follows.−1, and the value of α is 0.09173.The values of stress and strain were substituted into Equations (7) and (8), considering that strain is 0.1. The linear relationship obtained under these conditions is shown in For all stress states, Equation (9) can be obtained.Equation (10) can be obtained by taking the logarithm of both sides of Equation (9). Equation (10) is expressed as follows.Q value, 572.8915 kJ/mol. The intercept of the straight line presented in Q values. The value of lnA is found to be 55.9645.The relationship between Q, A, β, α, n, and m) associated with the constitutive model. The relationship between material constant and true strain is shown in The method mentioned above is followed to select the strain values  in the range of 0.05–0.2 to calculate the material parameter values . The nodes corresponding to the output layer can usually be represented by simple linear functions. The action function (basic function) in the hidden layer node locally responds to the input signal. The schematic diagram of the RBF neural network is shown in px is the p-th input sample, ic is the i-th center point, h is the number of nodes in the hidden layer, and σ is the expansion coefficient (determined based on the distribution of the data center). The variance is determined as follows to avoid the generation of a flat or significantly sharp RBF.The activation function of the RBF neural network can be expressed as follows:ijw is the weight between the i-th hidden layer and the j-th output layer node. The output of the RBF neural network can be expressed as follows:jd is the actual output value, and F is the fitness of the optimization model. The process of obtaining  using the optimization algorithm is the training process of the network.Appropriate structural and parameter designs should be considered to construct the RBF neural network. The structural design considers the number of hidden layers b and the number of nodes h presented in each hidden layer. The parameter design takes into consideration the data center c of the basis function, the extended parameter σ, and the weight w of the output node. The training error corresponding to the RBF neural network can be expressed as follows:To obtain the proper network parameters, the number of the hidden layers, the nodes in each hidden layer, and the expansion coefficient are adjusted many times using the trial-and-error method. When the errors between the experimental and predicted values are acceptable, i.e., the correlation coefficient is larger than 0.99, the corresponding network parameters are obtained. Therefore, an RBF neural network model  based on the above network model is established. The model consists of two hidden layers, and the number of nodes in each hidden layer is 40. The expansion coefficient is 0.01. The prediction accuracy of the model is shown in Stress index m is often used to analyze the primary deformation mechanism associated with a material subjected to thermal deformation. When m is close to 2, the predominant deformation mechanism involves grain boundary slip. When the value of m is 3, the primary deformation mechanism involves dislocation viscous slip. When m is in the range of 4–6, the process of deformation primarily involves the process of dislocation climbing. As shown in The magnitude of the deformation activation energy is influenced by the deformation mechanism ,30. The (1)The DRX process of the TC21 titanium alloy during high-temperature deformation is observed. The flow stress of the material initially increases and then decreases gradually as the strain increases, which results from the effect of DRX. The gradual decrease can be attributed to the appearance of necking and micropores, which results in material fracture.(2)T and T increases, flow stress gradually decreases.The high-temperature rheological behavior of the TC21 titanium alloy is influenced by (3)The Arrhenius and RBF neural network constitutive models, which can accurately describe the deformation process of materials, have been built and the models can well forecast the flow stress of materials at high temperatures. The results obtained from the RBF neural network are more accurate than those obtained using the Arrhenius model.(4)The relationships between the stress index and the deformation activation energy with strain indicate that the processes of grain boundary slip and viscous slip of dislocations result in material deformation. DRX primarily results in the softening of the material.The high-temperature deformation behavior of the TC21 titanium alloy has been studied by conducting high-temperature tensile tests. The conclusions made have been presented as follows:


After the first detection of Middle East respiratory syndrome coronavirus (MERS-CoV) in camels in Jordan in 2013, we conducted 2 consecutive surveys in 2014–2015 and 2017–2018 investigating risk factors for MERS-CoV infection among camel populations in southern Jordan. Multivariate analysis to control for confounding demonstrated that borrowing of camels, particularly males, for breeding purposes was associated with increased MERS-CoV seroprevalence among receiving herds, suggesting a potential route of viral transmission between herds. Increasing age, herd size, and use of water troughs within herds were also associated with increased seroprevalence. Closed herd management practices were found to be protective. Future vaccination strategies among camel populations in Jordan could potentially prioritize breeding males, which are likely to be shared between herds. In addition, targeted management interventions with the potential to reduce transmission between herds should be considered; voluntary closed herd schemes offer a possible route to achieving disease-free herds. Middle East respiratory syndrome (MERS) coronavirus (MERS-CoV) represents 1 of 3 major zoonotic coronaviruses to have emerged with global impact in the past 2 decades, alongside severe acute respiratory syndrome coronavirus (SARS-CoV-1) in 2002–2003 and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) from 2019 onward  and 550 km of desert border with Saudi Arabia to the south and east .In the 2014–2015 study, because of the absence of an adequate sampling frame, we conducted nonprobabilistic sampling among clients of a centrally located private veterinary practice in Al Quwayrah (Aqaba governorate). During the study period, the Al Quwayrah clinic closed (February 2015); the final 53 herds included in the study were recruited through local contacts of government veterinarians working in the study area. We collected serum samples from the onset, whereas collection of nasal swab specimens began in March 2015 and occurred in the final 53 herds only.In the 2017–2018 study, we conducted multistage cross-sectional random sampling by using MoA-supplied lists of camel owners for Aqaba and Ma’an governorates organized by 4 local administrative areas . We collected serum samples and nasal swab specimens from the onset.<12 camels per herd; in herds of <12, we sampled all camels, subject to accessibility and owner permissions. A structured questionnaire regarding potential risk factors for MERS-CoV infection was administered in the local dialect on paper (2014–2015 study) or on Android tablets using the application Open Data Kit (2017–2018 study) (https://getodk.org) to herd owners face-to-face at the time of sampling or by telephone after sampling. A veterinary surgeon clinically examined all camels included in the study to assess general health before sampling.In both studies, to encourage owner compliance, we sampled Blood samples were collected in 8 mL serum vacutainer tubes and centrifuged at 2,000 RPM for 10 min, followed by serum collection and storage at −20°C. Nasal swab specimens were placed in viral transport medium and chilled before storage at −20°C (2014–2015 study) or −80°C (2017–2018 study). All laboratory testing of samples was performed at the Diagnostic Laboratory, Veterinary Health Centre, Jordan University of Science and Technology .https://www.sinobiological.com) before blocking with 1% milk. MERS-CoV S1-specific antibodies were detected by using anti-llama IgG horseradish peroxidase-conjugated antibodies  and subsequently developed with peroxidase-substrate reagent (KPL). Optical densities were measured at 405 nm and positivity at 3 times mean negative camel serum samples collected from United States–bred dromedary camels confirmed to be MERS-CoV–free. This assay does not cross-react with antibodies to bovine coronavirus, OC43, or SARS-CoV-1  according to the manufacturer’s instructions <18 months after sample collection. Extracted RNA was used in a 1-step real-time reverse transcription PCR (rRT-PCR) UpE MERS-CoV assay performed on a QIAGEN Rotor-Gene instrument, with positivity set at a cycle threshold value of <40, on the basis of standard operating procedures as described in Corman et al.  and ran regression models for identification of risk factors. Because of the differences in sampling strategy, weighting was conducted by region for the 2014–2015 study and by subregion for the 2017–2018 study. In both studies, we excluded camels We conducted univariate analyses by using mixed-effects regression with herd as a random effect and camel serologic status considered a binary outcome. All potential risk factors were analyzed as categorical variables, with the exception of camel age and herd size, which were analyzed as continuous variables. Variables were herd level with the exception of age, sex, racing camel, and nasal discharge. For the 2017–2018 study (data were missing in the 2014–2015 study), we constructed a composite variable “closed herd,” which we defined as herds in which no borrowing, lending, purchasing, racing, or contact with local or distant herds occurred.https://cran.r-project.org) and generated mixed-effects models by using the glmer function of the R package lme4 version 1.1–21.We considered variables with a p value of <0.2 for inclusion in the multivariate models, with the exception of any variables missing >10% of their values. We used the Pearson R coefficient and a threshold of 0.4 to compare collinearities between variables; we excluded colinear variables from the same multivariate model and tested in separate models. We conducted multivariate models by using mixed-effects regression with herd as a random effect and constructed using a backward stepwise method, removing the least significant variable at each step while p>0.1, unless the variable was considered an a priori factor  or the removal of the variable demonstrated a significant effect on the other variables (a change in log odds of >10%). We repeated model creation by using a forward stepwise method, beginning with a priori variables and adding new variables in order of significance, keeping variables if they showed significance of p<0.1 or changed the log odds of other risk factors by >10%. We performed all statistical analyses in R version 3.5.1  representing 97 herds (median herd size 11 [IQR 5–22]). We obtained blood samples from an average of 4.5 camels/herd and collected nasal swab specimens from 65% of included camels. The questionnaire was completed for 93 of 97 herds; we excluded 4 herds (17 camels) that lacked questionnaire data from the analysis of risk factors. A total of 21 questionnaires were completed at the time of sampling, and 72 were completed subsequently by telephone.<6 months of age, of which 4 were seropositive (44.4%). After we excluded these calves from the dataset, the adjusted seroprevalence was 88.0% (95% CI 84.1–91.4). No nasal swab specimens tested positive for MERS-CoV RNA on rRT-PCR.In total, 128 sampled camels (from 22 herds) were from Ma’an region and 305 (from 75 herds) were from Aqaba region. MoA records indicated an estimated population of 4,436 camels (317 herds) in Ma’an region and 3,314 camels (265 herds) in Aqaba region; we weighted adjusted seroprevalence accordingly. Of 433 camels sampled, 381 were seropositive for MERS-CoV, an unadjusted seroprevalence of 88.0% and adjusted seroprevalence of 86.8% (95% CI 82.8–90.3). Of these, 9 camels were >1 seropositive camel , resulting in an unadjusted herd-level seroprevalence of 95.9% and adjusted herd-level seroprevalence of 92.3% (95% CI 83.3–97.1); median herd sample seroprevalence was 100% (IQR 80%–100%)  , 3. HighIn univariate analysis, age, sex, herd size, number of herds nearby, quarantine >3 days after purchase, borrowing of breeding males, and water source were all found to be associated with seropositivity at p<0.2, although we identified no significant correlations for these variables , 5. QuarVariables in the final multivariate model results were age, herd size, borrowing of males for breeding purposes, and water source . We noteBlood samples and nasal swab specimens were collected from 404 camels (median age 5 years [IQR 3–8 years]) in 121 herds; an average of 3.3 camels were sampled per herd (median herd size 9 [IQR 4–17]). The questionnaire was administered to all 121 herd owners; 114 questionnaires were completed at the time of sampling, and 7 were completed subsequently by telephone. In total, 90 camels (29 herds) were sampled from Ma’an East, 70 (21 herds) Ma’an West, 152 camels (36 herds) from Aqaba East, and 92 (35 herds) from Aqaba West. MoA records described an estimated 1,909 camels (138 herds) in Aqaba East, 1,405 camels (127 herds) in Aqaba West, 3,563 camels (198 herds) in Ma’an East, and 873 camels (119 herds) in Ma’an West; we weighted adjusted seroprevalence accordingly.<6 months of age were seropositive (66.7%), which compares with 18 (22.8%) of 79 among camels >6 months–2 years of age . After removal of calves <6 months from the dataset, the adjusted seroprevalence was 70.2% (95% CI 65.0–75.2) among 119 herds.Of 404 camels sampled, 264 were seropositive for MERS-CoV, for an unadjusted seroprevalence of 65.3% and an adjusted seroprevalence of 70.2% (95% CI 65.6–74.7). Of these, 26 of 39 camels >1 seropositive camel , resulting in an unadjusted herd-level seroprevalence of 77.3% and adjusted herd-level seroprevalence of 77.0% (95% CI 69.8–83.0); median herd sample seroprevalence was 75% (IQR 25%–100%)  , 3. The No nasal swab specimens tested positive for MERS-CoV RNA on rRT-PCR. Nasal discharge was noted in 8 camels (2.6% [95% CI 1.4%–4.8%]) at the time of sampling .In the univariate analysis, the following 12 variables were found to be associated with seropositivity at p<0.2: region, age, sex, herd size, number of herds nearby, herd being kept as a single group throughout the year, contact with local herds, borrowing of camels for breeding purposes, lending of camels for breeding purposes, use of camels for racing, water source, and closed herd status , 5. We iVariables in the final multivariate model results were region, sex, age, herd size, borrowing for breeding purposes, water source, and closed herd status . EvidencPrevious studies have described MERS-CoV seroprevalence among camel populations worldwide; however, substantial knowledge gaps remain, in particular with regard to factors associated with higher risk for infection, which might provide insights into viral transmission routes between and within camel herds .In Jordan, as in other countries in the region, many herd owners do not own a breeding male camel because of cost or ease of management; instead, they borrow stud bulls from neighboring herds or send breeding females to herds that have a bull. These practices serve to provide spatial connectivity between infected and uninfected herds; this effect is potentially compounded by the immunosuppressive stresses of transport and joining a new herd and by the effects of male rutting behavior, in which oronasal secretions are sprayed over, or close to, breeding females  might be explained by several factors. The factors include differences in sampling strategy (nonprobabilistic vs. probabilistic), an absence of sample collection during the 2017–2018 summer period , and a possibly limited introduction of new MERS-CoV variants into the population between the study periods, with geographic spread over time (<2 weeks) ; maintaining a closed herd where possible, including the potential for voluntary closed herd management schemes; and quarantine practices of