Mycobacterium tuberculosis. Using a null mutant of the related, vaccine strain Mycobacterium bovis BCG, we show that MmpL7 is also involved in the transport of the structurally related phenolic glycolipid (PGL), which is also produced by the hypervirulent M. tuberculosis strain HN878, but absent in M. tuberculosis H37Rv. Furthermore, we generated an in silico model of M. tuberculosis MmpL7 that revealed MmpL7 as a functional outlier within the MmpL-family, missing a canonical proton-relay signature sequence, suggesting that it employs a yet-unidentified mechanism for energy coupling for transport. In addition, our analysis demonstrates that the periplasmic porter domain 2 insert (PD2-insert), which doesn't share any recognisable homology, is highly alpha-helical in nature, suggesting an organisation similar to that seen in the hopanoid PD3/4 domains. Using the M. bovis BCG mmpL7 mutant for functional complementation with mutated alleles of mmpL7, we were able to identify residues present in the transmembrane domains TM4 and TM10, and the PD2 domain insert that play a crucial role in PDIM transport, and in certain cases, biosynthesis of PDIM.Mycobacterial membrane protein Large (MmpL7) is a Resistance-Nodulation-Division (RND) family transporter required for the export of the virulence lipid, phthiocerol dimycocerosate (PDIM), in  Mycobacterium tuberculosis, are rich in unique lipids that form a hydrophobic barrier, playing a role in resistance and virulence  and phenolic glycolipid (PGL) play an important role in virulence. PDIM and PGL share a common structural core, a diol backbone (phthiocerol), esterified with two methyl-branched fatty-acyl moieties (dimycocerosates) (p-hydroxybenzoyl-AMP-ligase (FadD22) catalyse the formation of long-chain p-hydroxyphenylalkanoate intermediates that primes the synthesis of PGLs (M. tuberculosis strain H37Rv has a frameshift mutation in pks15/1 resulting in a deficiency in PGL production (Two rosates)  Fig. 1)M. tubercrosates)  that is rosates) . The poloduction Fig. 1(A)M. tuberculosis strains such as those from the W-Beijing family , are involved in the translocation of mycobacterial lipids across the plasma membrane (Transport proteins termed membrane . MmpLs amembrane .M. tuberculosis the mmpL7 (Rv2942) gene is located in the PDIM/PGL biosynthesis cluster  and of the ABC-transporter drrABC, which is present in the same PDIM/PGL cluster, are also defective in PDIM-translocation, suggesting a complex and distinct transport mechanism, possibly driven by a multiple, interacting components (M. tuberculosis MmpL7 and its orthologues are the only MmpLs that do not possess conserved Asp/Tyr-residues located on the transmembrane helices 4/10 (TM4/TM10), critical for proton coupling of transport M. tubercllularly . Furtherransport . Other lransport . In contmponents . Also, uransport .M. tuberculosis complex, though the transport of PGLs in Mycobacterium marinum has been inferred to be linked to MmpL7  using Specialized Transduction (mmpL7 mutant with 14C-propionate to selectively label methyl-branched fatty acid-containing lipids (including PDIMs and PGLs). Apolar lipids from spent media (exported lipids) and cell pellets (intracellular lipids) were extracted using standard methods M. bovis rculosis . A similr pMV261  to generr pMV306  under itfadD22 which, as mentioned above, encodes a p-hydroxybenzoyl-AMP-ligase that primes PGL biosynthesis  B for funM. bovis BCG ΔmmpL7-strains carrying the D670R or E676A-substitutions showed complete loss of PDIM-biosynthesis, similar to what we observed in some of the above TM10-substitutions (e.g. Y837F). Notably both Ile611 and Asp673 were previously shown to be essential for the MmpL7-PpsE interaction (All substitutions apart from R610D and D673A reinstated PDIM-transport . Howevereraction . Similareraction .https://alphafold.ebi.ac.uk/entry/P9WJU7; ab initio prediction minimising model bias. We anticipated this to be of particular benefit for the interpretation of the mutations within the PD2-insert of MmpL, which is hindered by the lack of reliable homology templates and based on the available structural data, our only reliable predictions were of high alpha-helical propensity of the insert. The PD2-insert of the AlphaFold model is consistent with our prediction, being fully alpha-helical and forming a helical bundle, but it has to be noted that it has also the lowest confidence prediction score within overall model, which limits its predictive power. While the overall conformation of the PD2-insert is markedly different, the locations of the phenotypically pronounced mutations are consistent with the PD2-domain playing a docking-platform for protein–protein interactions (While this work was under review, we gained access to the AlphaFold v2.0 predictions  for MmpLractions . Furtherractions . Intriguin silico model of MmpL7 used in this study is not without limitations, it helps to inform future structural and functional studies of this medically important protein.In summary, our findings identify MmpL7 as a functional outlier within the MmpL-family, with a distinctive amino-acid signature with its TM-domain, suggesting a yet-unidentified mechanism for energy coupling. Furthermore, we have identified the alpha-helical PD2-insert domain of MmpL7 to be critical for its function. Given the role of other proteins in PDIM transport, it is likely that MmpL7 is part of a much larger complex for PDIM and PGL transport, likely providing a scaffold for co-ordinating transport with biosynthesis these virulence lipids. While the The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


Recent advances in automatic recognition systems based on deep learning technology have shown the potential to provide environmental-friendly plant disease monitoring. These systems are able to reliably distinguish plant anomalies under varying environmental conditions as the basis for plant intervention using methods such as classification or detection. However, they often show a performance decay when applied under new field conditions and unseen data. Therefore, in this article, we propose an approach based on the concept of open-set domain adaptation to the task of plant disease recognition to allow existing systems to operate in new environments with unseen conditions and farms. Our system specifically copes diagnosis as an open set learning problem, and mainly operates in the target domain by exploiting a precise estimation of unknown data while maintaining the performance of the known classes. The main framework consists of two modules based on deep learning that perform bounding box detection and open set self and across domain adaptation. The detector is built based on our previous filter bank architecture for plant diseases recognition and enforces domain adaptation from the source to the target domain, by constraining data to be classified as one of the target classes or labeled as unknown otherwise. We perform an extensive evaluation on our tomato plant diseases dataset with three different domain farms, which indicates that our approach can efficiently cope with changes of new field environments during field-testing and observe consistent gains from explicit modeling of unseen data. Plant disease recognition concerns many farmers and researchers in agriculture. Once a plant is affected by diseases, the damage can be easily propagated to the entire crop, causing then several productions and economical losses . In convRecent works often rely on classification or detection systems that distinguish between diseases in real-time for various types of crops. Classification methods predict the type of disease using the features of the whole input image, while detection methods estimate both localization and type of disease. These systems have achieved higher performance when the trained models are evaluated in the same or at least similar farm conditions . HoweverExisting approaches for plant disease recognition make a closed-set assumption and perform supervised domain adaptation. This assumption is, however, unrealistic as for most real applications, the target domain contains multiple images, but only a small part of it belongs to the classes of interest. The universe of plant diseases is limited to those that have been included in the model during training. Also, this type of domain adaptation requires labels for the new data. In practice, much of this information is novel to the system and is often associated with one of the source categories, which could lead to wrong predictions during inference. This problem is frequently also related to the training data, as it is difficult to obtain and scarce . AlthougEarly works in deep learning have studied the open-set problem, which is concerned with approaches that are aware of unseen data . In thisTo summarize, the main contributions of this article are as follows:-We propose an architecture to address the paradigm of novel data for plant disease recognition. Our proposed method leverages the capabilities of our previous works to address more complex challenges of new greenhouse scenarios.-Our approach allows performing open set self and across domain adaptation between different greenhouse farms evaluated in our tomato diseases dataset. Particularly, we study domain adaptation using data collected at three different farms. All claims are experimentally evaluated.-We provide a set of guidelines to analyze the logic behind the recognition of novel data. This approach can allow us to make the system more adaptable to real-world environments.-We provide theoretical insight and empirical evaluation to demonstrate the capabilities of the proposed system to enhance the performance of plant disease detectors.The remainder of the article is introduced as follows. Section “Related Works” presents an analysis of related works in open set recognition and plant diseases recognition. Section “Open Set Self and Across Domain Adaptation for Plant Disease Recognition” describes the core of our open-set domain adaptation approach. Section “Experimental Results” shows the experimental settings and results. Finally, Section “Conclusion” concludes the article and presents some guidelines for future works in the field.In this section, we briefly review recent works related to the proposed approach. According to the constraints of the recent advances in plant disease recognition, these methods fall mainly into closed set domain adaptation. In this direction, open set domain adaptation is referred to as a potential solution to overcome these issues, as a more robust system that can address challenges in real farm conditions.Plant disease recognition focuses on the estimation of symptoms that occur in the plants due to disease contamination and cause risks to the crops. Recent advances have shown prominent results to address the problems of plant diseases using non-destructive media such as images . This reMethods based on detection, on the other hand, synthesize samples more objectively by providing the type of disease and location in the image through the class probability and the bounding box information, respectively. In our previous work , we presAlthough both classification and detection strategies have achieved higher performance when trained and evaluated in the same or similar field conditions with defined classes. The situation turns out to be more complex, such as when a system is applied to real-field conditions, an unknown world of objectives could appear in the scene. In this case, a weak performance could be observed, and the problem can be associated with either the capacity of the network to generalize well in the presence of domain shift or the dataset could not cover the large variety of target domains. A system is then severely affected by visual variations and other objects in the scene. Therefore, our proposed research addresses these issues through open set domain adaptation.The interest in studying domain adaptation techniques for computer vision problems has been recently increased as a way to address realistic problems that can be faced in various applications. In real-world recognition tasks, which are limited by various factors, it is usually difficult to collect training samples that cover all types of variations. A more realistic scenario is, therefore, to treat the problem as an open set recognition approach. Differently from closed-set domain adaptation which focuses on mitigating the impact of the domain gap between source and target domains using mainly feature adaptation  and geneBased on the above concept, several strategies have been recently proposed to deal with the issues associated with open set domain adaptation aiming to compensate the domain shift between the source and target datasets  studied In open set domain adaptation, the level of openness also matters. This is measured by the proportion of unknown classes in the target domain. In our work, we apply the treats mentioned above and explore methods to learn plant disease diagnosis models with open set domain adaptation. We aim to handle domain-shift among the samples in the dataset to further build a framework capable of dealing with complex cases of real greenhouse scenarios. Also, to the best of our knowledge, there is no available literature on the application of open set domain adaptation in the area of plant disease recognition.In this section, we describe the assignment of the target samples to categories of the source domain. Also, we introduce the training strategies to address these challenges. Finally, we show how the mapping from the source domain to the target domain is estimated from the previous definitions, and how this influences to label samples that do not match the desired conditions as “unknown.”y represent the universe of samples in the dataset that should be diagnosed. Among those samples, data can be either part of specific categories or unknown, as shown in Cs =  represent a set of n classes in the dataset. Open set recognition has been traditionally implemented by dividing the dataset into known and unknown categories. However, in the task of plant diseases, as a real-world application, data is collected not only on a single farm but across multiple farms LF = . Therefore, although we study the same type of crop, the conditions and plant states can differ from farm to farm. Plants at each farm could be affected by similar or different types of diseases and, also depending on the scenario of application, various conditions such as illumination and background could be observed. Additionally, once deployed, a model may encounter cases that could belong to any of the known categories or declare them as unknown otherwise. In this setting, we want to prevent misdiagnosis of data and instead recommend an additional procedure for those particular samples. Let Cs while avoiding misdiagnosis of data from target classes Ct. Additionally, a background class is included in the source domain to add context information of the greenhouse environment.To address open set domain adaptation, we study the actual scenario where unknown data is presented only in the target domain. ns labeled samples and a target domain nt unlabeled samples. The source domain consists of a set of classes Cs that are also part of the target domain. However, the target domain is further associated with additional classes that represent unknown data. Then, training domain adaptation can be addressed by assuming a certain level of openness in the dataset. This openness level refers to the relationship of classes used for training, testing, and target. Following  Bounding box detection, (2) Open set domain adaptation.The bounding box detector is based on our previous work for plant disease recognition using a refinement filter bank . This arThis is the core of the proposed research at which we address the problems associated with domain shift between data used as source and target for the training and test operations  is maximized, by also maximizing the response of Gn with respect to the background samples. Training the source domain model involves using a cross-entropy loss function in two steps: First, Ds, where CNNs, negative instances Dn are generated from bn and training Es,Gs,Gn using Ls and Ln represent the source and negative loss, respectively. Once the source domain is trained, the system owns features from both source data and negative data.where -Domain adaptation: By domain adaptation, we aim to cover the gap of the domain-shift between the source and target data. Like the source model, domain adaptation starts with a given set of classes nt unlabeled samples of the target farm. In this case, features from the source domain model (source farm) are utilized as an initial point to transfer information from known labeled classes and background class to the target domain (target farm), then Fs and G are frozen. An input data nt goes in two directions: first, it is passed through Fs and G using finetuning. Then, features are obtained by Ft from CNNt and Et to further, by classification in Gt generating the features of the target domain. The output Gs and Gt followed by the softmax function σ. At this point, the task is to maximize the probability in case the sample belongs to one of the known classes by where, Finally, the output of the system is an image with the detected plant diseases if they coincide with any of the corresponding known classes of the source domain. Otherwise, these regions are labeled as unknown.In line with , we eval-Intersection-over-Union metric (IoU): We utilized a threshold of 0.5 to capture true positive detections generated by the model, as:where A and B represent the ground-truth and predicted box, respectively.-Mean Average Precision score (mAP): mAP is the area under the precision-recall curve calculated for all classes.r, and where, Following , the usu-Normalized accuracy over the known classes (OS*):-Normalized accuracy over all classes including the unknown (OS):k represents the number of known classes.where -Harmonic mean of OS* (HOS):In this section, we evaluate the performance of the proposed open set domain adaptation approach for plant disease recognition. We design the experiments to support our claims, then: (1) we provide a framework for plant diseases recognition that can operate in real greenhouse scenarios, (2) we address the problem of domain shift between the datasets used for training and testing, respectively, (3) our model can efficiently transfer the features of the source domain to the target domain and perform recognition in unseen data of different farm environments.We carried out experiments and validate the performance of our approach on our tomato plant diseases dataset ,2018. ThThe dataset consists of 12 types of tomato plant diseases, pests, and physiological disorders, plus two additional classes that contain healthy leaves and regions of the background, respectively. Details on the dataset are presented in F1, F2, F3. This is a particular case of domain adaptation of various scenarios toward building a more robust system by taking the visual characteristics from one place to another. Here, we assume that the conditions of each greenhouse farm are different, for instance, in terms of illumination, materials of the structure, and surrounding objects. Also, because some diseases or pests can appear commonly throughout the farms, while others appear only in some of them. A representation of the farms used for data collection is presented in To make our dataset able to be used for domain adaptation such as the representation shown in During our study, we used data collected on both modern and traditional greenhouse structures. Modern greenhouses appear to have clear backgrounds, hydroponic crop management, and automatic control of the cultivation processes, while more traditional greenhouses tend to have darker backgrounds due to the structure materials, and also plants are cultivated in the soil. This, consequently, can be reflected in the types of diseases and pests that can be found on each farm.For our study, we selected the most common tomato plant diseases and physiological disorders that occur in Korean farms such as leaf mold, gray mold, canker, powdery mildew, tomato yellow leaf curl virus (TYLCV), tomato chlorosis virus (ToCV), plague, magnesium deficiency, and physical damage. Also, some pests like miner, whitefly, whitefly egg. Additionally, we included another class of healthy leaves and a background class that is particularly used for adding contextual information about the visual characteristics of the greenhouse and surrounding areas of the plant. The availability of data by farm and class is presented in Considering the data availability for each class, to build up the source and target datasets for domain adaptation, our experiments are focused on the following combinations across three different farms: F1-to-F2, F1-to-F3, F2-to-F1, F2-to-F3, F3-to-F1, F3-to-F2. Correspondingly, we train the source domain using data from one farm and transfer its features to the other domain.To assign, the number of classes that are used for training and testing the source and target domains, respectively, we used the formula described previously in Eq. 1 with different levels of openness. In this way, we evaluate the response of the system to the percentage of known and unknown classes. We used different values of openness such as 0, 33, 62, and 73%, respectively. 0% represents the case of closed-set recognition, which is the case when we apply the same number of classes for training and testing. We applied the settings mentioned above for training and testing the model across three different domain farms. The complete model is implemented in the following two stages:We used the architecture of Our Filter Bank v2 (proposed) detector provides an accurate yet efficient alternative to obtain refined regions while avoiding the false positives in the final prediction. We trained and validated the detector on the entire tomato plant diseases dataset to obtain the set of bounding boxes and their corresponding classes. We used an Intersection over Union (IoU) threshold of 0.5. A representation of the bounding boxes generated by the detector can be seen in Once the regions of interest corresponding plant diseases have been obtained from the output of the detector, we defined the known and unknown categories to be used as part of the source and target domains, also taking into account the information of the farm. We used an ImageNet pre-trained ResNet-50 model as the base CNN feature extractor , where t-Source domain: We trained the model on the source dataset with known classes. We used a batch size of 64 for 30,000 iterations. Validation is performed every 30 iterations and the learning rate has been adjusted to 0.001.-Target domain: Like training the source domain, we used a batch size of 64 to train the target domain with the known and unknown classes for 30,000 iterations. However, validation is performed every 500 iterations and the learning rate has been adjusted to 0.0001.We evaluated the performance of the bounding box detector and the open set domain adaptation module across the combinations of three farms, with the openness levels presented in In this experiment, we evaluate the performance of the bounding box detector compared to other existing methods, and report the mAP calculated with Eq. 5. As mentioned earlier, the core of the bounding box detector is based on our previous work using filter banks for plant disease recognition . The onlIn this experiment, we evaluate the performance of domain adaptation from the source to the target dataset over the known classes. To calculate this value, we used the metric introduced in Eq. 7. The system efficiently recognized samples from the known categories. However, as the level of openness increases, the performance showed some decay in all cases. Among the combinations of farms, we found that F1-to-F3, and F3-to-F1, were the most challenging cases. On one side, Farm 1 (F1), as shown earlier in In this experiment, we evaluate the capability of the model to address the recognition of data from both known classes and unknown in the target dataset. To calculate this value, we used the metric presented in Eq. 8. In this case, recognizing unknown data over the wide spectrum of variations presented in the target dataset is more challenging. Unknown data could include samples that do not match any of the known classes and are presented only in the target domain. The results demonstrate a further performance decay at a larger level of openness, but the system is still able to recognize unknown data.As in the previous experiment, a closed set configuration generates better performance than the open set. However, what we intend to demonstrate exactly with our work is a more realistic scenario to improve the ability of a model to be able to recognize new unknown classes. This could be the case when the system finds unknown or new diseases on new farms. Therefore, the level of openness gives the idea of this scenario.As presented earlier in the section “Quantitative Results” and Eq. 9, HOS represents a more realistic estimation of the recognition problem, as it provides a high score only if the algorithm performs well both on known and on unknown samples, independently of the number of classes. Therefore, using HOS penalizes large gaps between OS* and UNK. Following , we compThis experiment also validates the domain adaptation across the three farms. We evidence that transferring features from F1-to-F2 and F2-to-F3 farms, results in higher accuracy than the other cases. However, the performance decays as the openness value is increased. To further validate the performance of the proposed approach, we present the confusion matrices of domain adaptation from one farm to another. We selected Farm 1 to Farm 2 (F1-to-F2) to visualize the changes for two values of openness from 0% representing the closed-set case, and 33% representing the open-set case, respectively. First, In this article, we presented an approach for open-set domain adaptation for plant disease recognition to allow existing systems to operate in new environments with unseen conditions and farms. Our system specifically addressed diagnosis as an open set problem by mapping the features of the source and target domains to potentially improve the performance of the known classes while treating changes that happen in real farm conditions as unknown information. The main framework consists of two modules that perform bounding box detection and open set domain adaptation. We performed an extensive evaluation on our tomato plant diseases dataset across three different domain farms. An interesting future direction is, to explore the adaptation of the model to more farms with the possibility of extending the study to more diseases and variations, as well as to other types of crops. Also, improve the potential of the model to compensate for the deterioration in performance due to the level of openness.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors.AF designed the system, performed the experiments, and wrote the manuscript. DP and SY advised in the design of the system and analyzed the strategies to find the best method for efficient plant diseases recognition. TK provided support in the data collection. All authors 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.


Associations between perceived discrimination and relative deprivation have been observed among both general and migrant populations. However, it is unclear how, and under what conditions, perceived discrimination relates to relative deprivation, a subjective cognition and affective experience in which individuals or groups perceive themselves as disadvantaged, compared to their peers. Therefore, this study aimed to construct a moderated mediation model to examine the roles of locus of control and duration since migration in the relationship between perceived discrimination and relative deprivation among Chinese rural-to-urban migrant adolescents.We conducted a cross-sectional study using a convenience sampling method in three coastal cities in southeast China. We recruited 625 Chinese rural-to-urban migrant adolescents, who completed a battery of questionnaires assessing perceived discrimination, relative deprivation, locus of control, and demographic variables. Regression-based statistical mediation and moderation were conducted using the PROCESS macro for SPSS.After controlling for sex and age, perceived discrimination was positively associated with migrant adolescents’ relative deprivation, and external locus of control partially mediated this connection. Furthermore, the mediating effect was moderated by the duration of the migration. In relatively recently migrated adolescents, perceived discrimination was significantly related to relative deprivation through a greater external locus of control; however, this indirect association was not significant for adolescents with long-term migratory duration.The results of our analysis expand our understanding of the link between perceived discrimination and relative deprivation. Moreover, these findings may provide practical guidance for interventions among Chinese rural-to-urban migrant adolescents to raise their social status and improve their mental health by addressing the macro-social psychological causes of relative deprivation. In this study, the internal consistency of the scale (Cronbach’s α) was 0.85.We assessed migrant adolescents’ perceived discrimination using the Perceived Personal Discrimination Scale developed by Liu and Shen . This scvalidity , 8. The How do you compare your family’s economic status with that of your urban counterparts? How satisfied are you with this situation?). The scale consists of 20 items rated on a 7-point Likert scale. The items of the cognitive dimension range from 1 (very good) to 7 (very bad), while the items of the emotional dimension ranged from 1 (very satisfied) to 7 , strong test–retest reliability over four weeks . In this study, the internal consistency of the scale was 0.93.We used the Relative Deprivation Scale for Migrant Adolescents (RDS-MA), developed by Xiong , to meas = 0.80; , and gooI often find out what’s going to happen before it actually happens” vs. “For me, it’s better to believe in fate than to struggle”). The total scale score ranges from 0  to 23 . The scale has been widely used in adolescent groups with good reliability and validity ; see Table B = 0.33, p < 0.001, 95% CI ), and the association between locus of control and relative deprivation was also significant . Meanwhile, the direct path from perceived discrimination to relative deprivation was still significant , indicating that locus of control partially mediated the relationship between perceived discrimination and migrant adolescents’ relative deprivation . The indirect association accounted for approximately 22.9% of the total variance. Thus, Hypotheses 1 and 2 were supported.The key variables in our model are continuous variables, and a significant correlation exists between the variables for use in the moderated mediation model test. First, we conducted Model 4 (a single mediation model) of the SPSS PROCESS macro, compiled by Hayes , to examB = 0.15, p < 0.001, 95% CI ), and this association was significantly moderated by the duration since migration . Furthermore, although perceived discrimination was positively related to relative deprivation , this association was not significantly moderated by duration since migration . This indicates that duration since migration moderates the relationship between locus of control and relative deprivation. Thus, Hypothesis 3b was supported.We further conducted Model 15 (a moderated mediation model) of PROCESS macro  to assessimple slope = 0.07, t = 1.27, p > 0.05) than for those with short-term migratory duration . Additionally, the conditional indirect effect test indicated that the indirect association between perceived discrimination and relative deprivation through locus of control was moderated by duration since migration  and high (1 SD above the mean) levels of duration since migration, using the original scores . The simThis study aimed to examine how (mediating mechanisms) and when or for whom (moderating mechanisms) perceived discrimination influences migrant adolescents’ relative deprivation. The results show that locus of control acted as a mediator and that duration since migration acted as a moderator in the relationship between perceived discrimination and relative deprivation. The mediating effect of locus of control was found to differ according to adolescents’ different migratory durations. The indirect link between perceived discrimination and relative deprivation through external locus of control was stronger for migrant adolescents with a short-term migratory duration. Overall, our findings align with social comparison theory , 20 and Consistent with previous studies , 15, we In line with theoretical expectations and previous studies , 56, we The present study also found an association between external locus of control and relative deprivation, which suggests that migrant adolescents with an external locus of control reported higher levels of relative deprivation. According to the theory of locus of control , as an iSupporting our hypothesis, we observed that the link between external locus of control and relative deprivation was weaker for adolescents with long-term migratory duration than for those with short-term migratory durations. Previous research  indicateLazarus and Folkman  propose However, contrary to our expectation, the direct link between perceived discrimination and relative deprivation was not moderated by duration since migration. It could be argued that time-related effects are related to psychological mechanisms , explaining judgments about relative deprivation, rather than general evaluations of perception of discrimination. In other words, locus of control appeared to be the more proximal variable affecting migrant adolescents’ relative deprivation, while perception of discrimination appeared to be a more distal variable, at least among our study participants. In general, among the many factors that affect individual psychosocial development, environmental factors  are distal variables, while individual factors  are proximal variables . Distal Our findings have clinical implications for Chinese migrant adolescents who are struggling to adapt to their new surroundings and promote community harmony. First, parents and teachers should guide migrant adolescents to foster the positive attributions of their new urban environment, build coping skills that can increase their internal locus of control, and support them in ways that can improve their status . Such positive ways of articulating their emotions, fair treatment, and opportunities for individual growth may reduce the relative deprivation of the adolescent migrant population.Second, urban communities should work toward a fair and inclusive social atmosphere for all residents and always bear in mind that migrants come in to help build their local economies, through which all residents benefit. Specifically, urban communities should strengthen publicity and report positive typical cases of the migrant population to change the urban residents’ negative attitudes toward them. To address this social concern, Balingue  proposesThird, city governments and administrators should ensure that changes in household registration are straightforward, so that migrant adolescents can enroll in local schools readily at academic levels appropriate to their current education; further, having local household residency means that they have complete access to higher-level schools and social welfare, which promotes their personal development in their city lives. Finally, government departments and relevant managers should listen to and seriously consider the opinions of the migrant population, formulate practical and feasible policies and measures, and supervise their implementation. Specifically, they should lower the threshold for migrant workers to settle in the city, promulgate laws and regulations to ensure that the children of migrant workers can enter urban public schools smoothly, and regularly issue living allowances and social welfare to migrant workers’ families.Although our study had a sizable sample, appropriate measures, and robust analytical methods, it also had several limitations. First, it had a cross-sectional design, which could not confirm the causal relations we assumed among the core variables. Thus, future studies should adopt quasi-experimental designs to further examine the relationships and mechanisms between perceived discrimination and relative deprivation among migrant adolescents. Second, although the self-report method has been widely used to assess perceived discrimination and relative deprivation, and although “self” is the best source of information about perceptions, future research should collect data using multiple methods  and informants . Third, although many environmental and individual variables may affect the relationship between perceived discrimination and relative deprivation, we only focused on two: locus of control and duration since migration. Therefore, future research should consider more proximal variables  to accurately clarify when and how perceived discrimination influences migrant adolescents’ relative deprivation. Fourth, although this study measured cognitive and affective components of relative deprivation, we did not measure behaviors or behavioral intention components. Future research should therefore use more comprehensive indicators  to measure relative deprivation more objectively. Finally, we only selected migrant adolescents from Fujian province in southeast China as participants, which may limit the representativeness of the sample. Therefore, the findings of this study should be interpreted with caution. Future studies should expand the sample size and representation of migrant adolescents to further validate our findings.This study provides a clearer understanding of the mechanisms between perceived discrimination and relative deprivation among Chinese migrant adolescents, as well as the mediating effect of external locus of control and the moderating effect of duration since migration. Furthermore, this study was the first to examine the formation mechanisms of relative deprivation among Chinese migrant adolescents from two perspectives: perceived discrimination and locus of control. The results of this study align with the social comparison theory and developmental systems theory. These results also have implications for improving the mental health of migrant adolescents and the harmony of communities.


It was found that the flow stress constitutive model could be effectively established by the Arrhenius based hyperbolic sine equation with an activation energy of about 389.1 kJ/mol. The thermal processing maps were developed based on the dynamic material model at different strains. It’s shown that the safe region for high temperatures in a very broad range of both deformation temperature and deformation strain and only a small unstable high deformation region, located at low temperatures lower than 950 °C. The deformation microstructures were found to be fully recrystallized microstructure in the safe deformation region and the grain size decreases along with decreasing temperature and increasing strain rate. Whereas the deformation microstructures is composed by grain refinement-recrystallized grains and a small fraction of non-recrystallized microstructure in the unstable deformation region, indicating that the deformation behaviors controlled by continuous dynamic recrystallization. The Hall Petch relationship between microhardness and the grain size of the high temperature deformed materials indicates that high strength low-density steel could be developed by a relative low temperature deformation and high strain rate.The thermal processing parameters is very important to the hot rolling and forging process for producing grain refinement in lightweight high-manganese and aluminum steels. In this work, the high temperature deformation behaviors of a low-density steel of Fe30Mn11Al1C alloyed with 0.1Nb and 0.1V were studied by isothermal hot compression tests at temperatures of 850–1150 °C and strain rates between 0.01 s The impact toughness reduced significantly with the Al content increasing. Acselrad et al. [Facing with the requirements of weight-lightening of metallic equipment, low-density steel has attention because of its significantly reduced density, high ductility, high toughness and possible applications in engineering ,2,3. It d et al.  have stuFor structural purposes, a good combination of mechanical strength and fracture toughness is desirable. In order to obtain both high ductility and high toughness, low-density steel has to be solution treated to suppress κ-carbides, which takes place in the cooling or aging process ,3,9,10. Different from the traditional austenitic steel, such as 304 austenitic stainless steels and twin induced plasticity (TWIP) steels, the low-density Fe-Mn-Al-C steel was designed with the considerable of light element addition, such as 6–12% Al, 20–30% Mn and 0.80–1.50% carbon ,19,20,21In this work, the thermal process behaviors of an austenitic low-density steel micro-alloyed with Nb and V were studied by various strain rates and temperatures uniaxial compression tests. The effects of deformation temperature and the deformation strain rate on the thermal flow behaviors and the microstructure evolution were discussed. The objective of this work is constructing a thermal processing map of the designed low-density steel for the optimization of thermal deformation parameters to obtain the combination of strength, ductility and toughness by the austenite grain refinement above temperature of κ′-carbide precipitate .−1 to 10 s−1. The compression experiments were carried out according to the processing parameters as shown in −1 and a hold time of 5 min at the deformation temperature. The compression tests were carried out at a given temperature and a given deformation rate until the true strain reaching to 0.9. After the compression deformation, the specimens were immediately quenched into cold water to reserve the microstructure at the high temperature.The chemical composition of Fe30Mn11Al1C0.1Nb0.1V steel is shown in Microstructures were examined by a field emission gun scanning electron microscope (FEG-SEM) with an electron backscatter diffraction (EBSD) detector. The mapping was performed at 20 kV and a step size of 0.5 μm. The average grain size was processed by Channel 5 software for grains having minimum misorientation angles of 15° (excluding twin boundaries). Samples for the EBSD examination were electropolished in a solution of 7% perchloric acid and 93% ethanol at room temperature. The microhardness test was carried out on FM-300 microhardness tester with a load of 300 g and a dwell time of 10 s.−1, the flow stress increases quickly after yielding and then decreases gradually after the peak stress, which indicating a transition of overall flow behavior from the work hardening stage to a softening stage. In addition, the yield stress, the peak stress and the transition strain from hardening to softening are increased with decreasing deformation temperature, implying that the dislocation storage capacity and the dislocation structure stability increases with decreasing deformation temperature. The steady-state flow behaviors of the Fe30Mn11Al1C0.1Nb0.1V steel deformed at different temperatures imply that the microstructure experiences a continuous evolution during high temperature deformation process. Similar to the flow behaviors at a compression strain rate of 0.01 s−1, the yield stress, peak stress, and transition strain all increase with decreasing deformation temperature with strain rates of 0.1 s−1, 1 s−1 and 10 s−1, as revealed in −1 and 1.0 s−1 as shown in The true stress-strain curves obtained by thermal compression tests under different deformation temperatures and strain rates are shown in −1 under deformation temperatures of 850 °C, 950 °C, and 1050 °C are shown in −1, a fully recrystallized microstructure can be seen as shown in The grain boundary maps of specimens deformed at the strain rate of 1 s−1 and 10 s−1. This means that the deformation temperature of 1050 °C is very suitable for obtaining a fully recrystallized and grain refinement microstructure.In order to examine the effect of strain rate on the microstructure at 1050 °C, the microstructures of the specimens deformed at 1050 °C with different deformation rate are shown in −1. While, the area fraction of the recrystallized region is over 95.4% and the recrystallized grain size is about 3.8 μm in the specimens deformed at 950 °C and strain rate of 10 s−1.As shown in A1, A2, A3, β, α, n1 and n are material constants with α = β/n1, σ is the flow stress, R is the gas constant (8.314 J mol−1 K−1), T is the hot deformation temperature, Q is the deformation activation energy.The stress-strain curves as shown in ollowing ,31,32,(pσ), or strain (pε) dependent flow stress. Generally, for materials prone to dynamic recovery, the steady-state stress value is often selected as the characteristic stress value. While for materials prone to dynamic recrystallization, the peak stress value is often selected as the characteristic stress value [pσ) is applied to calculate the parameters, as described in Equation (3) to Equation (5). According to Equation (3), (4), the constants of n1 and β could be calculated as shown in n1 = 6.75, β = 0.034 and α = β/n1 = 0.0050, n = 4.636 could be obtained.The characteristic stress can either be the initial yield stress, the peak stress , Q = 427.0 kJ/mol for Equation (4) and Q = 389.1 kJ/mol for Equation (5), as shown in 2 for the regression indicates a high reliability of the calculated deformation energy according to Equation (5), thus Q = 389.1 kJ/mol is believed to be the nominal deformation energy and constitutive relationship between the peak stress, strain rate and deformation temperature could be rewritten in Equation (6) for Fe30Mn11Al1C0.1Nb0.1V.By applying linear regression, the activation energy of deformation η is a dimensionless constant, which reflects the relationship between the energy consumed by the evolution of the internal microstructure and the energy consumed in the ideal linear state. It can be expressed as Equation (7).m is the strain rate sensitivity, which could be calculated by K is the material constant, and m is the sensitive index of strain rate.Processing maps were established in this work by the dynamic material model (DMM), where the mechanical process is considered as a power dissipation system . DMM is lnσ and lnb is a constant.In this work, the cubic spline difference method is used to fit the relationship between the function Based on the criterion for microstructural instability at a constant temperature reported by Prasad and Kumar, the condition can be described as Equation (9) .(9)ξ were obtained, as shown in −1 (gray area B in −1 (red zone A in According to the above analysis, the processing map generated by the superimposition of power dissipation map and the instability maps of the low-density steel at different strains (The grain refinement microstructure of the Fe30Mn11Al1C0.1V0.1Nb steel developed by hot working contain a certain level density of dislocation, which is caused by work hardening in the relative low temperature of 850-950 °C. The high dislocation density provids driving pressure for the nucleation of grain crystallization. Compared with other Fe-Mn-Al-C austenitic steels, the present samples under discussion contain the dispersed VC and NbC particles. The particles provide pinning pressure retarding the grain boundary motion , which i−1 under deformation temperatures of 850 °C is 391HV. As the deformation temperatures are increased up to 950 °C, 1050 °C, and 1150 °C at the strain rate of 10 s−1, the microhardness of the deformed specimens is decreased to 321HV, 302HV, and 280HV, respectively. There was a similarly change in microhardness at strain rates of 0.01 s−1, 0.1 s−1 and 1.0 s−1 as shown in The microhardness and grain size of specimens deformed under different deformation temperatures and strain rates are shown in −1, which may be resulted from the continuous dynamic recrystallization mechanism during thermal deformation process. The average austenite grain size increases with increasing deformation temperature and decreasing strain rate as shown in Similarly, the average grain size of austenite has a strong dependence of the deformation temperature as shown in HV is the microhardness, 0HV is the fractional microhardness, HPK is the Hall Petch slope and d is the grain size. Using the measured microhardness and average grain size of specimens deformed under different deformation temperatures and strain rates, the relationship between the microhardness HV and the grain size of d can be obtained. It can be seen that the strengthening contribution of the grain boundary is manifested by the Hall–Petch hardening slope of 349 HV·μm0.5, which is equivalent to about 1047 MPa·μm0.5, higher than that of the full recrystallized low-density steel of 719 MPa·μm0.5 reported by Massardier-Jourdan et al. [The relationship between the grain size and the microhardness is plotted in n et al. . This din et al. ,39 and h−1 to 10 s−1 were investigated with the objective of understanding the deformation’s controlling mechanism and the thermal processing parameters for low densitysteel. Conclusions can be drawn as following.High temperature deformation behaviors and microstructural evolution of a low-density Fe30Mn11Al1C0.1Nb0.1V steel at deformation temperature ranging from 850 °C to 1150 °C and strain rate varying from 0.01 sThe developed physical-based constitutive equation well predicts the flow behavior of the steel. In this point, the value of activation energy was found to be 389.1 kJ/mol. The constitutive equation for the high temperature deformation of the under discussion low-density steel is:−1 and the temperature range of 900–1150 °C with fully recrystallized microstructure, the other locates at the strain rate range of 0.01-0.1 s−1 and the temperature range of 850–950 °C with partial recrystallized microstructure.Processing maps were developed at strains of 0.1, 0.2, 0.4, 0.6, 0.8, and 0.9. The domain of safe region is found to be in two parts: one locates at the strain rate range of 1–10 s−1, which resulted from the continuous dynamic recrystallization during thermal deformation process. It provides a new grain refinement route to lightweight steel that grain refinement high strength low-density steel can be developed by relative low temperature deformation.A grain refinement microstructure can be obtained with the average grain size of 2–4 μm when deformed at relative low temperature 850–950 °C and with relative high deformation strain rates of 1–10 s0.5.A strong Hall Petch relationship between microhardness and grain size of present steel implies a strong strengthening contribution of grain boundary, which is manifested by the high Hall Petch slope of 349 HV·μm


Fusarium graminearum. Our data identified 14 PX domain-containing proteins in F. graminearum, all of which were required for plant infection and deoxynivalenol (DON) production, with the exception of FgMvp1 and FgYkr078. Furthermore, all the PX domain-containing proteins showed distinct localization patterns that were limited to the endosomes, vacuolar membrane, endoplasmic reticulum, cytoplasm, and hyphal septa/tips. Remarkably, among these proteins, FgBem1 targeted to surface crescent and septal pores and was retained at the septum pores even after actin constriction during septum development. Further analyses demonstrated that the surface crescent targeting of FgBem1 solely depended on its SH3 domains, while its septum and apex anchoring localization relied on its PX domain, which was also indispensable for reactive oxygen species (ROS) production, sexual development, and pathogenicity in F. graminearum. In summary, our study is the first detailed and comprehensive functional analysis of PX domain-containing proteins in filamentous fungi, and it provides new insight into the mechanism of FgBem1 involved in septum and apex anchorage mediated by its PX domain, which is necessary for sexual development and pathogenicity of F. graminearum.The Phox homology (PX) domain is a membrane recruitment module that binds to phosphoinositides (PI) mediating the selective sorting and transport of transmembrane proteins, lipids, and other critical cargo molecules via membrane trafficking processes. However, the mechanism of vesicular trafficking mediated by PX domain-containing proteins in phytopathogenic fungi and how this relates to the fungal development and pathogenicity remain unclear. Here, we systematically identified and characterized the functions of PX domain-containing proteins in the plant fungal pathogen  Fusarium graminearum is a filamentous ascomycete that causes a devastating disease of cereal crops and is the predominant causal agent of most severe wheat diseases worldwide (Fusarium head blight (FHB) gives rise to severe wheat yield losses in China  and deoxynivalenol (DON) in infected grains, which are detrimental to the health of human and livestock  domain is a membrane recruitment module that binds to phosphoinositides (PI) on the cytoplasmic leaflets of different organelles. It is primarily involved in the selective sorting and transport of transmembrane proteins, lipids, and other critical cargo molecules via membrane trafficking processes . SpecialSNX8 significantly increased the bacterial Shiga toxin (Stx) transport to the trans-Golgi network (TGN), whereas the transport of the plant toxin ricin was slightly inhibited, indicating a critical role of Snx8 in the retrograde transport of cargo molecules  has been suggested to function in anterograde protein transport from the Golgi to the endosome  was identified as an RGS protein and required for the discharge of ascospores from perithecia  is known to mediate endocytic recycling through early endosomes and is required for hyphal morphogenesis and pathogenesis  protein homolog, is required for vacuolar morphology and endocytosis. ΔMovam7 showed significant defects in hyphal growth, conidial formation, and virulence of M. oryzae . Characterization using gene deletion mutants revealed that all the PX domain-containing proteins except FgMvp1 and FgYKR078 are important for virulence and DON production in F. graminearum. Interestingly, we found that the PX domain of FgBem1 is important for septum and apex anchoring in growing hyphae, which is involved in septation-associated nuclear division and reactive oxygen species (ROS) generation during hyphal apical growth in F. graminearum.Although PX domain-containing proteins widely exist in the eukaryotes, system-wide characterization of PX domain family proteins, particularly in subcellular localization and biological function, are poorly reported. Herein, we identified 14 PX domain-containing proteins in Aspergillus nidulans, F. graminearum, M. oryzae, and N. crassa) and S. cerevisiae (an acknowledged model organism) genomes (https://fungidb.org/fungidb/) via BLASTP analysis. Among them, 14 annotated genes  were found in F. graminearum  domains. Members of groups 3, 6, 11, 12, and 14 contain a membrane-curvature-sensing Bin/amphiphysin/Rvs (BAR) domain and therefore are considered SNX-BAR subfamily. Each member of groups 4 and 10 contains a PX-associated domain termed PXA in addition to the PX domain. Group 5 members were homologs of Bem1 protein,s which contain four conserved domains, including the PX domain, two Src homology 3 (SH3) domains, and PB1 (a proline-rich domain mediating specific protein-protein interactions) domain. These results reveal that the PX domain-containing proteins are well conserved in filamentous fungi and have distinct roles/localizations due to domain expansion throughout evolution.Members of the PX domain-containing family possess a conserved PX-domain acting as a phosphoinositide-binding motif. Using InterPro terms, 52 PX domain-containing proteins were retrieved from four model filamentous fungi  domain (Rho-like GTPases). Download FIG S1, TIF file, 0.7 MB.Phylogenetic analysis of putative PX domain-containing proteins in fungi. A neighbor-joining tree was constructed based on the amino acid sequences of representative fungal PX domain-containing proteins. Numbers at nodes represent the percentage of their occurrence in 10,000 bootstrap replicates; only nodes supported by 50 bootstraps or more are shown. The PX domain-containing proteins are separated into 14 different clades (designated groups 1 to 14). The colored boxes in the protein domain architecture represent the homeodomains. PX, Phox homology domain; PXA, PX-associated domains; RGS, regulator of G protein signaling domain; BAR, Bin/amphiphysin/Rvs (BAR) domain; PB1, proline-rich domain mediating specific protein-protein interactions; SH3, Src homology 3 domain; PLD, phospholipase D domain; PH, pleckstrin homology domain; SNARE, soluble Copyright © 2021 Lou et al.2021Lou et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the F. graminearum, we tagged each of the proteins with green fluorescent protein (GFP), respectively, under the control of their respective native promoters. The resulting transformants were confirmed by PCR using the primers indicated in F. graminearum.To determine the subcellular localizations of the PX domain-containing proteins in 10.1128/mBio.02324-21.2FIG S2F. graminearum. The localizations of FgSnx4-GFP, FgSnx41-GFP, FgVps5-GFP, FgVps17-GFP, FgSnx3-GFP, FgMvp1-GFP, FgYpt35-GFP, FgYpr097-GFP, FgSpo14-GFP, FgSnx19-GFP, FgMdm1-GFP, FgYkr078-GFP, and FgBem1-GFP in the conidia or mycelia grown on CM or TBI (DON-inducing) medium were observed by fluorescence microscopy. Download FIG S2, TIF file, 2.9 MB.Localization patterns of the PX domain-containing proteins in Copyright © 2021 Lou et al.2021Lou et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mBio.02324-21.7TABLE S1Table S1, DOCX file, 0.03 MB.Strains, plasmids, and PCR primers used in this study. Download Copyright © 2021 Lou et al.2021Lou et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the F. graminearum appear as punctate structures in the fungal cells  and 100 μm nocodazole (the microtubule-destabilizing agent), respectively, compared with the DMSO control. Bars, 5 μm. Download Copyright © 2021 Lou et al.2021Lou et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the F. graminearum, we deleted the genes coding for each of the protein products except FgVPS5, FgVPS17, FgSNX4, FgSNX41, and FgVAM7, which have already been characterized in previous studies (28–HPH) gene as a selectable marker using homologous recombination strategy  Targeted gene deletion of FgSPO14. EcoRI and KpnI double-digested DNAs showed a 1,793-bp band in PH-1 and 2,770-bp band in mutants. (C) Targeted gene deletion of FgSNX3. NcoI-digested DNAs showed a 2,142-bp band in PH-1 and 1,833-bp band in the mutants. (D) Targeted gene deletion of FgYPT35. KpnI digested DNAs showed a 2,117-bp band in PH-1 and 4,155-bp band in the mutants. (E) Targeted gene deletion of FgYPR097. ClaI-digested DNAs showed a 5,329-bp band in PH-1 and 7,260-bp band in the mutants. (F) Targeted gene deletion of FgYKR078. HindIII-digested DNAs showed a 1,705-bp band in PH-1 and 2,846-bp band in the mutants. ΔFgYKR078-1 was verified as a negative transformant. (G) Targeted gene deletion of FgSNX19. XhoI-digested DNAs showed a 2,829-bp band in PH-1 and 5,477-bp band in the mutants. (H) Targeted gene deletion of FgMDM1. HindIII-digested DNAs showed a 2,000-bp band in PH-1 and 3,527-bp band in the mutants. (I) Targeted gene deletion of FgMVP1. ClaI-digested DNAs showed a 3,261-bp band in PH-1 and 4,070-bp band in mutants. Download FIG S4, TIF file, 1.0 MB.Southern blot analyses showing confirmation of targeted gene deletion. (A) Targeted gene deletion of Copyright © 2021 Lou et al.2021Lou et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the F. graminearum, we inoculated the wild type and these gene deletion mutants on CM media containing 1.5 M KCl (ionic and osmotic stress inducer), 0.7 M NaCl (ionic and osmotic stress inducer), 1 M d-sorbitol (osmotic stress inducer), 10 mM H2O2 (oxidative stress inducer), 0.02% SDS , 200 μg/ml CFW , and 3 mg/ml Congo red (CR) , respectively. The colony diameters were analyzed after 3 days, and then the relative growth inhibition rates (RGI) were measured  SDS, 200 μg/ml CFW, and 1 mg/ml CR, respectively. Colony diameters (B) and mycelial radial growth inhibition rates (C) were quantified after incubation on CM media supplemented with different stress-inducing agents after 3 days postinoculation. Red asterisks indicate statistically significant difference (P < 0.05). Download FIG S5, TIF file, 2.8 MB.Sensitivity of the wild-type strain PH-1 and all PX domain family genes deletion mutants to osmotic stresses, oxidative stress, and cell wall-damaging agents. (A) Colonies of PH-1 and all gene deletion mutants on CM media supplemented with 1 M KCl, 0.7 M NaCl, 1 M Copyright © 2021 Lou et al.2021Lou et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the F. graminearum, loss of FgVps5 and FgVps17, which served as retromer complex subunits in yeast, causes significant defects in development and virulence  of approximately 13.2. However, seven of the mutants, including ΔFgbem1, ΔFgspo14, ΔFgsnx3, ΔFgypt35, ΔFgsnx19, ΔFgypr097, and ΔFgmdm1, had severe defects in virulence, with average disease indexes of 1.8, 1.2, 7.1, 4.2, 3.6, 2.2, and 3.2, respectively  were restored to the levels observed in the PH-1  during the process of septa formation. Because LifeAct-RFP binds specifically to F-actin, it has been widely used as a marker for tracing the constricting ring during septation processes among filamentous fungi . Consist10.1128/mBio.02324-21.9Video S2F. graminearum. Download Movie S2, AVI file, 3.4 MB.Comotility of FgBem1-GFP and LifeAct-RFP during the septum development in Copyright © 2021 Lou et al.2021Lou et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the Fgbem1 mutant may, in turn, cause a defect in nuclear division. A histone-GFP (H1-GFP) fusion construct was transformed into the protoplasts of PH-1 and ΔFgbem1 mutant, respectively. As expected, GFP signals were observed in the nuclei of both conidia and hyphae of the H1-GFP transformants from the PH-1 and ΔFgbem1  of the nuclei in the wild-type strain PH-1 were distributed evenly within the hyphae, and the average number of nuclei per hyphal cell was 2.5  fluorescence probe and observed by confocal microscopy. DCFH-DA has been used as a chemical reporter for quantifying the intracellular ROS level in previous studies   compared to FgBem1ΔSH3-1 and FgBem1ΔSH3-1 mutants, which still failed to form perithecia   and C. Irithecia  and E. Crithecia . It was rithecia . These rF. graminearum and established their subcellular localizations in normal growth and toxin-induced conditions. To our knowledge, this is the first detailed and comprehensive functional analysis of PX domain-containing proteins in fungi. In addition, we deeply analyzed one PX domain-containing protein, FgBem1, in septation-associated nucleus division and ROS generation during hyphal apical growth, which are important for fungal development and pathogenesis. These findings also provide new insights into PX domain-involved septal development in filamentous fungi.PX domain-containing proteins are poorly studied in filamentous fungi, especially in plant-pathogenic fungi. In this study, we systematically identified and characterized the functions of PX domain-containing proteins in F. graminearum, while 49 PX domain-containing proteins exist in mammals, and those are roughly grouped into two categories, 34 candidate SNXs and 15 other PX domain-containing proteins  domains except for the N-terminal PX domain . FunctioFgBEM1 resulted in defect in fungal polarize growth. In S. cerevisiae, Bem1 is involved in symmetry breaking and polarization during bud formation, where it appears to constitute a parallel mechanism to actin-based cell polarization  was found to interact with NoxR, a regulatory component of NADPH oxidases involved in ROS production . Growth rates on CM plates and conidiation in liquid carboxymethylcellulose (CMC) medium were evaluated as described previously , and 1 mg/ml Congo red (CR), respectively, were prepared in CM agar plates (The wild-type strain (PH-1) and all the PX domain-containing gene deletion mutants of eviously . For senr plates . For sexr plates . All exppKNTG plasmid by using ClonExpress II one-step cloning kit , respectively. All resulting plasmids were verified by sequencing analysis and are listed in Protoplast preparation and fungal transformation were performed as described previously . To deleΔPX-GFP fusion construct in which the PX domain was deleted, we first used PCR to amplify the FgBem1ΔPX-GFP-1 and FgBem1ΔPX-GFP-2 fragments from genomic DNA of the wild-type strain PH-1 using the primer pairs FgBEM1-CF/FgBEM1-CR-NPX and FgBEM1-CPX-F/FgBEM1-CR. Then, FgBem1ΔPX-GFP-1 and FgBem1ΔPX-GFP-2 were seamlessly cloned to the KpnI/HindIII-digested pKNTG by using ClonExpress II one-step cloning kit, designated FgBem1ΔPX-GFP. The resulting plasmid FgBem1ΔPX-GFP was verified by sequencing analysis. FgBem1ΔSH3-1-GFP, FgBem1ΔSH3-2-GFP, and FgBem1ΔPB1-GFP fusion constructs were also generated using a similar approach. All the primers used are listed in FgBEM1-truncated constructs were transformed into the ΔFgbem1 mutant to assay the functions of each domain.To generate the FgBem15 conidia/ml were inoculated into liquid trichothecene biosynthesis induction (TBI) media as described previously (Infection assays on flowering wheat heads and wheat coleoptiles were conducted as previously described . For DONeviously . CultureFgbem1 expressing the H1-GFP construct were grown on CM agar for 2 days and stained with CFW. To examine the effects of the microtubule-destabilizing agent nocodazole and the actin inhibitor latrunculin A (LatA) on the localization of FgBem1-GFP in apical hyphae, FgBem1-GFP-expressing strain was grown on CM agar for 2 days, and then a block of CM agar containing the colony margin was excised and placed directly on a coverslip, which was treated with dimethyl sulfoxide (DMSO) (as control), nocodazole , or LatA , respectively. After incubation for 30 min, the samples were observed under a Nikon A1 confocal microscope. For time-lapse measurement of septum development, FgBem1-GFP- and LifeAct-RFP-expressing strains were grown on CM agar plates for 2 days. Using the same strategy described above, FgBem1-GFP and LifeAct-RFP signals were observed using the Nikon A1 confocal microscope at 488 nm and/or 561 nm for 24 min. Images were taken every 2 min. The elapsed times are indicated in the videos. To analyze the effects of FgBem1 deletion on ROS accumulation, the PH-1 and Fgbem1 mutants were grown on solid CM for 2 days, and then the vegetative hyphae were stained with DCFH-DA fluorescent probe and observed under a Nikon A1 confocal microscope.For hyphal branch assays, the “inverted agar block method” of preparing and staining samples was used as described previously . FM4-64


In contrast to the hexagonal αp grain with limited slip systems, the body-centered βt matrix has more slip systems, however the hindering effect of αs phases on dislocation slip leads to the different deformability of the αp phase and βt matrix. The equiaxed αp grains are more prone to deformation and rotation to coordinate the overall deformation. The shear band leads to the formation of sub-grain boundary and even the fragmentation of αp grains. As a result, the microvoids tend to nucleate at the grain boundary, phase interface, slip band, and shear band. The inhomogeneous deformation in the plastic deformation zone around the crack tip is the primary cause of damage. The crack propagation caused by microvoids coalescence advances along the grain boundaries and phase interfaces in the form of intergranular, and along the activated slip systems and shear bands in the form of transgranular. Pinpointing the situation in the equiaxed microstructure and combining that in other typical microstructures will help to summarize the universal deformation and fracture mechanisms of metastable β titanium alloy, and provide a basis for alloy design and microstructure tailoring.Understanding the mechanisms of deformation and fracture of metastable β titanium alloys is of great significance for improving formability and service life. By combining the in-situ tensile test, TEM characterization and EBSD analysis, the tensile deformation behavior, activation of slip systems, crack initiation, and propagation of a high strength metastable β titanium alloy (Ti-5Cr-4Al-4Zr-3Mo-2W-0.8Fe) with equiaxed microstructure are investigated. The equiaxed microstructure is composed of primary α (α Titanium alloys are the fundamental materials in the fields of aerospace, marine, and medicine, and have been the long-standing research focus of material scientists . In partRecently, many works have been conducted on the deformation and fracture mechanisms of metastable β titanium alloys. Wang et al. ,8 studieIn-situ characterization techniques can observe the deformation, damage, and fracture of materials in real time, so they are widely used in titanium alloy research. Zhang et al.  observedIn this work, the tensile deformation and fracture mechanisms of a strength and plasticity well-matched metastable β titanium alloy Ti-5Cr-4Al-4Zr-3Mo-2W-0.8Fe (Ti-54432) with equiaxed microstructure are studied by an in-situ tensile test under SEM. Combined with TEM characterization and EBSD analysis, the microstructure changes during deformation, damage, and fracture were studied, including the activation of slip systems, the elongation, sliding and rotation of grains, the formation of shear band and sub-grain, the initiation and propagation of crack, as well as the morphology and composition of the fracture. Meanwhile, the underlying mechanism and influence rule of the deformation and fracture were discussed.β) of the alloy was determined to be 860 ± 5 °C by the metallographic method. Some flakes were cut by wire EDM from the forged billet to prepare the tensile specimen, and the slow-feeding method of wire walking was adopted in the cutting process to ensure the surface quality of the specimen. The test material is a new high strength metastable β titanium alloy Ti-54432, which is produced by Northwest Institute for Nonferrous Metal Research, China. The chemical composition (wt.%) of this alloy is 4.10 Al, 5.33 Cr, 3.99 Zr, 2.63 Mo, 2.09 W, 0.83 Fe, 0.08 O, and balance Ti. A cast ingot was fabricated by the vacuum self-consuming arc-melting three times. The ingot was successively forged at 1150 °C and 950 °C (in β phase region) to refine the grains. Then the β-forged billet was re-forged at 830 °C (in a+β phase region). In each of the above three forging steps, the height reduction of the billet was more than 50%. The β transus temperature . Several interruptions were performed during the in-situ tensile process to observe the microstructure evolution of the alloy, at this moment the tensile speed was reduced to 0. The observation area was framed near the tip of the V-notch on the specimen. The XRD analysis of the phase component of the alloy was conducted by a Bruker D8 Advance X-ray diffractometer  with Cu-Kα. The fracture morphology was observed by a field emission JSM-6460 SEM  at an accelerating voltage of 15 kV. The crystallographic orientation was acquired by EBSD analysis combined with the HKL Channel 5 software  at the acceleration voltage of 20 kV and a step size of 0.7 μm. The EBSD specimens were electrochemically polished in an electrolyte mix of perchloric acid and glacial acetic acid with a volume proportion of 1:16 at a voltage of 60 V, an electric current of 0.7 A, and a dwelling time of 30 s. The microstructure characteristics were characterized by a JEM-200 CX TEM  at 200 kV, and the TEM specimens were taken from the severe plastic deformation zone near the fracture on the tensile specimen. In other words, the farther away from the fracture, the less plastic deformation, thus the sampling location should be as close to the fracture as possible. In detail, some flakes were cut by wire EDM, and sanded down to about 40 μm. Then some 3-mm diameter disks were cut from the flakes and twin-jet electropolished in a solution of 60% methanol, 35% butanol, and 5% perchloric acid (in volume) at 30 V and −20 °C.c+a> type slip systems with large critical shear stress, only the activation of <a> type slip systems in the α phase is considered here. The crystallographic orientation of α grain in the interest region was determined by EBSD data analysis. The SF is used to evaluate the difficulty of slip system’s activation, and the calculation formula of its value is [n represents the unit vector in the normal direction of slip plane, s represents the unit vector in the slip direction, and cT represents the unit vector in the stress axis direction in crystal coordinates. The larger the SF value of a slip system is, the easier it is to be activated, and the slip system with the largest SF value is the principal slip system.The activated slip systems were identified by analyzing the slip trace and the calculated value of the corresponding SF . Since tvalue is ,18,19: of this angle when analyzing the slip traces, and the calculation formula of θc:TS is the unit vector of the intersecting line between the slip plane and the specimen observation surface. TS = n × cN, where cN is the unit vector in the normal direction of the specimen observation surface. In general, the criterion of ±5° is used to match θc and θm. If the deviation between θc and θm is less than 5°, the slip system is determined to be activated, while if the deviation is greater than 5°, the slip system is considered to be inhibited.In addition, there is an effect of the angle between the slip plane in a specific slip system and the stress axis on the activation of the slip systems. Therefore, whether the slip system is activated, can be judged by comparing the calculated value p) phase and the transformed β (βt) matrix phase, but also a small quantity of the short rod-like secondary α (αs) phase on the βt matrix can be observed. The XRD pattern confirms the coexistence of α and β phases, and the quantitative analysis shows that the α phases account for 55.4% (volume fraction) and the remaining 44.6% is accounted by the β phases. Due to the low solution temperature, a large number of αp grains are formed during the solution process, which inhibit the nucleation and precipitation of the αs phase on βt matrix during the subsequent aging process, thus the amount of the αs phase is small.p grains can be measured. Two adjacent equiaxed αp grains are labeled as the measurement objects, and the length between the farthest two points in the two grains are measured. The initial distance between the two points in the tensile specimen with a strain of 4.8% is 5.304 μm. When the strain increases to 5.9% and 7.5%, the distance between the two points becomes 5.362 μm and 5.450 μm, respectively. In other words, the strains of equiaxed grains are respectively about 1.09% and 2.75% when the corresponding strain increments of the specimen are 1.1% and 2.7%. The strain of equiaxed αp grains and the macroscopic strain of alloy are very close, indicating that the deformation of the alloy is mainly contributed by the deformation of equiaxed αp grains, and the adjacent grains elongate along the tensile direction simultaneously to coordinate the deformation [In the process of tensile deformation, the substructure evolution in the grain depends on the cumulative (maximum) misorientation ,21,22. Tp grains identified by the SF calculation and slip trace analysis, and the corresponding grains are labeled in the inset figure. These activated slip systems can be classified to two pyramidal slip systems, two basal slip systems and three prismatic slip systems. The activation of slip systems inside grain depends on the relationship between the crystallographic orientation of grain and the loading direction; for example, the slip system with the normal direction perpendicular to the loading direction is easily activated [a> pyramidal slip systems with high CRSS are usually difficult to activate unless there is a large angle rotation of the αp grain in which the pyramidal slip is located [p grains even when these grains undergo severe plastic deformation. That is, the crystallographic orientation of the equiaxed αp grains and its relationship to the stress axis determine whether and which internal slip systems can be activated.ctivated . In the ctivated ,17,24. I located . Howeverp grains and βt matrix of undeformed alloy, and the diffraction patterns prove the existence of α and β phases. After tensile deformation, the equiaxed αp grains are obviously elongated in the tensile direction, and there are many dislocations at the grain boundaries. Although the βt matrix has more slip systems, the hindering effect of αs phases on dislocation slip leads to the formation of many dislocation tangles in the βt matrix. When the slip transfer between the αp grains and βt matrix is insufficient, the shear bands are generated in the severely deformed αp grain, resulting in the fragmentation of αp grains into smaller grains [p grains are relatively softer and more prone to the dislocation slip because the precipitation of the αs phase in the βt matrix increases the strength of the βt matrix and inhibits the dislocation slip [s phase precipitates on the β matrix [s phase.r grains ,27. It cion slip ,28. The β matrix . We belip grains, and the crystallographic orientation of the grains can satisfy the deformation driven by a slight dislocation movement. Subsequently, further deformation leads to the increase of slip lines and the activation of multiple slip systems, and the deformation incompatibility between α and β phases forces the αp grains to rotate [p grain. To coordinate the overall deformation, both α and β grains rotate and slide relative to each other, making that the microvoids are easy to nucleate at the slip band, shear band, and grain boundary [To intuitively understand the morphologic evolution of grains and the slip transfer at the grain boundary in the Ti-54432 alloy during tensile deformation, a schematic diagram is drawn as shown in o rotate . At thiso rotate ,32. Meano rotate , some inboundary . As the p grains and βt matrix are analyzed by EBSD, and the deformation features of the crack tip plastic zone is revealed in the inverse pole figure (IPF) map and Kernel average misorientation (KAM) map. The corresponding KAM values near the crack tip are high, especially at the αp/αp grain boundary, αp/βt phase interface, and αs/βt phase interface, which indicate that there is obvious inhomogeneous deformation in this region [s region . In contp grains promotes the activation of slip systems in the adjacent βt matrix [p and βt phases, the slip transfer may occur between them. We propose that the slip transfer is an important mode of the coordinated deformation of two phases, since the continuous slip lines are observed in the equiaxed αp grains and adjacent βt matrix [p grain and βt matrix. However, even though multiple slip systems are activated in the βt matrix, it is difficult for slip transfer to occur between two adjacent grains if the crystallography orientations of the αp and β grains are severely mismatched. In the propagation process, the crack will turn to the grain with the more suitable crystallographic orientation and activated slip systems. Predictably, once the well matching relationships of the crystallography orientation between the grains in the crack propagation direction are restored, the crack will continue to straight propagate rapidly. Besides the transgranular propagation in the grains, the crack also propagates in the intergranular mode at the interfaces with a large number of microvoids and microcracks, including the αp/αp grain boundary, αp/βt phase interface, and αs/βt phase interface. For the same microvoids coalescence fracture, the crack propagation may be only transgranular in the bimodal microstructure [The morphology, crystallographic orientation, and internal activated slip systems of the α and β phases in the equiaxed microstructure affect the crack propagation behavior, and thus determine the fracture mode of the alloy, as shown in the SEM images in t matrix . When tht matrix . At the tructure , while iAt present, there are two limitations that need to be further considered. Firstly, the in-situ study of deformation and fracture is incomplete. In-situ tensile tests are observed under SEM and can monitor some phenomena related to fracture, such as damage generation and development, crack initiation and propagation, and fracture morphology. However, the microstructure responses related to deformation during tensile process, such as dislocation multiplication and slip, mechanical twinning, shear band, and sub-grain formation, are lacking in real time, which may require in-situ TEM characterization. Secondly, the dependence of deformation and fracture on the stress state is unclear. In recent years, a large number of studies have shown that the mechanical properties of titanium alloys under tensile and compressive stresses are obviously different. However, the current in-situ tests are carried out under tensile stress, and the deformation and fracture behaviors of alloys under compressive stress are not well understood. Supplementing the in-situ studies under compressive stress may be able to better explain the tension and compression asymmetry of titanium alloys during deformation.s phases precipitated on the β matrix. Due to the difference in the number of potential slip systems, the pure β phase is bound to deform more easily than the α phase. With the increase of the volume fraction of αs phases, the precipitation strengthening effect on the β matrix is enhanced, and the strength of the β phase increases, approaching or even surpassing the strength of the α phase. In addition to the volume fraction, both the equiaxed α grains and α lamellae, their size is also a key factor affecting the mechanical properties of alloys. Therefore, clarifying the quantitative relationship is the premise of microstructure tailoring aimed at properties optimization.In future, there are two implications that may be reflected in the following work. Firstly, the universal mechanisms of deformation and fracture of metastable β titanium alloys should be summarized and refined. Together with previous works, the deformation and fracture mechanisms under several typical microstructures have been revealed. Among them, some deformation modes and influencing factors are shared, such as the dislocation slip and crystallographic orientation, which may be the decisive factors affecting the mechanical properties of the alloys. For these, a targeted in-depth study may be closer to the physical nature behind the phenomenon. Secondly, the quantitative study on the relationship between the microstructure characteristic parameters and the deformation and fracture related performance indexes needs to be conducted. Just as there is a controversy about which α phase or β phase is more difficult to deform, we hypothesize that the key problem lies in the volume fraction of the αp grains and βt matrix, and there is the short rod-like αs phase precipitated in the βt matrix. Although the body-centered β phase has more potential slip systems than the hexagonal α phase, the precipitation strengthening of the αs phase makes the βt matrix more difficult to deform. Therefore, the deformation of αp grains in the main contribution to the overall deformation of the alloy, is mainly characterized by elongation, rotation, and fragmentation.The initial microstructure of the alloy before deformation is mainly composed of equiaxed αp grains. At the beginning of deformation, the original grain orientation and single slip can satisfy the deformation requirements. As the deformation increases, the multiple slips are activated and the grains rotate and slide. The adjusted crystallography orientation is helpful to the slip transfer between the two phases and the coordination of the overall deformation. Under large deformation, the shear bands are formed in α grains, and slip bands are formed in β grains, which develop into sub-grain boundaries.The basal slip systems and prismatic slip systems are the main activated slip systems in equiaxed αUnder severe deformation, the stress concentration and inhomogeneous deformation in the plastic deformation zone around the crack tip are the primary cause of damage. The microvoids nucleate at the grain boundary, phase interface, slip band, and shear band, and then grow and coalesce into microcracks, which then expand into a primary crack. The crack propagates along the activated slip systems and shear bands in the form of transgranular, and also along the grain boundaries and phase interfaces in the form of intergranular. The conversion of the two forms depends on the orientation relationship of the grains along the propagation path and the activation of slip systems within the grains.The tensile deformation and fracture mechanisms of a high strength metastable β titanium alloy (Ti-54432) with equiaxed microstructure were investigated by in-situ tensile tests. The microstructure responses during deformation, damage, and fracture were characterized and analyzed, including the activation of slip systems, the deformation, sliding and rotation of grains, the formation of shear band and sub-grain, the initiation and propagation of crack, as well as the morphology and composition of the fracture. The main findings can be drawn as follows: