The cattle sector is the most important economic production unit of the Irish farming and agri-food sector. Despite its relevance, there has been limited quantitative information about the structure of differing cattle production types and of the connections between them. This paper addresses this gap by providing, for the first time, an overview of the Irish cattle population structured by enterprise type.We collected data from the cattle register for the period 2015 to 2019 and assigned registered herds to one of 18 different herd types using a recently published herd type classification approach. This allows, for the first time, to exploring changes in enterprise types and subtypes over time, and describing the movements between these subtypes and from these subtypes to slaughter.The overview and associated classification presented in this study will form the basis for a number of future comparative studies, including cross-sectoral assessments of profitability, estimation of the extent of animal health losses on Irish cattle farms or structural analysis of Greenhouse Gas (GHG) emissions across production systems.The online version contains supplementary material available at 10.1186/s13620-022-00212-x. The cattle sector in Ireland, from farm to processing and export is an important indigenous industry and a vital part of the agri-food sector. In 2019, there were approximately six and a half million cattle in Ireland, kept on more than 100.000 farms. In the same year, total annual Irish milk production was around 7.9 billion litres .Despite the importance of these industries to the national economy, there has been limited quantitative information about the different cattle production types within Ireland, and of the connections between them. This information is useful to inform discussion on a range of relevant issues, including a better understanding of disease epidemiology in infectious disease eradication planning and surveillance.A qualitative understanding of differing enterprise types within the major production systems of beef and dairy is recognised. For example, some dairy farms raise most of their replacement animals and keep surplus animals for fattening, while others introduce new heifers or cows from outside the herd and sell animals not used for milk production . Furtherwww.icbf.com) classifies breeding herds into dairy, beef and mixed. Due to the specialization of Irish cattle farming this classification rarely provides the level of detail required for a number of applications, including epidemiological risk analysis and effective policy development. This is particularly evident in the area of animal health management, most recently through the introduction of the European Animal Health Law (AHL). For the bovine sector, with the new EU regulations the herd management type became an important criterion for shaping the sampling strategy of national surveillance and control programs. With regard to BoHV-1 eradication as an example, the AHL prescribes certain rules on how herds can achieve disease-free status. These rules often interact with the specifics of herd management going beyond the categories of dairy, beef and mixed herds.In Ireland, demographic cattle data at the animal and herd level is available through the Animal Identification and Movement database (AIM) maintained by the Department of Agriculture, Food and the Marine (DAFM), but is primarily categorised at the level of beef and dairy . The IriBrock et al  recentlyData for this analysis were obtained from the AIM database maintained by DAFM. In the AIM database, each registered animal in the Republic of Ireland is listed, along with its unique tag number (ID) so that its sex, breed, and birth date, as well as the identity of its birth herd and dam, can be ascertained.Calving in most Irish herds is seasonal, with a strong bias to spring calving with peak numbers born in February . TherefoThe AIM data is available at the animal level, meaning each animal is recorded with its own unique identity (ID) or tag number, along with its sex, breed and birth date, as well as the IDs of its herd and dam. These data were aggregated at the herd level so that we had one dataset for each year comprising those herds with one or more registered animals present during at least one of the time points of interest. While aggregating the data at the herd level a number of demographic decision parameters were calculated for each individual herd . The variables were chosen according to the classification scheme proposed by . In a neThe next step was to assign each herd to the different herd management types identified in Ireland using a recently published classification concept presented in . In theiSelf-organising maps are an unsupervised learning algorithm used to project high-dimensional input data onto a low-dimensional map while preserving the topological properties of the input data . Due to The original decision tree after  to classIn brief, the complete classification procedure was: First, all herds with registered animals in May and September or at all three time points were classified as before (left part of the tree). Second, the so-called non-continuous herds were classified. For these latter herds, most of our classification parameters cannot be calculated  was performed separately during each year of interest to account for possible changes in classification of the enterprise type between years.The Irish cattle population is represented by seven main herd types, whose distributions at national and provincial levels are shown in Fig. Structural differences in the composition of the cattle population, based on assigned herd type, were found between the provinces Fig. . The disFigure n = 11.000), four subtypes were identified, reflecting their differing management practices  sells their male calves within a few weeks after birth (subtype D), while sufficient female calves are kept and reared as replacement heifers. However, in 2019 more than 2.000 herds also reared their male calves and were classified as subtype dairy rearing male (DRm). Non-rearing dairy herds are also present in the Irish dairy sector, which sell most or all their calves and comprise two further dairy subtypes. Dairy non-rearing contract herds (DnR_C) comprised approximately 1.000 herds in 2019. These herds move their female calves to external contract rearing farms  where they are reared and later inseminated before returning to their birth herd as in-calf heifers.Among herds classified as dairy herds  which source replacement cows through the purchase of non-homebred animals.Beef breeding herds (commonly referred to in Ireland as suckler herds) are by far the most common herd type in Ireland, with almost 50.000 herds registered  in 2019 being assigned to this class Fig. . Five diIn 2019, more than 15.000 store or rearing herds were registered in the Republic of Ireland Fig. . These hFattening (F) herds are confinement feeding operations where cattle are fed primarily finishing. More than 15.000 registered herds were classified as fattening in Ireland (16%) in 2019. Mixed (M) herds are dual purpose operators and produce milk, but also have another cattle enterprise, focused on beef production. Trading (T) herds are characterised primarily on the basis of having a very low proportion of animals that remain in them for more than 30 days. While they represent a very small proportion of all herds (0.7%), they play an important role in animal movements between other herd types and from other herd types to slaughter and export. Herds that could not be assigned to an enterprise type by our classification tree were termed unclassified. The majority of these are “seasonal” herds with registered animals at only single points of time in the year.The total number of eligible herds  declined by 4.9% between the start and finish of the observation period, from 109.413 in 2015 to 104.014 in 2019. The total number of herds that ceased trading (closures) and were established (openings) between 2015 and 2019, by herd type, is presented in Fig. The data from Fig. Figure Figure In beef herds, a steady decline in the number of animals has been observed since 2016. There were more than 2.64 million animals registered in beef herds in 2016, falling by 7.6% to 2.44 million in 2019.A slight but positive increase in cattle numbers was observed in the Irish dairy sector, increasing by 3.5% from 1.95 million in 2015 to 2.02 million animals in 2019 , they are relatively well represented in the trading network, with more than 200.000 in- and out-moves, respectively (excluding movements to slaughter and export via trading herds).n = 891.386 movements), from beef herds (438.846), from dairy herds (258.038), and from mixed herds (222.224). Only a small proportion of animals destined for slaughter were transported via trading herds to the slaughterhouse . This increase was facilitated by the abolition of milk quotas in 2015. Since then, milk production has increased significantly  this allThe pattern of activity within the Irish cattle system that is described in section “Herd subtypes” is also evident from the analysis of between-herd movements and movements to slaughter see Fig. . For exaFrom a methodological point of view, the allocation of herds to a certain production type is a simple and transparent process, as straightforward classification rules are followed. Further, the decision tree on which this work is based  can be First, and possibly most relevant, there are benefits from an epidemiological perspective for strategic planning of control and surveillance measures. In the context of the new European Animal Health Law (AHL), the herd management type has become an important criterion for consideration when shaping the sampling strategy of national surveillance and control programmes . This isIn this study, we have also shown how the classification can be used to analyse transport data in a structured way according to enterprise type, providing a robust framework for a generalised overview of inter-herd trade dynamics. There has been considerable work to date on representing Irish cattle movements (e.g. ) and anaIn addition, the overview and associated classification presented in this study will form the basis for a number of future comparative studies. With the classification system at hand it is now possible, for example to compare the emission of greenhouse gases for different farming systems between and within main herd types. This was not possible before, as to the authors knowledge no classification system exists that was classifying production types beyond dairy beef and mixed. Formally, with the new classification system it is possible to quantitatively compare e.g. Dairy non-rearing contract herds (DnR) against typical self-breeding dairy herds (D) in relation to economic output, greenhouse gas emission and biomass distribution, making it possible to identify specialized farming systems that might be more efficient.Additional file 1. Variables for the classification of herd types.Additional file 2. Number of trading partners per enterprise type.Additional file 3. Irish cattle herd characteristics by enterprise type.


Gene therapy requires an effective and safe delivery vehicle for nucleic acids. In the case of non-viral vehicles, including cationic liposomes, the structure of compounds composing them determines the efficiency a lot. Currently, cationic amphiphiles are the most frequently used compounds in liposomal formulations. In their structure, which is a combination of hydrophobic and cationic domains and includes spacer groups, each component contributes to the resulting delivery efficiency. This review focuses on polycationic and disulfide amphiphiles as prospective cationic amphiphiles for gene therapy and includes a discussion of the mutual influence of structural components. Gene therapy is a modern and promising method for treating severe hereditary and acquired diseases, including COVID-19 immunization, through the delivery of therapeutic nucleic acids (NAs) that can replace a damaged gene (pDNA), provide a new one, or block the expression of an unwanted protein  . The dirOvercoming these factors requires the development of special delivery vehicles. At present, viruses ,6, whichsn-glycero-3-phosphoethanolamine, DOPE) . Li. Li35c adelivery ,61.36a–c, l-lysine decreased TA . Th. Th40d–fA library of CAs (more than 1200 compounds) was developed using combinatorial chemistry methodology, in which both the hydrophobic  and the cationic  domains varied . The resBased on these findings, multiple CAs were selected for extended biological studies, which showed that the efficiency of NA delivery to primary macrophages exceeded that of commercial transfectants. In contrast, the transfection of HeLa or HepG2 cells by the selected amphiphiles was poor.44 with TETA , were selected. For this, liposomes containing CA/PEG-lipid /Chol (42:10:48 mol.) were formed. These CAs commonly contained various diamines or TETA. The most effective formulation (achieving more than 90% suppression of target gene expression) was based on compound ith TETA . The del45а–d and 46а–d with various hydrophobic domains (TETA and propylenediamine were chosen as cationic domains). Of these, the most effective were amphiphiles with a hydroxyl group in the hydrophobic domain, while the domain itself was bound to the polyamine with an ether or amide linker   were used to deliver ribonucleoprotein complexes to GFP-HEK cells. Compounds 80a, 81a, 83a, 84a had the highest efficacy, comparable to or exceeding that of Lipofectamine 2000. An increase in the number of hydrophobic domains to three (78b–81b) increased the delivery efficiency of ribonucleoprotein complexes to GFP-HEK cells [79a with two hydrophobic domains had practically no TA, while the use of an analog (79b) with three hydrophobic domains made it possible to transfect stem cells more efficiently than was possible with the commercial transfectant Lipofectamine 2000. An increase in the number of hydrophobic domains to four negatively affected TA regardless of the cationic domain. A study of the effect of the length of the hydrophobic domain demonstrated that shortening the chain length to 12 atoms significantly impaired TA, while including 14–18 atoms in the chain provided no efficiency difference [A library of disulfide CAs 78–89  with varur atoms . CLs comEK cells  and humaEK cells . Notablyfference .79a–84a [85c, 87c, 88c were highly efficient in delivering mRNA to all four investigated cell lines . Compound 86c was effective on all cells except NIH 3T3. The results of all four compounds were comparable or slightly inferior to that of Lipofectamine 2000. Compounds 84c and 89c demonstrated extremely low aggregation stability and delivery efficiency.Subsequently, the library of disulfide derivatives was expanded using a cholesterol residue in the structure of the hydrophobic domain, which reduced cytotoxicity relative to that of long-chain analogs 79a–84a . CompounEach element of the CA structure performs a specific function and influences the TA. Hydrophobic domains are involved in the protection of NAs and promote the fusion of lipoplexes with cell membranes. Aliphatic hydrocarbon substituents usually represent these domains with a length of 10 to 18 carbon atoms, tocopherol, or sterols. The type of hydrophobic domain determines both the structure of the vesicles that a CA forms in the aqueous phase and its subsequent interaction with biological membranes. Liposomal formulations promote more efficient NA delivery than that accomplished by micelles or other types of nanoparticles . Mostly,An increase in the length of aliphatic hydrocarbon chains usually increases TA ,58,88,89When sterol derivatives are used as hydrophobic domains, one should prefer natural compounds, which do not cause significant toxicity. In this case, it is optimal to use a common and widely available sterol such as cholesterol ,78,85, al-lysine [l-histidine, the presence of which in the CA structure did not improve the NA delivery [The positively charged CA domain is responsible for the electrostatic interactions of amphiphiles and/or liposomes with NAs, the formation of stable lipoplexes, and the interaction of complexes with cell membranes. An increase in the number of amino groups in the structure of the cationic domain leads to an increase in the TA ,81,82,85l-lysine ,86,90,92delivery .Linkers, connecting hydrophobic and cationic domains, determine the stability and biocompatibility of the CAs and play a key role in the efficiency of NA delivery. The most commonly used are ether, ester, carbamate, amide, and disulfide linkers. Among the most effective linkers imparting low toxicity to CAs are carbamoyl ones ,43,52,87Multiple studies have proven that a close arrangement of the hydrophobic and cationic domains complicates both domain’s functioning and interferes with the formation of liposomes. The introduction of a spacer into the CA structure and an increase in its length increase NA delivery efficiency ,88,89,91Low cytotoxicity is an important feature of CLs. First generation of CAs containing quaternary ammonium head was rather toxic, but numerous recently developed polycationic amphiphiles provided non-toxic transfection . As mentIn this review, we focused on the cationic amphiphiles, which are the major but not sole component of CLs. Development of additional components, such as targeting  or stealPositive charge of CLs provides an effective binding and protection of NAs during transfection but also attracts serum proteins and other components during in vivo administration. Interaction with serum components may cause fast blood clearance of lipoplexes. Therefore, searching for possibilities to mask excessive CLs positive charge is required. One of such ways may be a use of PEG-lipids, which, however, decrease both lipoplex and NAs internalization into the cell. Alternatively, a coating of CLs or lipoplexes with additional lipid (polymer) shell may be also considered.Sophistication of liposomal formulations turns its manufacturing procedure into novel challenges including scale-up and quality control stages. A possible solution to overcome existing difficulties might be a shift from the single large-scale to the multiple parallel small-scale production. For this purpose, microfluidic technologies become very attractive due to their ability to provide continuous and reproducible liposome preparation. Recent progress in microfluidics allows to use this technique for production of rather complex nanoparticles containing biopolymers and even whole cells .In conclusion, the development of effective and safe CLs for the delivery of therapeutic NAs requires employment of the right combination of structural elements in the CA molecule to promote the formation of both the liposomes and their complexes with NAs while avoiding interference and overcoming biological barriers. Once within the target cell, the complexes must release NAs with a high efficiency to provide biological/therapeutic effect.


Caring for sick or disabled relatives is a key model for understanding the effects of chronic stress on immunity/inflammation; biomarkers which are part of an index of allostatic load. Here, we examine whether caring and allostatic load are predictive of future illness/disability and if the association between caring and illness/disability is mediated by allostatic load.Using data from the Understanding Society Wave 2 (2011) and Wave 9 (2017–2019) datasets in the UK, a sample of 471 of caregivers and 2,151 non-caregivers  were compared on allostatic load and future illness/disability.p < .001); higher allostatic load was associated with increased risk of illness/disability in the future. In an unadjusted mediation model, allostatic load mediated the association between caregiving and future illness/disability. However, after controlling for confounding, the mediation became non-significant.Caregivers had higher allostatic risk scores, for total as well as immune and non-immune biomarkers, and were more likely (23.3% vs 17.4%) to have an illness/disability in the future compared to non-caregivers. Moreover, caregiving was responsible for a 33% future illness/disability risk. Further, allostatic load was also predictive of excess risk (OR = 1.18, 95% CI = 1.08 – 1.26; These results confirm that caregiving and allostatic load are damaging for future health. Results are also discussed in relation to public health aspects of caregiving. •Caregivers have a greater risk of future illness and disability relative to non-caregivers.•Allostatic load is higher among caregivers.•Risk of illness and disability continued beyond cessation of caring.•Caregiving and allostatic load predicted future illness and disability. ResearcA key criticism of previous caregiver and immunity studies are methodological critiques. For examples, there is often a lack of control for confounding factors such as caregivers health status, i.e. they may already have poorer health, as already be immunologically compromised, or have very small sample sizes which are prone to sampling selection biases ; they do not control for other health indices (e.g. medication) and behavioural lifestyle factors  that may indirectly influence outcomes .In addition, to addressing these issues, our intention is to build on the previous research. In particular, we will explore the impact of caregiving on allostatic load and whether or not this is predictive of future illness and disability. To our knowledge this is the first study to examine this. Allostatic load is viewed as a measure of the cumulative burden on multiple physiological systems including the metabolic , endocrine (e.g. Dehydroepiandrosterone (DHEA), cortisol) and the immune (e.g. C-reactive protein) of the body as it attempts to adapt to life’s demands . CaregivIn order to overcome issues of small sample size and selection bias, this study will use a longitudinal population-based study to examine the impact of caregiving on future risk of illness/disability and whether this is predicted by allostatic load. We expect that, after controlling for confounding, caregivers will have 1) higher scores of allostatic load, and 2) greater risk of future illness/disability. 3) The association between caregiving and illness/disability will be mediated by allostatic load.22.1Our data was obtained from Wave two (2011-13) and Wave 9 (2017–2019) of the Understanding Society study in the UK . The datInformation about caregiving was ascertained from two questions “Is there anyone living with you who is sick, disabled or elderly whom you care for or give special help to ?” and “Do you provide care for or service or help for any sick, disabled or elderly person not living with you?” Similar Yes/No format for both. People who answered ‘no’ to these caring questions served as our non-caregiver control group. We dichotomized several of our socio-demographic variables including relationship status (married/partnered vs single/divorced/widowed) education level (college education vs high school or less) and ethnicity (Caucasian/white vs other). Given the role of medication in influencing the immune and hormonal system  particip2.22.2.1long-standing physical or mental impairment, illness or disability? By ‘long-standing’ I mean anything that has troubled you over a period of at least 12 months or that is likely to trouble you over a period of at least 12 months. Participants had to respond yes or no, coded as 1 and 0, respectively.Our main outcome measure, taken from Wave 9 dataset, and was captured by asking participants “Do you have any 2.2.2Caregivers also report poorer well-being relative to controls . Thus, t2.2.3For the biological assessment  a nurse visited each person’s home for blood draws and objective measurement. In accordance with the original definition T. E. , we used2.3p’s for Kolmogorov-Smirnov and Shapiro-tests were >. 05 and nor outliers were identified for allostatic load. Further, slight changes in degrees of freedom reflect missing data on some lifestyle or demographic data. Following this, test of differences were first conducted to examine group differences across caregiver groups. Following this, we found differences on age, gender and educational status which are predictive of allostatic load, thsu we created a propensity score matching variable by regressing these variables on our caregiver group. This new variable was then included as a co-variate in our main analyses. For we conducted hierarchal logistical regressions with covariates factors entered at Step 1, and caregivers and non-caregiver groups at Step 2, and well-being in Step 3 and allostatic load in Step 4. Partial Eta-squared (η2), R2 and odds ratio (OR) were used as an indicators of effect size. To see whether the association between caregiving and future illness/disability was mediated by allostatic load, we also tested a mediation model (using model 4 in Process).33.1p = .60. Thus, these groups were pooled and examined as one caregiving group. As can be seen from 2), of 0.005. It is worth nothing that dual carers had a higher allostatic load compared to the other caring groups. Caregiver were also more likely to report poorer well-being (SF-12), (η2), of 0.002. Further, the differences between caregivers and non-caregivers were evident for both immune and non-immune measures of allostatic load .There were no differences between caregivers inside the home, outside the home and dual caregivers on future illness/disability,χ2 (2) = 1.01, p = .002, compared to non-caregivers. Moreover, we also explored whether this differed whether or not caregivers were still caring or not in 2017–19; these groups did not differ, p = .77.In terms of our outcome, a higher percentage of caregivers reported having a long-standing illness/disability in 2017–19 See , χ2 (1) 3.2p = .03). In Step 4, allostatic load was also significant, contributing to 18% of this excess risk. In sensitivity analysis we entered immune and non-immune indices of allostatic load simultaneously in Step 4, and the non-immune parameters proved predictive of future illness/disability. The immune indices were non-significant.In Step 1 of our hierarchical logistics regressions we entered propensity score, age, sex, marital status and eating fruit and vegetables as these were likely confounding. This was followed by entering well-being at Step 2, caregiver group at Step 3, and allostatic load in Step 4. As seen in This analysis was followed by a mediational model (Model 4 in PROCESS) to see if the association between caregiving and illness/disability was mediated by allostatic load. In unadjusted analyses, we found evidence of mediation  such that caregivers who had higher allostatic risk also had greater illness/disability in the future. However, after controlling for confounding the mediation became non-significant . After controlling for several potential confounding factors  well-being, caregiving and allostatic load proved predictive. Following adjustment, being in the caregiving category was associated with a 33% increased risk of future illness/disability while allostatic contributed to an18% excess risk. In sensitivity analyses of immune and non-immune indices, it was the non-immune  that proved predictive. Moreover, with over 6.5 million caregivers in the United Kingdom , this exThe increased risk of illness and disability in caregivers is similar to that found elsewhere . Here, wWhile for allostatic load our findings concur with smaller scale studies showing that caregivers have higher risk of allostatic load . While iwear and tear on physiologic systems and our narrow window of assessment of caregiving, i.e., five months prior to blood assessment may not be an ideal measure of the chronicity of caregiving. Further, while our findings were significant, especially after controlling for confounding, the effect sizes are relatively small and as such our results should be interpreted with caution. There are also several strengths to the study including it is much larger than the vast majority of caregiver biomarkers studies, and it had an unbiased sample. It also controlled for a multitude of confounding factors from health, lifestyle and socio-demographic factors which appeared important.There are several limitations of the present study including the lack of care recipient illness type; caregiver stress has been found to vary across disability types . Also thIn conclusion, the current study extends on the previous caregiving literature in several ways. There is an excess risk of future illness/disability in caregivers relative to non-caregivers. The risk was evident irrespective of cessation of caregiving, perhaps suggestive of a scarring effect. We also found that while caregivers has lower allostaic load this did not mediate the association between caregiving and future illness/disability. Moreover, we find these effects for did not vary by caring location  and withstood adjustment for several confounding factors.The authors have no conflicts of interest.


Erbium metal with purity ≥ 99% was cold rolled to 30%, 40%, 50%, and 60% deformations and the Er metal of 60% deformation was annealed at different temperatures for 1 h. The effect of cold rolling deformation and annealing on the microstructure and texture evolution of Er metal was investigated by XRD, EBSD, Microhardness tester, and OM. P is the orientation index, which is used to judge the preferred orientation. The research results showed that grains were broken and refined gradually with increasing deformation, the average grain size was 3.37 µm, and the orientation distribution was uniform for 60% deformation; deformation twins appeared in the grain when the deformation was less than 40%, which contributed to the generation of (0001) plane orientation. Comparing with the initial state, the (01 Er2O3 flectrics . Moreove storage ,6. Furth storage ,8,9. ErbHowever, as-cast Er metal can’t satisfy the specifications of industrial sputtering targets, the grains usually need to be refined and crystal orientation should be optimized by rolling and annealing. Most rare earth metals have hexagonal close-packed (HCP) structure and fewer slip systems; therefore, the plastic deformation is very difficult because the rolling deformation process can easily cause stress concentration and lead to cracking ,11. TherIn the present study, the cold rolling and annealing of Er metal has been performed. The influence of cold rolling deformation and heat treatment temperature on the microstructure, texture, and hardness of the Er metal target were studied, and the deformation mechanism and optimum annealing process were obtained.−1 Pa , then the samples were cooled down naturally with the furnace. The schematic diagram of the cold rolling process was shown in The commercially pure Er metal (99 wt.%) samples with dimensions of 50 mm × 25 mm × 12 mm were cold rolled by total deformation of 30%, 40%, 50%, and 60%, with 0.3 mm reduction during each pass , and the stress relief annealing was at 520 °C for 1 h when the deformation was increased by 10%. The rolling was performed at a speed of 150 r/min by means of a two-high mill rolling machine  with a roll diameter of 500 mm. The 60% cold-rolled Er samples were vacuum annealed at 460 °C, 500 °C, 540 °C, 620 °C, 660 °C, 700 °C, 740 °C, 780 °C, and 820 °C for 1 h in a tube furnace with the vacuum degree less than 10The samples were surface-grinded with silicon carbide sandpaper from 200 mesh to 5000 mesh and mechanically polished with 0.5 µm diamond spray polishing agent, samples for EBSD measurements need to be further polished with argon ion. The microstructure, grain orientation, grain boundary distribution, and grain size on the rolling direction (RD) and normal direction (ND) plane at the mid-width of the sample were observed by the MM-6 optical microscope  and Electron Backscattered Diffraction (EBSD). EBSD analysis was carried out on using ZEISS-Sigma500 scanning electron microscope equipped  with an Oxford-symmetry accessory. The grain orientation on the RD–ND plane was analyzed by Philips PANalytical X-ray Diffraction (XRD) unit  with Cu-Kα radiation. The hardness (HV0.3) was tested by an HVS-1000A Vickers microhardness tester, which indirectly reflected the recrystallization during the annealing process; the load of the microhardness tester was 3 N and the load time was 10s. The RD-ND surface of Er samples was shown in . et al. . When thP is introduced, calculated as following:R is intensity ratio of the two characteristic peaks of the sample, and R0 is intensity ratio of the two characteristic peaks of the sample with no preferred orientation, which is used as a criterion for orientation. R can be expressed as Equation (2):1K1L1) and (H‘1K′1L′1), respectively, with preferred orientation. 0R is calculated as following:0K0L0) and (H′0K′0L′0), respectively, without preferred orientation. When the orientation index P equals 1, it indicates that there is no preferred orientation, when p deviates from 1, it indicates that the preferred orientation occurs. The greater the deviation, the more obvious the preferred orientation [In order to further analyze the texture evolution in the cold rolling process, the crystal orientation on RD-ND plane of Er metal is analyzed by XRD. The results are shown in entation ,32,33.0 is the  The grains of Er metal are gradually broken and refined with increasing deformation. When the deformation reaches to 60%, the grains are dominated by fine deformed structures, the average grain size is 3.37 µm, and the crystal orientation distribution is uniform;(2) The deformation mechanism is transformed from the synergistic way of twinning and slip to the slip-dominated deformation at greater than 40% deformation and the generation of twins contributes to the formation of (0001) orientation;(3) Compared with the initial as-cast state, the crystal orientation of (01(4) For the 60% cold rolling deformation of Er metal, the deformed microstructure was replaced by fine equiaxed grains as the annealing temperature increased. The average grain size is 24.53 µm and the grain size distribution is uniform after the optimal annealing process of 740 °C × 1 h.


Thin-walled curved box beam structures especially rectangular members are widely used in mechanical and architectural structures and other engineering fields because of their high strength-to-weight ratios. In this paper, we present experimental and theoretical analysis methods for the static analysis of thin-walled curved rectangular-box beams under in-plane bending based on 11 feature deformation modes. As to the numerical investigations, we explored the convergence and accuracy analysis by normal finite element analysis, higher-order assumed strain plane element, deep collocation method element, and inverse finite element method, respectively. The out-of-plane and in-plane characteristic deformation vector modes derived by the theoretical formula are superimposed by transforming the axial, tangential, and the normal deformation values into scalar tensile and compression amounts. A one-dimensional deformation experimental test theory is first proposed, formulating the specific contributions of various deformation modes. In this way, the magnitude and trend of the influence of each low-order deformation mode on the distortion and warping in the actual deformation are determined, and the significance of distortion and warping in the actual curved beams subjected to the in-plane loads is verified. This study strengthens the deformation theory of rectangular box-type thin-walled curved beams under in-plane bending, thus providing a reference for analyzing the mechanical properties of curved-beam structures. Because of their high strength-to-weight ratios, the curved beam members and particularly those with rectangular cross-sections are widely used in mechanical engineering and building structures. This geometry is the first choice for engineering applications when working in the direction of a fixed load. However, studies of the accuracy of the strength and deformation of thin-walled curved beams have mainly considered the distortion and warping of thin-walled curved beam structures and the complexity of other factors, causing difficulties in obtaining the mechanical properties of thin-walled curved beam structures. The earliest research on thin-walled beams  began wiHowever, previous literature has made assumptions regarding deformation theory, the rigidity of the section, and the effect of curvature, in the above articles on thin-walled curved beams, whether in finite-element analysis, linear viscoelastic analysis , one-dimAs to the finite element analysis factors of plane problems on beam structures, accuracy and convergency are the most important issues among them. In one of the most recent works in the fields of assumed strain elements, Rezaiee-Pajand published some numerical articles –21 relatSome researchers studied the surrogate of FEM by deep learning, which mainly trains the deep neural networks from datasets obtained from FEM. Hongwei Guo et al.  proposedTo enable shape sensing analyses of beam structures undergoing bending deformation, Marco Gherlone et al. demonstrated that the recently presented iFEM for beam is reliable when experimentally measured strains are used as input data , 25. AdnIn this paper, we present experimental and theoretical analysis methods for the static analysis of a thin-walled curved rectangular box beam under in-plane bending based on 11 feature deformation modes. The experimental method for measuring the actual deformation of each section in a curved beam structure is proposed, using a one-dimensional deformation experimental test theory. Numerical investigations is explored the convergence and accuracy analysis by normal finite element analysis, higher-order assumed strain plane element, deep collocation method element, and inverse finite element method, respectively. The out-of-plane and in-plane characteristic deformation modes derived in the formula are superimposed by transforming the axial, tangential, and normal deformation values into tensile and compression components. The one-dimensional deformation experimental test theory is first proposed, formulating the specific contributions of various deformation modes. In this way, the magnitude and trend of the influence of each low-order deformation, distortion, and warping mode on the actual deformation are determined, and the significance of distortion and warping in actual curved beams subjected to in-plane loads is verified. This theory can be used in cases where it is difficult to measure and apply the measurement of the deformation distribution of various elastomer materials and structures to better understand the mechanical properties of the structure. The actual influence of distortion and warping in high-order deformation on deformation is reflected by actual data and a predictive curve. The low-order and high-order deformation modes of the box curved beam subjected to an in-plane load are thus obtained.ρ, Φ, and y, where ρ is the radial coordinate, Φ is the angular coordinate starting from the fixed end, and y is the ordinate. The height and width of the thin-walled curved beam structure are h and b, respectively, and the thicknesses of the wall are t1 and t2, respectively. The cantilever beam structure of the thin-walled curved beam mechanism is fixed at one end, and the load is applied to the other free end. The load in the plane comprises the tangential force T, radial force V, and in-plane bending moment M.The geometrical dimensions of the thin-walled curved box beam shown in In order to determine the contour displacement field, the cross-section analysis of the thin-walled curved rectangular box beam is utilized. Firstly, research on all deformations of the predecessors is performed, and the high-order deformation is extracted by removing the low-order deformation components from the deformation . A featux- and the y-axis. Modes 4th-6th are thus torsional distortion, uniaxial symmetric bending distortion, and biaxial symmetric bending distortion; modes 10th and 11th are torsional warping and bending warping, respectively, in the out-of-plane high-order deformation.According to the Timoshenko theory, the six low-order deformations include the translational displacement of the section by torsion, axial expansion, and rotational displacement about the The actual deformation of the cross-section of all thin-walled curved rectangular box beams is the superposition of vectors of the above low- and high-order deformations . The lowy because of the curvature. This deformation can be decomposed into symmetric, asymmetric, and high-order modes, namely, low-order and high-order modes.In order to obtain the in-plane section deformation function of the thin-walled curved box beam, it is necessary to consider the eleven types of section deformation modes listed in the previous section  of the contour displacement component of the rectangular section of the curved beam [The deformation expression ved beam  can be eψrjχb(sj) represents the shape function of the wall of the beam and is used to describe the shape of the line in the profile of the section; j indicates the jth wall, r is the direction of the deformation in the section wall, such as  r = n, s, φ, respectively, for the normal, tangential, and radial directions. χb denotes the generalized displacement of a point on the midline of the section, which is used to describe the amplitude variation of the in-plane and out-of-plane feature deformations along the axis.The load of the analyzed curved beam is the in-plane bending load, while ignoring the unloading effect and shear lag. An approximate shape function must be derived using a one-quarter or one-half model of the entire section model. Because both the geometry and the external load are curved by the inner plane, the curvature is symmetric about the two axes of the hollow rectangular section.α, s) on the midline of the section at time t: the axial component α, s, n) direction is positive. The three displacement components can be expressed in the form of the sum of finite terms by modal superposition method, namely,χri represents the generalized displacement of a point  on the midline of the section at time t, which is used to describe the amplitude change of the in-plane and out-of-plane characteristic deformation along the axis; while φri(s), ψri(s), and ϕri(s) also known as generalized coordinates, it is used to describe the shape change of the section along the center line of the section profile. Considering the influence of the axial and tangential displacement due to the deflection of the wall, the three-dimensional displacement component vector is obtained from the two-dimensional displacement component vector expressed asr obtained by Equation; Ω represents integral space of the s coordinate along the midline of the section; p is the force sequence vector of the body distribution acting on the middle surface; and q is the force sequence vector of the surface distribution acting on the beam surface; s-coordinate along the center line of the section, and the third integral range refers to the direction of the beam axis. The kinetic energy is expressed asThe kinematic equation of the curved beam segment is established by the energy method, it is necessary to determine the strain energy, external force potential energy, and kinetic energy of the structure; the strain energy of a curved beam is express asH represents Hamilton and L represents Lagrange function. The Hamilton principle and Lagrange function are used to derive the kinematic differential equation of the curved beam. The Hamilton principle can be expressed asφ = 0°, acting as fixed supports, and a point load is applied at the free end of the beam; this load will create a moment under total loads of 300 N and 1000 N and the forces applied at the position of φ = 90° along the y-axis. Analysis is carried out by linearly increasing the load from zero to a maximum value given by the expected total capacity of the structure, whose model is meshed into thin shell elements having the material parameters of E = 200 MPa. Finite model is meshed into 809 elements and 2046 nodes with thin shell element, which the results obtained under the load of 1000 N in shown in A full model representation includes supports and loading, in which the top end of the beam are totally restrained at the position of 𝒫:Generally speaking, the field variable selection is the most important step in finite element technology. Obviously, the accuracy of this approximation depends on the interpolation function assigned to the element. Different types of mathematical functions can be used to express these functions. However, one of the most stable forms is a polynomial basis of degree 𝒫. (b) Additional carrier—according to the formula method, its definition is different. In some formulas based on displacement, it is called a degree of freedom vector. In the displacement field method, these parameters are obtained by various methods, but in the natural assumed strain method, these strain states are based on the application of a series of optimal criteria to reduce the stress, according to the degree of freedom and derived from the distribution of geometric shapes. The number of degrees of freedom required. The most commonly used best criteria are balance and compatibility equations. In addition to forcing a minimum energy level to the finite element, applying balance can also increase the convergence speed of the resulting element.The approximation field consists of two main parts: (a) interpolation function—the order of this function determines the accuracy and speed of convergence to achieve a precise response. The degree of convergence and sustainability gradient depends on the degree of The traditional plane element analysis needs to be divided into a very fine mesh to get accurate results, and the convergence speed is too slow. In order to improve the convergence speed and accuracy of finite element analysis for planar elements, in this paper, we adopted a new robust membrane finite element for the analysis of plane problems proposed by Rezaiee-Pajand, which has triangular geometry with four nodes and 11 degrees of freedom for the element, in which each of the three vertex nodes has three degrees of freedom, two displacements, and one drilling. The fourth node that is located inside the element has only two translational degrees of freedom. Three different meshes are used to analyze this structure, namely, 1 × 6, 2 × 12, and 4 × 24. These mesh styles are named based on the number of quadrilateral elements used in them. Of note, to analyze using triangular elements, each quadrilateral element is divided into two triangular elements.7 and 0.25, respectively, and its inner radius and thickness are 4.12 and 0.1 unit. The beam is analyzed by three different mesh and the results of vertical displacement of the structural tip are listed in In order to compare the convergence rate and accuracy of various element types, tip deflection of the curved beam under applied load was computed, which proposed element in the analysis of curved structures; a thin curved cantilever beam loaded by a transverse load at its free end is analyzed. The beam is made of the elastic material with a modulus of elasticity and Poisson's ratio equal to 10As shown in Even the obtained results show that triangular plane elements with four-node and 11 degree of freedoms  perform This deep collocation method can be seen as a truly mesh-free method without the need of background grids.This section introduces the deep collocation method used to solve the Kirchhoff board bending problem. This method is a widely used method to find the numerical solutions of normal, partial differential, and integral equations. Among the control theories, it is a popular method for trajectory optimization. Usually, a set of randomly distributed points  is used to represent the required trajectory, which minimizes the loss function while satisfying a set of constraints. The collocation method is often relatively insensitive to instability and is a feasible method for training deep neural networks.w. Therefore, a loss function can be constructed to find optimal hyperparameters by approximating the boundary conditions to the minimization of the governing equations. The purpose of this section is to seek a series of approximate deflection parameters to minimize the loss function (θ). If the function value is small, the approximate deflection value is very close to the value that satisfies the driving equation. The thin plate bending problem solved by the deep collocation method can be simplified into an optimization problem. In the deep learning Tensorflow framework, various optimizers are used. One of the most widely used optimization methods is the Adam optimization algorithm, which is also used in the numerical research of this article. The idea is to decrease at the collocation point xi, using the Adam-based learning rate αi, and then the process in Equation  normal strength steel, and the steel plate with a thickness of 2.2 mm is flame-cut.b = 120 mm, height h = 150 mm, wall thickness t = 2.2 mm, section radius of centerline R = 1 m, and central angle φ = 90°. The experimental sample structure is welded from four Q235-grade steel plates to form a thin-walled curved beam; it was stored at rest for half a year after welding. In order to prevent the influence of uniqueness on the data, four samples with the same materials and parameters were tested. To research the properties of a thin-walled curved box beam, the fixed end fully and rigidly constrained by a rigid plate. The in-plane load V is applied as a radial load of 1000 N, and the load direction is as shown in The beam is of width ijk as shown in i represents the section of the central axis of the curved beam section with the point O as the origin, and curve PQ is the central axis of the section of the thin-walled curved box beam i = 1, 2, 3, 4, and 5, with point P as the reference point, according to the angle Φi of Φ1 = 1.72°, Φ2 = 22.5°, Φ3 = 45°, Φ4 = 67.5°, and Φ5 = 88.28°, as shown in P of the section is the constraint position.The mark number of each detection point is Q represents the loading position, as shown in j represents the jth wall of the ith section, which has four walls.Point A indicates the wall of the outer diameter, and the other wall plates are arranged in clockwise order, with wall plate B located below, C at the inner diameter, and D above, k represents the position of the axially arranged resistance strain gauge where the jth wall of the i section is located; each wall has three positions in every section, for a total of 12 positions in each section. k = 1 is the starting test point, the other test points are arranged clockwise, where the o'clock position relative to the edge of the wall is e = 20 mm in order to avoid the welding position of the end, point 2 is the center position of each wall, and points 3 and 1 are symmetric about point 2  of the various deformation modes. By substituting this coefficient into the formulas of low-order, distortion, and warping effects, the respective influences are obtained, and the distribution of the test points at each position of the curved beam is used to infer the distribution of the entire curved beam. It thus can be determined whether there is any shape deformation effect that can be ignored. The strain value of each position of each wall of each section measured by the experimental method is scalar and composed of the axial, normal, and tangential strains. Although the actual deformation on the theoretical level is formed by distortion and warping in the low- and high-order deformation, the influence of the various deformation forms on the actual deformation must be determined. This study proposes an experimental test theory, which first calculates the expressions of various deformation modes in the axial, normal, and tangential components and then uses the undetermined coefficient method to calculate the influence coefficient uL is the low-order effect and uD and uW are the effects of distortion and warping, respectively.The one-dimensional deformation effect formula for the thin-walled curved rectangular box beam is shown in Equation , where uψLχb is a low-order shape function matrix concluding the six basic low-order deformation modes, while χbL is a low-order influence coefficient matrix. Equation (17) illustrates the distortion effect, where ψDχb and χbD represent the distortion effect shape function matrix and the distortion influence coefficient matrix, respectively, and the distortion types include bending and torsional distortion.The low-order effect indicates six kinds of deformation mode in Equation (16), where ψWχband χbW represent the warping function matrix and warping influence coefficients matrix, respectively, and the warping forms include two types of bending and torsion warping as shown in Equation(18).For the warping influence equation, Low-order effect:Distortion effect:Warping effect:This theory can be used in cases where it is difficult to measure the feature deformation modes, and it is impossible to distinguish between the out-of-plane and in-plane deformation modes. At the same time, the theory can be applied to the measurement of the deformation distribution of various elastomer materials and structures, to better understand the mechanical properties of the structure.x-axis and the y-axis, respectively. The characteristic deformations 1st, 2nd, and 3rd are normal deformations, and the characteristic deformations 7th, 8th, and 9th are axial deformations. The above deformation modes are, respectively, used as the main forms of low-order deformation, and the expression [The deformation of the lower-order features can cause the main deformation of the section, which is the main deformation feature. The six low-order deformations include translational displacement by torsion, axial expansion, and rotation of the section about the The high-order influence mainly comprises of the influences of distortion and warping, which is analyzed based on Zhang et al. . The disψsjχb(sj) can be approximated as a cubic polynomial:The cross-sectional dimension of the curved beam section is much larger (>5) than the wall thickness thus satisfying the applicable range of the Euler-Bernoulli beam theory. Therefore, the normal deformation function aji , where i denotes the power of the polynomial and j denotes the jth shape deformation. Because the force mode determines the curved rectangular shape, the distortion type is only a single axis-symmetrical distortion type, so j can only be equal to 5, and the Euler-Bernoulli beam theory is determined by combining the geometric symmetry of the section and the bending moment balance condition. The other basic assumptions that must be used include:Right angles must be maintained at the junctions of adjacent wallsThe lateral expansion and contraction of the wall section can be ignoredThe displacement on the same wall plate can be continuously differentiatedThe displacement of both sides of the same node is continuousThe superscripts “+” and “-” indicate the approach directions of the tangential coordinates in Equation ; aji  = 0). For the cubic polynomial describing the quarter-model deformation function, the eight unknown coefficients are determined according to the following conditions:(1)A and B must satisfyAccording to the displacement continuity condition, the tangential displacement and the normal displacement at the intersection of the faces (2)According to the bending moment balance condition at the corner joint, the deformation on both sides is opposite to that at the angular joint:(3)x-axis:According to symmetric conditions about the (4)y-axis:According to the antisymmetric conditions on the (5)According to the assumption that the corners are kept at right angles, the angles of the two side wall plates are equalBecause the bending distortion is independent of the torsion, the deformation characteristics are shown in According to the principle of normalization, the amplitude of middle point of wall A shown in The eight linear independent equations in the simultaneous Equation(21), and the symmetric relationships yield the normal deformation function of the model:Because the axial deformations are orthogonal to each other, the axial deformation becomes 0:According to the rule of strain measurement, the above deformation is not axially deformed, that is:Warping is caused by uneven expansion of the siding. Based on the identified deformation characteristics of the characteristic deformations 10 and 11, the function is approximated by a quadratic polynomial:The coefficients in the formula are consistent with those mentioned in the previous section and are determined by the section deformation characteristics and geometric symmetry characteristics. According to the central symmetry characteristic of the torsional warping deformation, the biaxial symmetry characteristic of the cross-section can be simplified to solve the torsion warping model of the section to the shape function of the half-model shown in .A and B are, respectively, opposed to SAand SB, the axial shape at the symmetry point becomes 0, that is,Because the deformations on walls (2) The slopes at the two points of the central symmetry are equal, and the displacements are opposite. Taking the two end points of the wall plate as an example, joint continuity is assumed:For the half-model, the next six conditions are used as the boundary conditions for determining the plane profile deformation function of the two side wall plates of the corner node:The six linear independent equations in the simultaneous Equation (23) are solved and substituted into the quadratic polynomial. The axial deformation function of the half-model of the rectangular section:According to the warping deformation characteristics and geometric symmetry characteristics of the warping deformation, the bending warping model of the section is simplified to be solved by the half-model as shown in (1)A is symmetrical about the center point of wall A, and the slope of the axial deformation function at the symmetry point is 0:The deformation on wall (2)B is 0:The axial deformation of wall (3)A shown in The amplitude of the center point of wall According to the half-model shown in The six linear independent equations are combined in the joint solution, solved, and substituted in the quadratic polynomial. According to the symmetry characteristics, the axial deformation function of the model is obtained:χbL, χbD, and χbW for each section is solved and returned to the original equation to obtain the values. The effects of distortion and warping in the low- and high-order modes under the action of the thin-walled curved beam under the in-plane load are clearly shown in The above is the expression of the shape function of each feature deformation mode. After substituting the above equation into the experimental structure, each coefficient A is negative, that is, a compression value, while those at the locations of wall C are positive, or tensile, the value of 2 position approaches 0. The value of B and D walls near wall A, that is, the 3 locations in wall B and 1 position in wall D are positive values, that is, tensile, while the far wall A is negative, and the value of the 2 position point is the middle value of the 1 position point and the 3-position point value.As the experimental results show, the values of 60 positions in the walls are as shown in Taking a typical 3-position point in wall A and 1-position point in wall B as examples, the strain change of the entire curved beam is represented. The value is as shown in The low-order effect is the main influencing factor of the deformation which is marked by red line, and the influence value is larger than those for distortion and warping. The low-order effect value becomes larger and then smaller, and the maximum value occurs at the second test point of 22.5° which finally approach 0.The distortion effect is mostly compression amount which is marked by blue line, for tensile effect; the value changes from large to small and becomes larger after approaching 0. The maximum value also occurs at the 22.5° test position, before decreasing again and approaching 0. In warping marked by green line, the influence has a positive value, that is, the tensile effect. The influence value first changes from large to small. The minimum extreme point also occurs near the 22.5° position; the value then increases to the maximum extreme point at 45° test position and then decreases until it approaches 0.uL1,  uL2, uL3, uL7, uL8, and uL9. The influences of the six low-order strains show that the low-order deformation, except the ninth characteristic deformation mode, is near 0, and the influences are smaller than that of the ninth characteristic deformation mode. In the low-order deformation effect, the ninth characteristic deformation mode has the greatest influence, which means that the displacement clockwise along the y-axis is the largest. This direction is consistent with the direction of load application, which determines the low-order influence throughout the deformation, with a trend matching the overall low-order effect.For low-order influence, the above actual deformation value derivation and one-dimensional test theory can obtain the deformation form and its composition. The respective influences of various low-order characteristic deformations under the action of in-plane loading are illustrated in x-axis, and the deformation from the constrained position to 32° along the curved beam is in the direction of the outer diameter along the x-axis. From this area to the load application position, the negative direction along the x-axis is the reverse translation of the inner diameter, and the influence value changes from small to large. The maximum extreme point occurs at 45° and then becomes smaller until it approaches zero. The second characteristic deformation mode of low-order deformation has no effect, that is, the curved beam under the in-plane load has no translation along the y-direction. The third and seventh deformation modes have little influence, that is, rigid rotation along the z-axis and the axial expansion are both negative and gradually approaching zero. Rigid rotation around the x-axis (the eighth deformation mode) is clockwise, and the influence value changes from large to small and finally approaches zero.The first deformation mode is translation along the The 10th deformation mode in the warping effect is the torsional warping effect, and the 11th mode is the bending warping effect. A new method of one-dimensional deformation experimental test theory has been proposed for calculating and judging the contributions of low-order deformation effect and the high-order distortion and warping effects to the actual deformation of a thin-walled curved rectangular box beam under in-plane bending loads. Because the vector of deformation cannot be directly measured, we converted vectors in three directions containing axial, tangential, and normal deformations into superimposable tensile and compressive amounts, which are scalars that can be superimposed and matched with experimental data. As to the finite element analysis of plane problem on curved beam, four nodes triangular element performs better property on higher accuracy and faster convergence, then the deep collocation method element; more neurons and hidden layers could obtain better results, while the inverse finite element method experimentally measured strains are used as input data, in which more strain gauges should be used. This theory can be implemented in elastomeric materials and structures to measure the deformation distribution of various elastomer materials and structures and other mechanical properties of the structure. An experimental test system was established to obtain the deformation distribution of the entire curved beam structure at any position. The deformation value of the entire curved beam structure under the bending load was determined by marking 60 test locations evenly distributed on the four walls at five cross-sectional locations on the curved beam structure. The significance of the distortion and warping components in the actual deformation and the main role of low-order deformations are verified. The magnitude and variation trends of the eleven deformation modes are obtained; no radial translation of the curved beam structure occurs under in-plane bending.


Numerous proteins have been suggested to play a role in neurodegenerative disorders such as Alzheimer’s disease (AD). A well-known protein, the complement protein C1q, has meanwhile entered the field and might stimulate further research in this area.C1q is a small molecule in the shape of a bunch of flowers [Neuronal plasticity is a major feature of brain development and brain function. It is determined by both biological and environmental factors such as the everyday use of brain functions.The breakdown of synapses in the context of autoimmune processes, e.g., during brain development, is also known as synaptic pruning. Two complement proteins are involved in the microglia-dependent pruning mechanisms. C3b, the active form of the C3 protein, can opsonize neurons and is recognized by microglia by means of its C3 receptor. Furthermore, the C1q protein affects synaptic pruning in two ways: on the one hand, synapses tagged by C1q are recognized by the C1q receptor expressed on microglia. On the other hand, C1q may increase the amount of activated C3 , 3. WherBoth Stephan et al.  and ReicMoreover, our own studies in the same mouse strain indicated an increase of about 50% within the dentate gyrus during a time interval of only 5 months in older wild type animals Fig. c. Using Alzheimer’s disease pathology may affect brain C1q levels in two ways. On the one hand, the effect of age may even be enhanced during this disease. It has been reported that APP23 mice at the age of 9 months showed significant greater C1q mRNA levels compared to the wild type control, which is even more pronounced with increasing age . This isOn the other hand, the Alzheimer pathology itself may influence C1q expression. For example, in mice treated intraventricularly with β-amyloid (Aβ) a significant increase of C1q levels occurred .Reichwald et al.  as well Besides the effect on soluble C1q AD also promotes the accumulation of C1q in form of aggregates. C1q plaques only occur in animal models of AD and are mainly located in the cortex and the hippocampus (Fig. In addition to the descriptive data reporting a correlation between C1q levels, age and AD discussed above, there is also evidence for a direct involvement of C1q in the pathophysiology of this disease. Hong et al.  demonstrIn summary, it can be assumed that synaptic pruning mechanisms can be aberrantly reactivated particularly during older age and thereby contribute to neurodegeneration. This is in line with the well-known phenomenon that age constitutes a major risk factor for neurodegenerative disorders such as AD. Complement proteins such as C1q may play a role as “eat me” signals in this context and opsonize synapses for microglia-mediated phagocytosis. These mechanisms constitute a good example of the cooperation of the cellular and non-cellular immune responses in the context of inflammatory processes. Future research should address the following questions: What is the exact role of C1q during age and neurodegeneration in the human brain? Is an increase of C1q triggering neurodegeneration or is it simply a consequence of other ongoing neurodegenerative processes? May C1q serve as a general marker for neurodegeneration or is there a difference between various forms of dementia? Can C1q serve as a putative biomarker either by quantification in CSF alone or in conjunction with other complement markers  or is it


Long non-coding RNAs (LncRNAs) exert important regulatory roles in chronic myeloid leukemia (CML). In this study, we aimed to investigate the potential role and molecular mechanism of lncRNA ADORA2A antisense RNA 1 (ADORA2A-AS1) in CML. We found that the expression of ADORA2A-AS1 was upregulated in CML. Further, knockdown of ADORA2A-AS1 inhibited the proliferation, induced apoptosis, arrested cell cycle, and enhanced imatinib sensitivity in CML cells. Besides, ADORA2A-AS1 promoted the expression of transforming growth factor-beta receptor 1 (TGFBR1) and ATP binding cassette subfamily C member 2 (ABCC2) via sponging miR-665, thereby exerting a tumor-promoting activity. Collectively, our results confirmed the oncogenic effect of ADORA2A-AS1 in CML, indicating that ADORA2A-AS1 is a promosing therapeutic target for CML.  Chronic myeloid leukemia is a malignant clonal hematopoietic stem cell disease that accounts for about 20% of adult leukemia . CML is LncRNAs are a class of non-coding transcripts with a length of more than 200 nucleotides, which constitute a large part of the human gene transcriptome. Studies have reported that lncRNAs regulate neighboring or distant genes by serving as signaling, guiding, isolating, or scaffolding molecules . LncRNAsIn this study, we first analyzed the expression trend of ADORA2A-AS1 in CML patient samples. Then, RNA interference technique was used to knock down ADORA2A-AS1 in CML cells to study the changes of CML cell viability, apoptosis, cell cycle and sensitivity to treatment. In addition, bioinformatics prediction and experimental techniques were used to explore the mechanism of ADORA2A-AS1 in CML cells. This experiment will elucidate the occurrence and development of CML at the epigenetic level, thus providing potential intervention targets for its treatment.Bone marrow samples were collected from 13 patients with CML and 8 healthy controls at Department of Hematology of the First Affiliated Hospital of Harbin Medical University from June 2021 to December 2021. CML was confirmed by clinical examination, cell morphology analysis, immunology, and histochemical staining. Samples were collected from all patients prior to any therapeutic intervention. Patients were divided into the CP phase (8 cases) and AP phase (5 cases), and the characteristics of the patients were listed in (Table S1). Ten healthy volunteers included 6 males and 4 females, with an average age of 36.4 (35.6–56.8). This study was approved by the Ethics Committee of the First Affiliated Hospital of Harbin Medical University. All participants signed written informed consent before the study.2 at 37°C.The K562, KCL22 and HEK-293 T cells were obtained from the Cell Bank of the Chinese Academy of Sciences . The KCL22 cells were maintained in Iscove’s modified Dulbecco’s medium  supplemented with 10% fetal bovine serum (FBS). The K562 and HEK-293 T cells were cultured in RPMI 1640 medium  containing 10% FBS. All cells were incubated in a humidified incubator with 5% COThe CML cells were lysed with RIPA Lysis Buffer  and then centrifuged. The supernatant was collected and stored at −20°C. Protein concentration was measured with a Bicinchoninic Acid Protein Assay Kit . After the mixture with loading buffer, 50ng protein was separated in 10% sodium dodecyl sulfate-polyacrylamide gel electrophoresis gel and then transferred to an activated polyvinylidene fluoride membrane . The membrane was sealed in the blocking solution at room temperature for 10 min and incubated with the diluted primary antibody overnight at 4°C. After washing 3 times, the membrane was soaked in secondary antibody solution for 1 h. Finally, enhanced chemiluminescence  was performed to visualize protein bands, and Image J software  was used for density analysis, and glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was used as an internal reference to quantify protein expression levels. Antibodies against TGFBR1 and ABCC2 were purchased from Abcam . GAPDH monoclonal antibody was obtained from Proteintech –ΔΔCt.For CML cells or samples, total RNAs were extracted with TRIzol Reagent . The extracted RNAs were then reversed to cDNA using the EasyScript One-step gDNA Removal and cDNA Synthesis Supermix kit . Quantitative real-time PCR was performed with an SYBR green PCR kit . Primer sequences are provided in (Table S2). GAPDH was used as an internal control for ADORA2A-AS1. U6 was used as an internal control for miR-665. The relative expression levels of the transcripts were calculated using 23 cells/well) were seeded into 96-well plates and then cultured for 0, 24 h, 48 h and 72 h 10 μL of CCK-8 reagent was added and incubated for 3 h. Lasty, the absorbance was measured at 450 nm. For EdU assay, 5 × 103 cells were seeded into 96-well plate and cultured for 48 h. Then, cells were dyed with EdU reagent for 2 h and stained with DAPI solution. The images was collected with a fluorescence microscope Cell proliferation was measured by CCK-8 Cell Proliferation and Cytotoxicity Assay Kit  and Cell-Light EdU DNA Cell Proliferation Kit . For CCK8 assay, the transfected CML cells . The results are expressed as mean ± standard error of the mean (SEM) based on at least three independent replicates. Student’s t-test or one-way analysis of variance was used for two or multiple group comparisons. In this study, we aimed to investigate the role and mechanism of lncRNA ADORA2A-AS1 in CML. We first used qRT-PCR to examine the expression of ADORA2A-AS1 in CML and healthy control. Then, the effects of silencing ADORA2A-AS1 on cell proliferation, apoptosis and cell cycle were evaluated in a series of cell experiments. Furthermore, the potential miRNAs regulated by ADORA2A-AS1 were predicted and verified by RIP and luciferase assay. Rescue experiments confirmed that miR-665 could reverse the carcinogenic effects of ADORA2A-AS1. Besides, TGFBR1 and ABCC2 were identified as the targets of ADORA2A-AS1/miR-665 and meadiated their role in CML.The expression of ADORA2A-AS1 was examined in bone marrow samples from CML patients (n = 13) and healthy individuals (n = 10) by qRT-PCR. The results revealed that the levels of ADORA2A-AS1 were dramatically increased in CML patient samples compared with those in healthy individuals .Figure Given that ADORA2A-AS1 expression was elevated in CML, we wondered whether ADORA2A-AS1 affects cellular biology. Three siRNAs targeting ADORA2A-AS1 were designed to silence its expression, and si-ADORA2A-AS1#2 showed a better silencing efficiency in both K562 and KCL22 ) and wasTo further explore whether exhaustion of ADORA2A-AS1 influences cell apoptosis and cell cycle in CML, flow cytometry analysis was performed. As described in ). These http://starbase.sysu.edu.cn/) [The potential miRNAs interacting with ADORA2A-AS1 were analyzed using the online database starBase 3.0 (edu.cn/) , and miRCommercial miR-665 mimic were transfected into K562 and KCL22 cells to increase miR-665 expression. As shown in ), miR-66Previous studies have reported that TGFBR1 and ABCC2 were the direct targets of miR-665 in other cancers . TherefoPrevious studies reported that ABCC2 was involved in the imatinib sensitivity of CML And the above experiments indicate that ADORA2A-AS1 and miR-665 can regulate the expression of ABCC2. Therefore, we next explored whether ADORA2A-AS1 and miR-665 affects the imatinib sensitivity in CML cells. The results demonstrated that knockdown of ADORA2A-AS1 enhanced imatinib-induced apoptosis of K562 cells, while miR-665 inhibitor suppressed imatinib-induced apoptosis and attenuated the pro-apoptotic effect of ADORA2A-AS1 silencing . MoreoveAt present, tyrosine kinase inhibitors (TKIs) are still one of the main therapies for the treatment of CML, which greatly improves the survival rate of patients with CML by targeting the abnormal BCR-ABL . HoweverEmerging evidence has shown that abnormally expressed lncRNAs exert key regulatory roles in the occurrence, development, and drug resistance of CML . For exaNow that we have identified the role of ADORA2A-AS1 in CML, it is of great significance to understand its potential mechanism for further discovering new CML targets and improving the treatment of CML. Previous studies have confirmed that lncRNAs sponge miRNAs to regulate the expression of miRNA target genes . In presThe specific expression of lncRNA in CML provides the possibility for its clinical application. Several lncRNAs have been defined as biomarkers, and some of them are undergoing clinical trials ,37. In oOur study demonstrated that ADORA2A-AS1 is upregulated in CML, and knockdown of ADORA2A-AS1 inhibits cell proliferation, induces cell apoptosis and cell cycle arrest, and enhances imatinib-induce apoptosis through regulating miR-665, with TGFBR1 and ABCC2 as its downstream effectors. Together, these findings suggest that targeting ADORA2A-AS1 may represent a novel and important therapeutic strategy for CML.Click here for additional data file.


Imitation learning is an effective approach for an autonomous agent to learn control policies when an explicit reward function is unavailable, using demonstrations provided from an expert. However, standard imitation learning methods assume that the agents and the demonstrations provided by the expert are in the same domain configuration. Such an assumption has made the learned policies difficult to apply in another distinct domain. The problem is formalized as domain adaptive imitation learning, which is the process of learning how to perform a task optimally in a learner domain, given demonstrations of the task in a distinct expert domain. We address the problem by proposing a model based on Generative Adversarial Network. The model aims to learn both domain-shared and domain-specific features and utilizes it to find an optimal policy across domains. The experimental results show the effectiveness of our model in a number of tasks ranging from low to complex high-dimensional. The demand for autonomous agents capable of mimicking human behaviors has grown significantly in recent years. For example, self-driving vehicles, assistive robots, and human–computer interaction fields rely on the ability of agents that can not only make optimal decisions but also behave like humans , which c1.Humans tend to imitate the goal of a task rather than a particular behavior of the expert ,7.2.Humans can recognize structural differences  and similarities between the expert and themselves in order to adapt their behaviors accordingly .Imitation learning works by extracting information about the behavior of the expert and learning a mapping between the observation state and demonstrated behavior ,5. UnforThe first aspect of human imitation can be modeled using Inverse Reinforcement Learning (IRL) ,10. IRL The problem is formalized as domain adaptive imitation learning, which is a process of learning how to perform a task optimally in a learner domain, given demonstrations of the task in a distinct expert domain . In ordeA features extractor, which is capable of deriving domain-shared and domain-specific features, is proposed.The DAIL-GAN model is proposed. The model leverages adversarial training  to learnA comprehensive experiment on both low and high-dimensional tasks is conducted to evaluate the performance of the proposed model.Unlike most previous studies in domain adaptive imitation learning, this work proposes a model that aims to learn both domain-shared and domain-specific features. Such features enable the agents to learn optimal policies without being affected by the shift between two domains. The learning procedure can be achieved within one training process by utilizing adversarial training . In summThe rest of this paper is organized as follows. In Imitation learning has been a popular method for training autonomous agents from expert demonstrations . A simplUnfortunately, the common major weakness of the above-mentioned models is that they require the experts to provide demonstrations in the same configuration and domain as the learners. Thus, the presence of a shift between the expert and learner domains may lead to a significant performance deterioration of those models. A popular approach is to employ a domain adaptation, which attempts to recover the learned policy from one domain and to adapt it to a different domain. The work in  proposedOur method is different from previous methods ,35, whicJ:t.In this section, we formalize the domain adaptive imitation learning as a Markov decision problem. A Markov Decision Process (MDP)  horizon  is reprex without reward is defined as x and y, However, in the domain adaptive imitation learning setting, the reward function is not given beforehand. Therefore, the MDP for a domain ce versa . An examor ML≥ME .x, which is a sequence of state–action pairs. Then, a set of demonstrations Furthermore, F, G, and D that holds different responsibilities.In this section, we introduce our proposed DAIL-GAN model. The model relies on learning the domain-shared and domain-specific features in order to recover expert behaviors and adapt them to the learner domain. The architecture of our proposed model is illustrated in x is input into the feature extractor F to produce a feature vector F is trained to capture the structural similarities or the shared features between F is defined as:A state–action pair D is designed to distinguish between expert feature vector D receives a feature vector G aims to generate an action The discriminator ial loss  for bothThe optimal policy is achieved using a RL-based policy gradient, which relies on reward signal F and the generator G are optimized to minimize the feature extractor loss x, we want to judge whether F. Overall, our full objective function is:During the learning phase, we aim to learn domain-shared features between We wish to find a saddle point, where:F.At the saddle point, the Algorithm 1: DAIL-GAN1:Input2:    3:F, generator G and discriminator DRandomly initialize feature extractor network 4:for i = 0, 1, 2, … do5:    Sample an expert demonstration 6:F with the gradient    Update the parameters of feature extractor network 7:    Update the discriminator parameters with the gradient8:    Update policy 9:end for10:Output11:    The algorithm of the proposed model is outlined in Algorithm 1.In this section, the performance of the proposed DAIL-GAN model is evaluated by comparing with various baseline models on a number of tasks ranging from low to complex high-dimensional. The details of the experimental settings and evaluation results are presented in the following subsections.Low-dimensional tasks:–Pendulum–Acrobot: Expert domain is Pendulum and learner domain is Acrobot.–Pendulum–CartPole: Expert domain is Pendulum and learner domain is CartPole.–Acrobot–CartPole: Expert domain is Acrobot and learner domain is CartPole.To provide expert demonstrations, for each task, the Trust Region Policy Optimization method  is firstHigh-dimensional tasks:–Door–Door: The expert and learner domains have different friction parameters. The friction parameter in expert domain is –Hammer–Hammer: The expert and learner domains have different mass of the hammer. The mass of the hammer in expert domain is 0.253442, while, in the learner domain, it is 1.0.We also use 20 expert demonstrations for each task. The demonstrations are collected from humans using the Mujoco HAPTIX system  and publIn this experiment, five simulated environments were considered: Pendulum , AcrobotTrust Region Policy Optimization (TRPO)  is a ReiGAMA-PA : The modThe performance of the proposed DAIL-GAN model was evaluated in comparison with the following baseline methods:F, G, D networks of the proposed model. The network hyperparameters are shown in Deep feed-forward networks with two hidden layers are used for three In this subsection, the evaluation results of the proposed DAIL-GAN model on low- and high-dimensional tasks are presented to highlight its superior capability in domain adaptive imitation learning.In the expert demonstration of the Pendulum–Acrobot task in For the Acrobot–CartPole task in The above observations show that the proposed DAIL-GAN model not only succeeded in imitating the expert behaviors but also adapted the learned policies well to a distinct learner domain. Meanwhile, although the GAMA-PA  could leIn this subsection, the performance of the proposed DAIL-GAN versus the referenced models on the high-dimensional task is assessed. The average cumulative rewards of the evaluated models are shown in As illustrated in This section discusses the overall performance of the proposed DAIL-GAN model, followed by the importance of the feature extractor.The quantitative and qualitative results assessed from the previous section have shown the potential of the proposed DAIL-GAN model in tackling the domain adaptation problem in imitation learning. On both low- and high-dimensional tasks, DAIL-GAN could imitate expert behaviors from their demonstrations. In particular, the policies acquired by DAIL-GAN could even generate natural and human-like behaviors despite the high complexity of the Door–Door and Hammer–Hammer tasks. This indicates that the proposed DAIL-GAN could scale up to a complex manipulation task with a high-dimensional state and action space. Furthermore, the proposed model could adapt the learned policies to a distinct learner domain and accomplish low-dimensional tasks without being affected by the presence of domain shift between expert and learner domains. Although the success rate remained limited and depended on the complexity of the tasks, the proposed model can be improved to provide a better performance toward practical real-world imitation learning tasks.F. The feature extractor aims to learn both domain-shared and domain-specific features between expert and learner domains. In The promising performance of the proposed DAIL-GAN also praises the effectiveness of the proposed feature extractor In this paper, we proposed a novel model for domain adaptive imitation learning, in which a feature extractor was introduced to learn the domain-shared and domain-specific features. The comprehensive evaluation on both low and high-dimensional tasks demonstrates that the policies learned by the proposed model can imitate expert behaviors and adapt them to a distinct learner domain. Thus, the potential of our proposed model and the effectiveness of the feature extractor were verified. In future work, we intend to extend the proposed model to improve its performance on more complex real-world imitation tasks.


Solanum lycopersicum SPX-domain-containing protein gene family. The Solanum lycopersicum genome contains 19 SPX-domain-containing protein genes. These SPX-domain-containing protein genes were located in seven of the 12 chromosomes. According to the different conserved domains, the proteins encoded by those genes could be divided into four SPX-domain-containing protein families, which included SPX Family, SPX-ERD1/XPR1/SYG1(SPX-EXS) Family, SPX-Major Facilitator Superfamily (SPX-MFS) Family and SPX-Really Interesting New Gene (SPX-RING) Family. Phylogenetic analysis of SPX-domain-containing protein genes in Arabidopsis thaliana, Solanum tuberosum, Capsicum annuum and Solanum lycopersicum classified these genes into eight clades. Expression profiles derived from transcriptome (RNA-seq) data analysis showed 19 SPX-domain-containing protein genes displayed various expression patterns. SPX-domain-containing protein may play different roles in phosphate nutrition of Solanum lycopersicum different tissues and development stages. And, this study can provide the selection of candidate genes for functional research and genome editing in Solanum lycopersicum phosphate ions (Pi) nutrition.The SYG1, PHO81, and XPR1 (SPX) domain is named after the suppressor of yeast gpa1 (Syg1), yeast phosphatase (Pho81) and the human Xenotropic and Polytrophic Retrovirus receptor1 (XPR1). SPX-domain-containing proteins play pivotal roles in maintaining phosphate ions (Pi) homeostasis in plant. This study was to genome-wide identification and analysis of  Proteins containing the Syg1/Pho81/XPR1 (SPX) domain play an important role in maintaining phosphate ions (Pi) homeostasis at the cell level . In yeasThere are four members of the SPX family in Arabidopsis, named AtSPX1, AtSPX2, AtSPX3, and AtSPX4. AtSPX1 and AtSPX3 are positive regulators in plant adaptation to phosphate starvation, and AtSPX1 and AtSPX3 act as negative regulators of some phosphate starvation induced (PSI) genes . In riceArabidopsis thaliana) and non-vascular (Physcomitrella patens) plants, SPX-EXS family plays important roles in the acquisition, translocation and allocation of phosphate  genome contains six members of SPX-EXS family, named SlPHO1;1, SlPHO1;2, SlPHO1;3, SlPHO1;4, SlPHO1;5, SlPHO1;6  were identified and designated OsNLA1 and OsNLA2  is a major vegetable crop worldwide and a model plant for biological and genetic research of fruit development, domestication and stress responses .The domesticated tomato  databases. And then, BLASTp searches were performed employing the SPX corresponding protein sequences from Arabidopsis against the SGN (https://solgenomics.net/) databases. Finally, all the retrieved non redundant hypothetical protein sequences were submitted to the Pfam database (http://pfam.xfam.org/) (http://smart.embl-heidelberg.de/)   and the erg.de/)  to identhttp://expasy.org/) (https://solgenomics.net/) databases. The structures of the SPX-domain-containing protein genes were predicted using the online tool GSDS (http://gsds.gao-lab.org/) (Each SPX-domain-containing protein sequence were submitted to the ExPaSy (sy.org/)  to compuab.org/) .https://itol.embl.de).Phylogenetic tree was constructed with the MEGA 7.0 software  using thhttp://pfam.sanger.ac.uk/) (https://prosite.expasy.org/) (The protein domains in the SPX-domain-containing protein family were identified using the online program Pfam (.ac.uk/)  and the sy.org/) .http://ted.bti.cornell.edu/). The expression profiles of the Solanum lycopersicum SPX-domain-containing protein genes were estimated by RPKM values (reads per kilobase per million mapped reads) and a heat map was constructed to show the different expression profiles by the TBtools software (version No. 0.6739) (The transcriptome data (RNA-seq) of gene expression in different tissues including bud, flower, leaf, root, 1 cm fruit, 2 cm fruit, 3 cm fruit, mature green, breaker, and breaker after 10 days were downloaded from the Tomato Functional Genomics Database ( 0.6739) .https://www.uniprot.org) databases, the SGN (https://solgenomics.net/) databases, the Pfam database (http://pfam.xfam.org/) and the SMART website (http://smart.embl-heidelberg.de/), a total of 19 SPX-domain-containing proteins were identified  method using MEGA (version 7)  . As showThe phylogenetic relationships and structural analysis of the 19 SPX-domain-containing genes showed that the members within each subfamily (SPX-MFS and SPX-RING) had the same exon–intron structures, but the members within each subfamily (SPX and SPX-EXS) showed a certain degree of complexity . The SPXSolanum lycopersicum. The SPX family possessed eight members, SPX-EXS family contained five members, SPX-MFS family had four members, and SPX-RING family possessed two members.All the 19 SPX-domain-containing proteins contain SPX domain in the C-terminal portion . The fouSolanum lycopersicum SPX-domain-containing protein genes in different tissues and fruit developmental stages, we constructed a heat map using a published tomato RNA-seq dataset from the Tomato Functional Genomics Database (http://ted.bti.cornell.edu/)  –S5. The ll.edu/) , S5. In ll.edu/) , S5. Thell.edu/) , S5. Anoll.edu/) , S5. Onell.edu/) , S5.Solanum lycopersicum genome encodes 19 SPX-domain-containing proteins, covered all of the four families including SPX Family, SPX-EXS Family, SPX-MFS Family and SPX-RING Family.SPX-domain-containing proteins are vital in plant maintaining Pi homeostasis at the cell level . In planSolanum lycopersicum genome, we identified seven SPX family members . The SPX family genes have the similar gene structure with three exons and two introns between rice and Arabidopsis, except for OsSPX5, which possessed two exons and one intron  have the similar gene structure with Arabidopsis, and the rest two (Solyc00g149970 and Solyc02g088250) were different from Arabidopsis. Further analysis indicates that the gene structure of Solyc00g149970 was similar to OsSPX5, while Solyc02g088250 was different from Arabidopsis and rice, which possessed six exons and five introns. The SPX family protein structures are more divergent in Solanum lycopersicum, which have one, two or even three SPX domains. Various expression patterns for SPX family genes were observed in rice and Arabidopsis  were dominantly expressed in root tissues.Some members of the SPX-EXS Family could perform Pi loading in the xylem or play an important role in the root-to-shoot Pi-deficiency signaling network . In Arabeostasis . In Solang stage . SPX-EXSng stage . In Riceng stage . In non-ng stage . The traSolanum lycopersicum, we identified four members for SPX-MFS family, but their functions were not clear. AtPHT5;1 and AtPHT5;3 showed a similar expression pattern which were expressed in most tissues, while AtPHT5;2 was expressed in guard cells, vascular tissue and pollen  had some similarities with OsSPX-MFS3 such as expressing significantly higher than others in various tissues.The three SPX-MFS2 . The fouSolanum lycopersicum SPX-RING family consists of two members, and the functions about Pi homeostasis need to be explored in depth. AtNLA was expressed higher in root and stem than in seedling, flower, rosette and cauline leaves, and lower than in siliques  had been found. The results of expression analysis showed that the 19 SPX-domain-containing protein genes displayed various expression patterns. The above results implied that they may play different roles in phosphate nutrition of Solanum lycopersicum different tissues and development stages. These results provide references for the further study of Solanum lycopersicum SPX-domain-containing proteins family genes. Moreover, this study provides the selection of candidate genes for functional research and genome editing in Solanum lycopersicum phosphate nutrition.A total of 19 SPX-domain-containing protein genes were identified in http://ted.bti.cornell.edu/).The data that support this study are available in the Tomato Functional Genomics Database (10.7717/peerj.12689/supp-1Supplemental Information 1Click here for additional data file.