The strengthening mechanism of 9Cr–1.5Mo–1Co and 9Cr–3W–3Co heat resistant steel was studied by tensile test and microstructure analysis. At the same temperature, the yield strength of 9Cr–3W–3Co heat-resistant steel is higher than that of 9Cr–1.5Mo–1Co heat-resistant steel. Microstructure analysis proved that the strength of 9Cr–1.5Mo–1Co and 9Cr–3W–3Co heat-resistant steel is affected by grain boundary, dislocation, precipitation, and solid solution atoms. The excellent high temperature mechanical properties of 9Cr–3W–3Co heat-resistant steel are mainly due to the solution strengthening caused by Co and W atoms and the high-density dislocations distributed in the matrix; however, 9Cr–1.5Mo–1Co heat-resistant steel is mainly due to the martensitic lath and precipitation strengthening. It is a hot topic in today’s society how to reduce greenhouse gas emissions and environmental pressure [2 emissions under ensuring social and economic development, it is necessary to improve the efficiency of thermal power generation. To achieve this goal, it is necessary to the increase the temperature and pressure of the main steam engine, which puts forward higher requirements for materials [The consumption of coal is the main source of air pollution, especially the emission of COpressure ,2. Thermaterials ,4,5,6,7.aterials ,9,10.23C6 carbides and MX carbonitrides [The unique tempered martensite microstructure of 9–12% Cr heat-resistant steel is the key to keep good mechanical properties at a high temperature. The strengthening mechanisms are mainly grain boundary strengthening, precipitation strengthening, dislocation strengthening, and solution strengthening ,12. The nitrides ,15,16, wnitrides ,18. FinaLots of work has been done on the strengthening mechanism of 9–12% Cr martensitic heat-resistant steel. Yao Du et al.  found th9Cr–1.5Mo–1Co and 9Cr–3W–3Co heat-resistant steel are two important members of 9–12% Cr martensitic heat-resistant steel. However, there are few studies in the literature comparing the strengthening mechanism of these two kinds of heat-resistant steels. 9Cr–1.5Mo–1Co heat-resistant steel is the heat-resistant material currently used in 620 °C thermal power generation units, while 9Cr–3W–3Co heat-resistant steel is a newly developed type of steel used in 650 °C thermal power generation units. The high temperature tensile properties of the two steels were compared and the mechanism was discussed. It is of great significance to the application and development of 9Cr–3W–3Co heat-resistant steel. At the same time, it is conducive to the development of new materials.In this paper, 9Cr–1.5Mo–1Co and 9Cr–3W–3Co heat-resistant steels are taken as the research objects. The microstructure and high-temperature mechanical properties of 9Cr–1.5Mo–1Co and 9Cr–3W–3Co heat-resistant steels are studied comparatively, and their strengthening and toughening mechanism is revealed.3 (5 g) + HCl (15 mL) + H2O (80 mL) for 20 s, and the corroded sample is washed and dried with alcohol. The microstructure and the distribution of the main elements were observed and analyzed by optical microscope (OM), scanning electron microscope (SEM), and electron probe microanalyzer (EPMA). SEM was performed on JSM-6700F  with an operating voltage of 15 kV. The EPMA scan was performed on EPMA-8050G  with an operating voltage of 15 kV. The transmission sample was prepared by the tenupol-5 electrolytic double spray instrument .The double spray liquid was 4% perchloric acid ethanol solution, which is cooled to −30 °C by liquid nitrogen, and the voltage was 20 V. Precipitates, laths, and dislocations were observed on thin foils using JEM-2100 emission transmission electron microscopy  at 200 kV. Thermodynamic calculation was performed by Thermo-Calc software  and TCFe7 database was used.The steel used in this study was melted in an electric furnace, refining furnace, and vacuum refining furnace; then obtained by sand casting; and finally normalized at 1000 °C and tempered at 700 °C, and the chemical composition is shown in −1. After the tensile test, scanning electron microscope (SEM) was used to observe the morphology and analyze the fracture mechanism.Zwick Z1100TEW universal material testing machine  was used to measure the high temperature tensile properties. The sample size for high temperature tensile property test is shown in The high temperature tensile curves of 9Cr–1.5Mo–1Co and 9Cr–3W–3Co heat-resistant steel are shown in The relationship between the temperature and strength of 9Cr–1.5Mo–1Co and 9Cr–3W–3Co heat-resistant steel is shown in The relationship between the temperature and plasticity of 9Cr–1.5Mo–1Co and 9Cr–3W–3Co heat-resistant steel is shown in The tensile fracture of 9Cr–1.5Mo–1Co and 9Cr–3W–3Co heat-resistant steel at different temperatures is shown in The OM images of 9Cr–1.5Mo–1Co and 9Cr–3W–3Co heat-resistant steel are shown in SEM images of 9Cr–1.5Mo–1Co and 9Cr–3W–3Co heat-resistant steel are shown in 23C6, while MX precipitate may be formed inside the martensitic lath, and Co element does not participate in the formation of precipitate, which is evenly distributed in the matrix.The results of surface element in electron probe microanalyzer(EPMA)of 9Cr–1.5Mo–1Co heat-resistant steel are shown in 23C6, while MX precipitate may be formed inside the martensitic lath, and Co element does not participate in the formation of precipitate, which is evenly distributed in the matrix.The results of surface element in EPMA of 9Cr–3W–3Co heat-resistant steel are shown in TEM images of the matrix microstructure of 9Cr–1.5Mo–1Co and 9Cr–3W–3Co heat-resistant steel are shown in 23C6 precipitates and MX precipitates. The structure of these two precipitates is shown in 23C6 precipitates, the metal atoms are distributed in 4a, 8c, 32f, and 48h of the cell, while the C atoms are mainly located in 24e. In the MX precipitate, the metal atom is located at the top of the cell and the center of the crystal surface, while the non-metal atom is located at the center of the edge and the center of the crystal cell. Because of the different positions of formation, M23C6 precipitates are formed at the lath boundary and sub grain boundary, MX precipitates are formed inside the martensitic lath, M23C6 precipitates are elliptical, and MX precipitates are spherical.TEM images of precipitated phases in 9Cr–1.5Mo–1Co and 9Cr–3W–3Co heat-resistant steel are shown in The dislocation distribution in 9Cr–1.5Mo–1Co and 9Cr–3W–3Co heat-resistant steel is shown in Through the above results, it is not difficult to find that the overall mechanical properties of 9Cr–3W–3Co heat-resistant steel are significantly better than those of 9Cr–1.5Mo–1Co heat-resistant steel, which provides the possibility for 9Cr–3W–3Co heat-resistant steel to be widely used in 650 °C thermal power generating units. This part is mainly used to explain the reason that the properties of 9Cr–3W–3Co heat-resistant steel are better than those of 9Cr–1.5Mo–1Co heat-resistant steel.9Cr–1.5Mo–1Co and 9Cr–3W–3Co heat-resistant steel belong to 9–12% Cr martensitic heat-resistant steel. Chromium equivalent directly affects the overall performance of heat-resistant steel. The chromium equivalent is calculated as follows :NCE = %CAfter calculation, the chromium equivalent of 9Cr–1.5Mo–1Co heat-resistant steel is 8.992, while that of 9Cr–3W–3Co heat-resistant steel is 7.783. Compared with In addition to grain boundary strengthening, the strengthening methods of 9–12% Cr martensitic heat-resistant steel mainly include solution strengthening, precipitation strengthening, and dislocation strengthening. It can be seen from the above experimental results that the microstructures of 9Cr–1.5Mo–1Co and 9Cr–3W–3Co heat-resistant steel are similar, and there are four strengthening ways mentioned above. Therefore, it is necessary to distinguish the effect of the other three strengthening mechanisms on the two heat-resistant steels.γ is the boundary surface energy per unit area, VF and VBF are volume fraction, and d is the size of dispersed particles, respectively. D represents the size of structural elements, that is, the grain/subgrain size or lath thickness. It can be seen from the formula that the pinning force of precipitation relative to grain boundary or martensitic lath boundary is mainly related to the volume fraction and the size of precipitated phase. With the increase of the volume fraction of precipitates, the pinning effect of precipitates is stronger, that is, it is more favorable for the strength of heat-resistant steel. With the increase of the size of precipitates, the pinning effect of precipitates is weaker, that is, it is more unfavorable for the strength of heat-resistant steel. 23C6 in 9Cr–1.5Mo–1Co and 9Cr–3W–3Co heat-resistant steel calculated by Thermo-Calc software. It can be seen from the figure that the precipitation of M23C6 in 9Cr–1.5Mo–1Co heat-resistant steel is significantly greater than that of M23C6 in 9Cr–3W–3Co heat-resistant steel, which is mutually confirmed with the experimental results in The pinning force PZ of the precipitated relative grain boundary or sub grain boundary and the pinning force PB for the martensitic lath can be expressed as follows :(2)PZ=3γM is the Taylor coefficient, G is the shear modulus, b is the length of Burgers vector, and ρ is the dislocation density. It can be seen that the strength of the material increases with the increase of dislocation density. According to the analysis results in The strengthening effect of dislocation strengthening on steel can be calculated by the following formula :(4)σρ=0.Solution strengthening is mainly related to the radius difference between the solute atom and matrix atom. The larger the radius difference between the solute atom and matrix atom, the stronger the strengthening effect on the matrix. Therefore, in 9Cr–1.5Mo–1Co and 9Cr–3W–3Co heat-resistant steels, except for the small-size non-metallic elements such as C, N, and B, which are strengthened in the form of interstitial atoms, the metal elements such as Co, W, and Mo can also strengthen 9Cr–1.5Mo–1Co or 9Cr–3W–3Co heat-resistant steel in the form of replacement atoms. According to At the same temperature, the overall yield strength of 9Cr–3W–3Co heat-resistant steel is better than that of 9Cr–1.5Mo–1Co heat-resistant steel. At room temperature, the yield strength of 9Cr–3W–3Co heat-resistant steel is 628 MPa, while that of 9Cr–1.5Mo–1Co heat-resistant steel is 530 MPa. When the tensile temperature is increased to 700 °C, the yield strength of 9Cr–3W–3Co heat-resistant steel is 204 MPa, while that of 9Cr–1.5Mo–1Co heat-resistant steel is 164 MPa.The strength of 9Cr–3W–3Co heat-resistant steel and 9Cr–1.5Mo–1Co heat-resistant steel is affected by grain boundary, dislocation, precipitation, and solid solution atoms, but the width of the martensitic lath of 9Cr–1.5Mo–1Co heat-resistant steel is smaller than that of 9Cr–3W–3Co heat-resistant steel, and the dislocation density of 9Cr–3W–3Co heat-resistant steel is larger than that of 9Cr–1.5Mo–1Co heat-resistant steel.The excellent high-temperature mechanical properties of 9Cr–3W–3Co heat-resistant steel are mainly attributed to the solution strengthening caused by Co and W atoms and the high-density dislocations distributed in the matrix, while the precipitates are mainly pinned dislocations and hinder the movement of dislocations. However, the high temperature strength of 9Cr–1.5Mo–1Co heat-resistant steel is mainly due to the refinement and precipitation of the martensitic lath and the pinning of the grain boundary.On the basis of the analysis of the tensile properties and strengthening mechanism of 9Cr–1.5Mo–1Co and 9Cr–3W–3Co heat-resistant steel at different temperatures, the following conclusions are obtained:


Human Genome VariationCorrection to: 10.1038/s41439-020-00115-9 published online 18 September 2020After online publication of this article, the authors noticed two errors in the “Discussion” and “Acknowledgements” section of the article.On page 3, right side, line 24–28, Discussion;“In this epidemiological study, the m.1555 A>G mutation was identified in 1 out of 1,683 participants from the general population of the Iwaki district (0.06%), which is slightly lower than the frequency in previous reports”.should be“In this epidemiological study, the m.1555 A>G mutation was identified in 1 out of 1,683 participants from the general population of the Iwaki district (0.06%), which is slightly lower than the frequency in previous reports from other countries”.On page 5, right side, line 2, Acknowledgements;“This work was supported by JST COI [Grant Number JPMJCE1302]”.should be“This work was supported by JST COI [Grant Number JPMJCE1302], Japan Agency for Medical Research and Development , Ministry of Health, Labour and Welfare (H29-Nanchitou(Nan)-Ippan-031, 20FC1048)”.The original article has been corrected.The authors apologize for any inconvenience caused.


A decline in visuospatial Working Memory (vWM) is a hallmark of cognitive aging across various tasks, and facing this decline has become the target of several studies. In the current study we tested whether older adults can benefit from task repetition in order to improve their performance in a vWM task. While learning by task repetition has been shown to improve vWM performance in young adulthood, little is known on whether a similar enhancement can be achieved also by the aging population. By combining different behavioral and electrophysiological measures, we investigated whether practicing a specific task (delayed match-to-sample judgement) over four consecutive sessions could improve vWM in healthy aging, and which are the neurophysiological and cognitive mechanisms modulated by learning. Behavioral data revealed that task repetition boosted performance in older participants, both in terms of sensitivity to change  as well as capacity estimate . At the electrophysiological level, results indicated that only after task repetition both target individuation (as evidenced by the N2pc) and vWM maintenance (as reflected by the CDA) were modulated by target numerosity. Our results suggest that repetition learning is effective in enhancing vWM in aging and acts through modifications at different stages of stimulus processing. Changes to various mechanisms operating at different stages of stimulus processing have been suggested to explain the age-related reduction in vWM. Three main changes have been proposed: (1) Primary changes in early perception  A decreased efficiency of attention selection16, so that older individuals cannot discard irrelevant information that is later encoded in the WM buffer; (3) A loss in the capacity of the short-term storage19 where to-be-remembered material is retained.Cognitive aging is associated with a decline in the domain of visuospatial working memory (vWM)20, facing its age-related decline has become the target of several studies. In the current study we tested whether older adults can benefit from task repetition in order to improve their performance in a vWM task. We relied on electrophysiological (EEG) measures to maximize the possibility to isolate which mechanisms (if any) underlying task execution are influenced by learning via task repetition.Since vWM is involved in the execution of several tasks that affect everyday activitiesn-back) produces positive outcomes, both after a variable number of sessions performed in different days21 , and within the same session27. These enhancements are usually visible in terms of increase in accuracy and/or speed, and have been associated with a modulation of the neural activity at different stages of stimulus processing .Previous studies on young adults have indicated that repetition of different vWM tasks  simple and rapid way to evaluate whether a specific ability can be enhanced in older adults . Second, when coupled with the isolation of the specific mechanisms associated with vWM improvement, it may provide useful prompts for the development of longer-term cognitive interventions specifically targeting these mechanisms (see33 for a review on long-term WM trainings).The literature on learning by task repetition in older participants is scarce. In most of the studies on older individuals, repetition practice of a specific vWM task is exploited over an extended period of time for a different experimental aim, namely to provide evidence in favor of transfer gain to untrained tasks39), which was in turn successfully adopted from neurophysiological studies on non-human primates45. Strong convergence has also been found between areas activated while performing the DMTS and the brain regions usually associated with the WM network . DMTS is particularly suitable for the aim of the present study for two reasons. First, differently from other tasks probing vWM functioning (e.g. n-back), the defined sequence of events in the DMTS task allows for the temporal isolation of (at least some) specific mechanisms subserving vWM. Indeed, in this task participants first have to encode the stimuli and then deploy an attention-based selective mechanism that allows them to individuate the target elements. Once the relevant items are selected, they are retained in a vWM buffer, where they can ultimately be compared to a probe array48. In terms of EEG measurements, some event-related responses have been widely identified as markers of the cognitive mechanisms implicated in DMTS: (1) the N1, an early visual component of perceptual encoding50; (2) the N2 posterior-contralateral (N2pc), an attention-related response of target individuation53; (3) the contralateral delay activity (CDA), a late sustained component indexing active item maintenance in the vWM buffer39. Crucially, the amplitude of both N2pc and CDA is sensitive to the number of items that need to be simultaneously individuated/memorized and reaches a plateau that is in line with the behavioral performance of the individual39.To investigate the effect of learning by task repetition, we capitalized on a delayed match-to-sample paradigm (DMTS). In the past decade, research on the electrophysiological correlates of vWM functioning has extensively used this task , an impaired or delayed distractor suppression mechanism was associated in the elderly with changes in the CDA14, indicating that older participants were retaining also irrelevant items. Changes were also found in the N2pc15, and were linked to an age-related deficit at an earlier cognitive stage, namely during target selection.Second, together with concurrent EEG recordings, DMTS judgements have recently been used in the aging population57), i.e. the ability to discriminate signal from noise; (2) the decision bias (c57), i.e. the response approach adopted by the participant; (3) the estimate of the vWM capacity limit (k59). This allowed us to test whether any age-related effect of learning (if present) would be reflected in a shift of response bias or in a genuine change of discrimination sensitivity and/or vWM capacity limit.In the present study, young and older participants performed a lateralized DMTS task over four consecutive days, with electrophysiological recordings in the first and last session. We investigated three behavioral indexes and three electrophysiological measures typically elicited by DMTS tasks. Specifically, to increase our knowledge of the effect of task repetition on behavioral performance we considered three different measures: (1) the sensitivity threshold , the N2pc (target individuation) and the CDA (WM maintenance). We were interested in how task repetition influenced the load-related modulations of these EEG responses, as such modulation is indicative of the efficiency in discriminating the to-be-memorized items.The overarching aim of the study was to obtain a deeper understanding of the changes following learning by task repetition, with a special focus on aging individuals. At the behavioral level, we asked whether repetition practice would selectively improve vWM capacity or it would lead to a general enhancement encompassing also the discrimination ability. At the electrophysiological level, we considered the presence of a significant difference between memory loads (i.e. load-related modulation) as a proxy of the efficiency in discriminating the memoranda. Therefore, we considered that if such modulation is present only after task repetition, it is indicative of a learning effect . By making use of the detailed temporal information present in EEG recordings, we additionally tested for the presence of such modulation across the different levels of stimulus processing, namely early perception (N1), attentive selection (N2pc) and memory retention (CDA). If learning exclusively influences the capacity to maintain elements in memory, we expect to observe different patterns of load-related modulations in the two sessions only in the CDA latency. In contrast, if repetition-learning also influences earlier perceptual or attentional mechanisms, a different pattern of load-related modulations should be evident in the N1 and/or in the N2pc, with a potential cascade effect on the CDA.62. Twenty-eight healthy older adults and 25 healthy young adults participated in the study. Data from one young and four older adults were excluded from the analyses due to excessive noise during EEG recording. The final sample consisted of 24 healthy older adults  and 24 healthy young adults . All reported normal or corrected-to-normal vision and a negative history of neurological or psychiatric disorders. Written informed consent to participate in the study was obtained prior to testing. The study was approved by the Ethics Committee of the University of Trento and conducted in accordance with the 2013 Declaration of Helsinki.Sample size was determined a priori, based on previous practice/training studiesOlder adults were administered a battery of neuropsychological tests in order to assess their cognitive status. The duration of the neuropsychological assessment was approximately 50 minutes. In order to avoid fatigue, the assessment was split and administered prior to each of the two intermediate sessions . The exclusion criterion was set to more than one test score below the cut-off values. No data were excluded for this reason. The mean scores for each cognitive test are reported in Table 39, identical to the one of a previous study56. Stimuli were colored and light grey dots , presented on a dark grey background . For dots presented in the ‘relevant’ hemifield (targets) the colors selected were red , blue , yellow , light green  and purple ; colors used for the ‘irrelevant hemifield’ (distractors) were orange , light blue , dark green , pink  and brown . Each stimulus array consisted of one, two or four colored dots independently presented in each hemifield together with light grey dots, resulting in the same or different number of colored dots across the two hemifields . Throughout the experiment, the total number of stimuli presented on the screen was kept constant . Stimulus arrays were arranged within an invisible 8 (rows) × 10 (columns) (13.8° × 16.4°) rectangular grid centered at the center of the screen, where a white fixation cross was present for the entire trial procedure. Stimulus positions were randomized on each trial, with the constraint that colored dots never appeared in the extreme rows and columns or in the columns closest to the fixation cross.Participants performed a lateralized delayed match-to-sample taskParticipants sat in front of a 19-inch LCD monitor  and performed a change detection task on lateralized stimuli  were augmented in trial number and vice-versa.The same task was performed over four consecutive days. The choice of the number of sessions was driven by the need to strengthen practice-related effects beyond test–retest gains. During the first and last day , participants completed a total of 720 trials  divided into 15 blocks of 48 trials each, after performing a practice block of 10 trials. In Session 1 and 4, the EEG signal was simultaneously recorded. During Session 2 and 3, the task was performed with a semi-adaptive procedure. Specifically, participants completed a total of 384 trials divided into eight blocks of 48 trials each. The total number of trials for each target numerosity depended on the accuracy obtained at each memory load during the previous day EEG was continuously recorded using 29 active electrodes with a sampling rate of 1000 Hz, a time constant of 10 s as low cut-off and 250 Hz of high-cutoff. AFz served as ground and the right mastoid as the on-line reference. Horizontal ocular movements were recorded using two electrodes placed on the outer canthi of both eyes. Electrode impedances were kept below 20 kΩ.64, ERPLAB65 and FieldTrip66. All the pre-processing steps were separately performed on each EEG session. Data were down-sampled to 250 Hz and filtered with a low-frequency cutoff of 0.1 Hz and a high-frequency cutoff of 40 Hz. Additionally, to remove the 50 Hz line noise, a notch band-pass filter (width: 2 Hz) was also applied. All channels were re-referenced to the average of the left and right mastoids. Independent component analysis  was applied to the whole dataset for each session of each participant to manually identify and correct for eye blinks, saccades, muscle and cardiac activity. Epochs with correct responses were then segmented from − 200 to 900 ms relative to the onset of the memory array. Baseline correction was applied by subtracting the mean activity during a 200 ms pre-stimulus recording period. Beyond removing ICAs related to saccades, trials with originally uncorrected horizontal eye movement activity exceeding ± 50 µV  were removed68. Also, trials with any channel activity exceeding ± 80 µV were rejected. Finally, epochs were collapsed across change condition  and target side , to obtain activity contralateral and ipsilateral to the target, regardless of the actual cue direction/target position . For each session, three main conditions were extracted depending on the load . After pre-processing, for the Young group the mean number of epochs yielding correct responses retained for the average in Session 1 was 199.46  for Load1, 196.33 (82%) for Load2 and 172.63 (72%) for Load4, while in Session 4 the mean number of epochs retained was 201.13 (84%) for Load1, 199.29 (83%) for Load2 and 173.88 (72%) for Load4. In the Old group, the mean number of epochs in Session 1 was 184.75 (77%) for Load1, 176.38 (73%) for Load2 and 135.83 (57%) for Load4, while in Session 4 there were 188.21 (78%) epochs for Load1, 186.38 (78%) for Load2 and 147.63 (62%) for Load4.The continuous EEG signal was then processed off-line using EEGLAB72; homogeneity of variances75).Not all behavioral and electrophysiological variables satisfied the assumptions of normality and homogeneity of variances (see Supplementary Tables 55) which better represents the vWM limit of the participants. Therefore, for the analyses of all behavioral data we computed the indexes on the basis of Load4. We measured sensitivity (d’), response criterion (c57) and memory capacity /] × load. Load refers to the number of colored target dots that participants had to remember, i.e. four76). Following previous studies56, hit rates were defined as ‘different’ responses in change conditions, while false alarms were ‘different’ responses in no-change trials. It follows that, in the present study, a conservative response criterion is defined as the tendency to answer ‘Same’ on change trials and is represented by positive values; vice-versa, a more liberal behavior was considered the tendency to answer ‘Different’ on no-change trials and is represented by negative values.vWM capacity is usually computed using the highest memory load condition  was then conducted on d’, c and k measures with Age  as between-subjects and Session  as within-subjects factor.56, to obtain a precise temporal evolution of the ERP components, the analysis was performed in three consecutive steps by identifying the temporal windows of interest without any a priori selection. This data-driven approach allowed us to take into account slight latency differences in the onset and development of the components that might be present between the two age groups.Following previous studies15 collapsed across Load condition  and Session (i.e. Session 1 and 4) in a region of interest (ROI) comprising electrodes O1/2, P7/8 and PO7/8 (see77). Then, separately for young and older adults, we followed a moving window approach with a non-overlapping window length of 20 ms, from the onset of the memory array to 900 ms post-onset. For each time window, we used a one sample t-test to assess whether the mean amplitude in that time window differed from zero. A significant difference lasting for at least two consecutive time windows (i.e. 40 ms) was considered reliable. This procedure resulted in three distinct significant time ranges for each group . We then averaged the starting and ending time point respectively of the previously identified windows across groups, in order to obtain common time windows for both young and older participants for subsequent analysis. The final resulting latencies were 120–160 ms, 230–350 ms and 450–900 ms, roughly reflecting the ERP components usually found in the literature around those latencies: N1, N2pc and CDA.First, for each participant of each group a single difference wave was computed by subtracting ipsilateral from contralateral-to-target activitySecond, within each one of these latencies, the mean amplitude was computed for each load, session and age group and a main ANOVA was conducted with Age as between-subjects factor and Session, Load and Latency  as within-subjects factors.56).Third, significant main or interaction effects resulting from the main ANOVA were then separately investigated in each session, latency and age group , again employing the 20-ms consecutive time window approach  for ANOVAs and Cohen’s d for t-tests79. For dependent-samples t-tests, the following Eq.  or Huynh–Feldt (when G–G epsilon > 0.75) correction was used, and only adjusted p values are reported. All follow-up pairwise comparisons were conducted through t-tests and correction for multiple comparisons , in order to reduce Type 1 error, was performed using the False Discovery Rate (FDR) procedure80). We thus used a less ambiguous approach to investigate learning-related changes in the efficiency of the mechanisms subserving vWM, and tested for the presence, in each session, of a load-related modulation. Such modulation acts as a proxy of the efficiency in discriminating one vs two vs four items during the different processing stages .As behavioral and EEG data provide different information, we adopted a between-sessions approach for the behavioral measures, and a within-session approach for the EEG measures. As the absolute values of the behavioral indexes  have an intrinsic meaning (e.g. k = 3 indicates that the WM limit is around three elements), comparing them between sessions can actually quantify the amount of learning. In contrast, the amplitude values of the ERP components do not have a similar intrinsic meaning, therefore comparing them between sessions might lead to ambiguous results  between colored targets and colored distractors did not influence the performance (see Supplementary Information for further details). We therefore collapsed trials across number of distractors for further analysis.In line with a previous studyF = 25.724, p < 0.001, ηp2 = 0.359) and Age  = 38.79, p < 0.001, ηp2 = 0.457).The mixed ANOVA showed significant main effects of Session  = 8.383, p = 0.006, ηp2 = 0.154) and a Session × Age interaction  = 6.921, p = 0.012, ηp2 = 0.131) were significant. First, to characterize the behavioral approach as conservative, neutral or liberal, one-sample t-tests against zero  were performed for each group at each session. The t-tests resulted significant in both groups and both at Session 1 and 4, revealing that all participants exhibited a conservative behavior  = 10.849, padjusted < 0.001, d = 2.215; Session 4, t(23) = 12.015, padjusted < 0.001, d = 2.453. Young: Session 1, t(23) = 7.344, padjusted < 0.001, d = 1.499; Session 4, t(23) = 11.405, padjusted < 0.001, d = 2.328). The subsequent follow-up comparisons revealed that the Young group became more conservative across the practice period (t(23) = − 3.574, p = 0.002, d = 0.745). For the Old group, no difference was instead significant between Session 1 and 4 (t(23) = 0.243, p = 0.810, d = 0.043). Moreover, the two groups differed in their criterion at Session 1 (t(46) = 3.128, padjusted = 0.006, d = 0.922), where the Old group was more conservative, but not at Session 4 (t(46) = 0.917, padjusted = 0.364, d = 0.270)  = 8.172, p = 0.006, ηp2 = 0.151) and Age  = 47.097, p < 0.001, ηp2 = 0.506), and a significant Session × Age interaction  = 18.343, p < 0.001, ηp2 = 0.285). From the Session × Age interaction, higher WM capacity of the Young with respect to the Old group was evident at both Session 1 (t(46) = − 8.107, padjusted < 0.001, d = 2.391) and 4 (t(46) = − 4.69, padjusted < 0.001, d = 1.383). Moreover, in young participants there was no significant improvement across sessions (t(23) = 0.904, p = 0.376, d = 0.188). In the Old group, an improvement across sessions was instead evident (t(23) =  − 5.799, p < 0.001, d = 1.209).The mixed ANOVA on k values at Load4 revealed significant main effects of Session  = 4.663, p = 0.036, ηp2 = 0.092), Load  = 20.241, p < 0.001, ηp2 = 0.306), Latency  = 19.558, p < 0.001, ηp2 = 0.298) and Age  = 5.514, p = 0.023, ηp2 = 0.107). Significant interactions emerged for Latency × Age  = 4.694, p = 0.011, ηp2 = 0.093), Session × Latency × Age  = 5.142, padjusted = 0.009 as G–G ε = 0.872, ηp2 = 0.101), Load × Latency  = 32.034, padjusted < 0.001 as G–G ε = 0.744, ηp2 = 0.411) and Session × Load × Latency × Age  = 2.866, padjusted = 0.030 as G–G ε = 0.798, ηp2 = 0.059).The main mixed ANOVA showed significant main effects of Session  through a series of pairwise comparisons run over consecutive 20-ms steps. Each pairwise comparison separately tested Load1 vs Load2 and Load2 vs Load4 conditions within-session. Only differences between conditions lasting for at least two consecutive 20-ms windows were considered reliable (see Methods for details).ts ≥ 2.490, all psadjusted ≤ 0.038, all ds ≥ 0.508), with Load2 exhibiting larger negative amplitude than Load1, while no difference emerged in the N1 and N2pc time windows . There was instead no significant difference between Load2 and Load4 in any latency range , but also in the N2pc latency , always with Load2 having larger negative values than Load1, while they did not differ in the N1 . Still no difference emerged from the comparison between Load2 and Load4  and in the CDA latency , with larger negative amplitude for Load2 than Load1, while they did not differ in the N1 . There was still no difference between Load2 and Load4 in any latency , practice anticipated such distinction during the N2pc range. Conversely, in the old group the two lowest memory loads (one versus two targets) differed only at Session 4, both in N2pc and CDA latency.For the sake of completeness, further post-hoc comparisons were performed on the difference computed by subtracting Load1 from Load2 amplitude between sessions (Supplementary Table t(23) = 0.243, p = 0.811, d = 0.050; Old: t(23) = − 0.810, p = 0.426, d = 0.165). Moreover, the difference in eye movements between Session 1 and Session 4 did not correlate (Pearson’s correlation) to any significant pre/post difference of any of the behavioral and EEG measures that were used  were compared between Session 1 and Session 4 via a t-test. No significant difference was observed between sessions in the number of eye movements in both groups , we suggest that in our experimental design d’ might reflect a behavioral component of performance (change sensitivity) that is tightly linked to target individuation. Therefore, our results would indicate that, following task repetition, older participants became more proficient in individuating the colored targets presented in the memory display together with irrelevant material (i.e. the grey dots), as reflected by the presence of a load-related modulation in the N2pc only during Session 4. This, in turn, allowed for a better sensitivity when a change in the stimulus display took place, as reflected by a practice-related increase of d’ values.First, task repetition improved the sensitivity threshold of older participants, as indicated by an increase in the ability to distinguish change from no-change trials (d’). At the electrophysiological level we observed that the amplitude of the N2pc was modulated by memory load only in Session 4. Because of these results, and given previous findings indicating an association between sensitivity (as measured via d’) and the N2pc85 have systematically linked the age-related attenuation of the N2pc to an impairment in the ability to individuate relevant items, which in turn negatively affects the performance of the elderly in tasks relying on such cognitive process . Hence, the present results appear promising as they reveal that the mechanism of attention individuation can be efficiently improved even through task repetition over few sessions.Previous electrophysiological studies55. In the present study, results indicated that while the CDA amplitude was not modulated by the number of targets before practice , it started to index one versus two memory load during the last session . Given that the CDA has been linked to vWM capacity measured through k values in DMTS (see86 for a review), these results support the use of task repetition as a tool to improve vWM capacity in aging individuals when performing a DMTS.Second, the results on older adults showed higher k values after task repetition (i.e. from ~ 1.7 in Session 1 to ~ 2.1 in Session 4). At the neural level, we found that the CDA response was modulated by memory load only after learning in the elderly. Similarly to the N2pc, the typical numerosity-related modulation of the CDA, considered as the neural counterpart of the lower vWM capacity exhibited by older participants in this task, is also reduced by aging50. We hypothesize that this might be due to the task used here, because some previous studies88 using different tasks showed that changes in the N1 were evident in aging individuals following a visual perceptual training, so when perceptual abilities were specifically taxed .Task repetition did not influence early perceptual abilities, as evidenced by no practice-related effects in the N189; but see90). As for old adults, practice had no influence on early perception (no effect on the N1).Some effects of task repetition were evident also in young adults. Similar to older participants, at the behavioral level, task repetition enhanced the discrimination ability (d’), while EEG data revealed a load-dependent modulation of the N2pc only in the last session (similarly to what observed in old adults). This result seems to further underline the potential link between N2pc and d’ measures. In contrast, we did not find an improvement in vWM capacity, as measured through k values, which remained around three elements across all sessions. The absence of vWM improvement was in line with the electrophysiological data, where the CDA was modulated by target numerosity both before and after task repetition in young participants. Accordingly, the load-dependent modulations  were significantly different only between one and two targets. No difference was instead observed in the CDA amplitude between two and four memory loads, which could be associated with the vWM capacity estimate plateauing at three items in young participants , probably because there is an overlap between the shorter-lasting N2pc and the more-sustained CDA onset, and (3) there may be task-unrelated inter-session variability in the EEG signal92. More in general, the present results are similar to previous research indicating a between-sessions effect in behavioral measures mainly reflected in a within-session load-related modulation of EEG data93.Further between-sessions comparisons on the N2pc and the CDA (see Supplementary table 95).Finally, repetition practice had an influence also on the decision bias (c) adopted by young participants. While both age groups showed an overall conservative attitude when performing the task (i.e. a tendency to answer ‘same’), only young participants strengthened such bias across sessions. It is not clear why task repetition should impact the response bias only in young adults. We speculate that this may be related to a decrease in alertness as a consequence of task repetition (as observed in vigilance tasks97 for examples in this direction), being specifically focused on learning by task repetition. However, the outcomes of future EEG studies on task repetition may reveal useful hints when designing long-term WM training programs that specifically aim at an overall enhancement of WM abilities, and they could be considered as a biomarker for training success. For instance, such interventions could be designed to selectively train the cognitive processes that were mostly susceptible to changes, as reflected by the EEG measures from procedures similar to the present one. Third, we interpreted learning-related effects on the cognitive mechanisms subserving vWM based on the results obtained through EEG within-session comparisons , which might be considered an implicit way to draw conclusions in light of the fact that there are no strong between-sessions effects.Three limits of the present study should be noted. First, as there was no follow-up assessment, the duration of the effects found in the study is currently unknown. Second, the study was not designed to consider benefit generalization to other untrained tasks/domains  can be achieved already after two sessions of task repetition. This information may be instrumental for the design of future research on complex long-term interventions, which aim at generalizing training gains to different cognitive domains.To conclude, the present study showed that task repetition is beneficial for various aspects related to vWM in healthy aging. Specifically, in older adults learning by task repetition boosted the mechanism of target individuation, presumably allowing individuals to retain more elements in the vWM buffer. According to the literature, age-related vWM reduction is due to changes in early perceptionSupplementary Information


An increasing volume of evidence suggests that SETDB1 plays a role in the tumorigenesis of various cancers, classifying SETDB1 as an oncoprotein. However, owing to its numerous protein partners and their global-scale effects, the molecular mechanism underlying SETDB1-involved oncogenesis remains ambiguous. In this study, using public transcriptome data of lung adenocarcinoma (ADC) and squamous-cell carcinoma (SCC), we compared tumors with high-level SETDB1 (SH) and those with low-level SETDB1 . The results of principal component analysis revealed a transcriptomic distinction and divergence between the SH and SL samples in both ADCs and SCCs. The results of gene set enrichment analysis indicated that genes involved in the “epithelial–mesenchymal transition,” “innate immune response,” and “autoimmunity” collections were significantly depleted in SH tumors, whereas those involved in “RNA interference” collections were enriched. Chromatin-modifying genes were highly expressed in SH tumors, and the variance in their expression was incomparably high in SCC-SH, which suggested greater heterogeneity within SCC tumors. DNA methyltransferase genes were also overrepresented in SH samples, and most differentially methylated CpGs (SH/SL) were undermethylated in a highly biased manner in ADCs. We identified interesting molecular signatures associated with the possible roles of SETDB1 in lung cancer. We expect these SETDB1-associated molecular signatures to facilitate the development of biologically relevant targeted therapies for particular types of lung cancer. Lung cancer, which is the leading cause of cancer-related morbidity and mortality worldwide , can be Lung cancer is a molecularly heterogeneous disease, and grasping the underlying biology is essential to develop effective therapies against it. This is especially the case if the relatively higher mutation rates in NSCLC are considered; the mutation frequency has been shown to be 3.5 and 3.9 per megabase (Mb) in ADC and SCC, respectively, and these values are approximately twofold higher than the mean rate of 1.8 per Mb across all tumor types . A high Human cancer genomics studies have recognized that point mutations, translocations, deletions, and gene amplification events frequently occur in genes encoding histone-modifying enzymes such as histone methyltransferases, demethylases, acetyltransferases, and deacetylases . One of Although the findings of studies on SETDB1 have consistently indicated its oncogenic role in various cancers, the broad ranges of genomic targets and protein partners of SETDB1 , along with their global effects, make it challenging to prove an immediate connection of SETDB1 with certain tumors. Therefore, the molecular mechanism of SETDB1-involved oncogenesis remains largely unknown. In this study, based on the SETDB1 levels, we attempted subtyping of the lung ADC and SCC samples to measure how SETDB1 overexpression quantitatively and qualitatively alters the global gene expression profiles of ADC and SCC samples. For this, we utilized public lung transcriptome datasets available on The Cancer Genome Atlas (TCGA) site and analyzed the gene sets involved in the cellular processes that are suggested to be associated with SETDB1 function. We identified interesting molecular signatures associated with the roles of SETDB1 in lung cancer and hope that these findings facilitate the development of biologically relevant targeted therapies for particular lung cancer types.To investigate the effect of SETDB1 overexpression in non-small cell lung cancers (NSCLC), we utilized the genomic datasets of lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) deposited in The Cancer Genome Atlas . All plots were generated under R-environment or by MS-EXCEL.SETDB1 gene copy number and the expression level, the copy-number alteration (CNA) data were download from cBioPortal, and TCGA RNA-seq HTseq-count datasets of LUAD  and LUSC carcinoma  were downloaded using “TCGAbiolinks”  and histology (LUAD or LUSC) to generate box plots for the SETDB1 gene CNA vs. the expression levels. Correlations between SETDB1 CNA and expression in ADC and SCC were calculated using “cor” function with “method = pearson.”To examine the relationship between the iolinks” . The RNAiolinks” , and theSETDB1 levels and the top 20% (n ≅ 100) and the bottom 20% (n ≅ 100) of them were arbitrarily chosen as SETDB1-high (SH) and SETDB1-low (SL) group, respectively, for transcriptomic analysis. In ADC samples, the mean expression levels of the selected SH and SL groups were 4,211.15  and 1,562.89  in normalized counts, respectively, while in SCC samples they were 3,691.52  and 1,468.37 , respectively. Regarding normal samples, we used all normal samples  provided in the TCGA ADC and SCC datasets weather they were paired with tumor samples or not. Of 108 samples, 59 were from the TCGA ADC expression datasets and 49 were from the SCC datasets, with 33 and 17 of them paired with the tumor samples.The tumor samples were ranked by For survival analysis, clinical data for TCGA LUAD (ADC) and LUSC (SCC) patients were downloaded using “GDCquery,” “GDCdowload,” and “GDCprepare_clinic” functions in “TCGAbiolinks,” and datasets for SETEDB1 high and low patients were extracted and labeled as “Top100” and “Bot100,” respectively. Survival analysis was performed, and result was plotted using “TCGAanalyze_survival” function in “TCGAbiolinks.”2. Cells were transfected with 4 μg of a CMV promoter-driven RFP-SETDB1 expression plasmid per well using Lipofectamine 3000 (Invitrogen) and were observed for RFP signal in fluorescence miscroscope immediately before harvest 48 h after transfection.The human NSCLC cell line A549 was maintained in RPMI 1640 medium  supplemented with 10% heat-inactivated fetal bovine serum , 2 mM glutamine and antibiotics (100 U/ml penicillin and 100 μg/ml streptomycin) at 37°C in a humidified atmosphere of 5% COGAPDH gene were calculated using QuantStudio Design and Analysis Software (Thermo).Total RNAs were extracted from A549 cells and reverse transcription was performed by incubating 1 μg of DNase I−pretreated RNA with Superscript III enzyme (Invitrogen), 20 μM oligo−dT primers (Invitrogen), and 50 ng random hexamers (Invitrogen) at 50°C for 1 h. Five nanograms of the synthesized cDNAs was used for real−time quantitative PCR  with the specific primer sets for individual target genes . PCR wasSETDB1 expression dependent transcriptomic divergences in lung cancer samples, the TCGA LUAD, or LUSC samples were ordered based on the expression levels of SETDB1, and top-ranked and bottom-ranked 100 samples were selected; they were named as SH and SL groups, respectively. For t-distributed stochastic neighbor embedding (t-SNE), “Rtsne”1, a R wrapper for t-SNE, was used with “perplexity = 30” and “pca = true.” Then, transcriptomic data of SH and SL samples were extracted from the earlier DESeq2-normalized expression dataset for the differential expression analysis. For gene set enrichment analysis (GSEA), gene collections for “HALLMARK,” “KEGG pathways,” and “GO Biological Process” from MSigDB v7.0  were identified, and the results were visualized on volcano plots and heatmaps generated by “plot” and “ggplot” functions in R.In addition to the fGSEA that was generated based on group means, single sample GSEA (ssGSEA) was performed using gene set variation analysis (GSVA) , a R pacM-values for variance stabilization, and differentially methylated CpG sites (DMCs) were identified using “limma”  and annotated with genomic features  using “bedtools”  of TCGA of LUAD and LUSC samples were downloaded using “TCGAbiolinks,” and the pre-processed and scaled methylation beta-values for SH and SL samples were extracted. Next, beta-values were converted to edtools” . FinallySETDB1 transcript levels in the two most frequent histologic subtypes of NSCLC, ADC, and SCC. We inspected the genome and transcriptome data that are publicly available from the cBioPortal and The Cancer Genome Atlas (TCGA). The SETDB1 gene amplification was more frequent in ADC samples than in SCC ones  and normalized ES  that estimate the degree to which the genes in the terms are overrepresented at either the top or the bottom of the ranked list of genes, the selected terms mostly showed negative ES/NES values. It indicated that those genes in the designated collections were mostly underrepresented in the SH samples in both the ADC and SCC groups . OverallCDH2/NCAD, were significantly underrepresented in the SH samples in both ADCs and SCCs  expression  in the SH samples in the ADCs  were largely underrepresented in the SH samples , 3E. Thithe ADCs . The undp < 0.002) in the ADC-SH samples with positive ES values: “RNA_METABOLIC_PROCESS,” “PRE_MIRNA_PROCESSING,” “PRODUCTION_OF_SMALL_ RNA_INVOLVED_IN_RNA_INTERFERENCE/GENE_SILENC ING_BY_RNA,” “RNA/NCRNA_EXPORT_FROM_NUCLEUS,” etc.  in association with ARGONAUTE-1 and -2 . The genS,” etc.  althoughSETDB1 level, we performed single sample GSEA using gene set variation analysis (GSVA), which assesses separate enrichment scores for each pairing of a sample and gene set (independent of phenotype labeling) to find out to what extent the genes in a certain gene set are coordinately upregulated or downregulated within a sample (FDR < 1 × 10–5) showed that, consistent with the result in To further test if the ADC and SCC samples could be divided by a sample . The GSVBecause SETDB1 is one of epigenetic players, or epidrivers, that function as a writer, reader, and/or eraser of chromatin marks , the altp < 0.02) between the SH and SL samples in both ADCs and SCCs, and the “HISTONE_UBIQUITINATION” genes differed in the ADCs  between the SH and SL samples. The genes in the H3K4, H3K9, H3K36, and H4K20 methylation categories significantly differed (p < 0.01) in the ADCs, whereas only H3K4 and H3K9 category genes differed in the SCCs (As shown in the ADCs . We furtthe SCCs . The incn = 154) encoding effector enzymes and mediators that directly participate in the modification of nucleosomes  were mostly of lower methylation in the SH samples, whereas they were of higher methylation in the SCCs , as illustrated in The antithetical pattern of the methylation change was also observed around the CpG island (CGI) regions. In the ADCs, the differentially methylated CpGs identified between the SH and SL samples  and lung squamou-cell carcinoma  were downloaded from Genomic Data Commons Data Portal , and copy number alteration data were obtained from cBioPortal .Gene expression datasets for TCGA LUAD (Y-KK: conception, design, and administrative support. BM: collection and assembly of data. Y-KK and BM: data analysis and interpretation. Both authors wrote and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Pomegranate cultivation has expanded significantly in the last two decades. However, there is limited information on its fertilization requirements and the effect of macronutrient availability on its reproductive development. Two commercial pomegranate cultivars—“Wonderful” and “Emek”—were grown in 500-L containers for 3 years, using a fertigation system. Development and reproduction indices were measured to explore the trees’ responses to elevated levels of nitrogen (N), phosphorus (P) and potassium (K) in the irrigation solution. Andromonoecy rate was affected by nutrient levels only in the first year of the experiment, with higher levels of N and P leading to a greater proportion of hermaphrodites out of total flowers. P level had a positive effect on the total number of hermaphrodites per tree in both varieties. Differences recorded between hermaphroditic and staminate flowers included nutrient concentrations and dry weight. Fruit set and aril number were positively affected by N concentration in the irrigation solution. We conclude that only a severe deficiency of N and P affects the andromonoecy trait, and that at the levels examined in this study, K hardly influences pomegranate reproduction. Solanum carolinense revealed that pollen grains from male flowers are more vigorous than those from bisexual ones [About 2% of known plant species are andromonoecious—a floral dimorphic sexual system in which an individual plant bears both hermaphroditic  and staminate  flowers . Severalual ones . These rual ones . The ratual ones ,10.Pomegranate is an andromonoecious plant, defined by large hermaphroditic flowers along with smaller staminate ones, containing the remains of degenerated female organs . The staAs staminate flowers are considered to bear better pollen, and because nutrients affect pollen quality in general , our hyp−1) was examined in macro- and microelement concentrations in the flowers of cv. “Wonderful” trees. N concentrations in hermaphroditic and staminate flowers were similar. Increasing N levels in the irrigation solution, up to 40 mg L−1, increased N concentration in the flowers, which leveled off at higher N levels. The concentrations of sulfur (S), iron (Fe), manganese (Mn), and zinc (Zn) increased with N level in the irrigation solution, whereas a negative impact was recorded for P, calcium (Ca), copper (Cu), and molybdenum (Mo) concentrations in both flower types. N levels had no significant impact on K, magnesium (Mg), or boron (B) concentrations in staminate flowers. At most N levels tested, staminate flowers of “Wonderful” trees had significantly higher concentrations of K, Ca, Fe, Mg, Mn, and Mo compared to hermaphroditic ones , medium (70 mg N L−1 for both cultivars) and high (200 mg N L−1 for “Wonderful” and 100 mg N L−1 for “Emek”). All three N levels positively affected the N concentration in both hermaphroditic and staminate flowers of both cultivars (We compared “Emek” and “Wonderful” trees subjected to three different N levels in the irrigation solution (see ultivars A. Mn and−1), medium (10 mg P L−1), and high (20 mg P L−1) (levels were the same for both cultivars). An increasing P level in the irrigation solution positively affected P concentration in both hermaphroditic and staminate flowers, for both cultivars  in the irrigation solution positively affected only K concentration in “Emek” staminate flowers (An increasing K level (low: 20 mg K L flowers C. N and  flowers C. K leve−1) reduced hermaphrodite numbers in “Wonderful” trees  than staminate flowers (~0.5 g) of both cultivars for all treatments . N and PThe proportion of fruit out of the total number of hermaphrodites (fruit set) was not affected in “Emek” by nutrient levels and was around 80% in 2017 and 60% in 2018 . In “WonIn “Wonderful”, the number of arils per fruit decreased under low levels of N in the irrigation solution, but the response to higher levels of N was inconsistent . This inNutrients are key players in plant physiology. Reproduction is a major developmental process, and nutrients are well known to participate in and affect sex expression in higher plants . The firIn our experiment, nutrient levels affected the number of hermaphrodites per pomegranate tree more than the number of staminate flowers. Hermaphrodite number was positively associated with N and P levels . As the As expected from their larger size, hermaphroditic flowers weighed significantly more than staminate ones, at all N, P, and K levels . However−1 had the highest recorded fruit set (−1), which gave the lowest result. This result is surprising because N, P, and K have been found to affect the yields of many fruit trees [A significant effect of nutrient levels on fruit set was found only for the higher N treatments, mostly in 2017, in which 40 mg Lruit set . In 2018it trees ,36,37, ait trees . HoweverAril (seed) number varies among pomegranate cultivars  and thisIn summary, the various reproductive indices—the number of flowers per pomegranate tree, the proportion of hermaphrodites, fruit set, flower dry weight and the number of arils per fruit—were relatively stable and were usually affected only by an acute shortage of one of the nutritional elements. These data emphasize the pomegranate’s ability to maintain a constant fertility potential over a wide range of macroelement availability. Fertigation management in soilless-medium containers enables the frequent and continuous application of macronutrients in the water solution . This syTaken together, we revealed that N, P, and K are not the main factors affecting the reproductive development of pomegranate—either flowering or fruit set. We found that the andromonoecy trait is affected mostly by N and P deficiency, and that K has barely any influence on pomegranate reproduction.−1. During all 3 years of the experiment, the trees were fertigated daily and excessively, at rates designed to achieve 30% leaching fraction. The trees were pruned in February 2017 and 2018, after leaf drop. Trees were topped 2 m above the perlite surface, keeping only three to four main outward-growing branches. All stem-sprouting branches were removed.In July 2015, two-year-old “Wonderful” and “Emek” pomegranate plants were transplanted into 500-L containers filled with perlite , at the Gilat Research Center, Israel. Between July 2015 and February 2016, all trees were fertigated with 70 mg N L−1, 10 mg P L−1, and 100 mg K L−1. The other essential mineral nutrients were supplied with the irrigation solution as follows: Ca 20 mg L−1, Mg 24 mg L−1, S 84 mg L−1, Fe 500 μg L−1, Mn 250 μg L−1, Zn 125 μg L−1, Cu 19.0 μg L−1, Mo 13.5 μg L−1, and B 190 μg L−1. The low level of N was 10 mg L−1 for “Wonderful” and 20 mg L−1 for “Emek”. The high level of N was 200 mg L−1 for “Wonderful” and 100 mg L−1 for “Emek”. Other elements were as in the control treatment. P treatments were equal in both cultivars: 1 mg P L−1 for the low level and 20 mg P L−1 for the high level. K treatments were also equal in both cultivars: 20 mg K L−1 for the low level and 200 mg K L−1 for the high level.Differential fertilization treatments were begun in March 2016, in two separate experiments. In one experiment, we compared three levels of N, P, and K—low, medium, and high—in both cultivars. The control fertilizer treatment, which was defined as “medium level” was 70 mg N L−1 (90% NO3− and 10% NH4+ w/v), which included the three levels of the first experiment, to get a better response curve. The other mineral nutrients were supplied with the irrigation solution as previously described [In the other experiment, we studied the effect of eight N concentrations on “Wonderful” trees: 5, 10, 20, 40, 70, 100, 150, and 200 mg Lescribed . Both exPrior to the blooming season in each year, a plastic net was placed under each tree. The net was larger than the canopy radius. All dropped flowers were collected from the net during the entire season. The hermaphrodite and staminate flowers  were disThe 10 flowers of each type sampled from each tree during the 2018 blooming season were washed, oven-dried at 70 °C for 3 days, and their dry weight measured. Fruit set was measured for each treatment in 2017 and 2018 as the percentage of fruit per tree out of the total hermaphrodites. The arils were separated by hand from five fruits per tree. The number of arils per fruit was calculated by dividing the total aril weight by the average weight of a single aril per fruit.P ≤ 0.05 was considered significant.Data were analyzed by least squares fitting and determined as nonlinear regression functions in GraphPad Prism (GraphPad Software Inc.). Fertilizer treatments and harvest years were examined by two-way ANOVA . The eff


Malus domestica Borkh.) during fruit development and maturation. Volatile compounds detected in the fruit of both cultivars exhibited similar trends across different developmental stages. In the early stages of “Ruixue” fruit development (60 days after full bloom), there were fewer volatile compounds, mainly aldehydes (87.0%). During fruit maturation (180 days after full bloom), the types and amounts of volatile compounds increased, mainly including esters (37.6%), and alkenes (23.2%). The total volatile concentration, the types of major volatile compounds, and their relative content in both cultivars varied across different stages. Gene expression analysis indicated that the upregulation of MdLOX, MdAAT2, and MdADH3 was associated with increased aroma compound content, especially esters, during fruit development in both cultivars. Changes in the expression of MdArAT, MdACPD, MdADH3, MdAAT2, and MdLOX may lead to differences in volatile compounds between apple cultivars.Aromatic volatile compounds are important contributors to fruit quality that vary among different cultivars. Herein, headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry was used to determine changes in volatile compounds and related gene expression patterns in “Ruixue” and “Fuji” apples ( Malus domestica Borkh.) is one of the most popular fruits worldwide, renowned for nutritional and taste quality, and high economic value. The quality of apple fruit is valued by consumers. The process of apple fruit development is regulated by multiple biochemical factors that affect the color, texture, flavor, and nutrition of fruit [Apple  biosynthesis of fatty acid-derived compounds, (ii) biosynthesis of amino acid derivatives, and (iii) biosynthesis of terpenoids ,7. The fbutyrate ,13.MdAAT1 gene has been shown to be involved in the production of fruit volatile esters such as hexyl acetate, butyl acetate, and 2-methylbutyl acetate found in “Royal Gala” apple [Recent studies have shown that some functional genes play potential roles in fruit aroma synthesis. Genes encoding enzymes with alcohol transaminase (AAT) activity have been isolated and characterized in various fruit species including strawberry, banana, and apple . The MdAa” apple . In addia” apple . In tomaa” apple . Moreovea” apple ,19.Previous research on apple fruit aroma has mainly focused on the characterization of volatile profiles in specific cultivars. However, the molecular mechanisms underpinning the differences in volatile compounds produced by various cultivars, and the key factors affecting the synthesis of volatile compounds in apple, remain unclear. The “Fuji” cultivar is the most widely consumed throughout the world, while the recent “Ruixue” cultivar bred by crossing “Pink Lady” and “Fuji” cultivars has a unique flavor. The commercial harvest stage of “Ruixue” is ~5 days later than that of “Fuji”. Herein, we selected “Ruixue” and “Fuji” apples with considerably different flavors to explore changes in the volatile compounds and expression patterns of aroma-related genes during fruit development. In particular, we clarified the material basis and basic characteristics of the formation of volatile compounds in apple fruit. This work could provide useful data for the quality evaluation of apple fruit based on the biosynthesis of volatile compounds.Changes in the quality parameters of the two apple cultivars are shown in In “Ruixue” apple, the total volatile concentration continued to increase as fruit matured, from 633.50 μg/kg at 60 DAFB to 4488.42 μg/kg at 210 DAFB A. A totaIn “Fuji” apple, the increasing trend in total volatile concentration was essentially similar to that observed in “Ruixue” apple A. The toMdAADC, MdMCAT, MdLOX, MdACP2, MdACPD, MdPDC2, and MdAAT2) and their relative expression levels increased with fruit development. Class II comprised three genes  and their relative expression levels increased first then decreased with fruit development. Class III comprised two genes (MdMACT and MdAOS) whose relative expression levels continuously decreased with fruit development , for which relative expression levels increased in the early stages then decreased in the later stages of fruit development. There were three genes in class II  with relative expression levels that first increased with fruit development, then decreased by 195 DAFB, then increased again by 210 DAFB. Class III also included three genes  whose relative expression levels decreased first then increased with fruit development . Meanwhile, the major volatile compounds in mature “Fuji” fruit were hexyl isovalerate, α-farnesene, 2-hexenal, hexyl hexanoate, and butanoic acid 3-methyl-pentyl ester (195–210 DAFB). These results indicate that variation in the composition and content of aroma compounds leads to differences in aroma flavor between the two apple cultivars.The contribution of each compound to the specific aroma of fruit depends on the activity of relevant enzymes, the specificity and availability of substrates, and the presence of other compounds . Herein,LOX gene family are involved in the synthesis of apple aroma [MdLOX gene changed drastically with fruit maturation, and a 140-fold increase was observed at 210 DAFB compared with that at 60 DAFB. In “Fuji” apple, relative expression of the MdLOX gene also changed substantially, and a 250-fold increase was observed at 180 DAFB compared with that at 60 DAFB. When MdLOX expression began to increase, the ester content also increased sharply in both “Ruixue” and “Fuji” apples. In addition, hyperoxide lyase (HPL) is involved in the ester biosynthetic pathway, and HPL silencing reduces the production of most C6 volatiles in potato leaves [MdHPL to MdAOS may affect the biosynthesis of apple aroma [Based on RT-qPCR data, we explored the possible mechanisms underpinning the differences in aromatic volatile compounds between apple cultivars and the key factors affecting aroma synthesis in apple fruit. Straight-chain volatile compounds reportedly depend, to a large extent, on the availability of fatty acid-derived precursors . Therefole aroma . Herein,o leaves . It has le aroma . Our resMdPDC2 expression levels increased significantly in “Ruixue” and “Fuji” apples with increasing maturity. In addition, our results indicate that the relative expression of MdADH3 increased sharply in “Ruixue” apple at 180 DAFB compared with 60 DAFB, but this change was not seen in “Fuji” apple.Pyruvate decarboxylase (PDC) and ADHs are involved in the conversion between aldehydes and alcohols. PDC has been demonstrated to play a vital role in regulating the supply of pyruvate, one of the precursors for the formation of ethyl esters . In the MdArAT gene was expressed differently in the two apple cultivars, and a clear increasing trend was observed in “Fuji” apple during fruit development. However, such a change was not evident in “Ruixue” apple, and only the expression level of MdACPD increased obviously markedly in this cultivar during fruit development. LeAADC1 or LeAADC2 overexpression in transgenic tomato plants results in up to a 10-fold increase in emissions of products from this pathway in fruit [MdAADC increased with fruit development in “Ruixue” apple, while its expression in “Fuji” fruit first decreased then increased with increasing maturity. In addition, fatty acid biosynthesis is one of the key pathways for aroma biosynthesis in apple fruit. Acyl carrier proteins (ACPs) are key factors involved in the fatty acid biosynthetic pathway [MdACP2 was relatively large and irregular in “Ruixue” apples during fruit development, while its expression level was relatively stable in “Fuji” apple. This expression difference could be one of the reasons for the difference in fruit aroma between the two apple varieties.Plant ArAT acts on L-tyrosine and L-phenylalanine. This enzyme is responsible for the direct decarboxylation of phenylalanine and it promotes the formation of fruit flavor . Herein,in fruit . Herein, pathway . VariatiMdAAT2 are positively correlated with AAT enzyme activity and ester production in apple, except in the later maturation stage. This suggests that MdAAT2 is involved in the regulation of ester biosynthesis, and that post-translational modification may be involved in the regulation of AAT activity [MdAAT2 increased considerably, while the content and number of ester compounds also increased. This result is consistent with previous findings for “Golden Delicious” and “Granny Smith” apples [AAT is a key enzyme involved in the biosynthesis of ester compounds. Its main function is related to the binding of the acid donor acyl-CoA to alcohol receptors in the final key step of ester biosynthesis . Northeractivity . The res” apples .Malus domestica Borkh.) cultivars, “Ruixue” and “Fuji”, were produced at the apple experimental station of the Northwest A&F University . The station lies within the continental monsoon zone  in Baishui County, Shaanxi Province, China. The elevation of the experimental site is 830 m, and it has an average annual temperature of 11.6 °C and average annual rainfall of 567.6 mm.Two apple . Fruit samples without pests and mechanical damage were selected and used for subsequent analyses. The fruits of the same replicate group were scraped with a peeler to scrape the skin about 1 mm thick from the carcass, mixed into a sample, quickly frozen in liquid nitrogen and stored at −80 °C for later use.Total soluble solids were determined using a digital hand-held refractometer . Acidity was measured using a fruit acidity meter . The sugar:acid ratio was obtained by dividing the soluble solid content by the acidity. Each measurement was repeated three times.−1). After sealing and capping with tin foil. We placed it on a magnetic stirring heating plate and balance for 10 min. Subsequently, Solid phase microextraction head was inserted into the headspace of the sample bottle that had been equilibrated. After 40 min of adsorption, the extraction head was inserted into a gas chromatography inlet for thermal desorption at 250 °C. After 2.5 min of desorption, the extraction head was removed, and volatile compounds in the extract were analyzed using a GC-MSQP-2010 gas chromatograph-mass spectrometer .Headspace solid-phase microextraction was used to extract volatile compounds from fruit peel samples. A 5 g sample was accurately weighed into a 50 mL bottle containing 1 g of sodium chloride, a stirrer bar, and 10 µL of the internal standard 3-nonanone ) and supplemented by manual analysis to confirm their identities. The quantification of volatile compounds was based on peak area normalization to obtain the relative mass percentage of each compound, and 3-nonanone was used as the internal standard.Mass spectrometry was performed using electrospray ionization, with an electron bombardment energy of 70 eV, an ion source temperature of 240 °C, a transmission line temperature of 240 °C, and an initial acquisition period of 2 min. Mass spectra of unknown volatile compounds were searched and matched against the NIST05 mass spectral library  method, as described previously , with slMdActin was selected as the constitutively expressed reference gene and a sample (specified with an arbitrary number of 1) was used as a calibrator for “Ruixue” apples at 60 DAFB.Relative expression levels of 12 candidate genes encoding enzymes involved in aroma synthesis  were analyzed by quantitative real-time PCR (RT-qPCR) with cDNA as a temperate. Gene-specific primers were designed using Primer 5.0  and all primer sequences are listed in −∆∆CT method [qRT-PCR analysis was performed using ChamQTM SYBRR qPCR Master Mix  with a Step OnePlus Real-Time PCR System . The cycling parameters were 95 °C for 30 s followed by 40 cycles at 95 °C for 5 s and 60 °C for 30 s. All reactions were performed in triplicate for each of the three biological replicates, and relative expression levels of the selected genes were calculated using the relative 2T method .Experimental data are presented as means ± standard error of three experimental replicates. All data were processed using Excel 2010 . A one-way analysis of variance (ANOVA) was performed on data using IBM SPSS 21.0 . Graphs were drawn using SigmaPlot 12.5 .n-hexanal, and 3-hexenal. With increasing maturity (165–210 DAFB), the content of aldehydes decreased, while the content and types of other volatile compounds  gradually increased. In “Ruixue” apple, there were α-farnesene, hexyl isovalerate, butanoic acid, 3-methyl-, pentyl ester, and hexyl hexanoate. In “Fuji” apple, there were mainly α-farnesene, hexyl isovalerate, hexyl butyrate, and butyl butyrate. Gene expression analysis revealed that increased expression of MdLOX, MdAAT2, and MdADH3 was associated with increased aroma compound content, especially esters, in “Ruixue” and “Fuji” apples during fruit development. Changes in the expression of MdArAT, MdACPD, MdADH3, MdAAT2, and MdLOX may have contributed to differences in volatile compounds between the two apple cultivars.This study showed that the major volatile compounds in apple were aldehydes during the early stage of fruit development (60–120 DAFB). In “Ruixue” and “Fuji” apples, they were mainly 2-hexenal, Based on the above results, the following can be concluded:n-hexanal, and 3-hexenal. When the maturity increases, the content of aldehydes decreases, while the content and types of other volatile compounds  gradually increase. In “Ruixue” apples, there are α-farnesene, hexyl isovalerate, butanoic acid, 3-methyl-, pentyl ester and hexyl hexanoate. In “Fuji” apples, there are mainly α-farnesene, hexyl isovalerate, hexyl butyrate and butyl butyrate.In the early stage of apple fruit development (60–120 DAFB), most of the volatile compounds in fruits are aldehydes. In “Ruixue” apples and “Fuji” apples, they are mainly 2-hexenal, MdLOX, MdAAT2, and MdADH3 is associated with increased aroma, especially esters, in “Ruixue” and “Fuji” apples during fruit development. Changes in the expression of MdArAT, MdACPD, MdADH3, MdAAT2, and MdLOX may lead to differences in volatile compounds between the two apple cultivars.Our comprehensive study demonstrates that increased expression of


Down syndrome (DS) is often characterised by intellectual disability with particular difficulties in expressive language. However, large individual differences exist in expressive language across development in DS. In the general population, one of the factors associated with variability in this domain is parental depression. We investigated whether this is also the case in young children with DS.Thirty-eight children with DS between 8 and 48 months of age participated in this study. Their parents reported on the children’s receptive and expressive vocabularies (MacArthur-Bates Communicative Development Inventory) and on parental depression. Furthermore, an experimenter-led standardized developmental assessment  was administered to the children to test five domains: gross motor, fine motor, visual reception, receptive language, and expressive language.A cross-sectional developmental trajectories analysis demonstrated that expressive language developed at a slower rate in children with DS whose parent reported depression than in those whose parent did not. No differences between groups were found in any other domain.Parental depression is associated with slower rate of expressive language development in young children with DS. These findings suggest that DS and parental depression may constitute a double hit leading to increased difficulties in the development of expressive language. Little is known about the relationship between parental depression and the development of young children with Down syndrome (DS). We found that parental depression is associated with a slower rate of expressive language development in the first four years of life in DS. These findings suggest that DS and parental depression may constitute a double hit leading to increased difficulties in the development of expressive language. Thus, family systems with this double vulnerability may be a particularly important target for intervention.1Down syndrome (DS) is caused by the presence of an additional chromosome 21. With an incidence of approximately 1 in 1000 live births, DS is the most common known genetic cause of intellectual disability . A charaAlthough expressive language development is particularly poor in DS, large individual differences exist in this domain  within the London Down Syndrome (LonDownS) Consortium , father (n = 5), or both parents (n = 3) reported a diagnosis of depression. These children (the 'Depression group') were closely matched to 19 children with DS whose parent(s) did not have depression  Research Ethics Committee (13/WA/0194) and from the Birkbeck Psychological Sciences Ethics Committee (121373). Prior to testing, informed consent was obtained from parents. Participants were given a small gift  in return for their participation.2.22.2.1Parents were administered a medical history questionnaire over the phone before the lab visit . They we2.2.2n = 19), of whom 14 had a diagnosis of depression at some point in their life. In the No-depression group (n = 19), 16 mothers, 2 fathers, and in one case both parents together completed the questionnaire.Parents filled in the MacArthur-Bates Communicative Development Inventory (CDI) for Words and Gestures (for ages 8-18 months) . Althoug2.2.3gross motor ; (2) fine motor , and motor control); (3) visual reception ; (4) receptive language ; and (5) expressive language (the ability to use sounds and language productively). These yield scores for each domain. MSEL is standardized for TD children between 0 and 68 months (from 0 to 33 months for the gross motor domain). This test has high internal consistency and test-retest reliability (n = 2), within five months (n = 1); No-depression group: within two months (n = 1), within three months (n = 1)). Analyses of the relationship between MSEL and CDI were run both with and without these five participants, to ascertain whether this affected the results.The Mullen Scales of Early Learning MSEL;  was admiiability , we used2.3intercept and gradient. The intercept describes the initial level of performance. The gradient represents the rate at which performance changes (increases or decreases) with age. The use of intercepts and gradients allows us to look beyond differences between means (which can only inform us of whether performance differs across groups or not) and provides insight into how performance differs in the two groups across developmental time.In order to investigate whether changes in scores differ across groups  and chronological age, we employed a cross-sectional developmental trajectory analysis . The metdelayed onset in development compared to the other group. If the interaction between Chronological age and Group is significant, then we can conclude that one group was developing more slowly than the other, exhibiting a slower rate of development.In this paper, cross-sectional developmental trajectories were initially constructed separately for each group by conducting a series of linear regression analyses to examine whether chronological age relates to performance on an outcome variable. Subsequently, the cross-sectional developmental trajectories were compared between the Depression and No-depression groups. This made it possible to evaluate whether the developmental trajectories differed between the groups in terms of their gradients and/or intercepts. To compare pairs of trajectories, a modified version of ANCOVA was employed to test for a main effect of Chronological age, Group, and the interaction between Chronological age and Group. If the main effect of Group is significant, then the intercepts of the two groups are significantly different. We could then conclude that the performance of one group exhibits 32R = .39, F = 10.81, p = .004) and No-depression group  = 68.64, p < .001). The Depression group trajectory plotted against Chronological age was compared with the No-depression group trajectory using a general linear model predicting overall CDI expressive language from Chronological age with Group as a between-subject factor. As expected, there was a significant main effect of Chronological age, F = 59.80, p < .001, ηp2 = .64, which indicates that across both groups, Chronological age significantly predicted CDI expressive language. The main effect of Group was not significant, F = 1.12, p = .298, ηp2 = .03. This indicates that the intercepts of the Depression and No-depression groups were not significantly different. However, the gradient was different between the two groups as indicated by a significant interaction between Group and Chronological age, F = 7.69, p = .009, ηp2 = .19. Thus, the Depression group showed a slower rate of development than the No-depression group.experimenter-led measure, we conducted the same developmental trajectory analysis as before – but with MSEL expressive language. First, we established the relationship between parent-reported CDI expressive language and experimenter-led MSEL expressive language (r(35) = .57, p < .001, chronological age at CDI partialled out). A near-identical result was obtained when chronological age at MSEL was partialled out (r(35) = .58, p < .001) or when the correlation was run only with participants for whom CDI and MSEL were administered within one month of each other (r(30) = .60, p < .001, chronological age at CDI/MSEL partialled out).To check that the finding for our parent report measure of expressive language also exists for our 2R = .22, F = 4.69, p = .045) and No-depression group  = 14.26, p = .002). The Depression group trajectory plotted against Chronological age was compared with the No-depression group trajectory using a general linear model predicting overall MSEL expressive language from Chronological age with Group as a between-subject factor  = 19.06, p < .001, ηp2 = .36, which indicates that across both groups, Chronological age significantly predicted MSEL expressive language. The main effect of Group was not significant, F = 1.20, p = .282, ηp2 = .03. This indicates that the intercepts of the Depression and No-depression groups were not significantly different. The interaction between Group and Chronological age approached significance, F = 3.12, p = .086, ηp2 = .08. This is broadly consistent with the above finding that expressive language (CDI) was developing at a slower rate in the Depression group than the No-depression group.We then conducted the cross-sectional developmental trajectory analysis with MSEL expressive language b. A signt factor b. As expFs > 47.38, ps < .001, ηp2s > .58), there were neither main effects of Group  < 0.78, ps > .384, ηp2s < .03) nor Group x Chronological age interactions  < 2.68, ps > .111, ηp2s < .08). Therefore, the Depression and No-depression groups did not significantly differ from each other on CDI receptive language, MSEL gross motor, MSEL fine motor, MSEL visual reception, or MSEL receptive language.The relationship between parental depression and expressive language in DS may be domain-specific because parental depression was not linked to any other outcome measure . While comparisons of group trajectories found significant main effects of Chronological age on the outcomes  as these groups may have a greater risk of developing poor outcomes.One of the pathways which could explain the relationship between parental depression and expressive language is through the context of parent-child interaction (for a review, see The interaction between a parent and child provides an important context for the child’s expressive language development – a domain in which children with DS particularly struggle . ParentaConsidering that parent-child interaction is an important context for learning more broadly, it is unlikely that parental depression solely impacts expressive language. Indeed, findings from studies of typically developing children show that even though the expressive language domain is often the first domain to be negatively impacted by parental depression , perhapsIt is important to emphasize that the effect of parental depression may not necessarily be an environmental effect. Difficulties in expressive language in children with DS may be due to an inherited genetic vulnerability – which in parents would be associated with depression and in infants/toddlers with DS exacerbate the difficulties in the domain with the highest vulnerability – expressive language. Future studies could investigate these questions as well as possible gene-environment correlations and gene-environment interactions .There are some limitations of the current study which are important to consider. The main result of the effect of depression on CDI expressive language was based on a parental report of depression and expressive language. It is possible that parents who report depression also report more negatively on their child’s performance . HoweverAnother limitation of this study is that it is based on a convenience sample. It is possible that our sample does not represent those parents with depression who find it particularly challenging to deal with their child with DS, as these parents may find it difficult to allocate time and other resources to take part in research studies. Thus, it is possible that the current study actually underestimates the impact of parental depression on development in children with DS, since in studies of TD children, severe parental depression has been found to have greater negative effects on development . FurtherIn order to understand the link between parental depression and language outcomes in children with DS, future studies should also obtain more detailed information on parental depression, such as onset, duration, and severity, as these have been related to developmental outcomes in typically developing children . ObtainiThis study represents an important step towards understanding how parental depression relates to developmental outcomes in children with DS. Although a number of studies have investigated the impact of parenting on development in DS (for reviews, see Hana D’Souza: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Supervision, Visualization, Writing - original draft, Writing - review & editing. Amanda Lathan: Conceptualization, Data curation, Formal analysis, Methodology, Visualization, Writing - original draft, Writing - review & editing. Annette Karmiloff-Smith: Conceptualization, Funding acquisition, Methodology, Project administration, Resources, Supervision. Denis Mareschal: Project administration, Resources, Supervision, Writing - review & editing.None.


Schneider and Laplanche, Acta Mater. 204, 2020). The data related to recrystallized microstructures comprise raw backscatter electron (BSE) micrographs (tif-files) obtained using a scanning electron microscope (SEM) for six grain sizes in the range [10–160 µm], optical micrographs of the alloy with the largest grain size (d = 327 µm), pdf-reports and tables presenting the corresponding grain-size distributions  and crystallite-size distributions , the annealing twin thicknesses (t), the average number of annealing twin boundaries per grain (n), and the average Taylor factor (M) of each recrystallized microstructure. These are benchmark datasets that may serve to develop new algorithms for the automated evaluation of microstructural parameters. Such algorithms would help to speed up the analyses of microstructures and improve their reliability. Furthermore, several groups pointed out that in addition to the mean grain size, other microstructural parameters such as the grain size distribution  and the average number of twins per grain  may affect some material properties . Therefore, an effort was made here to determine and report almost all the microstructural parameters describing recrystallized microstructures of FCC alloys. The mechanical-properties data are provided as excel-sheets in which the raw stress-strain curves can be found. Compression tests for alloys with different grain sizes were performed at room temperature. Additional compression tests and tensile tests for the grain size d = 160 µm were performed at temperatures between 77 K and 873 K. Characteristic mechanical properties, such as yield stresses at 0.2% plastic strain (0.2%σ) and Hall-Petch parameters (0σ and yk) are given for all temperatures in the tables below. Moreover, the Hall-Petch parameters as well as the mechanical data reported in the present study could be used for data mining and implemented in programs used for alloy design.In the present article, we present a data compilation reflecting recrystallized microstructures and the corresponding mechanical properties of an equiatomic, single-phase face-centered cubic (FCC) CrFeNi medium-entropy alloy (MEA). For the analysis, interpretation, and discussion of the data, the reader can refer to the original research article entitled “Effects of temperature on mechanical properties and deformation mechanisms of the equiatomic CrFeNi medium-entropy alloy”, see Ref. ( These data may be useful for other researchers in the community of high- and medium-entropy alloys.•The equiatomic CrFeNi alloy may also be interesting for researchers in the fields of austenitic stainless steels and Fe-based superalloys. Our data may improve the understanding of these complex engineering alloys and also help to further optimize them.•The microstructural data compilation consists of BSE and optical micrographs of recrystallized FCC microstructures, tables and pdf-files reporting the corresponding grain/crystallite-size distributions, the thickness distribution of annealing twins and their density as well as the texture of the alloys. These data could be used to further improve the automated analysis of microstructures, e.g. algorithms for image analysis.•i.e., stress strain curves) could be used to further improve the automated analysis (machine learning) of yield stress, work hardening rate, ultimate tensile stress, homogeneous elongation and strain to fracture.Our mechanical raw-data  reported here could be used to shed light on how these parameters are affected by chemistry, microstructure , and alloy parameters such as the stacking fault energy and the shear modulus.The normalized Hall-Petch parameters (133.3Fe33.3Ni33.3 (composition in at.%) medium-entropy alloy. Recrystallization heat treatments at temperatures lying in the range (1273 K–1573 K) for times between 15 min and 60 min yielded seven different recrystallized microstructures. BSE and optical micrographs of these microstructures were used in combination with the lineal intercept method to determine the grain- and crystallite-size distributions, see E-112 E-112 https://data.mendeley.com/datasets/7d826s3mhf/1 or be sent on request by email. y-axis in d) and crystallite (c) sizes, the BSE and optical micrographs were also used to assess the number of annealing twin boundaries per grain (n) and the distribution of annealing twin thicknesses (t), which are reported in Since 2004, high- and medium-entropy alloys (HEAs and MEAs) have attracted tremendous attention in various scientific fields d = (Ad × π/4)1/2  and c = (Ac × π/4)1/2, where Ad and Ac are the average cross sectional areas of the grains and crystallites, respectively. M) determined by EBSD.Besides the Heyn lineal intercept method applied to BSE and optical micrographs, electron backscatter diffraction (EBSD) was used to determine the mean grain and crystallite size distributions, see σ0) and the Hall-Petch slope (ky) were determined at room temperature following the procedure reported in Ref. G and Gb1/2, where G is the shear modulus and b is the Burgers vector. Both parameters were taken from Ref. σ0/G and ky/(Gb1/2)) are reported in To investigate the effect of grain refinement on mechanical properties, compression tests were conducted at 293 K for the seven grain sizes investigated in the present study. These data allowed us to plot the yield stress at 293 K as a function of the square root of the average grain/crystallite size. From these Hall-Petch plots, the intrinsic lattice strength (σ0.2%) determined at different temperatures for various grain and crystallite sizes are given in The Excel-sheets containing the corresponding stress-strain data can be found in the linked Mendeley Data repository under the “CrFeNi_Compression_Tests”-folder. This folder is divided into eight subfolders corresponding to the eight testing temperatures. The Excel-sheets in these folders are named using the three following characteristics: alloy composition, recrystallization heat treatment (temperature and time), and temperature of the compression test. For instance, the Excel-sheet for a compression test conducted at 473 K with a CrFeNi alloy that was recrystallized at 1473 K for 60 min is labelled as: “CrFeNi_1473 K_60min_473 K”. From the stress-strain datasets, the yield stresses at 0.2% plastic deformation , the ultimate tensile stresses (UTS), the uniform elongation (εuniform), and the elongation to fracture (εfracture) determined at different temperatures for a given grain/crystallite size are reported in εuniform and εfracture values, those marked with an asterisk were determined from the crosshead displacement and subsequently corrected by a correction factor (∼0.8) while the other values were directly estimated using an extensometer. For the detailed description of this procedure, the reader may refer to the related research article Additional tensile tests were performed at six different temperatures  on samples with a given grain size of 2d = 327 µm), the recrystallized materials were ground with SiC abrasive papers, polished with diamond suspensions, and vibropolished in a mixture of distilled water and colloidal silica (particle size: 0.06 µm). BSE micrographs were recorded in an SEM of type Quanta FEI 650 ESEM using a working distance of ∼10 mm. Acceleration voltages between 15 kV  and 20 kV (large grains) were chosen to optimize the BSE contrast. Four BSE images spaced 1 mm apart were collected for each grain size, except for the two coarsest microstructures. Here, two different methods were used to obtain micrographs covering sufficiently large surface areas. In the first method used for the second-largest grain size (d = 160 µm), nine BSE micrographs were collected and assembled, covering an area representative of the whole cross-section of a compression specimen, see d = 327 µm), the sample was etched using a Kalling II etching solution prior to imaging. This solution consists of 100 ml ethanol, 100 ml hydrochloric acid (32 vol.% in distilled water) and 5 g copper chloride. The specimen was hold in this solution for ∼5 s. The etched specimen was then rinsed, dried and observed in an optical microscope of type Zeiss Axio and three optical micrographs were mounted together and are shown in Except for the alloy with the coarsest grain size (d) and mean crystallite (c) sizes along with their corresponding distributions using the Heyn lineal intercept method with four horizontal and four vertical lines, see t) and corresponding uncertainties (∆t). Using the data for d and c and the equation n = (d/c - 1), the average number of annealing twin boundaries per grain (n) was calculated, see The BSE and optical micrographs were then used to determine the mean grain  and the EBSD method . Please note that in the latter case grain and crystallite sizes were calculated using d = (Ad×π/4)1/2 and c = (Ac×π/4)1/2, which assumes that grain and crystallites have an equiaxed geometry.Grain orientation maps were determined by electron backscatter diffraction (EBSD) in the above-mentioned SEM equipped with a Hikari XP camera . From these orientation maps, grain and crystallite size distributions  directly attached to the gage section. For the other temperatures, a different method was used since the extensometer could not be used in these cases. Here, the strains were directly calculated from the crosshead displacement and corrected by a correction factor. The correction factor was determined by analyzing the tensile data obtained at 77 K and 293 K using either the strains determined with the extensometer and those determined from the crosshead displacement. At 77 K and 293 K, the strains calculated using the cross-head displacement were found to represent 80% of the strains determined with the extensometer, resulting in a correction factor of ∼0.8. The resulting values are marked with an asterisk in Compression and tensile tests were conducted in a Zwick Roell XForce Z100 machine at temperatures ranging from 77 K to 873 K and at a nominal strain rate of 10Mike Schneider: Methodology, Investigation, Writing - Original Draft; Guillaume Laplanche: Writing - Review & Editing, Supervision.The authors declare that they have no known competing financial interests or personal relationships, which have, or could be perceived to have, influenced the work reported in this article.