The multi-environment genomic selection enables plant breeders to select varieties resilient to diverse environments or particularly adapted to specific environments, which holds a great potential to be used in rice breeding. To realize the multi-environment genomic selection, a robust training set with multi-environment phenotypic data is of necessity. Considering the huge potential of genomic prediction enhanced sparse phenotyping on the cost saving of multi-environment trials (MET), the establishment of a multi-environment training set could also benefit from it. Optimizing the genomic prediction methods is also crucial to enhance the multi-environment genomic selection. Using haplotype-based genomic prediction models is able to capture local epistatic effects which could be conserved and accumulated across generations much like additive effects thereby benefitting breeding. However, previous studies often used fixed length haplotypes composed by a few adjacent molecular markers disregarding the linkage disequilibrium (LD) which is of essential role in determining the haplotype length. In our study, based on three rice populations with different sizes and compositions, we investigated the usefulness and effectiveness of multi-environment training sets with varying phenotyping intensities and different haplotype-based genomic prediction models based on LD-derived haplotype blocks for two agronomic traits, i.e., days to heading (DTH) and plant height (PH). Results showed that phenotyping merely 30% records in multi-environment training set is able to provide a comparable prediction accuracy to high phenotyping intensities; the local epistatic effects are much likely existent in DTH; dividing the LD-derived haplotype blocks into small segments with two or three single nucleotide polymorphisms (SNPs) helps to maintain the predictive ability of haplotype-based models in large populations; modelling the covariances between environments improves genomic prediction accuracy. Our study provides means to improve the efficiency of multi-environment genomic selection in rice.The online version contains supplementary material available at 10.1186/s12284-023-00643-2. Rice is a staple crop feeding more than half of the human population  the potential of using genomic prediction enhanced sparse phenotyping to establish a cost-efficient multi-environment training set, and 2) whether using the haplotype-based genomic prediction models based on LD-derived haplotype blocks could improve the prediction accuracy relative to the marker-based approaches in rice.The first population is from Spindel et al.  includinThe second population comes from Liang et al.  incorporIndica lines. These RILs were phenotypically evaluated for DTH and PH in DS and WS of year 2014 in International Rice Research Institute (IRRI) and two locations in China, namely Jiangxi (JX) and Shenzhen (SZ), in year 2016. The combination of season/location and year was regarded as the environment, thereupon phenotypic data of four environments was available for the 1048 RILs. These RILs were fingerprinted by a customized rice 55 K SNP array  of genetic effects of all lines. Specifically, in the first stage, the spatial adjustment of field trial data was conducted in each environment by fitting the model: To infer the haplotypes, the genotypic data after quality control was specifically imputed and phased using SHAPEIT software  and haplotypes (haplotype-based model). Following variables were identically defined in all models: n et al. . In markThe first model considers no genotype-by-environment interaction formulated asThe second model explicitly portrays genotype-by-environment interaction asth environment. We assumed th environment, th environment. In our study, we specified t = 1 (one latent factor) in all the three populations.The third model is the factorial analytic (FA) model  with 5 folds in Burgueño et al.  to asses with 5 fThe 5-fold cross-validation was repeated 10 times, yielding 50 divisions of training and test sets. Finally, there were totally five hundred times (5 × 10 × 10) calibrations and predictions for each training set missing rate (10–90%). Considering the test set size in the cross-validations is just 1/5 of the size of total population, we combined the environment-specific genomic predicted genetic values of the lines in the five test sets per repeat of cross-validation and used the Pearson correlation coefficient between the combined genomic predicted genetic values and corresponding environment-specific BLUEs to assess the genomic prediction accuracy. To statistically compare the prediction accuracies of different genomic prediction models, we firstly transformed the accuracies (correlations) using Fisher's z transformation i.e., z = 0.5 × ln((1 + r)/(1 − r)) where r is the correlation. The Student’s t-test was used to test the difference based on the transformed correlations.The repeatability estimates of DTH in each environment of both the first and the second populations were all higher than 0.9  higher than those of marker-based model disregarding the trait and phenotyping intensity of training set. There was no benefit of segmenting the haplotype blocks into small fragments with two or three SNPs despite the VG and VGR models using small haplotype blocks occasionally outperformed their marker-based counterparts in PH  superior to the marker-based models in DTH except for a high phenotypic missing rate in the training set. As compared, the image in PH was conversed that the haplotype-based methods were basically inferior to the marker-based model especially for the FA model  held the superiority of using environmental covariates to depict environments and their relationships.Modelling the covariances between environments in multi-environment genomic prediction was overall advantageous in our study Fig.  found usn et al.  comparedn et al.  revealedn et al.  in a japWang et al.  proposedThe multi-environment genomic prediction can help to discover the elite rice varieties resilient to diverse environments or particularly suited to a specific environment. To improve the efficiency of multi-environment genomic prediction, sparse phenotyping can be used to establish a multi-environment training set. We demonstrated that a 30% phenotyping intensity in the multi-environment training set is sufficient to provide a comparable prediction accuracy to high phenotyping intensities for traits like PH and DTH. Basing on LD to identify haplotype blocks and accordingly making haplotypes for genomic prediction could capture local epistatic effects more reasonably compared to a fixed length of haplotypes. We demonstrated that the haplotype-based models are worth to be implemented in the prediction of DTH and segmenting the haplotype blocks into small fragments with two or three SNPs could maintain the predictive ability of haplotype-based models in large populations. Modelling the covariances between environments improves multi-environment genomic prediction accuracy irrespective of capitalizing on marker genotypes or haplotypes.Additional file 1: Table S1. Heritabilitiesand repeatabilities in each environment for days to headingand plant heightin the three rice populations.Additional file 2: Fig. S1. Distribution of best linear unbased estimatesof genetic values of lines fordays to headingandplant heightin each environmentof the first population. The title of each barplot indicates the environment. Fig. S2. Distribution of best linear unbased estimatesof genetic values of lines fordays to headingandplant heightin each environmentof the second population. The title of each barplot indicates the environment. Fig. S3. Distribution of best linear unbased estimatesof genetic values of lines fordays to headingandplant heightin each environmentof the third population. The title of each barplot indicates the environment. Fig. S4. Genetic diversity of thefirst,second, andthird populations described by Euclidean distance between lines based on SNP genotypic scores. The average clustering method was used to order the lines.Additional file 3: Table S2. Genomic prediction accuraciesof days to headingand plant heightin the three rice populations using three prediction modelswith different missing rates of environment-specific best linear unbiased estimatesof genetic effects of lines in the training set. The genetic effect was respectively described by SNP genotypes, haplotype using complete blocks, and short haplotypes containing two SNPsand three SNPswithin a haplotype block. The missing rate zeroindicates the training set holds complete environment-specific BLUEs. The asterisk and pound sign indicate the prediction accuracies of haplotype-based approaches were statistically significantlyhigher and lower than those of the marker-based approach.


Spatial working memory (SWM) is essential for daily life and deficits in this domain represent a common impairment across aging and several mental disorders. Impaired SWM has been closely linked to dysregulations in dorsolateral prefrontal cortex (DLPFC) activation.The present study evaluates the feasibility and maintenance of functional near-infrared spectroscopy neurofeedback (fNIRS-NF) training of the DLPFC to enhance SWM in healthy individuals using a real-time fNIRS-NF platform developed by the authors.We used a randomized sham-controlled between-subject fNIRS-NF design with 60 healthy subjects as a sample. Training-induced changes in the DLPFC, SWM, and attention performance served as primary outcomes.Feedback from the target channel significantly increased regional-specific DLPFC activation over the fNIRS-NF training compared to sham NF. A significant group difference in NF-induced frontoparietal connectivity was observed. Compared to the control group, the experimental group demonstrated significantly improved SWM and attention performance that were maintained for 1 week. Furthermore, a mediation analysis demonstrated that increased DLPFC activation mediated the effects of fNIRS-NF treatment on better SWM performance.The present results demonstrated that successful self-regulation of DLPFC activation may represent a long-lasting intervention to improve human SWM and has the potential for further applications. The WM refers to a limited functional system that can hold, update, and monitor a strictly limited amount of verbal and visuospatial information.,–It has been advocated that the active maintenance of location-specific representations in SWM is mediated by focal shifts of selective attention to the memorized location, and if focused attention is forced to orient away from the memorized location, WM accuracy will decline.,,,–Neurofeedback (NF) techniques have been a promising non-invasive means to improve cognitive control by regulating associated neural activity.,,–,,SWM and attention processes are known to be subserved by overlapping neural substrates, including lateral prefrontal regions together with parietal regions.22.1Sample sizes for the main analytic approaches were calculated using G*Power version 3.1, based on the recent study suggesting that if the goal of a NF study is to show that individuals can improve their behavior compared to baseline, effect sizes may be large.2.2,Participants were scheduled for six experimental sessions . The experimental group received real-time feedback from fNIRS channel 9 located over the right DLPFC, whereas the control group received feedback from one random participant who had previously undergone the experimental training (yoke feedback). The HR signals were assessed using an fNIRS System  at a sampling rate of 10 Hz. Locations of the optodes and feedback channel are displayed in ,,In line with previous fNIRS-NF studies, the feedback was based on the oxy-Hb signal.The online feedback was visually displayed to the participants in a similar way to a time series see . The hei2.4Participants were informed that the purpose of the fNIRS-NF training was to examine whether they could learn to upregulate their brain activity. Given that explicit strategy instruction is not necessary for successful NF-assisted acquisition of neural regulation,2.5To assess whether fNIRS-NF of the right DLPFC leads to generalized improvements in cognitive control, participants were administered the SWM task and the D-CAT.During the SWM task, participants were presented with sequences of visuospatial stimuli of varying lengths and repeated sequences of stimuli in the same (forward) or reversed (backward) order. The stimuli consisted of squares appearing at 25 different locations and were pseudo-randomized into 24 sequences. The sequence length varied between 3 and 10 stimuli and 3 sequences of each length in both the forward and backward tasks. Thus, the forward or backward task has 24 trials each in a single run, with each run lasting The D-CAT sheet consisted of 12 rows of 50 digits, each containing 5 sets of the numbers 0 to 9 arranged in random order. Participants were instructed to search for target number(s)  with a slash mark as quickly and as accurately as possible. During the second and third trials, it was stressed that all of the target numbers should be canceled without omission.2.6,The fNIRS raw data were preprocessed and analyzed using the NIRS toolbox in statistical parametric mapping (SPM)For the group-level analyses, beta estimates were obtained for each participant and channel. The primary outcome to determine the training success on the neural level was oxy-Hb change over the training runs in the target channel . To further control the unspecific effects of training or effects of mental effort on DLPFC activity, individual-level beta values from the target channel were subjected to group-level activation analyses comparing the experimental and the control group. Differences were considered significant using a channel-level threshold of 2.7Training-induced oxy-Hb changes in the DLPFC target channel served as the primary outcome to evaluate the training success on the neural level. First, channel-specific activity beta estimates were employed as dependent variables, and the effects of training were initially determined by employing mixed ANOVA models, including the between-subject factor Group  and the within-subject factor training Time (run2/run3/run4/run5/run6). Significant effects were explored by employing appropriate Bonferroni-corrected post hoc tests. Then channel-specific activity beta estimates were employed as dependent variables again, and the effects of training were further determined by employing one-way ANOVA models in each group including the within-subject factor training Time (run1/run2/run3/run4/run5/run6). Significant effects were also explored by employing appropriate Bonferroni-corrected post hoc tests.The total number of correctly recalled location sequences in the SWM task was employed as dependent variables, and the effects of training on the behavioral level were determined by employing mixed ANOVA models, including the between-subject factor Group  and the within-subject factor time (baseline/post/day2/day3/week1). Significant effects were further explored by employing appropriate Bonferroni-corrected post hoc tests. The total performance in the D-CAT task was used as the dependent variable in mixed ANOVA models which included a between-subject factor of group  and a within-subject factor of time (baseline versus post). These mixed ANOVAs were performed to test the effects of training on the attention performance. Associations between neural and behavioral training success were explored by employing analyzing correlations between training-induced DLPFC oxy-Hb changes and behavioral indices in the experimental group. Statistical analyses were carried out using SPSS version 22.0 .2.8After the training, all participants were asked to report the strategies they employed during the training. The reported strategies were qualitatively assessed by five independent raters . To control for different strategies between the training groups, the frequencies of the reported regulation strategies were compared between the experimental group and the control group using the Pearson 33.1While 55 out of the 60 participants completed the whole experiment procedure, initial examination of the primary neural outcome (oxy-Hb) and behavioral outcome  data quality identified 3 participants as outliers . Consequently, data from these participants were excluded from all analyses resulting in a total of 3.2The demographic information and psychopathological load of all participants were reported in 3.3Examining effects on the total number of correctly recalled locations in the forward tasks using mixed two-way ANOVA with the factors group  and time (baseline/post/day2/day3/week1) revealed a main effect of time  and Time (baseline versus post) revealed the main effect of time (3.4A mixed two-way ANOVA with the factors time (run2/run3/run4/run5/run6) and group  and the dependent variable DLPFC activity as measured by the beta values (oxy-Hb) from the target channel revealed a main effect of group  and the SWM performance changes.This model reflects a causal sequence in which In summary, these findings were consistent with other reports of training-driven modulations in prefrontal cortical activity, supporting training-induced neuroplasticity as the mechanistic basis of these behavioral training effects.3.6These improved SWM and attention performance results suggested that a common, underlying mechanism of cognitive control was enhanced by fNIRS-NF. To assess this further, we examined the regional specificity of the fNIRS-NF training by quantifying the effect of training on all PFC channels. A mixed ANOVA with the factors Time (run2/run3/run4/run5/run6), group , channel (channels 1 to 22), and the dependent variable brain activity as measured by the oxy-Hb beta values revealed a main effect of group (A one-way ANOVA with the factors time (run1/run2/run3/run4/run5/run6) and the dependent variable DLPFC activity as measured by the beta values (oxy-Hb) in the experimental group revealed a main effect of time in channels 9 and 13 , group , and channel (channels 1 to 24) and the dependent variable brain activity as measured by the oxy-Hb beta values revealed a main effect of channel  and parietal cortex  were calculated, and the Pearson’s correlation  and an adjacent channel (channel 13) suggesting that the training produced regional-specific increases in right DLPFC activation. Furthermore, the exploratory analysis revealed a significant group difference in NF-induced frontoparietal connectivity suggesting that the training may induce changes in the communication between core nodes of the SWM network. On the behavioral level, enhanced SWM performance in the forward task and improved attention performance in the D-CAT following the training of the fNIRS-NF were observed, importantly, the experimental group also demonstrated better SWM performance over the follow-up period for 2 days and 1 week. Moreover, exploratory analyses revealed that better SWM performance was associated with stronger training-induced increases in right DLPFC activity and increased DLPFC activity mediated the effects of fNIRS-NF treatment on better SWM performance providing evidence that the training-induced neural training effects critically contributed to the cognitive enhancement following training.–,Comparing the experimental group with the control group demonstrated that fNIRS-NF allowed participants to acquire regulatory control over regional-specific activation in the right DLPFC. Examining the activation changes within the groups further documented that right DLPFC activity significantly increased over the six training runs in the experimental but not in the control group. Consistent with but extending past research,–The DLPFC critically contributes to WM, with this region being considered to support the strategic control of working memory processing.,The present findings need to be interpreted in the context of limitations. First, the present study was conducted with a healthy and sex-biased sample. The potential sex differences and generalization of the present results to patient populations need to be examined in future studies. Second, despite some evidence for the effects of fNIRS-NF training of right DLPFC on SWM performance, the between-group comparisons of the indices of the backward task failed to reach statistical significance. The lack of robust effects in this domain may be explained in terms of forward and backward recall calling upon qualitatively distinct retrieval processes.In summary, the present findings demonstrated that real-time fNIRS-NF training allows healthy individuals to gain control over DLPFC activity and that this leads to improved SWM performance, which is maintained for at least 1 week after the training and in the absence of further online feedback. Both the behavioral and neural results of this study support the hypothesis that the right DLPFC plays a crucial role in SWM performance and may specifically support attentional control. We provide promising results with the therapeutic potential of fNIRS-NF to target SWM and its underlying neural basis, which are impaired in both aging populations and a range of psychiatric disorders.Click here for additional data file.


Objective. Diffusion-weighted MR imaging (DW-MRI) is known to quantify muscle fiber directionality and thus may be useful for radiotherapy target definition in sarcomas. Here, we investigate the variability of tissue anisotropy derived from diffusion tensor (DT) in the human thigh to establish the baseline parameters and protocols for DW-MRI acquisition for future studies in sarcoma patients. Approach. We recruited ten healthy volunteers to acquire diffusion-weighted MR images of the left and right thigh. DW-MRI data were used to reconstruct DT eigenvectors within each individual thigh muscle. Deviations of the principal eigenvector from its mean were calculated for different experimental conditions. Main results. Within the majority of muscles in most subjects, the mode of the histogram of the angular deviation of the principal eigenvector of the water DT from its muscle-averaged value did not exceed 20°. On average for all subjects, the mode ranged from 15° to 24°. Deviations much larger than 20° were observed in muscles far from the RF coil, including cases with significant amounts of subcutaneous fat and muscle deformation under its own weight. Significance. Our study is a robust characterization of angular deviations of muscle fiber directionality in the thigh as determined by DW-MRI. We show that an appropriate choice of experimental conditions reduces the variability of the observed directionality. Precise determination of tissue directionality will enable reproducible models of microscopic tumor spread, with future application in defining the clinical target volume for soft tissue sarcoma. It is widely believed that it would be desirable to replace ionizing radiation-based CT planning scans with MRI. In addition, more accurate radiotherapy target delineation with MRI guidance may lead to improved treatment outcomes in a variety of cancers  that encode directions of water diffusion as eigenvectors of the tensor. In muscle, the largest eigenvalue corresponds to the direction along the fibers  participated in this study, which was approved by the Institutional Review Board of Massachusetts General Hospital. Written informed consent was obtained from each subject. Volunteers were scanned feet first using a 3T MRI system  and an 18-channel torso phased array coil covering the left and right thighs. The imaging protocol consisted of (a) two high-resolution anatomical  scans, T1- and T2-weighted; (b) a diffusion-weighted (DW) spin-echo-based scan using an echo planar (EP) acquisition with fat suppression. Anatomical and diffusion-weighted MRI scans were acquired in the axial plane.b0 images with b0 = 50 s mm−2 and twelve diffusion-weighted images with b = 400 s mm−2 using 12 gradient directions. Spectral adiabatic inversion recovery (SPAIR) was used to suppress the fat signal. Twelve independent acquisitions were performed for each b = 400 s mm−2 diffusion gradient direction. The images without diffusion-weighting were independently acquired two times. T1- and T2-weighted acquisitions were used to match the anatomical location of the muscles in DW images.The DW-MRI acquisition consisted of two 3 as most of the images were acquired with this planar resolution. For each acquired DW-MRI scan, the DT was reconstructed from 12 diffusion-encoded gradient pulses using the imaging Python library DIPY  and fiber characteristics calculated from tractography  to assess feasibility of longitudinal studies of varying muscle conditions (Froeling et alet alInaccurate estimation of the principal eigenvector may be caused by chemical shift artifacts in fat infiltrated muscle tissue within voxels containing both muscle fiber and fat. This is expected to be a problem in elderly patients and patients with muscular dystrophy. In addition, special consideration should be given to STS subtypes derived from adipose tissue, such as myxoid liposarcoma. Although it is accepted that SPAIR and STIR fat suppression techniques are optimal for the best performance of DW-MRI sequences (Guirguis et alet alSubject motion can cause image artifacts and associated errors in DT reconstruction. The echo-planar imaging acquisition mode typical of DW-MRI allows acquisition of slices in less than 100 ms and is therefore less sensitive to bulk motion. To further reduce motion artifacts, faster acquisition modes (Wang This study demonstrates that the spatial variability of the principal eigenvector in DW-MRI can be reduced by an appropriate choice of experimental conditions. We show that the variability of the observed directionality can be reduced by positioning the patient with the leg immobilized and the coil covering the entire thigh. This in turn leads to highly reproducible models of microscopic tumor spread. Future applications include the definition of clinical target volumes for soft tissue sarcomas.


The study evaluates the impact of the time between commencing non-invasive ventilation (NIV) support and initiation of venovenous extracorporeal membrane oxygenation (VV-ECMO) in a cohort of critically ill patients with coronavirus disease 2019 (COVID-19) associated acute respiratory distress syndrome (ARDS).Prospective observational study design in an intensive Care Unit (ICU) of a tertiary hospital in Barcelona (Spain). All patients requiring VV-ECMO support due to COVID-19 associated ARDS between March 2020 and January 2022 were analysed. Survival outcome was determined at 90 days after VV-ECMO initiation. Demographic data, comorbidities at ICU admission, RESP  score, antiviral and immunomodulatory treatments received, inflammatory biomarkers, the need for vasopressors, the thromboprophylaxis regimen received, and respiratory parameters including the length of intubation previous to ECMO and the length of each NIV support , were also collated in order to assess risk factors for day-90 mortality. The effect of the time lapse between NIV support and VV-ECMO on survival was evaluated using logistic regression and adjusting the association with all factors that were significant in the univariate analysis.p = 0.02), lactate (p = 0.001), and days from initiation of NIV support to starting VV-ECMO (p = 0.04) were all associated with day-90 mortality.Seventy-two patients finally received VV-ECMO support. At 90 days after commencing VV-ECMO 35 patients (48%) had died and 37 patients (52%) were alive. Multivariable analysis showed that at VV-ECMO initiation, age  appeared to be a better predictor of mortality than the time between intubation and VV-ECMO. This haOur hypothesis is that the time lapse between NIV initiation and VV-ECMO is as important as the time lapse between ETI and VV-ECMO when considering initiating a patient on VV-ECMO due to hypoxemic ARF. To demonstrate this hypothesis, we studied all patients requiring VV-ECMO during the COVID-19 pandemics and we evaluated all mortality risk factors with special consideration to all those factors related to the use of NIV previous to VV-ECMO. The aim in this single centre study was to examine the relationship between the use of NIV and mortality in VV-ECMO patients during the COVID-19 pandemic and whether this could be correlated with the time spent on NIV rather than to the use of NIV, per se.We collected data prospectively from our intensive care unit (ICU) of our university hospital  in Spain. We included all adults with ARDS (according to Berlin definition)  who requAll demographic data, relevant comorbidities, and Sequential Organ Failure Assessment (SOFA) score at ICU admission were collected. SOFA and RESP  scores were noted on initiation of VV-ECMO. Antiviral and immunomodulatory treatments received, the need for vasopressors (yes/no), the thromboprophylaxis regimen received, and respiratory parameters including the length of each ventilation support were also collected. Criteria for ETI and VV-ECMO initiation followed the recommendations from the Spanish critical care society (SEMICYUC)  and ELSOFrom March 2020 to January 2022, 519 subjects with SARS-Cov-2 and ARDS criteria were admitted to the ICU. 429 patients (83%) had received some modality of NIV before ICU admission with a median (IQR) time of 1 (0–3) days. Within the first day from ICU admission 318 patients (61%) had been intubated but the other 201 (39%) subjects continued on NIV support from which 118 (59%) were finally intubated. Among these ARDS patients, for the purpose of our study we only evaluated the 72 subjects (14%) that were finally initiated on VV-ECMO  time from intubation to VV-ECMO was 4 (2-9) days whereas the complete median time spent from NIV support to ECMO  was 9 5-15) days. Table 5 days. TInterestingly, RESP score and D-dimer at ECMO initiation were higher among survivors whereas lactate was lower. Times from hospital to VV-ECMO, ICU to VV-ECMO, or intubation to VV-ECMO were all shorter in those patients who survived to day 90 although none of them were statistically significant. Multivariable analysis Table  showed tTo avoid collinearity between days NIV-ECMO and days ETI-ECMO, a different multivariable logistic regression model was performed for the time lapse from ETI to ECMO initiation and 90-day mortality Table . This neA recent large multicenter study showed an increased mortality in ECMO patients with COVID-19 associated ARDS who had received NIV previous to intubation . In the In our cohort of VV-ECMO patients with COVID-19 associated ARDS, the time lapse between NIV support and commencing VV-ECMO (together with age and lactate) seems a better predictor when evaluating survival than the time between intubation and VV-ECMO. Age and lactate are clearly related to mortality when VV-ECMO is initiated as in the majority of critically ill patients who present any kind of organ dysfunction , 21. In Although in our small cohort of patients the time lapse from ETI to VV-ECMO was not significant in terms of mortality it is one of the most employed criteria when evaluating the mortality risk of the technique as those patients initiated more than 7 days after ETI have proved to have worse outcomes than those initiated within the first week. This is probably related to a more advanced stage of ARDS in those patients with a delayed initiation of VV-ECMO when compared to those with an early initiation of VV-ECMO. However, this phenomenon is common to other forms of mechanical organ support in critically ill patients such as renal replacement therapy or invasive mechanical ventilation, where a delayed initiation strategy is associated with a worse outcome when organ support is finally required, but also with a much better outcome when patients finally do not require organ support .The -timing of the intervention- issue has been also explored in ECMO patients for prone positioning . Even inAn important confounding variable in the assessment of time from NIV initiation to VV-ECMO is the duration of invasive ventilation that somehow makes our hypothesis seem overly simplistic. This is an important limitation of our study design. In our study, no association was found between the use of NIV previous to VV-ECMO and mortality, although the small size of the population is another important limitation. The variables selected for the multivariable logistic regression model were based on the statistical significance and not on a clinically meaningful method due to the limited number of events (35 deaths). This should be considered also as a limitation of our study. On the other hand, the lack of information on the pressures applied to these patients and the absence of cross-sectional protocols does not allow the identification of patients who have been treated with high parameters perhaps comparable to those of intubated patients. It seems reasonable that patients with severe stages of ARDS may require higher pressures of NIV (CPAP or BIPAP) that with HFNC cannot be achieved. Some trials performed during the COVID pandemics have reported results in this direction revealing that higher pressures with NIV could be associated with lower rates of ETI or even mortality , 31. A cWe suggest, based on our findings, that the time spent between NIV support and initiation of VV-ECMO should be considered and evaluated as an interesting clinical parameter to be included in the mortality prediction scores for VV-ECMO although further and larger studies are needed to confirm our findings .


Nature Communications 10.1038/s41467-023-37096-6, published online 13 March 2023Retraction to: RETRACTION NOTEThe Editors retracted this article because of concerns regarding a number of figures presented in this work. These concerns call into question the integrity of the data and the article’s overall scientific soundness. An investigation conducted after its publication discovered several instances of image overlap and identified irregularities in the source data provided by the authors. The Editors therefore no longer have confidence in the integrity of the data in this article.All authors agree to this retraction.


Infectious diseases represent one of the major public health concerns on the global level. The emergence and re-emergence of different RNA viruses  remain a major concern for public health control worldwide ,4. UnderTen original research papers are published in this Special Issue related to SARS-CoV-2, each looking at different aspects. Five studies discuss the relationship between antibody kinetic profiles and protection. These studies provide important information about whether all infected individuals will have the same course of humoral response maturation or evolve with a unique binding and neutralizing capacity which is individual specific. We might be able to use a mathematical model to predict the protection time post-infection according to antibody binding kinetics.Currently, the detection of viral RNA by qRT-PCR remains the gold standard for the acute phase of infection. Can we use antibody detection as a diagnostic alternative? To address this, two studies in this Special Issue provide additional insights into improving the sensitivity and specificity of a diagnostic system for SARS-CoV-2 infections using either conserved full-length (N protein) or fragmented (RBD) antigens as the early serology detection system. However, more validation from new SARS-CoV-2/RNA-virus-infected cohorts will be important to ascertain the robustness of the identified “immune signature” for pathogen-specific identification. With the availability of better diagnostic alternatives, clinicians can then provide a well-informed disease intervention program for patients.More importantly, this Special Issue contains four studies and two reviews looking at the treatment strategies for managing COVID-19 patients, particularly in preventing severe clinical outcomes post-infection. We might learn lessons from other infectious diseases (HIV and RSV) or validate different antiviral treatment options in order to improve our understanding of virus infection mechanisms and disease severity development.RNA viruses apart from SARS-CoV-2 (influenza and CHIKV) remain a major concern for public health control globally and this Special Issue contains three studies that provide additional antibody knowledge in order to expand the development of better prophylactic and therapeutic strategies. We would like to thank all authors for publishing their high-quality research and reviews in this Special Issue. It has been a great pleasure working with all the talented researchers around the world. This Special Issue will serve as a platform to further improve the current knowledge of antibody responses against RNA virus infections. Hopefully, this platform will continue to facilitate the design and development of prophylactic and therapeutic strategies in preparation for future disease outbreaks.


Self-Serving leadership is a global phenomenon and requires both literary and practical attention to understand how it unfolds and impacts organizations. More specifically the investigation of this underexplored dark side of leadership in Pakistani service sector organizations has its unique significance. So, in this regard, the current study took the initiative to investigate the relationship between a Leader's self-serving behaviour and a follower's self-serving counterproductive work behaviour. Moreover, the underlying mechanism of self-serving cognitive distortions was proposed, with followers' Machivellianism strengthening the indirect relationship between leaders' self-serving behaviour with self-serving counterproductive work behaviour through the self-serving cognitive distortions. The proposed theoretical framework was explained by the Social Learning theory. This study adopted a survey method with the collection of data by utilizing the convenience sampling method, in three-time waves with peer-reported self-serving counterproductive work behaviours. The data was analyzed by utilizing confirmatory factor analysis to establish discriminant and convergent validity. Moreover, the hypotheses testing was done utilizing Hayes Process Macro 4 (Mediation) and 7(Moderated Mediation). The results proved that the self-serving cognitive distortions mediated the relationship between the leader's self-serving behaviour and the follower's self-serving counterproductive work behaviours. Moreover, it was established that the High Mach tendencies strengthed the indirect positive relationship between a leader's self-serving behaviour with self-serving counterproductive work behaviour through the self-serving cognitive distortions. It is important to note that the current study provides a view to the practitioners that formulation of effective policies and systems for identifying and discouraging the tendencies of Leaders' self-serving behaviour and employing people with low Mach Tendencies can avoid the self-serving counterproductive work behaviours harming the overall welfare of the organization. This neDark leadership behaviours like self-serving leadership are more prevalent in Asian countries and there is a clear need to study such underexplored areas of research in this region . AlthougIn response to a self-serving leader, the followers are reported to indulge in negative behaviours, like increased organizational deviance, and interpersonal deviance , a high The recent literature on self-serving leader behaviour answers the question of “why” employees indulge in negative work behaviours like organizational deviance and interpersonal deviance by investigating organizational identification as an underlying mechanism . HoweverThe existing literature reports different boundary conditions like organizational budget policy, ethical climate, employee perceptions of distributive justice, employee power distance orientation, and justice sensitivity play a contingent role in the relationship between self-serving leader behaviour and resultant different employee behaviours . Recentl22.1The link between self-serving leadership and self-serving counterproductive work behaviours through self-serving cognitions is based on the social learning theory. According to this theory, the follower learns and acquires self-serving behaviour by observing such behaviour in their leaders . This thIt is essential to mention that a significant tenet of social learning theory is that the existence and role of cognitions result in differences in behaviour within the same environment . It mean2.2Leader's self-serving behaviours generally impair the helping behaviour of employees  and boosH1Leader self-serving Behaviour has a positive relationship between self-serving and counterproductive work behaviour.Leader self-serving behaviour has been investigated with moral disengagement  but thisThe Social Learning Theory  conciselH2Leader self-serving behaviour has a positive relationship with self-serving cognitionsCognitive distortions are inaccurate ways of giving meaning to different life happenings . MoreoveSocial learning theory  providesH3Self Serving Cognitions have a positive relationship with self-serving counterproductive work behaviour9. But, the study takes a novel approach and proposes that self-serving cognitive distortions serve as an underlying mechanism between a self-serving leader and self-serving counterproductive work behaviours. Even, the literature supports this assertion as it evidences that both the oppressor and his or her victims have a high level of self-serving cognitive distortions, which in turn rationalize the externalization of aggressive and anti-social behaviour .The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


A comprehensive insight into the effects of subtalar- and mid-tarsal joint osteoarthritis on lower limb’s biomechanical characteristics during walking is lacking. Our goal was to assess joint kinematics and kinetics and compensatory mechanisms in patients with subtalar and mid-tarsal joint osteoarthritis.n = 10) and an asymptomatic control group (n = 10) were compared. Foot joint kinematics and kinetics during the stance phase of walking were quantified using a four-segment foot model.Patients with symptomatic and radiographically confirmed osteoarthritis of the subtalar and mid-tarsal (P = 0.001), whereas the tarso-metatarsal joint range of motion in the sagittal plane was greater in the pre-swing phase (P = 0.003). The mid-tarsal joint showed lower transverse plane range of motion in the patient group during the loading response and pre-swing phase (P < 0.001 resp. P = 0.002). The patient group showed a lower Tibio-talar joint peak plantarflexion moment (P = 0.004), peak plantarflexion velocity (P < 0.001) and peak power generation in the sagittal plane (P < 0.001), and a lower mid-tarsal joint peak adduction and abduction velocity (P < 0.001 resp. P < 0.001) and peak power absorption (P < 0.001).During pre-swing phase, the tibio-talar range of motion in the sagittal plane of the patient group decreased significantly (These findings suggest that patients with subtalar and mid-tarsal joint osteoarthritis adopt a cautious walking strategy potentially dictated by pain, muscle weakness, kinesiophobia and stiffness. Since this poorly responding population faces surgical intervention on the short term, we recommend careful follow-up after fusion surgery since biomechanical outcome measures associated to this post-surgical stage is lacking.The online version contains supplementary material available at 10.1186/s13047-023-00689-x. Osteoarthritis (OA) of the foot leads to chronic pain, is associated with walking difficulties and has functional implications with reduced quality of life . DespiteJoint degeneration may occur at the STJ or MTJ in an isolated way, however, the involvement of both joints has been reported in various patient groups . Next toInvestigating the kinetic behavior of the foot and ankle complex during walking encompasses several notable benefits for patients with STJ and MTJ osteoarthritis, such as individual management of conservative treatment options, general monitoring of foot function and presurgical planning. In addition, these multi-segment kinetic foot models might allow us to investigate the coping strategies of other foot joints.The aim of this study was to develop new insights in the mechanical function of the ankle and foot joints in patients with STJ and MTJ osteoarthritis. First, we hypothesized that patients with STJ and MTJ osteoarthritis would demonstrate lower range of motion and peak power joint kinetics in the tibio-talar and mid-tarsal joint compared to a healthy group. The second hypothesis was that adaptive biomechanical patterns would occur in the tarso-metatarsal and first metatarsophalangeal (MTP1) joint. Finally, we hypothesized that the patient group will report poor to moderate physical function and quality of life.An observational study was carried out at the University Hospital Leuven following ethical approval of the local ethical committee and all participants provided informed consent (ML9038).Between 2015 and 2022, 76 patients consulting our university hospital with symptomatic and radiographically confirmed arthritis of STJ and MTJ were invited to participate in the clinical motion analysis laboratory to collect a prospective database. All selected patients had a lack of satisfactory symptomatic response regarding conservative treatment, including foot orthoses, shoe modification and physiotherapy treatment . Since considerable heterogeneity existed within this population with respect to the medical background of the foot pathology, a further selection of patients was considered based on the following inclusion criteria: i) diagnosis of unilateral talonavicular, calcaneocuboid and subtalar osteoarthritis (at least a grade 2 Kellgren-Lawrence osteoarthritis score confirmed by two senior orthopedic surgeons) , ii) theReference data serving as the control group in the current study was selected from an existing database of asymptomatic male adults. Selection of the specific control data was based on the age and BMI of the patient data.Clinical and radiographic data were extracted from the patient electronic medical record. Standard weightbearing plain radiographs of the ankle and foot were used. Imaging was performed at the first consultation and at maximum 3 months before their gait analysis. Concerning the ankle, an anteroposterior, a mediolateral orientation and a mortise view were used. Concerning the foot, an anteroposterior, a mediolateral and a ¾ internal rotation view. Two senior foot surgeons evaluated the radiographs and a consensus meeting was organized regarding the scoring of the different joints on the Kellgren-Lawrence scale.2; RSscan International, Paal, Belgium) were an integral part of the walkway. Both plates were dimensionally matched and were built into the floor. An RSscan 3D synchronization box was used to synchronize and calibrate the plantar pressure and force data.Gait analysis was performed in the institutional’s Clinical Movement Analysis Laboratory. Data were obtained when the participants walked along 10-m walkway, surrounded by an optoelectronic motion capture consisting of 10 T-10 cameras . A force plate  and a superimposed pressure plate  to the participants’ feet and shanks following the marker placement protocol of the Instituto Ortopedico Rizzoli (IOR) Foot Model . SubsequData processing incorporated manual marker labelling and definition of the individual gait cycles using Nexus software . Following this post-processing routine, the IOR-4segment-model-1 described by Deschamps et al.  was applJoint centers were respectively defined as the midpoint between both malleoli , the midpoint between the navicular and cuboid bone , the second metatarsal base , and the projection of the MTP1 marker halfway to the floor (MTP1). Subsequently, ground reaction forces and moments (captured by the force and pressure plate) were distributed over the different segments of the IOR-4segment-model-1 using a validated proportionality scheme . For eveRegarding the joint kinematics the following outcome variables were investigated: 1) range of motion (RoM), defined as the difference between the maximum and minimum value in a kinematic waveform, and calculated for three subphases of stance including loading response (0–20% stance phase), midstance and terminal stance together (21–83% stance phase) and pre-swing (84–100% stance phase) .Finally, peak internal joint moment, peak dorsiflexion and plantarflexion angular velocity and peak power generation and absorption (joint moment multiplied with angular velocity) at the different joints were quantified as kinetic outcome measures.All patients completed the Short-Form-36 (SF-36)  and an aP-value of < 0.01. Mean differences  and effect size (ES) were also calculated. The effect size for Cohen’s d value was calculated and interpreted as follows: d = 0.20 , d = 0.50 (medium effect), d = 0.80 (large effect), and d = 1.30 (very large effect) [IBM SPSS Statistics 20  was used to perform the statistical analyses. Data were assessed for normality with the Shapiro–Wilk test. An independent samples t-test  or a Mann Whitney U test  was used to compare the demographic parameters. One-way ANCOVA tests were computed to analyze group differences between the zero-dimensional parameters of the control and patient group. Walking speed was considered as a covariate since the control group had a significantly higher walking speed. To guard against inflation of type I error but maintain statistical power across the multiple comparisons made up on the multiple variables it was decided to adjust the conventional alpha level and adopt a  effect) . Trends The degree of kinematic coupling was evaluated by calculating the cross-correlation coefficient of the angular displacement curves of adjacent segments across the stance phase . Based o2. Etiology of the pathogeneses were variable, despite the stringent in- and exclusion criteria. There was a significant decrease in walking speed (P = 0.004) and step length (P = 0.001) between both groups.Baseline demographic and clinical characteristics can be found in Table Inverse dynamics calculation in the patient population could not be calculated for one symptomatic subject. Therefore, statistical analysis was performed on only nine participants for the kinetic parameters (dropout = 1). The covariate, walking speed, was significantly related to one kinematic variable and several kinetic variables . A trend towards lower sagittal plane RoM in the tibio-talar joint during the loading response phase was observed in the patient group . No other significant differences were observed in this subphase of the walking cycle. The mid-tarsal joint RoM of the patient group was significantly lower in comparison with the control group . In contrast, a significantly higher RoM was found in the frontal plane of the Calc-Met angle of the patient group . In the tarso-metatarsal joint, a lower RoM was seen in the sagittal plane in the patient group ).In this phase the patient group demonstrated a significantly lower RoM in the frontal plane of the tibio-talar joint . In contrast, the sagittal plane RoM of the tarso-metatarsal joint of the patient group was significantly higher compared to the control group . The transverse plane data showed a significantly lower mobility of the mid-tarsal joint in the patient group compared to the control group .In the last phase of stance, the mean sagittal plane RoM in the tibio-talar joint of the patient group was significantly lower compared to the control group  was found. Moreover, a trend towards lower coupling between the Sha-Cal Inv/Ev movement and the Cal-Met DF/PF was observed .A trend towards a lower of coupling between the Sha-Cal Inv/Ev movement and the Cal-Met Inv/Ev movement  in peak plantarflexion moment (ES = 0.44), peak plantarflexion velocity (ES = 0.60), peak power generation in the sagittal plane (ES = 0.57) and peak inversion velocity in the frontal plane (ES = 0.26), being all lower in the patient group (Table P < 0.05) in peak dorsiflexion velocity (ES = 0.26) and peak power absorption in the frontal plane (ES = 0.26), representing lower values in the patient group compared with the control group . The peak power absorption in the transverse plane was significantly lower in the patient group (ES = 0.55) while the peak power generation showed a trend being higher in the patient group in the same plane (ES = 0.25) Table .No significant differences were observed in the tarso-metatarsal joint and MTP1 joint kinetics adaptive kinematic and kinetic joint patterns. It is reasonable to assume that the reported debilitating pain, functional limitations and limited physical health may be principal drivers behind the observed foot biomechanics. Even though the applied multi-segment foot model is not able to differentiate the kinematic behavior of the subtalar and tibiotalar joint separately, some significant differences were captured in the currently reported data. Evidence for a lower sagittal plane RoM was observed during loading response and pre-swing combined with less frontal plane RoM during midstance and terminal stance. The former may originate from the joint structural damage but may also represent a more cautious weight acceptance  at initial contact whereas the lower sagittal plane RoM during pre-swing may originate from weakness of the calf muscles. Another plausible explanation for the difference in pre-swing RoM may be the adoption of a pull-off strategy (passive propulsion) instead of a push-off pattern (active propulsion). The lower frontal plane tibio-talar joint RoM during midstance and terminal stance explains why this patient population reports difficulties with walking on uneven terrain and highlights probably a critical loss of foot mobile adapter function . Also, tFrom the cross-correlation coefficients one can assume that the synchronous motion between the rearfoot and the forefoot is affected. The latter is an important finding since this may play a considerable role in the overall success rate of surgical interventions. The authors believe that restoring these coupling mechanisms is unrealistic in advanced stages of OA using the current available treatment methods, and that this should be discussed with the patient, e.g. prior to fusion surgery.The peak plantarflexion moment in the sagittal plane of the tibio-talar joint was significantly reduced. This could be explained by pain or stiffness at the hindfoot joints, atrophy of the calf muscles and a reduced walking speed leading to the pull-off strategy. Evidence for the adoption of the latter strategy is also provided by the lower peak plantarflexion velocity and the lower peak power generation in the tibio-talar joint.The trend towards an increased transverse plane peak power generation at the mid-tarsal joint reflects a foot that has lost its mobile adapter function and may on the other hand originate from concomitant ankle and hindfoot malalignment. Deleu et al. reported similar observations in patients suffering from post-traumatic ankle osteoarthritis .The FFI and the VAS score have shown that OA of the STJ and MTJ has a relevant impact on daily life, with pain progressively occurring towards the evening and most reported with speed walking and long-distance walking. It is therefore not surprising that the current findings reflect a pain-induced walking pattern. Similar findings have been reported in patients following triple arthrodesis in the hindfoot .We recommend close follow-up of these patients since they have poorly responded to conservative treatment and will therefore enter a care pathway involving foot surgery. This pathway typically comprises fusion surgery of the involved joints and it is well known that the primary goals of this surgery are to decrease pain and to improve overall function. To our knowledge, there is a lack of published research on the post-op rehabilitation of patients undergoing this surgery and it is believed that the current study provides valuable information which may guide future research in this domain. The main limitation in this study is the inability to provide measurements of independent tibiotalar and subtalar join motion since the current study used a traditional passive motion capture system with retroreflective skin-markers , 34. In Lastly, participants had to perform the walking trials throughout a barefoot condition. Therefore, the effect of footwear on the walking pattern cannot be estimated, hence, it is well-known that a shod condition is often more comfortable for these patients .Key findings suggest that the involved joints are characterized by a different range of motion and peak joint kinetics and that the distal foot joints do not present major adaptive patterns. We conclude that these patients adopt a pull-off strategy probably dictated by pain, muscle weakness and kinesiophobia.Additional file 1:Table S-3. Sagittal, frontal and transverse plane kinetics (mean and standard deviation) of the Lisfranc and first metatarsophalangeal joint.


Physical activity (PA) levels are significantly lower in women from ethnic minorities and deprived areas compared to the general population throughout Cardiff owing to barriers affecting participation . Given the far-ranging benefits of PA, there was a need to explore this relationship and promote opportunities for this population.The lead researchers were employees of Cardiff Met Sport (the city's sport development body) and had stakeholder good links to develop this project.Our work specifically relates to - Healthy Wales Healthy Weight - Welsh Government's long-term obesity strategy. - One of the 7 goals in the Well-being of Future Generations Act (2015) Wales .https://www.movemoreeatwell.co.uk). - The Cardiff and Vale University Health Board's Physical Activity and Health Strategy that aims to reduce inequality through health (The target audience for this work were: a) Walk/run leaders/physical activity providers and community groups that specifically support women from ethnic minorities (n = 10). Our focus group explored the how organisations promote diversity in their activities, and the barriers in increasing community partcipation in PA. We also talked about co-producing a walking/running pilot in the area and invited people to take part. The key themese were trust-building, culturally sensitive activities and how resource-deprivation affects women. b) Adult women from ethnic minorities (n = 18). We targeted populations living in South Cardiff, Wales, UK an area of deprivation. We ran 3 in-person focus groups to understand experiences of women, and the motivators and barriers to taking part in PA. Caring roles acted as a barrier and shortage of women-only spaces were highlighted. Women were active in community run activites and keen to be active.We shared knowledge amongst walk/run leaders via e-mail and will organise a webinar to outline our findings, but are still finalising anlysis of this phase. We're developing the work by running a pilot intervention in Spring 2023.Our participatory research has shown how to overcome the challenges of recruitment in this population. Our findings have implications for sport delivery patners  and more widely shows that PA promotion cannot be separated from racial/political issues.


Arabidopsis thaliana spaceflight experiments deposited in the National Aeronautics and Space Administration’s GeneLab data repository. These data were reanalyzed for genes showing significant differential expression in spaceflight versus ground controls using a single common computational pipeline for either the microarray or the RNA-seq datasets. Such a standardized approach to analysis should greatly increase the robustness of comparisons made between datasets. This analysis was coupled with extensive cross-referencing to a curated matrix of metadata associated with these experiments. Our study reveals that factors such as analysis type  or environmental and hardware conditions have important confounding effects on comparisons seeking to define plant reactions to spaceflight. The metadata matrix allows selection of studies with high similarity scores, i.e., that share multiple elements of experimental design, such as plant age or flight hardware. Comparisons between these studies then helps reduce the complexity in drawing conclusions arising from comparisons made between experiments with very different designs.Spaceflight presents a multifaceted environment for plants, combining the effects on growth of many stressors and factors including altered gravity, the influence of experiment hardware, and increased radiation exposure. To help understand the plant response to this complex suite of factors this study compared transcriptomic analysis of 15  By contrast, in the terrestrial environment, these are key factors driving normal growth and development. In addition, gravitational forces on Earth govern a host of physical processes including gas and liquid flow that are important for normal plant function. Thus, the microgravity environment can lead to the development of anoxic regions around metabolically active plant tissues and altered patterns of evaporative and convective cooling that can impact leaf function and physiology8. Additionally, the increased radiation exposure inherent in spaceflight is likely to trigger its own array of responses within the plant. The combination of these spaceflight-linked effects is outside the evolutionary history of terrestrial biology and so it remains complicated to predict the effects of spaceflight on organisms. Yet, understanding the molecular and physiological responses of plants to these conditions remains an important goal for space biologists, not the least because plants are integral to many plans for life support on long-duration crewed missions and for colonization9.Spaceflight imposes a unique suite of environmental effects on biology. For example, microgravity severely curtails the signals normally generated on Earth from the intrinsic weight of a plant’s organs11 has aggregated many such omics datasets. Critically, the deposited data are associated with extensive metadata covering elements of each experiment’s design ranging from features of the hardware, radiation exposure and lighting regime to treatment duration, genotype and organism age. Such extensive and accurate metadata are critical to understanding the breadth of differences in experimental designs when making comparisons between studies. This insight is important as the flight hardware used, the analysis methodology employed  and other experimental parameters likely superimpose their own, often poorly defined, influences on the results . Indeed, recent analysis of rodent spaceflight data suggests differences in sample preservation eclipsed spaceflight-driven differences in mouse transcriptional profiling13. However, given the relatively few opportunities to conduct experiments in space, making comparisons between existing studies represents a potentially powerful approach to identify common responses in the often-limited available spaceflight data.One way to probe the responses of organisms to spaceflight is by analysis of changes in their transcriptomes, proteomes, metabolomes, genomes and epigenomes induced by exposure to this environment. In the field of plant biology the National Aeronautics and Space Administration’s (NASA’s) GeneLab data repositoryCOLD RESPONSIVE 78 (COR78), and likely shifts in ion transport processes. We also identified factors within the experimental design such as choice of flight hardware and especially assay technique  that can impose greater differences between datasets than the spaceflight treatment. Thus, the Matrix allows researchers to explore the wealth of plant biology transcriptomic data generated during spaceflight-related studies and provides an approach to better understand underlying factors impacting the robustness of comparisons made between the different datasets.We have therefore imported 15 spaceflight-related plant transcriptome datasets from the GeneLab data repository and manually curated the associated metadata to develop a metadata matrix . This approach allows the more robust design of comparisons between studies that share commonalities in experimental design. Our meta-analyses broadly confirmed the spaceflight-related changes in cell wall processes and oxidative stress that were highlighted in many of the original publications associated with each individual study. Additionally, Matrix-driven analyses helped reveal new response elements, such as conserved spaceflight-triggered changes in expression of the cold response gene 14 that aggregates these analyses into an interactive data exploration environment. Such comparative studies capitalize upon the unique insights of the researchers who performed each experiment and the tailored analytical tools and approaches they then employed to define differentially expressed genes (DEGs) in their original publications. We will refer to these studies as in-house analyses. However, the wide range of analytical pipelines used in such a primary literature-based approach inevitably imposes some limitations on the robustness of any conclusions that can be drawn between studies. This problem arises because differences in gene expression patterns between datasets likely involve both the effects of experimental treatments, such as growing plants in spaceflight versus a ground control, and of elements specific to the different analytical programs and statistical approaches used to analyze the data. Indeed, differences between results from different software packages analyzing the same raw transcriptomics datasets are well-documented in the literature15. Therefore, a complementary methodology was also applied by reanalyzing the plant studies used in our analysis via the common computational pipelines summarized in Fig. 16. Although this analysis pipeline is different from the one we have adopted, the standardizing of analysis across all datasets for the EMBL-EBI gene Expression Atlas is designed with the same goal in mind: to reduce the potential for generating artifacts that are caused by making comparisons between datasets that have been the subject of different initial data analysis methodologies.One method to assess the similarities and differences in transcriptome-level responses between different plant spaceflight experiments is to make comparisons using the results of the analyses already presented in the primary literature on each study. This approach can be further expedited using tools, such as the Test of Arabidopsis Space Transcriptome (TOAST) databasep < 0.01 to define a DEG. Our analysis pipeline also generates the more stringent adjusted p-value  that corrects the p-value for the false discovery rate associated with multiple testing. Although we have analyzed the p-value filtered results to encompass as broad a set of DEGs as possible, q-values are presented in the tables of Supplementary Data, to allow the reader to define DEG lists using this parameter. Similarly, a cut off related to fold-change in expression  is often used in the literature to limit the extent of the gene lists being analyzed. Again, we have opted not to apply such a fold-change cut off to maintain the most inclusive list of DEGs for analysis. However, fold-change in expression level is also presented in the Supplementary Data, allowing the reader to filter the gene lists using a fold-change cut off as appropriate for their analysesUsing our common analysis pipeline approach to comparing DEGs across all the Arabidopsis studies, batch effects  became readily evident. Thus, Principal Component Analysis (PCA) and Euclidean hierarchical clustering revealed that rather than the comparison between of spaceflight and ground control, whether RNA-seq or microarray was used to detect patterns of gene expression is the factor with the largest effect on separating studies . Pairwise factor correlation analysis was performed by inspection of the Matrix in Supplementary Data We next created a connectivity network visualization system using all the pairwise comparisons that can be made between the GLDS used in our analysis  hardware are conducted in the dark. Therefore, lighting and hardware are inevitably closely linked in our network analyses of current datasets. This observation also highlights the insight that could be gained by performing more studies that use the same hardware but with a range of lighting environments. Such analyses could help separate hardware-related effects on the plant during growth in space from those specifically triggered by the lighting environment under those conditions.GLDS-7, GLDS-37, GLDS-38, and GLDS-120 differ from each other by ecotype, hardware or experiment duration, each links to multiple other spaceflight experiments within the Matrix and form hubs in networks related to hardware and/or tissue sample type  and is of interest due to the high number of tissues and ecotypes in its experimental design that link to many other studies in the Matrix. GLDS-37 was conducted in the BRIC hardware and is extensively linked to other studies due to the large number of Arabidopsis ecotypes analyzed, as well as the many other Arabidopsis BRIC experiments available for comparison. GLDS-38 (BRIC) provides RNA-seq and paired proteomics data that likewise connect to many other BRIC datasets. GLDS-120 took place with a unique hardware setup , but contains multiple ecotypes, genotypes and light treatments that link it to many other studies in the Matrix. These connections to other studies suggest that comparisons within the local networks where each study acts as a hub are likely to be fruitful targets to extract common spaceflight-related responses.Such network analyses distinguish those studies sharing a high degree of network linkages within the Matrix, i.e., studies with a larger number of common features in their experimental design. Results of comparisons between such highly connected studies are candidates for more robust analyses due to these shared factors. For example, although the overall experimental designs behind ype Fig. . Thus, GGLDS-46, GLDS-136, and GLDS-144. These experiments use elements such as hyperbaric chambers, space radiation analog exposures, and microgravity simulation on clinostats and random positioning machines to mimic specific aspects of the spaceflight environment and so are more distant in design to the other spaceflight experiments. Thus, as shown in Supplementary Fig. p < 0.01) when compared to ground analog studies (average similarity 4.21 ± 1.36), which are most similar to other ground analog studies. This Matrix-driven network visualization then highlights the opportunity to design follow-up experiments that use these analogs of putative spaceflight stressors but where the design of the study is more interconnected to the factors seen in their closest spaceflight studies within the Matrix. Such aligned experimental designs could help increase the robustness of subsequent comparisons to the existing spaceflight data.Conversely, such network analyses also revealed studies that are the most distinct . One clear set of such studies are those designed around terrestrial spaceflight analogs such as We next asked if we could define factors within the metadata other than spaceflight treatment that help define clustering within the studies. We therefore took the expression level data for each individual sample replicate  from all the studies in the Matrix and calculated the Pearson’s correlation coefficient for all possible pairwise combinations , RNA-seq analysis revealed significantly more DEGs. Thus, in our study the RNA-seq is likely providing a broader dataset within which to find enriched Gene Ontologies likely leading to the increased number of groupings found by our analysis.Insights from the network of connections between the spaceflight-related datasets in the Matrix were then used to make comparisons between gene expression patterns seen in spaceflight treatments and ground control samples . Having defined the assay type (microarray versus RNA-seq) as one of the most important confounding factors when comparing spaceflight responsive transcripts across multiple datasets Fig. , the micThe top 20 enriched ontology groupings shared between the RNA-seq and microarray analyses are summarized in Fig. GLDS-17, GLDS-37, GLDS-38, GLDS-44, and GLDS-121 are all highly connected, especially for these factors. Thus, these studies all used etiolated seedlings grown in the dark in the Petri Dish Fixation Unit (PDFU) cassettes of the BRIC hardware. Additionally, samples were harvested at the young seedling stage of development (up to 12 days old) and all included a paired on-orbit and ground control design to allow for exploration of spaceflight-related patterns of DEGs22. Differences between the studies include ecotype and analysis type (microarray versus RNA-seq). Nevertheless, their high levels of similarity, especially at the level of the hardware and lighting used, suggested to us that they could provide an important set of similarly designed experimentation to help more robustly reveal common spaceflight responses. Additionally, all these studies have published in-house analyses from their respective research groups, allowing us to further test the relative merits of comparisons drawn between the in-house results from each original publication versus the common analytical pipeline that we have used in this study. Table p ≤ 0.01 alongside the overlap in these gene lists between the in-house and common pipeline analyses  when using a cutoff of p < 0.01 but also applying a threshold of 5-fold or greater for induction or repression in transcript level as measured on their microarray (to define the most strongly regulated genes). We therefore reanalyzed these microarray results using a pipeline similar to the original authors’ analyses (Affymetrix Express pipeline and the Probe Logarithmic Intensity Error (PLIER) approach for normalization21, with a significance setting p < 0.01) but now using no fold-change filtering  and the reanalysis of GLDS-12121 using PLIER, the results of our comparisons across all the microarray studies conducted with wild-type seedlings identified 86 spaceflight-related DEGs found across all studies  as an alternative, widely used tool to assess gene ontology enrichments agreed with the analysis outlined above and did not reveal any new significantly enriched ontology terms at p < 0.01. This observation suggests the Metascape analysis outlined in Fig. The five datasets were separated into microarray (three studies) and RNA-seq (two studies) groups and analyzed using the GeneLab pipelines outlined above before making an overall comparison across all the BRIC experiments. The significance threshold to identify DEGs was set at GLDS-37 and GLDS-38 represent the BRIC samples studied with RNA-seq, necessitating independent analysis from the microarray datasets discussed above. Again, we used data from the published in-house bioinformatics approaches20 and the GeneLab common analytical pipeline. Comparison of these approaches in the WT Col-0 samples in both datasets shows that at a threshold of p < 0.01, the common pipeline identified 701 and 951 new loci as showing altered expression in spaceflight from GLDS-37 and GLDS-38 respectively, or about 25% more loci than found in the original authors’ analysis (Table GLDS-37) and 2404 (GLDS-38) DEGs. Further, within this analysis, 164 loci were significantly differentially expressed in both GLDS-37 and GLDS-38 (Supplementary Data 27). Reanalysis with the Matrix approach was also able to reveal a fingerprint of hypoxia which has been predicted as an important factor impacting biology operating with the reduced convective gas movements inherent in a microgravity environment7 but which has previously proven difficult to observe in analyses of transcriptional responses of individual flight experiments using the BRIC. In addition, Gene Ontologies related to various aspects of ion transport are prominent in our analysis targeting future investigations focused on both anion and cation transport as likely to be a fruitful targets for further understanding the effects of spaceflight on plants.is Table . CompariLastly, since processes associated with responses to spaceflight are still largely unknown, Supplementary Data GLDS-37 and GLDS-38 to explore potential protein:protein networks, applying Metascape’s protein-protein interaction enrichment analysis and Molecular Complex Detection (MCode)28. These analyses take the lists of differentially expressed genes and mine an array of protein interaction databases  for enriched networks of physical interactions. MCode then allows a focus on highly connected hubs when the numbers of proteins in the network become very high. These analyses again revealed enrichment for a response network associated with ion transport and chaperone activity . This observation reinforces the idea that variation in experimental design and analysis approach may be obscuring some common patterns of response (see below). Within the BRIC-19 experiment that generated the data in GLDS-37, in addition to the Col-0 ecotype  and Ler-0 , two additional ecotypes were investigated . These same six genes were also differentially expressed across all the ecotypes in this study, reinforcing their likely common response nature. These 6 common genes were: AT1G74310 , AT1G58340 , AT5G52310 , AT4G11290 , AT5G09220 , and AT1G73480 . Analysis of these loci using the graph-based network analysis tool KnetMiner23 revealed broad connections to the plastid and membrane function  sucrose was used in the seedling media in GLDS-17 compared to 0.3–1% (w/v) in the other studies. Sucrose is generally added to the media of Arabidopsis seedlings to support the heterotrophic growth of the plants in the dark conditions in the BRIC. However, the higher sucrose in BRIC-17 [https://genelab-data.ndc.nasa.gov/genelab/accession/GLDS-17] was specifically added to facilitate comparisons between the seedlings in this experiment and a parallel set of cell cultures that required much higher sucrose for growth. The differences in gene expression between BRIC-17 [https://genelab-data.ndc.nasa.gov/genelab/accession/GLDS-17] seedlings and the other BRIC experiments then implies that changes in primary metabolism experienced by the etiolated seedlings in the BRIC may be an important factor in determining spaceflight related transcriptional responses, echoing the altered primary metabolism inferred from our meta-analysis across all the spaceflight datasets analyzed as part of the Matrix in Fig. 33 used microarray analysis in ground-based research to investigate the changes that take place in seedlings that experience low oxygen stress with or without the addition of external sucrose. Their analyses revealed that exogenous sucrose significantly alters patterns of anoxia-related transcriptional change. Thus, the increased sucrose concentration in the media found in GLDS-17 should dramatically affect the plant hypoxia response and so is likely to alter responses to this particular effect of the spaceflight environment.Some of these genes have been discussed in the individual analyses originally published on each BRIC experiment(s)GLDS-44 also does not show the conserved transcriptional responses seen in GLDS-37, GLDS-38, and GLDS-121 is less obvious as its experimental design is very similar to these other BRIC-based experiments and it was flown side-by-side on the same mission as GLDS-121. However, subtle features such as the seed planting density differed between these studies and so effects of plant density and competition might be superimposed on these results. This analysis then highlights how understanding the feature(s) in these experiments responsible for the differences in expression pattern offers enormous potential to define factors with wide-ranging effects on the plant spaceflight response; i.e., the difference(s) between GLDS-44 and GLDS-121 and the other BRIC experiments clearly had dramatic effects on the patterns of spaceflight-related gene expression and so exploring how these studies differ in design should help define some key spaceflight-response related factors.Precisely why GLDS-37, GLDS-38, and GLDS-121 identifies HSP101 as a common spaceflight response marker. Indeed, upregulation of Heat Shock Proteins (HSPs) in the spaceflight environment is well known35. Heat Shock Proteins are molecular chaperones associated with protecting and refolding proteins in response to cellular damage36. Consistent with the enriched clades corresponding to photosynthesis identified in Fig. HSP101 upregulation and its relationship to the chloroplast  and the spaceflight-associated DEGs identified in the seedlings from BRIC experiments. Similarly, the large number of plastid genes responding to spaceflight identified in seedling samples from BRIC-16/GLDS-4422 reinforces the idea that this organelle may be an important site of spaceflight-induced responses. However, it is important to note that the BRIC experiments we have analyzed were all conducted under dark growth conditions. Therefore, the light-driven reactions of photosynthetic electron transport that are a major source of plastid ROS production on Earth are not responsible for these spaceflight-related effects and so the source of any spaceflight-triggered ROS production within the plastid remains to be defined.Looking at the shared DEGs between COR78 as likely a part of a conserved transcriptional response of Arabidopsis on orbit in the BRIC hardware. Although originally identified as a cold induced transcript, COR78 is now known to be highly inducible in response to a range of abiotic factors ranging from wounding and salt exposure to osmotic stress, drought and even the hypobaric (low pressure) environments predicted for future large scale, space-based plant growth facilities41. A common feature of all these stressors is that they trigger signaling through ROS and induce oxidative stress. Indeed, COR78 expression is regulated through the same ROS-responsive transcriptional cascades  that modulates heat shock response elements such as HsfA342, providing a possible link to the heat shock factor component of the spaceflight response. In a further tantalizing link between the chloroplast and COR78 response, COR78 expression is co-regulated with elements of the plastid antioxidant system and indeed, its expression is thought to be tightly linked to the levels of H2O2 processing by the plant43.Our meta-analysis using the common GeneLab analysis pipelines also highlights In summary, the GeneLab database is accumulating an ever-increasing number of datasets that investigate the transcriptional effects of spaceflight on early plant development. This aggregation of information, along with careful curation represents a powerful resource to begin to understand spaceflight responses in these organisms. Spaceflight imposes some commonly encountered and some unique challenges when comparing datasets. Thus, as with all large omics-level analyses, differences in protocols and analysis pipelines can impact the robustness of comparisons. However, spaceflight also leads to further challenges related to an often-restricted capacity for biological replication and with limitations on experimental design dictated by available spaceflight hardware. We have begun to address some of these issues by applying a common analytical pipeline for datasets and then constructing a matrix of metadata to allow for sorting and comparison across studies driven by their known similarities and differences. In this work we focused on two elements to highlight the potential of this approach: (1) making broad comparisons across the entire sets of data to draw conclusions about confounding variables that likely superimpose differences on spaceflight datasets, and (2) making analyses focusing on the commonly used BRIC hardware to help researchers understand the possibilities offered by designing comparative analyses in the context of the Matrix metadata. However, the possible comparisons guided by this Matrix are vast and so there remain many more opportunities for the research community to draw new insights from Matrix-focused analyses.HSP101 and COR78 suggest that a fundamental disruption of the ROS and/or antioxidant systems related to the plastid may be accompanying plant growth in space.From the Matrix-driven exploration presented here, we found that: (1) comparisons across different transcriptome monitoring technologies (RNA-seq versus microarray) should be performed with great care as differences in the technology used can impose greater variation on results than the biological treatment (spaceflight versus ground control); (2) environmental conditions and hardware-related constraints in the experimental design produce smaller but also important differences that can confound interpretation of the spaceflight versus ground control comparisons; (3) when these factors are controlled for, comparisons across the breadth of spaceflight-related Arabidopsis experiments reveal alterations in general responses to environmental stresses, photosynthesis, and other elements of primary and secondary metabolism Fig. . These bIt is important to note limitations inherent in the approach we have applied to meta-analysis of these spaceflight datasets. Due to flight and hardware constraints, the spaceflight experiments collected for this meta-study were limited to young seedlings of Arabidopsis. Although some experiments have tested the viability of plants to reach mature and reproductive developmental stages these have generally not involved omics research. We must await the data from more studies throughout the phases of the plant life cycle to understand how well our developing insights from seedlings and young plants will apply to individuals at maturity. Similarly, we must await further studies on a wider array of plant species to extend these approaches beyond the plant most commonly grown in spaceflight, Arabidopsis.One further limitation on our approach is that at present we manually curate the import of each experiment’s metadata into the Matrix. However, the GeneLab data repository has standardized its metadata formats for both current and future datasets offering us the opportunity to automate both import and curation. This automated approach will be facilitated through GeneLab’s automatic programming interface (API) which offers a program-accessible link to the metadata files. Continual updates to the Matrix will allow the power of inferences drawn to grow as quickly as the new plant spaceflight datasets are deposited.HSP101, ABS4, COR78, PRX39, AAP2, and MAGL4 in response to spaceflight stressors.Our analysis of the BRIC datasets suggests that focusing on a few hardware options that can then be the subject of multiple flight studies would greatly add to the power of such comparative omics-level analyses. Nevertheless, the results presented here offer the promise that as these experimental data become available, meta-analyses across the broad plant biology omics data landscape will provide a powerful approach to supplement the insights drawn from analyses focused on each individual study in isolation. The Matrix analysis presented herein provides a toolset to help expedite the development of such new investigations. Additionally, while the scope of potential hypotheses generated by these analyses is extensive, the current Matrix meta-analysis highlights three specific focus areas for future research that may prove particularly fruitful. These include: (1) studies examining the effect of variable light regimes on space grown plant productivity and physiology, (2) analyses aimed at determining the potential causes of altered redox activities in the plastids of space flown plants, and (3) experiments examining the function of Arabidopsis thaliana  and (2) had at least 3 biological replicates per treatment . A summary of the 15 studies  that fulfill these requirements is presented in Table 44. Briefly, the microarray analysis pipeline used the R/Bioconductor software package limma45 to perform differential gene expression analysis. Background correction by the Robust Multichip Average (RMA) method and between array normalization by the quantile method46 were performed through the Bioconductor Oligo package47. Gene level estimation was generated using the Maximum Interquartile Range method and annotations were added using the Annotation-Db class gene annotations specific to Arabidopsis thaliana from the Bioconductor repository (www.bioconductor.org). In cases where multiple probes mapped to the same gene ID, representative probes were selected with the highest mean normalized intensity across all samples. Differential gene expression analysis used the linear model fit from the limma R package to perform pairwise comparisons for all groups. For each probe set, the variance of mean signal intensities was estimated, improved by an empirical Bayes method for combining variances of probes showing similar variability, and the significance of the difference between the means was evaluated with a t-test to obtain p-values. p-values were also adjusted to q-values to account for possible errors introduced through multiple hypothesis testing using the Benjamini and Hochberg method48 and so control for the false discovery rate. Details of the code used to process each dataset are available at https://github.com/nasa/GeneLab_Data_Processing/tree/master/Microarray/1-channel_arrays/GLDS_Processing_Scripts. Both the raw and processed data can be downloaded at https://genelab-data.ndc.nasa.gov/genelab/projects.The GeneLab data repository currently holds the largest number of publicly accessible datasets of omics-  based studies of biological, spaceflight-related studies. For our analyses of plant responses using this resource we focused on the results assessing changes in the transcriptome as the most numerous kind of dataset available. We included such studies based on the minimal criteria that they: (1) were performed on the most widely used plant model species, Lab) and  had at l49 and the RNA-Seq by Expectation Maximization approach (RSEM v1.3.1)50 along with the TAIR10 genome assembly51 accessed through Ensembl Plants53. Raw sequence data were trimmed and filtered with Trim Galore! (v0.6.2). The Arabidopsis thaliana Ensembl reference genome TAIR10, release 44, and respective GTF file were used to align trimmed reads with STAR (v2.7.1a) then the aligned reads were quantified using RSEM (v1.3.1). Quantification data was imported to R (v3.6.0) using the tximport package (v1.14.0) and normalized using the DESeq2 (v1.26.0) median of ratios method54. Differential expression analysis was performed with DESeq2 (v1.26.0) and pairwise comparisons of all groups were performed using the Wald test to generate p- and adjusted p-values, and the likelihood ratio test was used to generate the F statistic p-value. Gene annotations were assigned using the Bioconductor org.At.tair.db (v3.8.2), STRINGdb (v1.24.0)29, and PANTHER.db (v1.0.4)55 packages. Processing code for each RNA-seq dataset are available at https://github.com/nasa/GeneLab_Data_Processing/tree/master/RNA-seq/GLDS_Processing_Scripts and both the raw and processed data are deposited at https://genelab-data.ndc.nasa.gov/genelab/projects.The RNA-seq analysis pipeline used the universal RNA-seq aligner STAR v2.7.1ahttps://gilroy-qlik.botany.wisc.edu/a/sense/app/20aa802b-6915-4b1a-87bd-c029a1812e2b.The associated metadata for each dataset was aggregated using a combination of the information provided alongside each GeneLab data submission, parallel manual curation from the literature and through interviews with the primary researchers. The Matrix of this data is available as both Supplementary Data https://gilroy-qlik.botany.wisc.edu/a/sense/app/ab2250b5-ee3a-4da8-b5da-fe87d5f2dbe6/overview), KnetMiner23, Metascape24, Ensembl GO53, the Kyoto Encyclopedia of Gene and Genomes56, AraCyc57 and Reactome58 are noted in the text and figure legends. Principal Component Analysis (PCA), Multidimensional Scaling analysis (MDS), t-distributed Stochastic Neighbor Embedding (T-SNE), Weighted Gene Correlation Network Analysis (WGCNA) and K-means statistical analyses were performed using the iDEP.94 R-package59. For these analyses, the normalized counts were imported from the GeneLab data repository and processed using R-studio. The R programming language provides for the statistical analysis of data (https://www.r-project.org/about.html) within a commercial development environment called R-studio .When they have been employed in the data analyses, online tools such as the TOAST X-Species Transcriptional Explorer (Further information on research design is available in the Supplemental MaterialSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Reporting Summary